Employment and mental health among UK ex-service personnel during the initial period of the COVID-19 pandemic
Abstract: The COVID-19 pandemic has interrupted participation in the labour force and may have affected mental health, both directly through the effects of illness and isolation and indirectly through negative effects on employment. Former military personnel may be at particular risk as a result of both additional exposure to risk factors for poor mental health and barriers to labour market participation raised by the transition from military to civilian working environments. This article examines furlough and unemployment as a result of the COVID-19 pandemic among UK working-age ex-service personnel and its associations with poor mental health. Participants from an existing cohort study of Iraq- and Afghanistan-era UK Armed Forces personnel were invited to provide information on employment before the COVID-19 pandemic and how it has changed since the pandemic. Mental health was measured using the General Health Questionnaire and compared with data collected pre-pandemic. Although Veteran unemployment is not higher than civilian unemployment (4.7% and 4.8%, respectively, in September 2020), it rose during the pandemic from a lower level (1.3%). Part-time and self-employed Veterans were more likely than full-time employees to experience furlough or unemployment. A negative impact on employment was associated with the onset of new mental ill health. Employment of ex-service personnel was more negatively affected by the COVID-19 pandemic, possibly because ex-service personnel are mostly men, and men were more affected in the UK general population. This employment instability has negative consequences for mental health that are not mitigated by furlough.
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.