Contribution of mental ill health during military service to postservice benefit claims in the UK
Abstract: Objectives: While most UK military personnel transition successfully into civilian life, some experience unemployment and disability, which may be partly attributable to in-service factors. This study aims to determine the degree to which in-service mental health problems impact on postservice benefit claims. Methods: Using data from a cohort of 5598 recent leavers from regular service in the UK Armed Forces linked with data from the Department for Work and Pensions, we assessed associations between in-service mental health and postservice benefit claims, and the population attributable fraction (PAF) of benefit claims related to in-service mental health. An analysis with postservice mental ill health as mediator was performed to determine the degree to which the observed effects were a consequence of persistent illness, as opposed to remitted. Results: Mental illness occurring in-service predicted both unemployment and disability claims, partly mediated by postservice health (23%-52% total effects mediated), but alcohol misuse did not. Common mental disorder (CMD) (PAF 0.07, 95% CI: 0.02 to 0.11) and probable post-traumatic stress disorder (PTSD) (PAF 0.05, 95% CI 0.01 to 0.09) contributed to unemployment claims. Probable PTSD was the largest contributor to disability claims (PAF 0.25, 95% CI 0.13 to 0.36), with a smaller contribution from CMD (PAF 0.16, 95% CI 0.03 to 0.27). Conclusions: In-service mental ill health gives rise to benefit claims. These effects are only partly mediated by postservice mental health, implying that in-service (or pre-service) mental issues have carry-over effects into civilian life even if remitted. Better prevention and treatment of in-service PTSD symptoms may well reduce postservice disability claims.
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.