Early Service Leavers: a study of the factors associated with premature separation from the UK Armed Forces and the mental health of those that leave early
Abstract: Background: Approximately 18 000 personnel leave the UK Armed Forces annually. Those leaving before completing the minimum term of their contracts are called early Service leavers (ESLs). This study aims to identify characteristics associated with being an ESL, and compare the post-discharge mental health of ESLs and other Service leavers (non-ESLs). Method: A cross-sectional study used data on ex-Serving UK Armed Forces personnel. ESLs were personnel leaving before completing their 3–4.5 years minimum Service contracts and were compared with non-ESLs. Multivariable logistic regression was used to estimate odds ratios and 95% confidence intervals for the associations between Service leaving status with socio-demographics, military characteristics and mental health outcomes. Results: Of 845 Service leavers, 80 (9.5%) were ESLs. Being an ESL was associated with younger age, female sex, not being in a relationship, lower rank, serving in the Army and with a trend of reporting higher levels of childhood adversity, but not with deployment to Iraq. ESLs were at an increased risk of probable post-traumatic stress disorder (PTSD), common mental disorders, fatigue and multiple physical symptoms, but not alcohol misuse. Conclusions: The study suggests that operational Service is not a factor causing personnel to become an ESL. Current mental health problems were more commonly reported among ESLs than other Service leavers. There may be a need to target interventions to ESLs on leaving Service to smooth their transition to civilian life and prevent the negative mental health outcomes experienced by ESLs further down the line.
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.