A case-control study examining the association between service-related mental ill-health and dementia in male military veterans over the age of 65
Abstract: Dementia is currently incurable, irreversible and a major cause of disability for the world’s older population. The identification, and early intervention of modifiable risk factors, is therefore of increasing global priority. Prior scientific studies have suggested numerous risk factors which increase the chance of developing dementia, a number of which are suggested to occur at a greater frequency within military and military veteran personnel. One such risk factor is service-related mental ill health. This project aimed to determine whether service-related mental ill-health increases the risk that male military veterans have of developing dementia. The study compared the prevalence of service-related mental ill-health in male military veterans with dementia with those without dementia.
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.