Cross-sectional examination of physical Abuse victimization differences between lesbian, gay, bisexual, and heterosexual Service members in the U.S. military, 2018
Abstract: The primary objective was to analyze the association between sexual orientation and physical abuse victimization using a representative sample from the U.S. active-duty military population. The secondary objective was to determine if differences exist by sexual orientation in perceived barriers (e.g., stigma) to mental health care utilization among physical abuse victimization survivors. The 2018 Department of Defense Health Related Behaviors Survey (HRBS) (n = 17,166 active-duty respondents) was used for analysis. Weighted logistic regressions and Poisson regressions were used for multivariable analyses, controlling for demographic and military variables. Approximately 93.7% of respondents identified as heterosexual or straight, 2.3% identified as gay or lesbian, and 4% as bisexual. Bisexual active-duty service members had 1.5-fold greater odds of reporting any form of physical abuse victimization (adjusted odds ratio: 1.50 and 95% confidence interval: 1.07–2.10). However, there was no difference observed between gay/lesbian and heterosexual service members for physical abuse victimization. Among survivors of physical abuse victimization, bisexual (p = 0.0038) and gay (p < 0.0001) service members were more likely to report more than one mental health care barrier compared to their heterosexual counterparts. Bisexual service members were more likely to experience physical abuse victimization when compared to their heterosexual counterparts. In addition, gay and bisexual survivors of physical abuse were more likely to experience barriers to mental health care. Tailored interventions should explore strategies to prevent victimization and disparities in mental health care utilization by sexual orientation.
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.