Reconceptualizing combat-related posttraumatic stress disorder as an occupational hazard
Abstract: Combat is clearly an occupational hazard with direct implications for mental health. In the case of the military, the mission dictates how often a service member leaves the base camp and is exposed to the combat conditions that can result in PTSD. Those in other occupations, such as firefighters and police officers, face risky environments as well. This issue of occupational risk associated with PTSD has gone largely unrecognized as it relates to the diagnostic conceptualization of the disorder. Unfortunately, the current diagnostic criteria for PTSD do not adopt an occupational health model. Instead, the criteria are based on a victim-based medical model. This decision to combine occupational risk with victimhood has obscured the critical differences between the two and has limited the examination of their unique diagnostic and prognostic pathways. The purpose of this chapter is to propose a reconceptualization of combat-related PTSD. This reconceptualization reviews the role of the definition of trauma, the context of symptoms, and the understanding of functional impairment.
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.