Individual and Military Factors That Modify the Association Between Recent Sexual Trauma and Health Outcomes Among U.S. Service Members and Veterans
Abstract: Sexual trauma (ST), which includes both sexual harassment and sexual assault, is associated with a variety of adverse mental and physical health outcomes in military and civilian populations. However, little is known about whether certain individual or military attributes or prior experiences may modify the relationship between recent ST and mental or physical health outcomes. Data from a longitudinal cohort study of current and former military members were used to examine whether individual and military factors modify the association between recent ST and health outcomes (posttraumatic stress disorder, depression, multiple somatic symptoms, and insomnia). Results indicated that demographic (sex, sexual orientation, race/ethnicity) and military factors (service branch, service component, military separation) generally did not modify the main effect of ST on the outcomes examined. On the other hand, factors known to be protective (spirituality, social support) and risk factors (childhood trauma, combat deployment, and mental health status) did modify the effect of ST on multiple outcomes examined; notably, protective effects were diminished among those who experienced recent ST. Protective factors were associated with the lowest risk of adverse outcomes among those with no ST, while risk reduction was less among survivors of ST. Diminished impacts also were found for cumulative risk factors, with the influence of multiple individual risk factors associated with increased risk but in a subadditive manner. We conclude that the effect of recent ST on the outcomes examined was persistent in the presence of potential protective factors, but that it may be impacted by ceiling effects in combination with other risk factors.
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.