Beyond war and PTSD : The crucial role of transition stress in the lives of military veterans
Abstract: Although only a relatively small minority of military veterans develop Posttraumatic Stress Disorder (PTSD), mental health theory and research with military veterans has focused primarily on PTSD and its treatment. By contrast, many and by some accounts most veterans experience high levels of stress during the transition to civilian life, however transition stress has received scant attention. In this paper we attempt to address this deficit by reviewing the wider range of challenges, rewards, successes, and failures that transitioning veterans might experience, as well as the factors that might moderate these experiences. To illuminate this argument, we briefly consider what it means to become a soldier (i.e., what is required to transition into military service) and more crucially what kind of stressors veterans might experience when they attempt to shed that identity (i.e., what is required to transition out of military service). We end by suggesting how an expanded research program on veteran transition stress might move forward.
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.