Prevalence and Correlates of Military Sexual Trauma in Service Members and Veterans: Results From the 2018 Canadian Armed Forces Members and Veterans Mental Health Follow-up Survey
Abstract: Introduction:Military sexual trauma (MST) is an ongoing problem. We used a 2002 population-based sample, followed up in 2018, to examine: (1) the prevalence of MST and non-MST in male and female currently serving members and veterans of the Canadian Armed Forces, and (2) demographic and military correlates of MST and non-MST. Methods: Data came from the 2018 Canadian Armed Forces Members and Veterans Mental Health Follow-up Survey (n = 2,941, ages 33 years + ). Individuals endorsing sexual trauma were stratified into MST and non-MST and compared to individuals with no sexual trauma. The prevalence of lifetime MST was computed, and correlates of sexual trauma were examined using multinomial regression analyses. Results:The overall prevalence of MST was 44.6% in females and 4.8% in males. Estimates were comparable between currently serving members and veterans. In adjusted models in both sexes, MST was more likely among younger individuals (i.e., 33-49 years), and MST and non-MST were more likely in those reporting more non-sexual traumatic events. Among females, MST and non-MST were more likely in those reporting lower household income, non-MST was less likely among Officers, and MST was more likely among those with a deployment history and serving in an air environment. Unwanted sexual touching by a Canadian military member or employee was the most prevalent type and context of MST. Interpretation:A high prevalence of MST was observed in a follow-up sample of Canadian Armed Forces members and veterans. Results may inform further research as well as MST prevention efforts.
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.