Understanding the Mental Health Needs of a Community-Sample of UK Women Veterans
Abstract: Women are often underrepresented or entirely missing from veteran research, and there remains limited understanding of their mental health needs. The present study investigated the mental health needs of a community sample of UK women veterans. A total of 750/1680 (44.6%) participants completed an online survey. Data was collected on sociodemographic and military factors, mental health and wellbeing, and childhood adversity. Findings revealed a high prevalence and comorbidity of mental health difficulties, including common mental health difficulties (28.6%) and posttraumatic stress disorder (PTSD) (10.8%). Women veterans who were older, not working, held a lower rank during service, perceived less social support and experienced greater loneliness were more likely to report such difficulties. Results further revealed high childhood and military adversity, and wellbeing difficulties. Such findings provide insight into the needs of women veterans and have implications for providing appropriate support. Considerations of the generalizability of findings are discussed.
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.