Problematic anger in military and veteran populations with and without PTSD: The elephant in the room
Abstract: In this editorial, the authors discuss that given the prevalence and serious risks associated with problematic anger, including harm to self and others, it is critical that general health and mental health practitioners be prepared to support active duty personnel and veterans with relevant assessment and treatment. In addition, policy makers and service managers and leaders within government and military and veteran services should consider the assessment, prevention, early intervention, and treatment of problematic anger. Indeed, clinicians can effect change by routinely incorporating assessment, early intervention, and treatment for problematic anger into their repertoire of health-promotion activities and researchers can continue to evaluate and inform these clinical efforts through much needed empirical scrutiny.
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.