{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T10:03:57Z","timestamp":1771927437796,"version":"3.50.1"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T00:00:00Z","timestamp":1769385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The Military Health System offers geographically distributed SARS-CoV-2 incidence estimates to support critical national pandemic surveillance, but this has not been previously assessed. The objective was to identify confirmed, probable, and possible SARS-CoV-2 infections with laboratory and clinical evidence and compare cumulative incidence to the general United States population.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>An observational, retrospective epidemiologic study using medical records from the United States Military Health System (inclusive of active duty) collected from outpatient and inpatient facilities worldwide, both United States Military and non-military treatment facilities. Direct standardization to the general US population was used to calculate sex-adjusted cumulative incidence, stratified by age, for 10 Health and Human Services regions for active duty and non-active duty beneficiary populations, with Spearman\u2019s rho correlations for age and region strata.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among Military Health System beneficiaries, 2,219,987 cases were identified, with 27.4% laboratory-confirmed cases alongside 35.0% probable and 37.6% possible cases identified using clinical ICD-10-CM evidence. Peaks in cases occurred November 2020\u2013January 2021, August 2021\u2013September 2021, and January 2022. Age-stratified and sex-adjusted cumulative incidence across 10 geographical regions reflected these temporal patterns among both active duty (90% of age and region-specific correlation coefficients &amp;gt;0.7) and non-active duty beneficiaries (80% of age and region-specific correlation coefficients &amp;gt;0.7). Cumulative incidence was higher among active duty beneficiaries compared to the United States general population, particularly those ages 18\u201349\u202fyears, with adjusted cumulative incidence ratios consistently greater than 1. The cumulative incidence ratios for non-active duty beneficiaries were more consistent and closer to 1. The sensitivity analysis of laboratory-confirmed cases among active duty personnel demonstrated consistently lower adjusted cumulative incidence than United States general population.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Temporal patterns in cases among Military Health System beneficiaries reflect cases measured nationally by the Centers for Disease Control and Prevention. Applying a comprehensive algorithm of clinical and laboratory data from a large electronic health system, such as the Military Health System, may improve case capture during an emergent epidemic providing incidence estimates and regional impact in support of U. S. national surveillance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2025.1714024","type":"journal-article","created":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T07:11:15Z","timestamp":1769411475000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparing SARS-CoV-2 infections in the US Military Health System and national data: opportunities for future pandemic surveillance"],"prefix":"10.3389","volume":"13","author":[{"given":"Megan Clare","family":"Craig-Kuhn","sequence":"first","affiliation":[{"name":"Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences","place":["Bethesda, MD, United States"]},{"name":"Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc.","place":["Bethesda, MD, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laveta","family":"Stewart","sequence":"additional","affiliation":[{"name":"Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences","place":["Bethesda, MD, United States"]},{"name":"Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc.","place":["Bethesda, MD, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erica","family":"Sercy","sequence":"additional","affiliation":[{"name":"Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences","place":["Bethesda, MD, United States"]},{"name":"Henry M. 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