The impact of military service on the mental health of older UK veterans: A qualitative study
Abstract: Background: There may be ongoing psychological problems associated with military service later in life; yet as the elderly in the general population also suffer from mental health problems, whether such issues can be attributed to military service or are a feature of ageing remains unclear. This study aimed to explore veteran and nonveteran perceptions of the impact of their occupation on their psychological well-being later in life. Methods: Twenty-five veterans (≥65 y); 25 nonveterans (≥65 y); 10 veterans with diagnoses of mental health issues (≥65 y); and a close companion of all participants (≥18 y, spouse, child, and close friend) were recruited. Using a qualitative approach, participants completed semistructured qualitative interviews with measures of psychological adjustment used to describe the sample. Results: Veterans were found to experience higher levels of workplace stress and trauma exposure compared with nonveterans. When such challenges were positively appraised, veterans described increased confidence and resilience. Social support in response to occupational stress was central to veteran and nonveteran well-being, especially for those with mental health problems. Nonetheless, providing support was challenging for close companions, with many feeling overwhelmed and requiring additional guidance from the veteran's clinical care team. Conclusions: The findings delineate the impact of occupation on the well-being of older veterans and nonveterans. The results illustrated the psychological support needs and formal guidance desired by veterans, nonveterans, and their families, which could ultimately improve coping of both the individual and family.
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.