Eating Disorders in Military and Veteran Men and Women: A Systematic Review
Abstract: Objective: Eating disorders (EDs) have serious consequences for psychological and physical health. They have high mortality rates and are among the most costly disorders to treat. However, EDs remain understudied in military and veteran populations. The aim of this review was to examine prevalence estimates and associated symptomatology of EDs among military and veteran men and women and to identify factors that may put these individuals at risk for the development of an ED for the purposes of improving detection, intervention, and treatment. Method: A thorough literature review was conducted using the databases PsycINFO and PubMed. All articles with a focus on EDs in military/veteran samples were considered. Results: Studies reveal high prevalence estimates of EDs among military/veteran men and women. Unique features of military life may increase the risk for development of an ED, including: military sexual trauma, strict weight and physical fitness requirements, and combat exposure. A history of trauma was common in individuals diagnosed with an ED in military and veteran samples. Discussion: The high rates of EDs among military and veteran samples underscore the importance of further research, as well as the importance of screening and intervention efforts, in these understudied populations.
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.