Abstract: Homelessness among US veterans has been a focus of research for over 3 decades. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this is the first systematic review to summarize research on risk factors for homelessness among US veterans and to evaluate the evidence for these risk factors. Thirty-one studies published from 1987 to 2014 were divided into 3 categories: more rigorous studies, less rigorous studies, and studies comparing homeless veterans with homeless nonveterans. The strongest and most consistent risk factors were substance use disorders and mental illness, followed by low income and other income-related factors. There was some evidence that social isolation, adverse childhood experiences, and past incarceration were also important risk factors. Veterans, especially those who served since the advent of the all-volunteer force, were at greater risk for homelessness than other adults. Homeless veterans were generally older, better educated, and more likely to be male, married/have been married, and to have health insurance coverage than other homeless adults. More studies simultaneously addressing premilitary, military, and postmilitary risk factors for veteran homelessness are needed. This review identifies substance use disorders, mental illness, and low income as targets for policies and programs in efforts to end homelessness among veterans.
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.