Chronically homeless veterans with gambling disorder: Epidemiology, clinical correlates, and traumatic experiences
Abstract: Gambling disorder (GD) is often a concern for people living in poverty. Although GD has been correlated with homelessness, there has been no study of factors related to chronic homelessness among veterans with GD. This study used data from specialized homeless programs from the U.S. Department of Veterans Affairs Homeless Operations Management System to explore prevalence and correlates of chronic homelessness among veterans with GD in this program and to describe initial descriptive epidemiology. Chi-square tests, analyses of variance, and logistic regressions were conducted to examine differences in sociodemographic, military, clinical, and behavioral characteristics between veterans with versus without chronic homelessness. Of 6053 veterans with GD, 1733 (28.6%) had chronic homelessness. Veterans with versus without chronic homelessness were more likely to be older, male, unemployed, and of low educational attainment and report having spent fewer years in the military. Chronic homelessness was associated with elevated odds of mental health and medical diagnoses, traumatic experiences, incarceration, and suicidal thoughts. Veterans with versus without chronic homelessness more frequently reported needing substance use, medical and psychiatric treatments but expressed low interest in participation in psychiatric treatment. Veterans with GD and chronic homelessness have more clinical and behavioral concerns and needs for treatment, but participate in treatment at lower rates. It may be important to address both chronic homelessness and GD concurrently in order to effectively support veterans facing these challenges.
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.