Incidence and prevalence of eating disorders among U.S. military service members, 2016-2021

Abstract: Objective: Despite unique experiences that may increase eating disorder risk, U.S. military service members are an understudied population. The current study examined incidence and prevalence of eating disorder diagnoses in U.S. military personnel. Method: This retrospective cohort study utilized Military Health System Data Repository (MDR) data on eating disorder diagnoses (2016-2021). Active duty, Reserve, and National Guard U.S. military service members who received care via TRICARE Prime insurance were identified by ICD-10 eating disorder diagnostic codes. Results: During the 6-year surveillance period, 5189 Service members received incident eating disorders diagnoses, with a crude overall incidence rate of 6.2 cases per 10,000 person-years. The most common diagnosis was other/unspecified specified eating disorders, followed by binge-eating disorder, bulimia nervosa, and anorexia nervosa. There was an 18.5% overall rise in total incident cases across the surveillance period, but this trend was not statistically significant (p = 0.09). Point prevalence significantly increased across the 6-year timeframe for total eating disorders (p < 0.001). Period prevalence for 6-year surveillance period was 0.244% for total eating disorders, 0.149% for other/unspecified eating disorder, 0.043% for bulimia nervosa, 0.038% for binge-eating disorder, and 0.013% for anorexia nervosa. Discussion: Overall crude incidence estimates for total eating disorders were higher than reported in prior research that included only active duty Service members and required an eating disorder diagnosis code in the first or second diagnostic position of the medical record. Comprehensive and confidential studies are needed to more thoroughly characterize the nature and scope of eating disorder symptomatology within U.S. military personnel. Public significance: U.S. military service members are a vulnerable population with regard to eating disorder symptoms. Previously reported incidence and prevalence estimates using data from the Military Health System may have been underestimated due to overly stringent case definitions. Given personal and occupational barriers (e.g., career consequences), confidential studies of military personnel may provide more complete data on the scope of eating disorders to inform screening and clinical practice guidelines for military populations.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.

    Mental Health Peer reviewed