{"doi":"10.1093/milmed/usaf496","title":"Post-Deployment Polypharmacy and Opioid Receipt Among Active Duty Soldiers Returning from Afghanistan and Iraq: A Retrospective Study","abstract":"INTRODUCTION: There is extensive literature on polypharmacy and high-risk medication (HRM) receipt among populations older than 65, defined here as prescription patterns requiring monitoring receipt. Few studies, however, have investigated HRM receipt among active duty (AD) service members, and the research that does exist assesses HRM receipt within a limited timeframe. The present study expands on the existing literature on HRM receipt among AD soldiers by extending the study timeframe, considering new HRM definitions, examining patterns of overlap among types of HRM receipt, and evaluating persistence of meeting specific HRM definitions. MATERIALS AND METHODS: The study sample consists of Army soldiers returning from an Afghanistan or Iraq deployment in FYs 2008-2014. Study data were drawn from healthcare and pharmacy records from the Military Health System Data Repository, deployment records from the Defense Manpower Data Center Contingency Tracking System, and healthcare eligibility data from the Defense Eligibility and Enrollment Registration System. The study considered 3 HRM definitions determined by the Army to require monitoring (psychotropic or central nervous system depressant polypharmacy; polypharmacy with an opioid; and emergency department visits with an opioid prescription), as well as a fourth definition reflecting the Defense Health Agency's HRM surveillance policies (long-term opioid therapy). The study timeframe began 60 days before return from deployment and extended to 2 exposure periods: (1) the first post-deployment year and (2) the point at which the soldier left AD service or the end of FY 2017. Analyses focused on the elapsed time between return from deployment and initial HRM receipt, HRM prevalence within each exposure period, and overlap in types of HRM. RESULTS: High-risk medication receipt was observed in 28.3% of AD Army soldiers within their first post-deployment year and 55.9% of soldiers across their post-deployment AD lifetime. In both observation periods, polypharmacy with an opioid was the most common type of HRM. Within the first post-deployment year, 18.2% of soldiers met the criteria for 2 or more types of HRM receipt. Among those receiving HRM at any point within their post-deployment AD lifetime, the mean time elapsed between return from deployment to initial HRM receipt was 19.8 months. CONCLUSIONS: This study extends the knowledge base on HRM among AD soldiers by analyzing longitudinal data over an expanded timeframe, considering additional definitions, and examining patterns of overlap among types of HRM. The high prevalence of first-year and post-deployment AD career HRM and the average time to HRM receipt suggest that the first 2 years following deployment may be a critical period for HRM monitoring. Given that over half of AD Army soldiers received HRM in their post-deployment career period, findings underscore the value of Defense Health Agency post-deployment medication monitoring initiatives.","journal":"Military Medicine","year":2025,"id":578978,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9556,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":752597,"name":"Krista B. Highland","orcid":"0000-0003-3815-2571","position":1,"is_corresponding":false},{"id":800122,"name":"Natalie Moresco","orcid":"0000-0002-2468-6292","position":2,"is_corresponding":false},{"id":1313896,"name":"Jenneth Carpenter","orcid":null,"position":3,"is_corresponding":false},{"id":800121,"name":"Mark R. Bauer","orcid":"0000-0001-6221-2197","position":4,"is_corresponding":false},{"id":1140655,"name":"Ryan C. Costantino","orcid":"0000-0003-4554-3802","position":5,"is_corresponding":false},{"id":724140,"name":"Mary Jo Larson","orcid":"0000-0003-3417-2882","position":6,"is_corresponding":false},{"id":503273,"name":"Rachel Sayko Adams","orcid":"0000-0003-4450-0712","position":7,"is_corresponding":false},{"id":759604,"name":"Nick Huntington","orcid":"0000-0002-2341-6100","position":0,"is_corresponding":true}],"reference_count":12,"raw_metadata":null,"created_at":"2026-07-19T02:58:24.957414Z","pmid":"41124337","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}