Post-9/11 military Veterans' adjustment to civilian life over time following separation from service
Abstract: US military veterans face many challenges in transitioning to civilian life; little information is available regarding veterans' reintegration experiences over time. The current study characterized veterans' postdeployment stressful life events and concurrent psychosocial wellbeing over one year and determined how stressors and wellbeing differ by demographic factors.
Recent Post-911 veterans (n = 402) were assessed approximately every three months for 1 year. Participants were 60% men, primarily White (78%), and 12% Latinx; the average age was 36 years.
The frequency of stressful events decreased over time but was higher for men and minority-race veterans (independent of time since separation). Veterans reported high mean levels of posttraumatic stress disorder, anxiety, and insomnia symptoms, which improved slightly over time. Minority-race and Latinx veterans had higher symptom levels and slower rates of symptom reduction.
Veterans remain distressed in their overall transition to civilian life. Interventions to promote resilience and help veterans manage readjustment to civilian life appear urgently needed.
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.