Going It Alone: Post-9/11 Veteran Nonuse of Healthcare and Social Service Programs During Their Early Transition to Civilian Life
Abstract: Transitioning from military to civilian life is challenging for a substantial number of veterans.Successful transitions require veterans to function well in various well-being domainsincluding employment, education, financial, health, and social relationships. There are manyprograms and services designed to assist veterans transition to civilian life. However, veter-ans rarely avail themselves of supportive resources. This study examined veteran nonuse ofprograms and services within the first three months of their transition to civilian life. Resultsrevealed that male veterans often reported that they did not need programs. Femaleveterans and veterans from the lowest enlisted ranks were more likely to report that theydid not know if they were eligible for support programs. A small percentage of veteransindicated they had not found the right program or did not know where to go to get help.Veterans need clear information about available programs, eligibility requirements, where tolocate them, and how to identify which programs will benefit them. Future research shouldfocus on what predicts veteran use of programs and services, how use changes over time,and how programs and services should be advertised/marketed to different veteran popula-tions, particularly those at risk for poor transition outcomes.
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.