Eating disorder symptoms in female veterans: The role of childhood, adult, and military trauma exposure
Objective: Eating disorders are understudied among female U.S. military veterans, who may be at increased risk due to their high rates of trauma exposure and trauma-related sequelae. The current study sought to examine whether different types of trauma in childhood and adulthood confer differential risk for eating disorder symptoms (EDSs) in this population. Method: We analyzed survey data from a sample of female Veterans Health Administration patients (N = 186) to examine the association between 5 trauma types (i.e., childhood physical abuse, adult physical assault, childhood sexual abuse, adult sexual assault, and military-related trauma) and EDS severity. Results: Approximately 14% of the sample reported clinical levels (i.e., standardized Eating Disorder Diagnostic Scale score ≥16.5) of EDSs. Multiple traumatization was associated with increased EDSs. Adult physical assault, adult sexual assault, and military-related trauma were individually associated with more severe eating disorder symptomatology, though only military-related trauma was uniquely associated with disordered eating in the full model. Discussion: EDSs are common among female veterans, and trauma exposures are differentially associated with symptom severity. It is critical to assess for EDSs in female veterans, particularly those with a history of military-related trauma, to facilitate detection and appropriate treatment.
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.