Describing the profile of a population of UK veterans seeking support for mental health difficulties
Abstract: Background: Evidence suggests that veterans with mental health issues have poorer treatment outcomes than civilian counterparts. Understanding the difficulties faced by veterans could help focus treatments and improve outcomes. Aims: To survey a representative sample of treatment-seeking veterans to explore their mental health needs. Methods: A random sample of UK veterans who had engaged with a national mental health charity in the UK was drawn. Individuals completed questionnaires about their health, military experiences and pre-enlistment vulnerabilities. Results: Four hundred and three out of six hundred (67.2%) participants returned completed questionnaires. PTSD was the most commonly endorsed mental health difficulty (82%), followed by problems with anger (74%), common mental health difficulties (72%) and alcohol misuse (43%). Comorbidity was frequent; with 32% of those with PTSD meeting criteria for three other health outcomes versus only 5% with PTSD alone. Conclusions: Results indicate the complexity of presentations within treatment seeking veterans. These difficulties may partly explain the poorer treatment outcomes reported in veterans in comparison to the general public. As such, it would be prudent for interventions targeted at veterans with mental health difficulties to attempt to address the range of issues faced by this population rather than focus on a particular presenting problem.
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.