The Impact of Military service on Health and Well-being
Abstract: While it is known that some UK Armed Forces (UK AF) personnel and veterans experience physical and mental health problems, the possible future healthcare needs of military veterans are unknown. Aims: To estimate the number of military personnel who may experience physical and/or psychological health problems associated with their military service. Methods: Data were obtained via Freedom of Information requests to several sources, including Defence Statistics. Raw data from research studies were also used where available. Data were analysed using meta-analytic methods to determine the rate of physical, mental or comorbid health problems in AF personnel. Results: Musculoskeletal problems were the predominant reason for medical discharge from service. In terms of mental health, meta-analyses estimated that veteran reservists (part-time military members) previously deployed to operational areas had the highest proportion of general health problems (35%), previously deployed veteran regulars (those in full time military employment) and veteran reservists had the highest proportion of post-traumatic stress disorder (9%), and regular personnel with a deployment history had the highest proportion of alcohol problems (14%). Overall, our findings suggest that at least 67515 veterans are likely to suffer from mental and/or physical health problems at some point as a result of their service between 2001 and 2014. Conclusions: The results of this study highlight that the difficulties personnel may face are largely musculoskeletal or mental health-related. These findings may help with planning the provision of future physical and mental health care and support for those who serve in the UK AF.
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.