Female Veterans' risk factors for homelessness: a scoping review
Abstract: Homelessness is a problem among female Canadian Veterans. Approximately 15% of the Canadian Veteran population is female, yet female Veterans constitute approximately 30% of the homeless Canadian Veteran population. In response, the Standing Committee on Veterans Affairs has called for the investigation of homelessness among female Canadian Veterans to address this research gap. A scoping review was conducted on the lived experiences of homeless female Veterans to identify factors associated with homelessness. This review was the initial step in a larger research framework to investigate the lived experiences of homeless Canadian female Veterans. Fifteen articles met the inclusion criteria and were included for synthesis. Four themes were identified, corresponding with the period in which they occurred (pre-military service, post-military service, during military service, and across the lifespan). Several implications are clear. First, Canadian research on female Veteran homelessness is needed. Second, future research must use a framework that accounts for multifactorial and multi-dimensional issues, as well as a sex- and gender-based analysis lens.
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.