The impact of just and unjust war events on mental health need and utilization within U.S. Service members
Abstract: Soldiers are resilient to just war events, such as killing enemy combatants and life-threatening experiences, but these same soldiers appear to struggle with unjust war events, such as killing a noncombatant or being unable to help civilian women and children in need. This study is the first to examine how just and unjust war experiences are associated with clinical health service outcomes. Two samples of soldiers in different stages of readjustment from deployment were drawn from a longitudinal, survey-based study of a US Army brigade. Measures included items related to combat events, mental health utilization, perceived mental health need, PTSD, depression, and functional impairment. After controlling for other kinds of combat events, just war events (i.e., life-threatening events and killing enemy combatants) predicted outcomes in soldiers who are less than three months post-deployment, but only predicted 2 of 26 outcomes in soldiers one year post deployment. In contrast, unjust war events were found to be robust predictors of short-term and long-term outcomes related to mental health need and utilization, even after controlling for exposure to other combat events. The results extend previous longitudinal research that suggests that exposure to unjust war events carry a heavier long-term mental health burden than other types of events. Additionally, Soldiers exposed to unjust war events had an unmet need for care one year post deployment that was not directly tied to PTSD or depression. The results question the emphasis on life-threat within mental health pathogenesis models.
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.