Health and Wellbeing Study of Serving and ex-Serving UK Armed Forces Personnel: Phase 4
Abstract: The study has been running since 2003 with the aim of investigating the impact of deployment to Iraq (Operation TELIC) and Afghanistan (Operation HERRICK) on the health and wellbeing of serving and ex-serving personnel.
Data have been collected over three previous phases - Phase 1 (2004/06), Phase 2 (2007/09), and Phase 3 (2014/16), with the most recent phase taking place over 2022/23. In addition to examining key mental health outcomes such as CMD, probable PTSD and alcohol misuse, this phase also collected new data on additional topics relevant to UK serving and ex-serving personnel including complex PTSD (C-PTSD), loneliness and caring responsibilities.
For Phase 4 (2022/23), we followed up participants who took part in the previous phase in 2014/16. 4104 participants completed the survey. 69% of the sample had deployed to Iraq and/or Afghanistan and 72% had left service.
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.