Military Service Member and Veteran Reintegration: A Critical Review and Adapted Ecological Model
Abstract: Returning military service members and veterans (MSMVs) experience a wide range of stress-related disorders in addition to social and occupational difficulties when reintegrating to the community. Facilitating reintegration of MSMVs following deployment is a societal priority. With an objective of identifying challenges and facilitators for reintegration of MSMVs of the current war era, we critically review and identify gaps in the literature. We searched 8 electronic databases and identified 1,764 articles. Screening of abstracts and full-text review based on our inclusion/exclusion criteria, yielded 186 articles for review. Two investigators evaluating relevant articles independently found a lack of clear definition or comprehensive theorizing about MSMV reintegration. To address these gaps, we linked the findings from the literature to provide a unified definition of reintegration and adapted the social ecological systems theory to guide research and practice aimed at MSMV reintegration. Furthermore, we identified individual, interpersonal, community, and societal challenges related to reintegration. The 186 studies published from 2001 (the start of the current war era) to 2015 included 6 experimental studies or clinical trials. Most studies do not adequately account for context or more than a narrow set of potential influences on MSMV reintegration. Little evidence was found that evaluated interventions for health conditions, rehabilitation, and employment, or effective models of integrated delivery systems. We recommend an ecological model of MSMV reintegration to advance research and practice processes and outcomes at 4 levels (individual, interpersonal, organizational, and societal).
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.