We Also Served: The Health and Well-Being of Female Veterans in the UK
Abstract: Women’s integration into the UK Armed Forces has resulted from a number of policy changes over the decades. Women now make up 11% of the UK Armed Forces and veteran population. However, research focused on female veterans in the UK is limited and not enough is known about their health, well-being, and Service experiences. In recognition of this, in June 2020, the Cobseo Female Veteran Cluster Group, supported by NHS England and NHS Improvement,commissioned the VFI to undertake a scoping study into the health and well-being needs of female veterans in the UK, identify gaps in research utilising national and international research and to provide a framework for prioritising research and other activities in the UK going forward. This report details the findings.
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.