DSM-5 eating disorder prevalence, gender differences, and mental health associations in United States military Veterans
Abstract: Objective: Little is known about prevalence estimates of new and revised DSM-5 eating disorders diagnoses in general, and especially among high-risk, underserved and diverse eating disorder populations. The aim of the current study was to determine prevalence, gender differences and correlates of DSM-5 eating disorders in veterans. Method: Iraq and Afghanistan war era veterans (N = 1,121, 51.2% women) completed the Eating Disorder Diagnostic Scale-5 and validated measures of eating pathology and mental health between July 2014 and September 2019. Results: Overall more women than men (32.8% vs. 18.8%, p < .001) reported symptoms consistent with a DSM-5 eating disorder. Prevalence estimates (women vs. men) for the specific diagnoses were: Anorexia Nervosa (AN; 0.0% vs. 0.0%), Bulimia Nervosa (BN; 6.1% vs. 3.5%), Binge-Eating Disorder (BED; 4.4% vs. 2.9%), Atypical AN (AAN; 13.6% vs. 4.9%), Subclinical BN (0.0% vs. 0.2%), Subclinical BED (1.4% vs. 0.6%), Purging Disorder (2.1% vs. 0.7%), and Night Eating Syndrome (NES; 5.2% vs. 6.0%). Women were more likely to have BN or AAN, and there was no difference for BED or NES among genders. The eating disorder group had a higher mean BMI, and significantly greater eating pathology and mental health symptoms than the non-eating disorder group. Discussion: Approximately one-third of women, and one-fifth of men, reported symptoms consistent with a DSM-5 eating disorder diagnosis. These high prevalence estimates across genders, and associated mental health concerns, suggest an urgent need to better understand and address eating disorders in military and veteran 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.