A comparative analysis of medically released men and women from the Canadian Armed Forces
Abstract: Introduction: Musculoskeletal (MSK) injuries and mental health (MH) disorders are the leading causes of medical attrition in the Canadian Armed Forces (CAF). Historically, medical attrition rates have been higher for women than men. In order to better understand the medical release trends of men and women, a descriptive analysis of the medical reasons for release was undertaken. Methods: Administrative data sources within the Department of National Defence were used to identify medically released personnel together with their primary medical diagnosis and demographic characteristics, including sex, age, and rank. The analysis included 5,180 Regular Force personnel medically released between April 1, 2014 and March 31, 2017. Results: While overall trends in the reasons for medical release were sometimes similar for men and women, statistically significant differences between the medical release reasons of men and women were found in several of the sub-groups considered. These sub-groups included non-commissioned members (NCMs), officers, Air personnel, and members who had not deployed in the 10 years prior to their release, as well as personnel in the earlier and later stages of their career. Discussion: An increased understanding of the differences between medically released men and women is important for the development of future injury and illness prevention strategies, which have the primary objective of improving the health and operational readiness of serving members, as well as a secondary objective of lowering medical attrition rates to improve overall retention in the CAF.
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.