Health.mil Articles RSS Feed

‘It has been a life saver’: Department of War civilians in Japan discuss health care pilot program

Wed, 01 Apr 2026 00:00:00 Z

The Office of the Assistant Secretary of War for Health Affairs hosted a virtual information session March 10, 2026, to gather feedback on the effectiveness of the Pilot Health Insurance Enhancement for Department of War Civilian Employees In Japan.

Launched Jan. 1, 2025, the program provides supplemental health services for approximately 11,000 DOW civilian employees in Japan — addressing challenges when seeking medical care including language barriers and high up-front costs.

Susan Orsega, deputy assistant secretary of war for health services policy and oversight, highlighted that “participant insights and feedback play a critical role in shaping the future of the program, which has already helped many individuals get the care they need.”

Since its launch, the program has facilitated over 1,400 appointments and prevented over $1 million in upfront costs to patients. In collaboration with International SOS, or ISOS, the pilot program will continue through Sept. 29, 2026.

How the health insurance enhancement works

To start the information session, officials presented an overview of the pilot program’s key services:

Health Expert assistance through ISOS in finding providers, overcoming language barriers, and understanding local procedures
Streamlined cashless/claimless billing and reimbursement services through ISOS
Interpreter support

Current pilot eligibility is extended to DOW civilian employees with assignments in Japan enrolled in participating Federal Employee Health Benefit program plans, which include:

Federal Blue Cross Blue Shield
Foreign Service Benefit Plan
Government Employee Health Association
Hawaii Medical Service Association
Mail Handlers Benefit Plan
Nonappropriated Funds employees are eligible if enrolled in Aetna International

The pilot program’s services are at no cost to eligible employees, while standard copayments and cost-shares still apply per individual FEHB programs.

To use the pilot program, employees must make an initial 15- to 20-minute setup call with ISOS, which one beneficiary described in the town hall as “extremely easy.”

Translation and interpretation

Officials at the town hall identified language as a common barrier to health care for employees in Japan. The program provides a call center staffed with bilingual professionals, including nurses, to assist with:

Appointment scheduling
Arranging payment guarantees
Answering program questions, or providing guidance for navigating Japanese health care

In-person interpreter services are also available in some cases. On the topic of language barriers, one beneficiary in attendance shared they “couldn’t have navigated Japan’s health care without this pilot program.”

Officials also outlined specific best practices when navigating Japan’s health care system to foster a positive DOW employee reputation among Japanese providers, including being:

Proactive in calling ISOS as soon as care needs are identified
Courteous of provider’s time by cancelling appointments well in advance
Open-minded to the specialties offered in Japan and work with ISOS to find the best course of action
Understanding of limitations of provider availability in Japan

Streamlined billing assistance

Officials detailed how, during a call with ISOS, they will assist in:

Verifying coverage through a benefit review
Coordinating with insurance carriers for direct billing to prevent upfront costs wherever possible
In cases where direct billing isn’t possible, ISOS can help streamline reimbursements

Direct billing is powered by agreements with a large network of medical providers in Japan, allowing ISOS to issue guarantees of payment to providers — preventing patients from having to pay any upfront costs. Under the agreement, FEHB plans reimburse ISOS, and patients direct their regular copayment and shared costs to ISOS.

Looking ahead to future access to health care

Officials report strong advocacy for the program from users and ISOS has 98.7% success in collecting co-pays from patients after FEHB carriers process the directly billed claims.

Ultimately, the pilot program continues to gather feedback from users and evaluate its impact. The Undersecretary of War for Personnel and Readiness will review the program’s outcomes and determine how to sustain, scale, or adapt it prior to the program’s contracted conclusion in September 2026.

Many participants in the town hall voiced enthusiasm for the program’s continuation, with one beneficiary stating, “I very much appreciate the program and we’re hoping for an expansion. It has been a life saver.”

Reducing tobacco and nicotine use key to ‘having a healthy, fit force’

Wed, 01 Apr 2026 00:00:00 Z

Tobacco use remains the No.1 preventable cause of death in the U.S., according to the Centers for Disease Control and Prevention, and poses a readiness issue for the military.

The Military Health System is concerned that about one-third of service members still use tobacco and nicotine products — a rate higher than the general population (19%), said Department of War Principal Deputy Assistant Secretary of War for Health Affairs Dr. Stephen Ferrara, citing statistics from department-wide health behavior surveys.

Ferrara spoke on this topic at the National Press Club on the dangers of tobacco use in the military and how the MHS is addressing the issue at an event in March 2026 titled, “Serving Those Who Serve, Embracing Tobacco Harm Reduction.”

Acutely aware of the impacts on readiness, the MHS has long focused on reducing tobacco and nicotine use through a comprehensive approach involving medical, behavioral, and cultural interventions, said Ferrara.

In 2026, that commitment is deeper than ever with the DOW’s deeper emphasis on a healthy force.

“Prevention is in our DNA,” he said. “The way we succeed on the battlefield, of which we're in active operations as we speak, is by having a healthy, fit force.”

As a vascular and interventional radiologist with multiple tours of duty in the U.S. Navy for 25 years, Ferrara is keenly aware that tobacco and nicotine dependence have long been embedded “in the culture” of the military, which offered behavioral off-ramps to smoke.

Ferrara said, “old myths” that tobacco “increases your alertness” have been debunked. “We know now it actually leads to slower wound healing, slower injury healing … especially the cardiovascular” system,” he said.

Stressors associated with military service and cultural dependencies mean “people use different things, whether it's food, whether it's cigarettes, whether it's other habits … as a crutch or as an aid to help them deal with that stress that we asked them to endure.”

The MHS emphasizes a comprehensive approach to help people quit, which includes prevention methods, stop-smoking aids, and behavioral therapies.

Stepping down from tobacco

The MHS provides intermediate, less harmful steps to help service members and veterans put combustible cigarettes down. “We're looking at nicotine replacement or other things that will satisfy or try to give people a bridge so they cannot get the most toxic tobacco experience as a way to eventually get to full cessation,” said Ferrara.

Mitigating “the most harmful behaviors and with a path” away from nicotine and tobacco dependence requires team support, he said. “That's where partnerships come in with the medical community … your friends or your family or your command structure to continue to work that journey from the most harmful behavior to ideally, to not a harmful behavior, with all the intermediate way points.”

Ferrara said the first line of defense against tobacco can be providers, who raise the issue during the annual Periodic Health Assessment or other medical visits. The PHA specifically asks about tobacco or nicotine product use.

“If the answer is yes, we start to talk about how we can do cessation,” Ferrara explained. The MHS has abundant “pharmacologic, mental health therapies, all those things to help people, including other adjunctive therapies, to try to get people to get away from cigarettes.”

MHS works ‘hand in glove’ with the Department of Veterans Affairs

One important effort was the publication in January 2026 of the DOW and the Department of Veterans Affairs joint clinical practice guideline called “Tobacco Use Treatment.”

The CPG describes the critical decision points in tobacco use treatment and presents comprehensive evidence-based recommendations for providers and patients to reduce and then stop the use of tobacco and nicotine. Ferrara emphasized the CPG demonstrates both departments’ commitment to tobacco cessation and continuity in care in transition and afterward.

“Because every veteran was once in uniform, the continuity of care must be instantaneous,” he emphasized. “We'll get you taken care of, and then we need to pass the baton to the VA, and that's why we have that joint clinical practice guideline,” Ferrara explained. “You can see that we are hand in glove with that, and that's as it should be for the folks who serve their country.”

Behavior changes and telehealth

Ferrara highlighted how the military has “really leaned into technology,” including telehealth or virtual appointments for tobacco and nicotine cessation. These tools “support meeting service members where they are in their tobacco-reduction journey, and wherever they are stationed or deployed worldwide,” he said. “It's an exciting time to be in medicine with what's going on in AI (artificial intelligence) and we're being able to leverage those things, not only for diagnostics but also for therapeutics.”

Because most service members are young, “there's a huge appetite for” AI and telehealth, he said. “Most of our service members … grew up very comfortable with technology, and some of them actually prefer a virtual encounter to face-to-face” meetings with a provider.”

He reinforced, “You have to meet people where they are, and I think that's … really a person-centered approach.”

Goal is a healthier, more ready fighting force

“Reducing tobacco and nicotine use improves health, fitness, recovery, and long-term resilience — all of which directly affect the warfighter’s ability to fight and win,” Ferrara said.

“Through continued research, strong partnerships, and sustained leadership commitment, the department is taking concrete steps to drive down tobacco use and protect the health of the men and women who serve,” he stated. “This is about readiness today, and it is also about ensuring that those who serve our nation can enjoy healthier lives long after they retire their uniforms.”

Ferrara noted that he’s already seen a shift in tobacco use. He said over the 35 years that he has been associated with the military, usage and culture have changed, resulting in fewer people smoking.

“If I had the magic wand, I think we would have a culture where we model the behavior that we never get folks started. But until then, we are going to do everything from all the things we talked about and more to mitigate that risk and continue our tobacco harm-reduction techniques.”

Ready to quit? Accessing resources

TRICARE offers a multitude of TRICARE tobacco cessation services. TRICARE covers tobacco cessation counseling if you’re aged 18 or older and you live in one of the 50 states or the District of Columbia, as long as you use a TRICARE-authorized provider.
TRICARE covers prescription and over-the-counter tobacco cessation products at no cost to you if you use TRICARE Pharmacy Home Delivery or a military pharmacy. TRICARE doesn’t cover these products if you get them at a retail network or non-network pharmacy. A TRICARE-authorized provider must write the prescription. You must have a prescription for OTC tobacco cessation products such as nicotine replacement products, nasal sprays, inhalers, patches, gums, and lozenges.
You Can Quit2 is a DOW-supported education program that offers coaching, online tools, and in-person support locators to help you quit tobacco. YCQ quit plan helps you create a timeline that fits your needs so quitting tobacco is within reach.
You can also call your local military hospital or clinic to see if they offer tobacco cessation programs.
The VA offers a wealth of advice on quitting smoking and smokeless products.
If you still use chewing tobacco, do monthly self-checks of your mouth, tongue, throat, face, and neck to help you find possible early signs of damage or cancer.
Smokefree.gov from the National Cancer Institute has many resources and information, including coping with stress without tobacco, what to do if you have a setback quitting tobacco and managing cravings.
The Food and Drug Administration warns that smokeless products have their own dangers.
Smoking and tobacco use from the CDC includes information on nicotine pouches, menthol tobacco products, vaping, secondhand smoke, and much more.

Hospital honors ‘Fighting doctor’ and upholds highest standards of care

Thu, 26 Mar 2026 00:00:00 Z

This year, the Military Health System is celebrating 250 years of America by recognizing the Military Medicine’s enduring history of innovation and impact to the warfighter. Please join the Military Medicine 250 campaign!

Irwin Army Community Hospital, Fort Riley, Kansas, is named for Brig. Gen. Bernard Irwin, who served almost four decades in frontier and wartime posts, between 1849 – 1881. Known as the “fighting doctor,” Irwin did not treat Soldiers from a safe distance — he rode on horseback into battle to reach injured service members and later brought surgical care close to the front lines during the Civil War. The hospital honors Irwin’s namesake through high-quality care for warfighters and their families.

It is fitting that Irwin, for his dedication to warfighter readiness, was chosen as the namesake of a military hospital that honors and supports the 1st Infantry Division at Fort Riley — the U.S. Army’s oldest continuously serving active duty division, and its mission of deploying “in an expeditionary manner to conduct decisive action to fight and win in complex environments as members of a joint, inter-organizational, and multinational team.”

Early years shaped approach to military medicine

Irwin was born in Ireland June 24, 1830, and his family later moved to New York City. He earned a medical degree in 1852 and joined the U.S. Army as an acting assistant surgeon in the mid-1850s, according to his biography from the U.S. Army Medical Department Center of History and Heritage. Irwin was appointed assistant surgeon in 1856 and began a career that would take him across the expanding U.S. frontier.

He served at duty posts in New Mexico and Arizona, including Fort Union, Fort Defiance, and Fort Buchanan conducting field operations, according to his biography. These remote locations forced doctors to solve problems quickly, often with limited supplies and personnel.

While stationed in the Arizona Territory early 1861, he volunteered to take command of a small relief force to aid 2nd Lt. George Bascom and his Soldiers who faced attack near Apache Pass. A story about the event from the AMEDD Center of History and Heritage cites “Irwin and his men rode mules because they had no horses, pushed through severe conditions, reached the trapped unit, and helped drive off the enemy.” The U.S. Army later awarded Irwin the Medal of Honor in 1894 for “distinguished gallantry” in that fight. He also earned his “fighting doctor” moniker from the event.

Wartime hospital care

When the Civil War began, Irwin was assigned to the U.S. Army of the Ohio as the medical director (a term that pre-dates today’s “command surgeon” title) and took part in the campaign that ended in April 1862 at the Battle of Shiloh in Tennessee. There, he organized a tent field hospital the U.S. Army called a first of its kind and “the model upon which our later field hospitals were based.” The location was a surgical station in a farmhouse that was converted into a 300-bed surgical hospital, with an operating room, dispensary, kitchen, and other support spaces, according to the National Park Service.

With the success of the field hospital at Shiloh, Irwin proved concentrating care near the battlefield improved outcomes and reduced mortality, without the need to send wounded troops far distances.

Irwin continued to serve after Shiloh in senior medical roles. His biography indicates or shows promotions during the war, later positions across the western frontier, an assignment in October 1873 to the United States Military Academy at West Point, and retirement in 1894. Congress later promoted him to brigadier general on the retired list in 1904.

Hospital honors legacy through nationally accredited care

The original Irwin Army Community Hospital was dedicated in 1958. In 2016, the legacy hospital was replaced by a new, state-of-the-art $343 million facility, built to carry Irwin’s name into the future, With 47% more space, IACH staff was better positioned to serve about 50,000 beneficiaries, which includes active duty service members, family members, and retirees.

In 2016 and 2024, IACH received the Joint Commission’s “Gold Seal of Approval” for its commitment to providing safe, high-quality patient care. The accreditation involved a four-day onsite review, assessing the performance standards such as emergency management, infection prevention, leadership, medication management, national patient safety goals, and patient rights.

The recognition was “a testament to the unwavering commitment of our staff to upholding the highest standards of safe, quality care for our warfighters and their families,” said Col. Laudino Castillo, hospital commander.

The hospital continues to honor the “fighting doctor” through nationally accredited, exemplary warfighter care, bearing the namesake of Irwin, who “gave no thought to distance, danger, or hardship in answering the many calls for his help,” according to his biography.

2026 Military Health System Research Symposium: Submit your research or nominate outstanding health care work

Tue, 24 Mar 2026 00:00:00 Z

As the 2026 Military Health System Research Symposium, themed “Harnessing the Power of Military Medical Research,” is approaching, participants can submit their abstracts or nominations for the annual awards.

The call for abstracts to submit your research for the Department of War’s premier military medical science meeting closes March 31, 2026, (12 a.m. EST). You can explore sessions and submit at https://mhsrs.health.mil.

Nominations for the 2026 MHSRS Annual Awards are open through April 24, 2026 (12 a.m. EST). These awards honor the researchers and teams whose work strengthens readiness, accelerates medical discovery, and directly supports the deployed warfighter. This is your opportunity to spotlight the people and programs driving meaningful impact across the MHS.

Award categories include:

Distinguished Service
Outstanding Research Accomplishment (Individual/Military) and (Individual/Academia)
Outstanding Research Accomplishment (Team/Military) and (Team/Academia)
Outstanding Program Management (Team)
Warfighter Medical Research Public Communication

You can review full category descriptions and submission criteria on the MHSRS homepage: https://mhsrs.health.mil/MHSRS. You must be registered and logged in to submit an award.

Ensure the award narrative fits the award criteria in the category selected. The Award Review Committee compares the narrative against the criteria. In past years, failure to meet that category's criteria have eliminated some well written packages from award contention.

Please share with colleagues who may be interested.

About MHSRS

The MHSRS is the Department of War’s primary scientific gathering where military medical researchers, clinicians, scientists, and industry partners come together to share new discoveries and improve care for deployed service members.

Department of War and Department of Veterans Affairs strengthen partnership to enhance care for Nation’s warfighters and veterans

Tue, 24 Feb 2026 00:00:00 Z

A long-standing partnership between Irwin Army Community Hospital, Fort Riley, Kansas, and the VA Eastern Kansas Healthcare System is expanding specialty services and access to care for veterans and service members.

On Jan. 14, leaders from both met to discuss strengthening their partnership, in place since 2017, through a new resource-sharing agreement aligning staff, equipment, and specialties to close gaps in access, and advance medical care for warfighters, veterans, and their families. Through their agreement, the health systems align capabilities and specialties for enhanced care to veterans and active duty members, said Col. Laudino Castillo, commander of Irwin Army Community Hospital.

He noted the importance of the partnership given nationwide health care challenges with staffing, available capabilities, and access to specialty care. The key partnership allows them to “align those capabilities (and) align those specialties,” he said, which provides “enhanced care, not only to the veterans, but also to our active duty population — which is required in order to get ready to fight the nation’s wars.”

In 2017, the partnership provided care for more than 3,000 veterans. In 2026, the agreement is expanding access to radiology, optometry, and women’s health. Veterans who once drove to Topeka for imaging or specialty services can now receive many of those services at Fort Riley, saving hours on the road.

Rudy Klopfer, executive director of VA Eastern Kansas Healthcare System, noted that female veterans previously accessed mammography services in the community since medical clinics did not have that capability.

“Our faster-growing population in this area, as well as within the VA, is our female veterans,” Klopfer said. “What a great opportunity for those who have served in the military and now our veterans to come here and receive that service.”

The agreement works both ways. If one system lacks a specialty, the other can help fill the gap. Services such as pulmonology, cardiology, rheumatology, and sleep medicine may be available sooner through shared coordination — providing care for all “who have fought for our freedoms,” Klopfer said.

“Why not capitalize on what this hospital and staff can do for our veterans?” asked Castillo. “At some point, today’s active duty soldiers will be our veterans. Building that relationship now is so valuable for the care we give.”

Leaders emphasized that partnership also sharpen and sustain clinical skills. As providers treat more complex cases, they strengthen readiness and improve care for service members, veterans, and families.

“For me, a partnership is figuring out what we can offer and what they can offer, so we can actually enhance care,” Castillo said. “The bottom line is enhancing care.”

Number of tuberculosis tests and diagnoses of latent tuberculosis infection among U.S. Army active component service members, January 2014–December 2023

Sun, 01 Feb 2026 00:00:00 Z

Abstract

Tuberculosis (TB) remains a force health protection threat to the U.S. military, particularly in crucial populations at increased risk of exposure or re-activation. This analysis examined TB testing trends and the prevalence of latent tuberculosis infection (LTBI) among U.S. Army active component soldiers from 2014 through 2023, the first decade following a major policy shift to targeted testing. Defense Medical Surveillance System data indicate that a total of 339,465 TB tests were administered, primarily (81.0%) tuberculin skin tests. Of those tests, 22,762 (6.7%) were positive, leading to the identification of 18,018 (5.3%) LTBI diagnoses. Asian/Pacific Islander soldiers demonstrated the highest LTBI diagnosis proportion (10.2%), followed by non-Hispanic Black (8.6%), Hispanic (5.6%), and Non-Hispanic White (2.9%) soldiers; the data also include ‘other’ (6.8%) and ‘unknown/missing’ (3.6%) categories. Recruits exhibited a significantly higher LTBI diagnosis proportion (11.0%) than non-recruits (3.6%), highlighting a high prevalence of LTBI among incoming personnel at time of accession. A marked decline in testing volume—a 72% decrease from 2014 to 2023 in the annual numbers of tests administered—followed the 2013 U.S. Army Medical Command policy shift. The substantially higher average proportion (6.7%) of positive tests from 2014 to 2023 compared to the average from the pre-policy era (1.3%) of universal screening demonstrates the successful concentration of testing resources on those most at risk, thereby improving diagnostic yield within a low-prevalence military force. This analysis’s findings describe the epidemiological outcomes of the Army’s targeted testing policy and underscore the importance of ongoing, targeted surveillance to mitigate TB risks in military settings.

What are the new findings?

The 2013 policy that successfully transitioned the U.S. Army from universal tuberculosis screening to a targeted, risk-based strategy reduced testing volume by 72% over the next decade. The decline in tuberculosis testing volume coincided with a substantial increase in diagnostic yield, with the overall positivity proportion rising from 1.3% in the pre-policy era to 6.7% in 2023.

What is the impact on readiness and force health protection?

The 2013 policy revision to a targeted, risk-based tuberculosis testing strategy succeeded in focusing valuable public health resources on high-risk groups. The high prevalence (14.0%) of latent tuberculosis infection that has been identified in recruits confirms that accession is the most critical juncture for tuberculosis control within the Army. Slight but notable differences in testing type positivity suggests opportunity for further policy refinement.

Background

Tuberculosis (TB) remains a significant force health protection concern for the U.S. military, primarily due to the risk of activating latent tuberculosis infection (LTBI) and the potential for transmission in congregate settings.^1,2 A 2014 analysis in MSMR of TB testing in all branches of the U.S. Armed Forces, covering the period from 2004 through 2012, provides a critical baseline for the present analysis. During that era of routine annual screening, the prevalence of LTBI diagnoses was low and stable, ranging from just 0.9% to 1.6% annually among those tested.³ That report provides the context for the current analysis, which focuses on the U.S. Army in the decade following a major policy change.

In November 2013, the U.S. Army Medical Command (MEDCOM) published Regulation 40-64, The Tuberculosis Surveillance and Control Program, which fundamentally altered the Army’s approach to TB control.⁴ This directive shifted the strategy from universal annual testing to a targeted, risk-based testing model, aligning with modern public health principles advocated and then formally updated in May 2019 by the U.S. Centers for Disease Control and Prevention (CDC) and National Tuberculosis Controllers Association (NTCA), which revised sections of previous guidelines. The rationale for this change was to improve screening efficiency and reduce the high number of false positive results when testing large, low-prevalence populations, thereby avoiding unnecessary follow-up procedures and resource expenditures.^4,5

Under the targeted testing policy, routine TB testing is discouraged and is instead mandated only after a formal risk assessment. Key high-risk populations designated for testing include 1) all new recruits upon accession into service, 2) personnel who have deployed or traveled to TB-endemic regions, 3) individuals identified as close contacts of an infectious TB case, and 4) personnel with specific clinical or occupational risk factors, such as health care workers.^2,4 The objective of this analysis was to describe the trends of TB tests and LTBI positivity in Army active component soldiers from January 2014 through December 2023, the first full decade following the implementation of this targeted testing policy, and to compare these findings to the pre-2013 baseline.

Methods

The analysis population included all Army active component soldiers who had a TB test at any military hospital or clinic from January 2014 through December 2023. The data source was the Defense Medical Surveillance System (DMSS). Tests for TB were identified using a combination of immunizations, laboratory, and outpatient procedure data. The DMSS includes data for Army active and reserve component soldier immunizations received during military service and administrative (i.e., billing records) from inpatient and outpatient medical encounters for all Military Health System (MHS) beneficiaries when reimbursed through TRICARE. Laboratory data for interferon gamma release assays (IGRAs), which include QuantiFERONTB Gold Plus (QFT) and T-SPOT. QFT and T-SPOT tests are IGRAs used to detect TB infection; QFT measures overall amount of IFN-γ, or interferon gamma, while T-SPOT counts number of cells producing IFN-γ. TB tests performed during the surveillance period were provided by the Defense Center for Public Health–Portsmouth. All laboratory tests were classified as IGRA. Tuberculin skin tests (TSTs) were identified from immunizations or outpatient procedures, as depicted in Table 1. Outpatient procedures were used to identify additional IGRA tests (Table 1). When calculating the number of tests administered, an individual was counted once per day.

For the purposes of this analysis, a ‘positive’ test was any TST or IGRA test result recorded as “positive” in the database. A diagnosis of LTBI was defined as an individual with a record of a positive TB test who also received a corresponding International Classification of Diseases, 9th or 10th revision, Clinical Modification (ICD-9-CM/ICD-10-CM) code for LTBI (ICD-9-CM: 795.5x; ICD-10-CM: R76.11, Z22.7) (Table 1) in any diagnostic position within 30 days of the test. Demographic information was identified at the time of each test, including beneficiary type, age, sex, race or ethnicity, branch of service, and geographic region of the military treatment facility performing the TB test.

Under the post-2013 targeted testing policy evaluated in this analysis, Army personnel were eligible for TB testing based on a risk assessment.

Results

During the 10-year surveillance period (2014–2023), a total of 339,465 TB tests were administered to U.S. Army active component soldiers. Of these, 22,762 were positive, for an overall positivity proportion of 6.7%. This resulted in 18,018 individuals receiving a diagnosis of LTBI. As shown in Figure 1, the annual number of tests administered declined sharply over the surveillance period, from 82,295 in 2014 to 22,986 in 2023. Concurrently, the proportion of tests returning a positive result nearly doubled, showing a steady increase from 4.5% in 2014 to 8.5% in 2023.

The majority of tests were administered to soldiers who were male (n=270,057, 79.6%), non-Hispanic White (n=167,887, 49.5%), of enlisted rank (n=268,723, 79.2%), and ages 20-34 years (n=235,235, 69.3%). When evaluated by age, soldiers in the under age 20 years category had the highest positivity (8.3%); this age range represents the primary age for accession into the Army. Positivity was 7.3% for both the ages 20-24 and 30-34 years categories, followed by 6.8% for the age 25-29 years category (Table 2).

TST was the most frequently used method (n=274,473), accounting for 81.0% of all tests, while IGRAs (n=64,992) comprised the remaining 19.0% (Table 2).

While men accounted for a larger absolute number of positive tests and LTBI diagnoses, the positivity proportion was nearly identical between men (6.7%) and women (6.9%) (Table 2). Proportions of positive tests and LTBI diagnoses varied notably by racial and ethnic group. Asian/Pacific Islander soldiers had the highest proportions of positive tests (13.0%) and LTBI diagnoses (10.2%), followed by non-Hispanic Black soldiers (11.2% and 8.6%, respectively). In contrast, non-Hispanic White soldiers had the lowest proportions (3.5% and 2.9%, respectively) (Table 2).

A noticeable difference was observed based on recruit status. The proportion of positive tests among recruits was 14.0%, compared to 4.4% among non-recruits (Table 2). The IGRA test showed a slightly higher positivity proportion (7.8%) compared to the TST (6.4%). Enlisted personnel had a higher proportion of positive tests (7.8%) and LTBI diagnoses (6.1%) compared to officers (2.6% and 2.1%, respectively) (Table 2).

Considerable variability in test positivity was observed between military installations (Table 2). Among the 10 installations with highest LTBI test positivity, U.S. Army Garrison (USAG) Bavaria, Germany, which is the largest U.S. Army training area in Europe, comprising Grafenwoehr Tower Barracks and Hohenfels Joint Multinational Readiness Center, reported the highest proportion of positive tests (26.6%) along with USAG Yongsan-Casey in South Korea, with second-highest test positivity (26.4%). Installations that serve as large initial entry training sites, such as Fort Sill, Oklahoma (16.2%) and Fort Jackson, South Carolina (15.4%), also reported high positivity proportions. Conversely, the 10 installations with the lowest positivity for LTBI—Aviano Air Base, Italy; Barksdale Air Force Base (AFB), Louisiana; Dover AFB, Delaware; Ellsworth AFB, South Dakota; Hanscom AFB, Massachusetts; Joint Base Charleston, South Carolina; Keesler AFB, Mississippi; Kirtland AFB, New Mexico; Maxwell AFB, Alabama; and U.S. European Command—each had 0% test positivity (data not shown). This could potentially be due to effective control measures, low local TB prevalence, or even a small sample size.

Discussion

This analysis of over 339,000 TB tests in the U.S. Army active component from 2014 through 2023 shows clear epidemiological outcomes following the 2013 MEDCOM policy⁴ shift to targeted, risk-based screening. These findings should be viewed within the context of the greater U.S., where a diagnosis of active TB disease is relatively uncommon, with a civilian incidence rate (IR) of 2.9 cases per 100,000 persons in 2023.^6,7 This contrasts sharply with the U.S. military, where the risk is substantially lower, with an active TB disease IR estimated at less than 1 case per 100,000 persons.^4,8 Similarly, while a significant reservoir of infection exists in the U.S. general population, with an estimated 4.0% prevalence of LTBI,^6,7 the prevalence among military-age groups is estimated to be only around 1%.^4,8 The primary finding of this analysis is a sharp 72% reduction in the annual number of tests administered. The substantially higher average proportion of positive tests from 2014 to 2023 (6.7%) compared to the average from the pre-policy era of universal screening (1.3%) demonstrates the successful concentration of testing resources on those most at risk, thereby improving diagnostic yield within a low-prevalence military force.

Following the 2013 MEDCOM policy change, the decline in testing volume and corresponding rise in the positivity proportion are the expected and intended results of a successful targeted testing program. By focusing screenings on high-risk populations, such as recruits, personnel deploying to endemic areas, and close personal contacts, the policy effectively eliminated the testing of a large, low-risk population that previously diluted the overall positivity prevalence. The result is not necessarily an increase in overall LTBI within the Army, but rather an improved diagnostic yield and more efficient allocation of public health resources, a finding consistent with the stated goals of the policy.

The demographic and military characteristics associated with LTBI in this analysis are largely consistent with previous reports,^3,5 although the magnitude of these associations is more pronounced due to targeted testing. The elevated proportion of positive tests among recruits (14.0%) underscores that accession screening remains critical for identifying prevalent LTBI acquired prior to service. The disparities observed among racial and ethnic groups, particularly the high proportions among non-Hispanic Black (11.2%) and Asian/Pacific Islander (13.0%) soldiers, are also consistent with national trends.^6,9 These associations are likely confounded, however, by socio-economic factors and, most importantly, country of origin. Non-U.S. birth is a primary LTBI risk factor, and it is probable that this unmeasured variable accounts for a significant portion of the observed differences between racial, ethnic, and even rank categories.^6,10 The higher proportion of LTBI among enlisted personnel compared to officers, for example, is more likely a reflection of underlying demographic differences at accession than of occupational exposures during service.

The pronounced disparities among racial and ethnic groups warrant further consideration, particularly considering this analysis’s limitations. The absence of data on country of birth is a significant confounding variable that likely explains a substantial portion of observed differences. National data consistently show that a majority of TB cases in the U.S. occur in non-U.S. born individuals.⁹ It is highly plausible that the elevated LTBI proportions among Asian /Pacific Islander and non-Hispanic Black soldiers are more reflective of a higher prevalence of non-U.S. birth within those cohorts than of any inherent racial or ethnic predisposition. Future surveillance should aim to integrate country of birth data into the initial screening process, which would enable more precise risk stratification, distinguishing risk acquired prior to service from that acquired during a military career. New country of birth data would allow public health officials to design prevention and treatment strategies more effectively.

From a policy perspective, while the targeted screening strategy has proven successful in enhancing diagnostic yield, these findings highlight the ongoing need for vigilance. The high prevalence of LTBI identified in recruits (14.0%) confirms that the point of accession is the most critical juncture for TB control within the Army. Furthermore, the slight but notable difference in positivity between IGRA (7.8%) and TST (6.4%) tests suggests opportunity for policy refinement; this variance could be attributable to IGRA’s greater specificity, especially among individuals who may have received the Bacille Calmette-Guérin vaccine, or it may reflect its use in more selectively high-risk groups. Given these factors, the Army may consider recommending IGRA as the primary screening tool for specific high-risk recruit populations, such as those born in TB-endemic countries, to further optimize the accuracy and effectiveness of the TB control program.

There are several limitations to this analysis. First, the demographics of the 2 periods (all forces vs. Army), living conditions, and potential exposures in different geographic locations may contribute to some differences. Second, there are generalizability limitations, as results are specific to the U.S. Army active component, limiting the relevance to other MHS beneficiaries such as other service components, family dependents, and retirees. Third, the dataset lacked information on service members’ countries of birth, a crucial unmeasured confounder that, as discussed, likely influenced observed associations with race and ethnicity. Fourth, there are data completeness problems, as the race and ethnicity data had 6% unknown or missing entries, potentially biasing disparity analyses. Fifth, the definition of an LTBI case relied on the presence of an ICD-9-CM/ICD-10-CM code within 30 days of a positive test. This is a significant assumption, as administrative or clinical lapses may lead to misclassification; it is possible that some individuals with a positive test did not receive a corresponding diagnostic code, or vice versa, potentially leading to an under-estimation of the true LTBI burden. Sixth, this analysis assumes uniform implementation of the 2013 MEDCOM policy, but adherence likely varied over time and between installations. This inconsistent application of targeted testing could contribute to the variability in positivity and may have influenced the overall trends. Finally, these are observational data, so causality cannot be determined; external factors, such as changes in deployment patterns or recruitment demographics, may also have influenced the observed trends.

References

Mancuso JD, Tobler SK, Eick AA, Olsen CH. An evaluation of the completeness and accuracy of active tuberculosis reporting in the United States military. Int J Tuberc Lung Dis. 2010;14(10):1310-1315. Accessed Feb. 2, 2026. https://www.ingentaconnect.com/content/iuatld/ijtld/2010/00000014/00000010/art00014;jsessionid=1uqpi287q40iq.x-ic-live-03#supp
Sanchez JL, Hiser MJ. Tuberculosis as a force health protection threat to the United States military. Mil Med. 2015;180(3):276-284. doi:10.7205/milmed-d-14-00433
Armed Forces Health Surveillance Center. Brief report: number of tuberculosis tests and diagnoses of latent tuberculosis infection in active component service members, US Armed Forces, January 2004–December 2014. MSMR. 2014;21(12):8-10. Accessed Feb. 2, 2026. https://www.health.mil/reference-center/reports/2014/01/01/medical-surveillance-monthly-report-volume-21-number-12
U.S. Army Medical Command. MEDCOM Regulation 40-64: The Tuberculosis Surveillance and Control Program. Dept. of the Army, U.S. Dept. of War. Nov. 26, 2013.
Mancuso JD, Tribble D, Mazurek GH, et al. Impact of targeted testing for latent tuberculosis infection using commercially available diagnostics. Clin Infect Dis. 2011;53(3):234-244. doi:10.1093/cid/cir321
U.S. Centers for Disease Control and Prevention. Reported Tuberculosis in the United States, 2023. U.S. Dept. of Health and Human Services. 2024. Accessed Feb. 2, 2026. https://www.cdc.gov/tb-surveillance-report-2023/index.html
Miramontes R, Hill AN, Yelk P, et al. Tuberculosis infection in the United States: estimates from the National Health and Nutrition Examination Survey, 2011–2012. PLoS One. 2015;10(11):e0140881. doi:10.1371/journal.pone.0140881
Defense Health Agency. Weed Army Community Hospital Regulation No. 40-72: Medical Services Tuberculosis Surveillance and Control. Feb. 14, 2025. Accessed Feb. 2, 2026. https://weed-irwin.tricare.mil/portals/148/wach%20regulation%20no.%2040-72%20tuberculosis%20surveillance%20and%20control%202025.pdf
Williams PM, Pratt RH, Walker WL, et al. Tuberculosis: United States, 2023. MMWR Morbid Mortal Wkly Rep. 2024;73(12):265-270. doi:10.15585/mmwr.mm7312a4
Bennett DE, Courval JM, Onorato I, et al. Prevalence of tuberculosis infection in the United States population: the national health and nutrition examination survey, 1999–2000. Am J Respir Crit Care Med. 2008;177(3):348-355. doi:10.1164/rccm.200701-057oc

Author Affiliations

Epidemiology and Disease Surveillance, U.S. Army Public Health Command, West, Joint Base San Antonio–Fort Sam Houston: Dr. Stidham; Army Public Health Nursing, U.S. Army Public Health Command, West: LTC(P) Tyler

Acknowledgments

The authors would like to thank Dr. Sithembile Mabila, Armed Forces Health Surveillance Division, for assistance in obtaining DMSS data.

Disclaimer

The views expressed in this article are those of the authors and do not necessarily reflect the official policy nor position of the Department of the Army, Department of War, nor the U.S. Government.

Title 17, U.S. Code Section 105 provides that copyright protection under this title is not available for any work of the U.S. Government. Title 17, U.S. Code Section 101 defines a U.S. Government work as work prepared by a military service member or employee of the U.S. Government as part of that person’s official duties.

Guest editorial: CHAMPS: the Career History Archival Medical and Personnel System—a summary of career and medical records of the U.S. Armed Forces, 1980–2023

Sun, 01 Feb 2026 00:00:00 Z

Military service requires not only physical but mental as well as moral fitness. To qualify for service, recruits must meet standards in each area, demonstrating their abilities to meet the demands of military service.¹ Maintaining physical and mental fitness is necessary, as continued military career success is contingent on sustained health and fitness. Inability to physically or mentally meet the standards of the U.S. Armed Forces can result in no longer qualifying for service.^2-4

The Career History Archival Medical and Personnel System (CHAMPS) is a comprehensive archival database that collects and maintains career and medical related records for millions of U.S. service members of all branches of service: Army, Navy, Marine Corps, Air Force, Space Force, and Coast Guard. CHAMPS comprises over 1 billion career records from 1980 through 2022, with medical records from 2001 through 2023, for millions of active duty and activated reserve U.S. service members. On average, 212,493 new service members join the military each year (Table). This robust source of data creates a timeline of career and medical events as service members enter, progress through, and separate from service.

The CHAMPS database was created to be a comprehensive, longitudinal database of all career and health events throughout a service member’s military career. CHAMPS can be used to trace the complete trajectory of an individual service member’s career, from accession and occupational specialty to career progression and promotion, deployment history, and eventual separation. Medical history data in CHAMPS include diagnosis and procedure codes, vital and immunization history, and laboratory and radiology records for all inpatient and outpatient encounters within military hospitals and clinics in addition to civilian health care providers.

CHAMPS was designed to provide insight into the correlation between health characteristics and military careers. Thorough analysis of the timing and trajectory of career and health events creates a more robust understanding of the experiences of service members and the complex interplay between career and health in a service member’s life. This editorial presents an overview of the CHAMPS database, including available data fields, sources used, and example questions being answered with CHAMPS data. This editorial is intended to provide a comprehensive understanding of the utility and opportunities for research that CHAMPS presents, its existing and potential collaborations, as well as its significant analytical products to date, in an effort to help answer the most pressing questions about military health, readiness, and career outcomes. This study was approved by the Naval Health Research Center Institutional Review Board in compliance with all applicable federal regulations governing the protection of human subjects (NHRC.2021.002).

Data sources

CHAMPS includes demographic, career, and deployment military data from the Defense Manpower Data Center (DMDC) and health data from the Military Health System (MHS) Data Repository (MDR). Career events comprise 47% of the data in CHAMPS, with the remainder (53%) comprised of medical events (Table). All career and medical events are chronologically concatenated in CHAMPS.

CHAMPS incorporates monthly personnel-and career-related data including demographics, military occupational specialty, accession and separation, deployment start and stop dates, and deployment countries or duty locations from DMDC. Data from a total of 11,748,005 unique service members are housed in the CHAMPS database (Table). Records are uniquely identified using Social Security Numbers (SSNs). Demographic characteristics for each service member include full name, date of birth, SSN, Electronic Data Interchange Personal Identifier (EDI-PI), age, sex, race and ethnicity, education, marital status, and most recent home location prior to military service. Total records in CHAMPS are predominantly Army (42%), followed by Air Force (24%), Navy (21%), Marine Corps (11%), and Coast Guard (2%); Space Force data are still too limited to constitute a significant percentage. On average, from 1980 through 2022, over 80% of accessions are consistently new Army, Air Force, and Navy service members (Figure 1). CHAMPS contains historical medical data from 2001 through 2023, with medical-related information obtained from the MDR on an annual basis. MDR data include medical reimbursement information including dates, locations, and types of encounters; medical codes (e.g., International Classification of Diseases, Current Procedural Terminology, diagnosis-related group); prescriptions for all outpatient care at military hospitals and clinics (i.e., direct care) as well as civilian (i.e., purchased care) facilities; in addition to death date, when applicable, and status. In addition, detailed clinical and administrative data from military hospitals and clinics are available: appointments, referrals, laboratory and radiology orders and results, immunizations, vital records, and both inpatient and outpatient pharmacy records. Civilian care data are limited to health care administrative data billed to TRICARE.

Career-related information in CHAMPS reflects core aspects of a military service career, including promotions, duration of service, and events of significance both individually and historically, as the database spans decades and multiple major conflicts. CHAMPS data include each service member’s initial accession date to the military, rank (e.g., enlisted, E01-E09; officer, O01-O10; warrant officer, W01-W05), branch of service (Army, Navy, Marine Corps, Air Force, Space Force, Coast Guard), status (active duty, activated reserve or Guard), and occupation designator (duty, primary or secondary).

Information on career progression (e.g., promotions, demotions) can be found using rank and branch of service variables. Condition of discharge or reason for separation from the military is categorized and defined as: “dropped from strength or correction” (e.g., desertion, imprisonment, missing in action or prisoner of war, change in status); “entry into officer program” (e.g., officer commissioning, warrant officer program, military service academy); death (e.g., battle casualty, non-battle casualty such as disease, cause of death not specified); administrative separation (e.g., failure to meet behavioral and performance criteria such as character or behavior disorder, drug or alcohol misuse, ineptitude); medical separation (e.g., medical disqualification due to disability, condition existing prior to service, failure to meet weight or body fat standards); early release (e.g., school attendance, insufficient retainability, police duty, seasonal employment, national interest); end of active service (e.g., expiration of term of service due to end of contract without re-enlistment); re-enlistment (if immediate re-enlistment required); and retirement (e.g., service of 20+ years, medical retirement). Military discharge based on conduct and performance are divided into 2 categories: administrative discharge—e.g., honorable, general (under honorable conditions), or other than honorable—and punitive discharge (e.g., bad conduct or dishonorable).

Capabilities, collaborations, and future directions

The CHAMPS database offers numerous research possibilities, given the types, volume, and depth of information it contains. CHAMPS represents a prime opportunity for collaboration and data-driven exploration of the factors that affect not only the career and health outcomes of service members but the complex relationships among those factors. CHAMPS data reveal that some of the principal reasons service members separate from the military are required re-enlistment (37%), end of active service or expiration of term of service (23%), administrative separation or failure to meet behavioral and performance criteria (13%), and retirement (9%) (Table). If including only desired type of discharges—e.g., early release, end of active service, failure to meet behavioral or performance criteria, medical disqualification, retirement—the majority of service members separated because they reached the end of their contracts or chose not to re-enlist (Figure 2). Since early 2000s there has been a notable increase in medical discharges, comparable to a study published by the RAND Corporation.⁵

Career history information in CHAMPS can be compared with available medical information to estimate the relative influence of career- or medical-related factors on service member retention, and other related topics. Numerous medical conditions could be examined in relation to successful military service, to determine their prevalences and corresponding impacts on service member performance. Because CHAMPS passive data collection spans decades, it allows a longitudinal understanding of the relationship between career and health outcomes during time in service.⁶

The inclusion of individual identifiers such as SSNs allows for links with other data sources. Extant military datasets have been augmented with the CHAMPS database to answer pressing questions, resulting in published findings on studies of the impact of injuries on military career outcomes⁷; mortality rates and severe extremity injuries⁸; impacts of traumatic brain injury (TBI) and severe limb injury on suicide⁹; musculoskeletal and blast-induced injuries¹⁰; TBI and low-level blast exposure on adverse career outcomes¹¹; associations between concussion, severe TBIs, and early-onset of dementia¹²; brain injury and military alcohol misuse¹³; the relationship between mental health issues and attrition¹⁴; predictors of psychiatric disorders among combat veterans^15-18; tele-behavioral health, in-person, and hybrid treatment of U.S. service members¹⁹; Marine recruit health and the Recruit Assessment Program^16,20,21; and the limited duty Sailor and Marine Readiness Tracker System.²²

CHAMPS does not track service members (e.g., death records) after separation, as it is limited to data captured during activated reserve or active duty status, but CHAMPS can be linked to data sources that follow health characteristics after service (e.g., Department of Defense and Veterans Affairs Infrastructure for Clinical Intelligence, or DaVINCI).

CHAMPS has been used extensively as a resource at the Naval Health Research Center, both as a named compendium of data and as a program for specific data elements (e.g., DMDC, MDR) that provides expertise and support for data agreement development, links, management, and analysis. All projects utilizing CHAMPS through data sharing agreements are tracked and enumerated. CHAMPS has been utilized for an assessment of the functional outcomes of lumbar microdiscectomy using a standardized physical readiness test (PRT) in a military population; identification of factors associated with PRT failure among U.S. Navy active duty and reserve service members; identification of characteristics of service members assigned to shipboard duty associated with admittance to U.S. Navy Medicine’s temporary limited duty (LIMDU); utilization of event transaction data to investigate post-LIMDU career outcomes for sailors designated for return to duty, in collaboration with Naval Medical Center San Diego; retrospective review of pulmonary medicine patients diagnosed with bronchiectasis and creation of a bronchiectasis registry, generating hypotheses for future research; identification of patients diagnosed with basal cell carcinoma matched with prescription medication history, deployment history, and career history; linking the data of personnel with musculoskeletal injuries sustained during combat; and collaboration with the Department of Defense and Uniformed Services University Brain Tissue Repository to improve warfighter brain health.

CHAMPS is an invaluable resource utilized in a multitude of military health research topics, through the detection of precursor metrics of risk as well as protective factors associated with outcomes such as readiness, individual trajectories, specific health conditions, substance abuse, sexual assault, domestic violence, and suicide. Prior and ongoing projects that have utilized the wealth of longitudinal and individual information housed in the CHAMPS database demonstrate not only its current but continuously expanding capabilities, with significant potential for additional exploration and further collaborations.

References

Office of the Under Secretary of Defense for Personnel and Readiness. DoD Instruction 6130.03, Volume 1: Medical Standards for Military Service: Appointment, Enlistment, or Induction. U.S. Dept. of War. Updated May 28, 2024. Accessed Feb. 1, 2026. https://www.esd.whs.mil/portals/54/documents/dd/issuances/dodi/613003_vol01.pdf
Office of the Under Secretary of Defense for Personnel and Readiness. DoD Instruction 1332.18: Disability Evaluation System. U.S. Dept. of War. Nov. 10, 2022. Accessed Feb. 1, 2026. https://www.esd.whs.mil/portals/54/documents/dd/issuances/dodi/133218e.pdf
Hoge CW, Auchterlonie JL, Milliken CS. Mental health problems, use of mental health services, and attrition from military service after returning from deployment to Iraq or Afghanistan. JAMA. 2006;295(9):1023-1032. doi:10.1001/jama.295.9.1023
Mullinax M, Sorensen I, Dintaman J, Hisle-Gorman E. Effect of serious mental health and physical injuries and their treatment on career trajectories for military service members. Mil Med. 2025;190(11-12):e2466-e2473. doi:10.1093/milmed/usaf191
Simmons MM, Farmer CM, Cherney S, et al. Post 9/11 Trends in Medical Separation and Separation for Service Members with Posttraumatic Stress Disorder and Traumatic Brain Injury. RAND Corporation;2021. Accessed Feb. 1, 2026. https://www.rand.org/pubs/research_briefs/RBA1197-1.html
Gunderson EK, Miller MR, Garland CF, Naval Health Research Center. Career History Archival Medical and Personnel System (CHAMPS): Data Resource for Cancer, Chronic Disease, and Other Epidemiological Research. Report 02-06. Dept. of the Navy, U.S. Dept. of Defense;2002. Accessed Feb. 2, 2026. https://apps.dtic.mil/sti/citations/ADA419547
Trone DW, Villaseñor A, Macera CA. Negative first-term outcomes associated with lower extremity injury during recruit training among female Marine Corps graduates. Mil Med. 2007;172(1):83-89. doi:10.7205/milmed.172.1.83
Schmied EA, Boltz J, Levine JA, et al. All-cause and cause-specific mortality rates after severe extremity injuries among previously deployed active duty service members. PM R. 2023;15(10):1300-1308. doi:10.1002/pmrj.12954
Chung SY, Levine JA, Schmied EA, et al. Impacts of traumatic brain injury and severe limb injury on death by suicide: concurrent investigations using path analysis. J Head Trauma Rehabil. 2025;40(5):e420-e429. doi:10.1097/htr.0000000000001053
Belding JN, Englert R, Bonkowski J, Thomsen CJ. Occupational risk of low-level blast exposure and TBI-related medical diagnoses: a population-based epidemiological investigation (2005-2015). Int J Environ Res Public Health. 2021;18(24):12925. doi:10.3390/ijerph182412925
Belding JN, Bonkowski J, Englert R. Traumatic brain injury and occupational risk of low-level blast exposure on adverse career outcomes: an examination of administrative and medical separations from service (2005-2015). Front Neurol. 2024;15:1389757. doi:10.3389/fneur.2024.1389757
Belding JN, Bonkowski J, Englert R, Grimes Stanfill A, Tsao JW. Associations between concussion and more severe TBIs, mild cognitive impairment, and early-onset dementia among military retirees over 40 years. Front Neurol. 2024;15:1442715. doi:10.3389/fneur.2024.1442715
Woodruff SI, Hurtado SL, Simon-Arndt CM, Lawrenz J. An exploratory case study of environmental factors related to military alcohol misuse. BMC Public Health. 2018;18(1):902. doi:10.1186/s12889-018-5843-5
Schmied EA, Highfill-McRoy RM, Crain JA, Larson GE. Implications of psychiatric comorbidity among combat veterans. Mil Med. 2013;178(10):1051-1058. doi:10.7205/milmed-d-13-00135
Booth-Kewley S, Schmied EA, Highfill-McRoy RM, et al. Predictors of psychiatric disorders in combat veterans. BMC Psychiatry. 2013;13:130. doi:10.1186/1471-244x-13-130
Booth-Kewley S, Highfill-McRoy RM, Larson GE, Garland CF. Psychosocial predictors of military misconduct. J Nerv Ment Dis. 2010;198(2):91-98. doi:10.1097/nmd.0b013e3181cc45e9
Crain JA, Larson GE, Highfill-McRoy RM, Schmied EA. Postcombat outcomes among marines with preexisting mental diagnoses. J Trauma Stress. 2011;24(6):671-679. doi:10.1002/jts.20700
Baker DG, Nash WP, Litz BT, et al. Predictors of risk and resilience for posttraumatic stress disorder among ground combat marines: methods of the Marine Resiliency Study. Prev Chronic Dis. 2012;9:e97. doi:10.5888/pcd9.110134
Walter K, Glassman L, Levine J, et al. Telebehavioral health, in-person, and hybrid modalities of treatment delivery among US service members: a longitudinal observational study. JMIR Ment Health. 2026;13:e83809. doi:10.2196/83809
Larson GE, Booth-Kewley S, Highfill-McRoy RM, Young SY. Prospective analysis of psychiatric risk factors in marines sent to war. Mil Med. 2009;174(7):737-744. doi:10.7205/milmed-d-02-0308
Williams D, Yea JC, Zhu Y, Naval Health Research Center. Summary of Recruit Assessment Program Survey Prediction of Military Personnel Outcomes. Report 21-75. Dept. of the Navy, U.S. Dept. of Defense;2021. Accessed Feb. 1, 2026. https://apps.dtic.mil/sti/citations/AD1158054
McWhorter S, Simon-Arndt C, Carlson L. Overview of Navy Medicine’s limited duty patient population. Mil Med. 2024;189(3-4):820-827. doi:10.1093/milmed/usac348

Author Affiliations

Deployment Health Research Department, Naval Health Research Center, San Diego, CA: Mr. Haile, Mr. Bonkowski, Dr. Khodr, Dr. McAnany, LT Lausted, LT Carnes; Leidos, Inc., San Diego, CA: Mr. Haile, Mr. Bonkowski, Dr. Khodr, Dr. McAnany; Department of Health Professions Education, Uniformed Services University, Bethesda, MD: LCDR Biggs

Acknowledgments

We greatly acknowledge the support of an additional data management team member of the Career History Archival Medical and Personnel System (CHAMPS): Khider Allos, MCS, BSc. We also thank all current and previous members of the CHAMPS data management team for maintaining this important archival database since its inception.

Disclaimer

The views expressed in this article are those of the authors and do not necessarily reflect official policy or position of the Department of the Navy, Department of Defense, nor the U.S. Government.

LCDR Biggs, LT Lausted and LT Carnes are military service members. This work was prepared as part of their official duties. Title 17, U.S. Code Section 105 provides that copyright protection under this title is not available for any work of the U.S. Government. Title 17, U.S. Code Section 101 defines a U.S. Government work as work prepared by a military service member or employee of the U.S. Government as part of that person’s official duties.

The authors declare that they have no competing interests.

Report 25-70 was supported by the Office of Naval Research under work unit NMR11355-29. The study protocol was approved by the Naval Health Research Center Institutional Review Board in compliance with all applicable federal regulations governing the protection of human subjects. Research data were derived from approved Naval Health Research Center Institutional Review Board protocol NHRC.2021.002.

The datasets generated and analyzed during the current study are not publicly available due to personally identifiable information regulations, but they may be made available by the corresponding author on reasonable request and approval by the Naval Health Research Center Institutional Review Board/Privacy Office.

Surveillance snapshot: Adherence to disease and injury standardized surveillance categories in two U.S. Africa command exercises, 2024

Sun, 01 Feb 2026 00:00:00 Z

Disease and non-battle injury (DNBI) is a significant threat to military operations, historically exceeding combat injuries in deployed settings.^1-4 Disease and injury (D&I) surveillance supports health risk assessment for the purpose of instituting interventions as needed to promote and maintain the health of deployed forces.^5-7 Defense Health Agency Procedural Instruction (DHA-PI) 6490.03: Deployment Health, effective June 19, 2019, defines standardized surveillance categories for D&I reporting.⁶ While U.S. Department of War (DOW) policy prescribes electronic systems such as the Disease Reporting System internet (DRSi) and ESSENCE,⁶ the austere nature of expeditionary operations often necessitates reliance on paper documentation, where adherence to these guidelines has not been described.

D&I data from 2 exercises, African Lion and Flintlock, held in the U.S. Africa Command (USAFRICOM) Area of Responsibility (AOR) in 2024 were evaluated. The absolute and relative D&I burden from each exercise was calculated and compared with DHA-PI 6490.03 for category consistency and standardization. De-identified D&I surveillance data were obtained from the AFRICOM Surgeon’s Office, Southern European Task Force–Africa, and Special Operations Command–Africa. D&I entries were submitted by field medical teams—comprising Guard and active duty physicians, nurse practitioners, physician assistants, and combat medics—in accordance with exercise-specific reporting requirements, primarily using paper logs and consolidated after-action reports. The project was reviewed and approved by the Institutional Review Board of the Uniformed Services University of the Health Sciences.

During the African Lion 2024 exercise (33 days), 314 D&I cases were reported within 13 categories (Table). Only 8 of the 13 categories (61.5%) and 155 cases (49.4%) conformed to standardized surveillance guidelines in accordance with DHA-PI 6490.03. The Flintlock 2024 exercise (12 days) recorded 203 D&I events within 18 categories. Compared to the DHA-PI 6409.09 standardized categories, 7 of 18 categorizations (38.9%) and 113 D&I cases (55.7%) were recorded correctly. While the high relative burden of respiratory (upper) cases (23.2%) in African Lion and gastrointestinal cases (30.0%) in Flintlock suggest significant environmental threats, the use of standardized surveillance categories in only 49.4% and 55.7% of entries, respectively, limits the ability to meaningfully correlate these events with health risk assessments or location-specific risk mitigation.

This descriptive analysis demonstrated inconsistent adherence of D&I surveillance to published military guidelines. Reporting and categorization of D&I during these exercises highlights the need for enhancing technical and administrative readiness in austere, resource-limited operational environments. While DOW electronic health records (e.g., Theater Medical Data Store) are designed to feed into standardized reporting systems, use of paper documentation in these austere environments prevents this automation.

Accurate documentation is needed for actionable medical readiness and planning.^1,5 Furthermore, lack of adherence to standardized case definitions at the point of care limits the operational value of surveillance; a list of illnesses and injuries without proper classification is ineffective for ensuring force health protection. Even in austere environments, and perhaps especially in those environments, standardized and timely data are essential for early threat detection and operational decision-making.

Recommended courses of action to combatant commands include prioritization of efforts to improve D&I surveillance by incorporating surveillance strategy into operational plans and orders (Annex Q); modifying field documentation tools (e.g., Standard Form 600, Chronological Record of Medical Care) to include D&I checkboxes; and integrating preventive medicine assets to provide just-in-time training and data quality assurance.

References

Alcover KC, Howard K, Poltavskiy E, et al. Disease and non-battle injury in deployed military: a systematic review and meta-analysis. Mil Med. 2024;189(s3):21-30. doi:10.1093/milmed/usae033
Belmont PJ, Goodman GP, Waterman B, et al. Disease and nonbattle injuries sustained by a U.S. Army brigade combat team during Operation Iraqi Freedom. Mil Med. 2010;175(7):469-476. doi:10.7205/milmed-d-10-00041
Hauret KG, Pacha L, Taylor BJ, Jones BH. Surveillance of disease and nonbattle injuries during US Army operations in Afghanistan and Iraq. US Army Med Dep J. 2016:(2-16):15-23.
Kauvar DS, Gurney J. Exploring nonbattle injury in the deployed military environment using the Department of Defense Trauma Registry. Mil Med. 2020;185(7/8):e1073-e1076. doi:10.1093/milmed/usz481
Armed Forces Health Surveillance Branch. Absolute and relative morbidity burdens attributable to various illnesses and injuries among active component members of the U.S. Armed Forces, 2023. MSMR. 2024;31(6):2-10. Accessed Feb. 2, 2026. https://www.health.mil/news/articles/2024/06/01/msmr-health-care-burden-active-component
Office of the Under Secretary of Defense for Personnel and Readiness. DoD Instruction 6490.03: Deployment Health. U.S. Dept. of War. Jun. 19, 2019. Accessed Feb. 2, 2026. https://www.esd.whs.mil/portals/54/documents/dd/issuances/dodi/649003p.pdf
Chairman of the Joint Chiefs of Staff. Joint Publication 4-02: Joint Health Services, Incorporating Change 1, 28 September 2018. Accessed Feb. 2, 2026. https://cdmrp.health.mil/pubs/pdf/joint%20health%20services%20publication%20jp%204-02.pdf

Author Affiliations

School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD: Lt Col Rupert; Office of the Command Surgeon, U.S. Africa Command, U.S. Department of War: Lt Col Frankel, Ms. Dressner; Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences: Lt Col Sayers

Disclaimer

The views expressed are those of the authors and do not necessarily reflect the official view nor policy of the Uniformed Services University of the Health Sciences, U.S. Air Force, nor the Department of War. This work was prepared by military and civilian employees of the U.S. Government as part of their official duties and therefore is in the public domain and does not possess copyright protection. Title 17, U.S. Code Section 105 provides that copyright protection under this title is not available for any work of the U.S. Government. Title 17, U.S. Code Section 101 defines a U.S. Government work as work prepared by a military service member or employee of the U.S. Government as part of that person’s official duties.

Public domain information may be freely distributed and copied, but as a courtesy it is requested that the Uniformed Services University and authors be given appropriate acknowledgment.

The authors have no conflicts of interest to disclose.

Historical perspective: Post-infection symptoms in U.S. soldiers with malaria during the Second World War: major limitation to return to duty

Sun, 01 Feb 2026 00:00:00 Z

Abstract

Malaria proved decisive in determining the outcome of the Pacific theater during the Second World War. In 1943 alone, over 100,000 malaria cases were reported among the U.S. military in the Southwest Pacific and South Pacific. Thousands of sick soldiers were evacuated from their units and hospitalized for weeks or months of rehabilitation due to malaria. The primary challenge was not treatment of acute infections, as death rates were very low, but rather an inability to return recovered soldiers quickly to their units. Relapsing Plasmodium vivax malaria posed a particular problem, with many soldiers stationed at Guadalcanal or New Guinea suffering more than 10 relapses. Secondary gain from residual symptoms became apparent when around 1% of malaria patients were repatriated for ‘chronic malaria’. Future conflicts disrupted by infectious diseases will almost certainly include diffuse, post-infection symptoms that must be anticipated to prevent catastrophic warfighter attrition.

“However, the way the individual adjusted to the malaria and concurrent situational factors, contributed to the development of symptoms, to their perpetuation and intensification.”¹

Nearly all U.S. soldiers deployed to the Pacific theater during the Second World War were hospitalized at least once per year,² primarily for infectious diseases—such as malaria, scrub typhus, filariasis, and skin infections—rather than combat wounds.³ Malaria came close to being a decisive agent in the Pacific theater due to the sheer number of casualties it produced. Infection rates reached 250 per 1,000 men per year in the Solomon Islands and New Guinea.⁴ Figure 1 shows the variety of hospitalizations in Guadalcanal, in the Solomon Islands, in 1942-1943, with a majority due to malaria.³ At the end of 1942, entire units had been incapacitated by malaria in Milne Bay, New Guinea due to inadequate chemoprophylaxis and preventive measures, but fortunately after the combined Australian and U.S. forces had already defeated the Japanese invasion the previous August.⁵

From the viewpoint of military commanders, the most critical limitation of malaria infection was that infantry divisions withdrawn from the Solomon Islands or New Guinea became useless for further deployment for at least 6 months.⁴ Multiple relapses of malaria struck soldiers even while their divisions attempted to reconstitute in non-endemic areas such as Australia and Fiji. Military planners estimated that maintaining contact with the enemy by 1 division required at least 3, possibly as many as 5, divisions simply because of malaria casualties.^5,6 Relapsing malaria due to Plasmodium vivax was a common sequela that often led to multiple, sequential febrile attacks even when a soldier was removed from an endemic area.⁷

Two weeks of anti-malarial drug treatment was required for those sick enough to be hospitalized. Many soldiers were medically evacuated from combat zones due to limited medical support in forward areas.⁸ By 1943, the situation had become unsustainable. Eventually, improved regimens of enforced chemo-suppression with quinacrine, combined with better anti-mosquito measures, reduced new infections, and treatment regimens were shortened to 7 days. Use of 8-aminoquinolines to eliminate latent parasites causing relapse would have to wait for chemotherapeutic advances during the Korean War, however.⁹

While treatment with quinine and quinacrine (atabrine) proved successful, and death rates remained very low,⁶ a more insidious problem for the U.S. military emerged. Large numbers of soldiers developed chronic symptoms and weight loss that led to repatriation for ‘chronic malaria’. Chronic malaria was characterized not only by multiple relapses—10 were not unusual—but a failure to recover between nearly monthly febrile relapses. Soldiers suffering from chronic malaria populated a medical system designed to treat combat injuries, with 3,334 malaria evacuations to the U.S. from the South Pacific in 1943, and a similar number from the Southwest Pacific to Australia.^3,8

The magnitude of the problem prompted the U.S. military to designate entire Army general hospitals as specialty centers for tropical diseases: in Longview, Texas; Modesto, California; Swannanoa, North Carolina; and in Klamath Falls, Oregon, for the U.S. Navy and Marine Corps; in addition to the 105th General Hospital in Gatton, Australia.⁴ Those dedicated facilities were clearly preferrable to the tented field hospitals (Figure 2). The farther a malaria-infected soldier traveled from where he acquired infection, the better the treatment facilities became—and more removed the opportunity to return to his original unit. Secondary gains from continued symptoms increased proportionally. The concern over chronic malaria grew so severe that medical studies were initiated in both Australia and Fiji to determine better ways to limit disease casualties. After studying 3,358 malaria patients in Australia in 1943-1944, officials found a wide range of responses to malaria infection among service members.⁸ Many soldiers reported chronic weakness and a variety of ill-defined complaints including headaches, dizziness, nervousness, insomnia and tremor. In Fiji, largely working with soldiers from the Americal (23rd) Infantry Division, a group of psychiatrists conducted a medical and laboratory study of malaria groups at the 18th General Hospital,¹ and found similarly wide variation in soldiers’ abilities to deal with malaria infection. Those who tolerated the disease poorly primarily reported weakness and chronic fatigue, along with a host of ancillary complaints. Remarkably, the only definite physical finding from the studies of malaria casualties was that most soldiers had lost 10-20 pounds of body weight since developing malaria.

The results of those wartime studies concluded that the non-physical effects of malaria were largely psychosomatic in nature. It was ultimately determined that patients—and the U.S. Army—achieved better outcomes when chronic malaria’s psychosomatic element was recognized and its medicalization was minimized.⁸ One wartime study author observed, “The soldier is usually capable of remaining useful, even though sometimes in a limited capacity, so long as his morale remains satisfactory; and symptomatology only becomes severe when the adjustment of the person is faulty.”¹ Although malaria infection was nearly universal for frontline infantry, the vast majority of soldiers coped well with the stress and only required hospitalization when overcome by 40° Celsius fevers and uncontrollable rigors.

Neuropsychiatric casualties due to maladjustment were not new in the Pacific theatre. All humans have limitations on abilities to cope with stress, and soldiers in the Pacific theater found themselves in life-threatening situations in a tropical jungle, with malaria an added stress in an austere warfare environment. Soldiers whose coping mechanisms failed early showed up as combat stress casualties. In mid-1943, after landing on New Georgia in the Solomon Islands, the 43rd Division had been incapacitated by war neurosis and combat stress resulting in 16% medical evacuations.¹⁰ Fully 15% of medical evacuations from the South Pacific in 1943 were due to neuropsychiatric diagnoses, with likely considerable overlap with other diseases such as malaria.³

Chronic malaria manifested later in the World War II Pacific conflict, when soldiers consciously or unconsciously understood that illness would keep them from returning to a combat zone. Medicalizing the symptoms of either combat stress or malaria was counter-productive and likely extended soldier hospitalizations during the war. The treatment of combat stress casualties was subsequently developed with emphasis on proximity, immediacy, and expectancy—principles that greatly influenced recommendations for handling post-infection casualties. Malaria treatment units were created near the front lines and evacuation distances were minimized. These strategies conformed to the principles of combat stress treatment and succeeded even when the U.S. Army encountered drug-resistant malaria during the Vietnam War, proving highly effective for management of post-infection casualties.¹¹

While malaria is unlikely to recur as a major casualty-producing agent in current South China Sea scenarios, the recent COVID-19 pandemic demonstrated both our limited ability to predict future epidemics and the potency of chronic disabling conditions such as the poorly defined ‘long COVID’.¹² Current INDOPACOM (Indo-Pacific Command) military exercises can expose service members to scrub typhus, also likely to have post-infection symptoms, given its potential for cardiovascular damage.¹³ Most infectious diseases have post-infection symptoms, seen during World War II, with filariasis, and during the Vietnam conflict, with dengue infections.^14,15

Given the ability of disinformation to spread via the internet, along with the expectation of many soldiers that infections will cause chronic symptoms, future military medical officers will almost certainly find themselves in situations analogous to those in the South Pacific in 1943. Applying the same treatment principles established for combat stress neuropsychiatry—namely proximity, immediacy, and expectancy—for infectious diseases is likely to be successful in minimizing preventable casualties during any future conflict.

References

Tumulty PA, Nichols E, Singewald ML, Lidz T. An investigation of the effects of recurrent malaria: an organic and psychological analysis of 50 soldiers. Medicine (Baltimore). 1946;25:17-75. doi:10.1097/00005792-194602000-00002
Thomas HM. Southwest Pacific area. In: Medical Dept., U.S. Army; Coates JB, ed. Internal Medicine in World War II, Volume I: Activities of Medical Consultants. Office of the Surgeon General, Dept. of the Army;1961:473-568. Accessed Feb. 2, 2026. https://apps.dtic.mil/sti/tr/pdf/ada286772.pdf
Baker BM. South Pacific area. In: Medical Dept., U.S. Army; Coates JB, ed. Internal Medicine in World War II, Volume I: Activities of Medical Consultants. Office of the Surgeon General, Dept. of the Army;1961:569-623. Accessed Feb. 2, 2026. https://apps.dtic.mil/sti/tr/pdf/ADA286772.pdf
Hoff EC. Malaria. In: Medical Dept., U.S. Army; Coates JB, ed. Preventive Medicine in World War II, Volume VI: Communicable Diseases. Office of the Surgeon General, Dept. of the Army;1963.
Walker AS. The Island Campaigns (Volume III). In: Australia in the War of 1939–1945, Series Five: Medical. Australian War Memorial;1957. Accessed Feb. 2, 2026. https://www.awm.gov.au/collection/C1417326
Joy RJ. Malaria in American troops in the south and southwest Pacific in World War II. Med Hist. 1999;43:192-207. doi:10.1017/s002572730006508x
Shanks GD. Plasmodium vivax relapse rates in Allied soldiers during the Second World War: importance of hypnozoite burden. Am J Trop Med Hyg. 2022;107:1173-1177. doi:10.4269/ajtmh.22-0546
Levine HD. Medical experiences with American troops in the Pacific: with remarks on the diagnostic value of sternal puncture in malaria and on the innocuousness of hookworm infection. NEJM. 1946;235:933-938. doi:10.1056/nejm194612262352604
Zottig VE, Shanks GD. Historical perspective: the evolution of post-exposure prophylaxis for vivax malaria since the Korean War. MSMR. 2021;28(2):8-10. Accessed Feb. 2, 2026. https://www.health.mil/reference-center/reports/2021/02/01/medical-surveillance-monthly-report-volume-28-number-02
Hallam FT. War neuroses. Appendix G. In: Mullins W, ed. Neuropsychiatry in World War II, Volume II: Overseas Theaters. Office of the Surgeon General, Dept. of the Army;1973:1063-1069. Accessed Feb. 2, 2026. https://collections.nlm.nih.gov/catalog/nlm:nlmuid-0211560X2-mvpart
Modell W. Malaria and victory in Vietnam: the first battle against-drug-resistant malignant malaria is described. Science. 1968;162:1346-1352. doi:10.1126/science.162.3860.1346
Hitchcock S, Cintron SA, Kasuske L, Diaz FJ, Pierce J. Post-COVID-19 condition in military personnel. Mil Med. 2024;189:e1277-e1281. doi:10.1093/milmed/usad453
Suhr R, Belonogoff S, McCallum F, Smith J, Shanks GD. Scrub typhus outbreak among soldiers in coastal training area, Australia, 2022. Emerg Infect Dis. 2024;30:41-46. doi:10.3201/eid3014.240056
Russell PK, Ognibene AJ. Dengue and dengue shock syndrome. In: Ognibene AJ, ed. Internal Medicine in Vietnam, Volume II: General Medicine and Infectious Diseases. Office of the Surgeon General, Dept. of the Army;1982:91-98. Accessed Feb. 2, 2026. https://achh.army.mil/history/book-vietnam-genmedvn-ch05
Shanks GD, Smith JK. Lymphatic filariasis in soldiers exposed in INDOPACOM. MSMR. 2024;31(8):20-23. Accessed Feb. 2, 2026. https://www.health.mil/news/articles/2024/08/01/msmr-filariasis-indopacom
WW2 Military Hospitals: Pacific Theater of Operations and Minor Theaters. WW2 Medical Research Centre. 2024. Accessed Feb. 2, 2026. https://www.med-dept.com/articles/ww2-military-hospitals-pacific-theater-of-operations

Author Affiliations

Australian Defence Force Infectious Disease and Malaria Institute, Enoggera, Queensland, Australia; School of Public Health, University of Queensland, Brisbane, Australia

Acknowledgments

The author acknowledges the service and sacrifice of all those who served in the U.S. military during World War II and thanks the many unnamed military officers, scientists, historians, and medical librarians who unselfishly provided data and ideas for this manuscript, especially the librarians at the Australian Defence Force Library at Gallipoli Barracks, Queensland.

Disclaimer

The opinions expressed are those of the author and do not necessarily reflect those of the Australian Defence Force nor the Department of Foreign Affairs and Trade.

No specific funding was given for this work.

The author does not claim any conflicts of interest.

Reportable medical events at Military Health System facilities through week 44, ending November 1, 2025

Sun, 01 Feb 2026 00:00:00 Z

Reportable Medical Events (RMEs) are documented in the Disease Reporting System internet (DRSi) by health care providers and public health officials throughout the Military Health System (MHS) for monitoring, controlling, and preventing the occurrence and spread of diseases of public health interest or readiness importance. These reports are reviewed by each service’s public health surveillance hub. The DRSi collects reports on over 70 different RMEs, including infectious and non-infectious conditions, outbreak reports, STI risk surveys, and tuberculosis contact investigation reports. A complete list of RMEs is available in the 2022 Armed Forces Reportable Medical Events Guidelines and Case Definitions.¹ Data reported in these tables are considered provisional and do not represent conclusive evidence until case reports are fully validated.

Total active component cases reported per week are displayed for the top 5 RMEs for the previous year. Each month, the graph is updated with the top 5 RMEs, and is presented with the current month’s (October 2025) top 5 RMEs, which may differ from previous months. COVID-19 is excluded from these graphs due to changes in reporting and case definition updates in 2023.

For questions about this report, please contact the Disease Epidemiology Branch at the Defense Centers for Public Health–Aberdeen. Email: dha.apg.pub-health-a.mbx.disease-epidemiologyprogram13@health.mil

References

Armed Forces Health Surveillance Division. Armed Forces Reportable Medical Events. U.S. Dept. of War. Accessed Feb. 28, 2024. https://health.mil/reference-center/publications/2022/11/01/armed-forces-reportable-medical-events-guidelines
Defense Manpower Data Center. Department of Defense Active Duty Military Personnel by Rank / Grade of Service. U.S. Dept. of War. Accessed Feb. 28, 2024. https://dwp.dmdc.osd.mil/dwp/app/dod-data-reports/workforce-reports
Defense Manpower Data Center. Armed Forces Strength Figures for January 31, 2023. U.S. Dept. of War. Accessed Feb. 28, 2024. https://dwp.dmdc.osd.mil/dwp/app/dod-data-reports/workforce-reports
Navy Medicine. Surveillance and Reporting Tools–DRSI: Disease Reporting System Internet. U.S. Dept. of War. Accessed Feb. 28, 2024. https://www.med.navy.mil/navy-marine-corps-public-health-center/preventive-medicine/program-and-policy-support/disease-surveillance/drsi

Authors’ Affiliation

Defense Health Agency, Disease Epidemiology Branch, Defense Centers for Public Health–Aberdeen

U.S. Navy veteran and VA health care executive appointed as Department of War’s top medical leader

Mon, 12 Jan 2026 00:00:00 Z

Keith Bass, a retired U.S. Navy commander and career health care leader dedicated to delivering health services to active duty military and veterans, was sworn in Jan. 12, 2026, as the assistant secretary of war for health affairs.

Bass succeeds Dr. Lester Martinez-López, who held the position 2022-2024. Dr. Stephen L. Ferrara has been serving as the acting assistant secretary since January 2025 and will now serve as principal deputy assistant secretary of war for health affairs.

“It is the highest honor and a profound privilege to be entrusted with the health and well-being of our Nation's warfighters and their families,” said Bass, who was confirmed by the Senate on Jan. 5, 2026. “I am deeply committed to delivering the best health care possible and to continue in service to those who serve.”

As the former medical center director for West Texas VA Health Care System, Veterans Integrated Service Network 17, Bass managed health care services for more than 24,000 veterans, an operating budget of $153 million, and thousands of employees. He brings a decades-long career of overseeing comprehensive health care systems across government, the military, and the public.

During his career, Bass became the CIA’s first nonphysician director of the Office of Medical Services, leading teams of hundreds of physicians, nurses, physician assistants, and clinical psychologists who delivered health care to the agency’s workforce.

As a former director of the White House Medical Unit, he managed medical care to the president, the vice president, and their families. Before his position at the Department of Veterans Affairs, Bass was the senior vice president at GlobalMed, managing virtual patient care programs and telehealth services for agencies including the VA, Department of War, Defense Health Agency, and the White House.

Bass said he will leverage his comprehensive, interagency leadership experience to champion exceptional health care to warfighters and families he will serve.

“I am deeply committed to forging a seamless, world-class healthcare experience that supports our uniformed personnel and their families from their first day of service to their last, and continues to care for them as veterans,” he said. “Our warfighters and families deserve nothing less than the absolute best.”

Bass earned undergraduate degrees in psychology and rehabilitation science from Arkansas Tech University, and a master’s of science in rehabilitation counseling from University of Arkansas. He also holds a master’s in business administration and master’s of health care administration from Texas Women’s University, and a graduate certificate in legislative affairs from Georgetown University.

Top DOW doctor: ‘Fundamentally, our focus is to support the warfighter’

Mon, 12 Jan 2026 00:00:00 Z

Supporting the warfighter, sustaining medical skills, and strengthening the health care system are three key pillars of providing “health care for the people who defend our country,” says the top Department of War doctor.

Dr. Stephen Ferrara, an experienced clinician, combat veteran, educator, and health care leader, recently reflected on the pivotal work of the Military Health System in 2025, which delivered visible gains in readiness-focused partnerships, increased productivity, revamped credentialing and privileging process, and early deployments of artificial intelligence tools to give clinicians more time with patients.

On Jan. 20, 2025, Ferrara was appointed as the principal deputy assistant secretary of war for health affairs and immediately stepped in as the acting assistant secretary for health affairs until the official nominee, Keith Bass, was confirmed by the Senate Jan. 5, 2026.

Bass has now assumed duties as the assistant secretary.

Ferrara is a retired U.S. Navy doctor with 25 years on active duty and remains a practicing physician. Prior to rejoining the DOW, he was the Chief Medical Officer at the CIA. He also served as the deputy director for clinical operations for the National Capital Region, the DOW’s largest health care network. He currently serves as an interventional radiologist at Walter Reed National Military Medical Center and is a clinical professor of radiology and radiological sciences at the Uniformed Services University of the Health Sciences.

In this interview, Ferrara discussed the critical priorities for the MHS, such as upgrading infrastructure, maintaining clinical readiness, and strengthening the pipeline of talented professionals dedicated to delivering the best possible care for warfighters.

Following are edited excerpts from the interview, which can be viewed in its entirety on www.health.mil:

Question: You often talk about the “3 S’s” for the Military Health System: supporting the warfighter, sustaining our skills, and strengthening our chain. What do they mean?

Ferrara: Those principles underpin our mission: support the warfighter. We provide health care to the people who defend our country, and that's what makes us unique as a health care system. There are many great health care systems in America — but there's only one great American health care system that goes to war. Fundamentally, our focus is to support the warfighter.

Sustaining our skills means our learned skills must be maintained. I liken it to how our aviators have to get flight hours or how the trigger-pullers go to the range. For our health care professionals, it's working in our MTFs (military treatment facilities) where we're taking care of patients and keeping sharp. We have to be great because our warfighters deserve our very best.

On strengthening our chain: We stand on the shoulders of many great people, both in the military and in military medicine — so it's on all of us to make sure that we pass along that wisdom. Our MTFs are a giant force-generation platform. We graduate 16,000 medical technologists with a variety of skill sets every single year. If we were a university, we'd be the biggest one. We have graduate medical education programs. We have nursing training programs across our entire enterprise. We are always training people to be able to take that baton from us and continue to be a world-class health care system.

Question: It's been an action-packed year when you look back at 2025. How would you characterize a few of our greatest successes?

Ferrara: First of all, we perform on the big picture with great success, including increasing our partnerships. I think we've done a lot to bring in more patients, higher-complexity patients, which improves our force-generation and our skill-sustainment platforms. We've brought in more Medicare patients, and we’re really happy to be able to take care of them. Similarly, with the Department of Veterans Affairs, we've strengthened our partnership to take care of America’s veterans.

We've made significant efforts to reduce administrative burden and improve quality of life. I'm very sensitive to the burdens we place on those who care for patients, because that's what they love to do. We now have universal privileging, which is a significant breakthrough, as it eliminates low-value administrative work such as renewing privileges or obtaining transfer briefs. If you're good enough to work at one MTF, you can work across the enterprise. It also helps our mission by increasing agility, capacity, and capability by giving us the ability to utilize our personnel where and when we need them.

We rolled out ambient listening, an artificial intelligence-powered tool to help bring humanity back to health care. There’s the burden of how much note writing providers have to do, where people are taking work home. With this new tool, you can have a nice, captured conversation with your patient, talk to them, and really be focused and centered on the patient. Ambient listening rolled out at four sites this fall and we're looking to distribute that across the enterprise in 2026.

Question: Speaking about AI in a broader context, are there other priorities that you've set for the MHS to incorporate that technology?

Ferrara: We’re in a golden moment for medicine in terms of all the technology occurring, and AI is a big element of that. AI can also accelerate personalized medicine with the molecular and genetic techniques that we have. For drug discovery and development, AI can enable better therapies for patients.

Clinicians can use AI to make more rapid diagnoses and more rapid treatments, and technologies to empower and enable those frontline medics and corpsmen to be able to do prolonged field care. We’re leaning into our forward-deployable technology platforms, using tools enabling medics and corpsmen right at the point of injury to begin documenting the service member’s medical record. To support clinical decision-making, technology can provide access immediately to guide care for those service members, so we can maximize survivability in the war fight.

Question: You spent a significant amount of time this year going to the deck plate and visiting MTFs around the world. What are your biggest takeaways from those visits, and have you had the chance to incorporate any of the feedback that you've learned when you've been on the ground?

Ferrara: It's always really energizing and restorative for me to get out to the field and see where the work is done. I try to meet as many staff as I can, whether I'm in the operational unit or at an MTF. One of the things I share with them is why it's so important for me to go to the field. Here at the Pentagon, we’re making a lot of decisions, but we have such a large and complex health care system … sometimes the nature of the information gets heavily filtered by the time it gets to me.

When I go to those places, I can learn what it's like to be there. I don't think people at our MTFs or operational units are a mere row in an Excel spreadsheet. There's a lot more to it than that. These are people who are taking care of patients. It's really helpful for me to hear their stories, and I learn a lot from them. Whether it’s simple or about policy, I come back with a punch list of things to help fix.

Question: Can you talk a little bit about what happened at Walter Reed National Military Medical Center earlier in January 2025, one of your first experiences as acting assistant secretary?

Ferrara: Walter Reed, the President's hospital, experienced facility challenges including flooding. Like many of our facilities, they have aging infrastructure and deferred maintenance. I compliment their staff for moving heaven and earth to ensure they always took care of their patients. I went out there and walked the spaces, and then was able to go right to Congress and say, “here are the infrastructure problems, and we need support.” In the One Big, Beautiful Bill Act, they gave us $2 billion specifically because of those efforts. We can apply it to help close some of the gaps at our facilities with the greatest challenges for infrastructure. I think we came out of it really well, and that's where that kind of advocacy on the Hill can be invaluable.

Question: What are some of the biggest resource-related challenges and successes for the MHS right now?

Ferrara: I'm a people-oriented leader — I believe having not only the right number of people but also the right skill types and in the right places — because health care is a very personal craft that we do.

We faced challenges early in the year with our civilian teammates as we sought to preserve many positions, but we were very successful because we were able to show how valuable everyone on our team is.

In the last several months of the fiscal year, we implemented policies and increased revenue collections by about $700 million. That's really exciting, because we can take those resources and we can use them to hire people, work on infrastructure, and focus on things that we need.

The National Defense Authorization Act (Fiscal Year 2026) was passed, and we're getting a top-line increase as a department. I'm looking forward to more financial resources that we can then deploy to improve health care for our warfighters and beneficiaries.

Question: What’s the value of welcoming TRICARE For Life beneficiaries back into certain MTFs for patients and for providers?

Ferrara: First and foremost, we're a military family. The opportunity to welcome our TRICARE For Life patients, our seniors, back into the MTFs where they want to get their care is the right thing to do, because it's bringing people in who want to get care from us. They trust us. They've been with us for most of their life.

It also provides high-quality, outstanding care for patients. They get their medications, imaging, and specialist visits, and they have a great patient experience. It's great for skill sustainment when health care professionals are seeing more complex patients, continuing to hone their skills and keep them sharp.

Question: How do you see strengthening the partnership the MHS has with the Department of Veterans Affairs?

Ferrara: The VA can have more demand than they can supply, and we often have more supply; we have excess supply. It’s a great opportunity to meet both of our missions across the country. Veterans may have more complex medical issues than our young, active duty personnel who are often healthy. It provides a robust clinical mix for us and is culturally aligned. Veterans enjoy receiving care at our MTFs. It provides another source of revenue, and it's good for the taxpayer. It’s a great opportunity and unique in government, where we can have wins across the board.

One example of how this has been successful is in El Paso (William Beaumont Army Medical Center). The VA and the hospital have partnered, referring 12,000 surgical patients a year to us. This can generate high value for our combat readiness mission in neurosurgery, orthopedics, and general surgery.

Question: What message would you like to share with the MHS force?

Ferrara: I’d like to thank them for the work they do. During my site visits, one thing that inspires me is the common refrain: “We just find a way to get the job done.” I know that folks have innovation and ingenuity — but most of all, it's driven by a focus on mission, on taking caring of patients, and on being ready to defend the country. I am fighting hard on their behalf every day. I'll continue to do that. That's what I consider my top priority and my primary mission.

Surveillance snapshot: Chikungunya in Military Health System beneficiaries, 2020–2024

Thu, 01 Jan 2026 00:00:00 Z

Chikungunya is a mosquito-borne viral disease that can cause severe joint pain, fever, and other short- or long-term symptoms.¹ Chikungunya is endemic to tropical and subtropical regions, with cases and outbreaks recorded in more than 100 countries.² The U.S. Food and Drug Administration (FDA) recently approved 2 chikungunya vaccines: a live-attenuated vaccine called IXCHIQ in November 2023, and a virus-like particle vaccine called VIMKUNYA in February 2025.³ These vaccines are recommended for those traveling to high-risk areas. The FDA recently suspended the U.S. license for IXCHIQ in August 2025, however, citing vaccine safety concerns.⁴

This analysis was conducted to answer questions from military health leadership about the risk of chikungunya infection to service members and their families. The analysis employed data published in prior MSMR articles^5-7 to provide case counts for all Military Health System (MHS) beneficiaries from 2020 through 2024. Data were drawn from the Defense Health Agency’s Disease Reporting System internet (DRSi), and were confirmed via medical chart review.

Ten cases of chikungunya virus disease among MHS beneficiaries were documented from 2020 through 2024 (Table). Five cases were recorded in service members, 3 among family members (all spouses), and 2 in other beneficiary types (i.e., not service members or dependents). One case was acquired while on deployment to multiple locations in Southeast Asia; no other cases were related to official travel or deployment. Most cases were related to unofficial travel.

Polyarthralgia, or pain in multiple joints, was the most documented symptom (n=7). Other commonly reported symptoms included fever, rash, and myalgia. Two cases had long-term symptoms (i.e., lasting longer than 12 weeks), and 2 cases were hospitalized. No cases had evidence of prior chikungunya vaccination in their medical records.

The small number of cases, hospitalizations, and evidence of long-term symptoms reported in the past 5 years suggest that risk of chikungunya virus disease to MHS beneficiaries is small. Use of standard preventive measures including personal protective equipment and vaccination should, however, continue to be encouraged when indicated for service members and other beneficiaries traveling to high-risk areas.

References

U.S. Centers for Disease Control and Prevention. Chikungunya Virus. U.S. Dept. of Health and Human Services. Accessed Oct. 3, 2025. https://www.cdc.gov/chikungunya/index.html
U.S. Centers for Disease Control and Prevention. Areas at Risk for Chikungunya. U.S. Dept. of Health and Human Services. Accessed Oct. 3, 2025. https://www.cdc.gov/chikungunya/data-maps/index.html
U.S. Centers for Disease Control and Prevention. Chikungunya Vaccine Information for Healthcare Providers. U.S. Dept. of Health and Human Services. Accessed Oct. 3, 2025. https://www.cdc.gov/chikungunya/hcp/vaccines/index.html
U.S. Food and Drug Administration. FDA Update on the Safety of Ixchiq (Chikungunya Vaccine, Live), August 2022, 2025. U.S. Dept. of Health and Human Services. Accessed Oct. 3, 2025. https://www.fda.gov/vaccines-blood-biologics/safety-availability-biologics/fda-update-safety-ixchiq-chikungunya-vaccine-live
O’Donnell FL, Fan M, Stahlman S. Surveillance for vector-borne diseases among active and reserve component service members, U.S. Armed Forces, 2016-2020. MSMR. 2021;28(2):11-15. Accessed Nov. 18, 2025. https://www.health.mil/reference-center/reports/2021/02/01/medical-surveillance-monthly-report-volume-28-number-02
O’Donnell FL, Stahlman S, Fan M. Surveillance for vector-borne diseases among active and reserve component service members, U.S. Armed Forces, 2010–2016. MSMR. 2018;25(2):8-15. Accessed Nov. 18, 2025. https://www.health.mil/reference-center/reports/2018/01/01/medical-surveillance-monthly-report-volume-25-number-2
Stahlman SL, Langton RS. Surveillance snapshot: chikungunya in service members of the U.S. Armed Forces, 2016–2022. MSMR. 2023;30(12):11. Accessed Nov. 18, 2025. https://www.health.mil/news/articles/2023/12/01/msmr-chikungunya

Authors’ Affiliation

Defense Health Agency, Public Health Directorate, Armed Forces Health Surveillance Division, Epidemiology and Analysis Branch, Silver Spring, MD: Dr. Stahlman; Defense Health Agency, Defense Centers for Public Health–Aberdeen, MD: Ms. Scatliffe-Carrion, Dr. McCannon

Letter from the Editor in Chief

Thu, 01 Jan 2026 00:00:00 Z

Thank you for being one of the many MSMR readers in 2025. MSMR’s mission is to publish operationally relevant, timely, and descriptive epidemiological articles that provide accurate data on topics vital to the health, safety, and resilience of the U.S. Armed Forces. As a product of the Armed Forces Health Surveillance Division (AFHSD), within the Public Health Directorate (PHD) of the Defense Health Agency (DHA), MSMR is a peer-reviewed journal published each month on health.mil that is also indexed on PubMed and archived on PubMed Central (PMC).

The MSMR role, supporting the combined missions of AFHSD, PHD, and DHA, remains vital. The need for appropriate database utilization, information synthesis, and methodologically valid analysis remains the ‘gold standard’ of epidemiological surveillance and medical knowledge development. MSMR continuously strives for timeliness with careful deliberation, relevance with objectivity, and scientific validity focused on force readiness, force health protection, and force resilience.

Although we publish MSMR for both warfighter readiness as well as military and civilian public health surveillance, planning, and response—with many individuals and organizations both within and outside DHA to thank—it is our readers such as you who are in our thoughts when we assemble, edit, and publish each issue. 2025 has truly been a high water mark for MSMR due to increased content, particularly in special topical issues, and significantly enhanced readership metrics.

MSMR published three special issues in 2025, which enhanced MSMR focus on unique military readiness and force health protection concerns. Our 30th anniversary issue in April featured 10 articles covering many operationally important topics including, but not limited to, historical highlights, influenza modeling, global pathogen surveillance, HIV testing, in addition to the annual malaria case update. In May, MSMR published a military women’s health and readiness issue, which also included 10 reports, covering a breadth of topics from infertility and contraception trends to military women’s health and readiness research and female warfighter performance in extreme environments.

MSMR’s third special issue, in September, presented our annual review of illnesses and injuries within the active, reserve, and Guard components of the U.S. Armed Forces in addition to its Military Health System (MHS) beneficiaries. The issue examined numbers and trends in hospitalization and ambulatory visits, deployment morbidity burdens, selected medical evacuations and telehealth usage by the active component members. Publishing morbidity burdens for the entire MHS in one issue provides our readers with a valuable reference document of recent case numbers and trends.

Rigorous data collection, exacting analysis, manuscript writing and review, and painstaking submission for publication is hard work, and we appreciate and heartily thank each author in 2025 for their scholarship and dedication. The MSMR editorial staff deeply appreciates the quality and operational value of every submission. Our manuscript submissions in 2025 increased by nearly two-thirds, and those increased submissions resulted in greater MSMR content, providing our readers with even more accurate, timely, and clear epidemiological reporting.

We also heartily thank our subject matter expert reviewers. Our external reviewers provide robust assessments and insightful comments informed by their professional knowledge and years of expertise that assist our authors’ refinement of their manuscripts. For each original manuscript submitted, our double-blind peer review process involves two independent subject matter experts who contribute clinical and professional perspectives, enhanced analyses, and additional editorial rigor that improves the quality of MSMR reporting.

MSMR began archiving on PMC in January 2024, enabling free, open, permanent access to our peer-reviewed content. Over the past two years, readership of MSMR content on PMC has steadily grown, expanding our impact within the international scientific community. The MSMR online ‘hit’ rate on PMC was 50% higher in 2025 compared to 2024.

Our reach and readership continue to increase as the appetite for high quality, evidence-based, military health-specific information continues to grow. The Department of War public health community is focused on collecting, publishing, and applying the increasing knowledge base to positively influence health awareness and outcomes. MSMR’s advances in 2025 are the result of hard work by the MSMR staff in concert with the excellent manuscripts submitted by public health investigators and researchers, not only from the various DHA organizations, but civilian and international contributors as well. MSMR staff works in collaboration with DHA PHD staff to more broadly share the findings that result from the substantial medical data available within DHA and the MHS.

Each MSMR issue comes together over the course of months, beginning with manuscript submission by our authors, comprehensive internal review by our editors, external review by external subject matter experts, painstaking responses and revisions by the authors, meticulous copy editing, and publishing on health.mil, indexing on PubMed, and archiving on PMC. We could not accomplish our mission to publish this operationally relevant journal without our authors, reviewers and, of course, our readers. Many thanks to you all!

Our plans for 2026 are robust. We will continue to increase our published content, and aim to publish earlier within the month, to increase the timeliness of our reporting. To return to my first Letter form the Editor’s Desk, published in January 2024, our mission and dedication remain firm and unchanged. I wrote then and reiterate, “In the most recent Armed Forces Health Surveillance Division (AFHSD) Annual Report, MSMR is referred to as the “premiere medical peer-reviewed journal published by the AFHSD and Defense Health Agency (DHA),” which provides “evidence-based estimates of the incidence, distribution, impact and trends of illness and injury among U.S. military service members and associated populations.” MSMR has a distinguished legacy of excellence and professional rigor. As we begin our 31st year, the MSMR staff is honored to pick up and carry that standard further. MSMR continues to be vigilant and undaunted by the continued high stakes role of public health but successes of 2025 position us well to continue to serve “those who serve” in 2026.

Very Respectfully,
Robert Johnson, MD, MPH, MBA
Col (ret) USAF
Editor-in-Chief
Medical Surveillance Monthly Report

Guillain-Barré Syndrome clinical characteristics and outcomes among U.S. active component service members, 2014–2022

Thu, 01 Jan 2026 00:00:00 Z

Abstract

An examination of Guillain-Barré Syndrome (GBS) cases among U.S. active component service members from 2014 through 2022 revealed an incidence rate of 1.6 cases per 100,000 person-years. Individuals younger than age 20 years and those in basic training exhibited higher incidence. The type of antecedent event, either illness or immunization, was not associated with higher disability ratings at long-term follow-up. The analysis also quantified morbidity among service members with GBS, finding that 28.0% of cases had a subsequent chronic pain diagnosis, and 28.7% of cases were referred to the medical evaluation board. The need for neuropathic pain medication during the acute phase predicted poorer long-term functional outcomes. Furthermore, electrodiagnostic evidence of axonal or mixed nerve damage correlated with greater disability after 1 year. Although basic trainees had higher incidence, their long-term morbidity was comparable to other groups. These findings underscore the considerable impact that GBS can have on affected military personnel and identify factors associated with long-term complications.

What are the new findings?

There were 1.6 cases of Guillain-Barré syndrome per 100,000 person years among active component U.S. service members from 2014 through 2022. There was no association between persistent disability and associated antecedent event (e.g., infection or immunization). Many patients experienced incomplete recovery, with 28.7% resulting in medical board referrals. Persistent disability was independently associated with chronic pain diagnosis.

What is the impact on readiness and force health protection?

Despite the low incidence rate of the disorder, approximately 29% of U.S. service member GBS cases experienced incomplete recovery that required medical board referral. Service members appear to be at a higher risk for GBS during initial recruit basic training, potentially due to increased exposure to infections and immunization requirements at accession.

Background

Guillain-Barré syndrome (GBS) is an acute immune-mediated polyradiculoneuropathy. GBS stems from an autoimmune response related to an antecedent illness, immunization, or other immune reaction causing damage to myelin (acute inflammatory demyelinating polyneuropathy, or AIDP) or axons (acute motor axonal neuropathy, or AMAN) of the peripheral nerves and ganglia. AIDP is the predominant variant seen in North America.¹

GBS occurs with an overall worldwide incidence rate (IR) of 0.6–4.0 cases per 100,000 people per year with higher rates reported in North America, 2.2–4.2 cases per 100,000.^2-8 One study from 2009 found a slightly higher incidence of GBS in the active duty U.S. military population compared to the general population.⁵ It is more common in men and can affect all age groups.¹

Mortality due to GBS varies in reported studies, from 2% to 10%, with predictors including advanced age, mechanical ventilation, and cardiopulmonary complications.^1-4,6 Morbidity with severe disability can be seen in upwards of 20% of patients, with predictors including advanced age, mechanical ventilation, preceding diarrheal illness, and high-grade disability in the acute phase.^1-4 Pain is a common symptom upon presentation and can persist long term, significantly affecting quality of life.⁹

Classic clinical presentation of GBS manifests as a progressive ascending muscle weakness with decreased or absent deep tendon reflexes.⁶ Patients also present with sensory symptoms, ataxia, lower back pain, and cranial nerve involvement that range in severity.⁶ Autonomic dysfunction is also common and can be fatal.⁶ Variants include pure motor, pure sensory, Miller Fisher, pharyngeal-brachial, and paraparetic.⁶

There are no specific biomarkers associated with GBS. Diagnosis of GBS is typically based on a thorough history and clinical examination. Certain diagnostic tools may support diagnosis, including cerebrospinal fluid (CSF) analysis, serum antibody testing, magnetic resonance imaging (MRI), and electrodiagnostic studies.

The disease timeline is typically monophasic, with progression over 2 weeks and symptom nadir (i.e., most critically ill point) around 4 weeks after onset.⁶ Severity is variable, and up to one-fourth of cases require mechanical ventiliation.^6,8 Close monitoring and early initiation of intravenous immunoglobulins (IVIG) or plasma exchange (PLEX) is essential for accelerating recovery.⁷ Uncommonly, acute clinical presentation of GBS can herald another neurological disorder, such as chronic inflammatory demyelinating polyneuropathy (CIDP) or neurological presentation of other systemic diseases such as lupus or infection.

Antecedent respiratory or gastrointestinal illness can be identified in up to three-fourths of patients presenting with GBS.^1,8,10 Campylobacter jejuni is the most common prior infection, with 30% of cases in 1 study demonstrating serological evidence of the infection.¹⁰ Other infectious etiologies include Mycoplasma pneumonia, cytomegalovirus, Epstein-Barr virus, hepatitis E virus, Zika, dengue, and influenza.^1,8,10 Asymptomatic infections have also been detected by serological testing, which may suggest higher rates of antecedent illness.¹⁰

The risk of immunization-related GBS was originally based on the 1976 swine influenza vaccine, but studies investigating influenza immunization after 1976 had mixed results, with most showing no causal relationship.¹¹ Low, but increased risk of GBS following adenovirus-vectored COVID-19 vaccines was lower than the risk identified with the 1976 influenza vaccine.¹² The same study also found reduced risk of GBS with the messenger RNA (mRNA) COVID-19 vaccines.¹² The U.S. Centers for Disease Control and Prevention (CDC)’s Advisory Committee on Immunization Practices (ACIP) states that GBS is not a precaution for future immunizations, unless it occurred within 6 weeks of receiving a tetanus-toxoid-containing vaccine or an influenza vaccine.¹³

Immunizations are administered upon accession into military service, unless a service member provides prior documentation of prior immunization or serological testing showing presence of antibodies.¹⁴ Immunization administration upon military accession is recommended before or at the beginning of basic training, to help mitigate risk of contagious disease in close quarters environments.¹⁴ Additional immunizations such as yellow fever, Japanese encephalitis, and rabies may be required depending upon travel or area of operation requirements.¹⁴ COVID-19 immunization was mandated for all military members in August 2021; however, the mandate was rescinded in January 2023. Immunizations identified by the CDC of potential concern are influenza and tetanus vaccines, however, other vaccines have also been implicated, including yellow fever and rabies.^15,16

A previous military population study, of matched case-control design, evaluated the association between GBS and acute gastrointestinal infections and deployment from 1999 through 2007.⁵ That 2009 study identified a slightly higher incidence in the military cohort compared to the general population, but it was limited by retrospective database review without medical record review.⁵

The objective of the current study was to describe the incidence, clinical characteristics (including antecedent illness or immunization), clinical course, and electrodiagnostic findings of U.S. active component service members (ACSMs) with clinically confirmed GBS from 2014 through 2022. Due to the timeline chosen for data extraction, it includes 2 years of COVID-19 immunization in addition to yearly influenza immunization. An updated, comprehensive understanding of the clinical characteristics of GBS, its disease course, and their readiness implications will supply health care providers with knowledge that can aid patient education, improve prognostication discussions, and potentially assuage apprehensions about immunizations in relation to GBS risk.

Methods

Potential cases of GBS were identified as those with documentation of an International Classification of Diseases, 9th or 10th Revision, Clinical Modification (ICD-9-CM/ICD-10-CM) code (357.0 or G61.0, respectively) in an inpatient or outpatient medical encounter from January 1, 2014 through December 31, 2022 among ACSMs in the U.S. Army, Navy, Marine Corps, Coast Guard, Air Force, or Space Force. The data came from medical records maintained in the Defense Medical Surveillance System (DMSS) that the authors obtained from the Armed Forces Health Surveillance Division (AFHSD) in 2023. DMSS ICD-9-CM/ICD-10-CM code queries included diagnostic positions of 4 digits for outpatient records and 9 digits for inpatient records. The records examined included those from military hospitals and clinics as well as civilian medical facilities if reimbursement was sought through the Military Health System (MHS). The 2014 start date was chosen to capture treatment and prescription data through DMSS. The Walter Reed National Military Medical Center determined this project to be human subject research exempt from institutional board review.

A list of 401 potential cases identified in DMSS was sent to the primary investigator’s research team of neurologists and neurology residents for individual record review (Figure). Cases were excluded during individual chart review when the diagnosis code was entered with no other supporting information to confirm diagnosis, or the diagnosis was revised during the acute treatment period. Cases were also excluded if the diagnosis code referenced childhood or prior history of GBS before January 2014.

Following individual chart reviews, 191 cases were identified as acute presentations of GBS. Cases were validated based on a culmination of consistent clinical history, symptoms upon patient presentation, physical examination findings, and treatment choice consistent with GBS diagnosis. Supporting diagnostic evidence including CSF studies, serum antibody testing, lumbar spine MRI findings, and electrodiagnostic testing were also reviewed to aid case validation. Data collected for the 191 identified cases included patient demographics, clinical information, electrodiagnostic testing data, and related case outcomes.

The acute phase of GBS was considered as the time from initial clinical evaluation to either end of acute treatment course, final hospitalization, or acute rehabilitation discharge. Clinical information collected in the acute phase included presence of antecedent illness or prior immunization, timeline of symptom onset, diagnostics (e.g., laboratory, imaging, electrodiagnostic data), primary treatments, pain treatment, hospital care and complications, and disability rating, using the Modified Rankin Scale (MRS), at the most critically ill point (i.e., nadir) of acute presentation. Antecedent illness information was obtained from clinical history and review of clinical notes 30 days prior to presentation, for indications of acute illness appointments or infection treatments. Immunization information was obtained from clinical histories, reviews of medical chart immunization records, and reviews of clinical notes indicating immunization appointments within 30 days preceding patient presentation. Any immunization within 30 days was recorded according to type of immunization and date administered.

Clinical information collected after the acute phase included additional electrodiagnostic study data, time to recovery, chronic pain diagnosis, and long-term follow-up MRS. Electrodiagnostic testing results were classified as normal, demyelinating, or mixed/axonal. Demyelinating cases had isolated demyelinating features that could include prolonged or absent F-waves, prolonged distal latencies, or slowed conduction velocities. Axonal or mixed cases had either axonal features alone or axonal and demyelinating features. Axonal features could include low amplitude action potentials or spontaneous activity on electromyography.

Time to recovery was assessed by patient report of recovery, date returned to full duty, and medical evaluation board (MEB) referrals. MEB information was compared and validated with MEB information provided by the DMSS. Chronic pain diagnosis based on presence of ICD-9-CM or ICD-10-CM code (338.2 or G89) or documentation of chronic pain within the clinical note and was dichotomized as present or not.

During chart reviews, chronic pain diagnosis was included if it could be related to GBS diagnosis in documentation of chronic pain present since acute phase. A diagnosis of chronic pain was included if pain was a new symptom at time of GBS diagnosis and continued at 1 year follow-up or beyond. Cases were not included in the chronic pain category if chronic pain diagnosis was clearly attributable to another injury. Chronic pain diagnosis was independently confirmed by the primary investigator.

MRS determination was based on reviews of neurologists’ interpretations of clinical disability reports from histories and documented physical examination findings. If multiple clinical encounters were available after 1 year of follow-up, then the encounter closest to 1 year after the original diagnosis was used to determine MRS. The MRS scale is a disability scale graded from 0 to 6, with 0 indicating no symptoms present, 1 indicating minimal symptoms but ability to complete all usual activities, 2 indicating slight disability but able to perform daily activities without assistance, 3 indicating moderate disability requiring help and unable to walk alone without assistance,4 indicating moderate severe disability requiring assistance for own bodily needs, 5 indicating severe disability unable to attend own body needs without constant assistance, and 6 indicating death.¹⁷

All reviewers were trained on proper application of the MRS scale, and a small sampling of cases was provided to the reviewers prior to initiation of review to aid with inter-rater reliability. The primary investigator confirmed complementary assessments with all reviewers of the sample cases provided. A data collection sheet for the acute phase collected medical research council sum score, ventilation requirements, and medical complications during hospitalization, which aided determination of nadir MRS scores. The primary investigator independently confirmed all MRS scores with the data collection sheet and independent review.

The list of confirmed cases was returned to AFHSD for IR calculation. AFHSD used longitudinal personnel data in the DMSS to calculate the rate of clinically confirmed GBS per 100,000 person-years (p-yrs) of active component service. Person-time was censored at the incident diagnosis date. Person time for recruit basic training was identified using a standard AFHSD algorithm based on time in service, assigned military installation, branch of service, and other factors.

Case characteristics were described and compared by MRS and chronic pain diagnosis as outcomes after 1 year follow-up using Wilcoxon 2-sample tests and Chi-square or Fisher’s exact tests. To evaluate potential independent associations of case characteristics with each outcome, multivariable logistic regression models estimated adjusted odds ratios associated with several characteristics selected by the research team based both on their potential clinical relevance and association with the outcome in unadjusted analyses. Further adjustment was avoided to guard against model overfitting and to conform to the traditional minimum number of events per predictor (>10) in logistic regression. Low variance inflation factors in each model indicated that multi-collinearity was not a concern.

The dependent variables were MRS outcome and chronic pain diagnosis at 1 year or greater follow-up. MRS was defined as either 0 (asymptomatic) or greater than 0 (minimally symptomatic to severe disability). Independent variables included use of neuropathic pain treatment in the acute treatment phase, presence of pain in the acute phase, and significant disability (MRS>3) during the acute phase. A dichotomized MRS definition of greater than or equal to 3 was based both on observed elevated frequencies of each outcome in this stratum relative to lower MRS scores and by the small cell sizes in several MRS strata, which prevented further evaluation of MRS as an ordinal score in multivariable models. These analyses were performed using R (version 4.0.5).¹⁸

Results

The DMSS database identified 401 potential GBS cases by ICD-9-CM/ICD-10-CM codes alone (Figure). After individual chart reviews, 191 cases were identified as acute GBS. An IR of 1.6 confirmed GBS cases per 100,000 p-yrs was calculated for ACSMs from 2014 through 2022 (Table 1). Male service members had a slightly higher rate than female service members (1.7 and 1.0 cases per 100,000 p-yrs, respectively). There was a higher IR in those younger than age 20 years, at 3.6 cases per 100,000 p-yrs, and those in basic training recruit status (11.5 cases per 100,000 p-yrs).

An isolated antecedent illness within 30 days of diagnosis was noted in 94 of 191 (49.2%) cases (Table 2). Median time from illness start to diagnosis date was 11 days. Isolated immunization within 30 days of the date of diagnosis was seen in 28 (14.7%) cases. Median time from immunization to date of diagnosis was 15 days. Approximately one-fourth of cases, 46 of 191 (24.1%), had both an antecedent illness and immunization within 30 days of the preceding syndrome presentation.

The most reported symptoms were upper respiratory illness or influenza-like illness, which were seen in 101 (52.8%) cases. COVID infection was only noted in 4 (2.1%) cases. Influenza was the most common immunization received within the preceding 30 days (11.5%). COVID immunization had been received in 9 (4.7%) cases. Among the 28 basic training recruits, 9 of the 28 (32.1%) had a prior illness only, 7 of the 28 (25%) had a previous immunization only, and 12 of the 28 (42.8%) had both a concurrent preceding illness and immunization (data not shown).

Pain was reported during initial patient presentation in 141 of 191 (73.8%) cases (Table 2). Over three-fourths of cases demonstrated no significant disability to moderate disability at the most critical point of initial presentation, ranging from MRS 1 in 40 cases (20.9%), MRS 2 in 71 cases (37.2%), and MRS 3 in 33 cases (17.3%). Almost one-fourth of cases had moderate to severe disability at nadir, with MRS 4 (n=29, 15.2%) or 5 (n=16, 8.4%).

All but 14 patients were hospitalized for monitoring and management (Table 2). The median duration of hospitalization was 7 days (range 2–54 days). Eleven patients received no documented treatment. Most patients received IVIG as primary treatment (n=155, 81.2%). Eleven patients received IVIG in combination with PLEX. Seven patients received PLEX alone, and 7 received steroids alone for primary treatment. Neuropathic pain medication was given to 48.2% (n=92) of patients.

Over half of cases (n=108, 56.5%) received electrodiagnostic testing. Among the 108 cases with electrodiagnostic testing, results were interpreted as normal in 27 cases; evidence of isolated demyelinating features was noted in 53 cases; and 28 cases had axonal or mixed findings (Table 2). Serial electrodiagnostic studies were completed in 45 of 108 (41.6%) cases (data not shown). Follow-up studies were normal in 15 of 45 (33.3%) cases, with median follow-up testing at 51 days (range 7–896 days). Demyelinating features were present in 20 of 45 cases (44.4%), with median follow-up testing at 152 days (range 6–1,063 days). Axonal or mixed features were observed in 10 of 45 cases (22.2%) with median follow-up testing at 122 days (range 19–1,191 days). Five cases had more than 2 serial electrodiagnostic tests completed (4 of 5 were axonal, range 35–1,088 days). One case with primary axonal damage had persistent fibrillation potentials and positive waves more than 1 year after original diagnosis (data not shown).

Following the initial treatment course, 36 patients had recurrent or persistent symptoms without interval improvement after initial hospitalization (data not shown). One case (0.5%) was believed to be recurrence of GBS, 3.5 years after the initial episode. Five cases experienced symptom recrudescence within 90 days, attributed to the initial disease presentation, that were subsequently treated with second rounds of IVIG. Five cases had recurrent symptoms more than 90 days later, without other objective evidence of recurrence, with 1 receiving IVIG treatment again 6 years later.

Long-term follow-up more than 1 year after initial diagnosis was available for 177 cases. Five cases had no clear improvement after acute presentation and continued to report persistent symptoms at long-term follow-up. Following the acute phase, alternative diagnoses (i.e., other than GBS) were made or suspected in 34 cases. Among those 34 cases, CIDP was the ultimate diagnosis in 13 cases; multifocal motor neuropathy was diagnosed in 2 cases; and other neurological disorders were suspected in 19 cases, including 10 cases of functional neurological disorder. Median time to symptom recurrence for CIDP was 33 days, and 130 days for all others with polyphasic presentations.

After excluding the 34 cases with possible alternative long-term diagnoses, there were 143 cases of GBS with more than 1 year of follow-up (Table 3). MRS was extracted from clinical encounters at least 1 year after original diagnosis. During follow-up after 1 year, 73 of 143 (51.0%) cases had returned to baseline, with MRS 0. The outcomes of long-term follow-up for all other cases were distributed from MRS 1 in 46 (32.2%) cases, MRS 2 in 17 (11.9%) cases, MRS 3 in 3 (2.1%) cases, MRS 4 in 3 (2.1%) cases, and MRS 6 in 1 case with death unrelated to GBS.

The average time for return to duty in those with full recovery was 5 months (median 4 months, range 0.5–40 months). Type of antecedent clinical event and recruit status were not associated with higher MRS at 1 year follow-up (p=0.182 and p=0.077, respectively) (Table 4a). Patients with axonal or mixed electrodiagnostic results were more likely to have MRS greater than 0 at 1 year versus patients with demyelinating or normal electrodiagnostic testing (p<0.001) (Table 4a).

Chronic pain associated with GBS diagnosis was seen in 40 (28.0%) of the 143 cases with no other alternative diagnosis at long-term follow-up (Table 3). Chronic pain diagnoses were seen more commonly in patients with MRS greater than 0 at 1 year (p<0.001) (Table 4a). Among the 143 cases at long-term follow-up, MEB was initiated for 41 (28.7%) cases, and 93 cases (65.0%) returned to duty. Twenty-six of the 41 referred for MEB had a chronic pain diagnosis (data not shown).

Of the 40 patients with chronic pain diagnoses, 33 (82.5%) initially required neuropathic pain treatment (p<0.001) (Table 4b). In a multi-variable logistic regression model of relevant clinical characteristics as predictors of MRS outcome and chronic pain diagnosis at long-term follow-up, neuropathic pain treatment was associated with greater risk of MRS greater than 0 (OR 6.8; 95% CI 2.8, 17.7; p<0.001) and resultant chronic pain diagnosis (OR 7.9; 95% CI 2.7, 28.0; p<0.001) independent of reported pain and MRS at nadir (Table 5).

Discussion

This study provides an update on clinical characteristics and outcomes in GBS among a large military cohort. GBS shares an overall similar IR in the U.S. military population when compared to reported rates globally^1,4,7-9 but has a lower incidence when compared to directly to other North American and European cohorts (1.9 to 4.2 per 100,000 p-yrs).^2,7 GBS severity is variable, but there were no fatalities attributable to GBS in this cohort. This lack of mortality may be related to a lower rate of mechanical ventilation (10.4% vs. up to 23% in other studies^3,8) and an overall healthier and younger active duty military population.

Results of electrodiagnostic testing can be helpful in predicting long-term MRS outcomes, but only slightly more than half of cases in this study had electrodiagnostic testing available for review. This study did not identify a clear role for serial electrodiagnostic testing. Serial testing can be beneficial, however, if the diagnosis is in question or the patient has persistent or recurring symptoms. It is notable that 1 axonal case had persistent fibrillation potentials and positive waves in serial electrodiagnostic testing more than 1 year after initial diagnosis; this appears to have captured the natural course of the disease rather than representing a second pathology.

This study quantified morbidity associated with GBS in U.S. ACSMs, as seen in the 28.0% of cases associated with a subsequent chronic pain diagnosis, and in 28.7% of cases referred to the MEB. The 2009 military study reported 20% of service members with continued medical visits related to GBS 1 year post-diagnosis, and other studies report approximately 20% with long-term disability.^4,5 There were similar rates of reported pain in the acute period (~70%) and long-term follow-up period (~25%) compared to a recent civilian cohort.⁹ The need for treatment with neuropathic pain medication could be an early indicator for morbidity, as it was independently associated with MRS of greater than 0 at follow-up. This may represent an additional and relatively early clinical feature to consider when determining overall prognosis. Additionally, it may provide an impetus to consider earlier or more aggressive treatment in certain cases. Future prospective studies could provide further clarification.

Determination of the type of GBS, axonal versus demyelinating, can be helpful with understanding associated acute and chronic pain, prognostication, and differentiating GBS from other mimickers.^4,6,9 A recent study highlighted that acute pain may be more pronounced in axonal variants while chronic pain may be associated with demyelinating, however, this was in a cohort of Asian subjects, who typically have higher axonal rates.⁹ If there is diagnostic or prognostic uncertainty after the acute phase, electrodiagnostic testing should be pursued.

There was no significant association between type of preceding event (e.g., infection or immunization) and long-term morbidity. Those in basic training recruit status did, however, have a higher incidence when compared to other groups. The recruit population typically receives multiple immunizations upon arrival, and they are also housed in close quarters, increasing potential infection transmissibility. This was reflected in this analysis, as most recruits had a preceding illness or immunization within 30 days of symptom onset. This study was not designed to determine direct causal relationships with immunization.

The main limitation of this study is its retrospective design. While these findings can provide insights, correlation cannot be established. Utilization of medical encounter data is both limited by accuracy of documentation and subject to reporting bias. Cases may be under-reported due to reliance on appropriate ICD-9-CM/ICD-10-CM code placement. Risk factors cannot be assessed with this study design. Antecedent illness and prior immunization data can be incomplete if not documented in clinical notes, or if service members received immunizations out of network without records in MHS medical charts. Evaluating morbidity by using an MRS greater than 0 may over-estimate significant disability.

This study provides an important update on GBS in the active component population of the U.S. military for MHS clinicians and may help guide future research. Given the increased incidence during the recruit training period in this study, it is prudent for health care providers working with populations such as military recruits to consider referral to an appropriate level of care if suspicion of GBS arises, in addition to ensuring appropriate immunization counseling and addressing elements to help mitigate close quarters disease transmissibility. Despite the increased incidence of GBS in recruits, no evidence supported a higher risk of long-term morbidity in the U.S. active component population.

References

van den Berg B, Walgaard C, Drenthen J, et al. Guillain-Barré syndrome: pathogenesis, diagnosis, treatment and prognosis. Nat Rev Neurol. 2014;10(8):469-482. doi:10.1038/nrneurol.2014.121
Bragazzi NL, Kolahi AA, Nejadghaderi SA, et al. Global, regional, and national burden of Guillain–Barré syndrome and its underlying causes from 1990 to 2019. J Neuroinflammation. 2021;18(1):264. doi:10.1186/s12974-021-02319-4
Alshekhlee A, Hussain Z, Sultan B, Katirji B. Guillain-Barré syndrome: incidence and mortality rates in US hospitals. Neurology. 2008;70(18):1608-1613. doi:10.1212/01.wnl.0000310983.38724.d4
Chiò A, Cocito D, Leone M, et al. Guillain-Barré syndrome: a prospective, population-based incidence and outcome survey. Neurology. 2003;60(7):1146-1150. doi:10.1212/01.wnl.0000055091.96905.d0
Nelson L, Gormley R, Riddle MS, Tribble DR, Porter CK. The epidemiology of Guillain-Barré syndrome in U.S. military personnel: a case-control study. BMC Res Notes. 2009;26(2):171. doi:10.1186/1756-0500-2-171
Willison HJ, Jacobs BC, van Doorn PA. Guillain-Barré syndrome. Lancet. 2016;388(10045):717-727. doi:10.1016/s0140-6736(16)00339-1
Shahrizaila N, Lehmann HC, Kuwabara S. Guillain-Barré syndrome. Lancet. 2021;397(10280):1214-1228. doi:10.1016/s0140-6736(21)00517-1
Finsterer J. Triggers of Guillain-Barré syndrome: Campylobacter jejuni predominates. Int J Mol Sci. 2022;23(22):14222. doi:10.3390/ijms232214222
Papri N, Mohammed A, Rahman MM, et al. Pain determinants and quality of life in Guillain-Barré syndrome: a prospective cohort study. BMJ Neurol Open. 2024;6(2):e000925. doi:10.1136/bmjno-2024-000925
Leonhard SE, et al, IGOS Consortium. An international perspective on preceding infections in Guillain-Barré syndrome: the IGOS-1000 cohort. Neurology. 2022;99(12):e1299-e1313. doi:10.1212/wnl.0000000000200885
Salmon DA, Proschan M, Forshee R, et al. Association between Guillain-Barré syndrome and influenza A (H1N1) 2009 monovalent inactivated vaccines in the USA: a meta-analysis. Lancet. 2013;381(9876):1461-1468. doi:10.1016/s0140-6736(12)62189-8
Censi S, Bisaccia G, Gallina S, Tomassini V, Uncini A. Guillain-Barré syndrome and COVID-19 vaccination: a systematic review and meta-analysis. J Neurology. 2024;271(3):1063-1071. doi:10.1007/s00415-024-12186-7
Eiffert SR, Stürmer T, Thorpe CT, et al. Vaccine patterns among patients diagnosed with Guillain-Barré syndrome and matched counterparts in a Medicare supplemental population, 2000–2020. Vaccine. 2023;41(39):5763-5768. doi:10.1016/j.vaccine.2023.08.014
Headquarters, Departments of the Army, the Navy, the Air Force, and the Coast Guard. Army Regulation 40-562, BUMEDINST 6230.15B, AFI 48-110_IP, CG COMDTINST M6230.4G–Medical Services: Immunizations and Chemoprophylaxis for the Prevention of Infectious Diseases. U.S. Dept. of Defense. Oct. 7, 2013. Accessed Nov. 19, 2025. https://www.med.navy.mil/portals/62/documents/nmfa/nmcphc/root/field%20activities/pages/nepmu6/operational%20support/environmental%20health/bumedinst-6230-15b-immunizations-and-chemoprophylaxis.pdf
Staples J, Gershman M, Fischer M, U.S. Centers for Disease Control and Prevention. Yellow fever vaccine: recommendations of the Advisory Committee on Immunization Practices (ACIP). MMWR Recomm Rep. 2010;59(rr-7):1-27. Accessed Nov. 19, 2025. https://www.cdc.gov/mmwr/pdf/rr/rr5907.pdf
Hemachudha T, Griffin D, Chen W, Johnson R. Immunologic studies of rabies vaccination-induced Guillain-Barré syndrome. Neurology. 1988;38(3):375-378. doi:10.1212/wnl.38.3.375
Saver JL, Chaisinanunkul N, Campbell BCV, et al, XIth Stroke Treatment Academic Industry Roundtable. Standardized nomenclature for Modified Rankin Scale global disability outcomes: consensus recommendations from Stroke Therapy Academic Industry Roundtable XI. Stroke. 2021;52(9):3054-3062. doi:10.1161/strokeaha.121.034480
The R Foundation. The R Project for Statistical Computing. Accessed Nov. 19, 2025. https://www.r-project.org

Author Affiliations

Naval Aerospace Medical Institute, Pensacola, FL: LCDR Elliott; Epidemiology and Analysis Branch, Armed Forces Health Surveillance Division, Public Health Directorate, Defense Health Agency, Silver Spring, MD: Dr. Stahlman; Walter Reed National Military Medical Center, Bethesda, MD: Dr. Watson, Dr. Sedarsky; Landstuhl Regional Medical Center, Landstuhl, Germany: MAJ Denkensohn

Disclaimer

The views expressed in this report are those of the authors and do not necessarily reflect the official policy or position of the Defense Health Agency, Department of War, nor the U.S. Government.

LCDR Elliott and MAJ Denkensohn are military service members; Dr. Stahlman and Dr. Watson are employees of the U.S. Government. This work was prepared as part of official duties. Title 17, U.S. Code Section 105 provides that copyright protection under this title is not available for any work of the U.S. Government. Title 17, U.S. Code Section 101 defines a U.S. Government work as a work prepared by a military service member or employee of the U.S. Government as part of that person’s official duties.

Acknowledgment

LT Mark Boyer

Applying distinct approaches to racial and ethnic classification to the surveillance of obstetric and neonatal outcomes in the U.S. Military, 2010–2021

Thu, 01 Jan 2026 00:00:00 Z

Abstract

Traditional, mutually exclusive approaches to racial and ethnic classification obscure important differences within major demographic groups and among multiracial populations. This study offers a novel examination of obstetric and neonatal outcomes among pregnant U.S. military service members, by applying multiple approaches to racial and ethnic classification and presenting disaggregated data. Overall, 235,608 births were identified among pregnant service members from 2010 through 2021. Inclusion of service members who identified with each racial group, whether alone or in combination with any other group, increased the American Indian or Alaska Native and Native Hawaiian or Pacific Islander birth populations by 209.7% and 94.0%, respectively, when compared to mutually exclusive classifications. Prevalences of obstetric outcomes such as cesarean delivery varied among racial and ethnic groups, particularly Asian and Latino populations, for example, Asian Indian, 36.7%; Filipino, 32.3%; Chinese, 26.5%; Puerto Rican, 30.2%; Mexican, 23.2%; and between distinct multiracial populations. Disaggregated estimates ultimately increased visibility of multiracial and Native service members and elucidated patterns indiscernible in aggregated data. Wider adoption of disaggregated racial and ethnic data methods is needed to improve understanding of health outcomes in the Military Health System.

What are the new findings?

Reporting of non-mutually exclusive racial and ethnic groups as well as disaggregated Asian, Hispanic or Latino, and multiracial populations elucidates important differences in obstetric and neonatal outcomes.

What is the impact on readiness and force health protection?

The collection and reporting of disaggregated racial and ethnic data is crucial to promote understanding of populations of multiracial, Native, and national origins serving in the U.S. military. System improvements in access to and quality of Military Health System obstetric care are needed to address persistent racial disparities and improve force readiness.

Background

Racial and ethnic disparities in adverse obstetric and neonatal outcomes have been widely reported in the U.S. literature.^1-4 Despite concerted efforts to document and attend to disparities, traditional approaches to racial and ethnic classification often obscure important differences within major racial or ethnic groups (e.g., among diverse Asian and Latino populations) and among multiracial populations, resulting in a bias of averages.⁵ Additionally, classification methods that restrict racial and ethnic group counts to individuals identifying as single-race and non-Hispanic or Latino lead to significant suppression of American Indian or Alaska Native (IAN) and Native Hawaiian or Pacific Islander (NHPI) populations: just 23.3% and 39.2% of their national populations, respectively, identified as single-race and non-Hispanic or Latino in the 2020 U.S. Census.⁶

Assessments of racial and ethnic health disparities in the Military Health System (MHS) are limited by many of the aforementioned data concerns.^7-13 A more holistic assessment is crucial given the diversity of the population: in 2022, 26.8% of U.S. military service members identified with a historically racialized group (i.e., AIAN, Asian, Black or African American, NHPI, or multiracial), and 17.3% identified as Hispanic or Latino.¹⁴ The present study used 1) self-reported racial and ethnic data from personnel records and 2) population-level health care claims data to assess the prevalence of obstetric and neonatal outcomes among U.S. service members by disaggregated race and ethnicity. Additionally, prevalence estimates were calculated for each racial and ethnic group using 2 distinct methods of classification: a mutually exclusive and non-mutually exclusive approach.

Methods

Study population

The study population was derived from the U.S. Department of War Birth and Infant Health Research (BIHR) program. The BIHR program is an ongoing surveillance and research effort that identifies live births among military families and captures information on associated pregnancy and infant health outcomes.¹⁵ BIHR data comprise military demographic and personnel data from the Defense Manpower Data Center (DMDC) and administrative medical encounter data from the MHS Data Repository. The data repository includes records for all care paid for by TRICARE, the health care plan for service members, retirees, and their families. Covered care spans medical services received at military and civilian facilities within the U.S. and abroad and is available at no cost to active duty service members and their families.

BIHR data were used to identify all live births occurring from January 2010 through December 2021 among pregnant U.S. military service members. Same-sex multiples were excluded due to difficulties distinguishing their neonatal medical records. The study was approved by the Naval Health Research Center Institutional Review Board (protocol NHRC.1999.0003); informed consent was waived in accordance with criteria set forth by Title 32, Code of Federal Regulations, Part 219.

Measures

Self-reported race and ethnicity data were ascertained from DMDC military personnel records. Values from both the race and ethnicity data fields were considered when assigning race and ethnicity (Supplementary Table 1). The Army and Army Reserve do not allow service members to select multiple categories of race: Multiracial individuals must select a single racial group or “other”. Additionally, all service members can report only 1 ethnicity.

Data were categorized using 2 distinct approaches: 1) a mutually exclusive (‘alone’) and 2) non-mutually exclusive (‘alone or in combination’) approach. The mutually exclusive (‘alone’) approach first identified Hispanic or Latino individuals, and subsequently grouped non-Hispanic individuals into 1 of the following racial categories: AIAN, Asian, Black or African American, NHPI, multiracial, or unknown. If service members selected multiple categories, they were classified as multiracial. The non-mutually exclusive approach identified all individuals identifying with each group, whether alone or in combination with any other group (i.e., including people who would otherwise be classified as multiracial or Hispanic or Latino). For example, if an individual’s self-reported race was “Black or African American” and ethnicity was “Korean,” that individual was categorized as multiracial using the mutually exclusive (‘alone’) approach, and Black or African American, Asian, and Korean using the non-mutually exclusive (‘alone or in combination’) approach.

Risk factors (e.g., age) and indicators of socio-economic disadvantage (e.g., educational attainment, military rank) were identified and treated dichotomously: age at delivery (18-19 years vs. ≥20 years; <35 years vs. ≥35 years), educational attainment (bachelor’s degree or higher vs. less education), and military rank (officer vs. enlisted).

Three obstetric outcomes were ascertained using International Classification of Diseases, 9th and 10th Revisions (ICD-9/ICD-10), diagnosis codes: cesarean delivery, gestational hypertension, and gestational diabetes (Supplementary Table 2). Cesarean deliveries required notation on either the delivery record or the infant birth record. Gestational hypertension cases required record of associated codes on 1 inpatient or 2 outpatient encounters from 20 weeks estimated gestational age (EGA) to 6 weeks postpartum. Gestational diabetes cases required record of associated codes on 1 inpatient or 2 outpatient encounters from 28 weeks EGA to date of delivery. Cases of pre-existing hypertension and pre-existing diabetes in pregnancy or the year prior to pregnancy were excluded from gestational case definitions. Two neonatal outcomes were also ascertained using ICD-9/ICD-10 diagnosis codes in the infant medical record: pre-term birth (<37 weeks EGA) and low birth weight (<2,500 grams).

Analysis

The proportion of live births among pregnant U.S. service members was calculated for each racial and ethnic group, alone and alone or in combination with any other group, overall and stratified by age, educational attainment, and military rank. Estimates were presented in the style of a heat map, with color gradients from dark green (indicating lowest risk or disadvantage) to dark yellow (indicating greatest risk or disadvantage). The prevalence of each outcome, as well as 95% confidence intervals (CIs), were calculated for each racial and ethnic group, alone and alone or in combination with any other group. Prevalence was not reported when the numerator included less than 11 cases. Secondary analyses examined prevalence among specific, mutually exclusive racial and ethnic identity intersections (e.g., AIAN and White). Data management and statistical analyses were performed using SAS Enterprise Guide, version 7.1 (SAS Institute Inc., Cary, NC).

Results

The BIHR program captured 1,353,602 live births among U.S. military families from 2010 through 2021, of which 235,608 occurred to pregnant military service members. Analysis of race and ethnicity as an exclusive classification demonstrated births to White ‘alone’ pregnant service members comprised the plurality (47.7%), followed by Black or African American ‘alone’ (22.6%), Hispanic or Latino (16.0%), multiracial (4.1%), Asian ‘alone’ (3.7%), NHPI ‘alone’ (1.4%), and AIAN ‘alone’ (1.3%) (Table 1). When using a non-mutually exclusive racial and ethnic classification approach, the AIAN birth population increased by 209.7% (from 2,985 to 9,245) and the NHPI group increased by 94.0% (from 3,309 to 6,421).

Service members identifying as AIAN, Black or African American, and NHPI (both alone and alone or in combination) had higher proportions of pregnant service members younger than age 20 years at delivery and lower proportions of those who completed a bachelor’s degree and of officer rank in relation to other groups (Table 2). Patterns were variable among Asian and Latino ethnic groups. Pregnant Filipino service members had lower proportions of college graduates and officers compared with other Asian ethnic groups. Mexican service members demonstrated higher proportions of pregnant service members younger than age 20 years at delivery and lower proportions of those with college education and of officer rank in relation to Cuban and Puerto Rican service members.

The overall prevalence of cesarean delivery, gestational hypertension, and gestational diabetes among all live births was 27.5% (95% CI 27.4, 27.7), 13.4% (95% CI 13.3, 13.6), and 7.3% (95% CI 7.2, 7.4), respectively (Figure 1). As a mutually exclusive group, Black or African American service members had a high prevalence of cesarean delivery (31.9%; 95% CI 31.5, 32.3) and gestational hypertension (15.5%; 95% CI 15.2, 15.8), but a low prevalence of gestational diabetes (6.4%; 95% CI 6.2, 6.7). The prevalence of each outcome varied across Asian alone ethnic groups, but skewed below the overall estimate for gestational hypertension, ranging from 6.7% (95% CI 4.5, 8.8) among Chinese service members to 11.9% (95% CI 10.6, 13.3) among Filipino service members. For gestational diabetes, the prevalence among Asian ‘alone’ ethnic groups skewed above the overall estimate, ranging from 13.4% (95% CI 12.5, 14.4) for the ‘other’ Asian descent population to 18.6% (95% CI 15.5, 21.8) for Korean service members. Among Hispanic and Latinos, Puerto Rican and Cuban service members had high prevalences of cesarean delivery (30.2%; 95% CI 28.6, 31.9 and 34.2%; 95% CI 29.2, 39.3, respectively) compared with the overall prevalence and that among Mexican service members (23.2%; 95% CI 22.3, 24.0); however, gestational diabetes was less prevalent among Cuban service members (5.1%, 95% CI 2.7, 7.4) than Mexican service members (8.4%; 95% CI 7.9, 9.4). Gestational diabetes was higher among AIAN alone service members (9.8%; 95% CI 8.8, 10.9) compared to the population inclusive of multiracial and Hispanic or Latino individuals (8.1%; 95% CI 7.5, 8.7). The prevalence of all obstetric outcomes was low among White service members.

Overall prevalence of pre-term birth and low birth weight was 8.4% (95% CI 8.3, 8.5) and 5.0% (95% CI 4.9, 5.1), respectively (Figure 2). Black or African American service members had higher prevalences of pre-term birth (alone 11.0%; 95% CI 10.7, 11.3) and low birth weight (alone 8.0%; 95% CI 7.8, 8.2) relative to several other racial and ethnic groups. Prevalence estimates among Asian ‘alone’ and Hispanic or Latino ethnic groups revealed wide variations among both neonatal outcomes, although corresponding CIs were widened for some groups, due to smaller sample sizes. For example, pre-term birth ranged from 6.7% (95% CI 4.5, 8.8) among Chinese service members to 11.9% (95% CI 7.5, 16.3) among Asian Indian service members, and low birth weight ranged from 3.2 (95% CI 1.7, 4.8) among Chinese service members to 7.1 (95% CI 3.7, 10.6) among Asian Indian service members. Hispanic or Latino service members had lower prevalences of pre-term birth (7.9%; 95% CI 7.6, 8.1) and low birth weight (4.7%; 95% CI 4.5, 4.9) than the overall estimate, but prevalence was elevated among Puerto Rican service members (pre-term birth 10.1%; 95% CI 9.0, 11.1; low birth weight 5.8%; 95% CI 5.0, 6.6). The inclusion of multiracial and Hispanic or Latino individuals in estimates for AIAN and NHPI groups, as is reflected in the non-mutually exclusive groupings, resulted in a disproportionate increase in cases of adverse neonatal outcomes, although CIs overlapped.

Outcome prevalences also differed at racial and ethnic identity intersections (Table 3). Black or African American ‘alone’, Black or African American and other Hispanic descent, Filipino, and White and Puerto Rican service members had higher estimates of several adverse outcomes. In contrast, service members who identified as White ‘alone’, White and other Hispanic descent, White and Mexican, and Other Hispanic descent ‘alone’ frequently had lower estimates. Despite similar population sizes, there were also differences in the prevalences of cesarean delivery and gestational diabetes for AIAN ‘alone’ versus AIAN and White service members.

Discussion

This study reported the prevalence of selected obstetric and neonatal outcomes among U.S. service members by disaggregated race and ethnicity, revealing varying prevalence within and across racial and ethnic groups. Furthermore, we identified differences between distinct multiracial groups and by using mutually exclusive versus non-mutually exclusive classification structures. These differences are especially important for AIAN and NHPI service members, who are very likely to additionally identify as another race or ethnicity.

We add to a limited body of racial health disparities research conducted among pregnant U.S. military service members. The prevalence of pre-term birth among Black or African American service members was lower than that previously reported using 2003-2014 data (11.0% vs. 11.5%), while low birth weight was more prevalent in the present study (8.0% vs. 7.7%).¹⁵ Prevalence of both neonatal outcomes, as well as of cesarean delivery and gestational hypertension, remained higher among Black or African American service members compared with all other major racial and ethnic groups. These findings underscore the continued relevance of disparities previously identified for neonatal mortality, severe maternal morbidity, and pregnancy-related mortality, and counter suggestions that comprehensive health coverage alone eliminates health disparities.^8-10,16

Disaggregation of the population identifying as Asian or Pacific Islander elucidated marked differences for each group overall and across specific ethnic groups. For low birth weight, overall estimates were 4.1% among NHPI alone service members and 5.2% among Asian alone service members, whereas the aggregated estimate using data from 2003-2014 was 4.9%.¹⁵ Differences were also pronounced for gestational diabetes, with Asian alone service members having a 49.5% increased risk compared with NHPI alone service members. Findings parallel prior work documenting higher risk for gestational diabetes among Asian populations compared with other major racial and ethnic groups, as well as uniquely high risk among Asian Indian, Vietnamese, and Filipino ethnic groups.^17,18 We also noted prevalence estimates for Chinese and Korean service members that were higher than typically reported^17,18; this finding may reflect differences between civilian and active duty populations related to place of birth, childhood socio-economic status, education, and age at delivery. Observed differences among Asian ethnic groups provide further justification for acknowledgment of aggregation as a potential fallacy.⁵

More inclusive, non-mutually exclusive definitions of race and ethnicity proved particularly effective for capturing births among AIAN and NHPI service members, as the numbers of births attributed to these populations increased by 209.7% and 94.0%, respectively, if compared to a mutually exclusive approach. Although meaningful differences in estimates between mutually exclusive and non-mutually exclusive groups were difficult to ascertain due to wide CIs, with the exception of gestational diabetes, point estimates were consistently higher among the NHPI ‘alone or in combination’ population, indicating greater risk for multiracial NHPI populations. For AIAN service members, gestational diabetes was higher among those identifying as AIAN alone. Prior work has shown the AIAN ‘alone’ population experienced increased economic disadvantage and decreased life expectancy relative to the multiracial AIAN population,^19,20 whereas the multiracial AIAN population experienced increased depression and mental distress.²¹ Our findings of disparate estimates by multiracial identity, therefore, contribute further nuance to awareness of Native health in the U.S.

There are some notable limitations with military personnel race and ethnicity data that affected this work. First, in the Services overall, only 1 ethnic identity could be reported; and in the Army and Army Reserve (which accounted for nearly 40% of all births in this cohort), soldiers could not report identity with multiple racial groups. Consequently, the multiracial population was under-estimated. Second, although self-reported, these records remain subject to data entry and administrative errors. Finally, detailed ethnicity is available only for Asian and Hispanic or Latino groups, hampering understanding of diversity among White, Black or African American, and NHPI service members.

The approach to this work, through the presentation of estimates for mutually exclusive and non-mutually exclusive racial and ethnic groups, as well as specific groups comprising a significant proportion of the population, mirrors recommendations in the 2024 Office of Management and Budget guidance for the maintenance, collection, and presentation of racial and ethnic data.²² Our findings underscore that a singular, mutually-exclusive approach to racial and ethnic classification is insufficient for understanding racial health disparities: It disproportionately obscures AIAN and NHPI populations and homogenizes multiracial populations.^23,24 As the U.S. population is increasingly multiracial,²⁵ disaggregation will only grow more pertinent. Ultimately, while application of multiple approaches to racial and ethnic classification may not always be feasible, researchers should consider the implicit biases or assumptions reflected in their selected approach.²⁶ Greater attention to the collection and reporting of disaggregated racial and ethnic health data will improve understanding of health outcomes within and beyond the MHS.¹³

References

Creanga AA, Bateman BT, Kuklina EV, Callaghan WM. Racial and ethnic disparities in severe maternal morbidity: a multistate analysis, 2008–2010. Am J Obstet Gynecol. 2014;210(5):435e1-435e8. doi:10.1016/j.ajog.2013.11.039
Grobman WA, Parker CB, Willinger M, et al. Racial disparities in adverse pregnancy outcomes and psychosocial stress. Obstet Gynecol. 2018;131(2):328-335. doi:10.1097/aog.0000000000002441
Pollock EA, Gennuso KP, Givens ML, Kindig D. Trends in infants born at low birthweight and disparities by maternal race and education from 2003 to 2018 in the United States. BMC Public Health. 2021;21(1):1117. doi:10.1186/s12889-021-11185-x
Martin JA, Osterman M. National Vital Statistics Reports: Shifts in the Distribution of Births by Gestational Age: United States, 2014–2022. U.S. Centers for Disease Control and Prevention. 2024. Accessed Nov. 21, 2024. https://www.cdc.gov/nchs/data/nvsr/nvsr73/nvsr73-01.pdf
Gee GC, Chien J, Sharif MZ, Penaia C, Tran E. East is east...or is it? Racialization of Asian, Middle Eastern, and Pacific Islander persons. Epidemiol Rev. 2023;45(1):93-104. doi:10.1093/epirev/mxad007
U.S. Census Bureau. Hispanic or Latino, and Not Hispanic or Latino by Race. U.S. Dept. of Commerce. 2020. Accessed Feb. 19, 2024. https://data.census.gov/table/decennialdhc2020.p9?q=raceandethnicity&g=010xx00us&y=2020
Hall C, Bukowinski AT, McGill AL, et al. Racial disparities in prenatal care utilization and infant small for gestational age among active duty US military women. Matern Child Health J. 2020;24(7):885-893. doi:10.1007/s10995-020-02941-3
Hall C, Romano CJ, Bukowinski AT, et al. Severe maternal morbidity among women in the U. S. military, 2003–2015. Am J Perinatol. 2024;41(2):150-159. doi:10.1055/s-0041-1740248
Romano CJ, Hall C, Bukowinski AT, Gumbs GR, Conlin AMS. Pregnancy-associated and pregnancy-related deaths in the United States military, 2003–2014. Am J Obstet Gynecol. 2022;227(3):508e1-508e9. doi:10.1016/j.ajog.2022.04.020
Hamilton JL, Shumbusho D, Cooper D, et al. Race matters: maternal morbidity in the Military Health System. Am J Obstet Gynecol. 2021;224(5):512e1-512e6. doi:10.1016/j.ajog.2021.02.036
Ranjit A, Andriotti T, Madsen C, et al. Does universal coverage mitigate racial disparities in potentially avoidable maternal complications? Am J Perinatol. 2021;38(8):848-856. doi:10.1055/s-0040-1701195
Williams VF, Oh GT, Stahlman S, Shell D. Diabetes mellitus and gestational diabetes, active and reserve component service members and dependents, 2008–2018. MSMR. 2020;27(2):8-17. Accessed Jan. 20, 2026. https://www.health.mil/reference-center/reports/2020/02/01/medical-surveillance-monthly-report-volume-27-number-2
Mancuso JD, Young J, Rusiecki J, et al. Advancing health equity in the US military. J Health Dispar Res Pract. 2023;16(1):3.
Office of the Deputy Assistant Secretary of Defense for Military Community and Family Policy. 2022 Demographics: Profile of the Military Community. U.S. Department of Defense. Accessed Jan. 20, 2026. https://www.militaryonesource.mil/data-research-and-statistics/military-community-demographics/archive
Bukowinski AT, Conlin AMS, Gumbs GR, et al. Department of Defense Birth and Infant Health Registry: select reproductive health outcomes, 2003–2014. MSMR. 2017;24(11):39-49. Accessed Jan. 20, 2026. https://www.health.mil/reference-center/reports/2017/01/01/medical-surveillance-monthly-report-volume-24-number-11
Vereen RJ, Nestander M, Haischer-Rollo G, et al. Neonatal mortality and disparities within the Military Health System. J Perinatol. 2023;43:496-502. doi:10.1038/s41372-022-01598-w
Bane S, Abrams B, Mujahid M, et al. Risk factors and pregnancy outcomes vary among Asian American, Native Hawaiian, and Pacific Islander individuals giving birth in California. Ann Epidemiol. 2022;76:128-135e9. doi:10.1016/j.annepidem.2022.09.004
Pu J, Zhao B, Wang EJ, et al. Racial/ethnic differences in gestational diabetes prevalence and contribution of common risk factors. Paediatr Perinat Epidemiol. 2015;29(5):436-443. doi:10.1111/ppe.12209
Huyser KR, Takei I, Sakamoto A. Demographic factors associated with poverty among American Indians and Alaska natives. Race Soc Probl. 2014;6:120-134. doi:10.1007/s12552-013-9110-1
Burnette J, Gregg M. How much did Native American life expectancy drop during COVID-19? Econofact. Jan. 31, 2023. Accessed Feb. 19, 2024. https://econofact.org/how-much-did-native-american-life-expectancy-drop-during-covid-19
Asdigian NL, Bear UR, Beals J, Manson SM, Kaufman CE. Mental health burden in a national sample of American Indian and Alaska Native adults: differences between multiple-race and single-race subgroups. Soc Psychiatry Psychiatr Epidemiol. 2018;53(5):521-530. doi:10.1007/s00127-018-1494-1
U.S. Office of Management and Budget. Statistical Policy Directive No. 15: Standards for Maintaining, Collecting, and Presenting Federal Data on Race and Ethnicity. Updated Nov. 14, 2025. Accessed Jan. 20, 2026. https://spd15revision.gov
Quint J, Matagi C, Kaholokula JK. The Hawai’i NHPI data disaggregation imperative: preventing data genocide through statewide race and ethnicity standards. Hawaii J Health Soc Welf. 2023;82(10 suppl 1):67-72.
Urban Indian Health Institute. Toolkit: Best Practices for American Indian and Alaska Native Data Collection. Updated Aug. 26, 2020. Accessed Feb. 19, 2024. https://www.uihi.org/resources/best-practices-for-american-indian-and-alaska-native-data-collection
Jones N, Marks R, Ramirez R, Ríos-Vargas M. Improved Race and Ethnicity Measures Reveal U.S. Population Is Much More Multiracial: 2020 Census Illuminates Racial and Ethnic Composition of the Country. U.S. Census Bureau, U.S. Dept. of Commerce. Aug. 12, 2021. Accessed Feb. 19, 2024. https://www.census.gov/library/stories/2021/08/improved-race-ethnicity-measures-reveal-united-states-population-much-more-multiracial.html
Lam-Hine T, Forthal S, Johnson CY, Chin HB. Asking multicrit questions: a reflexive and critical framework to promote health data equity for the multiracial population. Milbank Q. 2024;102(2):398-428. doi:10.1111/1468-0009.12696

Author Affiliations

Deployment Health Research Department, Naval Health Research Center, San Diego, CA: Ms. Romano, Dr. Hall, Ms. Burrell, Ms. Bukowinski, Ms. Lanning, Ms. Maduforo, Ms. Magallon, Dr. Khodr, Ms. Gumbs, Dr. Conlin; Leidos, Inc., San Diego, CA: Ms. Romano, Dr. Hall, Ms. Burrell, Ms. Bukowinski, Ms. Lanning, Ms. Maduforo, Ms. Magallon, Dr. Khodr, Ms. Gumbs

Disclaimer

The views expressed in this report are those of the authors and do not necessarily reflect the official policy nor position of the Defense Health Agency, Department of War, nor the U.S. Government.

Report 24-21 was supported by U.S. Navy Bureau of Medicine and Surgery under work unit 60504.

The study protocol was approved by the Naval Health Research Center Institutional Review Board in compliance with all applicable Federal regulations governing the protection of human subjects. Research data were derived from approved Naval Health Research Center Institutional Review Board protocol NHRC.1999.0003.

Dr. Conlin is an employee of the U.S. Government. This work was prepared as part of her official duties. Title 17, U.S. Code Section 105 provides that copyright protection under this title is not available for any work of the U.S. Government. Title 17, U.S. Code Section 101 defines a U.S. Government work as work prepared by a military service member or employee of the U.S. Government as part of that person’s official duties.

Reportable medical events at Military Health System facilities through week 40, ending October 4, 2025

Thu, 01 Jan 2026 00:00:00 Z

Total active component cases reported per week are displayed for the top 5 RMEs for the previous year. Each month, the graph is updated with the top 5 RMEs, and is presented with the current month’s (September 2025) top 5 RMEs, which may differ from previous months. COVID-19 is excluded from these graphs due to changes in reporting and case definition updates in 2023.

References

Armed Forces Health Surveillance Division. Armed Forces Reportable Medical Events. Accessed Feb. 28, 2024. https://health.mil/reference-center/publications/2022/11/01/armed-forces-reportable-medical-events-guidelines
Defense Manpower Data Center. Department of Defense Active Duty Military Personnel by Rank/Grade of Service. Accessed Feb. 28, 2024. https://dwp.dmdc.osd.mil/dwp/app/dod-data-reports/workforce-reports
Defense Manpower Data Center. Armed Forces Strength Figures for January 31, 2023. Accessed Feb. 28, 2024. https://dwp.dmdc.osd.mil/dwp/app/dod-data-reports/workforce-reports
Navy Medicine. Surveillance and Reporting Tools–DRSI: Disease Reporting System Internet. Accessed Feb. 28, 2024. https://www.med.navy.mil/navy-marine-corps-public-health-center/preventive-medicine/program-and-policy-support/disease-surveillance/drsi

Authors’ Affiliation

Defense Health Agency, Disease Epidemiology Branch, Defense Centers for Public Health–Aberdeen

Case report: An atypical Ross River Virus infection in an Australian Army service member

Thu, 01 Jan 2026 00:00:00 Z

Arboviral diseases, transmitted by arthropods such as mosquitoes, represent a significant and ongoing threat to the health, readiness, and mission capability of U.S. military personnel deployed in endemic regions.^1,2 Ross River virus (RRV), an alphavirus transmitted by mosquitoes, is endemic to Australia and causes an average of 5,000 cases annually.³ RRV is also endemic as well as epidemic in many South Pacific Islands including Papua New Guinea, Solomon Islands, Fiji, American Samoa, New Caledonia, and Cook Islands.⁴ These countries are frequent locations for U.S. military training and joint operations (Figure 1).

RRV is not a new threat to U.S. military operations. In 1997, an outbreak of RRV-related epidemic polyarthritis (EPA) occurred among 19 U.S. Navy personnel during a joint exercise at the Shoalwater Bay Training Area in Queensland.⁵ A pre- and post-deployment serum survey of 2,500 U.S. marines deployed to Australia on 6-month training rotations confirmed RRV seroconversion, indicating RRV local transmission during training deployments.⁶

U.S. military presence in the South Pacific has increased recently, with several multi-national, joint exercises in response to strategic pressures arising from the expansion of China’s southwestern Pacific military presence. More than 35,000 military personnel, including Australian and U.S. forces, and representatives from over 19 nations took part in Exercise Talisman Sabre 2025, the largest military exercise ever held in Australia and the first in Papua New Guinea.⁷

RRV is the most frequently reported arboviral disease in Australia, with approximately over 63,000 cases recorded in Queensland alone from 1993 to 2020.^8,9 The ecology of RRV is complex: Over 40 mosquito species have been identified as potential vectors, and more than 18 wild and domestic animal species are suspected as amplifying hosts or reservoirs.¹⁰ These factors contribute to unpredictable and seasonal RRV outbreaks. RRV is particularly prevalent in the Northern Territory and Queensland, where human cases are reported year-round.¹¹

Although some RRV infections are asymptomatic or sub-clinical (approximate symptomatic-to-asymptomatic ratio 1:3), symptomatic cases can develop into EPA, a debilitating condition characterized by joint inflammation. Additional symptoms, such as rash, low-grade fever, malaise, myalgia, lymphadenopathy, headache, depression, and fatigue, may accompany EPA.^12-14 Atypical presentations have been reported, including cases with prolonged or relapsing symptoms, absence of rash or arthritis, neurological involvement, or unusual laboratory findings.

While most symptomatic RRV patients recover within 4–6 weeks, some experience persistent joint or muscle pain and fatigue for months to several years. In a 1996 study of long-term symptomatic cases, at 15 months 51% of respondents still had joint pain, and 45% had persistent tiredness and lethargy¹⁵; these symptoms were still common up to 30 months after infection. Joint pain is the most common and persistent symptom, with the 4 most common joints affected being ankles (75%), wrist (72%), knees (66%) and fingers (66%). While other affected joints had much lower incidences (4-47%).¹⁶ Such cases can pose diagnostic challenges, particularly in military or deployment settings where other vector-borne or febrile illnesses are also possible.

The pathogenesis of persistent arthritis remains unclear, although persistent infection of synovial macrophages has been documented for other alphaviruses.¹⁷ RRV-induced arthritis is characterized by inflammatory infiltrates comprised largely of mononuclear cells. Characterization of those infiltrates suggests that monocytes/macrophages are a major constituent of the infiltrate, while immune-histological studies of synovial biopsy samples have also identified CD4+ and CD8+ T lymphocytes within inflammatory infiltrates.^18,19

Symptoms similar to EPA may occur after infection with Barmah Forest virus (BFV), chikungunya virus (CHIKV), Epstein-Barr virus, Rubella virus, and Parvovirus B19. BFV co-circulates with RRV in Australia with approximately 1,600 cases annually.²⁰ Currently, there is no specific antiviral treatment or vaccine for RRV. Clinical management primarily targets symptom relief.

In accordance with the Australian Health Department, definitive laboratory diagnosis of RRV infection requires specific laboratory evidence, including virus isolation or detection of viral RNA (ribonucleic acid) by RT-PCR (reverse transcription-polymerase chain reaction) in serum collected within 6 days of illness onset. Alternatively, diagnosis may be based on serological evidence, such as seroconversion or a greater than or equal to 4-fold increase in immunoglobulin G (IgG) titre, provided there is no corresponding change in antibody levels to BFV. Detection of RRV-specific immunoglobulin M (IgM) in the absence of anti-CHIKV IgM or anti-BFV IgM is also considered confirmatory evidence.

Due to serological cross-reactivity among alphaviruses, particularly BFV and CHIKV, serological diagnosis must be carefully interpreted. Alphavirus-specific IgM antibodies usually last 1–3 months, with levels generally falling subsequently.²¹ Within 2 weeks of an elevated virus-specific IgM response, a virus-specific IgG level usually becomes detectable, with IgG levels persisting for a long period, likely providing lifelong protection.²²

We report here an atypical RRV infection in 2024 in an Australian Army service member. The study was approved by the Australian Departments of Defence and Veterans’ Affairs Human Research Ethics Committee (protocol DDVA HREC P204-20). This report serves to promote awareness among medical corps and force health protection officers for consideration of deployment-related RRV disease in differential diagnosis of patients with fever, arthralgia, or rash who have recently deployed to, or conducted exercises in Australia.

Case Presentation

During a routine pre- and post-deployment serological screening program, a concerning seroconversion was identified in an Australian Defence Force (ADF) service member who had recently returned from a 3-week deployment to Papua New Guinea in late April and early May 2024. The predeployment serum sample, collected in early February 2024, was negative for anti-RRV IgG/M and neutralizing antibodies (NAb). The post-deployment serum, however, collected in early May 2024, was positive for both anti-RRV IgG/M and NAb, at a dilution of 1:320. Negative serology for anti-BFV NAb ruled out cross-reactivity and supported a definitive RRV infection.

Clinical questioning confirmed strict adherence to mosquito bite prevention measures while deployed, including sleeping indoors with screened windows, wearing a permethrin-treated uniform, and consistent use of mosquito repellent. As a result, she sustained few mosquito bites in Papua New Guinea.

Further investigation revealed that the service member resided in a known RRV hotspot in Brisbane, Queensland—an area with ongoing community transmission. The service member recalled significant mosquito exposure in early February (~15 bites per day), 2 months prior to deployment, and developed monoarthritis in the right wrist on February 13th. Imaging studies (x-ray and ultrasound) found no structural injury, and blood examination was negative for rheumatoid factors or other arthritic markers. MRI (magnetic resonance imaging) in March confirmed right wrist joint inflammation-joint effusion/synovitis (Figure 2a).

Despite the service member’s background as a laboratory scientist and personal request for RRV testing, her general practitioner dismissed the possibility of RRV infection due to monoarticular involvement.

The service member’s symptoms persisted—with manageable pain—until approximately October 2024. Some residual discomfort continued until April 2025, largely triggered by over-use. Follow-up pathology testing for rheumatoid and other arthritic markers was again negative. Additional MRI in April 2025 confirmed mild synovitis (Figure 2b), and corticosteroid injection was administered.

Based on timing, exposure history and serological data, we concluded that the infection likely occurred at the service member’s home in Queensland rather than during overseas deployment.

Discussion

Queensland is the Australian state most affected by RRV, consistently reporting over 1,000 cases annually. A record-breaking number of mosquito samples tested positive for RRV during the 2023-2024 mosquito season (November–April), which coincided with a high number of human RRV cases. Samples from more than 1,225 mosquito traps were tested, with 116 traps yielding positive results, the highest number since 2016, when the current surveillance program began. In the first 4 months of 2024, 2,065 human RRV cases were reported in Queensland, the highest total since the 2019-2020 season. In the second week of March 2024, weekly cases peaked at 333, with over 50% in Southeast Queensland, where incidence was 2.4 times higher than the 5-year average.²³

As the Indo-Pacific area becomes a defining theater of 21st century strategic competition, northern Australia, including Queensland and the Northern Territory, has emerged as a crucial area for U.S. force presence and deterrence.²⁴ U.S. military personnel who are deployed to regions where RRV is endemic, including northern Australia, Papua New Guinea, or the Solomon Islands, may be at risk of infection even during short-term exercises or visits. Exposure risk is influenced not only by location but also by timing, duration, and type of activities during deployment.

U.S. military personnel are subject to insect-borne diseases and pest threats that can adversely affect their health and compromise important missions, whether deployed in combat operations, engaged in humanitarian relief, or conducting training. Malaria, as well as flaviviruses such as dengue and West Nile virus, and alphaviruses such as RRV, BFV and CHIKV, along with sandfly fever, scrub typhus, and several tick-borne diseases, continue to pose a significant threat to forces worldwide. The largest outbreak of RRV infection ever recorded, in the Pacific from 1979 to 1980, demonstrates the epidemic potential of the virus.²⁵

The experience of Zika virus outbreaks since 2015 and the explosive CHIKV outbreak in China 2025 underscores the serious threat posed to global health by the potential for previously obscure arboviruses to shift from their historical cycles of transmission.^26,27 This risk is amplified within a mobile population such as the U.S. military.

A further risk is the potential for RRV to be exported to other countries through asymptomatic infected individuals, whether military personnel or civilians. RRV-viraemic travelers have been linked to the spread and epidemics with RRV in the Asia-Pacific region before.²⁸ This risk is of particular concern for the U.S., given the presence of mosquitoes known to be RRV vectors.^4,29

Australia remains a key partner of the U.S. in joint training operations, with an estimated 2,500 U.S. marines and sailors rotating annually through northern Australia. Additionally, in 2024, approximately 656,000 U.S. citizens traveled to Australia for recreational purposes, highlighting the potential for both military and civilian exposure to these endemic arboviruses. Enhanced surveillance, diagnostic capacity, and medical awareness of RRV, preventive measures during and after deployment must be prioritized in both the U.S. Military Health System and joint force health support planning.

This case underscores the need for heightened clinical awareness among military medical providers. U.S. service members presenting with febrile illness or joint pain after deployment to Australia should be evaluated for RRV as part of a comprehensive differential diagnosis of vector-borne diseases. Because exposure risk may extend beyond deployment sites, both deployment and travel locations should be considered when developing differential diagnoses, which should include arboviruses not endemic to Australia, such as CHIKV, dengue, and Zika virus (ZIKV). A high index of suspicion based on travel location and seasonality is needed to ensure RRV is included in the differential diagnosis.

The U.S. Department of Defence Insect Repellent System is an effective mechanism for protecting military personnel from pests and insect-borne diseases.³⁰ Preventive measures—including the use of DEET (diethyltoluamide)-based repellents, wearing long-sleeved uniforms, and treating uniforms with permethrin—remain critical to force health protection. In addition, medical staff must be aware of the local disease ecology and incorporate arboviral infections into pre-deployment briefings and post-deployment health assessments.

References

Kizu J, Graham M, Izuagbe R, et al. Potential high arbovirus exposure in INDOPACOM during U.S. service member deployment or exercises in Papua New Guinea. MSMR. 2025;32(8):9-17. Accessed Nov. 12, 2025. https://www.health.mil/news/articles/2025/08/01/msmr-papua-new-guinea-arboviruses
Wellington T, Fraser JA, Kuo HC, Hickey PW, Lindholm DA. The burden of arboviral infections in the Military Health System 2012–2019. Am J Trop Med Hyg. 2023;108(5):1007-1013. doi:10.4269/ajtmh.22-0684
Proboste T, Bista D, Clark NJ, et al. Ascertainment of community exposure sites to Ross River virus during the 2020 outbreak in Brisbane, Australia. J Infect Dis. 2025;231(3):e501-e510. doi:10.1093/infdis/jiae578
Yuen KY, Bielefeldt-Ohmann H. Ross River virus infection: a cross-disciplinary review with a veterinary perspective. Pathogens. 2021;10(3):357. doi:10.3390/pathogens10030357
Hueston L, Yund A, Cope S, et al. Ross River virus in a joint military exercise. Commun Dis Intell. 1997;21(14):193. doi:10.33321/cdi.1997.21.40
Bell MG, Liu W, Hulsey WR, Schully KL. Low risk for Ross River virus infection in expeditionary forces training in Australia demonstrated by a serological survey of Marine rotational force–Darwin, 2012–2018. MSMR. 2023;30(9):11-16. Accessed Nov. 12, 2025. https://www.health.mil/reference-center/reports/2023/09/01/msmr-september-2023-volume-30-issue-9
Australian Government Dept. of Defence. Exercise Talisman Sabre 2025. Apr. 23, 2025. Accessed Nov. 12, 2025. https://www.defence.gov.au/newsevents/releases/2025-04-23/exercise-talisman-sabre-2025
Australian Centre for Disease Control. National Notifiable Diseases Surveillance System (NNDSS) Public Datasets. Updated Dec. 2, 2025. Accessed Jan. 20, 2026. https://www.cdc.gov.au/resources/collections/nndss-public-datasets?utm_source=health.gov.au&utm_medium=callout-autocustom&utm_campaign=digital_transformation
Qian W, Hurst C, Glass K, Harley D, Viennet E. Spatial and temporal patterns of Ross River virus in Queensland, 2001–2020. Trop Med Infect Dis. 2021;6(3):145. doi:10.3390/tropicalmed6030145
Suhrbier A, La Linn M. Clinical and pathologic aspects of arthritis due to Ross River virus and other alphaviruses. Curr Opin Rheumatol. 2004;16(4):374-379. doi:10.1097/01.bor.0000130537.76808.26
Qian W, Glass K, Harley D, Viennet E, Hurst C. Unpacking Ross River virus trends in Queensland: regional insights and predictive modeling. iScience. 2025;28(10):113446. doi:10.1016/j.isci.2025.113446
Liu W, Kizu JR, Le Grand LR, et al. Localized outbreaks of epidemic polyarthritis among military personnel caused by different sublineages of Ross River virus, northeastern Australia, 2016–2017. Emerg Infect Dis. 2019;25(10):1793-1801. doi:10.3201/eid2510.181610
Harley D, Sleigh A, Ritchie S. Ross River virus transmission, infection, and disease: a cross-disciplinary review. Clin Microbiol Rev. 2001;14(4):909-932. doi:10.1128/cmr.14.4.909-932.2001
Selden SM, Cameron AS. Changing epidemiology of Ross River virus disease in south Australia. Med J Aust. 1996;165(6):313-317. doi:10.5694/j.1326-5377.1996.tb124989.x
Westley-Wise VJ, Beard JR, Sladden TJ, Dunn TM, Simpson J. Ross River virus infection on the north coast of New South Wales. Aust N Z J Public Health. 1996;20(1):87-92. doi:10.1111/j.1467-842x.1996.tb01343.x
Harley D, Bossingham D, Purdie DM, Pandeya N, Sleigh AC. Ross River virus disease in tropical Queensland: evolution of rheumatic manifestations in an inception cohort followed for six months. Med J Aust. 2002;177(7):352-355. doi:10.5694/j.1326-5377.2002.tb04836.x
Malvy D, Ezzedine K, Mamani-Matsuda M, et al. Destructive arthritis in a patient with chikungunya virus infection with persistent specific IgM antibodies. BMC Infect Dis. 2009;9:200. doi:10.1186/1471-2334-9-200
Soden M, Vasudevan H, Roberts B, et al. Detection of viral ribonucleic acid and histologic analysis of inflamed synovium in Ross River virus infection. Arthritis Rheum. 2000;43(2):365-369. doi:10.1002/1529-0131(200002)43:2<365::aidanr16>3.0.co;2-e
Fraser JR, Cunningham AL, Clarris BJ, Aaskov JG, Leach R. Cytology of synovial effusions in epidemic polyarthritis. Aust N Z J Med. 1981;11(2):168-173. doi:10.1111/j.1445-5994.1981.tb04226.x
Liu W, Kizu JR, Matley DR, et al. Circulation of 2 Barmah Forest virus lineages in military training areas, Australia. Emerg Infect Dis. 2020;26(12):3061-3065. doi:10.3201/eid2612.191747
Zaid A, Burt FJ, Liu X, et al. Arthritogenic alphaviruses: epidemiological and clinical perspective on emerging arboviruses. Lancet Infect Dis. 2021;21(5):e123-e133. doi:10.1016/s1473-3099(20)30491-6
Kistner O, Barrett N, Bruhmann A, et al. The pre-clinical testing of a formaldehyde inactivated Ross River virus vaccine designed for use in humans. Vaccine. 2007;25(25):4845-4852. doi:10.1016/j.vaccine.2007.01.103
Queensland Dept. of Health. Record-breaking Ross River Virus Detections in Queensland Mosquitoes. Press release. Apr. 30, 2024. Accessed Nov. 12, 2025. https://www.news.com.au/technology/science/animals/ross-river-virus-detected-in-record-number-of-queensland-mosquitoes/news-story/b8c008d2b75c1fe244f62f43858ac8d0
Dziedzic S. Andrew Hastie calls for ‘transparency’ about US military’s growing presence in Australia. Australian Broadcasting Corporation News. Jun. 23, 2024. Accessed Nov. 12, 2025. https://www.abc.net.au/news/2025-06-24/hastie-demands-answer-about-us-militarys-growing-presence/105451834
Aaskov JG, Mataika JU, Lawrence GW, et al. An epidemic of Ross River virus infection in Fiji, 1979. Am J Trop Med Hyg. 1981;30(5):1053-1059. doi:10.4269/ajtmh.1981.30.1053
Zhang Y, Zhang Y, Fang Z, Ni S. China’s chikungunya virus outbreak is a wake up call. Nature. 2025;645(8079):41. doi:10.1038/d41586-025-02794-2
Barreto Santana LM, Andrêssa de Moura I, Ramos Tanaka YM, de Oliveira França RF. Zika virus: a review of biology, clinical impacts, and coinfections. Viruses. 2025;17(5):637. doi:10.3390/v17050637
Lau C, Weinstein P, Slaney D. Imported cases of Ross River virus disease in New Zealand: a travel medicine perspective. Travel Med Infect Dis. 2012;10(3):129-134. doi:10.1016/j.tmaid.2012.04.001
Claflin SB, Webb CE. Ross River virus: many vectors and unusual hosts make for an unpredictable pathogen. PLoS Pathog. 2015;11(9):e1005070. doi:10.1371/journal.ppat.1005070
Defense Health Agency. DoD Insect Repellent System. U.S. Dept. of Defense. Updated Sep. 22, 2025. Accessed Nov. 12, 2025. https://ph.health.mil/topics/entomology/pur/pages/dod-insect-repellentsystem.aspx

Author Affiliations

Australian Defense Force Malaria and Infectious Disease Institute, Gallipoli Barracks, Enoggera, Queensland, Australia: CAPT Graham, Dr. Liu, Dr. Pasay; QIMR-Berghofer Medical Research Institute, Brisbane, Queensland: CAPT Graham, Dr. Pasay; Walter Reed Army Institute of Research Engineering and Scientist Exchange Program, Enoggera: MAJ Vesely

Acknowledgments

The authors express their gratitude to all study participants, the Australian Defence Force Malaria and Infectious Disease Institute team. Special thanks to Prof. G. Dennis Shanks for his guidance and proofreading of the manuscript.

Disclaimer

The opinions and assertions contained herein are the private views of the authors authors and are not to be construed as official, nor as reflecting true views of the Australian Department of Defence or the Department of the Army. The investigators have adhered to the policies for protection of human subjects as prescribed in AR 70–25. Research data were derived from an approved Australian Department of Defence and Department of Veterans’ Affairs Human Research Ethics Committee Institutional Review Board protocol, DDVA HREC 204-20. The data are included in the manuscript. The study protocol was approved by the Australian departments of Defence and Veterans’ Affairs Human Research Ethics Committee Institutional Review Board in compliance with all applicable regulations governing the protection of human and animal subjects.

The authors declare no conflicts of interest. Joint Health Command of the Australian Defence Force funded this investigation. The funder had no role in the study design, data collection and analysis, decision to publish, or the preparation of the manuscript.

MAJ Vesely is a U.S. military service member. This work was prepared as part of official duties. Title 17, U.S. Code Section 105 provides that copyright protection under this title is not available for any work of the U.S. Government. Title 17, U.S. Code Section 101 defines a U.S. Government work as a work prepared by a military service member or employee of the U.S. Government as part of that person’s official duties.

This report has been reviewed by the Walter Reed Army Institute of Research. There is no objection to its publication.

Update: Cold weather injuries among the active and reserve components of the U.S. Armed Forces, July 2020–June 2025

Mon, 01 Dec 2025 00:00:00 Z

Abstract

From July 2024 through June 2025, a total of 806 members of the active (n=702) and reserve (n=104) components of the U.S. Armed Forces had at least 1 cold weather injury. Compared to the 2023-2024 cold season, the cold weather injury rates during the 2024-2025 cold season increased by 41.8% (from 38.6 to 54.7 per 100,000 person-years) and 45.8% (from 8.5 to 12.4 per 100,000 person-years) in the active and reserve components, respectively. The Army, Navy, and Marine Corps recorded their highest cold weather injury rates during the 2024-2025 season of the 5-year surveillance period. Frostbite was the most common cold weather injury in the Army, Navy, and Marine Corps, with the Marine Corps experiencing the largest surge in frostbite rates. Over the entire surveillance period, U.S. active component service member cold weather injury rates were generally higher among male service members, non-Hispanic Black individuals, and those under age 20 years.

What are the new findings?

The incidence rate of cold weather injuries among active component service members increased by over 40% between the 2023-2024 and 2024-2025 cold seasons, resulting in a 5-year rate of 41.5 per 100,000 person-years. This increase was primarily attributable to higher rates in the Army, Navy, and Marine Corps. The Marine Corps evinced the largest incidence rate increase (77.4%) during the 2024-2025 cold season. This year’s update expanded cold injury surveillance to include “other specified and unspecified effects of reduced temperature,” to provide a more comprehensive assessment of cold weather injuries.

What is the impact on readiness and force health protection?

Despite the terminology, cold weather injuries can occur in a variety of conditions, and in much warmer temperatures than expected, particularly during operations or training in wet or aquatic environments. It is essential that both service members and leadership understand the hazards in their environments, the risks to health, and proven prevention strategies, including weather-appropriate clothing, clean, dry socks and footwear, and proper protective gear for bodily extremities.

Background

Cold weather injuries are of significant military concern due to potential effects on service members (e.g., morbidity and potential disability) and the total force (e.g., adverse impacts on operations and costs of treatment).^1,2 In response, the U.S. Armed Forces have developed, and are continually improving, their training, doctrine, procedures, and protective equipment and clothing to counter the threat of cold environments.^3-6 Although these measures are effective when properly implemented, cold weather injuries continue to affect hundreds of service members each cold season due to exposures to both cold and wet environments.^7,8

Cold weather injuries can be broadly categorized in 2 major groups: those with a central effect and those primarily affecting the body’s periphery. Hypothermia occurs if the body cannot maintain a core temperature at or above 95°F. If skin temperatures reach 95°F, the body’s physiological response is triggered to minimize loss of heat and maintain core temperature for vital organ protection.^9,10 This response is achieved by decreasing blood flow to the extremities and redistributing warm blood to the body’s core.^9-11 Lack of blood flow to the extremities, even before a drop in core temperature, is the leading cause of peripheral cold injuries.

Initially, hypothermia may impair cognition (e.g., confusion, slurred speech, memory loss), heart rate, and breathing. Severe hypothermia can lead to loss of consciousness, pulmonary edema, coma, ventricular arrhythmias (including ventricular fibrillation), and asystole.^10,12,13 Freezing atmospheric temperatures are not required to produce hypothermia, particularly when water immersion is involved. Because heat loss occurs 2 to 5 times faster in water compared to air, core body temperature can start to drop in water temperatures as warm as 80°F.¹⁰

Peripheral cold injuries, which mainly affect the hands, feet, and face, can be further classified as either freezing injuries, such as frostbite, or non-freezing injuries, such as immersion foot. Freezing peripheral injury is defined as the damage sustained by tissues when skin temperatures fall below freezing, most frequently affecting tissues of the ears, nose, cheeks, chin, fingers, and toes.^10,11,14-16 A substantial proportion of patients with peripheral frostbite experience permanent changes in microcirculation and disruption of localized nerve functions (e.g., reduced sensation in affected area).15 Although most frostbite damage is minor, severe injury may lead to impaired functioning and inability to perform occupational tasks due to hypersensitivity to cold, chronic ulceration, vasospasm, localized osteoarthritis, or chronic pain.^11,15,17

Non-freezing peripheral injury includes a spectrum of localized injuries to the soft tissues, nerves, and vasculature of distal extremities that result from prolonged exposure to wet, cold (generally 32–59°F) conditions; the injury process is generally slower in warmer water.^10,11,14,18 Although most non-freezing peripheral injuries involve feet, any body part can be affected by the condition, including hands.¹⁹ When immersion foot injury occurs, the foot becomes hyperemic (i.e., increased blood flow), painful, and swollen with continuous exposure; progression to blistering, decreased blood flow, ulceration, and gangrene is gradual.^11,18,20

Environmental factors that increase risk of cold weather injury include specific geographic locations including high altitudes, prolonged outdoor exposure to temperatures 40°F and lower, wind speeds exceeding 5 miles per hour, wet conditions due to rain or snow, or submersion in cold water, in addition to lack of adequate shelter and clothing.¹⁹ Situational factors that increase risk of immersion foot include immobility, wet socks, and constrictive footwear.^20-22 Individual risk factors vary and include prior cold weather injury, improper acclimatization, dehydration, fatigue, inadequate nutrition, alcohol use, smoking, medications that impair compensatory responses (e.g., oral anti-hyperglycemics, beta-blockers, general anesthetic agents), and chronic disease (e.g., peripheral vascular disease, diabetes).^{10,11,16,20-22}

Continuous surveillance of cold weather injuries is essential to understand the magnitude of risk they pose, inform prevention efforts, and remind leaders of the hazards of training and operating in wet and cold environments. Department of Defense guidelines for reportable medical events (RMEs) require reporting of cases of hypothermia, freezing peripheral injuries (e.g., frostbite), and non-freezing peripheral injuries (e.g., immersion injuries, chilblains).²³

Since 2004, MSMR has published annual updates on the incidence of cold weather injuries affecting U.S. Armed Force members for the 5 most recent cold seasons.²⁴ The timing of these annual updates is intended to call attention to the recurring risks of such injuries as winter approaches in the Northern Hemisphere, where most members of the U.S. Armed Forces are assigned. Following a period of more limited scope, this update restored expanded cold weather injury surveillance last reported in 2017.²⁵ The current report now includes—in addition to frostbite, immersion injury, and hypothermia—unspecified cold injuries with “other effects of reduced temperature” for more complete case ascertainment.

Methods

This surveillance population included all individuals who served in the active or reserve components of the U.S. Armed Forces at any time during the surveillance period of July 1, 2020 through June 30, 2025. For analysis purposes, a cold season was defined as July 1 through June 30 intervals, to allow for complete representation of cold weather seasons with annual summaries and appropriate comparisons. Due to data availability that began in January 2023, Space Force service members were classified separately starting in the 2022-2023 cold season; previously they were classified as Air Force.

Records of cold weather injuries for freezing peripheral injuries (i.e., frostbite), non-freezing peripheral injuries (i.e., immersion hand, foot injuries), hypothermia, and unspecified cold weather injuries were identified from 2 sources: 1) RMEs submitted to the Disease Reporting System internet (DRSi) and 2) diagnostic codes from inpatient and outpatient medical encounters in the Defense Medical Surveillance System and in-theater records from the Theater Medical Data Store (which maintains electronic records of medical encounters of deployed service members). A cold weather injury case was defined by the presence of an RME or 1 of any of the following qualifying International Classification of Diseases, 10th Revision (ICD-10) codes in the first diagnostic position of an encounter for frostbite (T33*, T34*), immersion injury (T69.0*), hypothermia (T68*), or other effects of reduced temperature (T69.8, T69.9). Additional analyses were conducted to examine the distribution of cold injury types by services to further assess trends.

To estimate the number of unique individuals who experienced a cold weather injury each cold season, and to avoid inclusion of follow-up health care encounters, only 1 cold weather injury per individual per season was included in the counts of ‘any cold weather injury’. For analyses of specific cold weather injury types (frostbite, immersion injury, hypothermia, unspecified), individuals could contribute a maximum of 1 case per cold weather injury type per season to the ‘all cold weather injuries’ count. For example, if an individual was diagnosed or reported with an immersion injury at 1 point during a cold season, then with frostbite later in the same cold season, each different injury type would be included in injury-specific calculations. If a service member had multiple medical encounters for the same cold weather injury, only 1 encounter was included in this analysis. Hospitalization encounters were prioritized over ambulatory health care visits.

Annual seasonal incidence rates (IRs) of cold weather injuries among active component service members (ACSMs) were calculated as incident cold weather injury diagnoses per 100,000 person-years (p-yrs) of service. Annual seasonal IRs of cold weather injuries among reservists were calculated as cases per 100,000 persons, using the total number of reserve component service members for each cold season of the surveillance period. Person counts were used as the denominator for reserve component because the lack of start and end dates for active duty service periods precluded accurate person-time calculation.

Cold weather injuries are summarized by the locations where service members were treated for those injuries, identified by a Defense Medical Information System Identifier (DMIS ID) of a health care encounter. Because such injuries can occur during field training, temporary duty, or outside usual duty stations, DMIS IDs were utilized as proxies for locations where cold weather injuries occurred.

Results

2024–2025 cold season

From July 2024 through June 2025, a total of 806 members of the active (n=702) and reserve (n=104) components of the U.S. Armed Forces had at least 1 cold weather injury (Table 1).

In the active component, Army members had the highest rate of any cold weather injury (n=417, 95.5 per 100,000 p-yrs) during the 2024-2025 cold season, followed by members of the Marine Corps (n=147, 88.7 per 100,000 p-yrs), Air Force (n=85, 27.6 per 100,000 p-yrs), and Navy (n=48, 14.8 per 100,000 p-yrs). One active component Space Force member (10.6 per 100,000 p-yrs) and 4 active component Coast Guard members (10.0 per 100,000 p-yrs) were affected by cold weather injuries during the 2024-2025 cold season (Table 1, Figure 1). Within the reserve component, Army personnel accounted for 77.9% of the cold injury cases (n=81, 14.7 per 100,000 persons) in the 2024-2025 cold season (Table 1, Figure 2), although reservists in the Marine Corps (n=8, 20.6 per 100,000 persons) had higher rates of cold weather injuries.

Frostbite was the most common type of cold weather injury among active component Army (n=167, 35.1%, Table 2a), Marine Corps (n=63, 40.1%, Table 2d) and Air Force (n=49, 53.3%, Table 2c) members in 2024-2025, whereas immersion injury (n=15, 30.6%) and hypothermia (n=15, 30.6%) were the most common types of cold weather injuries among Navy service members (Table 2b).

Five cold seasons: July 2020–June 2025

The crude IR for all 5 cold seasons of any cold weather injury was 41.5 per 100,000 p-yrs for all ACSMs (Table 1). In the most recent cold season, 2024-2025, the crude IR of any cold weather injury for all ACSMs increased by 41.8%, from 38.6 per 100,000 p-yrs in 2023-2024 to 54.7 per 100,000 p-yrs in 2024-2025 (Table 1), the highest value documented during the 5-year surveillance period. Similarly, the crude IR of any cold weather injury for the reserve component increased by 45.8% in 2024-2025 (from 8.5 to 12.4 per 100,000 persons) from the prior season. Throughout the surveillance period, cold weather injury rates remained consistently higher among ACSMs in the Army and Marine Corps (Figure 1).

During the 5-year surveillance period, overall rates of all cold weather injuries in the active component were generally higher among service members who were male (except in the Marine Corps), non-Hispanic Black individuals, and among the 2 youngest age groups (ages <20 and 20-24 years) (Tables 2a–2d). When specific types of cold injury were considered, male and non-Hispanic Black service members had higher rates of frostbite in comparison to other types of injury (Tables 2a–2d). Among all cold weather injury cases reported within the active component during the 5-year period, the Marine Corps demonstrated the highest recruit cold weather injury rate (238.7 per 100,000 p-yrs). With the exception of the Marine Corps, enlisted personnel had higher rates of cold weather injury compared to officers (Tables 2a–2f).

Throughout the 5-year surveillance period, a total of 38 ACSMs (1.4% of total) were hospitalized. The Army (n=25) and Marine Corps (n=8) accounted for the majority (86.8%) of hospitalized cases (data not shown).

Patterns and trends in service branches

Army

Within the Army active component, total cold injury cases and IRs increased from 356 cases (74.6 per 100,000) in 2020-2021 to 476 cases (109.0 per 100,000) in 2024-2025, representing a 46.1% increase during the surveillance period (Table 2a, Figure 3a). Frostbite was the most common cold injury type overall, with rates increasing by 35.4% in 2024-2025 compared to the prior season. Army IRs increased most for unspecified injuries, with values nearly tripling over the surveillance period (from 11.9 to 33.7 per 100,000 p-yrs). Increases in immersion injuries and hypothermia were slight-to-moderate and less pronounced.

Navy

Among the Navy active component, total cases and IRs increased from 30 cases (8.8 per 100,000 p-yrs) in 2020-2021 to 49 (15.1 per 100,000 p-yrs) in 2024-2025, representing a 71.6% IR increase over the surveillance period (Table 2b, Figure 3b). The overall increase for the Navy was primarily driven by comparatively sharp rises in immersion injuries and hypothermia cases in 2024-2025, compared to prior seasons. The highest IR for the Navy during the 5-year surveillance period was seen for frostbite cases, followed closely by hypothermia. Counts and IRs of unspecified injuries were relatively lower and fluctuated over the surveillance period.

Air Force

Within the Air Force active component, total cold injury cases and IRs increased from 71 cases (21.6 per 100,000 p-yrs) in 2020-2021 to 92 (29.9 per 100,000 p-yrs) in 2024-2025 (38.4% IR increase), with the apex (100 cases, 31.9 per 100,000 p-yrs) during the 2023-2024 cold season (Table 2c, Figure 3c). The observed Air Force increase was largely attributable to rises in immersion injuries and hypothermia cases. Rates of unspecified injuries, the second most common cold injury following frostbite, fluctuated throughout the surveillance period.

Marine Corps

Among the Marine Corps active component, total cold injury cases and IRs increased from 114 cases (63.3 per 100,000 p-yrs) in 2020-2021 to 157 cases (94.8 per 100,000 p-yrs) in 2024-2025, representing a 49.8% increase during the surveillance period (Table 2d, Figure 3d). Frostbite was the dominant cold injury, in both counts and IRs. Frostbite IRs in the Marine Corps nearly tripled during the most recent cold season compared to the prior season (38.0 per 100,000 p-yrs in 2024-2025 vs. 13.7 in 2023-2024). Immersion injuries and hypothermia also showed notable increases over time within the Marine Corps, while unspecified injuries, although smaller in magnitude, also rose sharply in the 2024-2025 cold season.

Deployment-related cold weather injuries

During the 5-year surveillance period, a total of 82 cold weather injuries were diagnosed among service members deployed outside the U.S. (data not shown), of which 35 (42.7%) were frostbite, 33 (40.2%) were immersion injuries, 12 (14.6%) were hypothermia, and 2 (2.4%) were unspecified. Among the 28 cases of the 82 total deployment-associated cold weather injuries diagnosed during the 2024-2025 cold season, 17 were frostbite, 7 were immersion injuries, and 4 were hypothermia cases.

Geographic locations of cold weather injuries

During the 5-year surveillance period, 23 military locations reported at least 25 incidents of cold weather injury (1 per person per cold season) among ACSMs. Figure 4 charts the 2024-2025 seasonal numbers of cold weather injuries (1 per person per year) for each of those 23 locations, in addition to the median case numbers for the previous 4 cold seasons. The highest 5-year counts of incident cold weather injuries for seasons 2020 through 2025 were recorded at Fort Wainwright, Arkansas (n=335), Joint Base Elmendorf-Richardson, Arkansas (n=209), Marine Corps Base Camp Lejeune, North Carolina (n=115), Fort Carson, Colorado (n=104), and U.S. Army Garrison Bavaria, Germany (n=85) (data not shown).

Discussion

Overall rates peaked in 2024-2025 for any cold weather injury among the U.S. active and reserve components, increasing by 41.8% and 45.8%, respectively, from the 2023-2024 season. During the 5-year surveillance period, the active components of all services experienced increased IRs for cold injuries. During the 2024-2025 cold season, the Army, Navy, and Marine Corps active components experienced their highest rates of any cold weather injury for the entire 5-year surveillance period. The Coast Guard and Space Force average less than 5 cases per year among their ACSMs, thus, small changes in the numbers of cases annually will result in abnormally large fluctuations in the injury rate. Frostbite was the most common cold weather injury in the Army, Marine Corps, and Navy, while the Marine Corps saw the largest surge in frostbite rates. In contrast, immersion injuries and hypothermia were the main causes of increases in the Navy and Air Force. Rates of unspecified cold injuries also increased substantially within the Army and Marine Corps, but remained lower and more variable in the Navy and Air Force.

The simultaneous increase in both specific and unspecified case rates suggests true increases in cold weather injury occurrence in the 2024-2025 cold season. The increase in IRs could indicate heightened exposure to environmental risk factors. The long-term complications of non-freezing injuries are similar to, and equally debilitating as, those produced by frostbite: hypersensitivity to cold, chronic pain, and severe pain induced by walking.^17,18,20

Similar to previous MSMR reports, the highest cold weather injury rates were observed among service members who were male, those in younger age groups, and non-Hispanic Black individuals.^8,24 Increasing rates of cold weather injury have also been noted among service members in the United Kingdom (U.K.) military with similar demographic characteristics.^21,26,27 Differences in physiological responses to cold stress have been observed between various racial and ethnic groups, with individuals of African descent demonstrating greater vasoconstriction responses compared to individuals of Asian or Caucasian descent.^10,15,28 Signs and symptoms of cold weather injury (e.g., skin redness, blotchy skin) may initially be more difficult to see on service members with skin of darker color.^29,30 Service members, leadership, and medical personnel should be educated on the early signs and symptoms of cold weather injuries for a wide range of skin types.

When examining the demographic groups with increased rates within the services, it should be noted that there were differences in the most frequently observed cold weather injury types. Younger marines had higher rates of immersion injuries, while younger soldiers had higher rates of frostbite. Such differences could indicate different situational risk factors, such as specific training activities, occupational tasks, and geographic regions, for cold weather injury among the service branches. A study of U.K. service personnel noted that the most common situational risk factors for non-freezing peripheral injury were standing guard, as well as wet socks and boots.²¹ Unit leaders must be able to assess environmental, situational, and individual risk factors of their training and operational environments and understand how those factors increase risk of cold weather injuries for service members in their charge.

This analysis of cold weather injuries was unable to distinguish between injuries sustained during official military duties (e.g., training or operations) and those associated with unrelated or personal activities. This report expanded the scope of cold injuries beyond specified conditions (e.g., frostbite, immersion injury, hypothermia) to include “other specified and unspecified effects of reduced temperature.” That change contributed to an increased overall case count compared to last year’s report. The increase in cold injury IRs was observed uniformly for all services and specific injury types, suggesting a genuine rise in cold injury incidence rather than solely an artifact of broadened inclusion criteria.

Cold weather injuries can be prevented by ensuring proper clothing, including layers that can be added or removed according to environmental conditions and specific physical activities, along with footwear that is non-constrictive, dry, and regularly changed if wet.^9,10,22 Proper hydration and nutrition, avoidance of long periods of sedentary or immobile positions, and planning for appropriate shelter and opportunities for re-warming are also important.

Military training or mission requirements in cold and wet weather conditions can preclude immediate warm or dry shelter, ability to change wet or damp clothing, or even healthy physical activity.^2,3,11 To prepare for all circumstances posing a threat for cold weather injury, service members should be cognizant of, and able to identify, signs of cold weather injury in addition to environmental, individual, and situational risk factors. Service members should also be aware of protective measures for themselves and their fellow service members, whether during training, operations, combat, or recreational activities in wet or freezing conditions.

Acknowledgment

The editors would like to thank Erika Dreyer, MPH, Epidemiology and Analysis Branch, Armed Forces Health Surveillance Division, for analyzing the data presented in this report.

References

Candler WH, Freedman MS. Military medical operations in cold environments. In: Pandolf KB, Burr RE, eds. Medical Aspects of Harsh Environments, Volume 1. Office of the Surgeon General;2001:553-566.
Paton BC. Cold, casualties, and conquests: the effects of cold on warfare. In: Pandolf KB, Burr RE, eds. Medical Aspects of Harsh Environments, Volume 1. Office of the Surgeon General;2001:313-349.
Headquarters, Department of the Army. Technical Bulletin Medical 508. Technical Bulletin: Prevention and Management of Cold-Weather Injuries. U.S. Dept. of Defense. Apr. 1, 2005. Accessed Nov. 6, 2025. https://apps.dtic.mil/sti/tr/pdf/ADA433435.pdf
U.S. Army Training and Doctrine Command Headquarters, Department of the Army. TRADOC Regulation 350-29. Training: Prevention of Heat and Cold Casualties. U.S. Dept. of Defense. Jun. 15, 2023. Accessed Nov. 6, 2025. https://adminpubs.tradoc.army.mil/regulations/tr350-29.pdf
U.S. Army Training and Doctrine Command Headquarters, Department of the Army. TRADOC Regulation 350-6. Training: Enlisted Initial Entry Training Policies and Administration. U.S. Dept. of Defense. Aug. 28, 2025. Accessed Nov. 17, 2025. https://adminpubs.tradoc.army.mil/regulations/tr350-6.pdf
Castellani JW, O’Brien C, Baker-Fulco C, Sawka MN, Young AJ. Sustaining Health & performance in cold weather operations. Technical Note TN/02-2. U.S. Army Research Institute of Environmental Medicine. Oct. 2001. Accessed Nov. 6, 2025. https://apps.dtic.mil/sti/pdfs/ADA395745.pdf
DeGroot DW, Castellani JW, Williams JO, Amoroso PJ. Epidemiology of U.S. Army cold weather injuries, 1980–1999. Aviat Space Environ Med. 2003;74(5):564-570.
Armed Forces Health Surveillance Branch. Update: cold weather injuries, active and reserve component, U.S. Armed Forces, July 2013–June 2018. MSMR. 2018;25(11):10-17. Accessed Nov. 6, 2025. https://www.health.mil/reference-center/reports/2018/01/01/medical-surveillance-monthly-report-volume-25-number-11
Bubnis MA, Hulsopple C. Human performance and injury prevention in cold weather environments. Curr Sports Med Rep. 2022;21(4):112-116. doi:10.1249/jsr.0000000000000946
Haman F, Souza SCS, Castellani JW, et al. Human vulnerability and variability in the cold: establishing individual risks for cold weather injuries. Temperature (Austin). 2022;9(2):158-195. doi:10.1080/23328940.2022.2044740
Kowtoniuk RA, Liu YE, Jeter JP. Cutaneous cold weather injuries in the U.S. military. Cutis. 2021;108(4):181-184. doi:10.12788/cutis.0363
Jolly BT, Ghezzi KT. Accidental hypothermia. Emerg Med Clin North Am. 1992;10(2):311-327.
Biem J, Koehncke N, Classen D, Dosman J. Out of the cold: management of hypothermia and frostbite. CMAJ. 2003;168(3):305-311.
Imray CHE, Oakley EHN. Cold still kills: cold-related illnesses in military practice freezing and non-freezing cold injury. J R Army Med Corps. 2005;151(4):218-222. doi:10.1136/jramc-151-04-02
Ervasti O, Hassi J, Rintamaki H, et al. Sequelae of moderate finger frostbite as assessed by subjective sensations, clinical signs, and thermo-physiological responses. Int J Circumpolar Health. 2000;59(2):137-145.
Harirchi I, Arvin A, Vash JH, Zafarmand V. Frostbite: incidence and predisposing factors in mountaineers. Br J Sports Med. 2005;39(12):898-901. doi:10.1136/bjsm.2004.016097
Handford C, Thomas O, Imray CHE. Frostbite. Emerg Med Clin North Am. 2017;35(2):281-299. doi:10.1016/j.emc.2016.12.006
Hall A, Sexton J, Lynch B, et al. Frostbite and immersion foot care. Mil Med. 2018;183(suppl 2):168-171. doi:10.1093/milmed/usy085
van Dongen TTCF, Berendsen RR, de Jong FJM, et al. Frostbite: a systematic review on freezing cold injuries in a military environment. BMJ Mil Health. 2023:e002171. doi:10.1136/military-2022-002171
Imray C, Grieve A, Dhillon S, Caudwell, Xtreme Everest Research Group. Cold damage to the extremities: frostbite and non-freezing cold injuries. Postgrad Med J. 2009;85(1007):481-488. doi:10.1136/pgmj.2008.068635
Kuht JA, Woods D, Hollis S. Case series of non-freezing cold injury: epidemiology and risk factors. J R Army Med Corps. 2019;165(6):400-404. doi:10.1136/jramc-2018-000992
Imray CHE, Richards P, Greeves J, Castellani JW. Nonfreezing cold-induced injuries. J R Army Med Corps. 2011;157(1):79-84. doi:10.1136/jramc-157-01-14
Armed Forces Health Surveillance Branch, Defense Health Agency. Armed Forces Reportable Medical Events: Guidelines and Case Definitions. Defense Health Agency;2022. Accessed Nov. 6, 2025. https://www.health.mil/reference-center/publications/2022/11/01/armed-forces-reportable-medical-events-guidelines
Army Medical Surveillance Activity. Cold injuries, active duty, U.S. Armed Forces, July 1999–June 2004. MSMR. 2004;10(5):2-10. Accessed Nov. 6, 2025. https://www.health.mil/reference-center/reports/2004/01/01/medical-surveillance-monthly-report-volume-10-number-5
O’Donnell FL, Stahlman S, Oetting AA. Update: cold weather injuries, active and reserve components, U.S. Armed Forces, July 2012–June 2017. MSMR. 2017;24(10):12-21. Accessed Nov. 6, 2025. https://www.health.mil/reference-center/reports/2017/01/01/medical-surveillance-monthly-report-volume-24-number-10
Burgess JE, Macfarlane F. Retrospective analysis of the ethnic origins of male British Army soldiers with peripheral cold weather injury. J R Army Med Corps. 2009;155(1):11-15. doi:10.1136/jramc-155-01-04
Heil KM, Oakley EHN, Wood AM. British military freezing cold injuries: a 13-year review. J R Army Med Corps. 2016:162(6):413-418. doi:10.1136/jramc-2015-000445
Maley MJ, Eglin CM, House JR, Tipton MJ. The effect of ethnicity on the vascular responses to cold exposure of the extremities. Eur J Appl Physiol. 2014;114(11):2369-2379. doi:10.1007/s00421-014-2962-2
Taylor SC. Diagnosing skin diseases in skin of color. Dermatol Clin. 2023;41(3):xiii-xv. doi:10.1016/j.det.2023.03.001
Ohanenye C, Taliaferro S, Callendar VD. Diagnosing disorders of facial erythema. Dermatol Clin. 2023:41(3):377-392. doi:10.1016/j.det.2023.02.004

Update: Diagnoses of mental health disorders among U.S. active component service members, 2020–2024

Mon, 01 Dec 2025 00:00:00 Z

Abstract

Mental health disorders have long been recognized as a problem in a wide range of domains, including the military, resulting in significant impacts on general morbidity, health care provision, disability, and military discharges. From 2020 through 2024, a total of 560,035 U.S. active component service members were diagnosed with at least 1 mental health disorder. Annual incidence rates of mental health disorder increased steadily from 2020 until 2022, but adjustment disorder decreased since then, anxiety gradually increased, and the remaining conditions remained relatively unchanged. Most mental health disorder diagnoses were attributable to adjustment disorders, anxiety disorders, depressive disorders, post-traumatic stress disorder, alcohol-related disorder, and other mental health disorders. Historically, mental health disorders have often been misunderstood and stigmatized, leading to under-reporting, delayed treatment, and poor prognoses. Reflecting the unique stressors and cultural stigmas of military life, ongoing efforts to raise awareness, encourage help-seeking, and improve treatment options are essential to supporting the mental and emotional well-being of service members.

What are the new findings?

While the incidence of U.S. service members who were diagnosed with at least 1 mental health disorder remained stable from 2023 to 2024, the annual incidence rate of anxiety disorders demonstrated a continual increase from 2020 to 2024.

What is the impact on readiness and force health protection?

The sustained incidence of mental health disorders (11,534.1 per 100,000 person-years) diagnosed among U.S. active component service members in addition to significant variations in relation to sex, service branch, occupation, and length of military service, underscores the need for targeted interventions along with continued monitoring to ensure force readiness.

Background

Mental health is a significant public health issue for the U.S. military due to the unique stressors experienced by service members. Military service, especially deployment, is linked to higher rates of mental health issues both during and after service. While combat and deployments are major risk factors, even general military service can lead to mental health challenges. Mental health issues can manifest at any time but are particularly prevalent when individuals are in close proximity to combat situations or during the transition from active duty to civilian life.¹

In 2024, mental health disorders accounted for the largest total number of hospital bed days and second highest total number of medical encounters for members of the active component of the U.S. Armed Forces.² In general, incidence rates (IRs) of mental health disorders have been observed to be highest among Army soldiers, female service members, and those in younger age groups.^3-6 The most recent MSMR update on mental health disorders, in 2024, found the IR of any mental health diagnosis increased by almost 40% between 2019 and 2023, largely attributable to adjustment disorders, anxiety disorders, depressive disorders, post-traumatic stress disorder (PTSD), alcohol-related disorders, as well as ‘other’ mental health disorders.⁶ Mental health disorders often co-occur with other conditions, making professional diagnosis and personalized treatment plans crucial.

Despite the high prevalence and severity of mental health issues during military service, service members face challenges in accessing mental health treatment due to constraints including deployment, frequent relocation, limited mental health service capacity, and stigma associated with seeking care.⁷ Addressing mental health disorders in military service members necessitates increased awareness, expanded access to care, and a prioritized focus on evidence-based treatments. Due to the significant impacts of mental health issues, military leaders, policy-makers, researchers, and the public are urging governments to provide timely and appropriate mental health services to service members.¹ This report summarizes the numbers, types, and IRs of mental health disorder diagnoses among U.S. active component service members (ACSMs) over a 5-year surveillance period, 2020 through 2024.

Methods

The surveillance period for this report included January 1, 2020 through December 31, 2024. The surveillance population included all individuals who served in the active components of the U.S. Army, Navy, Air Force, Marine Corps, Coast Guard, or Space Force, at any time during the surveillance period. Due to Space Force personnel data availability for 2023 only, Space Force service members were combined with Air Force personnel for this analysis.

All data used to determine mental health diagnoses were derived from records routinely maintained in the Defense Medical Surveillance System (DMSS). DMSS records document both ambulatory health care encounters and hospitalizations of active component members of the U.S. Armed Forces in fixed military and civilian (if reimbursed through the Military Health System, or MHS) hospitals and clinics. Diagnoses were also derived from records of medical encounters of deployed service members documented in the Theater Medical Data Store (TMDS) in DMSS.

For purposes of analysis, mental health disorders were ascertained from records of medical encounters that included mental health disorder-specific diagnoses with International Classification of Diseases, 9th and 10th revisions (ICD-9/ICD-10) codes (ICD-9: 290–319; ICD-10: F01–F99) (Table 1) in the first or second diagnostic position. Although the MHS transitioned to ICD-10 coding on October 1, 2015, ICD-9 codes were included in this analysis, as some TMDS encounters still contain ICD-9 diagnoses, which were needed to identify and exclude prevalent cases in records before October 1, 2015. Diagnoses of pervasive developmental disorder (ICD-9: 299.*; ICD-10: F84.*), specific delays in development (ICD-9: 315.*; ICD-10: F80.*–F82.*, F88–F89), mental retardation (ICD-9: 317.*–319.*; ICD-10: F70–F79), tobacco use disorder and nicotine dependence (ICD-9: 305.1; ICD-10: F17.*), and post-concussion syndrome (ICD-9: 310.2; ICD-10: F07.81) were excluded from analysis.

Each incident diagnosis of a mental health disorder was defined using the corresponding Armed Forces Health Surveillance Case Definition.⁵ For most mental health disorders, a case was defined by either a hospitalization with an indicator diagnosis in the first or second diagnostic position; 2 outpatient or TMDS visits within 180 days documented with indicator diagnoses (from the same mental health disorder category) in the first or second diagnostic position; or a single outpatient visit in a psychiatric or mental health care specialty setting (defined by Medical Expense and Performance Reporting System [MEPRS] code beginning with ‘BF’) with an indicator diagnosis in the first or second diagnostic position.

The surveillance case definitions for schizophrenia, acute stress disorder, and eating disorders included some exceptions to the case parameters described. The case definition for schizophrenia required either a single hospitalization with a diagnosis of schizophrenia in the first or second diagnostic position or 4 outpatient or TMDS encounters with a diagnosis of schizophrenia in the first or second diagnostic position. Schizophrenia cases who remained in the military for more than 2 years after becoming incident cases were excluded, as those cases were assumed to have been mis-diagnosed. The case definition for acute stress disorders required 1 encounter with an indicator diagnosis in any diagnostic position, due to the transient nature of its symptoms. Eating disorder cases required 1 inpatient encounter with an indicator diagnosis in the first or second diagnostic position, or a single outpatient or TMDS encounter with an indicator diagnosis in the primary diagnostic position.

Service members diagnosed with 1 or more mental health disorders before the surveillance period (i.e., prevalent cases) were not considered at risk of incident diagnoses of the same conditions during the period. Service members diagnosed with more than 1 mental health disorder during the surveillance period were considered incident cases in each category in which they fulfilled the case-defining criteria. Service members could be considered incident cases only once in each specific mental health disorder category.

Results

Numbers and incidence rates of mental health diagnoses

During the 5-year surveillance period, 560,035 ACSMs were diagnosed with at least 1 mental health disorder; of those individuals, 268,480 (47.9%) were diagnosed with mental health disorders in more than 1 diagnostic category (Table 2). Overall, 1,007,037 incident diagnoses of mental health disorders were recorded in all diagnostic categories. The annual IRs of at least 1 mental health disorder increased from 8,430.2 per 100,000 person-years (p-yrs) in 2020 to 11,679.0 per 100,000 p-yrs in 2023, then decreased slightly to 11,534.1 per 100,000 p-yrs in 2024 (Table 2).

Over the entire surveillance period, 95% of all incident mental health disorder diagnoses were attributable to adjustment disorders (n=282,883, 28.1%), anxiety disorders (n=208,217, 20.7%), depression disorders (n=177,483, 17.6%), ‘other’ mental health disorders (n=124,142, 12.3%), PTSD (n=95,189, 9.5%), and alcohol-related disorders (n=69,248, 6.9%) (Table 2). In comparison, a relatively small number of incident diagnoses of personality disorders (n=15,668, 1.6%), substance-related disorders (n=15,275, 1.5%), bipolar disorder (n=8,654, 0.9%), other psychoses (n=3,838, 0.4%), eating disorders (n=3,678, 0.4%), schizophrenia (n=1,475, 0.1%), acute stress disorders, (n=1,191, 0.1%), and factitious disorders (n=96, 0.01%) contributed to the incident diagnoses of mental health disorders among ACSMs.

Annual IRs for adjustment disorders, alcohol-related disorder, personality disorders, substance-related disorder, bipolar disorder, eating disorders, and acute stress disorder increased steadily from 2020 until 2022 but then decreased, with adjustment disorders decreasing considerably thereafter. In contrast, anxiety increased gradually and steadily over the 5-year surveillance period, while conditions including depression, other mental health disorders, PTSD, other psychoses, and schizophrenia fluctuated (Table 2).

Co-occurring mental health diagnoses

Individuals with mental health disorders are often diagnosed with more than 1 mental health disorder. During the surveillance period, adjustment disorders were often co-diagnosed with other disorders, with 35.7% of substance-related disorders and 59.8% of personality disorders co-diagnosed with adjustment disorders.

Incident Diagnoses per 100,000 p-yrs

Depressive disorders were also often co-diagnosed with all other mental health disorders, ranging from 26.7% of substance-related disorder cases with co-diagnoses to 59.9% of bipolar disorder diagnoses. Incident cases of anxiety disorders were also co-diagnosed with factitious disorders (51.0%), bipolar disorder (47.3%), depressive disorders (46.0%), eating disorders (44.8%), PTSD (41.7%), personality disorders (41.3%), and acute stress disorder (36.4%) (Table 3).

Incidence rates of mental health diagnoses by sex

In general, most incident mental health disorder diagnoses were more prevalent among female service members, but alcohol- and substance-related disorders were more prevalent in male service members during the 5-year surveillance period. Schizophrenia was diagnosed at a higher rate in male service members in 2024 and 2020 (Figures 1a–2b).

Rates of mental health disorder diagnoses in male service members steadily increased until 2022, remaining relatively unchanged since then, with the exception of decreases in adjustment disorders, substance-related disorders, and personality disorders. Anxiety disorders increased throughout the surveillance period among male service members (Figures 1a, 1b).

Rates of mental health disorder diagnoses in female service members followed a similar pattern to those of male service members, with the exception of a slight decrease in anxiety disorders in 2024. Adjustment disorder IRs were the highest among women in 2024, followed by anxiety disorders, depressive disorders, other mental health disorders, and PTSD. During the 5-year surveillance period, eating disorders were 7–10 times more common in female ACSMs than in males, while personality disorders were 3.2–3.6 times more common among women (Figures 2a, 2b).

Incidence rates of mental health diagnoses by age

Rates of most mental health disorders varied by age, with adjustment disorders exhibiting the highest incidence among all age groups (Figure 3). Service members under age 20 years had the highest IR of adjustment disorder, compared to all other age groups. Rates of alcohol- and substance-related disorders, along with personality disorders, bipolar disorder, eating disorders, and schizophrenia, were highest for service members aged 20-24 years, while declining thereafter with increasing age. As age increased, PTSD increased, while adjustment disorders, anxiety disorders, depressive disorders, and acute stress disorders fluctuated. ACSMs ages 40-49-years had the highest IRs of anxiety and depressive disorders, and those older than age 50 years had the highest incidence of PTSD. After age 30 years, rates of adjustment disorders, anxiety disorders, and depressive disorders increased until ages 40-49 years, thereafter declining in those older than age 50 years.

Incidence rates of mental health diagnoses by service

Overall, IRs of mental health disorders were highest in the Army, specifically adjustment disorders, alcohol-related disorders, substance related disorders, anxiety disorders, PTSD, schizophrenia, other psychoses, and eating disorders. The Navy accounted for the highest IRs of depressive disorders, personality disorders, and bipolar disorder, while the Coast Guard accounted for the highest IRs of acute stress disorders (Figure 4).

Incidence rates of mental health diagnoses by occupation

Rates of adjustment disorders, anxiety disorders, depressive disorders, PTSD, personality disorders, bipolar disorder, eating disorders, and acute stress disorders were generally highest in health care occupations. Service members in combat-related roles exhibited the highest IRs of alcohol- and substance-related disorders, while those in motor transport had the highest rates of other psychoses and schizophrenia. By contrast, pilots and air crew personnel showed the lowest IRs of mental health disorders (Figure 5).

Incidence rates of mental health diagnoses by time in service

Rates of mental health disorder diagnoses differ by length of service, with highest IRs of schizophrenia, other psychoses, and acute stress disorders diagnoses occurring among ACSMs with less than 6 months of service. For those who served 12-36 months, the most common diagnoses were adjustment disorders, alcohol-related disorders, substance-related disorders, personality disorders, bipolar disorder, and eating disorders. Among those who served 36 months or longer, anxiety disorders, depressive disorders, and PTSD were most common (Figure 6).

Discussion

This report provides an update on incident diagnoses for mental health disorders among ACSMs of the U.S. Armed Forces from 2020 through 2024. Adjustment disorders, anxiety disorders, depressive disorders, PTSD, and alcohol-related disorder, along with other mental health disorders, consistently accounted for approximately 95% of all mental health disorder diagnoses during the 5-year surveillance period. IRs of anxiety disorders increased substantially from 2020 to 2024.

The increasing incidence of anxiety disorders and PTSD among ACSMs is complex, with multiple contributing factors including combat exposure, military culture and environment, personal and pre-existing factors, in addition to other stressors.^8,9 The consequences of these disorders can affect service readiness, military occupations, professional and personal relationships, long-term health, substance use, and potential suicidal ideation.¹⁰

Prior MSMR reports indicate that approximately one-third of anxiety disorder diagnoses from 2000 to 2011 had a co-occurring diagnosis of either adjustment or depressive disorder.¹¹ Co-occurring diagnoses persist in this report, which documents both adjustment disorders (42.1%) and depressive disorders (39.2%) as the leading 2 co-occurring diagnoses, from 2020 through 2024, for ACSMs with incident anxiety disorder diagnoses. Co-occurring mental health diagnoses represent a significant challenge, as they can increase both the complexity and severity of symptomology, complicate diagnosis and treatment, and affect overall prognosis. Mental health disorders affect male and female service members differently, with effects on both related prevalence and presentation of mental health conditions. During the 5-year surveillance period, most mental health disorders were more prevalent among female ACSMs, while alcohol and substance-related disorders were more common among male ACSMs. Female service members’ vulnerability to physical and mental health issues appears to be highly correlated with unwanted gender-based experiences, which may lead them more likely to report mental health problems than male service members.¹² In particular, the IR of eating disorders among female service members in this report was 7–10 times higher than that of male service members, similar to the results of a previous report.¹³ Eating disorders are complex conditions, difficult to treat and often co-occurring with other mental health conditions, making understanding each individual’s unique needs and experiences crucial for effective treatment.¹⁴ Differences in mental health disorder diagnoses between the sexes underscores the need for individualized treatment approaches that are sex-specific.

Consistent with previous findings, this report confirms age-related variations in mental health diagnoses, with service members aged 20-24 years exhibiting a particularly high incidence of mental health disorders during the 2020–2024 surveillance period.^3,15 While IRs varied by age group, each age group exhibited mental health problems that were particularly severe and unique to that age group.

From 2020 through 2024, the Army consistently reported higher IRs of most mental health disorders, likely due to its large size, frequent deployments, and high-stress missions.^6,15 While the Army has higher IRs overall, the Marine Corps is often viewed as the most mentally demanding branch due to its rigorous standards and intense emotional and psychological pressures.¹⁶ Effective management and prevention must take into account each branch of service’s distinct demographics, culture and missions, in order to fully address mental health.

As documented in a prior report,⁶ service members in health care occupations exhibited higher rates of diagnoses of most mental health disorders. Health care professionals often struggle to provide appropriate care for themselves, and when mental illness develops, tend to be reluctant to seek help when needed and neglect self-care.¹⁷ The higher rates of mental health disorders among those in health care occupations suggest an important need for future research on effective solutions to support the mental health of military health care personnel.

During the 5-year surveillance period, adjustment disorders generally had highest incidence rates during the early stages of military service. All mental health disorders continued to increase until mid-career, after which all mental health disorders decreased or remained stable through the later career stages, with the exception of anxiety disorders and PTSD.

There are several limitations in interpreting the results in this report. First, this report was compiled based on standardized administrative records and may not be reliable indicators of the true burden of mental health disorders among military service members. Second, this report may under-estimate the incidence of mental health disorders if service members do not seek appropriate care or receive care not routinely documented as ICD-9/ICD-10-coded diagnoses (e.g., from private practitioners, counseling or advocacy support centers, chaplains), or if mental health disorders were not diagnosed or reported on standardized records of care, or if diagnoses were mis-coded or incorrectly transcribed on centrally transmitted records. Conversely, some conditions may have been erroneously diagnosed or mis-coded as mental health disorders (e.g., screening visits), which may contribute to an over-estimation of the true burden of disease. Lastly, these analyses summarize the experiences of individuals while serving in an active component of the U.S. military and do not include mental health disorders or problems that affected members of reserve components or veterans of recent military service who received care outside the MHS.

In September 2024, the Department of Defense revised Instruction 6490.08, establishing a Department policy that promotes health-seeking behaviors for mental health services. The new policy emphasizes unrestricted, non-stigmatizing access to mental health care services, including voluntary substance mis-use education, as essential for maintaining the health and readiness of the total force.¹⁸ As the burden of mental health disorders continues to increase during a period of policy change, ongoing surveillance and further analyses are warranted to better understand the true burden of disease in addition to health care access and provision. The results from this report underscore the need for mental health services to address a range of mental health co-morbidities in ACSMs.

Mental health stigma is a primary barrier to help-seeking in the military, consistently identified as a major concern in military studies.¹⁹ Although stigma’s direct effect on care may be minimal,²⁰ a holistic approach that comprehensively addresses the complex needs of military personnel is crucial, providing integrated care from military and civilian providers that proactively works to reduce the persistent stigma associated with seeking mental health support.^21,22

The trends in this report demonstrate the ongoing need for mental health services among U.S. military members, documented in previous MSMR reports. Effectively addressing the increasing rates of anxiety disorders and PTSD in ACSMs requires evidence-informed prevention strategies, enhanced access to care, early intervention, appropriate and integrated treatment, strengthened support networks, along with ongoing research.¹ In addition, effective management of co-occurring disorders requires comprehensive assessment approaches complemented by treatment plans that are both individualized and integrated.^8,23

Acknowledgment

The editors would like to thank Jessica H. Murray, MPH, Epidemiologist, Epidemiology and Analysis Branch, Armed Forces Health Surveillance Division, for analyzing the data presented in this report.

References

Fikretoglu D, Sharp ML, Adler AB, et al. Pathways to mental health care in active military populations across the Five-Eyes nations: an integrated perspective. Clin Psychol Rev. 2022;91:102100. doi:10.1016/j.cpr.2021.102100
Armed Forces Health Surveillance Division. Absolute and relative morbidity burdens attributable to various illnesses and injuries among active component members of the U.S. Armed Forces, 2023. MSMR. 2024;31(6):2-10. Accessed Nov. 20, 2025. https://www.health.mil/news/articles/2024/06/01/msmr-health-care-burden-active-component
Armed Forces Health Surveillance Division. Update: mental health disorders and mental health problems, active component, U.S. Armed Forces, 2016–2020. MSMR. 2021;28(8):2-9. Accessed Nov. 20, 2025. https://www.health.mil/reference-center/reports/2021/08/01/medical-surveillance-monthly-report-volume-28-number-08
Armed Forces Health Surveillance Center. Mental disorders and mental health problems, active component, U.S. Armed Forces, 2000–2011. MSMR. 2012;19(6):11-17. Accessed Nov. 20, 2025. https://www.health.mil/reference-center/reports/2012/01/01/medical-surveillance-monthly-report-volume-19-number-6
Stahlman S, Oetting AA. Mental health disorders and mental health problems, active component, U.S. Armed Forces, 2007–2016. MSMR. 2018;25(3):2-11. Accessed Nov. 20, 2025. https://www.health.mil/reference-center/reports/2018/01/01/medical-surveillance-monthly-report-volume-25-number-3
Armed Forces Health Surveillance Division. Diagnoses of mental health disorders among active component U.S. Armed Forces, 2019–2023. MSMR. 2024;31(12):2-11. Accessed Nov. 20, 2025. https://www.health.mil/news/articles/2024/12/01/msmr-mental-health-update-2024
Shen YC, Bacolod M, Heissel J. Propensity of US military personnel to seek mental health care when community psychiatric capacity changes. JAMA Health Forum. 2023;4(10):e233330. doi:10.1001/jamahealthforum.2023.3330
Armenta R, Rush T, LeardMann CA. et al. Factors associated with persistent posttraumatic stress disorder among U.S. military service members and veterans. BMC Psychiatry. 2018;18(1):48. doi:10.1186/s12888-018-1590-5
Heward C, Li W, Chun Tie Y, Waterworth P. A scoping review of military culture, military identity, and mental health outcomes in military personnel. Mil Med. 2024;189(11-12):e2382-e2393. doi:10.1093/milmed/usae276
Tedla A, Asnakew S, Legas G, et al. Post-traumatic stress disorder among military personnel admitted at the Northwest Command level three military hospital, Bahir Dar, Ethiopia, 2022: an institution-based cross-sectional study. Front Psychiatry. 2024;15:1410630. doi:10.3389/fpsyt.2024.1410630
Armed Forces Health Surveillance Center. Anxiety disorders, active component, U.S. Armed Forces, 2000–2012. MSMR. 2013;20(10):2-6. Accessed Nov. 20, 2025. https://www.health.mil/reference-center/reports/2013/01/01/medical-surveillance-monthly-report-volume-20-number-10
Jaycox LH, Morral AR, Street A, et al. Gender differences in health among U.S. service members: unwanted gender-based experiences as an explanatory factor. Rand Health Q. 2023;10(2):8. Accessed Nov. 20, 2025. https://pmc.ncbi.nlm.nih.gov/articles/pmc10187550
Murray JH, Mabila SL, McQuistan AA. Trends in the incidence of eating disorders among active component service members, 2017 to 2021. MSMR. 2023;30(1):19-25. Accessed Nov. 20, 2025. https://www.health.mil/reference-center/reports/2023/01/01/medical-surveillance-monthly-report-volume-30-number-1
Touma DA, Quinn ME, Freeman VE, Meyer EG. Eating disorders in U.S. active duty military members and veterans: a systematic review. Mil Med. 2022;188(7-8):1637-1648. doi:10.1093/milmed/usac180
Russell PD, Judkins JL, Blessing A, Moore B, Morissette SB. Incidences of anxiety disorders among active duty service members between 1999 and 2018. J Anxiety Disord. 2022;91:102608. doi:10.1016/j.janxdis.2022.102608
Soberfirst Recovery. What military branch suffers the most from mental illness? 2025. Accessed Oct 30, 2025. https://soberfirstrecovery.com/what-military-branch-suffers-the-most-from-mental-illness
Braquehais MD, Vargas-Cáceres S. Psychiatric issues among health professionals. Med Clin North Am. 2023;107(1):131-142. doi:10.1016/j.mcna.2022.04.004
Aker JA, Military Health System Communications. Department of Defense works to dispel stigma of seeking mental health care. health.mil. Defense Health Agency, U.S. Dept. of Defense. 2023. Accessed Oct 16, 2025. https://health.mil/news/dvids-articles/2023/12/04/news458878
Campbell M, Auchterlonie JL, Andris Z, Cooper DC, Hoyt T. Mental health stigma in Department of Defense policies: analysis, recommendations, and outcomes. Mil Med. 2023;188(5-6):e1171-e1177. doi:10.1093/milmed/usab471
Kline AC, Panza KE, Nichter B, et al. Mental health care use among U.S. military veterans: results from the 2019-2020 National Health and Resilience in Veterans Study. Psychiatr Serv. 2022;73(6):628-635. doi:10.1176/appi.ps.202100112
Thorogood Z, Lozano JS. Enhancing mental health services for U.S. military personnel: a review of current practices. Psychology. 2025;16(5):551-560. doi:10.4236/psych.2025.165032
Piro L, Luo H, Jones K, et al. Racial and ethnic differences among active-duty service members in use of mental health care and perceived mental health stigma: results from the 2018 Health Related Behaviors Survey. Prev Chronic Dis. 2023;20:e85. doi:10.5888/pcd20.220419
Yule AM, Kelly JF. Integrating treatment for co-occurring mental health conditions. Alcohol Res. 2019;40(1):arcr.v40.1.07. doi:10.35946/arcr.v40.1.07

Trends in the prevalence of obesity among U.S. active component service members and civilians, 2013–2023

Mon, 01 Dec 2025 00:00:00 Z

Abstract

Trends in obesity among U.S. active component service members (ACSMs) and civilians are relevant to military recruitment and retention, as excess body weight is a common disqualification for military service. This study utilized measured height and weight data from the Military Health System Data Repository for ACSMs (cumulative n=12,262,745) and the National Health and Nutrition Examination Survey for civilians ages 17-62 years (cumulative n=19,334). Accounting for the design of each data source, the prevalence of obesity (body mass index≥30 kg/m²) and body mass index (BMI) distributions were calculated. Joinpoint software and polynomial regression were used to assess trends over time. From 2013 through 2023, obesity prevalence increased among ACSMs, from 14.7% to 24.2%. Although obesity rates among civilians were consistently higher, this gap narrowed over the course of the decade. The same pattern was seen in young men (ages 17-24 years). Civilians have greater proportions within the highest classes of BMI than ACSMs. Persistently high obesity prevalence among ACSMs overall and in young men, particularly since 2019, may affect military recruitment, retention, and ultimately, strength and readiness.

What are the new findings?

From 2013 through 2023, the prevalence of obesity increased significantly among U.S. active component service members, 2019 to 2023 in particular, while prevalence among civilians remained consistently high. The pattern of obesity is especially relevant in young men, the largest source of potential and newly accessed military recruits.

What is the impact on readiness and force health protection?

The persistently high prevalence of obesity among civilians and growing prevalence of obesity among active component service members in general, and among young men in particular, may affect military recruitment, retention, and ultimately, strength and readiness.

Background

The U.S. Department of Defense (DOD) experienced agency-wide recruitment shortfalls in 2022 and 2023.¹ Excess body weight is a common disqualification for recruitment and retention of military members.² Some authors have suggested that the high prevalence of weight-ineligible young people has compromised national security by reducing recruitment.³ Obesity also places a substantial burden on the Military Health System (MHS).⁴

Reports have found that the prevalence of obesity in U.S. military members increased slightly during the COVID-19 pandemic,⁵ but prevalence of obesity in the overall U.S. civilian adult population remained level.⁶ Examining whether trends in obesity prevalence are similar, when sex and age standardized, for the active component military and civilian populations, as well as for young men from both populations, is ultimately relevant to U.S. military strength and readiness.

The objective of this study was to examine trends over the past decade in the prevalence of obesity among U.S. active component service members (ACSMs) and civilians ages 17-62 years, both overall and by sex, to understand whether trends in these populations were similar or different. This study highlights trends in young men ages 17-24 years among both populations, to examine differences in the prevalence of obesity between potential civilian and newly accessed military recruits. Finally, this study visualizes the cross-sectional distribution of body mass index (BMI) at the end of the study period, in both men and women, to compare the age-standardized distribution of BMI categories between military and civilian populations.

Methods

Data sources

For the ACSM population, this study employed a census of medical records with measured height and weight data from January 1, 2013 through December 31, 2023 from the MHS Data Repository (MDR). An encounter record in MDR can be initiated by individuals seeking care or by a healthy individuals completing an annual physical examination requirement. For each calendar year (e.g., 2013, 2014, etc.), the first encounter that included a non-pregnant height and weight measurement for an individual was abstracted from the MDR and linked to demographic data from the Defense Medical Surveillance System (DMSS).⁷ The same individual could be represented in multiple years of this study period, but never more than once every given year. Records with missing racial or ethnic group or sex data were excluded (n=271,679, 2.2%).

For civilians, this study utilized measured height and weight as well as demographic data from 4 survey cycles of the National Health and Nutrition Examination Survey (NHANES): 2013-2014, 2015-2016, 2017-March 2020, and August 2021-August 2023. NHANES is a cross-sectional, interview- and examination-based survey representative of the U.S. civilian, non-institutionalized population, approved by the National Center for Health Statistics (NCHS) Ethics Review Board.⁸ Non-pregnant NHANES participants ages 17-62 years (i.e., ACSM age range) with measured height and weight were included in this study.

Body mass index categories

BMI was calculated as weight in kilograms divided by height in meters squared, rounded to 1 decimal place. BMI categories were defined as underweight (BMI<18.5), normal weight (BMI 18.5<25.0), overweight (BMI 25.0<30.0), and obesity (BMI≥30.0). Obesity was further classified as class 1 obesity (BMI 30.0<35.0), class 2 obesity (BMI 35.0<40.0), and class 3 obesity (BMI≥40.0).⁹ Records from ACSMs with BMI less than or equal to 12 or greater than or equal to 50 were considered implausible and excluded from this study (n=6,562, 0.1%).

Statistical analysis

Analyses were conducted using R version 4.4.0 including survey package version 4.4-2 (R Foundation), SAS-Enterprise Guide version 8.3 (SAS Institute, Inc.), and Joinpoint Regression Program version 5.4.0 (National Cancer Institute). A 2-sided p-value of less than .05 was used to determine statistical significance.

Prevalence of obesity

The crude prevalence of obesity among ACSMs was calculated both overall and by sex, age, racial and ethnic group, and branch of military service, for each year, 2013–2023. Because ACSM data are a census of the population, confidence intervals (CIs) were not calculated. Overall, and for every demographic group, the percentage point change and relative percentage change over the study period were calculated using the prevalence of obesity in 2013 and 2023.

For civilians, examination survey weights were used to estimate the crude prevalence of obesity overall and by sex, age, and racial and ethnic group, for each survey cycle; Korn and Graubard CIs were calculated, and estimates were evaluated for reliability according to the NCHS Data Presentation Standards for Proportions.¹⁰ Percentage point change and relative percentage change were not calculated for civilians due to the unequal lengths and spacing of NHANES survey cycles.

Overall prevalence of obesity for ACSMs and civilians were also standardized to the sex and age structure of the ACSM study population in 2023 to account for demographic composition differences between and within these populations over time.

Trends in obesity

Statistical testing for trends in obesity over time were conducted by sex, age, racial and ethnic group, and branch of military service to provide subgroup information, which is relevant for military retention and recruitment, particularly for young men (ages 17-24 years).

For ACSMs, Joinpoint software (using default settings and weighted BIC model) was used to identify inflection points in obesity prevalence over time, and to test whether apparent changes in slope at inflection points were significant. The difference in slope of trends before and after significant inflection points, measured in annual percentage point change, were reported.

For civilians, quadratic and linear trends in obesity prevalence over time were examined in regression models with the survey cycle modeled as an orthogonal polynomial, accounting for the unequal spacings and lengths of NHANES survey cycles, using the NCHS Guidelines for Analysis of Trends.¹¹ Because only 4 NHANES survey cycles were included in this study, Joinpoint software was not used to analyze civilian trends.

Distributions of body mass index

The prevalence of each BMI-defined weight category was calculated and used to visualize the 2023 distributions of BMI by sex among ACSMs in 2023 and the civilian population from August 2021 through August 2023, standardized to the age structure of the ACSM study population in 2023.

Results

Demographics of active component service members and civilians

This study included a cumulative total of 12,262,745 ACSM records of measured height and weight from the MHS Data Repository from 2013 through 2023 (Table 1). The demographic distribution of this study’s ACSM population is similar to active duty members in the DOD 2023 Demographics Report.¹²

This study included a cumulative total of 19,334 civilian participants from 4 survey cycles of NHANES (Table 1). NHANES estimates are representative of the U.S. non-institutional, civilian population.¹³

The population of ACSMs is younger (78.1% ages 17-34 years), with a higher percentage of men (82.9%) than the U.S. civilian population (39.3% ages 17-34 years, 50.0% men).

Obesity trends overall

Sex- and age-standardized prevalence of obesity in ACSMs increased from 14.7% in 2013 to 18.7% in 2020; from the joinpoint at 2020, obesity prevalence rose more rapidly, to 24.2% in 2023 (difference in slope before and after joinpoint 1.33, p<0.001) (Table 2). The standardized estimated prevalence of obesity among civilians, which was consistently higher than ACSMs throughout this period, increased from 31.3% (95% CI 29.2, 33.6) in the NHANES 2013-2014 survey cycle to 37.8% (95% CI 34.7, 40.9) 2017–March 2020 and then declined to 33.0% (95% CI 30.1, 36.0) August 2021–August 2023 (quadratic trend p=0.04, linear trend p=0.49) (Table 3).

Trends in male and female obesity

From 2013 through 2023, crude prevalence of obesity, in both men and women, was lower among ACSMs than civilians, although the difference narrowed over time (Figure 1). Prevalence of obesity in male ACSMs increased from 15.7% in 2013 to 18.1% in 2019; from the joinpoint at 2019 obesity increased more rapidly, to 25.3% in 2023 (difference in slope before and after joinpoint 1.42, p<0.001). Obesity increased in female ACSMs from 8.3% in 2013 to 12.0% in 2019; from the joinpoint at 2019 obesity increased more rapidly, to 19.6% in 2023 (difference in slope before and after joinpoint 1.38, p<0.001).

Estimated prevalence of obesity in male civilians increased from 33.5% (95% CI 30.1, 37.0) in 2013-2014 to 41.4% (95% CI 36.7, 46.3) during 2017–March 2020, then declined to 38.6% (95% CI 34.7, 42.7) during August 2021–August 2023 (quadratic trend p=0.04, linear trend p=0.05). In female civilians, obesity remained consistent from 2013-2014 (40.9%; 95% CI 37.7, 44.1) to August 2021–August 2023 (41.1%; 95% CI 35.7, 46.6) (quadratic trend p=0.67, linear trend p=0.85).

Trends in young male obesity

Among young male (ages 17-24 years) ACSMs, crude obesity prevalence increased from 7.9% in 2013 to 9.9% in 2019; from the joinpoint at 2019, obesity increased more rapidly to 15.1% in 2023 (difference in slope before and after joinpoint 0.98, p<0.001) (Figure 2).

Among young male civilians, estimated prevalence of obesity did not change significantly, from 21.1% (95% CI 15.3, 28.0) in 2013-2014 to 24.5% (95% CI 19.2, 30.6) during August 2021–August 2023 (quadratic trend p=0.08, linear trend p=0.35).

Distributions of body mass index

Age-standardized distributions of BMI according to sex, among ACSMs in 2023 and civilians during August 2021–August 2023, were visibly different (Figure 3). Male ACSMs demonstrated lower proportions in the highest classes of obesity (class 2 obesity 4.0%, class 3 obesity 0.7%) in comparison to the civilian male population (class 2 obesity 8.2%; 95% CI 6.5, 10.2 and class 3 obesity 5.7%; 95% CI 4.5, 7.1). This pattern was even more striking in women: Female ACSMs demonstrated even smaller proportions in the highest classes of obesity (class 2 obesity 3.6%, class 3 obesity 0.8%) when compared to women in the civilian population (class 2 obesity 9.0%; 95% CI 6.9, 11.5 and class 3 obesity 11.3%; 95% CI 9.7, 13.1).

Discussion

This study included newer data, collected after the COVID-19 pandemic, to describe trends in sex- and age-standardized prevalence of obesity over the past decade among ACSMs and civilians aged 17-62 years, as well as young men.

From 2013 through 2023, the prevalence of obesity in male and female ACSMs increased, while standardized estimated prevalence among civilians ages 17-62 years increased slightly but ended similar to the start of the decade. The difference in obesity prevalence between the populations apparently narrowed. Interestingly, a majority of the increase in ACSM obesity prevalence occurred recently, from 2019 until 2023. The pattern of increasing ACSM obesity prevalence and consistently high prevalence in civilians was also present in young men (ages 17-24 years), the largest source of potential military recruits as well as newly accessed military members. More than 1 in 5 young male civilians had obesity throughout the 10-year study period.

The growing prevalence of obesity among ACSMs overall and in young men, particularly since 2019, could lead to poorer retention of newly accessed recruits and an increased burden on the MHS. The persistently high obesity prevalence among civilians presumably reduces the pool of height- and weight-eligible potential military recruits, although other factors, such as education and medical conditions,¹⁴ are considered for U.S. military accession.

Force-wide changes within the DOD may explain the significantly greater 2019–2023 increase in obesity among ACSMs. In early 2020, Force Health Protection Guidance was published in response to the COVID-19 pandemic, limiting close contact and reducing workplace access, leading to suspended physical fitness testing requirements by the service branches.¹⁵ This period also saw the resolution of the War on Terror and withdrawal of troops from Iraq and Afghanistan in 2021, changing the military from a wartime to peacetime posture.¹⁶ These changes may have shifted emphasis from combat to non-combat occupations and reduced demand for exceptional physical capabilities.

This study has several strengths. Data collected from MDR and linked to DMSS provide a near census of ACSMs, due to the annual physical examination requirement. This study population closely matched the DOD 2023 Demographics Report.¹² NHANES data are nationally representative of the civilian, non-institutional population and do not rely on survey participants seeking health care.¹³ We used measured height and weight from both data sources, which is more accurate than relying on self-reported height and weight.¹⁷

This study has several limitations. Collection and interpretation of the data sources differed. DMSS provides a nearly complete, continuous census of ACSMs in MDR, while NHANES is a cross-sectional survey with a sample selected through a complex, multi-stage probability design. Statistical power to detect significant civilian trends was lower than for ACSMs due to the smaller NHANES sample size. Data from the upcoming Military Health and Nutrition Examination Study (MHANES) may be more directly comparable to NHANES.¹⁸ Furthermore, the first non-pregnant record of height and weight from MDR was used for ACSMs, biasing data selection from earlier in the calendar year, whereas NHANES data are collected throughout a calendar year. Seasonal variations in body weight may occur, although the magnitude is likely small.¹⁹ Additionally, it could not be ascertained whether ACSMs had obesity before joining the military or if they developed obesity after accession.

Another important consideration when interpreting the results of this study are the limitations of using BMI to define obesity. While BMI is simple, inexpensive, and widely accepted for obesity surveillance, it does not distinguish body fat from lean body mass, nor describes body fat distribution within an individual.²⁰ When comparing BMI distributions, it is apparent that a higher proportion of ACSMs than civilians are in the overweight and class 1 obesity categories; conversely, a higher proportion of civilians have class 2 and class 3 obesity (i.e., severe obesity). Some ACSM classifications of overweight or class 1 obesity are likely partially attributable to higher levels of fitness and lean muscle mass in ACSMs than in civilians. Body composition measurement has recently come under increased scrutiny and will be included in a rapid review of military standards.²¹ Other measures of adiposity that are better proxies for central adiposity, such as waist circumference and body composition scans, may reduce some limitations of using height and weight alone to define obesity.

Future studies could compare service-specific height and weight military accession standards with the findings from this study, to ultimately inform potential effects on military strength and readiness, as well as the burden of obesity on the MHS.

Author Affiliations

Epidemic Intelligence Service, U.S. Centers for Disease Control and Prevention, Atlanta, GA: MAJ Emmerich; National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD: MAJ Emmerich, Dr. Stierman, Dr. Ogden; Epidemiology and Analysis Branch, Armed Forces Health Surveillance Division, Public Health Directorate, Defense Health Agency, Silver Spring, MD: Dr. Mabila

Disclaimer

The findings and conclusions in this article are those of the authors and do not represent official position of the National Center for Health Statistics, U.S. Centers for Disease Control and Prevention.

References

Vergun D. DOD addresses recruiting shortfall challenges. DoD News. Dec. 13, 2023. Accessed May 12, 2025. https://www.defense.gov/news/news-stories/article/article/3616786/dod-addresses-recruiting-shortfall-challenges
U.S. Centers for Disease Control and Prevention. Unfit to serve. Physical Health. U.S. Dept. of Health and Human Services. Feb. 9, 2024. Accessed May 12, 2025. https://www.cdc.gov/physical-activity/php/military-readiness/unfit-to-serve.html
Maxey H, Bishop-Josef S, Goodman B. Un-Healthy and Unprepared: National Security Depends on Promoting Healthy Lifestyles from an Early Age. Council for a Strong America;2018. Accessed May 12, 2025. https://strongnation.s3.amazonaws.com/documents/484/389765e0-2500-49a2-9a67-5c4a090a215b.pdf
Knapik JJ, Farina EK, Steelman RA, Trone DW, Lieberman HR. The medical burden of obesity and overweight in the US military: association of BMI with clinically diagnosed medical conditions in United States military service members. J Nutrition. 2023;153(10):2951-2967. doi:10.1016/j.tjnut.2023.08.023
Legg M, Stahlman S, Chauhan A, et al. Obesity prevalence among active component service members prior to and during the COVID-19 pandemic, January 2018-July 2021. MSMR. 2022;29(3):8-16. Accessed May 12, 2025. https://www.health.mil/news/articles/2022/03/01/obesity-prev-msmr
Emmerich SD, Fryar CD, Stierman B, et al. Trends in obesity-related measures among US children, adolescents, and adults. JAMA. 2025. doi:10.1001/jama.2024.27676
Armed Forces Health Surveillance Division. Defense Medical Surveillance System. Defense Health Agency, U.S. Dept. of Defense. Updated Sep. 23, 2025. Accessed Nov. 25, 2025. https://www.health.mil/military-health-topics/health-readiness/afhsd/functional-information-technology-support/defense-medical-surveillance-system
National Center for Health Statistics, U.S. Centers for Disease Control and Prevention. NHANES Survey Methods and Analytic Guidelines. U.S. Dept. of Health and Human Services. Accessed May 12, 2025. https://wwwn.cdc.gov/nchs/nhanes/analyticguidelines.aspx
NHLBI Obesity Education Initiative Expert Panel on the Identification, Evaluation, and Treatment of Obesity in Adults (US). Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults: The Evidence Report. National Heart, Lung, and Blood Institute;1998. Accessed Nov. 25, 2025. https://www.nhlbi.nih.gov/files/docs/guidelines/ob_gdlns.pdf
Parker JD, Talih M, Malec DJ, et al. Vital and Health Statistics, Series 2 Number 175: National Center for Health Statistics Data Presentation Standards for Proportions–Data Evaluation and Methods Research. National Center for Health Statistics, U.S. Centers for Disease Control and Prevention;2017. Accessed Nov. 25, 2025. https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf
Ingram DD, Malec DJ, Makuc DM, et al. Vital and Health Statistics, Series 2 Number 179: National Center for Health Statistics Guidelines for Analysis of Trends–Data Evaluation and Methods Research. National Center for Health Statistics, U.S. Centers for Disease Control and Prevention;2017. Accessed Nov. 25, 2025. https://www.cdc.gov/nchs/data/series/sr_02/sr02_179.pdf
Military One Source. 2023 Demographics Profile. U.S. Dept. of Defense. 2024. Accessed May 12, 2025. https://www.militaryonesource.mil/data-research-and-statistics/military-community-demographics/2023-demographics-profile
National Center for Health Statistics, U.S. Centers for Disease Control and Prevention. About NHANES. National Health and Nutrition Examination Survey. U.S. Dept. of Health and Human Services. Updated Dec. 18, 2024. Accessed May 12, 2025. https://www.cdc.gov/nchs/nhanes/about/index.html
USA.gov. Requirements to Join the U.S. Military. U.S. General Services Administration. Updated Aug. 27, 2025. Accessed May 12, 2025. https://www.usa.gov/military-requirements
Under Secretary of Defense, Personnel and Readiness. Force Health Protection (Supplement 8): DOD Guidance for Protecting Personnel in Workplaces During the Response to the Coronavirus Disease 2019 Pandemic. U.S. Department of Defense. Apr. 13, 2020. Accessed May 12, 2025. https://media.defense.gov/2020/apr/13/2002280147/-1/-1/1/force-health-protection-guidance-supplement-8.pdf
Council on Foreign Relations. Timeline: The U.S. War in Afghanistan. Accessed May 12, 2025. https://www.cfr.org/timeline/us-war-afghanistan
Rowland ML. Self-reported weight and height. Am J Clin Nutr. 1990;52(6):1125-1133. doi:10.1093/ajcn/52.6.1125
Berryman C, Bukhari A, Hennigar S, et al. The Military Health and Nutrition Examination Study (MHANES) project overview: assessment of the health and nutritional status of the active-duty US Army (abstract). J Acad Nutr Diet. 2024;124(10):a79. https://www.jandonline.org/article/s2212-2672(24)00679-8/fulltext
Yanovski JA, Yanovski SZ, Sovik KN, et al. A prospective study of holiday weight gain. NEJM. 2000;342(12):861-867. doi:10.1056/nejm200003233421206
Neeland IJ, Poirier P, Després JP. Cardiovascular and metabolic heterogeneity of obesity: clinical challenges and implications for management. Circulation. 2018;137(13):1391-1406. doi:10.1161/circulationaha.117.029617
Secretary of Defense. Rapid Force-wide Review of Military Standards. U.S. Department of Defense. Mar. 12, 2025. Accessed May 12, 2025. https://media.defense.gov/2025/mar/12/2003666182/-1/-1/1/rapid-force-wide-review-of-military-standards-osd001952-25-res-final.pdf

Perinatal mental health conditions among U.S. active component service women, 2016–2022

Mon, 01 Dec 2025 00:00:00 Z

Abstract

Although mental health conditions are the leading underlying cause of maternal mortality, there is limited research on the prevalence of perinatal mental health conditions among active duty service women (ADSW). In this study of live-born deliveries among U.S. ADSW (n=62,729) with pregnancy start and end dates (i.e., dates of last menstrual period and infant delivery, respectively) from October 1, 2016 through December 31, 2021, International Classification of Diseases, 10th Revision, Clinical Modification diagnosis codes were used to identify mental health conditions: trauma and stressor-related disorders, anxiety and panic disorders, depressive disorders, suicidal ideation or attempt, and eating disorders. Data were collected through 1 year postpartum, until December 31, 2022. The prevalence of diagnosed mental health conditions from 1 year prior to pregnancy through 1 year postpartum was 33.8%. Trauma and stressor-related disorders were most prevalent (23.1%), followed by anxiety and panic disorders (16.9%), depressive disorders (14.6%), suicidal ideation or attempt (1.6%), and eating disorders (0.4%). The prevalence of mental health conditions was higher in the postpartum period (22.0%) compared to pregnancy (18.4%) and prior to pregnancy (15.0%). Overall, higher prevalence of these conditions was found among non-Hispanic Black ADSW (37.4%), and those who were unmarried (38.4%), never deployed (34.9%), or in the Army (37.4%) and Navy (36.4%).

What are the new findings?

One in 3 active duty service women were diagnosed with a mental health condition in the year preceding pregnancy through 1 year postpartum. Overall, non-Hispanic Black and junior enlisted active duty service women demonstrated higher prevalences of mental health conditions compared to all other racial and ethnic groups and military ranks.

What is the impact on readiness and force health protection?

Mental health issues can lead to early returns from deployment, which can adversely affect unit missions and cohesion. Service member retention is linked to mental health, with those who experience mental health conditions less likely to remain in military service. As the proportion of women serving in the military continues to increase, targeted perinatal mental health support and interventions should be prioritized to improve active duty service women's psychological well-being and maintain force readiness.

Background

Most (93.5%) U.S. active duty service women (ADSW) are of childbearing age (18-44 years), averaging 15,000 live births per year.¹ From 2017 through 2019, 22.7% of maternal mortality in the general U.S. population was attributable to mental health conditions, including deaths due to suicide or overdose.² While comparable maternal mortality data are not published for ADSW, during the same period 37.8% of ADSW received a mental health diagnosis during pregnancy or through 1 year postpartum.³

Recent research suggests that deaths from suicide or accidental overdose account for a much larger percentage of pregnancy-associated deaths among ADSW (39.4%) compared to civilian women (8.8–10.9%).⁴ The increased burden of mental health conditions among ADSW continues after military service, with as many as 46.7% of female veterans reporting perinatal depression, compared to 10% of civilian women.⁵ Existing research only provides the prevalence of any mental health condition versus specific diagnoses, without estimates for sub-populations of ADSW. This report describes the 2016–2022 prevalence of perinatal mental health conditions among ADSW, with data presented for 5 diagnostic categories: trauma and stressor-related disorders, anxiety and panic disorders, depressive disorders, suicidal ideation or attempt, and eating disorders.

Methods

Data sources

This study utilized data from the Department of Defense (DOD) Birth and Infant Health Research (BIHR) program, a population-level surveillance and research database that identifies live births among Military Health System (MHS) beneficiaries. Detailed information on BIHR data and methodologies have been described elsewhere.^6,7 BIHR includes military personnel data from the Defense Manpower Data Center (DMDC) and administrative medical encounter data from the MHS Data Repository; BIHR data are used to identify and link live births to birth mothers and military sponsors through the Defense Enrollment Eligibility Reporting System, to describe associated demographic and medical characteristics. These data are linked using the unique 10-digit identifiers (i.e., Electronic Data Interchange Personal Identifier) assigned to each person with a direct DOD relationship. Institutional Review Board approval (NHRC.1999.0003) for this study was obtained from the Naval Health Research Center, with informed consent waived in accordance with criteria set forth by 32 Code of Federal Regulations Section 219.116(d).

Study population

The source population for this study included all live-born deliveries among ADSW captured in BIHR data with pregnancy start and end dates (i.e., dates of last menstrual period, or LMP, and delivery, respectively) from October 1, 2016 through December 31, 2021. This timeframe was selected to include medical data that were captured exclusively after the transition to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM), which occurred on October 1, 2015. Deliveries were excluded if an ADSW did not have a record of TRICARE enrollment or any medical encounter data for at least 10 of 12 months during both the year preceding pregnancy and year following delivery.

Mental health conditions

Mental health conditions of interest were identified using ICD-10-CM codes and then grouped into 5 categories: trauma and stressor-related disorders (F43.x), anxiety and panic disorders (F40.x, F41.x), depressive disorders (F32.x, F33.x, F34.x), suicidal ideation or attempt (R45.851, T14.91), and eating disorders (F50.x), in accordance with the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision.⁸ Conditions were measured within 3 timeframes: pre-pregnancy (year prior to LMP), pregnancy (from LMP to date of delivery), and postpartum (year following date of delivery). For each diagnostic category and timeframe, cases were identified by the presence of diagnosis codes on 1 inpatient or 2 outpatient records on separate dates. This method was selected to improve reliability for capturing true mental health diagnoses and not exclusion or ‘rule out’ diagnoses, which are common with new mental health conditions, as many conditions exhibit overlapping symptoms. Diagnostic categories and timeframes were not mutually exclusive, meaning that ADSW with live-born deliveries could be identified with multiple mental health conditions within multiple mental health diagnostic categories at multiple timeframes in the study period. For example, if an ADSW had both an anxiety and depressive disorder diagnosis she would be represented individually in both diagnostic categories (anxiety or panic disorders and depressive disorders).

To provide the prevalence of diagnosed mental health conditions pre-pregnancy through 1 year postpartum, an overall composite variable was created to identify deliveries that met criteria for any mental health condition of interest during any timeframe. Variables were created to identify deliveries with any diagnosed mental health conditions of interest within each timeframe (pre-pregnancy, pregnancy, postpartum). To evaluate the prevalence of specific mental health conditions, variables were created for each diagnostic category (trauma/stress, anxiety/panic, depressive, suicidal ideation/attempt, eating disorders) assessed over the entire study period and within each timeframe (pre-pregnancy, pregnancy, postpartum). We also created a co-morbid mental health condition variable that summed the number of diagnosed mental health conditions (from the 5 diagnostic categories of interest) for each individual throughout the study period (1 year pre-pregnancy through 1 year postpartum). The count ranged 0–5 and was categorized as 0, 1, 2, or 3+.

Covariates

Demographic and military factors were obtained from DMDC files corresponding to the month of delivery. Variables included racial or ethnic group (i.e., American Indian or Alaska Native, Asian, Hispanic, multiracial, Native Hawaiian/Pacific Islander, non-Hispanic Black, non-Hispanic White, unknown), age at infant delivery (<20, 20-24, 25-29, 30-34, 35+ years), marital status (married, unmarried/unknown), military rank and pay grade (junior enlisted [E1-E4], mid-/senior enlisted [E5-E9], officer/warrant officer [O1-O10/W01-W05]), branch of service (Army, Navy, Air Force, Marine Corps, Coast Guard), and deployment history prior to infant delivery (ever deployed, never deployed). Deployment history was limited to deployments in support of post-September 11, 2001 (9/11) operations, predominately in or near the Middle East.

Statistical analysis

Frequencies and percentages were used to describe the prevalence of mental health conditions during the study period, and by demographic and military characteristics. Confidence intervals (CIs) were also calculated to assess differences between subgroups. Prevalence was not calculated for subgroups with less than 30 cases. Prevalence was calculated for the mental health conditions overall and by specific diagnostic category; measures were calculated throughout the entire study (1 year pre-pregnancy through 1 year postpartum) and by specific timeframe. All data management and statistical analyses were performed using SAS, Version 9.4 (SAS Institute Inc., Cary, NC).

Results

Analytic population

The source population included 62,729 live-born deliveries among 54,471 unique ADSW. After excluding deliveries among ADSW with less than 10 of 12 months of either TRICARE enrollment or medical encounter data before and after pregnancy, the final analytic cohort comprised 56,371 deliveries among 49,262 unique ADSW (89.9% of source population). Excluded deliveries were more likely to be among ADSW younger than age 20 years, of junior enlisted rank, and in the Marine Corps.

Prevalence of any mental health condition

Overall, 33.8% of deliveries were among ADSW diagnosed with at least 1 mental health condition of interest at any time during the study period (Table 1). Deliveries to non-Hispanic Black ADSW had the highest prevalence of any mental health condition (37.4%; 95% CI 36.6, 38.2) compared to all other racial and ethnic groups (range 23.6–34.3%). A higher prevalence of mental health conditions was found among deliveries to unmarried versus married ADSW (38.4%; 95% CI 37.5, 39.2 vs. 32.5%; 95% CI 32.1, 33.0). The prevalence of mental health conditions was lower among deliveries to officers (20.8%; 95% CI 20.1, 21.6) compared to those among junior enlisted (38.5%; 95% CI 37.3, 39.6) and mid- or senior enlisted ADSW (36.6%; 95% CI 36.1, 37.0).

ADSW in the Army had the highest prevalence of mental health conditions (37.4%; 95% CI 36.7, 38.1), followed by those in the Navy (36.4%; 95% CI 35.7, 37.2), compared to deliveries among ADSW in the Air Force, Coast Guard, and Marine Corps (range 22.7–31.0%). Those who had never deployed had a higher prevalence of mental health conditions compared to those who had a history of deployment (34.9%; 95% CI 34.4, 35.3 vs. 31.9%; 95% CI 31.2, 32.5), where the definition of deployment was limited to support of post-9/11 operations.

The prevalence of any diagnosed mental health condition increased over time during the perinatal period, from 15.0% (95% CI 14.7, 15.3) in the year prior to pregnancy to 18.4% (95% CI 18.1, 18.7) during pregnancy to 22.0% (95% CI 21.7, 22.4) in the year following pregnancy. Throughout the study cohort, 17.9% (95% CI 17.6, 18.2) of deliveries were to ADSW with 1 diagnosed mental health condition, 10.0% (95% CI 9.7, 10.2) were to ADSW with 2 mental health conditions, and 6.0% (95% CI 5.8, 6.2) were to ADSW with 3 or more mental health conditions. Of those diagnosed with any of the 5 mental health conditions during the study period, 29.4% had at least 2 diagnoses, and 17.7% had 3 or more diagnoses (data not shown).

Prevalence of specific mental health conditions

Throughout the entire study period, the most commonly diagnosed mental health conditions were trauma and stressor-related disorders (23.1%; 95% CI 22.8, 23.5), followed by anxiety and panic disorders (16.9%; 95% CI 16.6, 17.3), depressive disorders (14.6%; 95% CI 14.4, 14.9), suicidal ideation or attempt (1.6%; 95% CI 1.5, 1.7), and eating disorders (0.4%; 95% CI 0.3, 0.4) (Table 2). Similar to the overall prevalence of mental health conditions, when examined by specific diagnostic category, a higher prevalence of all diagnoses was found in deliveries of ADSW who were unmarried, of enlisted rank, in the Army or Navy, or who had never deployed.

For trauma and stressor-related disorders, deliveries to non-Hispanic Black ADSW had the highest prevalence (27.5%; 95% CI 26.7, 28.2) compared to all other racial and ethnic groups (range 17.2–22.8%). By age, the lowest prevalence for trauma and stress-related disorders was in deliveries among ADSW ages 30-34 years (18.5%; 95% CI 17.9, 19.1) compared to all other age groups (range 20.9–26.8%). The highest prevalence of trauma and stressor-related disorders was seen postpartum (14.2%; 95% CI 13.9, 14.5), compared to pre-pregnancy (10.3%; 95% CI 10.1, 10.6) and during pregnancy (9.4%; 95% CI 9.1, 9.6).

For anxiety and panic disorders, the lowest prevalence was found among deliveries to Native Hawaiian or Pacific Islander (8.1%; 95% CI 6.2, 10.0) and non-Hispanic Asian (11.1%; 95% CI 9.8, 12.4) ADSW compared to all other racial or ethnic groups (range 15.8–18.7%). Deliveries to ADSW in the Navy had the highest prevalence (19.2%; 95% CI 16.8, 19.9) compared to all other service branches (range 13.1–17.0%).

For depressive disorders, higher prevalence was found among deliveries to junior enlisted (17.4%; 95% CI 16.5, 18.3) and mid- or senior enlisted ADSW (16.2%; 95% CI 15.8, 16.5) compared to officers (7.3%; 95% CI 6.8, 7.8). The lowest prevalence of depressive disorders was seen pre-pregnancy (4.6%; 95% CI 4.5, 4.8) compared to during pregnancy (8.3%; 95% CI 8.1, 8.6) and postpartum (7.8%; 95% CI 7.6, 8.1).

For suicidal ideation or attempt, junior enlisted ADSW prevalence (3.4%; 95% CI 3.0, 3.8) was 8.5 times the prevalence among officers (0.4%; 95% CI 0.3, 0.5) and 2 times prevalence in mid- and senior enlisted ADSW (1.6%; 95% CI 1.4, 1.7).

For eating disorders, there were no significant differences in prevalence by demographic or military characteristics as well as timeframe.

Discussion

In this study of live-born deliveries among ADSW, 1 in 3 were diagnosed with a mental health condition 1 year prior to pregnancy through 1 year postpartum. Of those diagnosed with a mental health condition, 1 in 4 were diagnosed with a trauma and stressor-related disorder, which existing research has linked to an increase in suicide risk, particularly among women.^9-12 Mental health conditions are also associated with adverse pregnancy outcomes, such as pre-term birth and hypertensive disorders of pregnancy, with highest risk in those with trauma or stressor-related disorders.^13-16 This high prevalence (33.8%) of mental health conditions reveals the potential risk of adverse pregnancy outcomes among ADSW.^14,16,17 To our knowledge, this is the largest study to examine the prevalence of specific mental health conditions before and during the perinatal period among a sample of live births to ADSW.³

There is limited research focused on perinatal mental health conditions among ADSW. Abramovitz and colleagues investigated the prevalence of post-traumatic stress disorder (PTSD) (identified by diagnosis codes) among 134,244 pregnant ADSW from 2007 through 2014, utilizing the same data source (BIHR) as the current study. Abramovitz et al. found that 1.7% of ADSW had a diagnosis of PTSD from the year prior to pregnancy through the end of pregnancy.¹⁸ In contrast, this study estimated the prevalence of all trauma or stressor-related disorders, not just PTSD, and found a higher prevalence during pre-pregnancy (10.3%) and pregnancy (9.4%). A recent study of a nationally representative sample in the U.S. found a prevalence of a trauma or stressor-related disorder during pregnancy of only 0.2%, much lower than reported in this study and existing studies of military populations.¹⁴

Andriotti and colleagues utilized TRICARE claims data to identify new mental health cases in the 2 years prior to pregnancy, during pregnancy, and 2 years postpartum.¹⁷ Andriotti et al. provided limited details, however, for which mental health conditions were included in their study or which specific diagnosis codes were used to identify mental health cases. As in our study, Andriotti et al. found an increase in the prevalence of mental health conditions in the postpartum period (20%) compared to pregnancy (15%).¹⁷ Globally, the prevalence of perinatal mental health conditions is estimated to be 10% during pregnancy and 13% postpartum, which is lower than that found by Andriotti and colleagues¹⁷ as well as the current study of ADSW.¹⁹

A U.S. Government Accountability Office (GAO) report of perinatal mental health conditions among TRICARE beneficiaries, 2017–2019, found that 37.8% of ADSW had a diagnosed mental health condition during pregnancy or in the year postpartum.³ The GAO estimate is higher than this study’s estimated 33.8% prevalence, with several methodological differences between the 2 studies. First, the GAO report defined mental health cases by the presence of any mental health ICD-10 code (F01-F99) and only required 1 code, either inpatient or outpatient. The current study limited analysis to 5 categories of mental health conditions defined by the presence of either 1 inpatient ICD-10 code or 2 outpatient ICD-10 codes on separate days. We chose this method to improve reliability of capturing a true diagnosed case versus exclusion or ‘rule out’ diagnoses. Second, the GAO report only included mental health conditions diagnosed during pregnancy through 1 year postpartum, while this study also included mental health diagnoses in the year prior to pregnancy. Finally, this study only included live-born deliveries, while the GAO report included pregnancy losses and stillbirths. Despite these methodological differences, the results from the GAO report and this study are similar: Both found a higher prevalence of mental health conditions in non-Hispanic Black ADSW compared to other racial and ethnic groups, and a higher prevalence among ADSW in the Army and Navy compared to other service branches.

One key difference between the GAO report and this study is that the GAO found a higher prevalence of mental health conditions among ADSW who deployed, while this study found that ADSW who had not deployed had a higher prevalence. To capture deployment history, the GAO report relied on ICD-10 code (Z91.82), while this study obtained deployment data from DMDC limited to deployments in support of post-9/11 operations. This difference in methodology may explain the differing results.

This study also found lower prevalence among Native Hawaiian and Pacific Islander ADSW compared to all other racial and ethnic groups, which conflicts with existing, albeit limited, research.^20,21 This study’s population included 1,419 ADSW who identified as American Indian, Alaska Native, Native Hawaiian, or Pacific Islander, which provided a rare opportunity to evaluate perinatal mental health in these historically under-researched populations. Women of those racial and ethnic groups may face unique barriers to care due to living in remote geographic locations, lack of culturally appropriate screening tools, insufficient cultural congruency with health care providers, and other structural factors that increase their risk of poor health outcomes.²⁰ Those risks may be mitigated by military service, which provides access to health care and stable salaries, both factors that may help explain why rates of perinatal mental health conditions in this study were lower among Native Hawaiian and Pacific Islander ADSW compared to other racial and ethnic groups, including American Indian and Alaska Native service women.

Strengths of this study include the use of a large, population-based dataset of ADSW. All women in this study were employed, with access to health care, which provided a unique opportunity to examine differences in perinatal mental health by socio-demographic characteristics. Another strength of this study is that mental health conditions were evaluated at 3 distinct points in time—from 1 year prior to pregnancy, during pregnancy, and at 1 year postpartum—which allowed assessment of prevalence over time. Lastly, only live-born deliveries were included in this study, in recognition of the singular impact that experiencing a pregnancy loss or stillbirth may have on mental health.

Limitations of this study include the use of medical encounter data, which may be subject to coding accuracy and quality. The ICD-10-CM diagnosis codes utilized to identify mental health conditions have not been validated, but we attempted to improve accuracy of capture through our requirement of 1 inpatient or 2 outpatient diagnoses on separate days. The study observation period (2016–2022) included the COVID-19 pandemic, which may have had impacts on the prevalence and detection of perinatal mental health conditions.^22,23

Additionally, the prevalence of mental health conditions only reflects those actively seeking care or otherwise engaged in the health care system. Many service members may not seek health care due to stigma, despite access to these resources.^24-26 A recent GAO report²⁷ found that only 52% of U.S. service women who delivered at a military hospital or clinic received recommended perinatal mental health screenings, increasing risk of under-diagnosis.

The true prevalence of mental health conditions among ADSW before and during the perinatal period is likely much larger than reported in this study. Future research should include prospective screening studies to identify ADSW who are not seeking care for mental health but who may meet diagnostic criteria for a mental health diagnosis. Lastly, these findings are not completely generalizable to all ADSW because those excluded were more likely of younger ages and junior enlisted members, resulting in a study population biased towards slightly older ADSW with more time in service.

This study highlights the prevalence of perinatal mental health conditions among military sub-populations. ADSW have unique mental health and reproductive health needs as a result of stressors inherent to military life, including, but not limited to, potential (and sometimes sudden) deployment and engagement in armed conflict.^28,29

Mental health directly affects physical health. Military service members who are mentally fit are more likely to perform their duties efficiently, enhancing operational readiness. Mental health issues can lead to early returns from deployment, which can affect unit mission and cohesion. Ten percent of all aeromedical evacuations during operations Enduring Freedom, Iraqi Freedom, and New Dawn were due to psychiatric reasons.³⁰ Service member retention is also linked to mental health, with those experiencing mental health conditions less likely to remain in the military than those not experiencing mental health conditions.³¹

Enhanced screening and targeted management of mental health conditions may be a mechanism to improve service member retention and decrease adverse pregnancy outcomes among this population. Future research should employ qualitative methods to further explain differences in prevalence by demographic factors. Prospective studies are needed to evaluate effective screening practices and interventions for perinatal mental health conditions among ADSW.

Author Affiliations

Womack Army Medical Center, Ft Bragg, NC: MAJ Manzo; Yale School of Nursing, West Haven, CT: MAJ Manzo, Dr. Combellick, Dr. Womack; Leidos, Inc., San Diego, CA: Dr. Hall; Veterans Administration Connecticut Healthcare System, West Haven, CT: Dr. Combellick, Dr. Harpaz-Rotem, Dr. Womack; Yale University, New Haven, CT: Dr. Harpaz-Rotem; Air Force Medical Command, Falls Church, VA: Lt Col Phillips

Disclaimer

MAJ Manzo and Lt Col Phillips are military service members. This work was prepared as part of official duties. Title 17, U.S.C. Section 105 provides that copyright protection under this title is not available for any work of the U.S. Government. Title 17, U.S.C. Section 101 defines a U.S. Government work as work prepared by a military service member or employee of the U.S. Government as part of official duties. Report 25-16 was supported by the U.S. Navy Bureau of Medicine and Surgery under work unit 60504.

The views expressed in this article are those of the authors and do not reflect official policy nor position of the departments of the Army or Navy, Department of Defense, nor the U.S. Government. The study protocol was approved by the Naval Health Research Center Institutional Review Board in compliance with all applicable federal regulations governing human subject protection. Research data were derived from approved Naval Health Research Center Institutional Review Board protocol NHRC.1999.0003.

Acknowledgments

The authors are grateful to Ava Marie S. Conlin, DO, MPH, principal investigator of the Department of Defense Birth and Infant Health Research (BIHR) program at the Naval Health Research Center, who facilitated access to BIHR data and provided valuable feedback throughout the study design and execution; and to Celeste Romano, MS, epidemiologist with the BIHR program, who generously agreed to review the manuscript and provided valuable feedback.

References

U.S. Centers for Disease Control and Prevention. Women’s Reproductive Health, May 15, 2024. U.S. Dept. of Health and Human Services. Accessed Sep. 21, 2024. https://www.cdc.gov/reproductive-health/women-health/?cdc_aaref_val=https://www.cdc.gov/reproductivehealth/womensrh/index.htm
U.S. Dept. of Health and Human Services. Pregnancy-Related Deaths: Data from Maternal Mortality Review Committees in 36 US States, 2017-2019. Accessed Oct. 2, 2024. https://www.hrsa.gov/sites/default/files/hrsa/advisory-committees/infant-mortality/meetings/busacker-maternalmortality.pdf
U.S. Government Accountability Office. Report to Congressional Committees: Defense Health Care–Prevalence of and Efforts to Screen and Treat Mental Health Conditions in Prenatal and Postpartum TRICARE Beneficiaries. GAO-22-105136. May 2022. Accessed Sep. 23, 2023. https://www.gao.gov/assets/gao-22-105136.pdf
Romano CJ, Hall C, Bukowinski AT, Gumbs GR, Conlin AMS. Pregnancy-associated and pregnancy-related deaths in the United States military, 2003-2014. Am J Obstet Gynecol. 2022;227(3):508.e1-508.e9. doi:10.1016/j.ajog.2022.04.020
Smythe KL, Petersen I, Schartau P. Prevalence of perinatal depression and anxiety in both parents: a systematic review and meta-analysis. JAMA Network Open. 2022;5(6):e2218969-e2218969. doi:10.1001/jamanetworkopen.2022.18969
Bukowinski AT, Conlin AMS, Gumbs GR, et al. Department of Defense Birth and Infant Health Registry: select reproductive health outcomes, 2003-2014. MSMR. 2017;24(11):39-49. Accessed Oct. 2, 2025. https://www.health.mil/reference-center/reports/2017/01/01/medical-surveillance-monthly-report-volume-24-number-11
Khodr ZG, Bukowinski AT, Hall C, et al. The Department of Defense Birth and Infant Health Research Program: assessing the reproductive health of U.S. active-duty women. Semin Reprod Med. 2018;36(6):351-360. doi:10.1055/s-0039-1678751
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision. American Psychiatric Association;2022.
Akbar R, Arya V, Conroy E, Wilcox HC, Page A. Posttraumatic stress disorder and risk of suicidal behavior: a systematic review and meta-analysis. Suicide Life Threat Behav. 2023;53(1):163-184. doi:10.1111/sltb.12931
Fox V, Dalman C, Dal H, et al. Suicide risk in people with post-traumatic stress disorder: a cohort study of 3.1 million people in Sweden. J Affect Disord. 2021;279:609-616. doi:10.1016/j.jad.2020.10.009
Pompili M, Sher L, Serafini G, et al. Posttraumatic stress disorder and suicide risk among veterans: a literature review. J Nerv Ment Dis. 2013;201(9):802-812. doi:10.1097/nmd.0b013e3182a21458
Ramsawh HJ, Fullerton CS, Mash HB, et al. Risk for suicidal behaviors associated with PTSD, depression, and their comorbidity in the U.S. Army. J Affect Disord. 2014;161:116-22. doi:10.1016/j.jad.2014.03.016
Accortt E, Mirocha J, Jackman S, et al. Association between diagnosed perinatal mood and anxiety disorders and adverse perinatal outcomes. J Matern Fetal Neonatal Med. 2021;35(25):9066-9070. doi:10.1080/14767058.2021.2014450
Brown CC, Adams CE, George KE, Moore JE. Mental health conditions increase severe maternal morbidity by 50 percent and cost $102 million yearly n the United States. Health Aff. 2021;40(10):1575-1584. doi:10.1377/hlthaff.2021.00759
Ding XX, Wu YL, Xu SJ, et al. Maternal anxiety during pregnancy and adverse birth outcomes: a systematic review and meta-analysis of prospective cohort studies. J Affect Disord. 2014;159:103-10. doi:10.1016/j.jad.2014.02.027
Logue TC, Wen T, Monk C, et al. Trends in and complications associated with mental health condition diagnoses during delivery hospitalizations. Am J Obstet Gynecol. 2022;226(3):405.e1-405.e16. doi:10.1016/j.ajog.2021.09.021
Andriotti T, Ranjit A, Hamlin L, et al. Psychiatric conditions during pregnancy and postpartum in a universally insured American population. Mil Med. 2022;187(7-8):e795-e801. doi:10.1093/milmed/usab154
Abramovitz LM, Lutgendorf MA, Bukowinski AT, et al. Posttraumatic stress disorder in a cohort of pregnant active duty U.S. military servicewomen. J Trauma Stress. 2021;34(3):586-595. doi:10.1002/jts.22656
Gruszczyńska-Sińczak I, Wachowska K, Bliźniewska-Kowalska K, Gałecki P. Psychiatric treatment in pregnancy: a narrative review. J Clin Med. 2023;12(14):4746. doi:10.3390/jcm12144746
Owais S, Faltyn M, Johnson AVD, et al. The perinatal mental health of Indigenous women: a systematic review and meta-analysis. Can J Psychiatry. 2020;65(3):149-163. doi:10.1177/0706743719877029
Kisely S, Alichniewicz KK, Black EB, et al. The prevalence of depression and anxiety disorders in indigenous people of the Americas: a systematic review and meta-analysis. J Psychiatr Res. 2017;84:137-152. doi:10.1016/j.jpsychires.2016.09.032
Chmielewska B, Barratt I, Townsend R, et al. Effects of the COVID-19 pandemic on maternal and perinatal outcomes: a systematic review and metaanalysis. Lancet Glob Health. 2021;9(6):e759-e772. doi:10.1016/s2214-109x(21)00079-6
Zanardo V, Manghina V, Giliberti L, et al. Psychological impact of COVID-19 quarantine measures in northeastern Italy on mothers in the immediate postpartum period. Int J Gynaecol Obstet. 2020;150(2):184-188. doi:10.1002/ijgo.13249
Chu KM, Garcia SMS, Koka H, Wynn GH, Kao TC. Mental health care utilization and stigma in the military: comparison of Asian Americans to other racial groups. Ethn Health. 2021;26(2):235-250. doi:10.1080/13557858.2018.1494823
Ben-Zeev D, Corrigan PW, Britt TW, Langford L. Stigma of mental illness and service use in the military. J Ment Health. 2012;21(3):264-273. doi:10.3109/09638237.2011.621468
Sharp ML, Fear NT, Rona RJ, et al. Stigma as a barrier to seeking health care among military personnel with mental health problems. Epidemiol Rev. 2015;37:144-162. doi:10.1093/epirev/mxu012
U.S. Government Accountability Office. Report to the Committee on Armed Services, House of Representatives: Defense Health Care–DOD Should Monitor Mental Health Screenings for Prenatal and Postpartum TRICARE Beneficiaries. GAO-25-107163. Apr. 2025. Accessed Sep. 15, 2025. https://www.gao.gov/assets/gao-25-107163.pdf
Gawron LM, Mohanty AF, Kaiser JE, Gundlapalli AV. Impact of deployment on reproductive health in U.S. active-duty servicewomen and veterans. Semin Reprod Med. 2018;36:361-370. doi:10.1055/s-0039-1678749
Manzo LL, Dindinger RA, Batten J, Combellick JL, Basile-Ibrahim B. The impact of military trauma exposures on servicewomen’s pregnancy outcomes: a scoping review. J Midwifery Womens Health. 2024;69(5):634-646. doi:10.1111/jmwh.13620
Peterson A, Hale W, Baker MT, et al. Psychiatric aeromedical evacuations of deployed active duty U.S. military personnel during operations Enduring Freedom, Iraqi Freedom, and New Dawn. Mil Med. 2018;183:649-658. doi:10.1093/milmed/usy188
Schmied EA, Highfill-McRoy RM, Larson GE. Mental health and turnover following an initial term of military service. Mil Med. 2012;177(7):766-772. doi:10.7205/milmed-d-11-00430

Reportable medical events at Military Health System facilities through week 36, ending September 6, 2025

Mon, 01 Dec 2025 00:00:00 Z

Total active component cases reported per week are displayed for the top 5 RMEs for the previous year. Each month, the graph is updated with the top 5 RMEs, and is presented with the current month’s (August 2025) top 5 RMEs, which may differ from previous months. COVID-19 is excluded from these graphs due to changes in reporting and case definition updates in 2023.

Authors’ Affiliation

Defense Health Agency, Disease Epidemiology Branch, Defense Centers for Public Health–Aberdeen

References

Armed Forces Health Surveillance Division. Armed Forces Reportable Medical Events. Accessed Feb. 28, 2024. https://health.mil/reference-center/publications/2022/11/01/armed-forces-reportable-medical-events-guidelines
Defense Manpower Data Center. Department of Defense Active Duty Military Personnel by Rank/Grade of Service. Accessed Feb. 28, 2024. https://dwp.dmdc.osd.mil/dwp/app/dod-data-reports/workforce-reports
Defense Manpower Data Center. Armed Forces Strength Figures for January 31, 2023. Accessed Feb. 28, 2024. https://dwp.dmdc.osd.mil/dwp/app/dod-data-reports/workforce-reports
Navy Medicine. Surveillance and Reporting Tools–DRSI: Disease Reporting System Internet. Accessed Feb. 28, 2024. https://www.med.navy.mil/navy-marine-corps-public-health-center/preventive-medicine/program-and-policy-support/disease-surveillance/drsi

Follow up testing among male U.S. Air Force basic trainees diagnosed with chlamydia or gonorrhea, 2017–2023

Sat, 01 Nov 2025 00:00:00 Z

Abstract

While female U.S. Air Force and Space Force basic military trainees are screened universally for gonorrhea and chlamydia, male basic trainees are tested only when symptomatic or upon patient request. Epidemiology and follow-up testing of male basic trainees who test positive for gonorrhea or chlamydia in training is unclear. All active duty male basic trainees at Joint Base San Antonio–Lackland who tested positive for gonorrhea or chlamydia from 2017 through 2023 (50 of 182,726 total male trainees, 0.03%) were matched, 1-to-1, by age and accession date, with active duty female basic trainees who tested positive for the same pathogen. Medical records from military hospitals and clinics were reviewed for follow-up testing within 12 months of the initial positive test and subsequent diagnoses for chlamydia and gonorrhea up to 3 years afterwards, or July 1, 2024, whichever occurred first. Among 50 male basic trainees, 30 (60%) reported symptoms when presenting for testing. Most cases (86%) were due to chlamydia. Only 56% (n=28) of male trainees had follow-up testing within 1 year, compared to 76% (n=38) of matched female basic trainees (OR 0.4, 95% CI: 0.17, 0.95). Low screening for chlamydia and gonorrhea among male basic trainees may contribute to reduced follow-up testing and represents a missed opportunity to identify infections, prevent transmission, and reduce the burden of infection in this population.

What are the new findings?

Male basic military trainees who tested positive for gonorrhea or chlamydia had follow-up testing rates significantly below guideline recommendations. Rates of future infections among male basic trainees were not, however, statistically lower than female trainee rates of future infections.

What is the impact on readiness and force health protection?

These findings support universal gonorrhea and chlamydia screening for male trainees at higher risk for infection to reduce the impact of untreated infections on military readiness for individuals and their partners, in addition to facilitating provision of available methods of sexually transmitted infection prevention.

Background

Service in the U.S. military has been associated with increased risk of sexually transmitted infections (STIs) such as gonorrhea and chlamydia.¹ Before basic military training (BMT), all potential enlistees undergo medical evaluation including HIV testing, to ensure they meet criteria for accession, but they are not tested for chlamydia^2-6 or gonorrhea. BMT is an 8-week training program that is the sole point for civilian entry into the enlisted ranks of the U.S. Air Force and Space Force. During BMT, all trainees have access to universal, no-cost health care at both primary care clinics and emergency care facilities on base.⁷ Because male BMT trainees are not screened for gonorrhea or chlamydia, they are only tested if they are symptomatic or request testing.

A recent study of universally screened male Air Force BMT trainees found similar overall rates of chlamydia with female Air Force BMT trainees, although most infections were asymptomatic.⁸ Women entering U.S. Air Force and Space Force BMT are universally screened for chlamydia and gonorrhea due to known long-term sequelae of untreated infections, previously documented high rates of positivity, and guidelines recommending universal female screening. Positivity rates among female BMT trainees are approximately 0.3% for gonorrhea and 5.0% for chlamydia. With the exception of the Army, all services require universal screening for female BMT trainees.⁷

Current U.S. Centers for Disease Control and Prevention (CDC) guidelines recommend testing for re-infections 3 months after a gonorrhea or chlamydia diagnosis, regardless of patient sex or risk factors for future infection.⁵ Additionally, guidelines recommend that men at high risk for STIs, such as men who have sex with men, should be screened at least annually for chlamydia and gonorrhea. Annual chlamydia screening is required by all services for female service members under age 25 years.⁷

While the screening disparity between male and female BMT trainees is evident, it is unclear how this may affect future testing and STI diagnoses for male trainees who test positive for chlamydia or gonorrhea during BMT. Previously evaluated 2006-2021 data from 5,022 female BMT trainees who tested positive for gonorrhea or chlamydia showed a high follow-up testing rate (69.7%) within 1 year, as well as a relatively high rate (15.9%) of positivity upon repeat testing.² This study investigated the incidence of gonorrhea and chlamydia in male Air Force and Space Force BMT trainees from 2017 through 2023 and compared follow-up testing and clinical outcomes with female BMT trainees.

Methods

This retrospective matched cohort study evaluated all active duty male BMT trainees who tested positive (i.e., cases) for gonorrhea or chlamydia at Joint Base San Antonio–Lackland during any point in their BMT from 2017 through 2023. Additionally, during this study period, from November 2021 through March 2022, 352 male BMT trainees as well as active duty, reserve, and National Guard members were tested for gonorrhea and chlamydia as part of a previously published universal screening study that did not evaluate follow-up testing, so they were also included in this study. All male cases were matched 1-to-1 with female BMT trainees (i.e., controls) by age, date of military accession, and pathogen testing positive during training to determine sex-based differences in follow-up testing.⁸ Urinary testing for gonorrhea and chlamydia was performed by nucleic acid amplification testing (Hologic, Marlborough, MA) throughout the entire study period.²

For all positive male cases, a retrospective chart review in the Joint Legacy Viewer and MHS GENESIS electronic health records was performed. These systems include all military hospital and clinic records, regardless of geographic location. Variables including patient demographics, indications for testing, and testing facility were collected for each case. While current CDC guidelines recommend follow-up testing for re-infection at 3 months, this study evaluated whether a patient underwent repeat testing within 12 months of a positive test.²

Chart reviews identified positive laboratory test results for gonorrhea and chlamydia in BMT trainees. Test results for 3 years after original gonorrhea or chlamydia diagnosis were reviewed, or until July 1, 2024 if a period of 3 years following original diagnosis had not elapsed by initiation of data collection, as that period of time was used for a previous study.⁹

Nominal variables were compared by Fisher’s Exact Test due to small sample size, and continuous variables were compared by a Mann-Whitney U test due to non-parametric data distribution. Standard odds ratios (ORs) with 95% confidence intervals (CIs) were also calculated. A p-value less than 0.05 was pre-determined to be statistically significant.

This study was reviewed by the 59th Medical Wing Human Protections Office and determined to be exempt from Institutional Review Board approval due to its retrospective nature, and thus, consent was not obtained from subjects.

Results

Of the 182,726 male BMT trainees from 2017 through 2023, 50 active duty male trainees (0.03%) tested positive for gonorrhea or chlamydia during their 8 weeks of training (data not shown). Most cases (n=43, 86%) were due to chlamydia, with the remainder positive for gonorrhea (Table 1). There were no cases of co-infection among male BMT trainees (Table 1). During the same period, 5-6% of female trainees screened positive for chlamydia, and 0.2–0.4% screened positive for gonorrhea (data not shown).

The median age of male BMT trainees was 20 years (IQR 19-21). Most cases (n=44, 88%) were detected in primary care settings, with a minority of cases (n=4, 8%) diagnosed in the emergency department. The median time in training until diagnosis was 12.5 days (IQR 8-27).

Thirty male trainees (60%) had symptoms on presentation for chlamydia and gonorrhea testing, with dysuria (n=20, 40%) and penile discharge (n=15, 30%) the most common (Table 1). Nine (18%) male trainees were tested as part of the previously reported screening protocol,⁸ while 10 (20%) were tested after being notified of STI exposure by a partner (data not shown). Four (8%) additional male BMT trainees were asymptomatically screened for chlamydia and gonorrhea after presenting to a medical provider for another medical problem, including 1 service member who tested positive during HIV screening (data not shown).

Of the male BMT trainees with chlamydia or gonorrhea, 28 (56%) had repeat testing in 1 year, with 5 testing positive for chlamydia and 1 for gonorrhea (Table 2). Male trainees had statistically significant lower follow-up testing within 1 year compared to female trainees (56% vs. 76%; OR 0.41, 95% CI 0.17, 0.95) (Table 2). Despite this difference in follow-up testing, there was no statistically significant difference in chlamydia and gonorrhea diagnoses during the next 3 years: a total of 8 diagnoses among men versus 12 among women (OR 0.6, 95% CI 0.22, 1.63) (Table 2).

Discussion

This retrospective matched cohort study evaluated 50 male Air Force and Space Force BMT trainees who tested positive for gonorrhea or chlamydia from 2017 through 2023. The majority of male BMTs who tested positive in this study presented for testing due to symptoms consistent with an STI. Only 56% of the men in this study received follow-up testing within 1 year.

When compared to prevalence rates of gonorrhea and chlamydia among the universally-screened female BMT population, the rate observed among the male BMT trainee population in this study is much lower than expected. When universally screened, 4.8% of male BMT trainees tested positive for chlamydia.⁸ While the universal screening study included National Guard and reserve trainees in addition to active duty personnel, if that rate were applied to the population in this study, 8,771 cases of chlamydia would be expected among male BMT trainees. Given that only 43 cases of chlamydia were diagnosed in this study, it appears as though only 0.5% of expected cases of chlamydia were captured in this cohort. Notably, 9, or nearly 20%, of the cases in this study were identified through the previously published universal screening study. These results show that relying upon symptoms or partner notification likely missed thousands of infectious in the male BMT population.

Despite universal access to medical care, only 54% of male BMT trainees who tested positive for an STI in this study were re-tested within a year. CDC guidelines^5,11 recommend repeat testing in 3 months post-diagnosis due to the high risk of re-infection with the same or new STI pathogen. Similar to previous reports of follow-up testing in women in basic training, a relatively high positivity (18%) results on repeat testing. This finding suggests that a population with a bacterial STI who undergoes testing might be at greater risk for future infections in a male trainee population, and that there may be benefit from interventions such as Doxycycline Post-Exposure Prophylaxis (DoxyPEP), which has shown benefit in other populations, even decreasing incidence within a population.

There are several challenges related to STI testing in a military trainee population. First, due to the low reported incidence of STIs in BMT men, even if symptomatic, they are often not tested for bacterial STIs. Additionally, there is significant stigma related to STI positivity throughout the military that may be amplified in the BMT environment, the first stage of a service member’s military career, during which trainees experience significant stressors unrelated to their sexual health. Other unique challenges within the military population can contribute to lower than ideal follow-up testing rates. The majority of BMT trainees are assigned to a different duty station after graduation, resulting in lack of continuity of care that likely contributes to diminished follow-up testing, although notably, female BMT trainees with gonorrhea or chlamydia who moved to a different military base evinced a higher follow-up rate than women who stayed on the base where they originally tested positive. Finally, military members often have a career-long focus on maintaining mission readiness, and preventive medical care, which can potentially change an individual’s ‘mission ready’ status, is often avoided, as described in other military populations.^13,18

There are limitations to consider when interpreting these results. First, initial diagnoses and the start of data collection occurred within close temporal proximity. Although periods of time for follow-up testing were artificially shortened for some individuals, they should be similar for paired individuals, as matching was by accession date.

Second, the periods of service for men and women with chlamydia or gonorrhea may be different, which was not captured in this study and could lead to differences in observational time between men and women. Future studies could use person-time rates to adjust for varying follow-up durations.

In addition, patients empirically treated without testing were not captured, and the methodology did not allow ascertainment of the total number of male BMT trainees who tested negative for gonorrhea and chlamydia, and thus testing rates could not be determined.

This study did not evaluate extragenital testing, which has lower uptake compared to genital testing²² and could have identified more individuals, resulting in more conservative estimates of infection.

Furthermore, the small sample of 50 men and 50 women may have limited this study’s power to detect statistically significant differences between the 2 groups. Testing records before or after BMT for patients who did not test positive during the study period were not available for review, which likely contributed to an overall under-calculation of follow-up testing rates and new infection rates for both groups of BMT trainees.

There are potential benefits as well as drawbacks of implementing a universal STI screening program for male service members in the U.S. Air Force. While the true incidence of gonorrhea and chlamydia in this population is likely under-estimated due to asymptomatic infections and lack of routine screening, a screening program could identify individuals at risk and inform them of preventive health strategies. Furthermore, studies suggest that universal screening can be cost effective through the prevention of long-term health issues in female partners. Universal BMT male screening is not currently in place, however, due to the lack of long-term complications in men from untreated infections, its cost, and the administrative burden of testing. Despite these challenges, STI testing remains important for interrupting disease transmission, which has the potential to affect mission readiness through complications in female partners as well as increased HIV risk in both sexes.

Author Affiliations

Department of Medicine, Brooke Army Medical Center, Joint Base San Antonio–Fort Sam Houston, San Antonio, TX: Capt Powers, Maj Marcus; Trainee Health Surveillance, 559th Medical Group, Joint Base San Antonio-Lackland, TX: Lt Col Winkler, Brig Gen (ret) Casey, Ms. Osuna, Ms. Jung; Department of Medicine, Uniformed Services University of Health Sciences and Infectious Diseases Service, Brooke Army Medical Center: Maj Marcus

Disclaimers

The views expressed herein are those of the authors and do not reflect official policy nor position of the Defense Health Agency, Brooke Army Medical Center, the Department of Defense, or the U.S. Government.

The data that support the findings of this study are available on request from the corresponding author. All data are freely accessible. This study was reviewed by the Defense Health Agency San Antonio Market Institutional Review Board, protocol FWH20240006E, and determined to be exempt and informed consent not necessary.

References

Seña AC, Miller WC, Hoffman IF, et al. Trends of gonorrhea and chlamydial infection during 1985-1996 among active-duty soldiers at a United States Army installation. Clin Infect Dis Off Publ Infect Dis Soc Am. 2000;30(4):742-748. doi:10.1086/313742
Townsend LC, Stahlman SL, Escobar JD, et al. Positivity and follow-up testing of Chlamydia trachomatis and Neisseria gonorrhoeae infections in universally screened female basic military trainees. Sex Transm Dis. 2025;52(3):176-180. doi:10.1097/olq.0000000000002099
Webber BJ, Pawlak MT, Jones NM, Tchandja JN, Foster GA. Sexually transmitted infections in U.S. Air Force recruits in basic military training. MSMR. 2016;23(2):16-19. Accessed Oct. 3, 2025. https://www.health.mil/reference-center/reports/2016/01/01/medical-surveillance-monthly-report-volume-23-number-2
Gaydos CA, Howell MR, Pare B, et al. Chlamydia trachomatis infections in female military recruits. NEJM. 1998;339(11):739-744. doi:10.1056/nejm199809103391105
Workowski KA, Bachmann LH, Chan PA, et al. Sexually transmitted infections treatment guidelines, 2021. MMWR Morb Mortal Wkly Rep. 2021;70(4):1-187. doi:10.15585/mmwr.rr7004a1
Blackwell R. 59th Medical Wing Instruction 48-107 Aerospace Medicine: Control of Communicable and Other Reportable Diseases. Published online Dec. 3, 2019. Accessed Jun. 24, 2025. https://static.e-publishing.af.mil/production/1/59mdw/publication/59mdwi48-107/59mdwi48-107.pdf
Gaydos JC, McKee KTJ, Gaydos CA. The changing landscape of controlling sexually transmitted infections in the U.S. military. MSMR. 2013;20(2):2-4. Accessed Oct. 3, 2025. https://www.health.mil/reference-center/reports/2013/01/01/medical-surveillance-monthly-report-volume-20-number-2
Wade JK, Marcus J, Kieffer J, Kasper K, Smalley J. Prevalence of chlamydia and gonorrhea in US Air Force male basic trainees. Sex Transm Infect. 2024;100(2):125-126. doi:10.1136/sextrans-2023-055954
Hojnoski CE, Kieffer JW, Casey TM, et al. Follow-up of military blood donors who test positive for syphilis. Sex Transm Dis. 2023;50(10):652-655. doi:10.1097/olq.0000000000001836
Pleasure ZH, Lindberg LD, Mueller J, Frost JJ. Patterns in receipt and source of STI testing among young people in the United States, 2013–2019. J Adolesc Health Off Publ Soc Adolesc Med. 2022;71(5):642-645. doi:10.1016/j.jadohealth.2022.04.014
Schirmer P, Sharma A, Lucero-Obusan C, Oda G, Holodniy M. Trends in follow-up testing among patients positive for chlamydia and gonorrhea in the Veterans Health Administration, 2013 to 2019. Sex Transm Dis. 2023;50(5):906-907. doi:10.1093/ofid/ofac492.1839
Rose SB, Garrett SM, Stanley J, Pullon SRH. Chlamydia trachomatis and Neisseria gonorrhoeae retesting and reinfection rates in New Zealand health care settings: implications for sexually transmitted infection control. Sex Transm Dis. 2020;47(3):151-157. doi:10.1097/olq.0000000000001112
Hoffman WR, Aden JK, Barbera D, Tvaryanas A. Self-reported health care avoidance behavior in U.S. military pilots related to fear for loss of flying status. Mil Med. 2023;188(3-4):e446-e450. doi:10.1093/milmed/usac311
Harbertson J, Scott PT, Moore J, et al. Sexually transmitted infections and sexual behaviour of deploying shipboard US military personnel: a cross-sectional analysis. Sex Transm Infect. 2015;91(8):581. doi:10.1136/sextrans-2015-052163
Howell MR, Gaydos JC, McKee KTJ, Quinn TC, Gaydos CA. Control of Chlamydia trachomatis infections in female army recruits: cost-effective screening and treatment in training cohorts to prevent pelvic inflammatory disease. Sex Transm Dis. 1999;26(9):519-526. doi:10.1097/00007435-199910000-00007
Nevin RL, Shuping EE, Frick KD, Gaydos JC, Gaydos CA. Cost and effectiveness of chlamydia screening among male military recruits: Markov modeling of complications averted through notification of prior female partners. Sex Transm Dis. 2008;35(8);705-713. doi:10.1097/olq.0b013e31816d1f55
Allan-Blitz LT, Klausner JD. The impacts and consequences of sexually transmitted infections in the United States. Sex Transm Dis. 2025;52(5):285-289. doi:10.1097/olq.0000000000002126
Wright J. Department of Defense Instruction Number 6485.01: Human Immunodeficiency Virus (HIV) in Military Service Members. 2022. Accessed Jul. 5, 2025. https://www.esd.whs.mil/Portals/54/Documents/DD/issuances/dodi/648501p.pdf
Bachmann L, Barbee L, Chan P, et al. CDC clinical guidelines on the use of doxycycline postexposure prophylaxis for bacterial sexually transmitted infection prevention, United States, 2024. MMWR Morb Mortal Wkly Rep. 2024;73(2):1-8. doi:10.15585/mmwr.rr7302a1
U.S. Public Health Service. Preexposure Prophylaxis for the Prevention of HIV Infection in the United States–2021 Update: A Clinical Practice Guideline. U.S. Centers for Disease Control and Prevention, U.S. Dept. of Health and Human Services;2021. Accessed Dec. 2, 2025. https://stacks.cdc.gov/view/cdc/112360
Schmid AB, Okulicz JF, Mika W, Hakre S, Yabes JM. Self-perception of risk for HIV acquisition and calculated risk for HIV acquisition among active duty Air Force members with newly diagnosed HIV infection. Mil Med. 2024;189(9-10):e1851-e1856. doi:10.1093/milmed/usae087
Yabes J, Lamb CC, Hakre S, et al. Provider uptake of extragenital screening for gonorrhea and chlamydia in a cohort of Air Force members with incident HIV diagnosis. Medicine (Baltimore). 2022;101(42):e31209. doi:10.1097/md.0000000000031209

Sexual networks of U.S. military service members with chlamydia at Joint Base San Antonio, June–December 2023

Sat, 01 Nov 2025 00:00:00 Z

Abstract

Limited data on sexual networks in the U.S. military makes designing strategies to combat sexually transmitted infections (STIs) challenging. This retrospective evaluation assessed reported sexual networks of military service members with chlamydia, to inform future interventions for decreasing transmission of the infection. Thirty-two active duty service members at Joint Base San Antonio–Fort Sam Houston tested positive for chlamydia infection during the evaluation period, June through December 2023. Service members who tested positive for chlamydia were interviewed by Army Public Health Nursing staff and were asked to identify their sexual partners from the preceding 60 days, for routine contact tracing. Patient responses were then anonymized for comparisons of sexual networks of military service members—by sex, branch of service, and whether they were participating in military training or had completed training (“permanent party”). Service members with chlamydia were predominantly female (n=19, 59.4%), in the Army (n=18, 56.3%), and in military training (n=20, 62.5%). Of the 45 sexual contacts of the 32 service members identified through contact tracing, the majority (n=30, 66.7%) of those sexual contacts were civilians. Those still in military training were more likely to report sexual contacts who were also military service members, compared to permanent party service members (n=12, 50% vs. n=3, 14.3%, p=0.014). This evaluation determined that most service members who developed chlamydia were in sexual networks with only a single partner (n=22, 68.8%). These data should form an initial assessment of a military sexual network that needs to be confirmed in larger settings.

What are the new findings?

An analysis of sexual networks at Joint Base San Antonio–Fort Sam Houston involving 32 military service members with chlamydia found that sexual networks for service members who were in training had a greater proportion of sexual partners who were also in the military compared to service members who were not in training (50% vs. 14.3%, p=0.014).

What is the impact on readiness and force health protection?

In this population, the sexual networks of trainees diagnosed with chlamydia generally had a single partner, suggesting that broader testing strategies may be warranted to identify individuals who are at high risk for chlamydial infections.

Background

Compared to civilian populations, U.S. military service members have had a greater burden of sexually transmitted infections (STIs). For example, the rate of new chlamydia infections for male service members ages 20-24 years is 1.5 times greater than their civilian peers.^1,2

Current U.S. Centers for Disease Control and Prevention (CDC) guidelines recommend annual screening for women under age 25 years to prevent complications of STIs such as pelvic inflammatory disease, in addition to consideration for screening of men and women ages 25 years and older who are in populations with high incidence of infection.³ The U.S. military currently annually screens all women ages 25 years and younger, according to the CDC guidelines, and all services except the Army additionally screen women upon accession to military service. There is no universal screening program for men in the U.S. military.

The highest rates of chlamydia in the U.S. miliary are found in junior-enlisted women, and those age 24 years and younger.² Despite national U.S. military data suggesting a higher chlamydia burden among women, when universally screened, asymptomatic infections rates between male and female trainees are similar.^4,5 This underscores the need to evaluate increasing screening efforts among high-risk cohorts within the military.

Limited understanding of military sexual networks is a significant challenge for identifying high-risk cohorts within the military. Contact tracing serves as a useful epidemiological tool for uncovering such sexual networks, in addition to disrupting transmission events and re-infections.⁶ This retrospective evaluation utilized Army Public Health Nursing (APHN) contact tracing data to identify the sexual networks of service members infected with chlamydia at a single military base, to inform future interventions.

Methods

Active duty military service members who tested positive for chlamydia from June through December 2023 at Joint Base San Antonio–Fort Sam Houston were included in this evaluation of local sexual networks. Joint Base San Antonio–Fort Sam Houston supports 2 distinct groups: trainees and permanent party service members. Trainees, who are completing job-specific training following basic military training, live in congregate settings on base while fulfilling the requisite qualifications for their future military specialties. Trainees have restrictions on their abilities to physically leave their assigned military installations. Permanent party military service members, who have completed military training, have autonomy for their time off duty.

Military service members, regardless of training status, have universal access to no-cost medical care through Military Health System primary care, specialty, and emergency clinics. Women under age 25 years in the U.S. military are universally screened annually for chlamydia, while other populations are screened and tested based on symptoms and risk factors, as previously described.⁴ All military service members are tested for chlamydia using the Aptima Combo 2 assay (Hologic, Marlborough, MA), and those who test positive are interviewed by a trained APHN nurse, for contact tracing to identify their sexual partners for the 60 days preceding diagnosis. In addition, those service members receive education on prevention of STIs and their re-infection.

In this retrospective evaluation of local sexual networks, the contact tracing results were anonymized to only specify the sex and training status of the source patient, and the sex and military status of their partner(s). Comparison of nominal variables between trainees and permanent party service members was performed with Fisher’s Exact Test. A p-value of less than 0.05 was pre-determined to be significant.

Because this study involved analysis of de-identified, aggregate data only, it was classified as non-human subject research by the Brooke Army Medical Center Office of Human Research Protections Office (#23-17747) and consent was not required from participants.

Results

Thirty-two active duty service members tested positive for chlamydia at Joint Base San Antonio–Fort Sam Houston during the study period, June through December 2023, and underwent contact tracing with APHN staff. The majority of service members who tested positive were female (n=19, 59.4%). Most service members who tested positive were in the Army (n=18, 56.3%), followed by the Navy (n=10, 31.3%), and Air Force (n=4, 12.5%). There were more trainees (n=20, 62.5%) than permanent party service members (n=12, 37.5%) among those who tested positive.

Ten male service members reported only female partners, 2 reported only male partners, and 1 reported male and female partners. All 19 female service members reported only male partners. The median number (interquartile range) of partners reported was 1 (1-2). Of the 45 partners identified through contact tracing, the majority (n=30, 66.7%) were not affiliated with the military.

Sexual networks differed by service member sex as well as training status (Table). Both male trainees and permanent party members had majority female sexual partners (n=6, 85.7% and n=8, 72.7%, respectively) or who were civilians (n=5, 71.4% and n=9, 81.8%, respectively). Female permanent party members had predominantly civilian sexual partners (n=9, 90%) as well, while female trainees had majority military sexual partners (n=10, 58.8%). Service members in trainee status were more likely to have sexual contacts who were also military service members, when compared to permanent party service members (n=12, 50% vs. n=3, 14.3%, p=0.014).

Discussion

Despite elevated rates of chlamydia infection in military service members in comparison to their civilian counterparts, the sexual networks of the U.S. military population remain unknown.⁴ In this evaluation cohort, most patients reported only 1 partner within the 60 days preceding diagnosis. Additionally, female trainees were more likely to have sexual partners who were also military service members.

Published data on sexual networks in U.S. military population are limited.^7-9 In 1 previously published study of 2,453 shipboard U.S. active duty Navy and Marine Corps personnel, 67% of most recent sexual partners were either service members or military beneficiaries, and among women this result increased to almost 80%.⁷ In this retrospective evaluation, however, the majority of service members who tested positive for chlamydia had civilian partners. The findings of the prior study are consistent, however, with this evaluation’s finding of a majority military-affiliated sexual contacts among those in trainee status. In the earlier study, around 50% of women surveyed stated that they believed they had contracted an STI from a fellow service man, whereas 25% of service men stated that they believed they had contracted an STI from a service woman.⁷ Those data are also congruent with this evaluation, suggesting that small sexual networks might facilitate transmission to both men and women, as seen in civilian populations.¹⁰

Numerous studies have demonstrated multiple sexual partners as a risk for STI acquisition within the active duty military population.^8,9,11 Satterwhite et al. found, in the general U.S. population, regardless of sex, that reporting 2 to 4 sexual partners or 5 or more sexual partners within the past 12 months were significant predictors of reported STIs.¹² While this evaluation only investigated sexual networks from the preceding 60 days, the majority of active duty service members with chlamydia only reported 1 sexual partner. While this finding may represent under-reporting by service members to public health officers, or reflect the timing of contact tracing in relation to military duties, it is comparable to the number of sexual partners reported in other populations, such as college campuses.¹³ Similarly, 2008 survey data of military women suggested that a majority (68.3%) had only 1 sexual partner in the last 12 months.¹¹

These data, taken together, may imply that certain high-risk populations have limited sexual networks. Unfortunately, limited sexual networks are harder to identify via contact tracing for an infection that is frequently asymptomatic.¹⁴ Expanded or universal screening may be necessary to fully identify the STI burdens for such populations. Increased education efforts and prevention methods—such as condoms, which have demonstrated greater use by individuals with history of an STI,¹⁵ as well as doxycycline post-exposure prophylaxis in populations that may benefit—may be useful strategies for military bases to consider.^16,17 Studies that demonstrate the effectiveness of specific STI prevention practices within military populations are currently lacking, however.¹⁸

There are several limitations to this local evaluation of sexual networks. As a retrospective evaluation of anonymized records, the available data lack patient demographics, indications for testing or screening, setting of initial tests, and presence or absence of symptoms. An additional limitation is the small number of participants. Additionally, significant perceived stigma surrounds sexual health, and source patients are susceptible to reporting bias, potentially under-reporting their sexual contacts.^19,20 Notably, this project evaluated the sexual networks of service members who tested positive only for chlamydia, which may be different from sexual networks in relation to other STI transmission. Finally, as this sample is from 1 large military base, where the majority of testing is of female service members,²¹ the external validity and extrapolation potential of these data are unknown.

Contact tracing provides insight into the sexual networks of military service members at a military base, which can inform more targeted local prevention and intervention strategies. From these data, it appears that limited sexual networks exist among military service members diagnosed with chlamydia, especially among trainees. These findings, from an initial evaluation at a single base, should be replicated in larger populations to produce more robust data, analyses, and findings for determining optimal prevention strategies throughout the U.S. military services.

Author Affiliations

School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD: 2LT Peden; Army Public Health Nursing, Joint Base San Antonio–Fort Sam Houston, TX: Mr. Maddox, Ms. Stubblefield, Ms. Strahan, Ms. Cadena-Malek, Ms. Bell, CPT MendezLanda; Department of Medicine, Uniformed Services University of Health Sciences and Infectious Diseases Service, Brooke Army Medical Center, San Antonio, TX: Maj Marcus

Disclaimers

The views expressed in this manuscript reflect the results of research conducted by the authors and do not reflect official policy nor position of the Defense Health Agency, Brooke Army Medical Center, the Department of Defense, or the U.S. Government.

All authors agree with the submission of this manuscript and do not have any known conflicts of interest to report. This study protocol was reviewed by the Brooke Army Medical Center Institutional Review Board for ethical approval (#23-17747) and was determined to be non-human research.

References

U.S. Centers for Disease Control and Prevention. Sexually Transmitted Disease Surveillance 2021. US Dept. of Health and Human Services. 2022. Accessed Jun. 1, 2025. https://www.cdc.gov/sti-statistics/media/pdfs/2024/07/2021-std-surveillance-report-pdf_archived-2-16-24.pdf
Update: sexually transmitted infections, active component, U.S. Armed Forces, 2013–2021. MSMR. 2022;29(5):2-11. Accessed Oct. 6, 2025. https://www.health.mil/news/articles/2022/05/01/update-sti-msmr
Workowski KA, Bachmann LH, Chan PA, et al. Sexually transmitted infections treatment guidelines, 2021. MMWR Morb Mortal Wkly Rep. 2021;70(4):1-187. doi:10.15585/mmwr.rr7004a1
Townsend LC, Stahlman SL, Escobar JD, et al. Positivity and follow-up testing of Chlamydia trachomatis and Neisseria gonorrhoeae infections in universally screened female basic military trainees. Sex Transm Dis. 2025;52(3):176-180. doi:10.1097/olq.0000000000002099
Wade JK, Marcus J, Kieffer J, Kasper K, Smalley J. Prevalence of chlamydia and gonorrhea in US Air Force male basic trainees. Sex Transm Infect. 2024;100(2):125-126. doi:10.1136/sextrans-2023-055954
Eames KT, Keeling MJ. Contact tracing and disease control. Proc Biol Sci. 2003;270(1533):2565-2571. doi:10.1098/rspb.2003.2554
Harbertson J, Scott PT, Moore J, et al. Sexually transmitted infections and sexual behaviour of deploying shipboard US military personnel: a cross-sectional analysis. Sex Transm Infect. 2015;91(8):581-588. doi:10.1136/sextrans-2015-052163
Stahlman S, Javanbakht M, Cochran S, et al. Self-reported sexually transmitted infections and sexual risk behaviors in the U.S. military: how sex influences risk. Sex Transm Dis. 2014;41(6):359-364. doi:10.1097/olq.0000000000000133
Boyer CB, Pollack LM, Becnel J, Shafer MA. Relationships among sociodemographic markers, behavioral risk, and sexually transmitted infections in U.S. female Marine Corps recruits. Mil Med. 2008;173(11):1078-1084. doi:10.7205/milmed.173.11.1078
Jolly AM, Muth SQ, Wylie JL, Potterat JJ. Sexual networks and sexually transmitted infections: a tale of two cities. J Urban Health. 2001;78(3):433-445. doi:10.1093/jurban/78.3.433
Stahlman S, Javanbakht M, Cochran S, et al. A comparison of self-reported sexual risk behaviours between US civilian and active duty military women. Sex Health. 2015;12(3):272-5. doi:10.1071/sh14211
Satterwhite CL, Torrone E, Meites E, et al. Sexually transmitted infections among US women and men: prevalence and incidence estimates, 2008. Sex Transm Dis. 2013;40(3):187-193. doi:10.1097/olq.0b013e318286bb53
Scott-Sheldon LA, Carey KB, Carey MP. Health behavior and college students: does Greek affiliation matter? J Behav Med. 2008;31(1):61-70. doi:10.1007/s10865-007-9136-1
Detels R, Green AM, Klausner JD, et al. The incidence and correlates of symptomatic and asymptomatic Chlamydia trachomatis and Neisseria gonorrhoeae infections in selected populations in five countries. Sex Transm Dis. 2011;38(6):503-509.
Tremblay F, Courtemanche Y, Bélanger RE, Turcotte-Tremblay AM. A systematic review of the association between history of sexually transmitted infections and subsequent condom use in adolescents. BMC Public Health. 2024;24(1):1000. doi:10.1186/s12889-024-18322-2
Luetkemeyer AF, Donnell D, Dombrowski JC, et al. Postexposure doxycycline to prevent bacterial sexually transmitted infections. NEJM. 2023;388(14):1296-1306. doi:10.1056/nejmoa2211934
Bachmann LH, Barbee LA, Chan P, et al. CDC clinical guidelines on the use of doxycycline postexposure prophylaxis for bacterial sexually transmitted infection prevention, United States, 2024. MMWR Morb Mortal Wkly Rep. 2024;73(2):1-8. doi:10.15585/mmwr.rr7302a1
Hopkins D, Wilson C, Allard R. Sexually transmitted infections in U.S. military women: a scoping review 2000-2018. Womens Health Issues. 2021;31(suppl1):s43-s52. doi:10.1016/j.whi.2021.01.004
Uong S, Rosenberg ES, Goodreau SM, et al. Assessment of bias in estimates of sexual network degree using prospective cohort data. Epidemiology. 2020;31(2):229-237. doi:10.1097/ede.0000000000001151
Vargas SE, Thornton K, Norris C, et al. Sexual and reproductive health in military settings: a qualitative study. Mil Psychol. 2025;37(2):138-147. doi:10.1080/08995605.2024.2324644
Aleman-Reyes DM, Aden JK, Arroyo MA, Marcus JE. Impact of sex-based differences in testing practices on Chlamydia trachomatis and Neisseria gonorrhoeae infection rates in military service members. Int J STD AIDS. 2025:9564624251371827. doi:10.1177/09564624251371827

Update: Routine screening for antibodies to human immunodeficiency virus in the U.S. Armed Forces, active and reserve components, January 2020–June 2025

Sat, 01 Nov 2025 00:00:00 Z

Abstract

This report provides an update, through June 2025, of routine screening results for antibodies to the human immunodeficiency virus (HIV) among members of the U.S. military. The HIV-antibody seropositivity rates for active component service members from 2024 through mid-year 2025 were highest for the Navy (0.23 per 1,000 tested) and Marine Corps (0.22 per 1,000 tested), followed by the Army (0.17 per 1,000 tested), and lowest for the Air Force (0.13 per 1,000 tested) and Coast Guard (0.11 per 1,000 tested). Mid-year HIV seropositivity rates, in comparison to 2024, increased for active component service members of the Army but decreased or remained stable for all other services, as of June 2025.

What are the new findings?

From January 2020 through June 2025, approximately 7 million U.S. military service members among the active component, reserve component, National Guard) were tested for antibodies to HIV, and 1,463 were identified as HIV-antibody-positive (seropositivity 0.21 per 1,000 tested). Of the 1,463 new infections identified during this period, only 40 (2.7%) were among female service members.

What is the impact on readiness and force health protection?

The HIV-antibody screening program remains an important element of U.S. force health protection, particularly for men under age 35 years, for all branches of service and service components. The measurement of military retention for HIV-positive service members reflects changes in U.S. Department of Defense policies that allow asymptomatic individuals with undetectable viral loads to serve without restrictions.

Background

The U.S. Department of Defense (DOD) has conducted an active surveillance program for HIV since 1986. All service members of the active component, reserve component and National Guard are screened at specific points in time: prior to entry (all accessions must be HIV-negative prior to the start of service), before deployment or any change in status (e.g., change in component, between branches, or commissioning), and once every 2 years while a member of the U.S. military.¹ From 1990 through 2024, over 46 million tests for HIV antibodies were conducted to screen service members of the U.S. Armed Forces, resulting in the identification of 11,280 HIV new diagnoses (24.3 per 100,000 persons tested). While initial control efforts barred HIV-positive individuals from entering or serving in the military, leading to a precipitous drop in the rate of HIV diagnoses during the first decade of screening, the rate has remained stable for the last 2 decades.²

Infection with HIV remains a disqualifying diagnosis for entry into U.S. military service; however, in June 2022, the DOD amended policies to prevent HIV-positive service members with an undetectable viral load from being discharged or separated solely on the basis of HIV status.¹ In addition, HIV-positive personnel are not non-deployable solely for a positive status, as decisions related to deployability should be made on a case-by-case basis, justified by a service member’s ability to perform assigned duties.³

Summaries of HIV seropositivity for members of the U.S. military have been published with MSMR since 1995. The current report summarizes numbers and trends of newly identified HIV-antibody seropositivity from January 1, 2020 through June 30, 2025 among military members of 5 services under the active and reserve components of the U.S. Armed Forces, in addition to the Army and Air Force National Guard.

Methods

The surveillance population included all individuals eligible for HIV antibody screening from January 1, 2020 through June 30, 2025 while serving in the active or reserve components of the U.S. Army, Navy, Air Force, Marine Corps, or Coast Guard. Space Force service members were categorized as Air Force for this analysis. All individuals who were tested, and all initial detections of HIV antibodies, through U.S. military medical testing programs were ascertained from the Department of Defense Serum Repository (DODSR) specimens accessioned to the Defense Medical Surveillance System (DMSS).

An incident case of HIV-antibody seropositivity was defined as an individual with positive HIV test results on 2 different, serial specimens. Individuals who had just 1 positive result without a subsequent negative result were also defined as positive, to capture those who had yet to test positive for a second time. The total number of HIV-positive tests were acquired from DMSS to calculate seropositivity rates as a standardized methodology for all services.

Annual rates of HIV seropositivity among service members were calculated by dividing the number of incident cases of HIV-antibody seropositivity during each calendar year by the number of individuals tested at least once during the relevant calendar year. Rates were further stratified by service, component, and sex. Overall rates by age category were calculated for all services for the complete annual years 2020 through 2024.

Results

From January 2020 through June 2025, approximately 7 million service members (active component, Guard, reserve) were tested for antibodies to HIV, and 1,463 were identified as HIV-antibody-positive (seropositivity 0.21 per 1,000 tested) (data not shown). The male rate (0.26 per 1,000 tested) persisted above the female rate (0.03 per 1,000 tested) throughout the surveillance period, as only 40 women were identified as newly HIV-antibody-positive during this time. Age-specific HIV seropositivity rates are presented for complete annual years in Figure 1; service members 25 to 34 years continually represented the highest age-specific rates from 2020 to 2024. In 2023, the seropositivity rate for service members ages 45-54 years increased to 0.21 per 1,000 tested, corresponding to an increase from 1 HIV seropositive cases identified in 2022 to 12 cases in 2023 (data not shown).

U.S. Army, active component

From January 2024 through June 2025, a total of 445,309 U.S. Army active component soldiers were tested for HIV antibodies, and 77 were identified as HIV-antibody-positive (seropositivity 0.17 per 1,000 tested) (Table 1). During the surveillance period, annual seropositivity rates fluctuated between a low of 0.15 per 1,000 tested in 2024 and a high of 0.28 per 1,000 tested in 2021 (Table 1, Figure 2). Annual seropositivity rates for male active component soldiers were considerably higher than the seropositivity rates of female active component soldiers (Figure 2). In 2024, 1 new HIV infection on average was detected among active component soldiers per 8,051 screening tests (Table 1). Of the 389 active component soldiers diagnosed since 2020 with HIV infection, 242 (62.2%) were still in military service in 2025.

Army National Guard

From January 2024 through June 2025, a total of 286,365 U.S. Army National Guard members were tested for HIV antibodies, and 102 soldiers were identified as HIV-antibody-positive (seropositivity 0.36 per 1,000 tested) (Table 2). On average, 1 new HIV infection was detected in 2024 among Army National Guard soldiers per 3,309 screening tests. Of the 301 National Guard soldiers diagnosed since 2020 with HIV infection, 214 (71.1%) were still in service in 2025.

Army Reserve

From January 2024 through June 2025, a total of 127,024 U.S. Army Reserve members were tested for HIV antibodies, and 42 were identified as HIV-antibody-positive (seropositivity 0.33 per 1,000 tested) (Table 3). During 2024, on average 1 new HIV infection was detected among Army reservists per 3,965 screening tests. Of the 153 Army reservists diagnosed since 2020 with HIV infection, 105 (68.6%) were still in service in 2025.

U.S. Navy, active component

A total of 282,755 members of the U.S. Navy active component were tested for HIV antibodies from January 2024 through June 2025, and 65 sailors were identified as HIV-antibody-positive (seropositivity 0.23 per 1,000 tested) (Table 4). During the surveillance period, annual seropositivity rates fluctuated between a low of 0.16 per 1,000 tested in 2020 and a high of 0.29 per 1,000 tested in 2023 (Table 4, Figure 3). Annual seropositivity rates for male active component sailors were considerably higher than the seropositivity rates of female active component soldiers (Figure 3). During 2024, on average, 1 new HIV infection was detected among active component sailors per 4,990 screening tests. Of the 256 active component sailors diagnosed since 2020 with HIV infection, 181 (70.7%) were still in service in 2025.

Navy Reserve

From January 2024 through June 2025, a total of 45,073 members of the U.S. Navy Reserve were tested for HIV antibodies, with 9 sailors identified as HIV-antibody-positive (seropositivity 0.20 per 1,000 tested) (Table 5). On average, 1 new HIV infection was detected in 2024 among Navy reservists per 4,468 screening tests. Of the 33 reserve component sailors diagnosed since 2020 with HIV infection, 19 (57.6%) were still in service in 2025.

U.S. Air Force, active component

From January 2024 through June 2025, a total of 274,169 active component members of the U.S. Air Force were tested for HIV antibodies, and 37 Air Force members were diagnosed with HIV infection (seropositivity 0.13 per 1,000 tested) (Table 6). On average, 1 new HIV infection was detected in 2024 among active component Air Force members per 8,692 screening tests. Of the 143 active component Air Force members diagnosed since 2020 with HIV infection, 91 (63.6%) were still in service in 2025. During the surveillance period, seropositivity rates among male members ranged from a low of 0.08 per 1,000 tested in 2020 to a high of 0.16 per 1,000 tested in 2022 (Figure 4).

Air National Guard

From January 2024 through June 2025, a total of 85,121 members of the Air National Guard were tested for HIV antibodies, and 8 Air National Guard members were diagnosed with HIV infection (seropositivity 0.09 per 1,000 airmen tested) (Table 7). During 2024, on average 1 new HIV infection was detected among Air National Guard members per 13,930 screening tests. Of the 32 Air National Guard members diagnosed since 2020 with HIV infection, 24 (75.0%) were still in service in 2025.

Air Force Reserve

From January 2024 through June 2025, a total of 51,770 members of the Air Force Reserve were tested for HIV antibodies, with 9 Air Force reservists diagnosed with HIV infection (seropositivity 0.17 per 1,000 tested) (Table 8). On average, in 2024 1 new HIV infection was detected among Air Force reservists per 7,749 screening tests. Of the 38 Air Force reservists diagnosed since 2020 with HIV infection, 26 (68.4%) were still in service in 2025.

U.S. Marine Corps, active component

From January 2024 through June 2025, a total of 154,093 active component members of the U.S. Marine Corps were tested for HIV antibodies, and 34 were identified as HIV-antibody-positive (seropositivity 0.22 per 1,000 tested) (Table 9). Annual seropositivity rates rose from a low of 0.11 per 1,000 tested in 2021 to a high of 0.23 per 1,000 tested in 2024 (Table 9, Figure 5). In 2024, on average, 1 new HIV infection per 5,031 screening tests was detected among active component marines. Of the 100 active component marines diagnosed since 2020 with HIV infection, 58 (58.0%) were still in service in 2025.

Marine Corps Reserve

From January 2024 through June 2025, a total of 28,972 Marine Corps Reserve members were tested for antibodies to HIV, and 2 reservists were identified as HIV-antibody-positive (seropositivity 0.07 per 1,000 tested) (Table 10). During 2024, on average, 1 new HIV infection was detected among Marine Corps reservists per 10,730 screening tests. Of the 12 active component marine reservists diagnosed since 2020 with HIV infection, 5 (41.7%) were still in service in 2025.

U.S. Coast Guard, active component

From January 2024 through June 2025, a total of 28,188 active component members of the U.S. Coast Guard were tested for antibodies to HIV, and 3 were identified as HIV-antibody-positive (seropositivity 0.11 per 1,000 tested) (Table 11). During 2024, on average, 1 new HIV infection was detected among active component members of the U.S Coast Guard per 9,920 screening tests. Of the 5 active component Coast Guard service members diagnosed since 2020 with HIV infection, all 5 were still in service in 2025.

Coast Guard Reserve

From January 2024 through June 2025, a total of 5,307 U.S. Coast Guard Reserve members were tested for HIV antibodies, with none identified as HIV-antibody-positive (Table 12).

Discussion

The most current seropositivity rate (0.21 per 1,000 tested) reported for January 1, 2024 through June 30, 2025 remains consistent with the seropositivity rate reported in the prior annual report (0.22 per 1,000 tested from January 1, 2023 to June 30, 2025).⁴ The U.S. military has conducted routine screening for antibodies to HIV among all civilian applicants for service and all service members for more than 30 years.^5-8 In 1995, the U.S. Army tested approximately 1.1 million specimens annually, demonstrating an economically efficient, large-scale model for HIV testing.⁹ The first MSMR article to publish results from HIV screening programs indicates that antibody seropositivity rates in 1994 for the Army active duty (0.19 per 1,000 soldiers) and reserve component (0.23 per 1,000 soldiers) remain comparable to rates presented in 2025.¹⁰

A review of archived surveillance data also reflects improved retention of HIV-positive service members, in alignment with recent DOD policy that recognizes significant advances in the diagnosis, prevention, and treatment of the disease. From 1990 to 1994, a total of 889 active and reserve component soldiers were diagnosed with HIV-1 infection. By 1995, only 234 (26.0%) were still in service.¹⁰ Today, a comparative retention figure for active component Army service members has increased to 66.4%.

The 2022-2025 National HIV/AIDS strategy identifies youth ages 13-24 years as a priority population, based on increased risk for HIV transmission.¹¹ While the seropositivity results presented in this report do partially represent this priority population, as over 43% of all new HIV infections were identified in service members younger than age 25 years, these results should not be generalized to the U.S. population. Data from HIV screening in U.S. military populations are based on a negative test prior to entry, as well as voluntary service. Previous MSMR reports presented HIV screening results for civilian applicants to the military service; however, those data are no longer available in the Defense Medical Surveillance System (DMSS), as the U.S. Military Entrance Processing Command stopped reporting data to the DMSS at the end of calendar year 2020. Thus, the data presented in this report reflect service members who had a negative HIV test upon entry to military service, followed by a positive test during uniformed service.

Routine screening of civilian applicants for service and periodic testing of all active and reserve component members have been fundamental components of the military’s HIV control and clinical management efforts.¹² The most current HIV annual seropositivity rates indicate the HIV-antibody screening program remains an important element of force health protection, particularly for men younger than age 35 years, for all branches of service and components of the U.S. Armed Forces.

Acknowledgment

The editors would like to thank Gi-Taik Oh, MS, Principal Research Analyst, Epidemiology and Analysis Branch, Armed Forces Health Surveillance Division, for analyzing the data presented in this report.

References

U.S. Department of Defense. Department of Defense Instruction 6485.01: Human Immunodeficiency Virus (HIV) in Military Service Members. Updated Jun. 6, 2022. Accessed Oct. 17, 2024. https://www.esd.whs.mil/portals/54/documents/dd/issuances/dodi/648501p.pdf
Aumakhan B, Eick-Cost AA, Oh GT, Stahlman S, Johnson R. Four decades of HIV antibody screening in the U.S. military: a review of incidence and demographic trends, 1990–2024. MSMR. 2025;32(4):13-20. Accessed Oct. 9, 2025. https://www.health.mil/reference-center/reports/2025/04/01/msmr-vol-32-no-4-apr-2025
The Secretary of Defense. Secretary of Defense Memorandum for Senior Pentagon Leadership, Commanders of the Combatant Commands, Defense Agency and DOD Field Activity Directors: Policy Regarding Human Immunodeficiency Virus-Positive Personnel Within the Armed Forces. U.S. Dept. of Defense. Jun. 6, 2022. Accessed Jan. 13, 2026. https://media.defense.gov/2022/jun/07/2003013398/-1/-1/1/policy-regarding-human-immunodeficiency-virus-positive-personnel-within-the-armed-forces.pdf
Armed Forces Health Surveillance Center. Routine screening for antibodies to human immunodeficiency virus in the U.S. Armed Forces, active and reserve components, January 2019–June 2024. MSMR. 2024;31(10):2-10. Accessed Oct. 9, 2025. https://www.health.mil/reference-center/reports/2024/10/01/msmr-vol-31-no-10-oct-2024
Brown AE, Brundage JF, Tomlinson JP, Burke DS. The U.S. Army HIV testing program: the first decade. Mil Med. 1996;161(2):117-122. doi:10.1093/milmed/161.2.117
Armed Forces Epidemiological Board. Testing Interval for Human Immunodeficiency Virus (HIV-1) Infection in Military Personnel–2003-05. U.S. Dept. of Defense. Updated Mar. 29, 2004. Accessed Oct. 17, 2024. https://www.health.mil/reference-center/policies/2004/03/29/policy-memorandum---humanimmunodeficiency-virus-interval-testing
Office of the Under Secretary of Defense for Personnel and Readiness. Department of Defense Instruction 6485.01: Human Immunodeficiency Virus (HIV) in Military Service Members. U.S. Dept. of Defense. Updated Jun. 6, 2022. Accessed Oct. 17, 2024. https://www.esd.whs.mil/portals/54/documents/dd/issuances/dodi/648501p.pdf
Office of the Under Secretary of Defense for Personnel and Readiness. DoD Instruction 6130.03, Volume 1: Medical Standards for Appointment, Enlistment, or Induction. U.S. Dept. of Defense. Updated May 28, 2024. Accessed Oct. 17, 2024. https://www.esd.whs.mil/portals/54/documents/dd/issuances/dodi/613003_vol01.pdf?ver=b0uhh9e1k_mdtz4punu8aw%3d%3d
Brown AE, Burke DS. Cost of HIV testing in the U.S. Army. NEJM. 1995;332(14):963. doi:10.1056/nejm199504063321419
Army Medical Surveillance Activity. Supplement: HIV-1 in the Army. MSMR. 1995;1(3):12-15. Accessed Oct. 9, 2025. https://www.health.mil/reference-center/reports/1995/01/01/medical-surveillance-monthly-report-volume-1-number-3
Hiv.gov, U.S. Dept of Health and Human Services. National HIV/AIDS Strategy for the United States 2022–2025. White House Office of National AIDS Policy;2021. Accessed Jan. 13, 2026. https://files.hiv.gov/s3fs-public/NHAS-2022-2025.pdf
Okulicz JF, Beckett CG, Blaylock JM, et al. Review of the U.S. military’s human immunodeficiency virus program: a legacy of progress and a future of promise. MSMR. 2017;24(9):2-7. Accessed Oct. 9, 2025. https://www.health.mil/reference-center/reports/2017/01/01/medical-surveillance-monthly-report-volume-24-number-9

Brief report: Lyme disease forecasting in the U.S. Department of Defense: summary of one- to three-month forecasts, January–October 2024

Sat, 01 Nov 2025 00:00:00 Z

Lyme disease incidence has been increasing among U.S. Department of Defense (DOD) service members over the past 20 years, threatening the health and readiness of the force.¹ Syndromic surveillance of tick bite visits can provide timely information that might predict changes in tick-borne disease incidence and geographic spread.² Tick and tick-borne illness surveillance programs in the U.S. face many barriers, however, including inconsistent funding and limited capacities.³

Epidemic forecasting models can be used to enhance routine surveillance and inform public health policy.⁴ Previous research on Lyme disease forecasting has focused on general time series and machine-learning models; however, these models have demonstrated high percentage errors and limited predictive accuracy.^5,6 Further research is needed to explore alternative modeling approaches that may improve forecasting performance.

Since 2019, the Armed Forces Health Surveillance Division, Integrated Biosurveillance (AFHSD-IB) Branch, within the Defense Health Agency’s Public Health Directorate, has been conducting respiratory disease forecasting using syndromic surveillance data.⁷ Since 2024, AFHSD-IB has integrated vector-borne disease forecasting to provide situational awareness and inform public health responses from DOD senior leaders. This report aims to predict the number of future Lyme disease and tick bite encounters among U.S Military Health System (MHS) beneficiaries using outpatient encounter data and multiple forecasting methods.

Methods

Tick bite encounters were used as a proxy for tick exposure, while Lyme disease encounters served as a proxy for Lyme disease diagnoses in the absence of trusted case reporting. Encounter definitions were developed using internal criteria.

A single instance of a Lyme disease encounter was defined using the International Classification of Diseases, 10th Revision (ICD-10), discharge diagnosis code ‘A69.2’ and chief complaint terms “Lyme disease,” “erythema migrans,” or “bulls-eye rash,” or their misspellings, in the chief complaint field; any mention of a history of Lyme disease was excluded. Similarly, tick bite encounters were defined as health records mentioning “tick bite” and associated misspellings (including “tic” or “tik” for tick and “bit” for bite) in the chief complaint field.

Monthly direct-sourced outpatient encounter data for each military treatment facility were obtained from the DOD’s Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE). Data were then aggregated into 4 U.S. surveillance regions, selected based on their high volume of Lyme disease encounters in 2024: National Capital Region, New England, Tidewater, and West Point. Surveillance regions are collections of neighboring military installations, clinics, and hospitals used for disease surveillance and reporting. (Information about each surveillance region is shown in Supplementary Table 1.) Monthly encounter data from January 2021 through October 2024 were collected and used to generate 1- through 3-month forecasts for each outcome metric and surveillance region for the period January through October 2024. Models were trained each month, using data from January 2021 through the most recent month.

Several time series and machine-learning models were used for forecasting. The seasonal autoregressive integrated moving average (SARIMA) model is based on the widely used Box-Jenkins method for univariate time series forecasting.⁸ The error, trend, seasonal (ETS) model is another time series model that belongs to a special class of exponential smoothing models known as state-space models.⁸ The exponentially weighted moving average (EWMA) model is a highly sensitive model that effectively identifies subtle data pattern changes due to its weighting scheme, which is of particular importance when assessing rare outcomes.⁹ The vector autoregressive (VAR) model is a forecasting algorithm that can be used when 2 or more time series influence each other.¹⁰ The neural network (NNET) model is a machine-learning model that can adapt to changing inputs, generating the best possible results without needing to redesign the output criteria.¹¹ Prophet is open-source forecasting model created by Facebook that allows users to easily customize and produce high quality forecasts.¹² Finally, a baseline naïve model was created using data from the previous tick-borne season. An ensemble (ENS) model was also calculated as the average of all individual model forecasts.

A log-transformed weighted interval score (WIS) was used to measure accuracy of the model forecasts. WIS was previously established as a scoring method for respiratory disease forecasts,¹³ with lower values indicating more accurate forecasts. The median absolute percentage error (MAPE), another common metric for forecasting error, was also calculated. All analyses were performed in R software 4.4 (R Foundation for Statistical Computing, Vienna, Austria), including the fable¹⁴ and fabletools¹⁵ packages.

Results

Our analyses indicated that observed Lyme disease encounters increased from late winter to early spring, peaking during the summer months, except for West Point, which peaked in April. West Point also had the highest peak (34.5 per 100,000 MHS beneficiaries), followed by New England (31.4 in July), National Capital Region (13.9, June), and Tidewater (4.2, June) (Figure 1). Observed tick bite encounters increased from February until spring for each surveillance region. The National Capital Region had the highest peak (50.0 per 100,000 MHS beneficiaries in June), followed by West Point (39.5, April), New England (29.1, April), and Tidewater (18.9, May). Figure 1 also shows 1- through 3-month ahead horizon forecasts for the ENS model.

For Lyme disease encounter forecasts, the NNET model had the lowest median log-WIS across the 2-month (0.6) and 3-month (0.8) ahead horizons, while the ENS model had the lowest median log-WIS for the 1-month ahead horizon (0.7) (Figure 2). The ETS model had the lowest MAPE for Lyme disease encounters for the 1-month ahead horizon (33%), while the NNET model had the lowest MAPE for the 2-month ahead horizon (37%), and the ENS model had the lowest MAPE for the 3-month ahead horizon (28%) ahead horizon (Supplementary Figure 2). During the months with the highest activity (March–October), MAPE for the ENS model improved to 29% and 27%, respectively, for the 1-month and 3-month ahead horizons, while the MAPE for the NNET model improved to 29% for the 2-month ahead horizon, indicating greater forecasting accuracy during high activity periods when compared to the total surveillance time (Supplementary Table 2).

For tick bite encounter forecasts, the ETS model had the lowest score (1.0) for all horizons, as well as for the 1- (0.8), 2- (1.0), and 3-month (1.2) ahead horizons, followed by the NNET model (1.2, all horizons; 1.2, 1-month; 1.1, 2-month; and 1.2, 3-month) (Figure 2). The ETS model had the lowest MAPE across all 1-month (25%), 2-month (23%), and 3-month (24%) ahead horizons (Supplementary Figure 2). During the months of highest activity, MAPE for the ETS model improved to 21%, 22%, and 24% respectively for the 1-month, 2-month, and 3-month ahead horizons (Supplementary Table 2).

Discussion

This study produced 2 major findings. First, among all the time series and machine-learning models mentioned in our analysis, 3 models—ENS, ETS, NNET—provided the most accurate forecasts of Lyme disease and tick bite activity in MHS beneficiaries, with increased accuracy during peak activity periods. This finding suggests that these 3 models would be valuable as early warning signals for public health surveillance and preparedness. It is important to note, however, that model accuracy decreased at longer horizons. Second, MAPE estimates were higher than those reported in previous studies using SARIMA models, although accuracy was improved by focusing on periods of higher activity. Individual surveillance regions aligned with the findings of the aforementioned study.⁶

Limitations of this study must be considered. First, data lags and inconsistent reporting of Lyme disease cases within the MHS prevented validation of encounter data against confirmed case data. Consequently, sensitivity analysis of encounter definition robustness could not be performed, and therefore, results may not be representative of overall disease incidence. The training data set was derived from a 3-year period of encounter data, but additional historical data may be needed to properly train some of the models. Due to limited technology capacity, particularly hardware, this study also did not utilize more computationally intensive machine-learning models, such as long short-term memory and random forest models, potentially limiting the accuracy of the forecasts. In addition, these models are susceptible to over-fitting when there is insufficient training data.

Despite these limitations, this study provided the first quantitative evidence of the use of outpatient encounter syndromic surveillance data for forecasting Lyme disease and tick bite encounters in the MHS population. Lyme disease forecasting can provide vital information for anticipating the impact on military health and readiness, as well as informing effective public health responses and mitigation efforts within the DOD. Further research is required to explore additional models, more robust training data, and other covariates, including incident Lyme disease cases and other key predictors, such as host species, geographic factors, climate, and weather.

Authors’ Affiliation

Integrated Biosurveillance Branch, Armed Forces Health Surveillance Division, Public Health Directorate, Defense Health Agency, Silver Spring, MD

References

Melanson VR, Bateman SL, Hering K, et al. A review of force health protection aspects of Lyme disease in the U.S. military. Mil Med. 2025;190(3-4):e561-e568. doi:10.1093/milmed/usae415
Marx GE, Spillane M, Beck A, et al. Emergency department visits for tick bites: United States, January 2017–December 2019. MMWR Morb Mortal Wkly Rep. 2021;70(17):612-616. doi:10.15585/mmwr.mm7017a2
Mader EM, Ganser C, Geiger A, et al. A survey of tick surveillance and control practices in the United States. J Med Entomol. 2021;58(4):1503-1512. doi:10.1093/jme/tjaa094
Chowell G, Skums P. Investigating and forecasting infectious disease dynamics using epidemiological and molecular surveillance data. Phys Life Rev. 2024;51:294-327. doi:10.1016/j.plrev.2024.10.011
Sun YQ, Z XY, Fan TC, et al. Projecting Lyme disease risk in the United States: a machine learning approach integrating environmental, socioeconomic and vector factors. One Health. 2025;21:101111. doi:10.1016/j.onehlt.2025.101111
Kapitány-Fövény M, Ferenci T, Sulyok Z, et al. Can Google Trends data improve forecasting of Lyme disease incidence? Zoonoses Public Health. 2019;66(1):101-107. doi:10.1111/zph.12539
Bova ML, McGee SA, Elliott KR, Ubiera JI. Predicting COVID-19 and respiratory illness: results of the 2022-2023 Armed Forces Health Surveillance Division forecasting challenge. MSMR. 2024;31(5):24-30. Accessed May 8, 2025. https://www.health.mil/news/articles/2024/05/01/msmr-afhsd-ib-2022-respiratory-forecast
Khan DM, Ali M, Iqbal N, et al. Short-term prediction of COVID-19 using novel hybrid ensemble empirical mode decomposition and error trend seasonal model. Front Public Health. 2022;10:922795. doi:10.3389/fpubh.2022.922795
Alfasanah Z, Niam MZH, Wardiani S, et al. Monitoring air quality index with EWMA and individual charts using XGBoost and SVR residuals. MethodsX. 2024;14:103107. doi:10.1016/j.mex.2024.103107
Vector Autoregression (VAR): Comprehensive Guide with Examples in Python. Accessed Aug. 20, 2025. https://www.machinelearningplus.com/timeseries/vector-autoregression-examples-python
Kumar GS. Understanding and Building Neural Network (NN) Models. 2025. Accessed Aug. 20, 2025. https://builtin.com/machine-learning/nnmodels
Taylor SJ, Letham B. Prophet: Forecasting at Scale. 2017. Accessed Aug. 20, 2025. https://research.facebook.com/blog/2017/02/prophet-forecasting-at-scale
Bracher J, Ray EL, Gneiting T, Reich NG. Evaluating epidemic forecasts in an interval format. [Published correction in PLoS Comput Biol. 2022;18(10):e1010592. doi:10.1371/journal.pcbi.1010592.]. PLoS Comput Biol. 2021;17(2):e1008618. doi:10.1371/journal.pcbi.1008618
O’Hara-Wild M, Hyndman R, Wang E. fable: Forecasting Models for Tidy Time Series, R Package Version 0.4.0. 2024. Accessed Mar. 12, 2025. https://fable.tidyverts.org
O’Hara-Wild M, Hyndman R, Wang E. fabletools: Core Tools for Packages in the ‘fable’ Framework. R package version 0.5.0. 2024. Accessed Mar. 12, 2025. https://fabletools.tidyverts.org/index.html

Update: Sexually transmitted infections among active component members of the U.S. Armed Forces, 2016–2024

Sat, 01 Nov 2025 00:00:00 Z

Abstract

This report summarizes incidence rates and trends of the 5 most frequently occurring sexually transmitted infections (STIs) from 2016 through 2024 among active component service members of the U.S. Armed Forces. The data for this report were derived from medical and public health surveillance of chlamydia, gonorrhea, and syphilis as nationally notifiable diseases; case data for 2 additional STIs, human papillomavirus (HPV) and genital herpes simplex virus (HSV), are also presented. Chlamydia infections were the most common during the surveillance period, followed, in decreasing order of frequency, by HPV, gonorrhea, genital HSV, and syphilis. In 2024, both chlamydia and gonorrhea rates dropped to their lowest points of the period of surveillance, falling 25.5% and 26.4%, respectively, from their 2019 peaks. Declines were predominantly concentrated among service members younger than 25 years of age—who were the largest contributors to overall incidence. Notably, syphilis incidence rose steadily throughout the surveillance period, among all age groups, and both sexes, with steepest rises after 2021, increasing nearly 70%. Non-Hispanic Black service members continue to bear the highest syphilis burden, among whom incidence peaked in 2023, before declining approximately 15% in 2024. Syphilis rates continued to rise among all other racial and ethnic groups through 2024, with the largest relative increase, 456%, among non-Hispanic White service women under age 25 years. Genital HSV demonstrated a downward trend throughout the surveillance period, with overall incidence reaching its lowest point in 2024. Incidence of genital HPV also decreased among all service members, with a more pronounced decrease among men.

What are the new findings?

Chlamydia, gonorrhea, and genital HSV incidence rates dropped to their lowest points of the 9-year surveillance period. In contrast, total syphilis incidence rose among all age groups, and both sexes, with the highest incidence among service women ages 17-19 years. While syphilis incidence rates remain highest among non-Hispanic Black service members, its incidence has risen sharply in all other racial and ethnic groups, reflecting an evolving and expanding syphilis epidemiology within the military in addition to the general U.S. population.

What is the impact on readiness and force health protection?

STIs can adversely affect service member ability and availability to perform assigned duties and can result in serious medical sequelae if left untreated. Syphilis infection in reproductive age military women can cause miscarriage, stillbirth, or congenital syphilis, affecting women’s health, deployability, and overall force readiness, while increasing health care costs. Expanded prevention, testing, and treatment, along with comprehensive sexual health education, particularly targeting those younger than age 25 years, are warranted to curb transmission and maintain operational effectiveness.

Background

Sexually transmitted infections (STIs) represent one of the highest health care burdens attributable to infectious diseases among active component service members (ACSMs) of the U.S. Armed Forces.¹ A National Academies of Sciences, Engineering and Medicine committee, convened to provide recommendations for prevention and control of STIs in the U.S., concluded that military recruits and active duty service members warrant focused consideration due to their elevated risk of STIs.² While multiple and inter-related factors influence STI risk within military populations, the strongest risk factors are age and sex.³ Since the military population consists of young (mean age 26 years) and predominantly male (85%) service members, rates are not directly comparable to the general U.S. population, unless adjusted for those demographics.

The U.S. Centers for Disease Control and Prevention (CDC) publishes annual summaries of national surveillance data for notifiable diseases, including Chlamydia trachomatis (chlamydia), Neisseria gonorrhoeae (gonorrhea), and Treponema pallidum (syphilis), under federally funded control programs.⁴ Although relatively common bacterial STIs are curable with antibiotics, there is continued concern about the threat of multi-drug resistance.^5-7

Common viral STIs in the U.S. include infections caused by human papillomavirus (HPV) and genital herpes simplex virus (HSV).^8,9 While most HPV infections resolve spontaneously, a subset can persist and progress to HPV-associated cancers, including cervical cancer in women, as well as anal, penile, and oropharyngeal cancers in both sexes.¹⁰ Similarly, genital HSV can lead to recurrent genital ulcer disease with sustained transmission within the population due to asymptomatic shedding. Suppression of recurrent herpes is attainable using anti-viral medication, and a vaccine prevents infection from 4 of the most common HPV serotypes, as well as 5 additional cancerous types.¹¹

This report presents an update to the previous MSMR article on these 5 STIs among U.S. ACSMs, covering the surveillance period of 2016 through 2024.¹²

Methods

The surveillance population for this report consists of all ACSMs of the U.S. Army, Navy, Air Force, or Marine Corps who served at any time during the surveillance period of January 1, 2016 through December 31, 2024. Diagnoses of STIs were ascertained from medical administrative data and reports of notifiable medical events routinely provided to the Armed Forces Health Surveillance Division and maintained in the Defense Medical Surveillance System (DMSS) for health surveillance. STI cases were also derived from positive laboratory test results recorded in the Health Level 7 (HL7) chemistry and microbiology databases compiled by the Defense Centers for Public Health–Portsmouth.

The number of days in active service for each service member was ascertained, which were then aggregated to a total for all service members for each calendar year. The resultant annual totals are expressed as person-years (p-yrs) of service, used as the denominators for calculating annual incidence rates. Person-time not considered time at risk for an STI was excluded, such as the 30 days following each incident chlamydia or gonorrhea infection and all person-time following an initial diagnosis, medical event report, or positive laboratory test of HSV, HPV, or syphilis. Incidence rates were calculated as incident cases of a given STI per 100,000 p-yrs of active component service, with percent changes in incidence calculated by un-rounded rates.

An incident case of chlamydia was defined by either 1) a case-defining diagnosis (Table 1) in the first or second diagnostic position of a record of an outpatient or in-theater medical encounter, 2) a confirmed notifiable disease report, or 3) a positive laboratory test (for any specimen source or test type). An incident case of gonorrhea was similarly defined by 1) a case-defining diagnosis in the first or second diagnostic position of an inpatient, outpatient, or in-theater encounter record, 2) a confirmed notifiable disease report, or 3) a positive laboratory test (for any specimen source or test type). For both chlamydia and gonorrhea, an individual could be counted as having a subsequent case only if more than 30 days occurred between the dates recorded for each case-defining diagnosis.

An incident case of syphilis was defined by either 1) a qualifying International Classification of Diseases, 9th or 10th Revision (ICD-9/ICD-10) code in the first, second, or third diagnostic position of a hospitalization record, 2) at least 2 outpatient or in-theater encounters within 30 days with a qualifying ICD-9/ICD-10 code in the first or second position, 3) a confirmed notifiable disease report for any type of syphilis, or 4) a record of a positive polymerase chain reaction or treponemal laboratory test. Stages of syphilis (primary, secondary, late, latent) could not be distinguished because HL7 laboratory data do not allow for stage differentiation, and because a high degree of misclassification is associated with use of ICD diagnosis codes for stage determination.^13,14 An individual could be considered an incident case of syphilis only once during the surveillance period; those with evidence of prior syphilis infection were excluded.

Incident cases of genital HSV were identified by either 1) presence of requisite ICD-9/ICD-10 codes in either the first or second diagnostic positions of an outpatient or in-theater encounter record or 2) a positive laboratory test from a genital specimen source. Antibody tests were excluded because they do not allow distinction between genital and oral infections. Incident cases of genital HPV were similarly identified by either 1) presence of requisite ICD-9/ICD-10 codes in either the first or second diagnostic positions of an outpatient or in-theater encounter record or 2) a positive laboratory test from any specimen source or test type. Outpatient encounters for HPV with evidence of HPV immunization within 7 days before or after an encounter date were excluded, as were outpatient encounters with a procedural or Current Procedural Terminology (CPT) code indicating HPV vaccination, as such encounters were potentially related to vaccination administration. An individual could be counted as an incident case of HSV or HPV only once during the surveillance period. Individuals with diagnoses of HSV or HPV infection before the surveillance period were excluded.

To characterize trends during the surveillance period, age- and sex-specific percent changes relative to each group’s peak rate were calculated. Recent trends were assessed through annual percentage changes that compare 2024 with 2023. When notable differences in rates or trends were observed, absolute differences in incidence rates from peak levels were calculated to identify which age- and sex-specific groups contributed most to the overall decline. Results are presented as age- and sex-specific trends for the entire 2016–2024 surveillance period, and as recent changes from 2023 to 2024, for each STI. Incidence rates are expressed per 100,000 p-yrs.

Results

General incidence and distribution patterns

Chlamydia infections were the most common during the surveillance period, followed, in decreasing order of infection frequency, by HPV, gonorrhea, genital herpes, and syphilis (Table 2). Chlamydia accounted for the majority of reported STI cases during the surveillance period, with nearly twice as many cases as the combined total of the other 4 STIs, and nearly 5-fold higher than HPV, the next most frequently identified STI. Except for syphilis, incidence was generally higher in female service members; for gonorrhea, total incidence rates were similar between sexes.

The highest concentration of cases was among service members ages 20-24 years, who comprised over half of chlamydia (58%) and gonorrhea (52%) cases, as well as 36-42% of cases for the other 3 STIs. The 25-29-years age group accounted for the next largest proportion of cases for all STIs, comprising 19-27% of cases. HPV was most common (22%) STI among those aged 30-34 years.

Incidence rates for all 5 STIs were highest among those who had never married (among those with defined marital status) as well as non-Hispanic Black service members. With the exception of HPV, rates of infection were also highest among individuals with a high school education or less, and among junior enlisted members; in contrast, HPV revealed a different pattern, with the highest incidence observed among those with educations beyond high school, and among junior officers. Chlamydia and gonorrhea incidence were highest in the Army, syphilis and genital HPV incidence were highest in the Navy, while genital HSV rates were generally comparable among service branches.

Chlamydia

Age- and sex-stratified trends

Annual chlamydia rates continued to decline in 2024, extending the downward trend observed since 2020, as previously reported (Figure 1).¹² In 2024, chlamydia incidence fell to its lowest point in 9 years, to 1,395.9 cases per 100,000 p-yrs, a 43.8% decline from the 2019 peak rate of 2,484.1 cases per 100,000 p-yrs. The largest reductions from peak rates occurred in younger age groups, who accounted for most of the total incidence rate decline. Among female service members, over 88% of the total decline (12,144 fewer overall cases per 100,000 p-yrs; 46.5% decrease from 2019 peak) was among women ages 17-24 years (data not shown). Among male service members, the largest declines were among those aged 20-24 years (1,570 fewer cases per 100,000 p-yrs; 45.1% decrease from peak), followed by those aged 17-19 years (838 fewer cases per 100,000 p-yrs, 38.5% decrease), and 25-29 years of age (716 fewer cases per 100,000 p-yrs, 39.3% decrease) (data not shown).

Chlamydia rates among female service members were generally 3 times higher than among male service members throughout the 9-year surveillance period. Throughout the surveillance period, for individuals aged 17-19 years, rates were 7–9 times higher among women than men. Older groups (>age 30 years) accounted for a much smaller share of the total incidence and contributed minimally to overall declines in both sexes. Declines in chlamydia rates were consistent among all racial and ethnic groups (Figure 2).

Age- and sex-specific changes in 2024 versus 2023

Total chlamydia incidence rates declined by 11.8% (from 3,310.4 cases per 100,000 p-yrs in 2023 to 2,919.5 cases per 100,000 p-yrs in 2024) among female and 7.9% (from 1,155.6 cases per 100,000 p-yrs in 2023 to 1,064.7 cases per 100,000 p-yrs in 2024) among male service members. Declines among female service members were concentrated among those aged 17-24 years, with 17-19 year-olds driving the largest decrease: 12.5% (data not shown). In contrast, rates among male service members in the same 17-19-years age range increased by 9.7%. Divergent changes were also observed among older age groups (35-39 years), with incidence rates increasing (+10.2%) among women and decreasing (-4.4%) among men. In the 40 years and older age group, differences were minor, with slight declines in women (-5.5%) and small increases (+2.2%) in men.

Gonorrhea

Age- and sex-stratified trends

Gonorrhea incidence rates continued to decline for both female and male service members in 2024, following increases that peaked prior to 2020. These trends parallel those observed for chlamydia (Figure 3). The largest reductions in gonorrhea incidence occurred among younger age groups. Among female service members, total crude incidence decreased from 490.9 per 100,000 p-yrs at the 2018 peak to 254.9 per 100,000 p-yrs in 2024 (-48.1%), with those younger than age 25 years accounting for 72.4% of this reduction. Similarly, the total crude incidence among male service members declined from 347.0 per 100,000 p-yrs at the 2019 peak to 276.6 per 100,000 p-yrs in 2024 (-20.3%), with those younger than age 30 years accounting for nearly all (98.8%) of the total decline. Women older than age 25 years also experienced notable declines (range -42% to -66%), while incidence among men aged 30 years and older remained relatively stable over the surveillance period.

As observed with chlamydia infections, female service members aged 17-19 years demonstrated highest gonorrhea incidence, with rates nearly 3 times higher than their male counterparts. Sex disparities in other age groups were less pronounced, although men older than age 35 years tended to have higher gonorrhea rates than women of the same age (data not shown).

Age- and sex-specific changes in 2024 versus 2023

Gonorrhea incidence among service women declined by 9.3% from 2023 to 2024, with the largest decrease among those aged 30-34 years (-22.9%). The total rate among service men declined slightly (-1.2%), with no notable changes in age-stratified rates (data not shown).

Syphilis

Age- and sex-stratified trends

Syphilis incidence increased from 2016 through 2024 among all age groups, and both sexes, with the largest increases observed after 2021 (Figure 4). In 2024 a remarkable shift occurred in syphilis incidence: The total incidence rate among female service members surpassed that of male service members for the first time during the surveillance period (Figure 5). The steepest increase among service women was observed in those aged 17-19 years, among whom incidence rose nearly 4-fold, from a low of 52.7 per 100,000 p-yrs in 2018 to approximately 200 cases per 100,000 p-yrs from 2022 through 2024. Rates among women rose approximately 3-fold from 2016 to 2024 among those aged 20-24 years, from 28.7 cases per 100,000 p-yrs in 2016 to 95.4 cases per 100,000 p-yrs in 2024, with a peak of 107.1 cases in 2023. Although incidence rates were lower in the older female age groups, they demonstrated 3- to 5-fold increases.

Syphilis incidence among service men aged 17-19 years peaked at 124.4 per 100,000 p-yrs in 2022, before declining to 96.4 per 100,000 p-yrs in 2024, which still represented a 1.4-fold increase from the 2016 low of 67.7 per 100,000 p-yrs. The 20-24-years male age group showed a smaller (1.2-fold) increase from 2016 to 2024, while incidence rates among men ages 25-34 years increased 1.7- to 1.9-fold, reaching incidence rate levels comparable to the youngest age groups in 2024. Substantial rises in incidence were also observed among older males: 2.6-fold (from 21.5 to 55.2 per 100,000 p-yrs) among those aged 35-39 years and 2.2-fold (from 19.1 to 42.1 per 100,000 p-yrs) among those aged 40 years and older.

Syphilis burden was highest among non-Hispanic Black service members, with incidence rates 2- to 5-times greater (depending upon age group) than those of non-Hispanic White service members, who had the lowest incidence. Non-Hispanic Black men younger than age 25 years accounted for a disproportionate number of syphilis cases, and had the highest incidence rates, which peaked in 2023 at 271.9 per 100,000 p-yrs before declining to 197.1 (-27.5%) per 100,000 p-yrs in 2024. Rates among non-Hispanic Black women younger than age 25 years peaked at 196.9 per 100,000 p-yrs in 2023, followed by 15.1% decline to 167.1 per 100,000 p-yrs in 2024.

Despite lower baseline levels, other racial and ethnic groups demonstrated substantial increases in syphilis incidence throughout the surveillance period. In particular, among women younger than age 25 years, the largest relative increase, 455.5%, was observed among non-Hispanic White women, whose rate in 2024 was the highest (11.3 per 100,000 p-yrs in 2016 to 62.9 per 100,000 p-yrs in 2024), followed by service members in the ‘other’ (+398.1%, from 27.7 in 2017 to 137.8 in 2024) and Hispanic racial and ethnic categories (+303.4%, from 42.4 in 2016 to 171.0 in 2023).

In general, female service members had lower syphilis rates than their male counterparts, but among those aged 17-19 years, female rates exceeded male rates during 7 of the 9 years of surveillance. Rise in overall incidence of syphilis among service men was relatively modest, with the smallest increase (+27.6%) observed among Hispanic service members, and the largest (+68.2%) among non-Hispanic Black service members.

Age- and sex-specific changes in 2024 versus 2023

Changes in syphilis rates in 2024 compared to 2023 diverged by sex. Overall incidence increased from 79.5 per 100,000 p-yrs in 2023 to 84.9 per 100,000 p-yrs in 2024 (+6.7%) among women but declined 3.5% among men (from 83.5 to 80.6 per 100,000 p-yrs). Incidence among women increased among all age groups, except those aged 20-24 years, among whom syphilis declined by 11%, from 107.1 in 2023 to 95.4 per 100,000 p-yrs in 2024. Decreases among men were concentrated in younger age groups, particularly those aged 17-24 years (-16.9%, from 116.0 in 2016 to 96.4 in 2024). In contrast, incidence increased among older men, most notably those aged 35-39 years (+17.9%, from 46.8 in 2016 to 55.2 in 2024).

Genital human papillomavirus

Age- and sex-stratified trends

Crude annual incidence rates of genital HPV infections among all ACSMs decreased by 24.1%, from 511.3 cases per 100,000 p-yrs in 2016 to 388.0 cases per 100,000 p-yrs in 2024, with a more pronounced decrease among service men. On average, HPV rates in female service members were 10 times higher than those of male service members. Incidence rates of genital HPV infections among male service members overall followed a steadily downward trajectory, with a minor uptick in 2021, decreasing from a high of 220.6 cases per 100,000 p-yrs in 2016 to the lowest level, 119.7 cases per 100,000 p-yrs, in 2024 (-45.7%) (Figure 6). Incidence among female service members declined from a high of 2,278.8 per 100,000 p-yrs in 2016 to 1,775.7 cases per 100,000 p-yrs in 2024 (-22.1%), with the lowest point in 2022, at 1,584.4 cases.

Service women in the 17-19-years age group showed the largest reduction (-85.8%) in genital HPV, dropping from 381.3 per 100,000 p-yrs in 2016 to 54.0 per 100,000 p-yrs in 2024. Declines in older age groups were modest, ranging from approximately 19% to 29%. Among those aged 30-34 years—the female age group with the largest detection rate of HPV—incidence decreased by 23% from 3,694.2 to 2,845.4 cases per 100,000 p-yrs from 2016 to 2024.

Declines among service men were pronounced from their peak levels for most age groups. The magnitude of reduction progressively decreased with increasing age, with the greatest drop observed among those aged 17-19 years (-83.0%, from 61.7 in 2017 to 16.8 per 100,000 p-yrs in 2024), followed by those aged 20-24 years (-63.2%, from 238.9 in 2016 to 88.0 in 2024), and 25-29 years (-51.6%, from 286.4 in 2016 to 138.5 in 2024). Older groups of male service members experienced more gradual declines, with those aged 30-39 years experiencing an approximately 30% decrease over the entire surveillance period, and those aged 40 years and older group remaining largely stable, declining by only 2.8% from 137.7 in 2016 to 133.9 in 2024.

Age- and sex-specific changes in 2024 versus 2023

The magnitude of annual reduction in HPV incidence among service women from 2023 to 2024 progressively decreased with increasing age, from 59.9% (134.8 in 2023 to 54.0 per 100,000 in 2024) among those aged 17-19 years, to 1.4% (2,886.2 in 2023 to 2,845.4 per 100,000 p-yrs in 2024) among those aged 30-34 years. In older female age groups, this trend reversed, with rates among those aged 35-39 years increasing 4.1% (from 1,625.3 in 2023 to 1,691.3 per 100,000 p-yrs in 2024), and among those aged 40 years and older, rates increased 13.1% (from 1,035.4 in 2023 to 1,171.5 per 100,000 p-yrs in 2024). Among men, the youngest (17-19-years) age group continued to decline sharply, from 33.6 in 2023 to 16.8 in 2024 (-49.9%), with moderate levels of decline, 9–14%, among men aged 20-29 years. Similar to the HPV rate declines among women, incidence rates among men older than age 30 years showed a reversal of trending declines, rebounding 3-9%, which indicates a shift in the HPV burden towards older ages in both sexes.

Genital herpes simplex virus

Age- and sex-stratified trends

From 2016 through 2024, both female and male service members experienced substantial declines in HSV, but their extents and patterns differed. Total female incidence fell from 773.9 cases per 100,000 p-yrs in 2016 to 382.9 cases per 100,000 p-yrs in 2024 (-50.5%). Male incidence decreased from 180.9 in 2016 to 107.4 in 2024 (-40.6%). The largest declines for both male and female service members occurred among those aged 20-24 years (-53.8% and -48.6%, respectively). Other age groups also experienced notable decreases during the surveillance period, more pronounced among females. Women aged 17-19 years had approximately 3 times as many cases as their male counterparts, with the incidence rate per 100,000 revealing a female-to-male rate ratio of about 14:1. The rate ratio declined with increasing age.

Age- and sex-specific changes in 2024 versus 2023

In 2024, changes in patterns diverged by sex. Total incidence rates among women fell nearly 20%, from 478.0 in 2023 to 382.9 per 100,000 p-yrs in 2024, among all age groups except those aged 35-39 years, among whom HSV increased by 9.6%, from 228.5 to 250.3 per 100,000 p-yrs. In contrast, male trends indicated more variable results, with total rates declining by only 6% (from 114.4 to 107.4 per 100,000 p-yrs) while increasing by over 20% among individuals aged 17-19 years (from 41.2 to 53.5 per 100,000) and over age 40 years (from 60.9 to 73.2 per 100,000) (data not shown).

Discussion

This report provides a surveillance update on 3 nationally notifiable bacterial STIs—chlamydia, gonorrhea, and syphilis—as well as 2 viral STIs, genital HSV and HPV. Chlamydia was the most frequently reported STI during the surveillance period, with total cases and incidence rates exceeding those of HPV, the second-most common STI, approximately 5 times. Gonorrhea was the third most reported STI, followed by genital HSV and syphilis.

Chlamydia and gonorrhea

Chlamydia and gonorrhea are both bacterial infections that are frequently asymptomatic and typically tested together due to shared screening programs and diagnostic laboratory methods.¹⁵ Consequently, temporal trends in incidence rates for both infections tend to reflect the other, as observed in this analysis.

During the initial 4 years of the surveillance period, both chlamydia and gonorrhea showed upward trends, peaking in 2019, before declining in 2020. The declines for the 2 STIs persisted through 2024: 40% for both infections among service women, and over 20% for gonorrhea among service men. Women under age 25 years and men under age 30 years accounted for most cases, who, correspondingly, experienced the largest reductions in incidence.

The trend pattern observed over the past 5 years aligns with the recent CDC data, which indicate that between 2019 and 2023, the total rates of both chlamydia and gonorrhea decreased among general population women by approximately 12–14% and among men by approximately 8%.¹⁶ Gonorrhea, however, showed little change, or increased slightly, by approximately 2%, among men in the general population. Over the longer period, from 2014 through 2023, CDC data show that chlamydia rates in the general population diverged between sexes, increasing by 33.4% among men and decreasing by only 1.8% among women.

In contrast, in this analysis, which covers a comparable period, from 2016 through 2024, chlamydia rates among ACSMs demonstrated relatively steady downward trend, declining by more than 30% in both sexes. Corresponding rates for gonorrhea showed decreases by 10.1% among service men and 26.6% among service women. Chlamydia incidence rates were markedly higher among service members, with rates among males approximately 4–5 times, and females about 7 times, higher than those of civilian counterparts. Corresponding military to civilian ratios for gonorrhea were 1.4–2.7 times higher among men and 3.1–4.0 times higher among women.

Higher rates in military populations are likely due to a combination of demographic, behavioral, and structural factors. The military population is predominantly young, highly mobile, with frequent relocations and deployments, and often residing in close social environments, which are factors known to increase the risk of STI acquisition.^2,13 Additionally, the military implements aggressive screening programs (e.g., routine and mandatory testing among women younger than age 25 years) to maintain a fit and ready military force, coupled with no-cost access to preventive and primary care, which facilitate more comprehensive case detection.^17,18 Electronic health records within the Military Health System (MHS) further enable more complete disease burden capture for notifiable disease reporting. Nevertheless, these rate comparisons should be interpreted cautiously, as differences in surveillance and reporting practices between military and civilian populations may introduce surveillance bias.¹⁹

Laboratory and medical encounter data from service members in 2022 supplemented chlamydia case rates, as those cases had no medical event report and would have been unidentifiable without supplemental electronic health record data. Routine surveillance reports do not assess anatomical sites from gonorrhea case reports and laboratory records, which could provide more comprehensive understanding of extragenital infections in high-risk populations.

National guidelines recommend gonorrhea screening, including pharyngeal or rectal testing, at least annually for both men who have sex with men (MSM) and HIV-positive patients. Extragenital gonorrhea screening may be considered for women on the basis of reported sexual behaviors and exposure.²⁰ Despite these recommendations, extragenital screening for high risk civilian and military populations is under-used.^21,22 A recent assessment of extragenital STI screening by primary care physicians for HIV-positive male Air Force service members found that approximately one-third of patients had undetected STIs, the majority due to extragenital infections of the rectum and pharynx.²²

Syphilis

The trend in syphilis rates reveals a pattern that differs from the other 2 bacterial STIs, reflecting differing epidemiological factors and clinical dynamics. Total syphilis incidence trends mirror national trends in the civilian population. CDC data indicate that rates of primary and secondary syphilis among women in the general U.S. population increased nearly 5-fold nationally from 2015 through 2024, rising 392.9% from 1.4 to 6.9 cases per 100,000 population. In contrast, corresponding rates among men over the same period increased 29.4%, from 13.6 to 17.6 cases per 100,000 population. Syphilis rates were highest among non-Hispanic Black service members of both sexes, consistent with national data evidencing persistently elevated rates in this population. While syphilis incidence among non-Hispanic Black service members declined in 2024, they continue to bear the highest burden of infection, evincing the persistence of syphilis in this population and potentially reflecting ongoing challenges in delivering effective prevention, testing, and treatment services.

Conversely, syphilis incidence among service members of other racial and ethnic groups, especially women younger than age 25 years, continued to increase through the end of the surveillance period. The largest relative increase was observed among non-Hispanic White female service members, a group previously associated with the lowest syphilis burden, increasing 456% from 2016 to 2024. These trends correlate with the findings of a recent study that found declining syphilis incidence among historically highly-burdened population groups, while concurrently increasing in lesser-burdened groups.²³

The sharp rise in syphilis incidence observed among reproductive age women is a significant public health concern due to the risk of maternal and congenital syphilis. The rate of maternal syphilis among female MHS beneficiaries rose by 233% from 2012 to 2022, while the rate of congenital syphilis among newborn MHS beneficiaries increased by 355%.24 Nationally, the maternal syphilis rate increased by 222% from 2016 to 2022, and congenital syphilis cases rose 700% over the past decade, from 2015 to 2024.^25,16 These findings indicate critical gaps in prevention, screening and treatment for young service women, and reinforce U.S. Preventive Services Task Force recommendations for early syphilis infection screening in all pregnant women.²⁶

This cycle of syphilis resurgence appears to have begun in the early 2000s, in both civilian and military populations, with steady and notable increases among active component service members reported since the early 2010s.^27,28 Early increases were primarily attributed to MSM, a group also at elevated risk for HIV infection. Recent data suggest that among MSM, especially those under age 25 years, syphilis and HIV infections may increasingly co-occur, underscoring the need for integrated prevention and control strategies that address both infections concurrently.^16,29

Collectively, these findings suggest shifting syphilis epidemiology, from the moderate resurgence in 2010s described by Garges in 2016²⁸ to a sustained and broader increase across the force. Further studies are needed to understand the underlying drivers of these trends, including sexual behaviors and risk factors that are influenced by military service, the reach and effectiveness of existing prevention and screening programs, and unique “social context of soldiers, sailors, airmen, and marines”²⁸ affecting syphilis transmission.

Human papillomavirus

HPV rates among male and female service members declined steadily over the surveillance period, with the largest reductions, over 80%, observed in the youngest cohorts (ages 17-19 years) of both sexes. These data are consistent with CDC data that show the incidence of HPV infection, particularly in younger populations, declining significantly since the introduction of the HPV vaccine in 2006.³⁰ Specifically, the prevalence of vaccine type HPV strains (6, 11, 16, 18) dropped 86% among young women ages 14-19 years within the decade following vaccine introduction.³¹

Vaccination alone, however, does not fully explain the drastic decline observed among the youngest service members, who now represent almost a negligible proportion of the total recorded burden of HPV. Previous studies have documented sub-optimal vaccine uptake among service members.^32,33 Between 2007 and 2017, only approximately 27% of eligible service women and 6% of service men initiated HPV vaccination, and completion of the 3-dose series was even lower.³³ Contributing factors include inadequate awareness and education, lack of centralized vaccine monitoring within the MHS, the voluntary nature of vaccination, and the mobile lifestyle of service members.³⁴ It is also possible that HPV vaccination records are incomplete. The HPV vaccine is typically recommended during early adolescence (i.e., before military service), and prior vaccination may not have been reported or recorded, leading to an under-estimation of actual vaccination coverage

An additional factor contributing to the dramatic decline in rates of HPV detection among women ages 17-20 years is MHS implementation of updated national cervical cancer screening guidelines, which recommend delaying screening until age 21 years. Furthermore, routine screening is not recommended for men, resulting in under-detection within this population.¹⁵

In contrast, HPV detection rates were highest among women ages 20-39 years, with the greatest burden observed in those aged 30-34 years. A strong cohort effect was evident, with the magnitude of HPV rate reductions progressively diminishing with increasing age, and even reversing in 2024 among individuals older than age 35 years. This observed pattern likely reflects lower vaccination among older cohorts, HPV infection persistence, and expanded or more frequent screening practices in those age groups. Supporting this hypothesis, a recent study on cervical cancer screening modalities found that MHS screening practices align with national guideline updates, including increased use of HPV co-testing and expanded screening among women ages 30-64 years.³⁴ Concurrently, cervical cancer screening has decreased among women younger than age 21 years, consistent with recommendations to delay initiation of screening in that age group.³⁵

Targeted efforts are warranted to improve HPV vaccine awareness, accessibility, and completion among service members, and reinforcing education for both health care providers and personnel could strengthen vaccine uptake and help reduce the long-term burden of HPV-related diseases within the armed forces.

Herpes simplex virus

The trends in the incidence of genital HSV in the U.S. military are consistent with the CDC’s National Health and Nutrition Examination Survey (NHANES) rounds that show declining seroprevalence in the U.S. population since the late 1990s. National seroprevalence among individuals aged 14-49 years dropped from 18% in 1999-2000 to around 12% by 2015-2020.^36,37 Total incidence among service members decreased by roughly 40% to 50%, depending on the age group and sex, between 2016 and 2024.

No sexual risk behavior data were available for this report, but prior surveys of military personnel indicate increased behaviors of possible concern. The 2018 Department of Defense Health Related Behaviors Survey (HRBS) documented that 19.3% of active component respondents reported 2 or more sexual partners within the past year, with 34.9% reporting sex without condom use with a new partner in the past year—percentages almost double those in the 2011 survey.³⁸

This report has several limitations. Changes in incidence rates may reflect, at least in part, temporal changes in case detection, including more aggressive screening. Furthermore, STI diagnoses can be incorrectly coded. For example, STI-specific ‘rule out’ diagnoses or vaccinations (e.g., HPV vaccination) may be reported with STI-specific diagnostic codes, which would result in over-estimated STI incidence.

Cases of syphilis, genital HSV, and genital HPV infections based solely upon laboratory test results are considered ‘suspect’ because laboratory results cannot distinguish between active and chronic infections. Because incident cases of syphilis, genital HSV, and genital HPV were identified based upon a first qualifying encounter or laboratory result, it is likely most cases were acute and not chronic.

STI cases coded in the medical record using symptom codes (e.g., urethritis) rather than STI-specific codes may not be captured. In addition, counts of STI diagnoses reported herein may under-estimate actual diagnoses because some service members may have been diagnosed and treated by non-military health care providers (e.g., county health departments, family planning centers) that were not reimbursed, or in deployed settings (e.g., overseas training exercises, combat operations, aboard ships). Laboratory tests ordered from purchased care or in a shipboard facility, battalion aid station, or in-theater facility were not captured in this analysis.

Lack of standard service and installation practices for STI screening, testing, treatment, and reporting complicates interpretations of detected differences between services, military and demographic subgroups, as well as locations. For some STIs, detection of prevalent infection may occur long after initial infection. Standard STI screening, testing, treatment, and reporting among the services, along with consistent adherence, can improve detection and characterization of STI-related health threats. Continued behavioral risk reduction interventions are still required to counter STIs among military service members.

Acknowledgment

The editors would like to thank Jessica H. Murray, MPH, Epidemiology and Analysis Branch, Armed Forces Health Surveillance Division, for analyzing the data presented in this report.

References

Armed Forces Health Surveillance Center. Absolute and relative morbidity burdens attributable to various illnesses and injuries among active component members of the U.S. Armed Forces, 2023. MSMR. 2024;31(6):2-10. Accessed Oct. 20, 2025. https://www.health.mil/news/articles/2024/06/01/msmr-health-care-burden-active-component
National Academies of Sciences, Engineering and Medicine. Sexually Transmitted Infections: Adopting a Sexual Health Paradigm. The National Academies Press;2021. Accessed Sep. 22, 2025. https://nap.nationalacademies.org/catalog/25955/sexually-transmitted-infections-adopting-a-sexual-health-paradigm
Boyer CB, Gaydos CA, Geller AB, et al. Sexually transmitted infections in the U.S. military: a sexual health paradigm to address risk behaviors, unintended pregnancy, alcohol use, and sexual trauma. Mil Med. 2022;187(5-6):140-143.
U.S. Centers for Disease Control and Prevention. Sexually Transmitted Infections Surveillance 2023. U.S. Dept. of Health and Human Services. 2024. Accessed Jan. 13, 2026. https://www.cdc.gov/sti-statistics/media/pdfs/2025/09/2023_sti_surveillance_report_final_508.pdf
Maatouk I, Vumbugwa P, Cherdtrakulkiat T, et al. Antimicrobial resistance in Neisseria gonorrhoeae in nine sentinel countries within the World Health Organization Enhanced Gonococcal Antimicrobial Surveillance Programme (EGASP), 2023: a retrospective observational study. Lancet Reg Health West Pac. 2025;61:101663. doi:10.1016/j.lanwpc.2025.101663
World Health Organization. Growing Antibiotic Resistance Forces Updates to Recommended Treatment for Sexually Transmitted Infections. News release. 2016. Accessed Sep. 22, 2025. https://www.who.int/news-room/detail/30-08-2016-growing-antibiotic-resistance-forces-updates-to-recommended-treatment-for-sexually-transmitted-infections
Tien V, Punjabi C, Holubar MK. Antimicrobial resistance in sexually transmitted infections. J Travel Med. 2020;27(1):taz1. doi:10.1093/jtm/taz101
Lewis RM, Laprise JF, Gargano JW, et al. Estimated prevalence and incidence of disease-associated human papillomavirus types among 15- to 59-year-olds in the United States. Sex Transm Dis. 2021;48(4):273-277. doi:10.1097/olq.0000000000001356
Spicknall IH, Flagg EW, Torrone EA. Estimates of the prevalence and incidence of genital herpes, United States, 2018. Sex Transm Dis. 2021;48(4):260-265. doi:10.1097/olq.0000000000001375
de Martel C, Plummer M, Vignat J, Franceschi S. Worldwide burden of cancer attributable to HPV by site, country and HPV type. Int J Cancer. 2017;141(4):664-670. doi:10.1002/ijc.30716
Mangione CM, Barry MJ, Nicholson WK, et al. Serologic screening for genital herpes infection: US Preventive Services Task Force reaffirmation recommendation statement. JAMA. 2023;329(6):502-507. doi:10.1001/jama.2023.0057
Armed Forces Health Surveillance Division. Sexually transmitted infections, active component, U.S. Armed Forces, 2015–2023. MSMR. 2024;31(6):34-42. Accessed Oct. 20, 2025. https://www.health.mil/news/articles/2024/06/01/msmr-stis-2023
Garges E, Stahlman S, Jordan N, Clark LL. P3.69 Administrative medical encounter data and medical event reports for syphilis surveillance: a cautionary tale. Sex Transm Infect. 2017;93(suppl 2):a118. doi:10.1136/sextrans-2017-053264.304
Armed Forces Health Surveillance Branch. Use of ICD-10 code a51.31 (condyloma latum) for identifying cases of secondary syphilis. MSMR. 2017;24(9):23. Accessed Oct. 20, 2025. https://www.health.mil/reference-center/reports/2017/01/01/medical-surveillance-monthly-report-volume-24-number-9
Workowski KA, Bachmann LH, Chan PA, et al. Sexually transmitted infections treatment guidelines, 2021. MMWR Recomm Rep. 2021;70(4):1-187. doi:10.15585/mmwr.rr7004a1
U.S. Centers for Disease Control and Prevention. Sexually Transmitted Infections Surveillance 2024 (Provisional). U.S. Dept. of Health and Human Services;2025. Accessed Oct. 2025. https://www.cdc.gov/sti-statistics/annual/index.html
LeFevre ML. Screening for chlamydia and gonorrhea: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2014;161(12):902-910. doi:10.7326/m14-1981
Tanielian T, Farmer C. The US Military Health System: promoting readiness and providing health care. Health Affairs. 2019;38(8):1259-1267. doi:10.1377/hlthaff.2019.00239
Jordan NN, Gaydos JC, Garges EC. A comparative analysis of reported chlamydia and gonorrhea cases among US Army soldiers, 2015 to 2019. Sex Transm Dis. 2021;48(12):945-950. doi:10.1097/olq.0000000000001481
Li J, Armon C, Palella FJ, et al. Chlamydia and gonorrhea incidence and testing among patients in the Human Immunodeficiency Virus Outpatient Study (HOPS), 2007-2017. Clin Infect Dis. 2020;71(8):1824-1835. doi:10.1093/cid/ciz1085
Yabes JY, Lamb CC, Hakre S, et al. Provider uptake of extragenital screening for gonorrhea and chlamydia in a cohort of Air Force members with incident HIV diagnosis. Medicine (Baltimore). 2022;101(42):e31209. doi:10.1097/md.0000000000031209
U.S. Centers for Disease Control and Prevention. Table 25: primary and secondary syphilis–reported cases and rates of reported cases by age group and sex, 2018–2022. Sexually Transmitted Infections Surveillance 2022. U.S. Dept. of Health and Human Services. 2024. Accessed Jan. 13, 2026. https://www.cdc.gov/sti-statistics/media/pdfs/2024/11/2022-sti-surveillance-report-pdf.pdf
Do D, Rodriguez PJ, Gratzl S, et al. Trends in incidence of syphilis among US adults from January 2017 to October 2024. Am J Prev Med. Published online Apr. 2, 2025. doi:10.1016/j.amepre.2025.03.008
Kotas KS, Stahlman SL, Ying S, et al. Syphilis cases among pregnant women and newborns in the Military Health System, 2012–2022. MSMR. 2024;31(12):12-16. Accessed Oct. 20, 2025. https://www.health.mil/news/articles/2024/12/01/msmr-maternal-and-congenital-syphilis
Gregory ECW, Ely DM. Trends and characteristics in maternal syphilis rates during pregnancy: United States, 2016–2022. NCHS Data Brief. 2024;(496):1-8.
Curry SJ, Krist AH, Owens DK, et al, U.S. Preventive Services Task Force. Screening for syphilis infection in pregnant women: US Preventive Services Task Force reaffirmation recommendation statement. JAMA. 2018;320(9):911-917. doi:10.1001/jama.2018.11785
Stahlman S, Seliga N, Oetting AA. Sexually transmitted infections, active component, U.S. Armed Forces, 2010–2018. MSMR. 2019;26(3):2-10. Accessed Oct. 20, 2025. https://www.health.mil/reference-center/reports/2019/03/01/medical-surveillance-monthly-report-volume-26-number-3
Garges EC. Editorial: what’s old is new again: syphilis in the U.S. Army. MSMR. 2016;23(2):2-5. Accessed Oct. 20, 2025. https://www.health.mil/reference-center/reports/2016/01/01/medical-surveillance-monthly-report-volume-23-number-2
Aumakhan B, Eick-Cost AA, Oh GT, Stahlman SL, Johnson R. Four decades of HIV antibody screening in the U.S. military: a review of incidence and demographic trends, 1990–2024. MSMR. 2025;32(4):13-20. Accessed Oct. 20, 2025. https://www.health.mil/news/articles/2025/04/01/msmr-hiv-screening-1990-to-2024
Rosenblum HG, Lewis RM, Gargano JW, et al. Declines in prevalence of human papillomavirus vaccine-type infection among females after introduction of vaccine: United States, 2003–2018. MMWR Morb Mortal Wkly Rep. 2021;70(12):415-420. doi:10.15585/mmwr.mm7012a2
McClung NM, Lewis RM, Gargano JW, et al. Declines in vaccine-type human papillomavirus prevalence in females across racial/ethnic groups: data from a national survey. J Adolesc Health. 2019;65(6):715-722. doi:10.1016/j.jadohealth.2019.07.003
Maktabi H, Ludwig SL, Eick-Cost A, Yerubandi UD, Gaydos JC. Quadrivalent human papillomavirus vaccine initiation, coverage, and compliance among U.S. active component service women, 2006–2011. MSMR. 2012;19(5):16. Accessed Oct. 20, 2025. https://www.health.mil/reference-center/reports/2012/01/01/medical-surveillance-monthly-report-volume-19-number-5
Clark LL, Stahlman S, Taubman SB. Human papillomavirus vaccine initiation, coverage, and completion rates among U.S. active component service members, 2007–2017. MSMR. 2018;25(9):9-14. Accessed Oct. 20, 2025. https://www.health.mil/reference-center/reports/2018/01/01/medical-surveillance-monthly-report-volume-25-number-9
Ginn M, Stahlman SL, Fan MT, Baker Miller S. Trends in cervical cancer screening modality in the active component U.S. military, 2013–2023. MSMR. 2025;32(5):14-18. Accessed Oct. 20, 2025. https://www.health.mil/news/articles/2025/05/01/msmr-cervical-cancer-screening-trends
U.S. Centers for Disease Control and Prevention. HPV-associated cancers and precancers. Sexually Transmitted Infections Treatment Guidelines, 2021. U.S. Dept. of Health and Human Services. Updated Jul. 22, 2021. Accessed Oct. 20, 2025. https://www.cdc.gov/std/treatment-guidelines/hpv-cancer.htm
McQuillan G, Kruszon-Moran D, Flagg EW, Paulose-Ram R, National Center for Health Statistics. NCHS Data Brief No. 304: prevalence of herpes simplex virus type 1 and type 2 in persons aged 14-49: United States, 2015–2016. U.S. Centers for Disease Control and Prevention, U.S. Dept. of Health and Human Services. Feb. 2018. Accessed Oct. 20, 2025. https://www.cdc.gov/nchs/data/databriefs/db304.pdf
Xu F, Sternberg MR, Kottiri BJ, et al. Trends in herpes simplex virus type 1 and type 2 seroprevalence in the United States. JAMA. 2006;296(8):964-973. doi:10.1001/jama.296.8.964
Meadows SO, Engel CO, Collins RL, et al. 2018 Department of Defense Health Related Behaviors Survey (HRBS): Results for the Active Component. RAND Corporation;2021. Accessed Jun. 4, 2024. https://www.rand.org/pubs/research_reports/RR4222.html

The association between body mass index, physical fitness and COVID-19 hospitalization among male active duty U.S. Army soldiers, May 2020–November 2021

Wed, 01 Oct 2025 00:00:00 Z

Abstract

Few studies have investigated body mass index (BMI) and physical fitness factors related to coronavirus disease (COVID)-19 hospitalizations among U.S. active duty service members. This investigation examined associations between measures of physical fitness, BMI, and Army physical fitness test (APFT) performance with COVID-19 hospitalizations of U.S. Army active duty soldiers. From May 2020 through November 2021, 13,074 male soldiers were diagnosed with COVID-19 (90 hospitalized, 12,984 non-hospitalized) who also had an APFT and BMI record no more than 9 months from the COVID-19 diagnosis date. Female soldiers were excluded due to insufficient numbers of COVID-19 hospitalizations. In adjusted logistic regression models controlling for race and ethnicity as well as comorbidities, and including age, BMI, and their interactions, both BMI (adjusted odds ratio [aOR] 1.07; 95% CI 1.01, 1.14; p=0.021), and the age and BMI interaction were statistically significant (aOR 1.01; 95% CI 1.00, 1.02; p=0.004). Each additional year of age amplified the odds of hospitalization by an additional 1% for every 1 unit increase in BMI. Development and maintenance of a healthy body weight may reduce likelihood of COVID-19 hospitalization and sustain individual and unit health and medical readiness.

What are the new findings?

For male U.S. Army active duty soldiers, the association between having a higher BMI and COVID-19 hospitalization was amplified by age, indicating about a 1% increase in the odds of hospitalization per BMI unit for each additional year of age.

What is the impact on readiness and force health protection?

Maintaining a healthy body weight may reduce the risk of COVID-19 related hospitalization for military personnel. The U.S. Army’s Holistic Health and Fitness Program is one example of a comprehensive health program established to simultaneously enhance several facets of military health and fitness.

Background

Although the U.S. Centers for Disease Control and Prevention (CDC) has identified well-established risk factors—such as age, sex, race, comorbidities, vaccination status—for coronavirus disease (COVID)-19 hospitalization within the general U.S. population, limited research has explored the contributing factors specific to U.S. active duty service members.^1,2

Obesity (BMI≥30 kg/m²) is perhaps the most common comorbidity associated with COVID-19 severity, but obesity is related to several other chronic conditions including hypertension, type 2 diabetes, cardiovascular disease, lung disease, and sleep apnea, all of which have been independently associated with severe COVID-19 disease.^3-7 Additionally, overweight (BMI 25.0–29.9 kg/m²) or obesity increase risk of respiratory symptoms, such as shortness of breath, often associated with severe COVID-19 outcomes.^4,6,8 Service members are estimated to have higher overweight prevalence and lower obesity prevalence compared to the general U.S. population, with similar trends of higher overweight prevalence with older age.⁹

A 2021 CDC Morbidity and Mortality Weekly Report added further evidence that a higher BMI increases risk of severe COVID-19 outcomes (e.g., hospitalization, intensive care unit hospitalization, or death) in the general public.⁴ Epsi et al. (2021) reported that obesity was correlated with COVID-19 severity in a study of Military Health System (MHS) beneficiaries, in which active duty service members comprised over 50% of the study population.³ Early in the pandemic, studies described comorbidities associated with positive COVID-19 cases in the U.S. Army active duty population, and included obesity diagnosis codes in the medical records. Studies have yet to examine associations with BMI values obtained from periodic body composition assessments, such as the Army’s Digital Training Management System (DTMS) or vital records associated with medical encounters.^1,2

The active duty military population tends to be more physically fit, younger, and healthier (i.e., ‘the healthy soldier effect’ or ‘healthy worker effect’) compared to the general U.S. population due to accession requirements for health, ready access to medical care, and stringent standards of physical fitness and body composition.^10-12 The current U.S. Army Field Manual, volume 7-22, Holistic Health and Fitness, describes the Holistic Health and Fitness (H2F) Program that prescribes physical readiness training at least 5 to 6 times per week for a total of 5 to 7.5 hours in addition to rigorous fitness standards.¹³

Physical activity is 1 of 4 main modifiable risk factors identified by the CDC to reduce risk of some chronic diseases, which have been associated with severe COVID-19 outcomes.^6,14 Regular physical activity is generally associated with improved immune response, reduction in comorbid conditions, and reduction in systemic inflammation.^15,16 Regular physical activity has also been shown to reduce susceptibility to viral infection; however, this is dependent on meeting guidelines for exercise volume and intensity.¹⁷ Greater cardiorespiratory fitness may provide improved pro-inflammatory responses and increased antiviral host responses post-infection.^15,16 A meta-analysis of almost 2 million medical records demonstrated a reduction in risk of COVID-19 infection, hospitalization, and mortality for individuals who participated in regular physical activity (e.g., 500 metabolic equivalent [MET]-minutes per week, where 1 MET equals resting energy expenditure and MET-minutes is the product of METs achieved and task duration) compared to individuals who were inactive (0 MET-minutes per week).¹⁸

While prior studies have compared pre- and post-pandemic impacts on physical activity and BMI, few studies have described how physical fitness and BMI, prior to COVID-19 diagnosis, affected COVID-19 hospitalizations.^18-21 One large retrospective study in 2020 found that physically inactive patients diagnosed with COVID-19 were significantly more likely to experience severe COVID-19 outcomes including hospitalization, intensive care unit (ICU) admission, or death.²¹ This report describes associations between prior BMI and prior physical fitness performance with COVID-19 hospitalization while adjusting for age, race and ethnicity, vaccination status, and comorbidities.

Methods

Study population

The population for this retrospective cohort study included U.S. Army active duty soldiers with measured heights and weights and either 1) documented history of initial COVID-19 or 2) history of initial COVID-19 hospitalization from May 1, 2020 through November 30, 2021. (See Figure 1 for analysis population exclusions.) The beginning of the period was selected to capture the widespread use of the ICD-10-CM (International Classification of Diseases, 10th Revision, Clinical Modification) U07.1 diagnosis code for COVID-19. The end of the period was selected to capture cases before the initial wave of the Omicron variant, in December 2021.

Administrative medical data were obtained in December 2022 from electronic health records in the Military Health System Data Repository (MDR), and reportable medical event data were obtained from the Disease Reporting System internet (DRSi). The MDR is one of the most robust centralized sources of Department of Defense (DOD) health care data. MDR data utilized for this report included inpatient and outpatient medical encounters, immunizations, laboratory results, and pharmacy records.

COVID-19 hospitalizations were included if the first 2 positions of the diagnostic codes in the inpatient medical records contained 1 of the COVID-19 ICD-10-CM diagnosis codes (Table 1) and occurred within 30 days of the initial COVID-19 diagnosis or positive SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) polymerase chain reaction (PCR) laboratory result or DRSi medical event report.^2,22-25 Non-hospitalized COVID-19 encounters were defined by a COVID-19 ICD-10-CM diagnosis code (Table 1) in the first 2 diagnostic positions, a positive SARS-CoV-2 PCR laboratory result, or a confirmed DRSi case without a related inpatient record.

Vaccination status at the date of COVID-19 diagnosis was obtained from MDR immunization, outpatient, and pharmacy data using ‘CVX’, ‘CPT’, and ‘NDC’ codes. Soldiers completing a primary COVID-19 vaccination series were defined as those who had received the second dose of a 2-dose primary vaccination series or a single dose of a 1-dose primary vaccine product 14 days or more prior to a COVID-19 encounter. Soldiers with 1 dose of a 2-dose primary vaccination series were categorized as ‘partially vaccinated’, and others were categorized as ‘unvaccinated’.

A soldier was considered to have a comorbidity if a medical encounter contained an ICD-10-CM diagnosis code for that condition in any diagnosis position from January 1, 2019 and the date of the initial positive COVID-19 diagnosis. Comorbidities were selected using Clinical Classifications Software Refined (CCSR) categories from diagnostic codes similar to other research by the CDC, with a retrospective review period through January 1, 2019.^4,26,27 CCSR categories used included hypertension (CIR007, CIR008), coronary atherosclerosis and other heart disease (CIR011), chronic kidney disease (GEN003), diabetes (END002, END003), neoplasms (CIR categories beginning with ‘NEO’), chronic obstructive pulmonary disease and bronchiectasis (RSP008), and sleep wake disorders (NVS016).^4,26,27

Active duty soldier demographics (i.e., service, component, age, sex, race and ethnicity) were obtained in December 2022 from Defense Manpower Data Center (DMDC) personnel rosters. Age was calculated at the COVID-19 encounter date by date of birth. Race and ethnicity were categorized, based on data available in DMDC, as 1) non-Hispanic White—the reference population—2) non-Hispanic Black, 3) Hispanic, or 4) ‘other’ including those of Asian, Native Hawaiian/Pacific Islander, American Indian/Alaskan Native, or other race or ethnicity. BMI (displayed as kg/m²) was calculated using height (inches) and weight (pounds) closest to the initial COVID-19 encounter date using the formula (weight[lb]/height[in]² x 703). Measurements were recorded during periodic height and weight checks by unit personnel in Defense Training Management System (DTMS) body composition records, supplemented by MDR vital records recorded during medical encounters when no DTMS record was available. Records were included if the BMI measurement was no more than 9 months prior to the documented COVID-19 diagnosis date.

DTMS data for the Army physical fitness test (APFT) were used because those data were more readily available during the investigation period; the Army combat fitness test (ACFT) was not yet the U.S. Army fitness test of record. The APFT assessed physical fitness through performance on 3 timed events: 1) 2-minute push-ups, 2) 2-minute sit-ups, and 3) a 2-mile run. APFT event data were retained if the record occurred no more than 9 months prior to the initial COVID-19 diagnosis date, were considered ‘for record’, and each of the 3 events contained plausible values recorded (e.g., push-ups and sit-ups of 1-150 repetitions, 2-mile run times of 9.5–30 minutes). Implausible values accounted for less than 0.1% of all records.

Exclusions

Records were excluded if a soldier had a history of COVID-19 prior to the investigation start date, as identified via DRSi or the medical record, or were non-active duty (including activated National Guard or reserve). Female service members were excluded from the analysis due to an insufficient number (n=10) of hospitalizations after obstetric-related admissions were removed.

Statistical analysis

Differences in COVID-19 hospitalization by categorical variables were explored with chi-square tests; continuous variables were explored using univariate logistic regression. Crude and adjusted logistic regression models were fit to estimate odds ratios (ORs) and associated 95% confidence intervals (CIs). Adjusted logistic regression models used the outcome of COVID-19 hospitalization and age and BMI as main predictors, controlling for covariates that included race and ethnicity, vaccination status, comorbidities, and physical fitness characteristics. An interaction term between age and BMI was also included in the model.

Non-linearity was assessed using empirical logistic plots and the functional form with cumulative residual plots. When non-linearity was detected, models were fit as a linear term, polynomial degree, and restricted cubic splines, and the fit (i.e., AIC) of the linear term with the non-linear term was compared. Initial covariate selection was a priori, considering both linear and non-linear terms for each variable, as appropriate. Variables were excluded if the non-linear term did not improve the model fit compared to the linear term. Variables with less than 15 observations per category were excluded. There was strong evidence of non-linearity among the 3 APFT variables. Even after fitting different models with various functional forms of the 3 APFT variables, the model fit did not improve, and the APFT variables were omitted from the adjusted model. The final adjusted models included racial and ethnic group, age, BMI, comorbidities, and an interaction between age and BMI. Alpha levels were set to 0.05. Analyses were conducted using SAS, version 9.4 (SAS Institute Inc., Cary, NC).

Results

From May 1, 2020 through November 30, 2021, a total of 13,074 unique male Army active duty soldiers were identified as incident COVID-19 cases with a documented BMI and complete 3-event APFT no more than 9 months prior to the COVID-19 encounter date (Figure 1). Women were excluded from the analysis because only 10 hospitalizations of female soldiers for COVID-19 occurred, which was below the minimum required for analysis.

Table 2 summarizes the baseline demographic, physical fitness, and body composition characteristics of this cohort. The average male soldier was 26.5 years old (standard deviation [SD] 6.0) with a BMI of 26.6 (SD 3.4). Those male soldiers performed an average of 63.6 push-ups (SD 12.9), 67.3 sit-ups (SD 10.9), and completed the 2-mile-run in 14.9 minutes (SD 1.5) on the APFT (Table 2). The cohort was primarily non-Hispanic White (51.4%), unvaccinated (95.9%), with no histories of the selected comorbidities (91.4%) (Table 2). Compared with soldiers who were hospitalized, those not hospitalized were younger, with lower BMI, performed more sit-ups, and had a lower proportion of comorbidities (Table 2). Only 3% of soldiers were fully vaccinated during the study period, and just 4 of those were hospitalized; consequently, vaccination status was not incorporated in the adjusted model.

In unadjusted analyses, BMI (OR 1.11; 95% CI 1.05, 1.17), age (OR 1.04; 95% CI 1.01, 1.08), sit-ups (OR 0.97; 95% CI 0.95, 0.99), and comorbidities (OR 2.15; 95% CI 1.23, 3.75) were each significantly associated with COVID-19-related hospitalization (Table 3).

The final adjusted model included race and ethnicity, age, BMI, comorbidities, and the interaction term for age (mean-centered at 26.5 years old) and BMI (mean-centered at 26.6 kg/m²). In the adjusted model, the main effect of age was not statistically significant (aOR 1.01; 95% CI 0.98, 1.05), whereas the main effect of BMI was significant, with an additional 7% increase in the adjusted odds (aOR 1.07; 95% CI 1.01, 1.14) (Table 4). The age and BMI interaction was significant, for each additional year of age, the adjusted odds with a 1-unit increase in BMI is amplified by an additional 1%, and conversely each additional BMI unit amplifies the age effect by an additional 1% (Table 4, Figure 2).

Discussion

This study investigated the association between BMI, physical fitness, and COVID-19 hospitalizations in a subset of U.S. Army active duty soldiers with an APFT and body composition measures no more than 9 months prior to a COVID-19 medical encounter, either hospitalized or non-hospitalized. Prior physical fitness, as measured by APFT performance, in this cohort was not associated with COVID-19 hospitalization. In the adjusted logistic regression model, at the average age, each 1 unit increase in BMI increased odds of hospitalization by 7%. Additionally, there was significant interaction between BMI and age, with an additional 1% increase in odds of hospitalization for each unit increase in either BMI or age.

The lack of association between prior physical fitness and COVID-19 hospitalization found in this study is inconsistent with some studies which suggested that higher levels of prior physical fitness could lessen likelihood of COVID-19 hospitalization.^18,28-30 Differences in the methods that defined and measured physical fitness, along with the study populations, complicate direct comparisons between these results and those prior reports. Other papers have evaluated self-reported physical fitness or self-reported physical activity, which may introduce self-reporting and recall bias.^21,29 One report evaluating maximal exercise capacity, via peak METs, used fitness tests up to 2 years prior to SARS-CoV-2 infection and included a population unrepresentative of the U.S. population with a significantly higher hospitalization rate compared to other reports.³⁰

At least 1 study of U.S. service members identified self-reported fitness and exercise capacity decrements following SARS-CoV-2 infection.³¹ A specific threshold of physical fitness could potentially reduce hospitalization duration or intensity. Alternatively, physical fitness may reduce symptom duration or intensity during a non-hospitalized infection, which this report did not assess. This could also be due to the multifactorial nature of COVID-19 severity, in which other factors such as pre-existing health conditions, age, immune response, and genetic predispositions play critical roles. Additionally, the ‘healthy warrior effect’, attributed to rigorous physical and medical screening processes required for military service, health care access, and employment, may also positively affect clinical outcomes.¹⁰ Active duty soldiers who are generally healthier and more physically fit may experience lower morbidity, which could have influenced this study’s observed associations. Soldiers participate in regular physical activity to maintain required physical fitness standards, and several studies and a meta-analysis found that regular physical activity was associated with lower risk of COVID-19 infection, hospitalization, severe illness, and death.^18-21

The significant interaction found in this study between BMI and age underscores the compounded risk that higher BMI and increasing age pose for hospitalization. This finding aligns with existing literature that has identified obesity as a major risk factor for hospitalization, likely due to the association and interaction of COVID-19 with comorbidities such as hypertension, diabetes, and cardiovascular diseases.^2,4,32,33 Other reports that examined changes in service member BMI during the same period observed a significant increase in obesity, although the increases tended to be largest among service members younger than age 20 years.³⁴ The additional 1% increase in hospitalization risk per unit increase in BMI with age in this study suggests that some older individuals with higher BMI are particularly vulnerable, highlighting the need for targeted interventions in this group. This report differed from other studies that primarily relied on an ICD-10-CM diagnosis code to indicate obesity rather than measured heights and weights to calculate BMI.^1,2 This approach enabled us to better understand the relationship between BMI, age, and COVID-19-related hospitalization observed in our models.

This study has several limitations. Soldiers with a BMI and APFT record no more than 9 months from the COVID-19 diagnosis date limited the sample size to 20.5% of the original population, which could affect the generalizability of the results (Figure 1). The sample size available for soldiers with an APFT was considerably lower during this period, primarily due to fitness testing pauses during the initial stages of the COVID-19 pandemic (i.e., “lockdowns”). As the pandemic continued, the ACFT was gradually phased in, until established as the fitness test of record on October 1, 2022, resulting in fewer available APFT results. The ACFT data were incomplete and unavailable for use during the reporting period. It is also possible that ACFT performance may demonstrate different associations with COVID-19 hospitalizations than the APFT, given that it assesses additional physical fitness components (e.g., anaerobic fitness, muscular strength and power); ACFT results were not widely available during the period investigated, however. Because soldiers are automatically enrolled in TRICARE, the number of cases and related characteristics may have been under-estimated if soldiers sought care outside of the MHS TRICARE network or were unreported in DRSi. Vaccination status may have been under-estimated due to the accessibility of vaccinations at out-of-network facilities, such as pharmacies or mass vaccination sites.

COVID-19 hospitalizations may not be entirely preventable, but the results of this analysis suggest that risk is higher among military personnel with higher BMI and greater age. Resources available to soldiers such as H2F and Armed Forces Wellness Centers can provide individual guidance to maintain or improve BMI.

Author Affiliations

Preventive Medicine Division, Defense Centers for Public Health–Aberdeen, Aberdeen Proving Ground, MD: Mr. Smith, Mr. Marquez, Dr. Ambrose; Military Injury Prevention Division, Defense Centers for Public Health–Aberdeen: Dr. Pierce, Dr. Canham-Chervak

Disclaimer

The views expressed herein are those of the authors and do not reflect official policy nor position of the Department of Defense, Defense Health Agency, or U.S. Government. Mention of any non-federal entity or its products is for informational purposes only, and is not to be construed nor interpreted, in any manner, as federal endorsement of that non-federal entity or its products.

References

Kebisek J, Forrest LJ, Maule AL, Steelman RA, Ambrose JF. Prevalence of selected underlying health conditions among active component Army service members with coronavirus disease 2019, 11 February–6 April 2020. MSMR. 2020;27(5):50-54. Accessed Sep. 15, 2025. https://www.health.mil/reference-center/reports/2020/05/01/medical-surveillance-monthly-report-volume-27-number-5
Stidham RA, Stahlman S, Salzar TL. Cases of coronavirus disease 2019 and comorbidities among Military Health System beneficiaries, 1 January 2020 through 30 September 2020. MSMR. 2020;27(12):2-8. Accessed Sep. 15, 2025. https://www.health.mil/reference-center/reports/2020/12/01/medical-surveillance-monthly-report-volume-27-number-12
Epsi NJ, Richard SA, Laing ED, et al. Clinical, immunological, and virological SARS-CoV-2 phenotypes in obese and nonobese military health system beneficiaries. J Infect Dis. 2021;224(9):1462-1472. doi:10.1093/infdis/jiab396
Kompaniyets L, Goodman AB, Belay B, et al. Body mass index and risk for COVID-19–related hospitalization, intensive care unit admission, invasive mechanical ventilation, and death—United States, March–December 2020. MMWR Morb Mortal Wkly Rep. 2021;70(10):355-361. doi:10.15585/mmwr.mm7010e4
Rebello CJ, Kirwan JP, Greenway FL. Obesity, the most common comorbidity in SARS-CoV-2: is leptin the link? Int J Obes (Lond). 2020;44(9):1810-817. doi:10.1038/s41366-020-0640-5
U.S. Centers for Disease Control and Prevention. People with Certain Medical Conditions. U.S. Dept. of Health and Human Services. Accessed Sep. 9, 2021. https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-with-medical-conditions.html
Ng R, Sutradhar R, Yao Z, Wodchis WP, Rosella LC. Smoking, drinking, diet and physical activity: modifiable lifestyle risk factors and their associations with age to first chronic disease. Int J Epidemiol. 2020;49(1):113-130. doi:10.1093/ije/dyz078
Salome CM, King GG, Berend N. Physiology of obesity and effects on lung function. J Appl Physiol (1985). 2010;108(1):206-211. doi:10.1152/japplphysiol.00694.2009
Eilerman PA, Herzog CM, Luce BK, et al. A comparison of obesity prevalence: Military Health System and United States populations, 2009–2012. Mil Med. 2014;179(5):462-470. doi:10.7205/milmed-d-13-00430
McLaughlin R, Nielsen L, Waller M. An evaluation of the effect of military service on mortality: quantifying the healthy soldier effect. Ann Epidemiol. 2008;18(12):928-936. doi:10.1016/j.annepidem.2008.09.002
Under Secretary of Defense for Personnel and Readiness. Department of Defense Instruction Number 1304.26. Qualification Standards for Enlistment, Appointment, and Induction. U.S. Dept. of Defense. Updated May 29, 2025. Accessed Sep. 15, 2025. https://www.esd.whs.mil/portals/54/documents/dd/issuances/dodi/130426p.pdf
Chowdhury R, Shah D, Payal AR. Healthy worker effect phenomenon: revisited with emphasis on statistical methods: a review. Indian J Occup Environ Med. 2017;21(1):2-8. doi:10.4103/ijoem.ijoem_53_16
Headquarters, Department of the Army. Field Manual 7-22: Holistic Health and Fitness. Change 2. U.S. Dept. of Defense. Updated Aug. 2025. Accessed Sep. 15, 2025. https://armypubs.army.mil/epubs/dr_pubs/dr_a/arn44522-fm_7-22-002-web-7.pdf
Ng R, Sutradhar R, Yao Z, Wodchis WP, Rosella LC. Smoking, drinking, diet and physical activity-modifiable lifestyle risk factors and their associations with age to first chronic disease. Int J Epidemiol. 2020;49(1):113-130. doi:10.1093/ije/dyz078
Burtscher J, Millet GP, Burtscher M. Low cardiorespiratory and mitochondrial fitness as risk factors in viral infections: implications for COVID-19. Br J Sports Med. 2021:55(8)413-415. doi:10.1136/bjsports-2020-103572
Da Silveira MP, da Silva Fagundes KK, Bizuti MR, et al. Physical exercise as a tool to help the immune system against COVID-19: an integrative review of the current literature. Clin Exp Med. 2021;21(1):15-28. doi:10.1007/s10238-020-00650-3
Piercy KL, Troiano RP, Ballard RM, et al. The physical activity guidelines for Americans. JAMA. 2018;320(19):2020-2028. doi:10.1001/jama.2018.14854
Ezzatvar Y, Ramírez-Vélez R, Izquierdo M, Garcia-Hermoso A. Physical activity and risk of infection, severity and mortality of COVID-19: a systematic review and non-linear dose–response meta-analysis of data from 1 853 610 adults. Br J Sports Med. 2022;56(20):1188-1193. doi:10.1136/bjsports-2022-105733
Cho DH, Lee SJ, Jae SY, et al. Physical activity and the risk of COVID-19 infection and mortality: a nationwide population-based case-control study. J Clin Med. 2021;10(7):1539. doi:10.3390/jcm10071539
Lee SW, Lee J, Moon SY, et al. Physical activity and the risk of SARS-CoV-2 infection, severe COVID-19 illness and COVID-19 related mortality in South Korea: a nationwide cohort study. Br J Sports Med. 2022;56(16):901-912. doi:10.1136/bjsports-2021-104203
Sallis R, Young DR, Tartof SY, et al. Physical inactivity is associated with a higher risk for severe COVID-19 outcomes: a study in 48 440 adult patients. Br J Sports Med. 2021;55(19):1099-1105. doi:10.1136/bjsports-2021-104080
U.S. Centers for Disease Control and Prevention. ICD-10-CM Official Coding Guidelines: Supplement: Coding Encounters Related to COVID-19 Coronavirus Outbreak. U.S. Dept. of Health and Human Services. Accessed Mar. 4, 2022. https://www.cdc.gov/nchs/data/icd/ICD-10-CM-Official-Coding-Gudance-Interim-Advice-coronavirus-feb-20-2020.pdf
National Center for Health Statistics, U.S. Centers for Disease Control and Prevention. ICD-10-CM Official Guidelines for Coding and Reporting. U.S. Dept. of Health and Human Services. Accessed Mar. 4, 2022. https://www.cdc.gov/nchs/data/icd/ICD-10cmguidelines-FY2021-COVID-update-January-2021-508.pdf
National Center for Health Statistics, U.S. Centers for Disease Control and Prevention. ICD-10-CM Official Guidelines for Coding and Reporting FY 2021–UPDATED January 1, 2021. U.S. Dept. of Health and Human Services. Accessed Sep. 9, 2021. https://www.cdc.gov/nchs/data/icd/ICD-10cmguidelines-FY2021-COVID-update-January-2021-508.pdf
Armed Forces Health Surveillance Division. Armed Forces Reportable Medical Events Guidelines and Case Definitions. Defense Health Agency, U.S. Dept. of Defense. Accessed Aug. 14, 2023. https://www.health.mil/reference-center/publications/2022/11/01/armed-forces-reportable-medical-events-guidelines
Healthcare Cost & Utilization Project User Support. Chronic Condition Indicator (CCI) for ICD-10-CM. Agency for Healthcare Research and Quality. Updated Jul. 2025. Accessed Sep. 15, 2025. https://www.hcup-us.ahrq.gov/toolssoftware/chronic_icd10/chronic_icd10.jsp
Healthcare Cost & Utilization Project User Support. Clinical Classifications Software Refined (CCSR). Agency for Healthcare Research and Quality. Updated Nov. 2024. Accessed Sep. 15, 2025. https://hcup-us.ahrq.gov/toolssoftware/ccsr/ccs_refined.jsp
Liu J, Guo Z, Lu S. Baseline physical activity and the risk of severe illness and mortality from COVID-19: a dose–response meta-analysis. Prev Med Rep. 2023;32:102130. doi:10.1016/j.pmedr.2023.102130
Brandenburg JP, Lesser IA, Thomson CJ, Giles LV. Does higher self-reported cardiorespiratory fitness reduce the odds of hospitalization from COVID-19? J Phys Act Health. 2021;18(7):782-788. doi:10.1123/jpah.2020-0817
Brawner CA, Ehrman JK, Bole S, et al. Inverse relationship of maximal exercise capacity to hospitalization secondary to coronavirus disease 2019. Mayo Clin Proc. 2021;96(1):32-39. doi:10.1016/j.mayocp.2020.10.003
Richard SA, Scher AI, Rusiecki J, et al. Decreased self-reported physical fitness following SARS-CoV-2 infection and the impact of vaccine boosters in a cohort study. Open Forum Infect Dis. 2023;10(12):ofad579. doi:10.1093/ofid/ofad579
Jayanama K, Srichatrapimuk S, Thammavaranucupt K, et al. The association between body mass index and severity of coronavirus disease 2019 (COVID-19): a cohort study. PLoS One. 2021;16(2):e0247023. doi:10.1371/journal.pone.0247023
Malik VS, Ravindra K, Attri SV, Bhadada SK, Singh M. Higher body mass index is an important risk factor in COVID-19 patients: a systematic review and meta-analysis. Environ Sci Pollut Res Int. 2020;27(33):42115-42123. doi:10.1007/s11356-020-10132-4
Janvrin ML, Banaag A, Landry T, Vincent C, Koehlmoos TP. BMI changes among US Navy and Marine Corps active-duty service members during the COVID-19 pandemic, 2019–2021. BMC Public Health. 2024;24(1):2289. doi:10.1186/s12889-024-19699-w

Adverse pregnancy outcomes following COVID-19 infection or vaccination in active component U.S. military service women, 2021–2023

Wed, 01 Oct 2025 00:00:00 Z

Abstract

Prior studies have found a higher risk of adverse pregnancy outcomes due to COVID-19 infection; however, recent literature documents few adverse impacts to younger and otherwise healthy populations, but with limited information about military members. The study population comprised active component service women with a singleton delivery between 2021 and 2023. Adverse pregnancy outcomes were evaluated by COVID-19 infection and vaccination history, as well as by demographics and pre-existing comorbidities. During the surveillance period, 39,355 active component U.S. service women had a singleton delivery. After controlling for potential confounders in the adjusted logistic regression analysis, COVID-19 infection during pregnancy was associated with eclampsia (OR 2.18, p<0.05) and antepartum hemorrhage (OR 1.11, p<0.05), and COVID-19 infection prior to the start of pregnancy was associated with antepartum hemorrhage (OR 1.18, p<0.05). In comparison, after adjustment, COVID-19 vaccination during pregnancy and prior to start of pregnancy was not associated with increased odds of any adverse pregnancy outcome in active component service women. COVID-19 vaccines are recommended for pregnant women by the American College of Obstetricians and Gynecologists and, previously, the U.S. Centers for Disease Control and Prevention.

What are the new findings?

This analysis found no significant difference in adverse pregnancy outcomes among those who received a COVID-19 vaccine prior to delivery compared to women who did not, between 2021 and 2023. COVID-19 infection prior to start of pregnancy was associated with antepartum hemorrhage whereas COVID-19 infection during pregnancy was associated with eclampsia and antepartum hemorrhage.

What is the impact on readiness and force health protection?

The findings from this analysis suggest there is a benefit to vaccinating pregnant active component service women against COVID-19. There was no increased risk of these adverse pregnancy outcomes associated with receipt of a COVID-19 vaccine in this study population. In contrast, COVID-19 infection may be associated with increased occurrence of some adverse pregnancy events.

Background

COVID-19 infection during pregnancy has been associated with an increased risk of certain pregnancy complications such as pre-eclampsia and pre-term birth.^1,2 Severity of COVID infection may also play a role, as more severe infections have been more strongly linked to pre-term premature rupture of membranes.³ The increased risk for stillbirth and pre-eclampsia could be due to inflammatory changes affecting the placenta, and the need for intensive care associated with severe disease could result in the increased rates of pre-term delivery.⁴

In 1 cohort study of electronic health care records in southeastern Texas, COVID-19 infection before and during pregnancy were associated with spontaneous abortion.⁵ Other studies, however, found no association between COVID-19 infection and risk of miscarriage.⁶ One matched retrospective cohort study of over 170,000 pregnancies found a 12% higher risk for gestational diabetes following COVID-19 infection during the first 21 weeks of pregnancy.⁷ This association could be due to inflammation increasing insulin resistance, damage to the pancreas, or shared risk factors for more severe COVID-19 infection.^8,9 In contrast, studies of COVID-19 vaccination in pregnant women have not revealed increased risk of adverse maternal or neonatal outcomes including stillbirth, pre-term birth, hypertensive disorders, congenital malformations, or other conditions due to vaccination.^10-12 In fact, some studies have indicated that COVID-19 vaccination during pregnancy can reduce risk of stillbirth and pre-term birth.^13,14 Consequently, during the pandemic the U.S. Centers for Disease Control and Prevention (CDC) recommended that all pregnant patients remain up-to-date with COVID-19 vaccines before and during pregnancy.¹⁵ The American College of Obstetricians and Gynecologists also recommends that patients receive an updated COVID-19 vaccine or ‘booster’ at any point during pregnancy.¹⁶

Healthy women infected with COVID-19 during pregnancy primarily experience mild illness with limited or no significant adverse effects on the mother or neonate.^4,17 Women in active duty military service must maintain physical fitness standards and represent a relatively young and healthy population; as a result, it would be expected that COVID-19 infection would not increase risk of adverse pregnancy outcomes, in most situations. The objective of this study was to evaluate associations between COVID-19 infection during pregnancy and certain adverse pregnancy outcomes in active component U.S. service women who had a delivery between 2021 and 2023, with a review of any change in this association for women who received a COVID-19 vaccine during or prior to their pregnancy start dates. This study focused on adverse conditions that would be coded in the maternal record, since data from neonatal medical records were not available.

Methods

Study population

This cross-sectional study used inpatient and outpatient direct and purchased care medical encounter records from the Defense Medical Surveillance System (DMSS). The study population included U.S. active component service women who had a singleton delivery, either live or still birth outcome, from January 1, 2021 through December 31, 2023. International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes were used to determine singleton live (Z370) or still births (Z371). The first birth event in this surveillance period was used if a woman had multiple delivery events during the period. Deliveries were included if a woman was on active component duty during the 280 days preceding the delivery date. The pregnancy start date was calculated as the date 280 days prior to the delivery event.

Outcomes

The outcomes for this study were specific adverse pregnancy events diagnosed within 280 days preceding the first singleton delivery event during the surveillance period. Outcomes included antepartum hemorrhage or threatened abortion (ICD-10: O20* or O46*), gestational diabetes (O24.4*), eclampsia (O15*), pre-eclampsia (O14*), pre-term labor or delivery (O60*), premature rupture of membranes (O42*), and stillbirth (Z37.1). For the gestational diabetes analysis, individuals were excluded from the study population if they had an inpatient or outpatient diagnosis of ICD-10: E10* (type 1 diabetes), E11* (type 2 diabetes), O24.4* (gestational diabetes), or O24.9* (unspecified diabetes) prior to the start of pregnancy.

Exposures of interest

The exposures of interest in this study were COVID-19 infection before or during pregnancy and COVID-19 vaccination before or during pregnancy. The Armed Forces Health Surveillance Division (AFHSD) maintains a master list of COVID-19 cases for active component service members. These COVID-19 cases were identified from reports of positive antigen, polymerase chain reaction (PCR), and confirmed or probable tests that were entered into the Disease Reporting System internet (DRSi) prior to January 2023, and Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENSE) positive antigen and PCR tests that occurred on or after January 2023.

Anyone with multiple positive COVID-19 tests or reports was counted as 1 infection if both tests were within a 90-day period, consistent with guidelines from the CDC.¹⁸ A woman was categorized as having a COVID-19 infection during pregnancy if there was a documented COVID-19 infection within 280 days prior to her delivery event, and categorized as having COVID-19 infection prior to pregnancy if it occurred more than 280 days prior to the delivery event.

DMSS immunization data were utilized to determine COVID-19 vaccination status. A single dose of any of the following CVX codes met criteria for receiving a COVID-19 vaccination: 207, 208, 212, 221, 217, 211, 229, 300, 309, 312, 313, 510, 511, 502, or 210.¹⁹ These data are provided to DMSS from the MHS Information Platform (MIP) Immunizations Tracking System. DMSS only receives immunizations data for U.S. military service members.

Covariates

Covariates for this study included age, race and ethnicity, service branch, number of prior deliveries, and comorbidities diagnosed prior to the start of the pregnancy. Electronic Periodic Health Assessment (PHA) data were reviewed to determine a service member’s self-reported smoking status within the 2 years prior the start of the associated pregnancy. Smoking was used as a covariate for its documented link to adverse maternal health outcomes.¹³ Other lifestyle factors were not readily available from the PHA and thus were not included for covariate analysis. Pre-existing comorbidities were identified by having a diagnosis of that condition in any diagnostic position of an inpatient or outpatient encounter within 2 years prior to the delivery date (Table 1). For assessment of the number of prior deliveries, 1 delivery was counted every 280 days (ICD-10 codes Z37*, O80, O82). All births were classified as vaginal or cesarian section (ICD-10: O82* or inpatient procedure codes 10D00; outpatient CPT codes 59510, 59515, 59514, 00850, 00857, 01961, 01963, 01968, 01969; diagnostic group codes 370, 371). Age, race and ethnicity, and service branch were assigned based on demographic data for the member at the time of the delivery event.

Statistical analysis

Pearson chi-square tests were used to assess the relationship between exposures of interest and covariates with the adverse pregnancy outcomes. Adjusted logistic regression models were used to further explore the associations between the exposures of interest and study outcomes that were significant in the crude (unadjusted) analysis. These models adjusted for COVID-19 infection prior to the start of pregnancy, COVID-19 infection during pregnancy, COVID-19 vaccination prior to the start of pregnancy, COVID-19 vaccination during pregnancy, age, race and ethnicity, number of prior deliveries, and any previously diagnosed comorbidity. Covariates were selected for inclusion in the adjusted models based on being exposures of interest or significant potential confounders.

Results

A total of 39,355 active component service women experienced a singleton delivery between January 1, 2021 and December 31, 2023 (Table 2). Of those service women, 29,927 (76.0%) had vaginal deliveries, and 9,428 (24.0%) had cesarean sections. A total of 5,190 (13.2%) of these women had a documented COVID-19 infection during pregnancy, and 6,491 (16.5%) had a COVID-19 infection prior to pregnancy. Among women with an infection prior to the start of pregnancy, the first infection was a median of 233 days (IQR 110-402 days) prior.

A total of 9,236 (23.5%) active component service women received at least 1 COVID-19 vaccine dose during pregnancy, and 22,056 (56.0%) received a dose prior to start of pregnancy. There were 27,685 (70.3%) women who received a vaccine dose on or prior to the delivery event, less than the sum of women (n=31,292) who received at least 1 dose during and prior to start of pregnancy, because some women received a dose both prior to and during pregnancy. The percentage of women who received at least 1 dose by their delivery date increased each calendar year: 30% for deliveries in 2021, 91% for deliveries in 2022, 99% for deliveries in 2023.

Most service women had no documented prior deliveries (69.1%). Most service women were ages 20-34 years (86.9%), while non-Hispanic White service women comprised the largest racial and ethnic group (41.6%). Obesity (11.7%), immune-compromising conditions (11.2%), and metabolic disease (11.1%) were the most commonly diagnosed comorbidities within the 2 years prior to pregnancy.

Without adjusting for any potential confounders, antepartum hemorrhage was the most common adverse pregnancy outcome (20.9%), followed by premature rupture of membranes (15.0%), gestational diabetes (8.3%), pre-eclampsia (8.2%), pre-term labor or delivery (7.2%), stillbirth (0.8%), and eclampsia (0.1%) (Table 3). Black, non-Hispanic service women had the highest percentage of pre-eclampsia, eclampsia, antepartum hemorrhage, and stillbirth. Generally, prevalence of adverse pregnancy outcomes tended to be higher among service women with certain pre-existing comorbidities. For example, pre-eclampsia and antepartum hemorrhage were more prevalent among service women with cardiovascular disease. Pre-eclampsia, gestational diabetes, antepartum hemorrhage, and stillbirth were more common among service women with obesity.

In many cases, there was not a significant (p<0.05) difference in prevalence of adverse pregnancy outcomes in service women according to COVID-19 infection or vaccination status, with a few notable exceptions (Table 3). COVID-19 infection during pregnancy was associated with a higher percentage of eclampsia and antepartum hemorrhage; COVID-19 infection prior to start of pregnancy was associated with a higher percentage of antepartum hemorrhage and premature rupture of members; COVID-19 vaccination during pregnancy was associated with lower percentage of antepartum hemorrhage; and COVID-19 vaccination prior to the start of pregnancy was associated with a higher percentage of premature rupture of membranes and a lower percentage of pre-term labor or delivery.

After controlling for potential confounders in the adjusted logistic regression analysis, COVID-19 infection during pregnancy remained significantly and positively associated with eclampsia (OR 2.18, p<0.05) and antepartum hemorrhage (OR 1.11, p<0.05), and COVID-19 infection prior to start of pregnancy remained significantly and positively associated with antepartum hemorrhage (OR 1.18, p<0.05) (Table 4). After adjustment, COVID-19 vaccination prior to start of pregnancy was no longer associated with premature rupture of membranes. COVID-19 vaccination prior to start of pregnancy was, however, inversely associated (OR 0.86, p<0.05) with pre-term labor or delivery.

Discussion

This study found increased odds of eclampsia and antepartum hemorrhage, which includes threatened abortion or any bleeding during pregnancy, among active component service women with a documented COVID-19 infection during pregnancy. In contrast, COVID-19 vaccination during or prior to start of a pregnancy was not associated with increased odds of any adverse pregnancy outcome, after adjustment for potentially confounding factors. It is important to note that these findings cannot be generalized to the U.S. population, nor to earlier periods during the COVID-19 pandemic when vaccines were not widely available, and pre-existing immunity was low or non-existent. It is possible that by the period of analysis for this study, members of the study population may have already had COVID-19 illness and thereby developed natural immunity, which could not be identified. It is estimated that by June 2021 74% of active component service members had been exposed to COVID-19, either by prior infection or vaccination.²⁰

A mandate issued by the U.S. Department of Defense (DOD) on August 24, 2021 required service members to receive a COVID-19 vaccination by December 31, 2021. That requirement was rescinded in January 2023, however, by Section 525 of the National Defense Authorization Act.^21,22 This study concurs with prior research that reveals that receipt of a COVID-19 vaccine prior to or during pregnancy was not associated with any change in adverse pregnancy outcomes, including antepartum hemorrhage and stillbirth.^10-12 The results of this study are also consistent with a recently published article from the DOD’s Birth and Infant Health Registry, which found that COVID-19 vaccination was not associated with increased risk for pre-term birth, small size for gestational age, low birth weight, or neonatal intensive care unit admission among active duty service women who gave birth in 2021.²³

The highest percentage of adverse pregnancy outcomes occurred in Black, non-Hispanic service women, consistent with other research that reveals elevated levels of adverse pregnancy outcomes in this population.²⁴ This study population differs from most other studies, however, because military service women have access to robust medical care and surveillance during their pregnancies, which eliminates access to care as a potential confounder for the association between race and pregnancy outcome. Consequently, the findings in this study support the possibility of other factors besides access to care that contribute to the increased risk of adverse pregnancy outcomes in non-Hispanic Black service women.²⁵

Consistent with prior studies, preexisting comorbidities were associated with different types of adverse pregnancy outcomes.^26-28 Further studies should be conducted to validate the finding of potential associations between COVID-19 infection and eclampsia and antepartum hemorrhage, since it was not possible in this study to determine whether COVID-19 infection was the cause of those adverse outcomes.

Some limitations to this study are important to note. First, in this cross-sectional study design, temporality between COVID-19 infection or COVID-19 vaccination that occurred during pregnancy cannot be inferred with these adverse pregnancy outcomes. It is possible that some adverse pregnancy outcomes occurred prior to the documented COVID-19 infection. COVID-19 infection and vaccination prior to the start of pregnancy does infer temporality, however, which adds to the robustness of these findings.

Selection bias could have occurred in this study because pregnancies that ended in abortion, spontaneous or otherwise, were not included. If COVID-19 infection and adverse pregnancy outcomes are associated with spontaneous abortions, this would result in a negative bias, or an attenuation of the true association between COVID-19 infection and an adverse pregnancy outcome.

It is also unlikely that all COVID-19 infections during pregnancy were identified in this study, as at-home COVID test kits were rapidly deployed during the surveillance period. In addition, service women had the ability to test outside of the military’s medical system, resulting possible in misclassification for some categorized as without COVID-19 infection when they were potentially infected during their pregnancy. Similarly, women with no or mild COVID-19 symptoms may not have realized they were infected and, therefore, would not have tested.

This study considered women with any dose of any COVID-19 vaccine as vaccinated. As such, a misclassification bias is possible if these women were not fully vaccinated. Remaining up-to-date with COVID-19 vaccines was believed to provide the most benefit in the prevention of both severe adverse pregnancy outcomes and COVID-19 disease.¹⁵

Lastly, it should be noted that 52% (n=4,298) of the cases of antepartum hemorrhage had a diagnosis of O20.0 for “threatened abortion,” which can also be used to code bleeding during pregnancy. This coding could result in an over-estimate of antepartum hemorrhage cases, since bleeding during pregnancy is a more common and less severe outcome. Similarly, premature rupture of membranes may be over-estimated because 52% (n=3,079) of those cases had a diagnosis of O42.02, “Full-term premature rupture of membranes, onset of labor within 24 hours of rupture,” which suggests that, for half of these cases, the rupture occurred at or after 37 completed weeks of gestation.

This study provides insight on adverse pregnancy outcomes among pregnant U.S. active component service women. These findings suggest that COVID-19 vaccination is not associated with adverse pregnancy outcomes in this population. Future studies should review the prevalence of these outcomes in this population, refine and validate any associations with COVID-19 infection, along with the various levels of vaccination on adverse neonatal outcomes, and further investigate outcomes for pregnant active component service women of racial and ethnic minorities, to determine the reasons for these differences, given their equal access to no-cost medical care.

Author Affiliations

Lackland Trainee Health Squadron, Joint Base San Antonio-Lackland, TX: Maj Ching; Armed Forces Health Surveillance Division, Public Health Directorate, Defense Health Agency, Silver Spring, MD: Ms. Murray, Dr. Wells, Dr. Stahlman

Disclaimer

The content of this presentation is the sole responsibility of its authors and does not reflect the views nor policies of the Uniformed Services University of the Health Sciences, Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., U.S. Department of Defense, or the departments of the Army, Navy, or Air Force. Mention of trade names, commercial products, or organizations does not imply endorsement by the U.S. Government.

References

Boettcher LB, Metz TD. Maternal and neonatal outcomes following SARS-CoV-2 infection. Semin Fetal Neonatal Med. 2023;28(1):101428. doi:10.1016/j.siny.2023.101428
Xu K, Sun W, Yang S, Liu T, Hou N. The impact of COVID-19 infections on pregnancy outcomes in women. BMC Pregnancy Childbirth. 2024;24(1):562. doi:10.1186/s12884-024-06767-7
Palma A, Niño-Huertas A, Bendezu-Quispe G, Herrera-Añazco P. Association between the degree of severity of COVID-19 infection during pregnancy and preterm premature rupture of membranes in a level III hospital in Peru. Rev Peru Med Exp Salud Publica. 2023;40(4):432-440. doi:10.17843/rpmesp.2023.404.12957
Male V. SARS-CoV-2 infection and COVID-19 vaccination in pregnancy. Nat Rev Immunol. 2022;22(5):277-282. doi:10.1038/s41577-022-00703-6
Sandoval MN, Klawans MR, Bach MA, et al. COVID-19 infection history as a risk factor for early pregnancy loss: results from the electronic health record-based Southeast Texas COVID and Pregnancy Cohort Study. BMC Med. 2025;23(1):274. doi:10.1186/s12916-025-04094-y
van Baar JAC, Kostova EB, Allotey J, et al. COVID-19 in pregnant women: a systematic review and meta-analysis on the risk and prevalence of pregnancy loss. Hum Reprod Update. 2024;30(2):133-152. doi:10.1093/humupd/dmad030
Rincón-Guevara O, Wallace B, Kompaniyets L, Barrett CE, Bull-Otterson L. Association between SARS-CoV-2 infection during pregnancy and gestational diabetes: a claims-based cohort study. Clin Infect Dis. 2024;79(6):1386-1393. doi:10.1093/cid/ciae416
Govender N, Khaliq OP, Moodley J, Naicker T. Insulin resistance in COVID-19 and diabetes. Prim Care Diabetes. 2021;15(4):629-634. doi:10.1016/j.pcd.2021.04.004
Wu CT, Lidsky PV, Xiao Y, et al. SARS-CoV-2 infects human pancreatic β cells and elicits β cell impairment. Cell Metab. 2021;33(8):1565-1576.e5. doi:10.1016/j.cmet.2021.05.013
Prasad S, Kalafat E, Blakeway H, et al. Systematic review and meta-analysis of the effectiveness and perinatal outcomes of COVID-19 vaccination in pregnancy. Nat Commun. 2022;13(1):2414. doi:10.1038/s41467-022-30052-w
Ciapponi A, Berrueta M, Argento FJ, et al. Safety and effectiveness of COVID-19 vaccines during pregnancy: a living systematic review and meta-analysis. Drug Saf. 2024;47(10):991-1010. doi:10.1007/s40264-024-01458-w
Buekens P, Berrueta M, Ciapponi A, et al. Safe in pregnancy: a global living systematic review and meta-analysis of COVID-19 vaccines in pregnancy. Vaccine. 2024;42(7):1414-1416. doi:10.1016/j.vaccine.2024.02.012
Hui L, Marzan MB, Rolnik DL, et al. Reductions in stillbirths and preterm birth in COVID-19-vaccinated women: a multicenter cohort study of vaccination uptake and perinatal outcomes. Am J Obstet Gynecol. 2023;228(5):585.e1-585.e16. doi:10.1016/j.ajog.2022.10.040
Zels G, Colpaert C, Leenaerts D, et al. COVID-19 vaccination protects infected pregnant women from developing SARS-CoV-2 placentitis and decreases the risk for stillbirth. Placenta. 2024;148:38-43. doi:10.1016/j.placenta.2024.01.015
U.S. Centers for Disease Control and Prevention. COVID-19 Vaccination for Women Who Are Pregnant or Breastfeeding. U.S. Dept. of Health and Human Services. Accessed Sep. 21, 2025. https://www.cdc.gov/covid/vaccines/pregnant-or-breastfeeding.html
American College of Obstetricians and Gynecologists. ACOG Releases Updated Maternal Immunization Guidance for COVID-19, Influenza, and RSV. Accessed Sep. 21, 2025.
Wang CL, Liu YY, Wu CH, et al. Impact of COVID-19 on pregnancy. Int J Med Sci. 2021;18(3):763-767. doi:10.7150/ijms.49923
U.S. Centers for Disease Control and Prevention. About Reinfection. U.S. Dept. of Health and Human Services. Updated Jun. 14, 2024. Accessed Sep. 21, 2025. https://www.cdc.gov/covid/about/reinfection.html
U.S. Centers for Disease Control and Prevention. IIS: Current HL7 Standard Code Set CVX–Vaccines Administered. U.S. Dept. of Health and Human Services. Updated Dec. 18, 2024. Accessed Jan. 30, 2025. https://www2a.cdc.gov/vaccines/iis/iisstandards/vaccines.asp?rpt=cvx
Taylor KM, Ricks KM, Kuehnert PA, et al. Seroprevalence as an indicator of undercounting of COVID-19 cases in a large well-described cohort. AJPM Focus. 2023;2(4):100141. doi:10.1016/j.focus.2023.100141
Secretary of Defense. Memorandum: Mandatory Coronavirus Disease 2019 Vaccination of Department of Defense Service Members. U.S. Dept. of Defense. Updated Aug. 24, 2021. Accessed Aug. 28, 2023. https://media.defense.gov/2021/aug/25/2002838826/-1/-1/0/memorandum-for-mandatory-coronavirus-disease-2019-vaccination-of-department-of-defense-service-members.pdf
Secretary of Defense. Memorandum: Rescission of Coronavirus Disease 2019 Vaccination Requirements for Members of the Armed Forces. U.S. Dept. of Defense. Updated Jan. 10, 2023. Accessed Aug. 22, 2023. https://media.defense.gov/2023/jan/10/2003143118/-1/-1/1/secretary-of-defense-memo-on-rescission-of-coronavirus-disease-2019-vaccination-requirements-for-members-of-the-armed-forces.pdf
Hall C, Lanning J, Romano CJ, et al. COVID-19 vaccine initiation in pregnancy and risk for adverse neonatal outcomes among United States military service members, January–December 2021. Vaccine. 2025;51:126894. doi:10.1016/j.vaccine.2025.126894
U.S. Centers for Disease Control and Prevention. Pregnancy mortality surveillance system. U.S. Dept. of Health and Human Services. Updated Mar. 23, 2023. Accessed Sep. 21, 2025. https://www.cdc.gov/maternal-mortality/php/pregnancy-mortality-surveillance-data
Hall C, Bukowinski AT, McGill AL, et al. Racial disparities in prenatal care utilization and infant small for gestational age among active duty US military women. Matern Child Health J. 2020;24(7):885-893. doi:10.1007/s10995-020-02941-3
Ziert Y, Abou-Dakn M, Backes C, et al. Maternal and neonatal outcomes of pregnancies with COVID-19 after medically assisted reproduction: results from the prospective COVID-19-Related Obstetrical and Neonatal Outcome Study. Am J Obstet Gynecol. 2022;227(3):495.e1-495.e11. doi:10.1016/j.ajog.2022.04.021
Sayad B, Mohseni Afshar Z, Mansouri F, et al. Pregnancy, preeclampsia, and COVID-19: susceptibility and mechanisms: a review study. Int J Fertil Steril. 2022;16(2):64-69. doi:10.22074/ijfs.2022.539768.1194
Smith ER, Oakley E, Grandner GW, et al. Clinical risk factors of adverse outcomes among women with COVID-19 in the pregnancy and postpartum period: a sequential, prospective meta-analysis. Am J Obstet Gynecol. 2023;228(2):161-177. doi:10.1016/j.ajog.2022.08.038

Seasonal influenza hospitalization incidence rates among U.S. active component service members, 2010–2024

Wed, 01 Oct 2025 00:00:00 Z

Abstract

Despite a longstanding U.S. Department of Defense (DOD) requirement for seasonal influenza vaccination of active component service members (ACSMs), quantifying the impact of the DOD immunization program is challenging. To measure the burden of severe influenza among this highly immunized ACSM population, this study evaluated seasonal and cumulative seasonal influenza hospitalization rates among ACSMs from 2010 through 2024, stratifying by sex, age group, race and ethnicity, service branch, recruit site, and location (U.S. vs. non-U.S.). In contrast to Centers for Disease Control and Prevention (CDC) U.S. population data, the highest ACSM cumulative seasonal influenza hospitalization rate was in the age group under 25 years (9.3 per 100,000 person-years [p-yrs]) and recruits (70.1 per 100,000 p-yrs). Non-U.S.-based ACSMs had lower influenza hospitalization rates (4.8 per 100,000 p-yrs) compared to ACSMs in the U.S. (8.0 per 100,000 p-yrs). Within the DOD, cumulative seasonal influenza hospitalization rates were highest in the youngest age group, particularly among recruits. This may influence DOD influenza vaccine distribution priority considerations in the future.

What are the new findings?

Compared to U.S. national data, in which adult seasonal influenza hospitalization rates increase with age, the highest cumulative hospitalization rate among active component service members occurred in the youngest age group, those younger than age 25 years, especially in recruit settings.

What is the impact on readiness and force health protection?

Lower cumulative rates of seasonal influenza hospitalization in older age groups of active component service members help quantify the impacts of the longstanding DOD vaccination requirement for influenza. The higher burden of hospitalization among recruits offers DOD vaccine distribution priority considerations in the future.

Background

Influenza vaccines have been employed by the U.S. Department of Defense (DOD) since the 1940s and have been required annually since the 1950s for active component service members (ACSMs).¹ Each year, the DOD’s goal is to reach greater than 90% influenza vaccine compliance rates by January 15, a goal that is typically achieved, especially for ACSMs.² The DOD influenza program is challenged with shipping vaccine across the world in a timely manner. Differences in compliance groups are influenced by how quickly vaccines can be sent and used. Historically, non-U.S. locations have been prioritized for distribution first, while U.S. locations (including training sites) are hierarchized as lower in importance.

Quantifying the impact of the DOD influenza program is challenging, as vaccine effectiveness (VE) calculations through traditional, observational test-negative case control studies typically demonstrate lower VE compared to national data.³ Multiple factors may influence this observed lower VE with the DOD, including diminished antibody response to serial annual vaccinations, waning immunity during the influenza season, and study design limitations (i.e., adequate statistical power).⁴ Evaluating the burden of severe influenza illness among this highly vaccinated population may serve as a surrogate for vaccine performance.

The U.S. Centers for Disease Control and Prevention (CDC) Influenza Hospitalization Surveillance Network (Flu-Surv-NET) generates cumulative seasonal influenza hospitalization rates, stratified by age group, to define the national burden of influenza disease. Typically, the highest rates of influenza hospitalizations occur in older adults (≥50 years) and young children (0-4 years).⁵ Cumulative seasonal influenza hospitalization rates help quantify the burden of severe illness, but this has not been summarized previously for U.S. ACSMs. Analyzing DOD cumulative seasonal influenza hospitalization rates allows identification of higher risk ACSM groups and comparisons of the highly immunized military population to national trends.

The objectives of this study were to evaluate the cumulative seasonal influenza hospitalization rates of ACSMs by sex, age group, race and ethnicity, service branch, recruit site, and location (U.S. vs. non-U.S.). ACSM seasonal influenza hospitalization rates were also compared to CDC age group rates.

Methods

The population included all U.S. ACSMs during each influenza season, defined as September 1 through April 30, from the 2010-2011 through 2023-2024 seasons. Data from the Defense Medical Surveillance System (DMSS) and standardized laboratory data provided by the Defense Centers for Public Health–Portsmouth were utilized for the analysis.

Influenza hospitalizations were defined as 1 hospitalization with any of the defining diagnoses of influenza in the first or second diagnostic position (International Classification of Diseases, 10th Revision [ICD-10] codes J09-J11, International Classification of Diseases, 9th Revision [ICD-9] codes 487-488) or laboratory-confirmed influenza-positive result (rapid antigen, RTPCR, or culture influenza assay) with an indication that the individual was hospitalized. All hospitalizations meeting the inclusion criteria were included in the analysis. There were no exclusions. The incidence date was defined as the first date of hospitalization. An individual could be an incident case only once per influenza season.

For each influenza season, individual person-time began on September 1 or entry into active component service (whichever came last) and ended either April 30, last date in active component service, or incidence date for the hospitalization (whichever came first). Seasonal influenza hospitalization incidence rates (IRs) were calculated as the number of incident influenza hospitalizations divided by the number of person-years (p-yrs) for the season multiplied by 100,000. Incidence rates were calculated overall and stratified by sex, age group, race and ethnicity, service branch, recruit status, and location. Cumulative IRs were also calculated by combining data for the entire surveillance period. Comparisons were made to general U.S. age-stratified influenza hospitalization rates using the CDC Influenza Hospitalization Surveillance Network (FluSurv-NET) data.⁵

Results

Table 1 describes the total cumulative seasonal influenza hospitalizations among ACSMs from 2010 through 2024, stratified by sex, age group, race and ethnicity, service branch, recruit status and location (U.S. vs. non-U.S.). The overall cumulative influenza hospitalization rate was 7.4 per 100,000 p-yrs, with the highest rate among recruits (70.1 per 100,000 p-yrs). Higher hospitalization rates were observed in the youngest age group (<25 years; 9.3 per 100,000 p-yrs), women (9.7 per 100,000 p-yrs), Marine Corps members (13.9 per 100,000 p-yrs), and individuals located in the U.S. (8.0 per 100,000 p-yrs).

Seasonal counts and incidence rates of influenza hospitalizations with stratification by recruit status are shown in Figure 1. Overall counts varied by annual influenza season, with the largest number of influenza hospitalizations (n=145) during the 2019-2020 season. Counts and rates dropped significantly during the 2020-2021 season, coinciding with the COVID-19 pandemic. The largest number (41) of recruit influenza hospitalizations occurred during the 2023-2024 influenza season. Except for the seasons affected by the COVID-19 pandemic, incidence rates of influenza hospitalizations among recruits trended upwards during the surveillance period, with the highest rate (IR 218.5 per 100,000 p-yrs) observed during the 2023-2024 season.

Table 2 shows the influenza hospitalization counts and rates for recruits, stratified by age group, sex, race and ethnicity, and service branch. Among recruits, higher cumulative seasonal influenza hospitalization rates occurred in ages younger than 25 years (71.9 per 100,000 p-yrs), men (76.3 per 100,000 p-yrs), and Marine Corps members (178.7 per 100,000 p-yrs).

Figure 2 compares seasonal influenza hospitalization rates for ACSMs to CDC age groups. Seasonal influenza hospitalization rates were lower among ACSMs for all age groups compared to CDC age groups. Whereas CDC hospitalization rates increase with older age groups, the ACSM age groups were more comparable throughout each influenza season. When ACSMs younger than age 30 years were further stratified into younger than age 25 years and ages 25-29 years, the younger than age 25 year group had the highest influenza hospitalization rate among all age groups for over half the annual influenza seasons reported (data not shown).

Discussion

Cumulative seasonal influenza hospitalization rates help quantify the burden of severe illness in a population. In this study, cumulative seasonal influenza hospitalization rates from 2010 through 2024 reveal higher hospitalization rates among the youngest age group (<25 years) of ACSMs. This is counter to CDC national data in which adult influenza hospitalization rates increase with each age group. Hospitalizations within recruit populations drive this increased risk in the youngest DOD age group and in the Marine Corps. Military trainees have historically been vulnerable to acute respiratory disease due to relative immune compromise from physical, environmental, and psychological stress.⁶ Multiple studies have reported that recruits have a higher incidence of influenza-like illnesses compared to non-recruits.^7,8

Age-stratified influenza hospitalization rates from CDC national data were higher than the age-stratified ACSM rates. Influenza immunization has been a requirement for the DOD since the 1950s, with goals to reach at least 90% coverage each season.^1,2 Influenza vaccine coverage among individuals ages 18-49 years in the general U.S. population ranged from 26.9% to 38.4%, depending on the influenza season, from the 2010-2011 through 2023-2024 seasons.⁹ This differential vaccine coverage is likely a factor in why influenza hospitalization incidence rates among ACSMs were lower than CDC national data rates and do not increase incrementally with each older age group.

Locations outside the continental U.S. are the priority areas for DOD influenza vaccine distribution; however, the non-U.S. influenza hospitalization rate was lower than the rate for U.S. locations. This may be complicated by service members seeking care outside oversees DOD facilities. Future studies could examine influenza vaccination in DOD locations outside the continental U.S. versus U.S. populations. Regardless, the high influenza hospitalization rates in recruits should influence vaccine priority distribution strategies in the future. Areas of additional study need to evaluate factors associated with hospitalizations in the recruit setting and within the Marine Corps.

This study has several limitations. First, influenza hospitalizations were identified using ICD-10-CM (International Classification of Diseases, 10th Revision, Clinical Modification) billing code data, which is dependent on correct coding during inpatient stay and completeness. Inpatient diagnostic coding is entered by nosologists, however, which should ensure higher coding accuracy. The DMSS also has near-complete capture of all ACSM data, including outsourced data in addition to military hospitals and clinics.

Another limitation is the completeness of the laboratory data. Only laboratory testing requested by a military medical facility are captured in these data. This limitation could lead to an under-estimation of hospitalization rates; however, inclusion of ICD-10-CM hospitalization data should cover this gap. The laboratory data also do not indicate if a hospitalization was specifically for influenza, only that the individual testing positive for influenza was hospitalized, which could over-estimate the number of hospitalizations due to influenza. Data evaluating the influenza vaccine performance could not be determined against type or lineage of circulating virus. The incidence of hospitalization was low, along with a small unvaccinated population; thus, this study did not have adequate power to calculatevalid vaccine effectiveness estimates.

Although influenza hospitalizations are relatively rare in this population, likely due to the influenza vaccine requirements for service members, these results identify subpopulations within ACSMs at higher risk for severe influenza infections. DOD policies and vaccine distribution should consider these findings to ensure the health and readiness of U.S. service members.

Author Affiliations

Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD: Lt Col Sayers; Armed Forces Health Surveillance Division, Defense Health Agency, Silver Spring, MD: Dr. Ying, Dr. Eick-Cost

Disclaimers

The opinions and assertions expressed herein are those of the authors and do not reflect official policy nor position of the Uniformed Services University of the Health Sciences or U.S. Department of Defense. This work was prepared by military and contract employees of the U.S. Government as part of official duties and is in the public domain and has no copyright protection. Public domain information may be freely distributed and copied; as a courtesy, it is requested that the Uniformed Services University and authors are appropriately acknowledged.

References

Grabenstein JD, Pittman PR, Greenwood JT, Engler RJM. Immunization to protect the US Armed Forces: heritage, current practice, and prospects. Epidemiol Rev. 2006;28(1):3-26. doi:10.1093/epirev/mxj003
Defense Health Agency. Defense Health Agency Procedural Instruction: Guidance for the DoD Influenza Vaccination Program. 2020. Accessed Sep. 29, 2025. https://www.amlc.army.mil/portals/73/documents/1_%20guidance%20for%20the%20dod%20influenza%20vaccination%20program%20ivpv2.pdf?ver=ts6xhdygX851qguisiuuig%3d%3d
Lynch LC, Colemand R, DeMarcus L, et al. Brief report: Department of Defense midseason estimates of vaccine effectiveness for the 2018–2019 influenza season. MSMR. 2019;26(7):24-27. Accessed Sep. 29, 2025. https://www.health.mil/reference-center/reports/2019/07/01/medical-surveillance-monthly-report-volume-26-number-7
Sayers DR, Iskander JK. Influenza vaccine effectiveness and test-negative study design within the Department of Defense. Mil Med. 2023;188(11-12):289-291. doi:10.1093/milmed/usac436
U.S. Centers for Disease Control and Prevention. CDC Influenza Hospitalization Surveillance Network (FluSurv-NET). U.S. Dept. of Health and Human Services. Accessed Jul. 6, 2025. https://gis.cdc.gov/grasp/fluview/fluhosprates.html
Sanchez JL, Cooper MJ, Myers CA, et al. Respiratory infections in the US military: recent experience and control. Clin Microbiol Rev. 2015;28(3):743-800. doi:10.1128/cmr.00039-14
Coles C, Chen WJ, Milzman JO, et al. 2499. Burden of influenza like illness (ILI) among congregate military populations. Open Forum Infect Dis. 2018;5(suppl1):s750-s751. doi:10.1093/ofid/ofy210.2151
Eick AA, Wang Z, Hughes H, Ford SM, Tobler SK. Comparison of the trivalent live attenuated vs. inactivated influenza vaccines among U.S. military service members. Vaccine. 2009;27(27):3568-3575. doi:10.1016/j.vaccine.2009.03.088
U.S. Centers for Disease Control and Prevention. Flu Vaccination Coverage, United States, 2023–24 Influenza Season. U.S. Dept. of Health and Human Services. 2024. Accessed Jul. 28, 2025. https://www.cdc.gov/fluvaxview/coverage-by-season/2023-2024.html

Strategies for forecasting long COVID in the active component U.S. military

Wed, 01 Oct 2025 00:00:00 Z

Abstract

Long COVID, or post-acute coronavirus disease syndrome, represents a potentially serious threat to military readiness. Forecasts of future long COVID diagnoses could help prepare senior leaders for disruptions. Few studies predicting the incidence of long COVID have been published to date, however. Using existing COVID-19 and long COVID diagnoses, as well as demographic and outpatient encounter data, 1- to 6-month ahead and full 6-month forecasts were generated using time series and machine learning models trained on various covariate data. Forecasting models generated accurate predictions of long COVID diagnoses up to 6 months ahead of the forecasted date. Several model and covariate combinations were within 5% of the observed number of diagnoses over the full 6-month testing period, while monthly forecasts of long COVID diagnoses had median absolute percentage errors ranging from 3% to 10% for the best performing model combinations. Simple forecasting models and distribution-based forecasts that utilize existing clinical databases can provide accurate predictions of incident long COVID up to 6 months in advance and can be used to prepare for the burden of new long COVID diagnoses.

What are the new findings?

Accurate predictions of long COVID cases over a 6-month period were achieved by utilizing existing COVID-19 case and outpatient encounter data from January 1, 2020, through December 31, 2022.

What is the impact on readiness and force health protection?

Long COVID symptoms can cause disruptions to military readiness and prevent a healthy force, especially after surges in COVID-19 cases. The ability to use existing data sources to accurately predict future cases of long COVID allows senior leaders to anticipate and prepare for potential changes in the availability of service members.

Background

Long COVID, or post-acute coronavirus disease syndrome, has been well studied in the general population, although it has not been well established in the U.S. military. Internal, not yet published Defense Medical Surveillance System (DMSS) data from active component U.S. service members diagnosed with coronavirus disease 2019 (COVID-19) from January 2020 through December 2022 indicate that symptoms of long COVID may be present in up to 20% of service members, with cardiac symptoms in approximately 8% and respiratory symptoms in approximately 5% of service members (unpublished). Another study of active duty service members with COVID-19 diagnoses from March 2020 to November 2021 found cardiac symptoms in nearly 2% of service members more than 30 days after COVID-19 diagnosis.¹ At best, mild symptoms of long COVID could disrupt force readiness by causing unplanned training limitations and absences, while more severe symptoms could result in long-term disability or even death. It is, therefore, critical for senior U.S. Department of Defense (DOD) leaders to anticipate the burden of long COVID in advance to prepare for potential disruptions and to anticipate impacts on the military health care system resources.

Infectious disease forecasting, especially for influenza, has been conducted for decades. Various mechanistic, statistical, and time series models have been used for forecasting, as well as combined ensemble models. The U.S. Centers for Disease Control and Prevention (CDC) hosts annual forecasting challenges for influenza and COVID-19 aimed at predicting short-term incidence of cases and hospitalizations.² The CDC has found that ensemble models tend to be more stable and accurate for multiple forecasting locations and targets than individual models, including COVID-19 forecasting.^3-4

Long COVID is a long-term, post-infectious process of COVID-19, however, that is not contagious and requires a person to both be infected with COVID-19 and develop symptoms of long COVID after a specified period. Traditional time series methods for forecasting short-term COVID-19 and other respiratory disease activity may not be useful for forecasting long COVID cases, and little research has been published to date on efforts to predict the incident number of long COVID diagnoses utilizing existing case data, especially within the military population. Studies using clinical data in civilian populations found various models to be reasonably accurate, with AUROC (area under a receiver operating characteristic) values between 0.74 and 0.895.^5-7 Attempts have been made to use time series models to forecast incident cases of other diseases with long follow-up periods, such as Lyme disease, using clinical data, with mean absolute percentage errors around 8%.⁸

The purpose of this study was to develop predictive models to forecast future long COVID diagnoses and to compare the predictions of each model against observed long COVID diagnoses. To achieve this aim, this study utilized a cohort of COVID-19 cases, linked demographic and medical records, and longitudinal health encounter data.

Methods

The protocol for this study was approved by both the George Washington University Committee on Human Research Institutional Review Board and the Component Office for Human Research Protections of the Defense Health Agency Office of Research Protections.

Study population

The study population included a cohort of 464,356 active component U.S. service members with a confirmed case of COVID-19 at a U.S. military hospital or clinic, from January 1, 2020 through December 31, 2022. The U.S. active component includes full-time, active duty service members but excludes reservists or National Guard members.

Data were obtained from a master list of COVID-19 cases, defined as having either a positive SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) nucleic acid or antigen test or a COVID-19 reportable medical event (RME) in the Disease Reporting System Internet (DRSi) maintained by the Armed Forces Health Surveillance Division (AFHSD). The master list includes information relevant to a service member’s COVID-19 event, including vaccinations, re-infection status, and hospitalization.

Exposures and covariates

Covariates of interest in this study focused on measures of COVID-19 activity, including COVID-specific, COVID-like illness (CLI), and post-acute sequelae of COVID-19 (PASC) outpatient encounters, as well as risk factors for long COVID. Risk factors included sex, age, race and ethnicity, rank, COVID-19 hospitalization status, COVID-19 re-infection status, and COVID-19 vaccination status.

Demographic information for each COVID-19 case in the master positive list was taken from the Defense Medical Surveillance System (DMSS), a DOD-maintained database of health information that includes personnel, medical, immunization, pharmacy, health assessment, laboratory, and deployment data.⁹ Monthly aggregated outpatient encounters by military hospital or clinic were downloaded from the DOD Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE).

COVID-specific encounters were defined as any outpatient encounter with a discharge diagnosis containing the ICD-10 codes U07.1 or J12.81, while PASC encounters were defined as those containing the ICD-10 code U09.9. The CLI encounter definition is provided in Supplementary Table 1.

Case definition

The outcome of interest, long COVID, was assessed using the PASC definition developed and validated by the Defense Centers for Public Health–Portsmouth (DCPHP). Briefly, the definition requires a service member to have a positive SARS-CoV-2 nucleic acid test or a confirmed COVID-19 RME, and an International Classification of Diseases, 10th Revision (ICD-10) code from 1 of the mental health, neurological, cardiac, or respiratory diagnostic groups from 4 to 52 weeks after the COVID-19 event. Diagnostic groups and their ICD-10 codes are shown in Supplementary Table 2. A service member must not have the same diagnosis within that specific diagnosis group within 1 year prior to the COVID-19 event. Inpatient and outpatient datasets from DMSS were used to identify incidence of long COVID in this population.

Analyses

This study focused on longitudinal forecasts of long COVID in the U.S. active component population. To facilitate time series forecasting, long COVID, COVID-19, and outpatient encounter datasets were converted into time series by aggregating the monthly numbers of cases and encounters. COVID-19 cases were additionally stratified by risk factor. The number of monthly cases and encounters were plotted together to visualize the relationship between each metric and the outcome of long COVID.

The data were divided into training and testing datasets. The training dataset included data from January 1, 2020 through June 30, 2022, and the testing dataset included data from July 1, 2022 through December 31, 2022. Using the training data, 3 models were fit with long COVID diagnoses as the outcome: autoregressive integrated moving average (ARIMA), neural network, and vector autoregressive (VAR), in addition to an ensemble model that represented the average of the other 3 models. Different versions of each model were fit, with 21 in total that featured different data lags (unlagged, 3 month lag, and 6 month lag) and covariate data including PASC encounters, COVID-19 cases, COVID-specific encounters, CLI encounters, and demographics (age, sex, race and ethnicity, rank, re-infection status, hospitalization status, and vaccination status). All model and covariate combinations are shown in Table 1. Model fit statistics were assessed for the training period, including Akaike information criterion (AIC), sigma² (variance of forecast errors), root mean squared error (RMSE), and median absolute percent error (MAPE).

Models showing the best fit with the training data were selected for forecasting, including the models with all COVID-19 metrics and those with all metrics. The models using PASC encounters were also included for forecasting. Several baseline models were also created for comparison.

First, a seasonal NAÏVE was calculated using a 5-month lag of COVID-19 cases and 22% of COVID-19 cases diagnosed with long COVID in the cohort. The lag parameter represented the average time in months from the COVID-19 event date to the long COVID diagnosis date in the cohort, and the long COVID incidence parameter represented the percentage of COVID-19 cases diagnosed with long COVID in the sample.

Second, the distribution of the time from the COVID-19 event date to the long COVID diagnosis date in the cohort was estimated to be a Weibull distribution with a shape parameter of 1.56 and scale parameter of 5.81. A distribution of diagnosis times was calculated using the Weibull parameters, the long COVID incidence parameter described, and a minimum diagnosis time of 1 month and maximum of 12 months. The calculated distribution was applied to the time series of COVID-19 cases to create an estimate of expected long COVID diagnoses by month.

Similarly, an adjusted Weibull prediction was created using a long COVID incidence parameter that varied by risk factor. Based on factor-specific incidence of long COVID in the cohort, the parameter was estimated for sex (32% for females, 20% for males), race and ethnicity (21% for Asian, 22% for Hispanic, 27% for non-Hispanic Black, 21% for non-Hispanic White, 22% for ‘other’), age group (19% for <20, 21% for 20-34, 27% for 35-39, 30% for 40-44, 31% for 45+), rank (23% for enlisted, 19% for officers), COVID-19 re-infection status (22% for first infection, 26% for re-infection), and COVID-19 hospitalization status (22% for not hospitalized, 43% for hospitalized). The average calculated distribution was applied to the time series of COVID-19 cases to create an estimate of expected long COVID diagnoses by month.

Lastly, an ensemble model was calculated as the average of all models for each covariate and lag combination as well as overall.

Two sets of forecasts were generated for each model combination. First, the number of long COVID diagnoses during the entire 6-month testing period was forecasted using the training dataset. Second, for each month during the testing period (July–December), forecasts were generated for each remaining month in the testing period (through December 2022). Models used data through the end of the previous month for training. For example, data through July 31, 2022, were used to generate forecasts for August, September, October, November, and December 2022. Data through August 31, 2022 were used to generate forecasts for September, October, November, and December 2022. This continued through the end of the testing period. Seasonal naïve and ensemble forecasts were generated in both quantile and point formats to facilitate evaluation of the complete distribution of the forecasts. Forecasts using the Weibull distribution were only generated as a point forecast.

Forecasts were scored by comparing the predicted number of long COVID diagnoses in a period to the observed number. Monthly point forecasts were scored using a MAPE, and quantile forecasts were scored using a weighted interval score (WIS). Full 6-month point forecasts were scored using percentage error. WIS has been used previously by the CDC for scoring COVID-19 forecasting hub entries.¹⁰ All statistical analyses were conducted using R (version 4.1, R Foundation for Statistical Computing, Vienna, Austria), and an alpha (α) level of 0.05 was considered statistically significant.

Results

Table 2 shows demographic characteristics of COVID-19 cases in the training and testing datasets. Datasets were similar by age, race and ethnicity, rank, and COVID-19 hospitalization, although a larger proportion of the testing dataset was female (24.3% vs. 20.4%). COVID-19 re-infections were much more prominent in the testing dataset (19.2% vs. 5.5%), although this was expected, as the testing data were generated nearly 2 years into the COVID-19 pandemic. Figure 1 shows the time series of observed data used for training and prediction in this study. As expected, incidence of COVID-19 was higher than PASC, with COVID-19 cases peaking between 10,000 and 20,000 monthly cases each summer, and between 25,000 and 100,000 monthly cases each winter, while PASC cases peaked between 2,500 and 6,000 monthly cases. PASC peaks tended to follow peaks in COVID-19 activity by 2 to 3 months.

Table 1 shows model fit statistics for each combination of trained models during the training period. The lowest AIC was seen for the 3-month lag model containing all covariates (-103.8). This model combination also had the lowest sigma² (0.0), RMSE (0.4), and MAPE (0.05%) compared to other combinations. Other model combinations with a MAPE below 10% were the unlagged COVID-19 case model (8.8%), 6-month lagged COVID-19 encounter model (9.8%), unlagged all COVID-19 metric model (8.3%), and the 6-month lag all-COVID-19 metric model (9.9%). Graphs of the median fitted predicted values for each model combination and lag compared to observed data are shown in Supplementary Figure 1. All models appeared to fit the observed data visually, although the models with all covariates and those with only demographic covariates appeared to fit the data best.

Table 3 shows model scoring metrics for each ensemble and baseline model and forecasting horizons. For all forecasting horizons, the ensemble model using PASC encounters had the lowest median MAPE (9.2%) and weighted interval score (WIS) (206.6), followed by the 3-month lag ensemble model using all covariates (11.3% MAPE, 291.0 WIS), and the unadjusted Weibull model (MAPE 11.5%). Model performance varied between the 1-month ahead and 6-month ahead horizons. Figure 2 shows the observed compared to predicted values for each model and horizon. Ensemble models tended to predict a later peak than what was observed for the 1-month ahead through 3-month ahead forecasts, although this was less severe for the ensemble model using PASC encounters at the 2-month and 3-month ahead horizons. Weibull forecasts were more stable than ensemble model forecasts.

Table 4 shows the results of the full 6-month forecasts. During the forecasting period, from July through December 2022, 23,132 incident cases of PASC were observed. The 6-month lag ensemble model using all covariates had the lowest percent error over the 6-month period at -0.8% (22,960 predicted cases), followed by the unlagged ensemble model using all covariates (+4.3%, 24,174 predicted cases), adjusted Weibull model (-4.7%, 22,093 predicted cases), and the ensemble model using PASC encounters (+5%, 24,353 predicted cases). The seasonal naïve model had the highest percentage error, -71.6%, predicting only 13,479 cases during the 6-month period.

Discussion

This study aimed to use various forecasting models, including time series and machine learning models, as well as simple time-based distributions, to predict the number of incident long COVID diagnoses over a 6-month period utilizing various case, outpatient encounter, and demographic data. Forecasts were generated at the beginning of the study period for the entire 6-month period, and 1- to 6-month forecasts were generated for each month in the study period. Monthly forecasts ranged in accuracy, with the PASC encounter ensemble model having the lowest WIS for all forecasting horizons and a MAPE below 10%, as did the adjusted Weibull forecasts, which can be seen in Table 4 and Supplementary Figure 2. No pattern was seen for the 1- to 6-month ahead horizons, with some models performing better at earlier horizons and some performing better at later horizons. This contrasts with COVID-19 forecasts, which tend to perform worse as horizons increase.¹¹

Because long COVID is not an infectious process, it may not be useful to generate monthly forecasts of long COVID diagnoses, but instead generate forecasts for a specified period, to assist senior leaders and public health practitioners with planning for expected case burdens. To this end, forecasts of the entire 6-month period may be most useful. The ensemble model using all covariates and a 6-month lag was the most accurate, with a percent error of just -0.8% (-172 cases) over the study period. This is not unexpected, as the average time to a long COVID diagnosis was 5 months, so lagging covariate data by 6 months is a reasonable choice. Other models also had a percentage error within 5%, however, including the unlagged ensemble model using all covariates (+4.3%), the adjusted Weibull model (-4.7%), and the ensemble model using PASC encounters (+5.0%). These results are similar to estimates in a previous study of Lyme disease, another slow-developing disease.⁸ Despite having the best model fit using the training data, the 3-month lag all-covariate ensemble model had a percentage error of -10.2%, ranking only sixth best of the 8 models tested. This was not unexpected, as the lag in the full cohort was 5 months, which is closer to the 6-month lag model. It does not explain why the model performed worse than the unlagged ensemble model, however.

This study serves as a ‘proof of concept’ for long COVID forecasting, demonstrating how forecasting models can be used to predict incident long COVID cases up to 6 months in advance, utilizing clinical and demographic data. The study employed existing datasets and surveillance databases to accurately predict the numbers of long COVID diagnoses over a 6-month period.

This study has several limitations. First, models were only trained on COVID-19 cases from January 1, 2020 through June 30, 2022 and, therefore, do not reflect trends in long COVID in later years. Second, the study included the entire U.S., which may not be as useful as regional or single installation forecasts, a possible goal of future studies. Lastly, longer-term horizons, such as the 5- and 6-month forecasts, were limited to just 1 or 2 data points for each model, potentially limiting assessment of those horizons. Future research could focus on the utility of longer-term forecasts by expanding the study period to allow additional forecasts. Additional lag periods, such as the 5-month lag used for the baseline models, can be explored for the ensemble model forecasts.

This study demonstrates that accurate forecasting of long COVID incidence is possible, utilizing clinical, laboratory, and demographic data. Further research needs to determine if results are consistent in more recent time periods, and whether additional or more complex models improve accuracy.

Author Affiliations

Integrated Biosurveillance Branch, Armed Forces Health Surveillance Division, Silver Spring, MD: Dr. Bova, Dr. Russell; Department of Epidemiology, Milken Institute School of Public Health, George Washington University, Washington, DC: Dr. Bova, Dr. Magnus; Department of Medicine, School of Medicine and Health Sciences, George Washington University: Dr. Palmore; Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, George Washington University: Dr. Diao

Acknowledgments

Shauna L. Stahlman, PhD, MPH, Alexis A. McQuistan, MPH, Epidemiology and Analysis Branch, Armed Forces Health Surveillance Division, Silver Spring, MD

Disclaimer

The views expressed in this manuscript are those of the authors and do not reflect official policy nor position of the Defense Health Agency, Department of Defense, or the U.S. Government.

References

Mabila S, Patel D, Fan M, et al. Post-acute sequalae of COVID-19 and cardiac outcomes in U. S. military members. Int J Cardiol Cardiovasc Risk Prev. 2023;17:200183. doi:10.1016/j.ijcrp.2023.200183
Biggerstaff M, Alper D, Dredze M, et al. Results from the Centers for Disease Control and Prevention’s predict the 2013-2014 influenza season challenge. BMC Infect Dis. 2016;16(1). doi:10.1186/s12879-016-1669-x
McGowan CJ, Biggerstaff M, Johansson M, et al. Collaborative efforts to forecast seasonal influenza in the United States, 2015-2016. Sci Rep. 2019;9(1):2015-2016. doi:10.1038/s41598-018-36361-9
Cramer EY, Ray EL, Lopez VK, et al. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proc Natl Acad Sci USA. 2022;119(15):e2113561119. doi:10.1073/pnas.2113561119
Antony B, Blau H, Casiraghi E, et al. Predictive models of long COVID. EBioMedicine. 2023;96:104777. doi:10.1016/j.ebiom.2023.104777
Bergquist T, Loomba J, Pfaff E, et al. Crowd-sourced machine learning prediction of long COVID using data from the national COVID cohort collaborative. EBioMedicine. 2024;108:105333. doi:10.1016/j.ebiom.2024.105333
Wang WK, Jeong H, Hershkovich L, et al. Tree-based classification model for long-COVID infection prediction with age stratification using data from the national COVID cohort collaborative. JAMA Open. 2024;7(4):ooae111. doi:10.1093/jamiaopen/ooae111
Kapitány-Fövény M, Ferenci T, Sulyok Z, et al. Can Google trends data improve forecasting of Lyme disease incidence? Zoonoses Public Health. 2019;66(1):101-107. doi:10.1111/zph.12539
Rubertone MV, Brundage JF. The Defense Medical Surveillance System and the Department of Defense serum repository: glimpses of the future of public health surveillance. Am J Public Health. 2002;92(12):1900-1904. doi:10.2105/ajph.92.12.1900
Bracher J, Ray EL, Gneiting T, Reich NG. Evaluating epidemic forecasts in an interval format. PLoS Comput Biol. 2021;17(2):e1008618. doi:10.1371/journal.pcbi.1008618 [published correction in PLoS Comput Biol. 2022;18(10):e1010592. doi:10.1371/journal.pcbi.1010592].
Chharia A, Jeevan G, Jha RA, et al. Accuracy of US CDC COVID-19 forecasting models. Front Public Health. 2024;12:1359368. doi:10.3389/fpubh.2024.1359368

Update: measles, mumps, rubella and varicella among service members and other beneficiaries of the Military Health System, 2019–2024

Wed, 01 Oct 2025 00:00:00 Z

Abstract

Measles, mumps, rubella, and varicella (MMR/V) cases have decreased in the U.S. Military Health System (MHS) overall, but in recent years, increasing numbers of MMR/V outbreaks in the U.S. have led to a rise in reported cases among the civilian population. Data were queried from the Defense Medical Surveillance System to identify total number of confirmed and possible MMR/V cases among all MHS beneficiaries from 2019 through 2024. The total numbers of confirmed and possible cases among MHS beneficiaries included 8 confirmed and 71 possible cases of measles, 18 confirmed and 193 possible cases of mumps, 13 confirmed and 265 possible cases of rubella, and 251 confirmed and 4,554 possible cases of varicella. During the surveillance period the numbers of all confirmed and possible cases decreased. Among service members, most cases were either partially vaccinated, or vaccination records were not available.

What are the new findings?

In this 6-year surveillance period, cases of MMR/V decreased over time. No cases of measles were observed among U.S. service members during the surveillance period.

What is the impact on readiness and force health protection?

This report emphasizes the importance of continued vaccination against MMR/V to limit morbidity among U.S. service members, as evidenced by the lower number of cases among service members, who are required to be vaccinated, when compared to non-service members.

Background

Although the numbers of measles, mumps, rubella, and varicella (MMR/V) cases have drastically declined in the U.S. after vaccine implementation, outbreaks of these diseases still occur sporadically.^1,2 Fourteen measles outbreaks occurred in the U.S. between January 1 and May 8, 2025, accounting for 1,001 confirmed measles cases reported by 31 U.S. jurisdictions, 126 (12.6%) hospitalizations, and 3 deaths.³ Mumps outbreaks also continue to occur across the U.S., with cases drastically increasing in 2016 (n=6,366 cases) compared to the previous 5 years, during which cases ranged from 200 to 1,329 annually.⁴ Even though the number of total cases of mumps has decreased since 2016, with cases dropping below 500 cases per year, from 2021 through 2025, mumps cases are still reported annually, with 357 cases reported in 2024.⁴ Varicella cases have also drastically decreased since the introduction of the 2-dose vaccine in 2007, from an average rate of 215 cases per 100,000 population, 1994–1995, to 33 cases per 100,000 population.⁵ The median number of rubella cases reported annually, 2001–2004, was 14 (range 7-23), and rubella was declared eliminated in the U.S. in 2004.⁶ Rubella is no longer endemic to the U.S., with its annual 2005–2022 incidence remaining less than 1 case per 10 million population, with most reported cases in the recent past acquired while traveling or living outside the U.S.⁶ It remains important to monitor MMR/V cases in the U.S. Military Health System (MHS), as service members deploy to other countries where MMR/V is endemic, and viral outbreaks continue to occur within the U.S.

The Standing Order for Administering MMR/V vaccine among adults outlines the U.S. Department of Defense (DOD) policy for MMR/V vaccination.⁷ Military environments such as recruit training locations, barracks, and ships are conducive to the spread of MMR/V because service members live in close quarters. Military personnel are required to receive the MMR/V vaccine and provide documentation of 2 lifetime doses of MMR/V-containing vaccines, or serological evidence of immunity. If no documentation is available, 1 dose of MMR/V-containing vaccine is administered within the first 2 weeks of initial training, and the second dose is administered at least 4 weeks later. MSMR has previously reported on MMR/V cases among MHS beneficiaries, describing trends from 2010 through 2016 and 2016 through 2019.^8,9 From 2016 through June 2019, the total number of MMR/V cases were relatively low among MHS beneficiaries, with 5 confirmed cases of measles and 64 confirmed cases of mumps. None of the measles cases were among service members.⁹

This analysis provides an update on MMR/V cases from 2019 through 2024 to describe temporal trends among MHS beneficiaries. Additionally, this analysis stratifies cases by MMR/V immunization status to evaluate waning immunity and breakthrough infections among service members.

Methods

This retrospective cohort study included all MHS beneficiaries from 2019 through 2024. Demographic, immunization, and medical encounter data were obtained from the Defense Medical Surveillance System (DMSS). Because MMR/V are considered reportable medical events (RMEs), RME data for confirmed and possible cases were evaluated, in addition to International Classification of Diseases, 9th and 10th Revisions, Clinical Modification (ICD‐9/10‐CM) diagnostic codes from medical encounter data.

The Armed Forces Health Surveillance Division surveillance case definitions for MMR/V were used for this analysis. In summary, a ‘confirmed’ case was defined as an individual identified through an RME of MMR/V that was described as confirmed according to laboratory and epidemiological criteria.^10-13 A ‘possible’ case was defined as 1) a suspect, probable, unknown, or pending RME of MMR/V or 2) a record of an inpatient or outpatient medical encounter with a diagnosis of measles, mumps, rubella, or varicella in the primary diagnostic position.

For measles, mumps, and rubella cases, a disease-associated symptom in any other diagnosis position was also required in addition to the aforementioned RME or medical encounter requirement for possible cases.^10-13 Encounters with a record of MMR/V immunization or positive test for serological immunity to MMR/V within 7 days of the encounter date, or an ICD‐10‐CM diagnosis or a Current Procedural Terminology (CPT) code indicating MMR/V vaccination on the same day as the MMR/V diagnosis were excluded.^10-13

Vaccination status for service member cases was determined using the immunization data from the immunization table in DMSS. Immunization types for measles (03, 04, 05, 94), mumps (03, 07, 038, 94), rubella (03, 04, 06, 38, 94) and varicella (21, 36, 117, 94) were queried. A fully vaccinated case was an individual who had received 2 MMR/V vaccine doses at least 28 days apart, while any cases with 1 dose were considered partially vaccinated. Individuals without any vaccination information, or those with vaccination information after an incident case, were considered unvaccinated. Immunization exemption data were queried to determine cases that were exempt from the MMR/V vaccine. MHS beneficiaries were stratified by component and service. Due to the limited number of cases among service members, incident rates and any further analysis were not performed. The immunization table in DMSS does not have immunization data for non-service members; thus, the vaccination status of non-service members was not determined. All analyses were conducted using SAS‐Enterprise Guide (version 8.3).

Results

Measles

This retrospective study identified a total of 8 confirmed and 71 possible cases of measles among all MHS beneficiaries during the surveillance period (Table 1). No confirmed measles cases were among U.S. service members. Of the 71 possible measles cases, the majority (n=69, 97.2%) were among non-service member beneficiaries. Overall, both confirmed and possible cases of measles decreased during the surveillance period (Figure 1). Half of confirmed measles cases (n=4, 50.0%) and over half of possible measles cases (n=41, 57.7%) were among children ages 5 years or younger (Figure 2).

Mumps

A total of 18 confirmed and 193 possible mumps cases were identified among all MHS beneficiaries during the surveillance period. Half of confirmed mumps cases (n=9) occurred among service members. Among the 193 possible cases, a majority (n=130, 67.4%) were among non-service member beneficiaries (Table 1). The greatest annual number of confirmed cases (n=14) for all MHS beneficiaries occurred in 2019 (Figure 3). Cases were sporadically distributed among age categories (Figure 4). Of the 9 confirmed mumps cases among service members, 4 had been fully vaccinated, 2 partially vaccinated, and 3 had not been vaccinated (Table 2).

Rubella

A total of 13 confirmed and 265 possible rubella cases were identified among all MHS beneficiaries during the surveillance period. Six of the confirmed rubella cases occurred among active component service members. Among the 265 possible cases, a majority (n=241, 90.9%) were among non-service member beneficiaries (Table 1). Confirmed rubella cases peaked in 2022 (n=6), subsequently declining to 0 cases in 2024 (Figure 5). All confirmed rubella cases were among those aged 21 years and older (Figure 6). Among the confirmed service member cases, 3 had been partially vaccinated, and 3 cases had received an exemption from vaccination (Table 2).

Varicella

A total of 251 confirmed and 4,554 possible varicella cases were identified among all MHS beneficiaries during the surveillance period. The majority of confirmed varicella cases (n=179, 71.3%) and possible varicella cases (n=4,071, 89.4%) were among non-service member beneficiaries (Table 1). The overall trend in possible varicella cases declined by approximately 37% during the surveillance period (from 1,049 cases in 2019 to 666 cases in 2024). While the number of confirmed varicella cases remained relatively stable from 2020 through 2023, the subsequent increase to 51 confirmed cases in 2024 does not indicate a general decline over the surveillance period, as demonstrated by possible varicella case data (Figure 7). Nearly 23% (n=57) of confirmed cases were among children ages 5 years and younger (Figure 8). Among the 72 confirmed cases of varicella among service members, only 7 cases had been fully vaccinated, 48 cases had received an exemption from immunization, and 12 cases had not been vaccinated (Table 2).

Discussion

In this retrospective analysis from 2019 to 2024, no measles cases were identified among service members. The previous MMR/V report also demonstrated no confirmed measles cases among service members from 2016 to 2019.⁹ For non-service member beneficiaries, measles primarily affected children ages 5 years or younger, with 50% of confirmed cases and over 57% of possible cases occurring in this age group. A similar trend was observed in the general U.S. population, with 42% of all cases among children under age 5 years in 2024.³ This is especially of concern, as measles can cause serious health complications in children younger than age 5 years.¹⁴ It is important to note, however, that measles continued to decrease among all MHS beneficiaries throughout the surveillance period.

During the 6-year surveillance period, there were over double the number of confirmed cases of mumps compared to measles (n=18, n=8, respectively). In the last MSMR report of MMR/V cases among MHS beneficiaries, confirmed mumps cases were 12 times higher than measles cases.⁹ The increased number of mumps cases is consistent with continued mumps outbreaks across the U.S., particularly among fully vaccinated young adults.¹⁵ This may be attributed to the fact that the 2-dose MMR vaccine is less effective against mumps (86%) compared to the measles (97%).^15-17 This is evident in this study, with 22% (n=4) breakthrough mumps cases that were fully vaccinated during the surveillance period. In 2017, the Advisory Committee of Immunization practices recommended a third dose of MMR (MMR3) during mumps outbreaks; and it has been proposed that MMR3 be administered in late adolescence or prior to college to help improve mumps vaccine efficacy.¹⁸

Distribution of confirmed rubella cases was relatively similar in service members and non-service members. No confirmed rubella cases were among children or young adults (younger than age 20 years); most rubella cases were among adults aged 21-35 years. A larger number of possible rubella cases were identified among non-service members than service members, which may be attributed to the vaccination requirement for military service. Since rubella is no longer endemic to the U.S., cases among MHS beneficiaries were most likely acquired outside the U.S.; however, this analysis did not discern country of MMR/V acquisition.

Varicella afforded the most confirmed cases in both service members (n=72) and non-service members (n=179), and 90% (n=65) of all confirmed cases among service members were not fully vaccinated. Full vaccination against varicella among service members might decrease the number of cases among all MHS beneficiaries.

All MMR/V cases decreased from 2019 to 2020, coincident with the COVID-19 pandemic during which most people were socially distancing and taking extra hygiene precautions, such as wearing masks and frequently washing hands. The same is observed in the general U.S. population, from 1,274 cases of measles in 2019 that drastically dropped to 13 cases in 2020. There were also multiple mumps outbreaks in 2019 within the U.S. military, such as the outbreak aboard USS Fort McHenry in early 2019 and an outbreak in July 2019 among Army troopers in Italy.⁹ Such outbreaks are contributing factors to the high number of observed cases in 2019 compared to the rest of the surveillance period. Cases of mumps and rubella started increasing, however, again in 2023 and 2022, respectively. Similar to previous reports of MMR/V among all MHS beneficiaries,^8,9 a substantially higher number of possible cases were identified than confirmed cases. Since a diagnosis of an MMR/V in this study was considered a case if reported as a confirmed RME notification, cases identified from inpatient and outpatient records that were not reported as RMEs are not counted as confirmed cases, but as possible cases. This potentially led to under-estimating confirmed MMR/V cases within the MHS.

This analysis also included MMR/V vaccination status among service members, which was not considered in previous updates. This addition is useful for determining numbers of breakthrough cases and identifying cases that were unvaccinated, providing indication of the importance of MMR/V vaccination.

The results presented may, however, be subject to data limitations. A few confirmed mumps and varicella cases among service members had no evidence of either a vaccine record or immunization exemption. It is, therefore, probable that immunization information may be missing or subject to data entry errors for some service members, as MMR/V vaccination is a requirement for military service.

Overall, the number of all MMR/V cases were higher among non-service member MHS beneficiaries compared to service members. This finding is not surprising, since evidence of immunity for MMR/V is required for service members. As MMR/V outbreaks continue to occur in the U.S. continued monitoring of MMR/V cases within the MHS is essential to ensure a healthy force and military readiness.

Authors’ Affiliation

Epidemiology and Analysis Branch, Armed Forces Health Surveillance Division, Public Health Directorate, Defense Health Agency, Silver Spring, MD

References

Immunization Action Coalition. Vaccine History Timeline. Accessed Jun. 20, 2025.
U.S. Centers for Disease Control and Prevention. Atkinson W, Wolfe C, Hamborsky J. eds. Epidemiology and Prevention of Vaccine-Preventable Diseases. 12th ed. Public Health Foundation;2011.
U.S. Centers for Disease Control and Prevention. Measles Cases and Outbreaks. U.S. Dept. of Health and Human Services. Accessed May 13, 2025. https://www.cdc.gov/measles/data-research/index.html
U.S. Centers for Disease Control and Prevention. Mumps Cases and Outbreaks. U.S. Dept. of Health and Human Services. Accessed May 12, 2025. https://www.cdc.gov/mumps/outbreaks/index.html
Leung J, Harpaz R. Impact of the maturing varicella vaccination program on varicella and related outcomes in the United States: 1994–2012. J Pediatric Infect Dis Soc. 2016;5(4):395-402. doi:10.1093/jpids/piv044
U.S. Centers for Disease Control and Prevention. Manual for the Surveillance of Vaccine-Preventable Diseases. U.S. Dept. of Health and Human Services. Accessed May 13, 2025. https://www.cdc.gov/surv-manual/php/table-of-contents/chapter-14-rubella.html
Standing Order for Administering Measles Mumps Rubella Vaccine (Adult). U.S. Dept. of Defense.
Williams VF, Stahlman S, Fan M. Measles, mumps, rubella, and varicella among service members and other beneficiaries of the Military Health System, 2010-2016. MSMR. 2017;24(10):2-11. Accessed Sep. 9, 2025. https://www.health.mil/reference-center/reports/2017/01/01/medical-surveillance-monthly-report-volume-24-number-10
Williams VF, Stahlman S, Fan M. Measles, mumps, rubella, and varicella among service members and other beneficiaries of the Military Health System, 1 January 2016–30 June 2019. MSMR. 2019;26(10):2-12. Accessed Sep. 9, 2025. https://www.health.mil/reference-center/reports/2019/10/01/medical-surveillance-monthly-report-volume-26-number-10
Armed Forces Health Surveillance Division. Surveillance Case Definitions: Measles. Defense Health Agency, U.S. Dept. of Defense. Accessed May 13, 2025. https://www.health.mil/reference-center/publications/2015/09/01/measles
Armed Forces Health Surveillance Division. Surveillance Case Definitions: Mumps. Defense Health Agency, U.S. Dept. of Defense. Accessed May 13, 2025. https://www.health.mil/reference-center/publications/2015/05/01/mumps
Armed Forces Health Surveillance Division. Surveillance Case Definitions: Rubella. Defense Health Agency, U.S. Dept. of Defense. Accessed May 13, 2025. https://www.health.mil/reference-center/publications/2018/01/01/rubella
Armed Forces Health Surveillance Division. Surveillance Case Definitions: Varicella. Defense Health Agency, U.S. Dept. of Defense. Accessed May 13, 2025. https://www.health.mil/reference-center/publications/2018/01/01/varicella
U.S. Centers for Disease Control and Prevention. About Measles. U.S. Dept. of Health and Human Services. Accessed Jun. 17, 2025. https://www.cdc.gov/measles/about/index.html#:~:text=But%20measles%20can%20cause%20serious,and%20rubella%20(MMR)%20vaccine
Melgar M, Yockey B, Marlow MA. Impact of vaccine effectiveness and coverage on preventing large mumps outbreaks on college campuses: Implications for vaccination strategy. Epidemics. 2022;40:100594. doi:10.1016/j.epidem.2022.100594
U.S. Centers for Disease Control and Prevention. Measles Vaccination. U.S. Dept. of Health and Human Services. Accessed Jun. 17, 2025. https://www.cdc.gov/measles/vaccines/index.html
Kauffmann F, Heffernan C, Meurice F, et al. Measles, mumps, rubella prevention: how can we do better? Expert Rev Vaccines. 2021;20(7):811-826. doi:10.1080/14760584.2021.1927722
Lewnard JA, Grad YH. Vaccine waning and mumps re-emergence in the United States. Sci Transl Med. 2018;10(433):eaao5945. doi:10.1126/scitranslmed.aao5945

Reportable medical events at Military Health System facilities through week 31, ending August 2, 2025

Wed, 01 Oct 2025 00:00:00 Z

Total active component cases reported per week are displayed for the top 5 RMEs for the previous year. Each month, the graph is updated with the top 5 RMEs, and is presented with the current month’s (July 2025) top 5 RMEs, which may differ from previous months. COVID-19 is excluded from these graphs due to changes in reporting and case definition updates in 2023.

Authors’ Affiliation

Defense Health Agency, Disease Epidemiology Branch, Defense Centers for Public Health–Aberdeen

References

Armed Forces Health Surveillance Division. Armed Forces Reportable Medical Events. Accessed Feb. 28, 2024. https://health.mil/reference-center/publications/2022/11/01/armed-forces-reportable-medical-events-guidelines
Defense Manpower Data Center. Department of Defense Active Duty Military Personnel by Rank/Grade of Service. Accessed Feb. 28, 2024. https://dwp.dmdc.osd.mil/dwp/app/dod-data-reports/workforce-reports
Defense Manpower Data Center. Armed Forces Strength Figures for January 31, 2023. Accessed Feb. 28, 2024. https://dwp.dmdc.osd.mil/dwp/app/dod-data-reports/workforce-reports
Navy Medicine. Surveillance and Reporting Tools–DRSI: Disease Reporting System Internet. Accessed Feb. 28, 2024. https://www.med.navy.mil/navy-marine-corps-public-health-center/preventive-medicine/program-and-policy-support/disease-surveillance/drsi

Ambulatory health care visits among active component members of the U.S. Armed Forces, 2024

Mon, 01 Sep 2025 00:00:00 Z

What are the new findings?

In 2024 the rate of ambulatory visits in U.S. military and non-military medical facilities was 16.5 visits per person-year, 10.8% higher than the 2023 rate. Excluding administrative visits, the crude annual rate of 13.4 visits per person-year for illnesses and injuries in 2024 was 17.4% higher than in 2022 and 55.6% higher than in 2020. The numbers and rates of primary causes for ambulatory visits have increased in 16 of 18 diagnostic categories from 2020 to 2024, except for ‘other’ and COVID-19 diagnoses. Musculoskeletal, mental, and nervous system or sensory organ disorders remain the leading causes of ambulatory visits, with substantial increases from 2020 to 2024. Musculoskeletal disorders showed the largest absolute ambulatory visit increase, with 1,675,234 total additional visits in 2024 in comparison to 2020, followed by mental health disorders, which increased by 650,888 visits during the same period.

What is the impact on readiness and force health protection?

Disorders of the musculoskeletal, mental, and nervous system and sensory organ major diagnostic categories are already known to have significant impacts on the well-being of military personnel and operational readiness. Unaddressed musculoskeletal injuries and mental health disorders may lead to prolonged periods of unoccupied time, reduced ability to meet the physical and psychological demands of military service, and contribute to attrition.

Background

This report documents the frequencies, rates, trends, and characteristics of ambulatory health care visits in 2024 of active component members of the U.S. Army, Navy, Air Force, Marine Corps, and Space Force. Ambulatory visits of U.S. service members in fixed military and non-military (reimbursed through the Military Health System) hospitals and clinics are documented by standardized records that are routinely archived in the Defense Medical Surveillance System for health surveillance purposes. Ambulatory visits not routinely and completely documented within fixed military and non-military hospitals and clinics (e.g., during deployments, field training exercises, or at sea) are not included in this analysis.

As in prior MSMR reports, all records of ambulatory health care visits by active component service members were categorized according to the International Classification of Diseases, 10th Revision codes entered in the primary (i.e., first-listed) diagnostic position of the visit records. Incidence rates were calculated per 1,000 person-years. Percent change in incidence was calculated using unrounded rates.

Frequencies, rates and trends

In 2024, U.S. ACSMs completed 18,821,239 ambulatory visits for medical care, resulting in a crude annual rate—for all causes—of 16,563.4 visits per 1,000 p-yrs, or 16.5 visits per p-yr (Table 1). The observed rate represents an increase of 10.8% from 2023, despite the absolute number of visits continuing a decline from a peak in 2021 (Figure 1). Excluding the ‘other’ major diagnostic category, there were 15,220.739 documented ambulatory visits for illnesses and injuries (ICD-10: A00–T88, including relevant pregnancy ‘Z’ codes) in 2024, corresponding to a crude rate of 13.4 visits per p-yr, which is 17.4% higher than in 2022 (11.4 per p-yr) and 55.6% higher than in 2020 (8.6 per p-yr).

A ‘Z’ code in the first diagnostic position identifies administrative visits within the ‘other’ category that reflects the care related to other factors influencing health status and contact with health services (excluding pregnancy). After a sharp decline observed in Z-coded encounters in 20231 compared to 2021 and 2019, the frequency in 2024 remained relatively stable, with a slight 3.7% increase over the previous year (data not shown).

Ambulatory visits, by ICD-10 major diagnostic categories

As in prior years, the leading 5 major diagnostic categories in 2024 remained consistent, accounting for almost four-fifths (79.5%) of all ambulatory visits among ACSMs. Musculoskeletal system/connective tissue disorders (28.1%) rose to become the leading category in 2024, surpassing ‘other’ (19.1%), which was the dominant category in 2020 and 2022. Mental health disorders (14.5%), disorders of the nervous system and sensory organs (9.8%), and signs, symptoms and ill-defined conditions (7.9%) maintained stable rankings (Table 1). Rankings for other diagnostic categories were largely stable, with only a modest shift in endocrine disorders surpassing infectious diseases. In contrast, COVID-19 fell to the lowest rank, representing only 0.1% of visits, down from 0.8% in 2022, reflecting pandemic peak and decline.

Excluding the ‘other’ category, rates of ambulatory visits increased in all but 1 of the 17 major diagnostic categories of illnesses and injuries between 2020 and 2024. As in prior years, diagnostic ‘S’ codes (for injuries), as opposed to ‘T’ codes (burns and poisonings), accounted for nearly 90% of all ambulatory encounters within this major diagnostic category (data not shown). Excluding the ‘other’ major diagnostic category, COVID-19 was the sole diagnostic category to decline in both numbers and rates within the illness and injury major category, with visits decreasing by 62.9%. Musculoskeletal system conditions accounted for the highest growth in ambulatory visits, totaling an additional 1,675,234 visits (rate increase of 70.9%) from 2020 to 2024, followed by mental health disorders (650,888 more visits, 53.2% rate increase). Except for infectious diseases and pregnancy and delivery-related visits, all other diagnostic categories exhibited rate increases exceeding 40% (Figure 2). Infectious diseases increased by only 5%, while pregnancy and delivery-related visits increased by 27.8%.

Ambulatory visits, by sex

For both male and female ACSMs, joint pain comprised over 40% of all diagnoses within the musculoskeletal system category. Adjustment disorder was the leading diagnosis in the mental health category, representing approximately 20% among both sexes (Tables 2 and 3). Unspecified and iron deficiency types of anemia were among the leading diagnoses within the hematological and immune disorders major diagnostic category, accounting for 28.2% and 56.7% of diagnoses among service men and women, respectively (Tables 2 and 3). Unspecified viral infection and unspecified acute upper respiratory infection were the leading diagnoses in 2024 for infectious and parasitic diseases and disorders of the respiratory system, respectively (Tables 2 and 3). While congenital anomalies were not frequently diagnosed among women, nearly a quarter (24.4%) of the congenital anomalies in men were attributed to congenital deformities of feet, including congenital pes planus (flat foot) and congenital pes cavus (high arch) (Table 2).

In 2024, service men accounted for nearly three-fourths (71.6%) of all illness- and injury-related visits, but the annual crude rate for service women (21.3 visits per p-yr) was 82.8% higher than among service men (11.7 visits per p-yr) (data not shown). Excluding pregnancy- and delivery-related visits, which accounted for 9.4% of all non-Z-coded ambulatory visits among service women, the illness and injury ambulatory visit rate was 19.4 visits per p-yr, 65.9% higher than among service men.

Rates of illness- and injury-specific diagnoses among service women exceeded male rates by 50% in all major diagnostic categories except diagnoses for nervous system and sensory organs, circulatory system, digestive system, and injury (data not shown). Female rates were more than twice those of male rates for conditions in the hematological, mental, genitourinary, and endocrine-, nutritional- and metabolic-related disorder categories.

Relationships between age group and ambulatory visit rates were broadly similar among men and women within all diagnostic categories (Figure 2). Ambulatory rates for musculoskeletal system, mental health disorders, neoplasms, disorders in nervous, digestive, circulatory systems, and endocrine-, nutritional- and metabolic-related conditions rose more steeply with advancing age than other categories of illness or injury (Figure 2).

Eight of the 10 leading diagnoses among ambulatory visits were the same for male and female service members: pain in joint; lower back pain; adjustment disorders; pain in limb, hand, foot, fingers, or toes; posttraumatic stress disorder; cervicalgia (neck pain); unspecified anxiety disorder; and sleep apnea. Sleep apnea was the second-most frequent illness- or injury-specific primary diagnosis for men, but ninth for women. The difference in the rate rank order of mental disorders is also worth noting. Alcohol dependence and unspecified acute respiratory infections were the sixth and tenth most frequent diagnoses, respectively, for men but were not identified among the leading 10 causes of ambulatory visits for women, while generalized anxiety disorder and unspecified dorsalgia were among the 10 most common diagnoses for women (Tables 2 and 3).

Discussion

In 2024, ambulatory visits among ACSMs increased by 10.8% compared to 2023, reaching 16.5 visits per person-year, although remaining below the 2021 peak. Rates for all major diagnostic categories increased, with the exception of the ‘other’ major diagnostic category and COVID-19. The largest absolute increase in the number of ambulatory visits was observed for musculoskeletal system disorders, which surpassed the ‘other’ category as the most frequent diagnosis. Notable growth was also seen within the mental health, nervous system, injury, and respiratory system categories. While infectious, endocrine, circulatory, neoplasms, hematological, and congenital anomalies experienced modest increases, their rankings remained relatively stable, at the lower end of the spectrum. When excluding visits documented by ICD-10 Z codes, the rate of illness- and injury-specific ambulatory visits was approximately 17% higher than in 2022, and over 55% higher than in 2020. The rate of encounters for COVID-19 peaked in 2022 (when it ranked fourteenth) and then sharply declined to last place by 2024, reflecting the pandemic peak and decline.

The sex-specific rate ratio for illness and injury-specific ambulatory encounters showed that female service members used outpatient care more often than their male counterparts (21.3 vs. 11.7 visits per p-yr, respectively). This is consistent with a recent report based on the 2022 National Ambulatory Medical Care Survey indicating that civilian women use health care services approximately 1.8 times more than civilian men.² The crude annual rate of illness- and injury-related visits among ACSMs (13.4 visits per p-yr), however, far exceeds the rate of ambulatory visits among civilians ages 18-44 years (324.6 visits per 1,000 persons, or about 0.3 visits per p-yr).² Future analyses comparing the major diagnostic category rates to civilian counterparts may be useful to further elucidate the costs of readiness.

Several limitations should be considered when interpreting these findings. Unit level ambulatory care, care by non-credentialed providers (e.g., medics, corpsmen), and at deployed medical treatment facilities (including ships at sea) are not included. This summary does not reflect that the nature and rates of illnesses and injuries may vary between deployed and non-deployed ACSMs.

Prior reports described the number of virtual versus in-person ambulatory encounters, but data quality issues about the variable delineating this encounter type have also been identified; it is an area of active inquiry.

This summary is based on primary (i.e., first-listed) diagnosis codes reported on ambulatory visit records, and the current summary discounts morbidity related to co-morbid and complicating conditions that may have been documented in secondary diagnostic positions within health care records. The accuracy of reported diagnoses likely varies according to medical condition, clinical setting, care provider, and treatment facility, as the information is collected for non-surveillance purposes. Although specific diagnoses during individual encounters were potentially not definitive, final, or even correct, summaries of the frequencies, trends, and natures of ambulatory encounters among ACSMs provide descriptive evidence to inform further research and evaluation.

Rates and frequencies reported do not reflect unique individuals, but a rate of total ambulatory visits per person-year. This report documents all ambulatory health care visits but does not estimate incidence rates for the diagnoses described. These data provide descriptors for health care provision, which elevate rates for disorders requiring increased numbers of ambulatory visits. In contrast to common, self-limited, and minor illnesses and injuries that require little, if any, follow-up or continuing care, illnesses and injuries necessitating multiple ambulatory visits for evaluation, treatment, and rehabilitation are over-represented in this summary.

References

Armed Forces Health Surveillance Division. Ambulatory health care visits among active component members of the U.S. Armed Forces, 2023. MSMR. 2024;31(6):19-25. Accessed Aug. 21, 2025. https://www.health.mil/news/articles/2024/06/01/msmr-ambulatory-care-2023
Santo L, Peters ZJ, Guluma L, Ashman JJ. Visits to health centers among adults, by selected characteristics: United States, 2022. Natl Health Stat Report. 2024;22(211):cS353454. doi:10.15620/cdc/59282

Absolute and relative morbidity burdens attributable to various illnesses and injuries among active component members of the U.S. Coast Guard, 2024

Mon, 01 Sep 2025 00:00:00 Z

What are the new findings?

In 2024, injuries, mental health disorders, and musculoskeletal diseases were the categories of medical conditions associated with the most medical encounters, greatest numbers of members affected, and largest numbers of hospital days among active duty Coast Guard members, similar to Department of Defense active component service members. When compared to 2023, medical encounters rose by 6.2%, hospital bed days increased by 13.7%, and major category conditions increased by 5.7%. In 2024, COVID-19 accounted for 0.3% of total medical encounters, a decrease from 0.4% in 2023, and 0.2% of hospital bed days reported in 2024.

What is the impact on readiness and force health protection?

The major condition categories in this report present health challenges for members of the U.S. Coast Guard and affect their service readiness. Loss of duty availability related to illness and injury diminishes Coast Guard personnel readiness. Coast Guard members have unique occupational exposures that may benefit from specific risk reduction programs to mitigate these threats.

Background

The U.S. Coast Guard is a military service that operates under the authority of the U.S. Department of Homeland Security, providing law and maritime safety enforcement, marine and environmental protection, and military naval support.^1,2 It is the second smallest service of the U.S. Armed Forces, with approximately 45,940 active component service members, and the only military service operating outside the authority of the Department of Defense. Coast Guard personnel are eligible to use DOD health care facilities, but because many service members are not stationed near a DOD installation, the Coast Guard operates primary care clinics in areas with sufficiently large Coast Guard populations, which is limited to providing primary care.¹

Recent research indicates that Coast Guard beneficiaries (i.e., active duty service members, reservists entitled to specific care, retirees, dependents) face challenges obtaining care meeting access standards due to several factors, including staffing shortages at Coast Guard clinics, data gaps, a lack of information to ensure member assignments optimally address the health needs of dependents, and more.¹ A higher proportion of civilian hospitalizations among Coast Guard members has been noted³; this difference may extend to ambulatory care as well. The MSMR annual morbidity burden report excluded hospitalization data for the U.S. Coast Guard service members from 2016 through 2021 due to missing data.^3,4

To quantify the impacts of various illnesses and injuries among members of the active component of the U.S. Coast Guard in 2024, this summary report employs the same disease classification system and morbidity burden measures that were used in the general active component burden analysis.

Methods

The population for this analysis included all individuals who served in the active component of the Coast Guard at any time during the surveillance period of January 1, 2024 through November 30, 2024. The methodology for summarizing absolute and relative Coast Guard morbidity burdens in 2024 is identical to the methodology described on page 5 of this issue that determined the absolute and relative burdens attributed to various illnesses and injuries among the active component of the U.S. Armed Forces.

Results

In 2024, a total of 36,686 Coast Guard service members had 470,239 total medical encounters, which included 10,143 hospital bed days reported, for a rate of 0.28 hospital bed days per Coast Guard member who experienced at least 1 medical encounter, either ambulatory or hospitalization.

Morbidity burden, by category

In 2024, more active component Coast Guard members experienced medical encounters for injury (n=16,297) than any other morbidity-related category (Figure 1a). Second-most frequent in terms of hospital bed days, injury accounted for over one-fifth (22.1%) of all medical encounters (Figure 1b).

Mental health disorders accounted for more hospital bed days (n=5,376) than any other morbidity-related category, constituting over half (53.0%) of all hospital bed days, and fifth in terms of numbers of individuals affected (Figures 1a, 1b). Combined, injury and mental health disorders accounted for over three-fifths (65.8%) of all hospital bed days and more than two-fifths (42.1%) of all medical encounters.

Maternal conditions (pregnancy complications, delivery), accounted for a relatively large proportion of all hospital bed days (n=1,280, 12.6%) but a much smaller proportion of total medical encounters (n=4,094 0.9%) (Figures 1a, 1b). Maternal conditions were the most prevalent medical condition among female active component Coast Guard members. Women comprised approximately one-sixth (16.4%) of the active duty Coast Guard in 2024.

Medical encounters, by condition

In 2024, 5 disease-related conditions accounted for more than one-third (37.3%) of all illness- and injury-related medical encounters among active component Coast Guard members: other back problems (includes lower back pain, other dorsalgia), anxiety disorders, organic sleep disorders (e.g., obstructive sleep apnea, insomnia), arm/shoulder injuries, and knee injuries (Figure 2). Moreover, the 10 conditions associated with the most medical encounters constituted more than half (58.0%) of all illness- and injury-related medical encounters.

The disease-related conditions in 2024 that predominantly accounted for medical encounters among active component Coast Guard members were injuries, mental health disorders, and musculoskeletal diseases. Among the reported injuries, arm/shoulder (7.1%), knee (5.5%), foot/ankle (3.3%), and leg (2.6%) injuries accounted for the most medical encounters (Figure 2 and Table). Anxiety (8.2%), mood (4.8%), adjustment (4.5%), and substance abuse disorders (1.5%) were the four most frequent mental health disorder diagnoses. Other back problems (9.1%), all other musculoskeletal diseases (3.6%), and cervicalgia (2.2%) constituted the most medical encounters among musculoskeletal disorders. COVID-19 accounted for 0.3% of total medical encounters in 2024.

Individuals affected, by condition

The 10 categories of conditions that affected the most Coast Guard members in 2024 were all other signs and symptoms, upper respiratory infections, other back problems, refraction/accommodation, organic sleep disorders, anxiety, all other skin diseases, all other musculoskeletal diseases, arm and shoulder conditions, and respiratory and chest issues. COVID-19 affected 1,167 Coast Guard members, ranking thirty-sixth for number of individuals affected, a slight drop from thirty-third in 2023.

Hospital bed days, by condition

In 2024, substance abuse and mood disorders accounted for about two-fifths (40.5%) of all hospital bed days (Figure 3). Four mental health disorders (substance abuse, mood, anxiety, adjustment) and two maternal conditions (pregnancy complications, delivery) combined accounted for more than three-fifths (61.7%) of all hospital bed days (Table and Figure 3). About 12.8% of all hospital bed days were attributable to injuries. In 2024, 0.2% hospitalizations of active component Coast Guard members were due to COVID-19 (Table).

Discussion

Health care use within the Coast Guard was similar to the DOD when measured by total encounters and persons affected in 2024. The Coast Guard rate was 12.8 encounters per person (470,239 per 36,686 individuals), compared to the DOD rate of 12.3 encounters per person (14,197,058 per 1,150,913 individuals). The Coast Guard had a lower rate of hospitalization, however, with only 0.28 bed days per individual; the DOD reported 0.33 bed days per individual (378,693 per 1,150,913 individuals).

Compared to 2023, the number of Coast Guard medical encounters and hospital bed days increased by 6.2% and 13.7%, respectively, and the major category conditions increased by 5.7%. While the number of Coast Guard medical encounters and the number of major category conditions increased in 2024, the rate of change was significantly lower than in 2023 (13.3% and 12.7%, respectively). Conversely, the rate of change in hospital bed days in 2024 (13.7%) was significantly higher than in 2023 (2.3%). Mental health disorders resulted in more hospital stays than any other morbidity-related category, and mental health-related medical encounters increased by 15.5% compared to last year. In 2024, the number of individuals affected decreased by 2.0% compared to 2023.

This report is consistent with the major findings of prior annual reports on morbidity burdens among active component U.S. service members. Injuries, mental health disorders, and musculoskeletal diseases were the categories of medical conditions associated with the most medical encounters, largest numbers of affected service members, and greatest numbers of hospital bed days; maternal conditions accounted for the most hospital bed days, followed by mental health disorders. When examining ICD codes to the fourth digit character, Coast Guard and DOD service members shared many disease-related conditions: other back problems within the musculoskeletal disease major diagnostic category; arm/shoulder and knee injuries within the injury major diagnostic category; anxiety disorders in the mental health disorder major diagnostic category; and organic sleep disorders within the neurological condition major diagnostic category.

COVID-19 did not account significantly for medical encounters in 2024 compared to 2023: COVID accounted for 0.2% of hospital bed days in 2024, compared to none (0) in 2023. In addition to the waning of the pandemic, active component service members represent a relatively young and healthy population that is less likely to experience severe consequences from COVID-19 infection.

Preventable illnesses and injuries, which contribute disproportionately to morbidity and health care burdens, should be high priority targets for intervention, research, and resources. In a 2018 survey, Coast Guard members reported several mental health issues including serious psychological distress, failure to receive mental health services despite need, and other preventable risky health behaviors.⁵ Reliable access to health care is crucial for ensuring service members remain healthy and prepared for their missions. A lack of data hinders the Coast Guard from fully understanding health care accessibility issues, however.¹ To accurately portray the true burden of disease in this population, addressing and resolving the data gaps resulting from Coast Guard hospitalizations to civilian facilities is critical and should be a priority. Improving data collection processes and systems is crucial to addressing barriers to accessing health care.

Providing a matrix of major diseases each year enables the identification, in comparison with previous reports, of potentially avoidable health conditions among military personnel, and their proximate causes. Morbidity burden report findings can aid prioritization of effective interventions, provision of necessary care, and evaluation of their impacts and cost-effectiveness.⁶

References

Cohen C, Chan EW, Tong PK, et al. Health Services for Coast Guard beneficiaries: Improving access to care for active duty service members, reservists, dependents and retirees. RAND Homeland Security Research Division, Homeland Security Operational Analysis Center. 2025. Accessed Aug. 15, 2025. https://www.rand.org/pubs/research_reports/RRA3018-1.html
United States Coast Guard. U.S. Dept. of Homeland Security. Accessed Aug. 15, 2025. https://www.uscg.mil
Pillai S, Chau M, Kamara I, Thomas D, Iskander J. Brief report: hospitalizations among active duty members of the U.S. Coast Guard, fiscal year 2021. MSMR. 2023;30(2):3-5. Accessed Aug. 15, 2025. https://www.health.mil/news/articles/2023/02/01/active-duty-coast-guard-hospitalizations-fy-2021
Armed Forces Health Surveillance Branch. Hospitalizations among members of the active component, U.S. Armed Forces, 2015. MSMR. 2016;23(4):8-16. Accessed Aug. 15, 2025. https://www.health.mil/reference-center/reports/2016/01/01/medical-surveillance-monthly-report-volume-23-number-4
Pulkkinen AJ. Let’s talk about your behavioral health. My Coast Guard. U.S. Coast Guard. 2024. Accessed Aug. 15, 2025. https://www.mycg.uscg.mil/News/Article/3671040/lets-talk-about-your-behavioral-health
Devleesschauwer B, Maertens de Noordhout C, Smit GS, et al. Quantifying burden of disease to support public health policy in Belgium: opportunities and constraints. BMC Public Health. 2014;14:1196. doi:10.1186/1471-2458-14-1196
World Health Organization. The Global Burden of Disease: 2004 Update. World Health Organization;2008. Accessed Aug. 14, 2025. https://www.who.int/publications/i/item/9789241563710
Murray CJL, Lopez AD, eds. The Global Burden of Disease: A Comprehensive Assessment of Mortality and Disability from Diseases, Injuries, and Risk Factors in 1990 and Projected to 2020. Harvard University Press;1996:120-122.

Surveillance snapshot: Illness and injury burdens among reserve component members of the U.S. Coast Guard, 2024

Mon, 01 Sep 2025 00:00:00 Z

Morbidity burdens attributable to various illnesses and injuries among deployed active and reserve component service members of the U.S. Armed Forces, 2024

Mon, 01 Sep 2025 00:00:00 Z

What are the new findings?

Musculoskeletal disorders, in combination with administrative and other health services (ICD-10 ‘Z’ codes), accounted for more than half of the total medical encounters in 2024 among service members deployed to the U.S. Central Command or Africa Command. Lower back pain accounted for the most frequent musculoskeletal condition among male and female service members deployed to CENTCOM and AFRICOM.

What is the impact on readiness and force health protection?

Thorough examination of the most common causes of injury and illness during deployment can assist senior leaders in the development and implementation of strategies to reduce preventable medical issues, enhance force readiness, and ensure fighting strength.

Background

Each year, MSMR estimates illness and injury-related morbidity and health care burdens on the U.S. Armed Forces and the Military Health System, and this report updates previous analyses of these burden distributions among active and reserve component service members in deployed settings. While deployed service members are primarily selected from a subset of the active component, the reserve component contributes a substantial portion of U.S. deployed forces.

This report utilizes data from the Theater Medical Data Store, which documents service members’ inpatient and outpatient encounters while treated in an operational environment. TMDS receives medical data from Theater Medical Information Program-Joint applications, including AHLTA-Theater, TMIP-Composite Health Care System Cache, Mobile Computing Capability, Maritime Medical Modules, and the U.S. Transportation Command Regulating and Command and Control Evacuation System.¹

The health encounters of service members deployed to 2 specific theaters of operation, U.S. Central Command and U.S. Africa Command, are the subject of this report. While U.S. service members are deployed to all the geographic combatant commands, the largest concentrations without access to permanent medical facilities are in the CENTCOM and AFRICOM areas of operation.² While this report focuses on medical encounters of service members treated in CENTCOM and AFRICOM operational environments during the 2024 calendar year, future reports may incorporate other combatant commands as circumstances dictate and data become available.

Methods

The surveillance population included all individuals who served in the active or reserve components of the U.S. Army, Navy, Air Force, Marine Corps, or Space Force with health care encounters captured in the TMDS during the surveillance period. Analysis was restricted to encounters where the theater of care specified was CENTCOM or AFRICOM, or where the name of the theater of operation was missing or null; by default, this excluded encounters in the U.S. Northern Command, U.S. European Command, U.S. Indo-Pacific Command, or U.S. Southern Command theaters of operations. In addition, TMDS-recorded medical encounters where the data source was identified as Shipboard Automated Medical System, or where the military treatment facility descriptor indicated that care was provided aboard ship, were excluded from this analysis. Encounters from aeromedical staging facilities outside of CENTCOM or AFRICOM were also excluded.

Morbidity burdens attributable to various conditions were estimated by diagnosis distribution according to the 17 traditional categories of the International Classification of Diseases system, with an 18th category for COVID-19. Extended ICD-10 (10th Revision) code groupings were also reviewed for the most common diagnoses. The TMDS has not fully transitioned to ICD-10 codes, so some ICD-9 (9th Revision) codes were included. Primary diagnoses that did not correspond to an ICD-9 or ICD-10 code are not reported in this health care burden analysis.

Results

A total of 191,579 medical encounters occurred among 52,066 individuals deployed to Southwest Asia, the Middle East, and Africa in 2024. Of those 191,579 total medical encounters documented in 2024 among deployed service members, 227 (0.1%) were recorded as hospitalizations. Most medical encounters (n=146,384, 76.4%), individuals affected (n=42,344, 81.3%), and hospitalizations (n=181, 79.7%) occurred among male service members.

In 2024, the largest percentages of medical encounters among deployed service members were coded as musculoskeletal system/connective tissue disorders, followed by administrative and other health services (i.e., ‘Z’ codes, including factors influencing health status and health service contact) (Figure). The most common diagnosis within the musculoskeletal system/connective tissue disorders group was for unspecified lower back pain (ICD-10 codes beginning with M545) (Table). The percentage of total medical encounters attributed to other health services decreased from 32.1% in 2020 to 22.8% in 2024. COVID-19 accounted for only 0.3% of deployed service members’ total medical encounters in 2024 (Figure).

The percentages of in-theater medical encounters attributed to musculoskeletal system disorders increased from 2020 (23.4%) to 2024 (30.9%) (Figure). Unspecified lower back pain (M5450) was the most frequent ICD-10 diagnostic code for musculoskeletal encounters among both men and women (Table). The second-most frequent ICD-10 diagnostic code for musculoskeletal encounters among male service members was pain in the right shoulder (M25511), while for female service members it was pain in the right knee (M25561) (Table).

The percentages of in-theater medical encounters attributed to mental health disorders increased slightly during the surveillance period, from 5.8% in 2020 to 7.7% in 2024 (Figure). Unspecified reaction to severe stress (F439) accounted for the most frequent mental health disorder diagnoses, with a slightly higher percentage of in-theater encounters for this disorder among women (1.5%) than men (1.0%) (Table).

Discussion

As in prior annual reports of illness- and injury-related morbidity and care burdens in deployed settings, musculoskeletal disorders, in combination with administrative and other health services, accounted for more than half of the total medical encounters in theater. In prior reports during the surveillance period, encounters for COVID-19 screening contributed to an increase in encounters for administrative and other health services, as this specific Z code (Z1152) accounted for almost 5% of all in-theater medical encounters in 2022.³

This report documents an increased percentage of in-theater medical encounters for musculoskeletal disorders, consistent with the 2020-2024 increased rate of in-garrison ambulatory encounters for musculoskeletal disorders. The percentage of total ambulatory encounters attributed to musculoskeletal disorders in garrison (28.1%) was similar to the percentage observed in theater (30.9%).⁴ No absolute rate comparisons can be made due to the lack of in-theater denominator (person-time) data.

Some conditions, including diabetes, pregnancy, or congenital anomalies, often preclude service member deployment. Due to medical pre-screening, service members who are deployed demonstrate a lower rate of medical conditions that could interfere with deployment operations than their non-deployed counterparts. Deployed service members are also less likely to require medical care for pre-screened conditions.

When interpreting these results and analyses, several limitations of these data should be considered. Not all medical encounters in theaters of operations are recorded in the TMDS. Some care by in-theater medical personnel occurs at small, remote, or austere locations where electronic documentation of diagnosis and treatment is infeasible, and some emergency medical care for stabilization of combat-injured service members prior to evacuation may not be routinely captured in the TMDS. Due to the exigencies of deployment settings that can complicate accurate data reporting or transmission, this report may under-estimate the true burden of health care in the areas of operations assessed.

In any review that relies on ICD coding, some diagnosis misclassification should be expected due to coding errors within the electronic health record. Although the aggregated distributions of illnesses and injuries presented in this report are compatible with assessments derived from other examinations of morbidity in military populations (both deployed and nondeployed), instances of highly unlikely diagnostic codes for a deployed population have been observed. This misclassification bias is likely minor and non-differential.

Because this report only includes medical encounters from CENTCOM and AFRICOM, it does not describe any medical encounters from the recent deployment of troops to EUCOM, INDOPACOM, and SOUTHCOM. Each area of operation is unique, with vastly different medical assets, medical evacuation capabilities, and deployed service member populations. Consequently, the results from CENTCOM or AFRICOM may not be generalizable to other combatant commands.

References

Defense Health Agency, U.S. Department of Defense. Joint Operational Medicine Information Systems Theater Medical Data Store. Fact Sheet. Jul. 2019. Accessed Apr. 18, 2025. https://www.health.mil/reference-center/fact-sheets/2019/07/30/tmds-fact-sheet
The White House. Letter to the Speaker of the House and President pro tempore of the Senate Regarding the War Powers Report. Dec. 6, 2024. Accessed Apr. 18, 2025. https://bidenwhitehouse.archives.gov/briefing-room/statements-releases/2024/12/06/letter-to-the-speaker-of-the-house-and-president-pro-tempore-of-the-senate-regarding-the-war-powers-report-5
Armed Forces Health Surveillance Division. Morbidity burdens attributable to various illnesses and injuries among deployed active and reserve component service members of the U.S. Armed Forces, 2022. MSMR. 2023;30(7):2-5. Accessed Aug. 21, 2025. https://www.health.mil/news/articles/2024/07/01/msmr-deployed-morbidity-2023
Armed Forces Health Surveillance Division. Ambulatory health care visits among active component members of the U.S. Armed Forces, 2024. MSMR. 2025;32(9):21-27.

Absolute and relative morbidity burdens attributable to various illnesses and injuries among active component members of the U.S. Armed Forces, 2024

Mon, 01 Sep 2025 00:00:00 Z

What are the new findings?

Within the Military Health System in 2024, injuries, mental disorders, and musculoskeletal diseases were the major categories of medical conditions associated with the most medical encounters, greatest numbers of affected service members, and highest numbers of hospital bed days. Those three categories showed modest growth, increasing by about 0.8% compared to 2023. While reported health care encounters increased by 1.3% in 2024, the numbers of affected individuals and hospital bed days decreased by 4.4% and 2.9%, respectively.

What is the impact on readiness and force health protection?

The major categories of medical conditions in this report present health challenges among U.S. active component service members that can affect force readiness. Continuous health surveillance, morbidity trend analysis, and timely reporting of comprehensive summaries of the major health issues affecting the active duty force provides crucial evidence to line commanders, Military Health System leaders, and health care providers as they establish policies and priorities for effective health care management and treatment of U.S. service members.

Background

MSMR’s annual burden of disease reports are designed to provide accurate estimations of the general health status of U.S. military personnel, for prioritization of effective interventions with measurable impacts on force readiness.¹ In these reports, diagnoses are grouped to inform readers of the major factors and variables each year affecting health care provision within the Military Health System. Although the burden of disease within a health system can be classified into several categories, the majority of the global disease burden results from non-communicable diseases, followed by communicable diseases, maternal and neonatal diseases, nutritional diseases, and injuries.²

To broadly describe the morbidity burden among active component service members, since 2001 MSMR has used a classification system derived from the Global Burden of Disease Study.^3,4 This systematic classification, developed through a 30-year scientific effort, quantifies major diseases, risk factors, and intermediate clinical outcomes in a standardized manner, enabling comparisons between populations and health problems over time.^5,6 MSMR utilizes the GBD classification system in combination with an International Classification of Diseases, 10th Revision, Clinical Modification chapter-based system for categorizing hospitalizations and ambulatory care visits among the MHS population.

To improve the utility of this information, these classification schemes are refined by MSMR’s editorial staff. The major classification system for diagnoses, ICD-10-CM, features more than 68,000 separate codes.⁵ While the ICD-10-CM is organized in logical chapters, the groupings are not optimal for articulating burdens of disease within a military population. Consequently, some re-groupings of diagnoses are necessary to achieve a meaningful depiction of the burden in the military population.

The burden of disease experienced by ACSMs—a demographic characterized by youth, good health, and a predominantly male population—is assumed to substantially differ from the burden observed for the general U.S. and global populations. This divergence is attributable to a constellation of factors, including 1) pre-accession medical screening designed to ensure physical fitness for military service, 2) mandatory periodic health assessments and screenings, which potentially lead to earlier detection of certain conditions, 3) frequent use of outpatient services for readiness-related requirements, 4) unique environmental and lifestyle factors associated with military life and training, and 5) universal access to medical care without direct financial cost. These factors, collectively, contribute to distinct morbidity burden profiles within the ACSM population.

Individuals enlist or are commissioned into the active component typically between the ages of 17 and 25 years, with almost all members ending service by age 50 years. In 2024, the largest age group within the U.S. active component was 20-24 years, followed by 25-29 years, according to Defense Medical Surveillance System (DMSS) data. Women accounted for 19.4% of the active component in 2024.

Within the military population and its unique environment, categories of illness and injury requiring hospitalization have historically differed from illness and injury categories that result in the most outpatient visits. Added requirements for military readiness are likely a major factor in outpatient health care provision, but rarely for hospitalization. The categories of medical conditions that account for the most medical encounters generally within the Military Health System may differ from those that affect the most individuals, or those that result in the most debilitating or long-lasting effects among service members.⁴

This annual summary uses several health care burden measures to quantify the impacts in 2024 of various illnesses and injuries among members of the active component of the U.S. Armed Forces. Health care burden metrics include the total number of medical encounters, individuals affected, and hospital bed days. A consistent and comparative description of the burden of diseases and injuries, and sub-populations affected, should be an important element of health decision-making and planning processes, providing valuable information for where changes in policy or preventive emphasis may improve the medical readiness of the force.⁷

Methods

The population for this analysis included all individuals who served in the active components of the Army, Navy, Air Force, Marine Corps, or Space Force at any time during the surveillance period of January 1, 2024 through December 31, 2024. Each service member contributed medical records and person-time only for actual months served during the surveillance period.

All data in this analysis were derived from records maintained in the DMSS, which documents both ambulatory care encounters and hospitalizations of active component members of the U.S. Armed Forces. DMSS contains all encounters in military medical and civilian treatment facilities when reimbursed through the MHS. Encounters not routinely and completely documented within fixed military and non-military hospitals and medical clinics (e.g., during deployments, field training exercises, or at sea) were excluded from this analysis.

DMSS data for all inpatient and outpatient medical encounters of ACSMs during the surveillance period were summarized according to the primary (i.e., first-listed) diagnosis if reported with an ICD-10 code between A00 and T88, in addition to an ICD-10 code beginning with Z37 (“outcome of delivery”) or Department of Defense unique personal history codes DOD0101–DOD0105 (“personal history of traumatic brain injury”). This year, four new diagnostic groups were added for analysis: pain in foot, chronic rhinitis, neoplasm of uncertain behavior of skin, and disorder of pituitary gland.

All illness- and injury-specific diagnoses, defined by ICD-10 codes, are grouped into 25 burden of disease-related categories, comprised of 157 medical conditions, based on a modified version of the classification system developed for the GBD Study.⁴ This classification system was developed by the MSMR editorial staff in 2001 and is updated annually.

The GBD system groups diagnoses with common pathophysiological or etiological bases or significant DOD health policy importance. In this report, some diagnoses grouped into single categories in the GBD system (e.g., mental health disorders) were dis-aggregated to increase military relevance. In addition, injuries are classified by affected anatomical site rather than cause, as external causes of injuries are not required to be documented by providers.

The morbidity burdens attributable to various conditions were estimated based on the total number of medical encounters associated with each condition, i.e., total hospitalizations and ambulatory visits for the condition, with a limit of one encounter for an individual per condition each day; and numbers of service members affected by each condition, i.e., individuals with at least one medical encounter for the condition during the year; as well as total bed days during hospitalizations for each condition.

Results

Morbidity burden, by category

Provisional data indicate that affected ACSMs (n=557,980) experienced medical encounters due to injury more than any other morbidity-related category in 2024 (Figure 1a). Ranking third in terms of hospital bed days, injuries accounted for about one-fourth (23.5%) of all medical encounters (Figure 1b). The injury category combines ICD-10 ‘S’ (“injury”) and ‘T’ codes (“burns and poisonings”), but injuries account for about 98.1% of ambulatory encounters within the category (data not shown).

Mental health disorders accounted for more hospital bed days (n=195,726) than any other morbidity-related category, contributing over half (51.7%) of all hospital bed days, ranking fifth for individuals affected (Figures 1a, 1b). Together, the injury and mental health disorder categories accounted for over two-thirds (63.0%) of all hospital bed days and 42.3% of all medical encounters in 2024.

Maternal conditions (pregnancy complications and delivery) accounted for a relatively large proportion of all hospital bed days (n=54,348, 14.4%) but a much smaller proportion of medical encounters overall (n=203,467, 1.4%) (Figures 1a, 1b). As women comprised only 19.4% of the active duty force in 2024, these summary statistics understate the impact of these conditions among that group. Maternal conditions were the most frequent category for hospitalization among women in the active component.

Medical encounters, by condition

In 2024, almost one-third (33.4%) of all illness- and injury-related medical encounters were due to 5 medical conditions: other back problems (lower back pain, other dorsalgia), knee, arm/shoulder, organic sleep disorders (insomnia, obstructive sleep apnea), and anxiety (Figure 2). Moreover, the 10 conditions associated with the most medical encounters constituted more than half (55.3%) of all illness- and injury-related medical encounters.

The categories of conditions that accounted for the most medical encounters among ACSMs in 2024 were predominantly injuries, mental health disorders, and musculoskeletal diseases. Among reported injuries, knee (6.4%), arm/shoulder (6.2%), foot/ankle (3.7%), and leg (3.3%) resulted in the most medical encounters (Figure 2 and Table). Mental health disorder diagnoses resulted most frequently from anxiety (5.8%), adjustment (4.2%), mood (4.2%), and substance abuse disorders (2.7%). Other back problems (9.1%), all other musculoskeletal diseases (4.4%), and cervicalgia (1.8%) generated the most medical encounters from musculoskeletal diseases. COVID-19 accounted for 0.2% of total medical encounters in 2024, ranked fifty-eighth, declining from 0.3% in 2023.

Individuals affected, by category

In 2024, the 10 categories of conditions that affected the most service members were signs, symptoms, and other ill-defined conditions (all other signs and symptoms), musculoskeletal diseases (other back problems, all other musculoskeletal diseases), respiratory infections (upper respiratory infections) sensory organ diseases (refraction/accommodation), neurological conditions (organic sleep disorders), injuries (knee, arm/shoulder), respiratory diseases, and skin diseases (all other skin diseases). COVID-19 affected 23,173 ACSMs and ranked forty-seventh for members affected, a considerable decrease in rank from thirty-fifth in 2023.

Hospital bed days, by condition

Mood and substance abuse disorders accounted for nearly one-third (32.7%) of all hospital bed days in 2024 (Figure 3). Four mental health disorders (mood, substance abuse, adjustment, anxiety) and two maternal conditions (pregnancy complications, delivery) together accounted for almost two-thirds (60.7%) of all hospital bed days (Table and Figure 3). About 11.3% of all hospital bed days were attributable to injury. COVID-19 accounted for 0.1% of total hospital bed days among ACSMs (Table).

Relationships between health care burden indicators

There was a strong positive correlation between numbers of medical encounters attributable to various medical conditions with numbers of individuals affected by those conditions (r=0.85) (data not shown). The three leading causes of medical encounters were among the five medical conditions that most affected individuals (Table), while weak-to-moderate positive relationships were detected between numbers of hospital bed days attributable to conditions with numbers of individuals affected by those conditions (r=0.20), or numbers of medical encounters related to a medical condition (r=0.40). For example, substance abuse disorders and labor and delivery ranked high in terms of total bed days, these conditions affected relatively few ACSMs in 2024.

Discussion

This MSMR report provides the most recent data available for major disease classification and analysis comparable to previous reports. The total number of conditions reported in 2024 increased by 0.8% compared to 2023, and medical encounters increased by 1.3%. The numbers of affected individuals and hospital bed days decreased, however, by 4.4% and 2.9%, respectively. While numbers of individuals affected and hospital bed days decreased in 2024, the major diseases and conditions observed in this analysis are consistent with previous MSMR reports on the morbidity and health care burdens of the U.S. military.

Compared to 2023, both numbers of medical encounters and hospital bed days decreased for five major categories—mental health disorders, musculoskeletal diseases, respiratory diseases, maternal conditions, and blood disorders—while in the remaining categories, changes in numbers of medical encounters and hospital bed days were inconsistent. Injuries, mental health disorders, and musculoskeletal disorders were the categories in 2024 associated with the most medical encounters, highest numbers of affected service members, and greatest numbers of hospital bed days.

Only 9 of the 157 medical conditions that comprise this report, or just 5.7% of the listed conditions, accounted for slightly more than half (51.6%) of all illness- and injury-related medical encounters: two anatomical, site-defined injuries (knee, arm/shoulder), three mental health disorders (anxiety, adjustment, mood disorders), two musculoskeletal conditions (other back problems, all other musculoskeletal diseases), one sign, symptom or ill-defined condition (all other signs and symptoms), and one neurological condition (organic sleep disorders).

The pattern of illness and injury among U.S. ACSMs is distinct from other population groups, with different demographic distributions and occupational hazards. Injuries, mental disorders, and musculoskeletal diseases are identified in the literature as among the leading causes of morbidity and disability among service members throughout military history, affecting readiness and health care provision.^8-10 A previous study reported that injuries were the single leading cause of death, disability, hospitalization, outpatient visits, and manpower loss among U.S. military service members.⁸ Exposure to intense physical demands during training and in operational environments increases risk of musculoskeletal injury, which contributes to significant morbidity among military personnel.¹¹ Due to lifestyles that can be influenced by operational conditions, multiple combat missions, and familial separations, among other factors, a number of mental disorders including occupational stress, depression, and suicide are common among military personnel.⁹ Some studies have reported significant associations between major depressive disorder and deployment.¹⁰

Reporting on the burden of disease and injury includes reliable quantification of their physical and psychosocial health impacts, as well as risk factors, that can provide valuable information about a population’s health status, for optimal resource allocation for prevention and treatment. Accurate estimates can be used to predict expected health care use and costs, prioritize effective interventions, and evaluate their impacts and cost effectiveness.⁶ Current, accurate information on the scale of health disorders among service members, groups at significant risk, and trends in their health statuses over time are critical for policy-makers and commanders.

Preventing injuries and illnesses in service members requires not only routine injury and disease monitoring, but informed, pervasive understanding of the link between health-related factors and disease occurrence, a comprehensive medical surveillance system for successful prevention programs, and data-driven research prioritization. These surveillance, analysis, and reporting efforts can culminate in effective partnerships between commanders, policy-makers, and service members for direct actions to prevent disease and injury.^8,11

With psychosocial factors shown to be implicated in increased risk of back pain, for example, addressing related health care issues holistically, rather than divided among discrete categories, would be beneficial.^12,13 Integrated approaches to care not only address identified burdens of medical conditions but their associated risk factors. The unique health challenges of the military population share risk factors and medical conditions with the civilian population, with the added complexities of service experience and the nature of combat.¹⁴

References

Hernandez JBR, Kim PY. Epidemiology morbidity and mortality. In: StatPearls [internet]. Updated Oct. 3, 2022. StatPearls Publishing;2024. https://www.ncbi.nlm.nih.gov/books/nbk547668
Roser M, Ritchie H, Spooner F. Burden of Disease. 2024. Accessed Aug. 14, 2025. https://ourworldindata.org/burden-of-disease
World Health Organization. The Global Burden of Disease: 2004 Update. World Health Organization;2008. Accessed Aug. 14, 2025. https://www.who.int/publications/i/item/9789241563710
Murray CJL, Lopez AD, eds. The Global Burden of Disease: A Comprehensive Assessment of Mortality and Disability from Diseases, Injuries, and Risk Factors in 1990 and Projected to 2020. Harvard University Press;1996:120-122.
Murray CJL. The Global Burden of Disease Study at 30 years. Nat Med. 2022;28(10):2019-2026. doi:10.1038/s41591-022-01990-1
Devleesschauwer B, Maertens de Noordhout C, Smit GS, et al. Quantifying burden of disease to support public health policy in Belgium: opportunities and constraints. BMC Public Health. 2014;14:1196. doi:10.1186/1471-2458-14-11967
World Health Organization. WHO Methods and Data Sources for Global Burden of Disease Estimates 2000-2019. World Health Organization;2020. Accessed Jun. 4, 2024. https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_daly-methods.pdf
Jones BH, Perrotta DM, Canham-Chervak ML, Nee MA, Brundage JF. Injuries in the military: a review and commentary focused on prevention. Am J Prev Med. 2000;18(3 suppl 1):71-84. doi:10.1016/s0749-379(99)00169-5
Moradi Y, Dowran B, Sepandi M. The global prevalence of depression, suicide ideation, and attempts in the military forces: a systematic review and meta-analysis of cross sectional studies. BMC Psychiatry. 2021;21(1):510. doi:10.1186/s12888-021-03526-2
Packnett ER, Elmasry H, Toolin CF, Cowan DN, Boivin MR. Epidemiology of major depressive disorder disability in the US military: FY 2007-2012. J Nerv Ment Dis. 2017;205(9):672-678. doi:10.1097/nmd.0000000000000692
Lovalekar M, Hauret K, Roy T, et al. Musculoskeletal injuries in military personnel: descriptive epidemiology, risk factor identification, and prevention. J Sci Med Sport. 2021;24(10):963-969. doi:10.1016/j.jsams.2021.03.016
To D, Rezai M, Murnaghan K, Cancelliere C. Risk factors for low back pain in active military personnel: a systematic review. Chiropr Man Therap. 2021;29(1):52. doi:10.1186/s12998-021-00409-x
Meints SM, Edwards RR. Evaluating psychosocial contributions to chronic pain outcomes. Prog Neuropsychopharmacol Biol Psychiatry. 2018;87(pt b):168-182. doi:10.1016/j.pnpbp.2018.01.017
Alruwaili A, Khorram-Manesh A, Ratnayake A, Robinson Y, Goniewicz K. Supporting the frontlines: a scoping review addressing the health challenges of military personnel and veterans. Healthcare (Basel). 2023;11(21):2870. doi:10.3390/healthcare11212870

Hospitalizations among active component members of the U.S. Armed Forces, 2024

Mon, 01 Sep 2025 00:00:00 Z

What are the new findings?

The hospitalization rate among U.S. active component service members in 2024 at both military and non-military medical facilities was 47.3 per 1,000 person-years, the lowest since 2015, continuing the general declining trend observed over the previous nine years. It also represents a reduction of 14.0% from the 2015 peak, and 2.8% from the 2023 rate. As in prior years, over half (55.4%) of hospitalizations for active component members were associated with primary diagnoses in two categories: mental health disorders and pregnancy conditions.

What is the impact on readiness and force health protection?

As in prior years, mental health disorders, including substance abuse disorders, were associated with the longest median hospital stay, six days; 5% of hospitalizations for mental health disorders had durations greater than 30 days. Prolonged hospitalizations, after care, and early attrition due to these common disorders can diminish not merely individual but unit operational readiness.

Background

This report documents the frequencies, rates, trends, and distributions of hospitalizations among active component service members of the U.S. Army, Navy, Air Force, Space Force, and Marine Corps during calendar year 2024. Summaries are based on standardized hospitalization records at U.S. military and non-military (reimbursed through the Military Health System) medical facilities worldwide that are routinely maintained in the Defense Medical Surveillance System.

In this report, primary (i.e., first-listed) discharge diagnoses are considered indicative of the primary cause of hospitalization. As in prior MSMR reports, summaries are based on the first three digits of the International Classification of Diseases, 10th Revision codes of the primary discharge diagnoses. Hospitalizations not routinely documented by standardized, automated records, e.g., during field training exercises or while shipboard, are not available in a centralized location for health surveillance purposes and are excluded from this report. Incidence rates were calculated per 1,000 person-years. Percent change in incidence was calculated using unrounded rates.

Frequencies, rates and trends

In 2024, 58,860 hospitalizations were recorded for ACSMs of the U.S. Army, Navy, Air Force, Space Force, and Marine Corps (Table 1); 50.6% of these hospitalizations were in non-military facilities (data not shown), compared to 46.5% in 2023.

Between 2015 and 2024, the total crude hospitalization rates declined gradually from a high of 55.1 per 1,000 p-yrs in 2015 to a low of 47.3 per 1,000 p-yrs in 2024, representing a decrease of 14% during the 10-year surveillance period. For military facilities, the decline was more pronounced, falling from 36.8 per 1,000 p-yrs in 2015 to 23.4 per 1,000 p-yrs in 2024, about a 36% reduction over the same period. The hospitalization rates between 2015 and 2019 were relatively stable, fluctuating within a narrow range. In 2020, an inflection point occurred, with rates dropping more than 10% below the 2019 level. Although rates rebounded near pre-COVID-19 pandemic levels in 2021 and 2022, they subsequently resumed a decline, reaching their lowest levels in 2024 (Figure 1). Since 2020 was an atypical year due to COVID-19, causing disruptions in health care, this report mainly focuses on changes between 2022 and 2024.

Hospitalizations, by ICD-10 major diagnostic categories

In 2024, just 4 ICD-10 major diagnostic categories accounted for almost three-quarters (72.2%) of all active component hospitalizations: mental health disorders (29.2%), pregnancy and delivery (26.2%), injury (8.9%), and digestive system (7.9%) (Table 1). Consistent with findings for 2020 and 2022, hospitalizations for mental health disorders in 2024 accounted for more than any other major diagnostic category; 2009 was the last year in which any other diagnostic category—pregnancy and delivery—surpassed mental health disorder hospitalizations (data not shown).

The largest absolute reduction in hospitalizations occurred in the mental health disorders major diagnostic category, with 4,313 fewer hospitalizations in 2024 compared to 2022, translating into a 16.5% rate decrease (Table 1). The number (rate decrease) of pregnancy and delivery hospitalizations decreased by 1,537 (-9.1%) cases, musculoskeletal conditions by 433 (-12.0%) cases, and the ‘other’ category by 506 (-32.2%) cases. The steepest rate drop, of nearly 80% (234 fewer cases), occurred in COVID-19 hospitalizations. Additional categories with comparatively large declines included injury (-379, -2.7%), digestive system (-326, -2.4%), and signs, symptoms and ill-defined conditions (-258, -6.8%), further contributing to the overall downward trajectory.

At the same time, several major diagnostic categories increased in both frequency and rate of hospitalizations. The largest increases were observed in the respiratory system (225 additional cases, 22.6% rate increase), infectious and parasitic diseases (209, 28.1%), and skin and subcutaneous tissue (80, 16.3%) diagnostic categories.

Hospitalizations, by sex

The hospitalization rate (for all causes) for active component service women in 2024 was more than three times that of service men (112.2 per 1,000 p-yrs vs. 33.2 per 1,000 p-yrs, respectively). These data are consistent with hospitalization rate trends published in 2022 for women and men ages 18-44 years (95 per 1,000 p-yrs and 37 per 1,000 p-yrs, respectively) in the general U.S. population.¹ Excluding pregnancy and delivery, the rate of hospitalizations among women (42.2 per 1,000 p-yrs) was 29.2% higher than among men (33.2 per 1,000 p-yrs) in 2024 (data not shown). This rate difference was primarily due to hospitalizations for mental health disorders (female:male rate difference [RD] 4.8 per 1,000 p-yrs) and genitourinary systems (RD 2.3 per 1,000 p-yrs) (data not shown).

Relationships between age and hospitalization rates varied by major diagnostic category (Figure 2). Rates among women in all age groups were consistently higher for the mental health disorders, genitourinary system, nervous and sense organ diseases, digestive systems, neoplasms, endocrine, nutritional, metabolic, hematological, and immune disorders, and the ‘other’ diagnostic category. As in prior years, the sex gap was greatest for conditions in genitourinary system category, with females admitted at rates three to five times those of males of all age groups. Similarly, hospitalization rates for neoplasms, hematological and immune disorders were more than twice as high among women. In contrast, rates among men were higher than those among women in all age groups for conditions in the respiratory system category. Hospitalization rates of mental health disorders were 50% higher among younger women, under age 30 years, and were comparable among older age groups.

Hospitalization rates among both sexes generally increased with age for most diagnostic categories except mental health disorders, injury, skin and subcutaneous tissue, respiratory system, infectious and parasitic diseases, and COVID-19. Rates decreased for both sexes with increasing age for mental health disorders and were relatively stable among all age groups for infectious and parasitic diseases, skin and subcutaneous tissue categories, and hematological and immune disorders.

Most frequent diagnoses

Mental health disorders represented a significant portion of hospital admissions among ACSMs. Mental health disorder diagnoses, collectively, accounted for over 40% of all hospitalizations among men and women—excluding pregnancy and delivery. Adjustment disorders were the primary discharge diagnosis for both men (n=3,869) and women (n=1,127) (Tables 2 and 3) in 2024, accounting for nearly 30% of total mental health disorder hospitalizations. The next 4 most frequent mental health diagnoses, for both sexes, were alcohol- and depression-related disorders, including recurrent major depressive disorder (severe without psychotic features), and posttraumatic stress disorder. The pregnancy and delivery category constituted the top major diagnostic category for women, accounting for over three-fifths (62.6%) of all female hospitalizations (Table 3).

Other common causes of hospitalization, regardless of sex, included other and unspecified acute appendicitis; sepsis, unspecified organism; and other symptoms and signs involving emotional state; as well as other specified disorders of muscle for men and abnormal uterine and vaginal bleeding for women.

Durations of hospitalizations

When graphically represented, hospitalization durations demonstrate a highly right-skewed (positive) distribution, with the lower limit equal to one day and a mode of three days. Because length of hospital stay is not normally distributed, the median duration with interquartile range was chosen as the best measure of central tendency. The median (IQR) duration of hospital stays (for all causes) has remained generally stable at 3 (2-5) days but increased to 4 (2-6) days in 2022 and has remained at that level (Figure 3).

Median duration days of hospitalization varied substantially by major diagnostic category. The shortest durations of stays (median days, IQR) were observed for musculoskeletal system, genitourinary system, and digestive system hospitalizations (2 days, 2-6). The longest stays were for mental health disorder (6 days, 4-13) and ‘other’ (5 days, 3-16) hospitalizations. The remaining categories had a median of 3 (2-7) days.

Five percent of hospitalization stays exceeded 10 days for one half of ICD diagnostic categories: hematological and immune disorders (11 days), infectious and parasitic diseases (12 days), circulatory system (14 days), signs, symptoms and ill-defined conditions (22 days), nervous system and sensory organ diseases (23 days), neoplasms (24 days), injury (30 days), mental health disorders (34 days), and ‘other’ (primarily orthopedic aftercare and rehabilitation following prior illness or injury) (42 days) (Figure 4).

Hospitalizations, by service

Among active component members of the Navy, Air Force, and Space Force, pregnancy and delivery accounted for more hospitalizations than any other diagnostic category, while among ACSMs of the Army and Marine Corps, mental health disorders were the leading cause of hospitalization (Table 4). Prior to 2020, pregnancy and delivery were ranked first for both Navy and Air Force ACSMs. Among all the services, the crude hospitalization rate for mental health disorders in 2024 was highest among Army ACSMs (16.1 per 1,000 p-yrs).

Injury was the third leading hospitalization category among Army and Marine Corps ACSMs, 5.4 per 1,000 p-yrs and 5.0 per 1,000 p-yrs, respectively. Among Navy, Air Force and Space Force ACSMs, the third highest rate of hospitalizations was for the digestive system category, at 3.8, 3.0, and 2.8 per 1,000 p-yrs, respectively.

Discussion

The 2024 crude annual hospitalization rate marks the lowest recorded level since 2015, continuing a general downward trend observed over the last 10 years. The decline appears largely driven by reductions in hospitalizations in mental health disorders, pregnancy and delivery, musculoskeletal system, and ‘other’ categories. A significant decrease in hospitalizations in 2020 coincided with COVID-19 pandemic-related changes in health care provision, while the post-pandemic period saw a dramatic drop in COVID-19 hospitalizations.

As in past years, in 2024 mental health disorders accounted for more hospitalizations than any other major diagnostic category. Within the mental health disorders category, adjustment disorders, alcohol dependence, depressive disorders, and PTSD were among the leading primary discharge diagnoses for both men and women. At the same time, modest increases were observed in both hospitalization frequencies and rates for respiratory system, infectious and parasitic diseases, and skin and subcutaneous tissue categories. Neoplasms and circulatory system categories demonstrated small absolute declines but slight rate increases, likely due to denominator (person-time) or demographic fluctuations. Although the overall hospitalization rate continued to decline in 2024, these findings indicate that the downward trend was not uniform for diagnostic categories.

Certain limitations should be considered when interpreting these results. This summary is based on primary (first-listed) discharge diagnoses only, but in many hospitalized cases, multiple conditions can be present; for example, joint pain (category, musculoskeletal) may be co-listed with an injury (category, injury). In such cases, only the first-listed discharge diagnosis would be accounted in this report. Discharge coding among multiple categories could lead to under-estimation of hospitalization rates for common conditions. Since May 2023, DMSS data have been housed and analyzed from the Military Health System Information Platform. All military treatment facilities are now using GENESIS software to electronically capture medical care. Data completeness issues related to data transfers from GENESIS to the Medical Data Store to DMSS have improved significantly. Regardless of the electronic system used to capture hospitalizations, every hospitalization record requires completion of a discharge summary before the event record is reported in the system. Consequently, timeliness of reporting can still be an issue that may lead to under-estimates of true counts and rates of hospitalizations for the most recent year of reporting. As a result, direct comparison between the 2024 data and data from prior years should be interpreted with caution.

Reference

National Center for Health Statistics, Centers for Disease Control and Prevention. Table: people with hospital stays in the past year, by selected characteristics—United States, selected years 1997–2019. National Hospital Care Survey. U.S. Dept. of Health and Human Services. Accessed Aug. 25, 2025. https://www.cdc.gov/nchs/data/hus/2020-2021/hospstay.pdf

Medical evacuations out of U.S. Central and U.S. Africa Commands among the active and reserve components of the U.S. Armed Forces, 2024

Mon, 01 Sep 2025 00:00:00 Z

What are the new findings?

Non-battle injuries constituted the most frequent diagnostic categories for service members medically evacuated in 2024 from U.S. Central Command and U.S. Africa Command. Of the 714 CENTCOM service members and 171 AFRICOM service members evacuated for medical reasons in 2024, hospitalization was required for 228 (31.9%) and 42 (24.6%), respectively. Most service members evacuated from CENTCOM or AFRICOM were returned to full duty status after their post-evacuation hospitalizations or outpatient evaluations.

What is the impact on readiness and force health protection?

In 2024, evacuations for disease and non-battle injuries from U.S. CENTCOM and AFRICOM were similar to numbers observed in 2022 and 2023. Non-battle injuries and mental health disorders are the leading causes for medical evacuations and should remain the focus for future prevention efforts.

Background

This report summarizes the nature, numbers, and trends of conditions for which military members were medically evacuated from the U.S. Central Command or Africa Central Command operations in 2024, with historical comparisons to the previous four years. During deployed military operations, initial medical care is provided by military medical personnel stationed within the operational theater, but some injuries and illnesses require medical care outside the theater of operation. In such cases, affected individuals may be transported to a permanent military medical facility, usually in Europe or the U.S., for definitive diagnosis or care. Because medical evacuations are resource-intensive, they are employed for serious medical conditions, some of which are directly related to participation in, or support of, military operations. Other medical conditions that are unrelated to operational activities but necessitate medical evacuation may be preventable.

With completion of the withdrawal of all U.S. military forces from Afghanistan on August 31, 2021, followed by the conclusion of the U.S. combat mission in Iraq on December 9, 2021,^1,2 U.S. military operations were substantially reduced in the CENTCOM area of responsibility. To sustain counterterrorism operation successes, force deployment continues in all AORs, in addition to assistance, advice, and accompaniment of selected partners’ security forces.³

This report only includes medical evacuations from CENTCOM and AFRICOM, without describing any medical evacuations from troop deployment to the U.S. European Command, U.S. Indo-Pacific Command, or U.S. Southern Command. MSMR has historically reported medical evacuations from CENTCOM due to large numbers of service members deployed for named operations including Operation Iraqi Freedom, Operation Enduring Freedom, and Operation New Dawn. The AFRICOM AOR was added to this annual report in 2021 due counterterrorism force deployment.³ Future reports may review medical evacuations from other AORs, as required by leadership interest or changing operational tempos.

Methods

The surveillance population for this analysis includes all members of the active and reserve components of the U.S. Army, Navy, Air Force, Space Force, and Marine Corps deployed to the CENTCOM or AFRICOM AORs for any length of time from January 1, 2020 through December 31, 2024. Medical evacuations by the U.S. Transportation Command from the CENTCOM or AFRICOM AORs to a medical treatment facility outside the operational theater were assessed from records maintained in the TRANSCOM Regulating and Command & Control Evacuation System. CENTCOM and AFRICOM evacuation data are presented separately.

Medical evacuations were classified by the cause and nature of the precipitating medical condition, based on information in relevant evacuation and medical encounter records. All medical evacuations were classified as battle injuries or non-battle injuries and illnesses, based on entries in the TRAC2ES evacuation record. Evacuations due to non-battle injuries and illnesses were further classified into 18 illness and injury categories based on International Classification of Diseases, 9th and 10th Revisions diagnostic codes reported in medical encounter records following evacuation.

All records of hospitalizations and ambulatory visits at a permanent military medical facility in the U.S. or Europe within an interval of 5 days preceding to 10 days following the reported date of each medical evacuation were identified from Defense Medical Surveillance System data. The primary (i.e., first-listed) diagnosis for either hospitalization or earliest ambulatory visit after evacuation was used to classify the condition that necessitated the evacuation. If the first-listed diagnostic code specified an external cause of injury (ICD-9 ‘E’ code, ICD-10 ‘V’, ‘W’, ‘X’, or ‘Y’ codes) or an encounter for a condition other than a current illness or injury, the secondary diagnosis specifying illness or injury (ICD-9, 001–999; ICD-10, A00–T88, U07.1, U09.9) was used. If no secondary diagnosis was provided, or if the secondary diagnosis also was an external cause code, the first-listed diagnostic code of a subsequent encounter was used.

Results

In 2024, there were 714 medical evacuations from the CENTCOM AOR and 171 from the AFRICOM AOR. These medical evacuations were required to be associated with at least 1 subsequent medical encounter at a permanent medical facility outside the operational theater, within the requisite inclusion timeframe (Table 1). Non-battle injuries accounted for the most medical encounters after an evacuation from both CENTCOM (n=198, 27.7%) and AFRICOM (n=52, 30.4%) (Table 1). Mental health disorders accounted for the second-most medical encounters following a CENTCOM evacuation (n=196, 27.5%).

Annual CENTCOM medical evacuations attributable to battle injuries were highest in 2020 (n=59) and subsequently decreased in 2021 (n=7), 2022 (n=3), 2023 (n=14) and 2024 (n=20), following the conclusion of major combat operations (data not shown). Annual CENTCOM medical evacuations attributable to non-battle injuries also declined, from 1,134 to 694 during the 2020–2024 surveillance period (Figure). Annual medical evacuations from AFRICOM attributable to battle injuries peaked at four in 2020, falling below this number in 2021 (n=1), 2022 (n=2), 2023 (n=1) and 2024 (n=0) (data not shown). Notably, the annual number of AFRICOM medical evacuations attributable to non-battle injuries and diseases remained much lower than CENTCOM during the 2020–2024 surveillance period (Figure).

Demographic and military characteristics

The leading major diagnostic categories following evacuations from CENTCOM in 2024 were non-battle injuries for men (n=173, 30.7%) and mental disorders for women (n=49, 32.9%). In AFRICOM, the leading major diagnostic categories in 2024 were non-battle injuries for both men (n=45, 30.2%) and women (n=7, 31.8%) (Table 1). Female CENTCOM service members had a higher proportion of medical evacuations for mental health disorders compared to male CENTCOM service members (32.9% and 25.8%, respectively) (Table 1).

The largest numbers and proportions of evacuees from CENTCOM and AFRICOM involved non-Hispanic White service members, those aged 20-24 years, members of the Army, and senior enlisted personnel. Most medical evacuations from CENTCOM (86.7%) and AFRICOM (85.4%) were assigned routine precedence (Table 2).

Most frequent specific diagnoses

Among men and women in both AORs, the leading 3-digit ICD-10 code for mental health disorders (F43) indicated reaction to severe stress and adjustment disorders (Table 3). This ICD-10 code represented over two-thirds of the mental disorder diagnoses among men in CENTCOM and women in both AORs (data not shown). In CENTCOM, evacuations for other joint disorders and wrist/hand fractures were the second- and third-most common 3-digit ICD-10 codes for men (Table 3).

Disposition

Hospitalization was required for 228 (31.7%) of CENTCOM (n=714) and 42 (24.6%) of AFRICOM (n=171) medical evacuees in 2024 (data not shown).

Discussion

In 2024, only 20 (2.8%) medical evacuations from CENTCOM and none (0) from AFRICOM were associated with battle injuries in TRAC2ES records. Evacuations for disease and non-battle injuries from CENTCOM and AFRICOM in 2024 remained similar to numbers observed in 2022 and 2023. These trends reflect the continued counterterrorism force deployment throughout CENTCOM and AFRICOM AORs.³

The leading diagnoses of AFRICOM non-battle injuries were not clustered around any specific ICD-10 code but were distributed among diagnoses such as muscle and tendon injuries and fractures. This heterogeneity of injury type may be due to the large proportion due to occupational hazards in the deployed environment. Classification by cause of injury, rather than affected body system, may be more appropriate for this population; the ICD chapter for external causes of morbidity codes is intended for secondary coding purposes and is not mandatory, however. Consequently, completeness and specificity of these external cause codes for injury-related diagnoses may vary according to coding practices.⁴

The leading diagnoses of CENTCOM non-battle injuries were also heterogenous and included unclassified joint disorders, fractures, dislocation and sprains, and tendon injuries. The proportion of CENTCOM medical evacuations attributed to mental health disorders in 2023 (27.5%, n=199) and 2024 (27.5%, n=196) represents a sustained decline after increasing proportional trends reported in 2020 (27.1%, n=323), 2021 (33.3%, n=321), and 2022 (38.6%, n=267).^5-8 The proportions of medical evacuations due to mental health disorders are considerably higher than the proportion (11.6%, n=5,892) described by a MSMR report that examined evacuations from Iraq during a 9-year period from 2003 through 2011.⁹

Several important limitations should be considered when interpreting these results. Demographic data for the deployed population, i.e., person-time for individuals eligible for medical evacuation, are not readily available. The lack of deployed individual person-time precludes calculation of stratified and overall rates for medical evacuations.

Most causes of medical evacuations were estimated for this report from primary (i.e., first-listed) diagnoses in DMSS recorded during hospitalizations or initial outpatient encounters following evacuation. Diagnoses recorded in theater through the Theater Medical Data Store are not reflected in this analysis. In some cases, clinical evaluations at fixed medical treatment facilities following medical evacuation may have eliminated serious conditions that were clinically suspected while in theater, resulting in possible misclassification errors.

Battle injuries rely on proper classification in the TRAC2ES system. Misclassification errors may occur, and given the small number of battle injuries, any misclassification will have a disproportionate effect.

References

C-SPAN. White House Event: President Biden Remarks on US Withdrawal from Afghanistan. Aug. 31, 2021. Accessed Apr. 18, 2025. https://www.c-span.org/program/white-house-event/president-biden-remarks-on-us-withdrawal-from-afghanistan/602343
Kullab, S, The Associated Press. US formally ends combat mission in Iraq. Military Times. Dec. 9, 2021. Accessed Apr. 18, 2025. https://www.militarytimes.com/news/your-military/2021/12/09/us-formally-ends-combat-mission-in-iraq
The White House. Letter to the Speaker of the House and President pro tempore of the Senate Regarding the War Powers Report. Dec. 6, 2024. Accessed Apr. 18, 2025. https://bidenwhitehouse.archives.gov/briefing-room/statements-releases/2024/12/06/letter-to-the-speaker-of-the-house-and-president-pro-tempore-of-the-senate-regarding-the-war-powers-report-5
Canham-Chervak M, Schuh-Renner A, Stahlman SL, Rappole C, Jones BH. External cause coding of injury encounters in the Military Health System among active component U.S. service members, 2016-2019. MSMR. 2025;32(2):2-9. Accessed Aug. 21, 2025. https://www.health.mil/news/articles/2025/02/01/msmr-injury-cause-coding
Armed Forces Health Surveillance Division. Medical evacuations out of the U.S. central command, active and reserve components, U.S. Armed Forces, 2020. MSMR. 2021;28(5):28-33. Accessed Aug. 21, 2025. https://www.health.mil/reference-center/reports/2021/05/01/medical-surveillance-monthly-report-volume-28-number-05
Armed Forces Health Surveillance Division. Medical evacuations out of the U.S. central and U.S. Africa commands, active and reserve components, U.S. Armed Forces, 2021. MSMR. 2022;29(6):27-33. Accessed Aug. 21, 2025. https://www.health.mil/news/articles/2022/06/01/medevac-msmr
Armed Forces Health Surveillance Division. Medical evacuations out of U.S. central and U.S. Africa commands among active and reserve components, U.S. Armed Forces, 2022. MSMR. 2023;30(7):6-10. Accessed Aug. 21, 2025. https://www.health.mil/news/articles/2023/07/01/medical-evacuations
Armed Forces Health Surveillance Division. Medical evacuations out of U.S. central and Africa commands among the active and reserve components of the U.S. Armed Forces, 2023. MSMR. 2024;31(7):2-6. Accessed Aug. 21, 2025. https://www.health.mil/news/articles/2024/07/01/msmr-medical-evacuations-2023
Armed Forces Health Surveillance Center. Medical evacuations from Operation Iraqi Freedom/Operation New Dawn, active and reserve components, U.S. Armed Forces, 2003-2011. MSMR. 2012;19(2):18-21. Accessed Aug. 21, 2025. https://www.health.mil/reference-center/reports/2012/01/01/medical-surveillance-monthly-report-volume-19-number-2

Absolute and relative morbidity burdens attributable to various illnesses and injuries among non-service member beneficiaries of the Military Health System, 2024

Mon, 01 Sep 2025 00:00:00 Z

What are the new findings?

In 2024, mental health disorders accounted for the largest proportions of morbidity and health care burdens that affected the pediatric and younger adult age groups of non-service member Military Health System beneficiaries. Among adult beneficiaries older than age 45, musculoskeletal diseases was the leading diagnostic category for medical encounters. While provision of care from purchased care reimbursements or military medical facilities varied by age category, a majority of non-service member beneficiaries received care exclusively from private sector facilities.

What is the impact on readiness and force health protection?

Military Health System beneficiaries are a diverse, heterogeneous population of service members, retirees, and family members from all branches of military service under the U.S. Department of Defense. Each category of beneficiaries presents its own demographic, enrollment, and health care use patterns. The 2024-2029 Military Health System Strategy calls to attract and re-attract beneficiaries to military medical facilities, to improve efficiency, enrich the clinical experience for the ready medical force, and consciously fulfill the nation’s promise to care for Military Health System beneficiaries. Routinely documented and reported trends in health care use and diagnostic patterns can help senior leaders improve resource allocation within the Military Health System to maximize efficiency, medical readiness, and the readiness of the medical forces.

Background

The Military Health System, a global, integrated health delivery system, is tasked with ensuring the medical readiness of the U.S. Armed Forces while fulfilling the individual health care needs of eligible military personnel and their dependents.¹ The MHS network comprises military hospitals and clinics worldwide (collectively called the “ direct care system”), complemented by programs that enable care in the private sector through the TRICARE insurance program. While the first mission of the MHS enables the National Defense Strategy through a medically ready force, the inter-related mission to provide a medical benefit commensurate with the service and sacrifice of the U.S. Armed Forces extended TRICARE eligibility to approximately 9.4 million beneficiaries in fiscal year 2024.^2,3

MHS beneficiaries are a diverse and heterogeneous population of service members, military retirees, and family members from all branches of military service under the authority of the Department of Defense.² Accordingly, each beneficiary category presents its own demographic, enrollment, and health care provision patterns. In fiscal years 2024 through 2029, the Military Health System Strategy prioritizes stability for the direct care system through a dedicated strategic objective to “attract and reattract beneficiaries to military treatment facilities, to improve efficiency and enrich clinical experience for the Ready Medical Force, and consciously fulfill the promise our nation makes to care for our beneficiaries.”³

Beneficiaries enrolled in TRICARE, including many family members of service members and eligible retirees (primarily those aged 64 years and younger), may receive care at fixed military hospitals and clinics, or from private sector health care facilities that supplement direct military medical care. An important element of beneficiary care is the transition from TRICARE to Medicare. Once an individual reaches age 65, and becomes eligible for Medicare, TRICARE eligibility ends. If individuals enroll in Medicare, they receive a Medicare gap insurance, known as TRICARE for Life (TFL), funded through mechanisms outside of the Defense Health Program. While Medicare-eligible individuals remain eligible for direct care at military medical facilities, such care is contingent upon resource availability. Consequently, distribution of health care burden estimates should be considered in relation to beneficiary age category and source of care when interpreting health care provision data among MHS beneficiaries.

This report represents an updated summary of health care burdens among non-service member MHS beneficiaries during calendar year 2024. Health care burdens were quantified using a classification system derived from the Global Burden of Disease Study,^4-7 in combination with diagnostic groupings from the International Classification of Diseases,10th Revision, Clinical Modification chapter-based system for categorizing hospitalizations and ambulatory visits. This report presents stratified estimates for four age groups of health care recipients, with Medicare-eligible beneficiaries (over age 65 years) considered separately, as most of their care is provided and paid by non-MHS resources.

Methods

The surveillance population included all non-service member MHS beneficiaries who had at least one hospitalization or outpatient medical encounter from January 1 through December 31, 2024, with either a military hospital, clinic or health care provider, or through a private sector facility or provider (if reimbursed through TRICARE or through Medicare with a co-payment by TFL). All inpatient and outpatient medical encounters for this analysis were summarized according to the primary (i.e., first-listed) International Classification of Diseases, 10th Revision (ICD-10) codes that indicate the natures of illnesses or injuries (A00–T88). Nearly all records of encounters with first-listed diagnoses coded with ‘Z’ (care other than for a current illness or injury, e.g., general medical examinations, after care, vaccinations) or ‘V’, ‘W’, ‘X’, or ‘Y’ (indicators of the external causes but not the natures of injuries) were excluded from the analysis; encounters with a code of Z37 (“outcome of delivery”) in the primary position were retained.

For summary purposes, all illness and injury-specific diagnoses (as defined by ICD-10) were grouped into 157 burden of disease-related conditions and 25 major morbidity categories, based upon a modified version of the classification system developed for the Global Burden of Disease Study. This year, four new diagnostic groups were added: pain in foot, chronic rhinitis, neoplasm of uncertain behavior of skin, and disorder of the pituitary gland. The methodology for summarizing absolute and relative morbidity has been used annually since 2014 and is described elsewhere.⁸ Results were stratified by source of health care (direct care, i.e., military hospitals and clinics vs. non-direct care, i.e., private sector medical facilities) and by age group (0-17 years, 18-44 years, 45-64 years, 65 years and older). For analysis of morbidity burdens within the youngest age group, developmental disorders were included in the general category of mental health disorders.

Results

In 2024, the population of non-service member MHS care recipients included more female (56.8%) than male (43.2%) beneficiaries. Adults aged 65 years and older accounted for the highest number of individuals receiving health care (n=2.04 million, 33.0%), followed by pediatric beneficiaries aged 17 years and younger (n=1.46 million, 23.7%), adults ages 18-44 years (n=1.37 million, 22.2%), and older adults ages 45-64 years (n=1.30 million, 21.0%) (Table 1).

A total of 6,180,903 non-service member MHS beneficiaries had 90,357,451 recorded medical encounters in 2024. Over half (50.9%) of these medical encounters were among 2,042,408 MHS beneficiaries aged 65 years or older (Table 1). Provision of care for this age group was almost exclusively outsourced, with 91.0% of individuals age 65 years or older having medical encounters or hospital bed days documented only from purchased care reimbursements at private sector facilities (Table 2).

Among TRICARE-eligible beneficiaries (under age 65 years), provision of care was also primarily exclusively from outsourced care. Adults ages 18-44 years received approximately one-third of their care exclusively from military clinics and hospitals (14.1%) or a combination of direct and outsourced care (20.4%) (Table 2). The three most frequent morbidity-related categories accounting for the most medical encounters among TRICARE-eligible beneficiaries included mental health disorders, signs or symptoms of ill-defined conditions, and injury (Figure 1a). Mental health disorders also represented the leading category for hospital bed days among beneficiaries under age 65 years, followed by maternal conditions (Figure 1b).

Pediatric beneficiaries under age 18 years

Pediatric patients accounted for 15.0% of all medical encounters, 23.7% of all individuals affected, and 8.1% of all hospital bed days among non-service member MHS beneficiaries in 2024 (Table 1). On average, each pediatric beneficiary had 9.3 medical encounters during the year. Provision of care for pediatric patients was primarily through exclusive use of purchased care reimbursement in private settings (68.9%), followed by a combination of direct and outsourced care (19.8%). Only 11.3% of pediatric patients received all medical encounters or hospital bed days from direct MHS care (Table 2).

In 2024, mental health disorders represented the largest burden of disease among pediatric beneficiary medical encounters (38.7%, n=5,260,830) and contributed to the highest number of hospital bed stays (58.1%, n=295,259) (Figures 2a, 2b). On average, pediatric beneficiaries affected by a mental health disorder experienced 15.9 medical encounters during the year specifically related to this morbidity category (data not shown). More than two-thirds (69.2%) of all medical encounters for mental health disorders among pediatric beneficiaries were attributed to three groups of disorders: autistic disorder and pervasive developmental disorders (33.8%), developmental disorders of speech and language (24.4%), and attention-deficit hyperactivity disorders (11.0%) (Figure 2c). Pediatric patients affected by an autistic disorder had, on average, 41.2 autism-related encounters per individual (data not shown). Despite the high numbers of encounters associated with these 3 categories of mental health disorders, over two-thirds (68.6%) of hospital bed days related to mental health disorders were attributable to mood disorders. Among all mood disorder-related bed days, over 50% were attributed to two diagnostic categories: recurrent severe major depressive disorder without psychotic features (30.6%, ICD10: F332) and disruptive mood dysregulation disorder (28.5%, ICD10: F3481) (data not shown).

Perinatal conditions, or medical issues occurring within one year of birth, accounted for the second highest number of hospital bed days (n=45,612, 9.0%) in 2024 among pediatric beneficiaries, after mental health disorders (Figures 2a, 2b). Pediatric beneficiaries affected by malignant neoplasms had, on average, 12.6 neoplasm-related encounters per individual. The highest numbers of malignant neoplasm-related encounters and hospital bed days were attributable to leukemias (data not shown).

Respiratory infections (including upper and lower respiratory infections and otitis media) accounted for more medical encounters among pediatric beneficiaries (10.1%) compared to any older age group of beneficiaries (Figures 2b, 3b, 4b, 5b).

Beneficiaries ages 18–44 years

Non-service member beneficiaries ages 18-44 years accounted for 14.9% of all medical encounters, 22.2% of all individuals affected, and 9.8% of hospital bed days in 2024 (Table 1). On average, each individual aged 18-44 years affected with an illness or injury (of any cause) had 9.8 medical encounters during the year. Provision of care for beneficiaries ages 18-44 was primarily through exclusive use of purchased care reimbursement in private settings (65.5%), followed by a combination of direct and outsourced care (20.4%). Only 14.1% of beneficiaries ages 18-44 years received all medical encounters or hospital bed days from direct MHS care (Table 2).

Mental health disorders accounted for the most medical encounters (n=3,304,305, 24.6%) among adult MHS beneficiaries ages 18-44 years in 2024 (Figures 3a, 3b), also representing over one-fifth (20.7%) of total hospital bed days, and, on average, 8.5 mental health disorder-related encounters per individual. Anxiety disorders (35.9%), mood disorders (29.2%), and adjustment disorders (14.8%) accounted for over three-quarters (79.9%) of all medical mental health disorder encounters (data not shown). Mood and substance abuse disorders accounted for over three-quarters (47.1% and 28.6%, respectively) of hospital bed days for mental health disorders.

Maternal conditions accounted for more than two-fifths (n=274,180, 44.6%) of all hospital bed days among adults ages 18-44 years, as well as, on average, 6.7 medical encounters per affected individual (Figures 3a, 3b). Of the 274,180 hospital bed days for maternal conditions, 62.4% were attributed to pregnancy complications and 20.2% to infant deliveries (data not shown).

Malignant neoplasms, as a diagnostic group, resulted in 6.9 encounters, on average, per individual in 2024. Of the 104,672 medical encounters for malignant neoplasms among adults ages 18-44 years, 32.8% were attributed to malignant neoplasm of the breast (data not shown).

Beneficiaries ages 45–64 years

Non-service member beneficiaries ages 45-64 years constituted approximately one-fifth (19.2%) of all medical encounters, 21.0% of all individuals affected, and 12.5% of hospital bed days in 2024 (Table 1). Each affected individual aged 45-64 years had, on average, 13.3 medical encounters during the year. Provision of care for beneficiaries ages 45-64 years was primarily through exclusive use of purchased care reimbursement in private settings (71.8%), followed by a combination of direct and outsourced care (20.4%). Only 7.8% of beneficiaries ages 45-64 years received all medical encounters or hospital bed days from direct MHS care (Table 2).

Of all morbidity-related categories, musculoskeletal diseases accounted for the most medical encounters (n=2,469,102, 14.3%) among older adult beneficiaries ages 45-64 years (Figures 4a, 4b); back problems accounted for 41.9% of these musculoskeletal disease-related encounters (data not shown). Injury represented the highest proportion of hospital bed days (17.4%), second to cardiovascular disease (16.2%) among adults ages 45-64 years (data not shown). Digestive diseases (9.3%) and malignant neoplasms (7.9%) accounted for larger percentages of total hospital bed days among beneficiaries of this age group, compared to other age groups.

Malignant neoplasm of the breast represented the leading cause of neoplasm-related encounters (25.9%) in adult beneficiaries ages 45-64 years (data not shown).

Medicare-eligible beneficiaries, ages 65 and older

Non-service member beneficiaries aged 65 years and older accounted for the most medical encounters (50.9%) and more than 2.3 times the number of hospital bed days in 2024 than all other age groups combined. On average, each affected individual in this age group had 22.5 medical encounters during the year (Table 1). The provision of care for Medicare-eligible beneficiaries ages 65 and older was primarily through exclusive use of purchased care reimbursement in private settings (91.0%); only 2.1% received all medical encounters or hospital bed days from direct MHS care (Table 2).

Musculoskeletal diseases (n=6,856,411, 14.9%) and cardiovascular diseases (n=6,323,595, 13.7%) together represented the leading causes for medical encounters among beneficiaries aged 65 years or older, while injury (n=955,546, 21.9%) and cardiovascular diseases (877,678 days, 20.1%) were the leading diagnostic categories for hospital bed days (Figures 5a, 5b). Back problems accounted for a little more than one-third (35.2%) of all musculoskeletal disease-related medical encounters (data not shown).

Discussion

This report documents the overall health care burden of disease among non-service member MHS beneficiaries received through direct care at military hospitals and clinics, in addition to purchased care reimbursements from private sector facilities. In 2024, a substantial majority of non-service member MHS beneficiaries received medical care exclusively at private sector facilities, as only 8.1% of all ambulatory encounters and 4.4% of hospital bed days in 2024 were from direct care at military medical facilities.

The National Ambulatory Medical Care Survey of 2019 documented a substantially lower rate of ambulatory visits (3.2 visits per p-yr)⁹ among the general U.S. population than among non-service member MHS beneficiaries (14.6 visits per p-yr) reported here. This higher rate of ambulatory visits among non-service member beneficiaries compared to national civilian data was observed for all age groups. Since the National Ambulatory Medical Care survey includes uninsured individuals, financial barriers to care may explain a portion of the lower overall use rate among the general U.S. population, while the families of uniformed personnel require more medical procedures in practice, which is reflected in the composition of the most common directly-provided and purchased procedures.^10,11

As in previous years, mental health disorders were the leading cause for medical encounters within the pediatric (0-17 years) and young adult (18-44 years) beneficiaries age groups, although the proportion of medical encounters attributed to mental health disorders was markedly lower among young adult (24.6%) than pediatric (38.7%) beneficiaries. Developmental disorders were a significant factor for pediatric beneficiary health care, with almost 70% of medical encounters for mental health disorders attributable to autistic disorder and pervasive developmental disorders, specific developmental disorders of speech and language, or attention-deficit hyperactivity disorders.

The leading diagnostic categories for medical encounters and hospitalizations among adult beneficiaries also reflects 2023 data.¹² Among adults older than age 45 years, musculoskeletal diseases continue to represent the leading medical encounter diagnostic category. As in prior years, maternal conditions in adult beneficiaries ages 18-44 years accounted for the highest proportion of hospital bed days. Injury and cardiovascular diseases represent the leading diagnostic category for hospitalization among those aged 45 years and older.

When comparing 2023 and 2024 ambulatory encounters (90,192,185 vs. 90,357,451, respectively) and hospital bed days (6,083,009 vs. 6,261,731, respectively) among non-service member MHS beneficiaries, both remained relatively stable. Since this report does not include person-time nor approximate rates, annual comparisons are not proportionate to changes in the numbers of beneficiaries procuring care. While this report aims to describe morbidity-related diagnoses for all MHS beneficiaries, the data are limited to beneficiaries who received care at military hospitals and clinics, or at private sector medical facilities and reimbursed through TRICARE (as primary or secondary insurance) or through Medicare, if TFL was also billed. Certain forms of care provision, such as that paid with other health insurance and not billed to TRICARE, or paid directly by the patient (or family member), are not captured in this report.

The Military Health System Strategy for Fiscal Years 2024-2029 calls for additional capacity, to facilitate the return of patients including non-service member beneficiaries to military hospitals and clinics, improve their access to care, and increase opportunities for sustaining military clinical readiness for medical forces while delivering quality care to beneficiaries.^1,12 The need to “attract and reattract” beneficiaries to the direct care setting may be reflected in the data throughout this report, which indicate a substantial proportion of medical encounters and hospitalizations for non-service member MHS beneficiaries exclusively from private sector care. Continued evaluation of health care provision and diagnostic patterns may aid senior leaders’ allocation of resources for realization of the current MHS strategy and goals.

References

Mendez BHP, Congressional Research Service. Defense Primer: Military Health System. In Focus (10530). Library of Congress. Updated Oct. 2024. Accessed Aug. 5, 2025. https://www.congress.gov/crs-product/if10530
Analytics and Evaluation Division, Defense Health Agency. Evaluation of the TRICARE Program: Fiscal Year 2024 Report to Congress: Access, Cost, and Quality Data Through Fiscal Year 2024. Office of the Assistant Secretary of Defense (Health Affairs), U.S. Dept. of Defense. Accessed Aug. 5, 2025. https://health.mil/reference-center/reports/2024/09/23/annual-evaluation-of-the-tricare-program-fy24
Military Health System. Military Health System Strategy: Fiscal Years 2024-2029. Defense Health Agency, U.S. Dept. of Defense. Accessed Aug. 5, 2025. https://health.mil/reference-center/publications/2023/12/15/mhs_strategic_plan_fy24_29
Murray CJ, Lopez AD, Jamison DT. The global burden of disease in 1990: summary results, sensitivity analysis and future directions. Bull World Health Organ. 1994;72(3):495-509. Accessed Aug. 26, 2025. https://iris.who.int/bitstream/handle/10665/41177/9241561750_en_part2.pdf;jsessionid=3145C7676FA5B9E812A71046BA6326A2?sequence=2
World Health Organization. The Global Burden of Disease: 2004 Update. World Health Organization;2008. Accessed Aug. 26, 2025. https://www.who.int/publications/i/item/9789241563710
Murray CJL. The Global Burden of Disease Study at 30 years. Nat Med. 2022;28(10):2019-2026. doi:10.1038/s41591-022-01990-1
Roser M, Ritchie H, Spooner F. Burden of Disease. Our World in Data. Updated Feb. 2024. Accessed May 2, 2024. https://ourworldindata.org/burden-of-disease
Murray CJL, Lopez AD, eds. The Global Burden of Disease: A Comprehensive Assessment of Mortality and Disability from Diseases, Injuries, and Risk Factors in 1990 and Projected to 2020. Harvard University Press;1996:120-122.
Santo L, Kang K., National Center for Health Statistics. National Ambulatory Health Care Survey: 2019 National Summary. Centers for Disease Control and Prevention, U.S. Dept. of Health and Human Services 2019. Accessed May 24, 2024. https://www.cdc.gov/nchs/data/ahcd/namcs_summary/2019-namcs-web-tables-508.pdf
Frakes MD, Gruber J, Justicz T. Public and private options in practice: the Military Health System. Am Econ J Econ Policy. 2023;15(4):37-74. doi:10.1257/pol.20210625
Wooten NR, Brittingham JA, Pitner RO, et al. Purchased behavioral health care received by Military Health System beneficiaries in civilian medical facilities, 2000-2014. Mil Med. 2018;183(7-8):e278-e290. doi:10.1093/milmed/usx101
Armed Forces Health Surveillance Division. Absolute and relative morbidity burdens attributable to various illnesses and injuries among non-service member beneficiaries of the Military Health System, 2023. MSMR. 2024; 31(7):11-20. Accessed Aug. 26, 2025. https://www.health.mil/news/articles/2024/07/01/msmr-mhs-beneficiaries-2023
Mincher R, Military Health Systems Communications. Military Health System Stabilization: Rebuilding Health Care Access Is Critical to Patient’s Well-Being. U.S. Dept. of Defense. 2024. Accessed May 10, 2024. https://www.defense.gov/news/news-stories/article/article/3652092/military-health-system-stabilization-rebuilding-health-care-access-is-critical

Surveillance snapshot: Illness and injury burdens among reserve component members of the U.S. Armed Forces, 2024

Mon, 01 Sep 2025 00:00:00 Z

Reportable medical events at Military Health System facilities through week 27, ending June 30, 2025

Mon, 01 Sep 2025 00:00:00 Z

Reportable Medical Events are documented in the Disease Reporting System internet by health care providers and public health officials throughout the Military Health System for monitoring, controlling, and preventing the occurrence and spread of diseases of public health interest or readiness importance. These reports are reviewed by each service’s public health surveillance hub. The DRSi collects reports on over 70 different RMEs, including infectious and non-infectious conditions, outbreak reports, STI risk surveys, and tuberculosis contact investigation reports. A complete list of RMEs is available in the 2022 Armed Forces Reportable Medical Events Guidelines and Case Definitions.¹ Data reported in these tables are considered provisional and do not represent conclusive evidence until case reports are fully validated.

Total active component cases reported per week are displayed for the top five RMEs for the previous year. Each month, the graph is updated with the top five RMEs, and is presented with the current month’s (June 2025) top five RMEs, which may differ from previous months. COVID-19 is excluded from these graphs due to changes in reporting and case definition updates in 2023.

Authors’ Affiliation

Defense Health Agency, Disease Epidemiology Branch, Defense Centers for Public Health–Aberdeen

References

Armed Forces Health Surveillance Division. Armed Forces Reportable Medical Events. Accessed Feb. 28, 2024. https://health.mil/reference-center/publications/2022/11/01/armed-forces-reportable-medical-events-guidelines
Defense Manpower Data Center. Department of Defense Active Duty Military Personnel by Rank/Grade of Service. Accessed Feb. 28, 2024. https://dwp.dmdc.osd.mil/dwp/app/dod-data-reports/workforce-reports
Defense Manpower Data Center. Armed Forces Strength Figures for January 31, 2023. Accessed Feb. 28, 2024. https://dwp.dmdc.osd.mil/dwp/app/dod-data-reports/workforce-reports
Navy Medicine. Surveillance and Reporting Tools–DRSI: Disease Reporting System Internet. Accessed Feb. 28, 2024. https://www.med.navy.mil/navy-marine-corps-public-health-center/preventive-medicine/program-and-policy-support/disease-surveillance/drsi

Surveillance snapshot: Telehealth services among active component members of the U.S. Armed Forces, 2020–2024

Mon, 01 Sep 2025 00:00:00 Z

Telehealth in the Military Health System has long been an important tool for providing care in deployed and non-deployed settings.¹ The U.S. Department of Defense uses telehealth for primary care, medication management,² and other services including outpatient care. Certain types of care provided at fixed military hospitals and clinics, as well as health care encounters outside the military medical system that are billed to TRICARE, are also provided through telehealth.³

This Surveillance Snapshot presents trends in telehealth service use and identifies the 10 most frequent diagnoses addressed via telehealth among U.S. active component service members using Defense Medical Surveillance System outpatient and demographic records from January 2020 through December 2024.

Telehealth services were identified by having a virtual appointment type or by using the Common Procedural Terminology code modifiers 98966–98969, 99374–99380, 99339–99444, 99421–99423, 98000–98007, G0320–G0321, G0425–G0427, G0459, G0508–G0509, D9995, G2061–G2063, C7900–C7902, T1014. The use of telehealth was defined as having at least 1 telehealth encounter per patient per day; if a patient had multiple telehealth encounters per day, the first record was retained as the qualifying encounter. Reasons for telehealth encounters among ACSMs were determined using International Classification of Diseases, 10th Revision codes associated with each telehealth visit. The rate of telehealth encounters was calculated per 10,000 encounter records and stratified by year, patient demographics, and type of care (military clinics or purchased care).

A total of 2,924,428 telehealth encounters were provided to over 1,007,453 ACSMs during the study period. The overall crude rate of telehealth per 10,000 encounters demonstrates an upward trajectory from 2020 to 2024, rising from 228.3 to 515.3 (Table). Over the 5-year study period, women used telehealth at a higher rate than men (345.0 vs. 304.7 per 10,000 encounters, respectively). Direct care from military hospitals and clinics accounted for most telehealth encounters from 2020 through 2024.

The leading 10 reasons for telehealth encounters from 2020 through 2024 were other general symptoms and signs, encounter for other administrative examinations, encounter for immunization, occupational Health Periodic Health Assessment examination, obstructive sleep apnea, low back pain, other specified counseling, pain in right knee, adjustment disorder with mixed anxiety and depressed mood, and pain in left knee (data not shown).

The highest rates observed in 2024 were among male ACSMs (523.7 per 10,000 encounters), those aged 30-34 years (560.2 per 10,000 encounters), Space Force ACSMs (1,185.8 per 10,000 encounters), those treated in a military clinic (771.2 per 10,000 encounters), and ACSMs of other races or ethnicities (543.8) (Table).

The steady increase of telehealth encounter rates from 2020 through 2024 indicates a growing role for virtual care among ACSMs.

Authors’ Affiliation

Defense Health Agency, Epidemiology and Analysis Branch, Armed Forces Health Surveillance Division, Public Health Directorate, Defense Health Agency, Silver Spring, MD: Mr. Adegboye, Dr. Mabila

References

Madsen C, Banaag A, Koehlmoos TP. Analysis of telehealth usage and trends in the Military Health System, 2006-2018. Telemed J E Health. 2021;27(12):1346-1354. doi:10.1089/tmj.2020.0474
Vaudreuil R, Langston DG, Magee WL, Betts D, Kass S, Levy C. Implementing music therapy through telehealth: considerations for military populations. Disabil Rehabil Assist Technol. 2022;17(2):201-210. doi:10.1080/17483107.2020.1775312
Gilder T, Banaag A, Madsen C, Koehlmoos TP. Trends in telehealth care during the COVID-19 pandemic for the Military Health System. Telemed Rep. 2023;4(1):147-155. doi:10.1089/tmr.2022.0042

2025 Military Health System Research Symposium Kicks Off, Showcasing the ‘Highest Standards’ of Military Medicine

Tue, 05 Aug 2025 00:00:00 Z

The 2025 Military Health System Research Symposium, the premier scientific meeting of the Department of Defense focusing on the unique medical needs of the warfighter, launched on Aug. 4 in Kissimmee, Florida.

Dr. Stephen Ferrara, acting assistant secretary of defense for health affairs, expressed his gratitude to researchers in his opening remarks, noting, “It’s a real honor to be here with so many dedicated military and civilian researchers.”

“There are few prouder legacies in military medicine than our research legacy. This audience sits atop broad shoulders, on the shoulders of giants, but you've never rested on that legacy,” said Ferrara. “On the contrary, you're challenged by it. You challenge yourselves to live up to the highest standards, the standards set by those who came before you, not only the famous names, but also the quiet heroes who toiled and innovated anonymously for mission, for country, but most of all for their patient.”

Touching on the conference’s theme, “Supporting the Deployed Warfighter through Military Medical Research,” Ferrara said, “When I talk about the 3 ‘S’s’ that comprise our mission—supporting our warfighter, sustaining our clinical skills, and strengthening our chain—your work exemplifies these priorities.”

Ferrara addressed the challenges facing military medicine brought about by the changing nature of the battlefield.

“The next fight won’t look like the last one,” he said. “The lines are blurred. The frontlines are everywhere. And in this new environment, everything and everyone is a target—including our medics, our supply lines, our communications, even our care platforms.”

“What does this all tell us?” continued Ferrara. “It tells us that military medicine—and military medical research—must evolve from the inside out. We can’t afford to develop in isolation and test in comfort. We must design for the fight we’re going to face, not the one we remember.”

Ferrara said the path forward is clear.

“That means training in chaos, not in calm. It means testing technologies under fire, not under fluorescent lights. And it means asking, ‘Will this help a medic save a life when they are the only one left standing?’ The answer must be yes.”

Supporting the Deployed Warfighter through Military Medical Research

MHSRS provides a unique opportunity for researchers, healthcare professionals, and DOD leaders to share the latest research findings and advances in combat casualty care, military operational medicine, clinical and rehabilitative medicine, and infectious diseases.

The 2025 call for submissions drew 2,744 research abstracts in 69 scientific topic areas covering the four MHSRS focus areas—warfighter medical readiness, expeditionary medicine, warfighter performance, and return to duty—a 15% increase over 2024. The 473 oral presentations chosen by peer review represent the best of this year's submitted abstracts.

The plenary featured remarks from Brig. Gen. (Dr.) Zivan Aviad Beer, surgeon general of the Israeli Defense Forces discussing the IDF Medical Corps' advancements in military medicine during nearly two years of conflict.

“The story of the IDF Medical Corps is one of innovation and transformation,” said Beer. “Our ability to rapidly adapt and transition from ideation to execution makes the medical corps unique, and allowed us to save more lives than ever before.”

A panel discussion on “Drone Warfare and the New Resilience Paradigm” closed the opening session.

“Today, we'll see, hear, and I hope you will feel, the physical and mental injury inflicted by a new type of war, the relentless drone warfare,” said co-moderator Dr. John Holcomb, professor of trauma and acute care surgery at the University of Alabama Birmingham Department of Surgery.

The panel featured three members of the Ukraine Defense Force who spoke of their experiences and knowledge gained facing the challenge of drone warfare.

A series of awards were presented during the symposium’s opening session, recognizing distinguished service to the MHS, and outstanding individual and team achievements. Additional awards will be presented later in the conference for the “Young Investigator” competition and the scientific poster presentations.

The conference also features an exposition showcasing displays from government, military, industry, and academic institutions. Government and military agencies participating in the expo include the Air Force Research Laboratory, 711th Human Performance Wing, Airman Biosciences Division, the National Intrepid Center of Excellence, and the Naval Medical Research Command.

Visit the 2025 MHSRS spotlight page on Health.mil for more news and highlights from this symposium and engage with us on social media using the #MHSRS2025 hashtag.

Surveillance Snapshot: Trends in Hypertension and Hypertensive Disease Among Active Component U.S. Service Members, 2018–2023

Fri, 01 Aug 2025 00:00:00 Z

Hypertension, defined as persistent abnormal elevation of blood pressure above 130/80 mmHg, is estimated to have affected more than 47% of U.S. adults between 2021 and 2023.^1,2 Essential hypertension comprises the majority (95%) of hypertension cases and has no identifiable cause, while secondary hypertension stems from underlying medical conditions such as renal or endocrine disorders.^3,4 As a major risk factor for cardiovascular disease, hypertension can lead to heart and kidney damage if uncontrolled, which highlights the importance of early intervention on modifiable risk factors such as diet and exercise. This study aimed to examine the trend in annual incidence of hypertension and hypertensive disease, as well as the annual percentage of high blood pressure measurements, among active component service members between 2018 and 2023, using data from the Defense Medical Surveillance System.

Incident cases of essential hypertension (International Classification of Diseases, 9th Revision codes 401*; International Classification of Diseases, 10th Revision codes I10*), secondary hypertension (ICD-9: 405*; ICD-10: I15*), and hypertensive crisis (ICD-10: I16*; no equivalent ICD-9 code) were identified by the presence of a single inpatient or outpatient encounter with a diagnosis listed in any diagnostic position. Hypertensive heart or kidney disease (ICD-9: 402*–404*; ICD-10: I11*–I13*) cases required documentation of an inpatient encounter or at least 2 outpatient encounters within 60 days of each other with the diagnosis listed in the first or second diagnostic position. Periodic Health Assessment data were utilized to describe the annual percentages of service members who had one or more high blood pressure measurements, among those who had at least one recorded blood pressure measurement available. A high blood pressure measurement was defined by systolic blood pressure greater than or equal to (≥) 130 mmHg or diastolic blood pressure greater than or equal to (≥) 80 mmHg.

Incidence of diagnosed essential hypertension increased from 128.2 to 189.1 per 10,000 person-years (2018–2023), with a temporary decrease in 2020 likely related to reduced health care access during the COVID-19 pandemic (Figure). The percentage of service members who had at least one recorded high blood pressure measurement increased from 41.5% to 47.4% during the same period, with the largest annual increase occurring between 2019 and 2020. Secondary hypertension decreased from 4.0 per 10,000 person-years (p-yrs) in 2018 to 2.3 per 10,000 p-yrs in 2023 (Table). Hypertensive heart or kidney disease and hypertensive crisis remained stable (averaging 1.5 and 2.8 per 10,000 p-yrs, respectively).

The increase in essential hypertension among U.S. military personnel is consistent with recent increasing trends of risk factors including obesity and type 2 diabetes,⁵ and suggests that military fitness requirements alone are insufficient to prevent the development of hypertension. Military members did not, however, show increased rates of more severe hypertensive conditions, possibly indicating protective factors within military health care or lifestyle. In 2017, the definition for high blood pressure was lowered from 140/90 mmHg to 130/80 mmHg, which raised concerns that increased diagnoses of essential hypertension could be attributed to previously un-diagnosed individuals.⁶ The consistent increase in elevated blood pressure measurements on PHAs suggests a real increase, however, not just more diagnoses occurring under the new guidelines.

Author Affiliations

Epidemiology and Analysis Branch, Armed Forces Health Surveillance Division, Public Health Directorate, Defense Health Agency, Silver Spring, MD: Dr. Stahlman; Navy and Environmental Preventive Medicine Unit TWO, Navy and Marine Corps Force Health Protection Command, Medical Corps, U.S. Navy, Norfolk, VA: LCDR Tantlinger

Disclaimer

The views expressed in this Surveillance Snapshot are those of the authors and do not necessarily reflect official policy nor position of the Department of the Navy, Department of Defense, or the U.S. Government.

References

National Heart, Lung, and Blood Institute. National Institutes of Health. What Is High Blood Pressure? Published Apr. 25, 2024. Accessed Jul. 10, 2025. https://www.nhlbi.nih.gov/health/high-blood-pressure
Fryar CD, Kit B, Carroll MD, Afful J. Hypertension prevalence, awareness, treatment, and control among adults age 18 and older: United States, August 2021-August 2023. NCHS Data Brief. 2024;(511):cS354233.
Carretero OA, Oparil S. Essential hypertension. Part I: definition and etiology. Circulation. 2000;101(3):329-335. doi:10.1161/01.cir.101.3.329
Hegde S, Ahmed I, Aeddula NR. Secondary hypertension. In: StatPearls. StatPearls Publishing;2023.
Stiegmann RA, Payne CB, Kiel MA, Stahlman SL. Increased prevalence of overweight and obesity and incidence of prediabetes and type 2 diabetes during the COVID-19 pandemic, active component service members, U.S. Armed Forces, 2018 to 2021. MSMR. 2023;30(1):11-18. Accessed Jul. 28, 2025. https://www.health.mil/news/articles/2023/01/01/diabetes-during-covid19
Jones DW, Ferdinand KC, Taler SJ, et al. 2025 AHA/ACC/AANP/AAPA/ABC/ACCP/ACPM/AGS/AMA/ASPC/NMA/PCNA/SGIM Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. JACC. In press. Preprint Aug. 14, 2025. Accessed Aug. 25, 2025. https://www.jacc.org/doi/10.1016/j.jacc.2025.05.007

Potential High Arbovirus Exposure in INDOPACOM During U.S. Service Member Deployment or Exercises in Papua New Guinea

Fri, 01 Aug 2025 00:00:00 Z

Abstract

Arboviruses pose a significant health threat to U.S. military personnel deployed in the U.S. Indo-Pacific Command region. In 2023 we conducted a sero-epidemiological study to determine the arboviruses circulating in 185 Papua New Guinea military personnel, using the neutralizing antibody assay. Overall, sero-positivity rates among the 185 PNGMP tested were: anti-Zika virus (ZIKV), 87% (n=161); anti-Japanese encephalitis virus, 62.2% (n=115); anti-Ross River virus, 44.3% (n=82); anti-Murray Valley encephalitis virus, 39.5% (n=73); anti-chikungunya virus, 33.5% (n=62); anti-Barmah Forest virus, 10.8% (n=20); and anti-West Nile virus, 5.9% (n=11). The monotypic NAb sero-positivity rates for dengue virus serotypes were: anti-DENV-1 94.6% (n=175), anti-DENV-2 93% (n=172), anti-DENV-3 95.1% (n=176), and anti-DENV-4 31.4% (n=57). These findings indicate that the majority of PNGMP had prior exposure to DENV and ZIKV, with a notable proportion exposed to CHIKV, RRV, JEV, and MVEV, and lower levels of exposure to BFV and WNV. Low or moderate prior exposure may leave individual PNGMP immunologically naïve and more susceptible to infection and disease upon first exposure. Furthermore, secondary DENV infections with a different serotype can increase risk of severe disease due to immune enhancement mechanisms such as antibody-dependent enhancement. Understanding these exposure patterns is crucial for assessing population risk and informing surveillance and prevention strategies. U.S. soldiers exercising or deploying to Papua New Guinea should adhere to strict preventive measures for minimizing mosquito bites and reducing their risk of arboviral infections.

What are the new findings?

To our knowledge, this study provides the first comprehensive examination of arbovirus sero-positivity rates in Papua New Guinea military personnel following the COVID-19 pandemic. After examining sero-positivity of 11 arboviruses, we found a majority of PNGMP with neutralizing antibodies to dengue and Zika viruses, with some NAb to chikungunya, Japanese encephalitis, Ross River, and Murray Valley encephalitis viruses. Sero-prevalence to Barmah Forest and West Nile viruses was less common.

What is the impact on readiness and force health protection?

This study shows the potential circulation of multiple mosquito-borne viruses in Papua New Guinea. The alarmingly high prevalence of arbovirus in tested Papua New Guinea military personnel reveals the significant risk of environmental exposure to mosquito-borne diseases in that country. These study findings indicate the threat posed to U.S. combatant commands operating in the region, illustrating the need for robust preventive measures that minimize bites from infected mosquitoes. Implementation of thorough vector control strategies and personal protective measures will be critical for mitigating existing arbovirus risks for personnel traveling to or exercising in Papua New Guinea.

Background

Arboviral diseases, transmitted by arthropods such as mosquitoes and ticks, are a significant health threat to deployed U.S. military personnel and the U.S. Military Health System. Arboviruses pose continual risk, and continue to emerge and challenge force health protection. With new outbreaks and evolving vector ecology across the Indo-Pacific region, risk of both endemic and emerging arboviruses remains. Updated sero-surveillance helps to track trends, guide prevention, and strengthen operational readiness.

The U.S. military has a long history of combating arboviral diseases, particularly dengue virus, Zika virus, and chikungunya virus, with DENV the most prevalent.¹ DENV infections have remained a persistent operational threat to the U.S. military since the Spanish–American War.¹ The first isolation of DENV was achieved by Dr. Albert Sabin in 1944, derived from a U.S. soldier who experienced acute illness while deployed to Papua New Guinea.² The original New Guinea C strain of DENV, now known to be a DENV-2 serotype, was followed in the same year by additional isolation, of DENV-1, from another sick soldier stationed in Papua New Guinea.³

Papua New Guinea, the world’s third largest island country, is located in the Southwest Pacific, within the U.S. Indo-Pacific Command area of responsibility. During World War II, Papua New Guinea was a crucial operational theater for the war in the Pacific, with a Japanese invasion in 1942 and subsequent Allied campaign—of primarily Australian and American forces—trying to expel the Japanese. A significant number of mosquito-borne pathogen infections were reported among active Allied troops during WWII after deployment to Papua New Guinea,^1,4 with DENV disease one of the major causes of morbidity among soldiers.⁵

Recently, U.S. military presence in Papua New Guinea has increased, with several multi-national, joint exercises conducted in response to strategic pressures arising from the expansion of China’s military presence in the Southwest Pacific. Surveillance conducted in Papua New Guinea in 2019 revealed a high prevalence rate of ZIKV and moderate CHIKV infections among Papua New Guinea military personnel located at Manus Island and Wewak barracks.^6-8 ZIKV and CHIKV diseases are the second and third most significant arboviral disease threats to U.S. military, particularly during deployments to high-risk regions.⁵ Aedes mosquitoes, the primary vector transmitting ZIKV, are found in nearly 200 U.S. military installations worldwide. From January 1, 2013 through December 31, 2022, 212 ZIKV cases were reported among U.S. service members, based on data from the Disease Reporting System internet and laboratory records from the Composite Healthcare System.⁹ Meanwhile, CHIKV cases within the MHS are increasing, with a significant proportion of those infected experiencing long-term rheumatic complications.¹⁰

Other arboviruses such as Ross River virus, Barmah Forest virus, Japanese encephalitis virus, and Murray Valley encephalitis virus infections have also been reported in Papua New Guinea.^11,12 RRV and BFV, both considered typical Australian arboviruses, are the leading causes of human arboviral diseases in that country,¹³ which is less than 100 miles south of Papua New Guinea. JEV and MVEV are endemic in Papua New Guinea and northern Australia.¹⁴ In 2016 and 2017, RRV and BFV outbreaks occurred among Australian Defence Force personnel in the Shoalwater Bay Training Area of Queensland, in northern Australia.^15,16 A joint training exercise in 1997 resulted in at least 8 U.S. service members contracting RRV among approximately 9,000 U.S. marines and Australian soldiers participating in ground exercises in Queensland.¹⁷ In 2022, a JEV outbreak in Australia resulted in 45 reported cases and 7 deaths. No new human cases of JEV were recorded in Australia from December 2022 until December 2024, when a JEV case was reported in Victoria, in southern Australia. Although the origin of the outbreak remains uncertain, it is believed that migratory birds or wind-blown mosquitoes may have introduced JEV from Papua New Guinea to Australia.¹⁸

There are a few licensed vaccines currently available for arboviruses, and those that are available carry several caveats. The CHIKV vaccine, Ixchiq, approved by the U.S. Food and Drug Administration in 2023, is not yet widely licensed in other countries. For DENV, Dengvaxia (CYDTDV) is approved in several countries but is only recommended for individuals ages 9-45 years with confirmed prior dengue infection. Due to the risk of severe disease in sero-negative recipients, Dengvaxia is not ideal for broad public health use. In contrast, safe and effective vaccines that have long existed for JEV and yellow fever virus are included in routine immunization programs in endemic regions.

For many other medically important arboviruses—including ZIKV, RRV, and MVEV—no licensed human vaccines currently exist, highlighting a significant gap in global prevention efforts. Furthermore, there are no specific anti-arboviral treatments for those diseases; clinical management primarily focuses on symptom relief.

Prevention of mosquito-borne arboviral diseases largely relies on personal protective measures, including long-sleeved uniforms, bed nets, permethrin treatment of uniforms and nets, and DEET (N,Ndiethyl-meta-toluamide)-based mosquito repellents. Given the widespread presence of mosquito vectors in tropical and subtropical areas, arboviruses will likely continue to spread beyond their original regions of discovery, making force health protection in INDOPACOM increasingly challenging.

Use of antibody testing methods such as ELISA (enzyme-linked immunosorbent assay) for determining arbovirus exposure for surveillance is becoming a significant challenge due to broad cross-reactivity among the alphavirus and flavivirus families co-circulating in the same area.¹⁹ Risk of co-infection further complicates serological interpretation.

Although a few serological studies—most conducted in the 1970s—have documented arbovirus exposure and outbreaks in Papua New Guinea, in addition to our own surveillance efforts in 2019, comprehensive epidemiological data remain limited due to lack of diagnostic capacity, inconsistent clinical case reporting, and weak surveillance infrastructure in much of Papua New Guinea. The COVID-19 pandemic placed additional strain on Papua New Guinea health systems. As a result, the true distribution, burden, and temporal trends of arboviral diseases in Papua New Guinea remain incompletely defined.

This study aims to address this important gap in data from Papua New Guinea by providing updated arbovirus serology data from PNGMP stationed at Lae Barracks on the country’s eastern coast (Figure 1). We measured neutralizing antibodies against key arboviruses in this population. Our 2019 survey found high ZIKV and moderate RRV and CHIKV exposure at Wewak and Manus barracks. This follow-up study evaluates whether exposure patterns have shifted, especially post-COVID-19 pandemic.

Methods

The study was approved by the Papua New Guinea Medical Research Advisory Committee (MRAC, no. 18-21) and the Department of Australian Defence and Veteran Affairs Human Research Ethics Committee (DDVA HREC, no. 084-18, no. 157-19). Written informed consent was obtained from all participants.

Study Population Demographics

This study was part of an infectious disease surveillance program conducted by the ADF in conjunction with the Papua New Guinea Defence Force. A total of 185 PNGMP from Lae Barracks were recruited between April 20, 2023 and May 9, 2023. A convenience sample of serum specimens from PNGMP present at Lae Barracks during this period was collected. Participants provided written informed consent for additional infectious disease testing. Additional demographic and exposure data, including serum collection dates, age, sex, travel and vaccination history, and mosquito bite prevention measures, were self-reported via a study questionnaire. Due to security requirements, information on military occupational specialty, rank, and deployment history were not collected. The age range of participants was 24–60 years, with a median age of 35 years. The largest participant age group was 30-39 years (38.9%, n=72), followed by 20-29 years (27%, n=50). The cohort was predominantly male (96.2%, n=178). Forty-two participants reported domestic travel (22.7%), primarily to the capital, Port Moresby, or East or West Highlands regions. Ninety-nine participants (53.5%) reported travel history to Australia, but only 11 (5.9%) had traveled to Australia within 3 months prior to blood collection. All participants were asymptomatic at the time of blood withdrawal, with no reported history of prior infectious disease infections and outcomes, and no reported prior vaccination for JEV and YFV. Demographic features and mosquito prevention practices reported by survey respondents are summarized in Table 1.

All serum samples were bar-coded, stored at -20°C and transported to Brisbane, Australia for neutralization analysis against the arbovirus strains as detailed in the Supplementary Table.

Cells, Viruses and NAb Assays

Vero cells (African green monkey kidney epithelial cells) and C6/36 mosquito cells were routinely cultured in the laboratory and used for micro-neutralization assays. The arboviral strains used for NAb assays in this study are listed in the Supplementary Table. The virus stock preparation and serum NAb titers were assessed using a micro-neutralization assay and modified according to methods previously described.^6-8 NAb titer greater than or equal to 20 for flaviviruses and greater than or equal to 10 for alphaviruses were considered positive.

Statistical Analysis

Data analysis was performed using GraphPad version 9.0 and an online Chi-square test calculator (https://www.socscistatistics.com/tests/chisquare2/default2.aspx) to compare the arbovirus sero-positivity proportions among different age groups. P-values less than or equal to 0.05 were considered statistically significant.

Results

Sero-Prevalence Determined by Pathogen-specific NAb

As shown in Table 2 and Figures 2–4, the 2023 PNGMP cohort exhibited very high sero-prevalence for DENV-1 (95%, n=175), DENV-2 (93%, n=172), and DENV-3 (95.1%, n=176), indicating widespread exposure or cross-reactivity NAbs among these serotypes. Mean NAb titers were also high for DENV-2–4, suggesting stronger or more frequent immune responses. Lower DENV-4 sero-prevalence (31%, n=57), with a significantly lower mean titer of 79.0, suggests that this serotype may be less commonly circulating or elicits a weaker immune response in this population. ZIKV was also prominent, with high sero-prevalence (87%, n=161) and a strong mean titer of 265.1, while JEV was also notable, with 62.2% (n=115) positivity and mean titer of 126.3. CHIKV (33.5%, n=62), RRV (44.3%, n=82), and MVEV (39.5%, n=73) showed moderate sero-positivity, indicating endemic but less dominant circulation. WNV (5.9%, n=11) and BFV (10.8%, n=20) demonstrated low sero-prevalence, suggesting either minimal exposure or low transmission rates in the study population. These findings reveal a high level of DENV and ZIKV exposure in PNGMP, with a still notable proportion exposed to CHIKV, RRV, JEV, and MVEV, and lower exposures to BFV and WNV.

In a Chi-square analysis, the sero-prevalence of RRV, CHIKV, DENV1-4, ZIKV, MVEV, and JEV did not significantly differ by age group (Table 2). Due to the low sero-prevalence of BFV and WNV, NAb sero-positivity rates were not compared by age groups. Sex-stratified analysis was not conducted due to the predominantly male study population.

Multiple NAb Positivity Among Alphavirus

Among alphaviruses, dual or multiple NAb sero-positivity patterns were observed: BFV+/CHIKV+ in 3 cases (1.6%), BFV+/RRV+ in 5 cases (2.7%), CHIKV+/RRV+ in 34 cases (18.3%), and triple positivity BFV+/CHIKV+/RRV+ in 7 cases (3.8%) (data not shown).

Multiple NAb Positivity Among Dengue Virus and Flavivirus

As shown in Table 3, nearly one-third (28.1%) of PNGMP in 2023 had pan-serotype immunity, or positivity to all 4 DENV serotypes. Moreover, 64.3% of participants were positive to three serotypes, suggesting multiple past infections or strong cross-reactive responses, likely due to co-circulation or sequential infections with different serotypes. Only a small fraction (5.4%) was positive to only 1 or 2 serotypes, and 2.2% were negative for all 4, indicating minimal or no prior DENV exposure in this subset.

A significant majority (92.9%) of study participants were sero-positive to four or more flaviviruses (Table 4), indicating an extensive exposure or high cross-reactive antibody responses. The largest groups consisted of individuals positive to six (31.4%) or 5 (30.3%) flaviviruses, reflecting multi-flavivirus circulation or repeated exposures. Only a small minority (5.9%) were positive to three or fewer flaviviruses, suggesting limited exposure in only this small portion of the participant population. Notably, two individuals (1.1%) were sero-positive to all eight flaviviruses tested, suggesting either uncommonly broad exposure histories or high cross-reactivity of their serum antibodies.

These data indicate a high burden of arbovirus transmission or immunological imprinting in the region, consistent with the co-endemicity of multiple flaviviruses and alphaviruses such as DENV, ZIKV, RRV, CHIKV, JEV, MVEV, and WNV.

Discussion

Arboviral infections represent substantial and ongoing threats to the operational readiness of U.S. military personnel deployed to endemic regions such as Papua New Guinea. These results, demonstrating high arbovirus sero-positivity and co-positivity rates among PNGMP in 2023, re-affirm that Papua New Guinea is a highly endemic area for multiple arboviruses. These findings have important implications for U.S. forces deployed under the recently expanded defense cooperation agreements between the U.S., Australia, and Papua New Guinea.

At the Lae Barracks in 2023, PNGMP exhibited extremely high DENV sero-positivity, with 98% of participants demonstrating NAb reactivity to at least 1 DENV serotype. The most frequently co-detected serotypes were DENV-1, DENV-2, and DENV-3, with DENV-4 showing the lowest prevalence. This distribution aligns with historical data indicating the persistent endemic circulation of DENV-1–3 in PNG and lower levels of DENV-4 introduction into Australia from Papua New Guinea from 1999 through 2020.^20-22

Accurate interpretation of DENV NAb results in hyper-endemic regions such as Papua New Guinea is complicated by repeated exposures to multiple DENV serotypes. Primary DENV infection provides life-long immunity to the infecting serotype virus but only transient, partial protection against the other three serotypes.²³ Secondary infections often result in broad, cross-reactive NAb responses that can persist long-term.²⁴ Moreover, other endemic flaviviruses in Papua New Guinea, including ZIKV, JEV, WNV, and MVEV, can also cross-react with DENV in NAb assays, further complicating serological interpretation.²⁵ Therefore, it is difficult to determine true viral exposure using the traditional greater than or equal to (≥) 4-fold NAb titer difference between real infection virus and its cross-reaction virus, as many PNGMP samples exhibited high NAb titers against multiple DENV serotypes and other flavivirus, mainly ZIKV.

Nevertheless, the high sero-positivity strongly suggests the ongoing co-circulation and repeated DENV exposure in this population. The risk of secondary DENV infections, which are associated with more severe disease through antibody-dependent enhancement, remains a concern in populations with high prior exposure. The apparent lack of reported severe dengue cases among PNGMP, however, may indicate that a substantial proportion of infections are asymptomatic or present only with mild, non-specific symptoms. Asymptomatic or sub-clinical infections represent the majority of DENV infections, particularly in endemic settings, and under-reporting or mis-classification of mild cases as malaria or undifferentiated febrile illness may also contribute to this observation. These findings underscore the importance of paired serological and clinical surveillance to fully understand the burden of disease and guide appropriate preventive strategies in PNGMP.²⁶

High ZIKV Nab sero-positivity (87%) among PNGMP 2023 at Lae Barracks shows no statistically signifi cant difference compared to the 2019 survey of PNGMP (65%) at Wewak and Manus Island barracks prior to the COVID-19 pandemic. This prevalence aligns with the high level of ZIKV sero-prevalence (49-63%) in Polynesia, which includes endemic regions,²⁷ and is significantly higher than the 15.6% average reported across the broader Western Pacific.²⁸ The elevated prevalence among PNGMP likely reflects increased exposure risks due to greater contact with mosquito habitats during field operations, higher endemicity in deployment locations, or insufficient use of personal protective measures. This underscores the need for targeted vector control, health education, and operational adjustments to reduce transmission risk and protect force health.

Despite widespread ZIKV exposure among PNGMP, our literature review found no reports of congenital ZIKV syndrome in Papua New Guinea, possibly due to limited diagnostic capabilities and under-reporting in the country. Additionally, time of exposure may play an important role, as the highest risk of CZS occurs in the first trimester of pregnancy. Cross-immunological protection afforded by high levels of DENV immunity has also been identified as a contributor to reduce CZS development.^29,30 The predominantly young adult male PNGMP cohort may not reflect exposure or infection rates among pregnant women, who are at greatest risk for CZS.

Similarly, CHIKV NAb sero-positivity (33.5%) among PNGMP mirrors rates observed in 2019, suggesting sustained, low level CHIKV endemicity following the 2012 Papua New Guinea outbreak. Cases of CHIKV imported to Australia from Papua New Guinea from 2016 to 2020 evidence Papua New Guinea’s role as a persistent source of CHIKV transmission risk.^8,31,32

Sero-prevalence data also confirm ongoing exposure of PNGMP to other arboviruses historically reported in Papua New Guinea—including RRV, BFV, JEV, WNV, and MVEV—with no significant variation by age, reflecting sustained transmission or early life exposure.

A key strength of this study is the use of the gold standard NAb assay to detect virus-specific antibodies, which correlate strongly with immune protection. While NAb assays cannot distinguish antibody isotypes (IgM vs. IgG)³³ and may retain low level cross-reactivity among related arboviruses, they remain the most reliable serological method. Cross-neutralizing antibodies between DENV and ZIKV are widely documented, with repeated DENV-exposed individuals often exhibiting stronger and more durable cross-neutralizing responses to ZIKV. Similarly, ZIKV infection can induce cross-neutralizing antibodies against DENV, but the extent and duration of this effect can vary.²⁵ To minimize false positives from cross-reactivity, we applied a conservative sero-positivity limit of greater than or equal to (≥) 1:20 NAb titer for flaviviruses, although precise exposure determination remains challenging in hyper-endemic settings like Papua New Guinea.

These findings have critical implications for U.S. military personnel deployed in Papua New Guinea. Personnel without prior exposure or natural immunity face heightened risks of infection and severe disease, particularly for arboviruses such as DENV, ZIKV, and CHIKV. Asymptomatic or mildly symptomatic infected personnel could inadvertently export arboviruses to other regions with competent vector populations, echoing the past global ZIKV and CHIKV outbreaks.

Given these risks, continuous arboviral and clinical surveillance among military and local populations is essential for detecting symptomatic and asymptomatic infections. Surveillance should also be complemented by entomological monitoring to track spatio-temporal changes in mosquito populations, including species diversity, vector competence, biting behavior, longevity, and dispersal capacity. Environmental factors such as rainfall and temperature, which influence mosquito breeding and activity, as well as zoonotic and sylvatic surveillance, for spillover risk detection, must be integrated for comprehensive risk assessment.

Expanded studies with larger, more demographically diverse cohorts and application of more specific serological assays (e.g., epitope-based ELISAs) will further clarify exposure patterns and guide force health protection measures. Although this study focused on PNGMP, it provides a valuable insight into the endemic risk landscape facing deployed U.S. forces. The endemic and emerging arboviruses circulating in Papua New Guinea pose a sustained threat to U.S. military readiness in this strategically important region. Proactive surveillance, force health education, vector control strategies, and vaccination will be essential to mitigate the risks and safeguard deployed personnel.

Author Affiliations

Australian Defense Force Malaria and Infectious Disease Institute, Gallipoli Barracks, Enoggera, Queensland: CAPT Kizu, CAPT Graham, LT Izuagbe, LCOL McPherson, Dr. Liu; Queensland Institute of Medical Research, Berghofer Medical Research Institute, Brisbane, Australia: Ms. Graham; Health Services, Papua New Guinea Defence Force, Port Moresby: Dr. Kaminiel

Acknowledgments

The authors express their gratitude to all study participants, the Health Service Department of the Papua New Guinea Defence Force, and the Australian Defence Force Malaria and Infectious Disease Institute team for their important support in conducting this investigation. Special thanks to Prof. Shanks for his guidance and proofreading of the manuscript.

Disclaimers

The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policies or positions of their affiliated institutions.

The authors declare no conflict of interest.

The Joint Health Command of Australian Defence Force funded this investigation. The funder had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

Gibbons RV, Streitz M, Babina T, Fried JR. Dengue and US military operations from the Spanish-American War through today. Emerg Infect Dis. 2012;18(4):623-630. doi:10.3201/eid1804.110134
Sabin AB. Research on dengue during World War II. Am J Trop Med Hyg. 1952;1(1):30-50. doi:10.4269/ajtmh.1952.1.30
Sabin AB. The dengue group of viruses and its family relationships. Bacteriol Rev. 1950;14(3):225-232.
Walker AS. The sandfly-dengue fever group. In Walker AS, ed. Clinical Problems of War. Australian War Memorial;1963:168-172.
Wellington T, Fraser JA, Kuo HC, Hickey PW, Lindholm DA. The burden of arboviral infections in the Military Health System 2012-2019. Am J Trop Med Hyg. 2023;108(5):1007-1013. doi:10.4269/ajtmh.22-0684
Grant R, Kizu J, Graham M, et al. Serological evidence of possible high levels of undetected transmission of Zika virus among Papua New Guinea military personnel, 2019. IJID Reg. 2022;4:131-133. doi:10.1016/j.ijregi.2022.07.006
Kizu JG, Graham M, Grant R, et al. Prevalence of Barmah Forest virus, chikungunya virus and Ross River virus antibodies among Papua New Guinea military personnel before 2019. Viruses. 2023;15(2):394. doi:10.3390/v15020394
Graham M, Kizu J, Devine G, et al. Seroprevalence of chikungunya virus among military personnel in Papua New Guinea, 2019. IJID Reg. 2022;3:34-36. doi:10.1016/j.ijregi.2022.02.009
Touchstone K, Matsumoto K, Seliga N. Surveillance snapshot: Zika virus among Department of Defense service members and beneficiaries, 2013-2022. MSMR. 2023;30(3):16. Accessed Jul. 28, 2025. https://www.health.mil/news/articles/2023/03/01/surveillance-snapshot
Stahlman SL, Langton RS. Surveillance snapshot: chikungunya in service members of the U.S. Armed Forces, 2016-2022. MSMR. 2023;30(12):11 Accessed Jul. 28, 2025. https://www.health.mil/news/articles/2023/12/01/msmr-chikungunyahttps://www.health.mil/news/articles/2023/12/01/msmr-chikungunya
Jonduo MH, Bande G, Horwood PF. Arboviruses of human health significance in Papua New Guinea. PNG Med J. 2012;55(1-4):35-44.
Hii J, Dyke T, Dagoro H, Sanders RC. Health impact assessments of malaria and Ross River virus infection in the Southern Highlands Province of Papua New Guinea. PNG Med J. 1997;40(1):14-25.
Knope KE, Muller M, Kurucz N, et al. Arboviral diseases and malaria in Australia, 2013-14: annual report of the National Arbovirus and Malaria Advisory Committee. Commun Dis Intell Q Rep. 2016;40(3):e400-e436. doi:10.33321/cdi.2016.40.43
Lobigs M, Marshall ID, Weir RC, Dalgarno L. Murray Valley encephalitis virus field strains from Australia and Papua New Guinea: studies on the sequence of the major envelope protein gene and virulence for mice. Virology. 1988;165(1):245-255. doi:10.1016/0042-6822(88)90678-2
Liu W, Kizu JR, Le Grand LR, et al. Localized outbreaks of epidemic polyarthritis among military personnel caused by different sublineages of Ross River virus, Northeastern Australia, 2016-2017. Emerg Infect Dis. 2019;25(10):1793-1801. doi:10.3201/eid2510.181610
Liu W, Kizu JR, Matley DR, et al. Circulation of 2 Barmah Forest virus lineages in military training areas, Australia. Emerg Infect Dis. 2020;26(12):3061-3065. doi:10.3201/eid2612.191747
Hueston L, Yund A, Cope S, et al. Ross River virus in a joint military exercise. Commun Dis Intell. 1997;21(14):193. doi:10.33321/cdi.1997.21.40
Mackenzie JS, Williams DT, van den Hurk AF, Smith DW, Currie BJ. Japanese encephalitis virus: the emergence of genotype iv in Australia and its potential endemicity. Viruses. 2022;14(11):2480. doi:10.3390/v14112480
Kasbergen LMR, Nieuwenhuijse DF, de Bruin E, Sikkema RS, Koopmans MPG. The increasing complexity of arbovirus serology: an in-depth systematic review on cross-reactivity. PLoS Negl Trop Dis. 2023;17(9):e0011651. doi:10.1371/journal.pntd.0011651
Jonduo M, Neave MJ, Javati S, et al. Genomic sequencing of dengue virus strains associated with Papua New Guinean outbreaks in 2016 reveals endemic circulation of DENV-1 and DENV-2. Am J Trop Med Hyg. 2022;107(6):1234-1238. doi:10.4269/ajtmh.21-1292
Moore PR, van den Hurk AF, Mackenzie JS, Pyke AT. Dengue viruses in Papua New Guinea: evidence of endemicity and phylogenetic variation, including the evolution of new genetic lineages. Emerg Microbes Infect. 2017;6(12):e114. doi:10.1038/emi.2017.103
Luang-Suarkia D, Mitja O, Ernst T, et al. Hyperendemic dengue transmission and identification of a locally evolved DENV-3 lineage, Papua New Guinea 2007-2010. PLoS Negl Trop Dis. 2018;12(3):e0006254. doi:10.1371/journal.pntd.0006254
Guzman MG, Alvarez M, Rodriguez-Roche R, et al. Neutralizing antibodies after infection with dengue 1 virus. Emerg Infect Dis. 2007;13(2):282-286. doi:10.3201/eid1302.060539
Ito M, Katakai Y, Ono F, et al. Serotype-specific and cross-reactive neutralizing antibody responses in cynomolgus monkeys after infection with multiple dengue virus serotypes. Arch Virol. 2011;156(6):1073-1077. doi:10.1007/s00705-011-0959-2
Salgado BB, Maues FCJ, Jordao M, et al. Antibody cross-reactivity and evidence of susceptibility to emerging flaviviruses in the dengue-endemic Brazilian Amazon. Int J Infect Dis. 2023;129:142-151. doi:10.1016/j.ijid.2023.01.033
Senn N, Luang-Suarkia D, Manong D, Siba PM, McBride WJ. Contribution of dengue fever to the burden of acute febrile illnesses in Papua New Guinea: an age-specific prospective study. Am J Trop Med Hyg. 2011;85(1):132-137. doi:10.4269/ajtmh.2011.10-0482
Aubry M, Teissier A, Huart M, et al. Zika virus seroprevalence, French Polynesia, 2014-2015. Emerg Infect Dis. 2017;23(4):669-672. doi:10.3201/eid2304.161549
Saba Villarroel PM, Hamel R, Gumpangseth N, et al. Global seroprevalence of Zika virus in asymptomatic individuals: a systematic review. PLoS Negl Trop Dis. 2024;18(4):e0011842. doi:10.1038/s41467-018-05458-0
Regla-Nava JA, Elong Ngono A, Viramontes KM, et al. Cross-reactive dengue virus-specific cd8(+) t cells protect against Zika virus during pregnancy. Nat Commun. 2018;9(1):3042.
Bird IM, Cavener V, Surendran Nair M, et al. Distinct replication kinetics, cytopathogenicity, and immune gene regulation in human microglia cells infected with Asian and African lineages of Zika virus. Microorganisms. 2024;12(9):1840. doi:10.3390/microorganisms12091840
Horwood PF, Reimer LJ, Dagina R, et al. Outbreak of chikungunya virus infection, Vanimo, Papua New Guinea. Emerg Infect Dis. 2013;19(9):1535-1538. doi:10.3201/eid1909.130130
Huang B, Pyke AT, McMahon J, Warrilow D. Complete coding sequence of a case of chikungunya virus imported into Australia. Genome Announc. 2017;5(19):e00310-e00317. doi:10.1128/genomea.00310-17
Kam YW, Pok KY, Eng KE, et al. Sero-prevalence and cross-reactivity of chikungunya virus specific anti-E2EP3 antibodies in arbovirus-infected patients. PLoS Negl Trop Dis. 2015;9(1):e3445. doi:10.1371/journal.pntd.0003445

Reportable Medical Events at Military Health System Facilities Through Week 22, Ending May 31, 2025

Fri, 01 Aug 2025 00:00:00 Z

Reportable Medical Events are documented in the Disease Reporting System internet by health care providers and public health officials throughout the Military Health System for monitoring, controlling, and preventing the occurrence and spread of diseases of public health interest or readiness importance. These reports are reviewed by each service’s public health surveillance hub. The DRSi collects reports on over 70 different RMEs, including infectious and non-infectious conditions, outbreak reports, STI risk surveys, and tuberculosis contact investigation reports. A complete list of RMEs is available in the 2022 Armed Forces Reportable Medical Events Guidelines and Case Definitions.¹ Data reported in these tables are considered provisional and do not represent conclusive evidence until case reports are fully validated.

Total active component cases reported per week are displayed for the top five RMEs for the previous year. Each month, the graph is updated with the top five RMEs, and is presented with the current month’s (May 2025) top five RMEs, which may differ from previous months. COVID-19 is excluded from these graphs due to changes in reporting and case definition updates in 2023.

Authors' Affiliation

Defense Health Agency, Disease Epidemiology Branch, Defense Centers for Public Health–Aberdeen

References

Armed Forces Health Surveillance Division. Armed Forces Reportable Medical Events. Accessed Feb. 28, 2024. https://health.mil/reference-center/publications/2022/11/01/armed-forces-reportable-medical-events-guidelines
Defense Manpower Data Center. Department of Defense Active Duty Military Personnel by Rank/Grade of Service. Accessed Feb. 28, 2024. https://dwp.dmdc.osd.mil/dwp/app/dod-data-reports/workforce-reports
Defense Manpower Data Center. Armed Forces Strength Figures for January 31, 2023. Accessed Feb. 28, 2024. https://dwp.dmdc.osd.mil/dwp/app/dod-data-reports/workforce-reports
Navy Medicine. Surveillance and Reporting Tools–DRSI: Disease Reporting System Internet. Accessed Feb. 28, 2024. https://www.med.navy.mil/navy-marine-corps-public-health-center/preventive-medicine/program-and-policy-support/disease-surveillance/drsi

Historical Perspective: Early U.S. Military Immunization Against Tetanus and Diphtheria: Historical Context and Current Importance

Fri, 01 Aug 2025 00:00:00 Z

Prior to the Second World War, toxoid immunizations for both tetanus and diphtheria had been developed but were not widely used in adults. Starting in 1941, however, the U.S. Army began extensive immunization with tetanus toxoid. Tetanus decreased dramatically, with only 12 tetanus cases (1 case per million) developing during the war, mostly in imperfectly immunized soldiers. Diphtheria immunization was more complicated, as many adults in 1941 had some natural anti-toxic immunity to diphtheria. A decision to not immunize the U.S. military against diphtheria was made due to low prevalence of the disease and high rates of adverse events. During the war, however, unexpectedly high rates of debilitating cutaneous diphtheria were seen in desert and jungle warfare, among prisoners of war, and amid epidemics of respiratory diphtheria in Europe. Those diphtheria cases resulted in the requirement for U.S. soldiers to be immunized starting in 1945. Adjusted toxoid doses post-war eventually arrived at an accepted dual toxoid regimen. Mass immunization remains the best prevention against diphtheria.

It was rather a surprise to learn that diphtheria was an important and widely prevalent tropical disease.¹ 1946

Vaccination of the U.S. Army began during the War of 1812, with Jennerian smallpox immunization substituting for variolation. A century elapsed before a second vaccine, for typhoid immunization of soldiers, began in 1912, following the Spanish American War.²

The third standard U.S. Army immunization was tetanus toxoid, a chemically inactivated toxin that is immunogenic but not toxic. Vaccination of U.S. Army personnel with tetanus toxoid began in 1941, in the months preceding U.S. involvement in World War II. This immunization program resulted from successful tetanus toxoid reports from the British and French armies, with approval requiring nearly a year of effort by the U.S. Army Surgeon General’s Office.³

Due to the low prevalence of respiratory diphtheria at the beginning of World War II, immunizing the U.S. Army with diphtheria toxoid, although medically possible, was determined to be militarily impractical.^3,4 The decision not to immunize the U.S. Army of the Second World War against diphtheria toxoid was a practical, short-term measure with negative consequences. Surprisingly large numbers of soldiers in both the North African desert and South Pacific jungles were incapacitated by chronic skin ulcers caused by Corynebacterium diphtheriae, and over 120 U.S. Army deaths were directly attributed to respiratory diphtheria. Ultimately, immunization against diphtheria was begun in 1945, due to large European diphtheria epidemics.^5,6

Tetanus

Tetanus was a major military problem in the trenches of the First World War, requiring literally millions of doses of equine anti-toxin for the wounded.⁹ The development of tetanus toxoid immunization in the 1920s was a technological breakthrough, allowing individuals to produce their own antisera. Wound prophylaxis against tetanus became toxoid boosting and not equine antisera. Purification of the immunogen to eliminate peptone products used in bacterial culture eliminated some allergic reactions³; this was in an era before randomized clinical trials, with evidence of efficacy largely based on comparison groups.

With the onset of another global war, the U.S. Army authorized tetanus toxoid in 1941 for overseas service, and then for all troops, after a year’s discussion of the proposed policy change.^2,3 Despite logistical challenges, vaccination of millions of soldiers with tetanus toxoid was largely accomplished with few problems or adverse events (Figure 1). The U.S. Army had 70 tetanus cases per million soldiers in the First World War compared to only 12 cases, most not fully immunized, or 1 per million soldiers, during the Second World War.³

The U.S. military problem of tetanus during World War II was largely solved by consistent toxoid immunization of the entire population.^2,4 As neither the German nor Japanese Armies routinely immunized against tetanus, most clinical tetanus during the Second World War occurred in prisoners of war. During the fighting around Manila in 1945, 473 tetanus cases, with 389 deaths, were described in Japanese Army prisoners, but none occurred in wounded U.S. soldiers.³ The Japanese Army belatedly attempted to develop its own tetanus vaccine in Jakarta, but inadequate inactivation of the toxin resulted in 900 iatrogenic deaths in Indonesians who served as unwilling product recipients.¹⁰

Diphtheria

Diphtheria toxoid had been developed in the 1920s and successfully prevented pediatric disease, as well as decreasing need for equine anti-diphtheria toxin. Respiratory circulation of toxigenic C. diphtheriae meant that in 1940 many U.S. adults had some immunity, as measured by the intradermal Schick test. Negative Schick results denoted an ability to neutralize a small intradermal dose of diphtheria toxin; positive tests indicated skin reactions with no antibody neutralization.¹¹

On the basis of Schick testing of 3,000 soldiers, in 1940 the U.S. Army calculated that a majority (55%) of soldiers already had some pre-existing diphtheria immunity.³ Mass screening of soldiers with Schick tests was decided to be medically possible but militarily impractical.⁴

Diphtheria vaccination was much more complex than tetanus vaccination, despite similarities in toxoid technology. Schick-negative individuals (i.e., with diphtheria antibodies) often had adverse reactions when immunized with diphtheria toxin, contributing to the resistance to mass vaccination of soldiers. Adverse events in Schick-negative soldiers included swollen arms and lost duty days, with hospitalization of several who were immunized.

This weighing of risks versus benefits was reasonable, based on the information available at the time, but it assumed a static environment, which is not typical of infectious disease epidemiology during war. There were only 122 cases of respiratory diphtheria in the U.S. Army in 1942, but by 1945 cases had increased to 3,455, mostly in Europe.³ Mortality in the U.S. Army also markedly increased, with 86 of 125 total diphtheria deaths in 1945 occurring overseas.³

During the war, epidemics of cutaneous diphtheria in soldiers in austere environments, such as the deserts of North Africa and jungles of the Southern Pacific, were occurring.^1,16 Desert or veldt sore was a diagnosis well-known from the First World War and returned to be problem during the Second World War, initially in the British Army in Palestine and then in the U.S. Army in the Pacific.^16-18 Chronic, debilitating ulcers resulted from toxigenic skin infections that healed very slowly. Some soldiers were removed from duty for months of rehabilitation due to chronic foot ulcers and had to be evacuated to specialist tropical disease hospitals in the U.S. (Figure 3).

Cutaneous diphtheria was probably the worst in Allied prisoner of war camps in Asia, where debilitating tropical skin ulcers, compounded by starvation and other infections, often began a downward cycle to prisoner demise.¹⁹ German PoW camps in the U.S. and U.K. also had problems with cutaneous diphtheria in un-immunized prisoners.²⁰

The massive, war-associated diphtheria epidemics during the Second World War led the U.S. Army to institute diphtheria toxoid vaccine in 1945 after Schick testing occupation soldiers, and then their families, in Germany.¹³ Post-war diphtheria rates in the U.S. Army approached 10 per 1,000 per year in 1946, when diphtheria accounted for 15% of all medical deaths and 45% of all infectious disease deaths (Figure 2).¹⁴

Universal immunization against diphtheria in the U.S. military did not occur with mass Schick testing, but instead resulted from decreased immunogen in the standard combined tetanus-diphtheria toxoid vaccine. The new formulation was accomplished by a series of studies among U.S. Navy recruits at Great Lakes Training Center.^12,15

Pharyngeal diphtheria, leading to systemic intoxication and potentially lethal myocarditis and neuritis, was effectively prevented by immunization and largely ceased to be a problem as universal dT immunizations became the standard both in children and adults after World War II.²

Commentary

Immunization with established toxoid vaccines eventually solved the military problem of exposure to toxigenic environmental bacteria. During the First World War, tetanus was a greatly feared disease that resulted from battlefield wounds. Nearly all medical officers had some experience with the often lethal disease.⁹ The opportunity to dispense with reactogenic equine antisera and introduce tetanus toxoid immunization was embraced by Army leadership. Although general tetanus immunization of the U.S. Army began in the months before U.S. entry into the Second World War, requirements for diphtheria vaccination were more complicated, and were delayed due to adverse events and difficulty with mass Schick testing of soldiers during mobilization.

The delay against universal diphtheria immunization during World War II had two adverse consequences: 1) hundreds of frontline infantry soldiers in both Africa and Asia were incapacitated by chronic skin ulcers that healed poorly because of infection with toxigenic C. diphtheriae and 2) soldiers had to be immunized while deployed at the end of World War II once diphtheria became a major epidemic disease in Europe.

Few modern medical officers have any experience with what many now think to be extinct diseases, despite the perpetual presence of those pathogens in our environment. Tetanus is still a problem in un-immunized populations.²¹ When public health systems failed to deliver universal toxoid immunization, diphtheria epidemics resulted, as seen in the former Soviet Union and Yemen.^7,8 When health systems collapsed in failed states such as Yemen and in conflict border areas of Pakistan, epidemic diphtheria resulted.^8,22

Fear of adverse events and practical issues in screening soldiers for pre-existing immunity against other diseases exists today, but failure to continue current toxoid vaccination policies could once again threaten U.S. soldiers with preventable illnesses from ubiquitous environmental bacterial toxins. Force protection measures, specifically immunization, must be maintained if the U.S. Army is not to rediscover the effects of diseases such as tetanus and diphtheria.

Author Affiliations

Australian Defence Force Infectious Disease and Malaria Institute, Gallipoli Barracks, Enoggera, Queensland; University of Queensland School of Public Health, Brisbane

Disclaimers

The opinions expressed are those of the author and do not necessarily reflect those of the Australian Defence Force or the U.S. Department of Defense.

No specific funding was given for this work.

The author is an employee of the Australian Defence Force, a retired U.S. Army officer, and claims no conflicts of interest.

Acknowledgments

The author thanks MAJ James Smith, RAAMC, for commenting on an earlier version of this manuscript, in addition to the many un-named military officers, scientists, historians, and medical librarians who unselfishly provided ideas and data for this manuscript, especially the librarians at the Australian Defence Force Library, Gallipoli Barracks, Queensland.

References

https://archive.org/details/BAMD1944-03No74/page/n87/mode/2up

Levine HD. Medical experiences with American troops in the Pacific: with remarks on the diagnostic value of sternal puncture in malaria and on the innocuousness of hookworm infection. NEJM. 1946;235(26):933-938. doi:10.1056/nejm194612262352604

Grabenstein JD, Pittman PR, Greenwood JT, Engler RJ. Immunization to protect the US Armed Forces: heritage, current practice, and prospects. Epidemiol Rev. 2006;28:3-26. doi:10.1093/epirev/mxj003

Long AP; Medical Department, United States Army. The Army immunization program. In: Armstrong GE, Coates JB, Hoff EC, eds. Preventive Medicine in World War II. Volume III: Personal Health Measures and Immunization. Office of the Surgeon General, Dept. of the Army;1955:271-333. Accessed Jul. 1, 2025. https://apps.dtic.mil/sti/tr/pdf/ADA286774.pdf

Long AP. Immunizations in the United States Army. Am J Public Health Nations Health. 1944;34(1):27-33. doi:10.2105/ajph.34.1.27

McGuinness AC; Medical Department, United States Army. Diphtheria. In: Hays SB, Coates JB, Hoff EC, eds. Preventive Medicine In World War II. Volume IV: Communicable Diseases Transmitted Chiefly Through Respiratory and Alimentary Tracts. Office of the Surgeon General, Dept. of the Army;1958;167-190. Accessed Jul. 1, 2025. https://apps.dtic.mil/sti/tr/pdf/ADA286770.pdf

Liebow AA, Bumstead JH; Medical Department, United States Army. Cutaneous and other aspects of diphtheria. In: Heaton, Coates JB, Havens WP, eds. Internal Medicine in World War II. Volume II: Infectious Diseases. Office of the Surgeon General, Dept. of the Army;1963:275-308. Accessed Jul. 1, 2025. https://apps.dtic.mil/sti/tr/pdf/ADA286773.pdf

Ohuabunwo C, Perevoscikovs J, Griskevica A, et al. Respiratory diphtheria among highly vaccinated military trainees in Latvia: improved protection from DT compared with Td booster vaccination. Scand J Infect Dis. 2005;37(11-12):813-820. doi:10.1080/00365540500262658

Al-Dar AA, Al-Qassimi M, Ezzadeen FH, et al. Diphtheria resurgence in Sada’a-Yemen, 2017–2020. BMC Infect Dis. 2022;22(1):46. doi:10.1186/s12879-022-07033-x

Shanks GD. How World War I changed global attitudes to war and infectious diseases. Lancet. 2014;384(9955):1699-1707. doi:10.1016/s0140-6736(14)61786-4

Baird JK, Marzuki S. War Crimes in Japan-Occupied Indonesia: A Case of Murder by Medicine. Univ. of Nebraska Press;2015.

Cheever F. A Schick survey of 18,000 naval recruits. Am J Public Health Nations Health. 1948;38(3):374-377. doi:10.2105/ajph.38.3.374

Edsall G. Immunization of adults against diphtheria and tetanus. Am J Public Health Nations Health. 1952;42(4):393-400. doi:10.2105/ajph.42.4.393

Schulze HA. Schick testing and diphtheria immunization of United States troops in Europe. Am J Public Health Nations Health. 1948;38(11):1527-1531. doi:10.2105/ajph.38.11.1527

Schulze HA. Diphtheria in the US Army in Europe. Med Bull US Army Force Europe Theater Off Theater Chief Surg. 1947;2(6):2-26.

Edsall G, Altman JS, Gaspar AJ. Combined tetanus-diphtheria immunization of adults: use of small doses of diphtheria toxoid. Am J Public Health Nations Health. 1954;44(12):1537-1545. doi:10.2105/ajph.44.12.1537

Liebow AA, Maclean PD, Bumstead JH, Welt LG. Tropical ulcers and cutaneous diphtheria. Arch Intern Med (Chicago). 1946;78:255-295. doi:10.1001/archinte.1946.00220030002001

Cameron J, Muir E. Cutaneous diphtheria in northern Palestine. Lancet. 1942;240(6225):720-723. doi:10.1016/s0140-6736(00)57985-9

Livingood CS, Perry DJ, Forrester JS. Cutaneous diphtheria: a report of 140 cases. J Invest Dermatol. 1946;7(g):341-364. doi:10.1038/jid.1946.42

Riddell GS. Cutaneous diphtheria: epidemiological and dermatological aspects of 365 cases amongst British prisoners of war in the Far East. J Royal Army Med Corps. 1950;95(2):64-87.

Fleck S, Kellam J, Klippen A. Diphtheria among German prisoners of war. Bull US Army M Dept. 1944;x(74):80-89. Accessed Jul. 1, 2025. https://archive.org/details/BAMD1944-03No74/page/n87/mode/2up

Pierce C. Notes from the field: tetanus in an unvaccinated man from Mexico–Oregon, 2022. MMWR Morb Mortal Wkly Rep. 2023;72(24):665-666. doi:10.15585/mmwr.mm7224a5

Ahmad J, Shah MY, Latif A, Sulaiman M, Khan K. Navigating diphtheria resurgence in Pakistan’s conflict-ridden and disaster-prone area. Disaster Med Public Health Prep. 2024;18:e288. doi:10.1017/dmp.2024.266

Health.mil Articles RSS Feed

‘It has been a life saver’: Department of War civilians in Japan discuss health care pilot program

How the health insurance enhancement works

Translation and interpretation

Streamlined billing assistance

Looking ahead to future access to health care

Reducing tobacco and nicotine use key to ‘having a healthy, fit force’

Stepping down from tobacco

MHS works ‘hand in glove’ with the Department of Veterans Affairs

Behavior changes and telehealth

Goal is a healthier, more ready fighting force

Ready to quit? Accessing resources

Hospital honors ‘Fighting doctor’ and upholds highest standards of care

Early years shaped approach to military medicine

Wartime hospital care

Hospital honors legacy through nationally accredited care

2026 Military Health System Research Symposium: Submit your research or nominate outstanding health care work

About MHSRS

Department of War and Department of Veterans Affairs strengthen partnership to enhance care for Nation’s warfighters and veterans

Number of tuberculosis tests and diagnoses of latent tuberculosis infection among U.S. Army active component service members, January 2014–December 2023

Abstract

What are the new findings?

What is the impact on readiness and force health protection?

Background

Methods

Results

Discussion

References

Author Affiliations

Acknowledgments

Disclaimer

Guest editorial: CHAMPS: the Career History Archival Medical and Personnel System—a summary of career and medical records of the U.S. Armed Forces, 1980–2023

Data sources

Capabilities, collaborations, and future directions

References

Author Affiliations

Acknowledgments

Disclaimer

Surveillance snapshot: Adherence to disease and injury standardized surveillance categories in two U.S. Africa command exercises, 2024

References

Author Affiliations

Disclaimer

Historical perspective: Post-infection symptoms in U.S. soldiers with malaria during the Second World War: major limitation to return to duty

Abstract

“However, the way the individual adjusted to the malaria and concurrent situational factors, contributed to the development of symptoms, to their perpetuation and intensification.”1

References

Author Affiliations

Acknowledgments

Disclaimer

Reportable medical events at Military Health System facilities through week 44, ending November 1, 2025

References

Authors’ Affiliation

U.S. Navy veteran and VA health care executive appointed as Department of War’s top medical leader

Top DOW doctor: ‘Fundamentally, our focus is to support the warfighter’

Surveillance snapshot: Chikungunya in Military Health System beneficiaries, 2020–2024

References

Authors’ Affiliation

Letter from the Editor in Chief

Guillain-Barré Syndrome clinical characteristics and outcomes among U.S. active component service members, 2014–2022

Abstract

What are the new findings?

What is the impact on readiness and force health protection?

Background

Methods

Results

Discussion

References

Author Affiliations

Disclaimer

Acknowledgment

Applying distinct approaches to racial and ethnic classification to the surveillance of obstetric and neonatal outcomes in the U.S. Military, 2010–2021

Abstract

What are the new findings?

What is the impact on readiness and force health protection?

Background

Methods

Study population

Measures

Analysis

Results

“However, the way the individual adjusted to the malaria and concurrent situational factors, contributed to the development of symptoms, to their perpetuation and intensification.”¹