Cohort profile update: the US Millennium cohort study-evaluating the impact of military experiences on service members and Veteran health
Abstract: This paper provides an update to the original cohort profile paper published a decade ago. The Millennium Cohort Study is the largest, longest-running, prospective study of current and former United States (US) military personnel and is sponsored by the US Departments of Defense (DoD) and Veterans Affairs (VA). While the original study aim, evaluating the health impact of serving in the military, has remained consistent, the spectrum of research topics has expanded to include areas such as social determinants of health. 260 228 military personnel enrolled across 5 panels between 2001 and 2021 (baseline age range: 25-35 years); participants are surveyed every 3-5 years. The original 21-year follow-up period was extended through 2068 to examine health across the lifespan. Longitudinal survey data are linked to data from DoD, VA, and external sources (e.g. medical records, deployment histories, vital statistics, and geospatial data).
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.