Decreasing alcohol use among young adults presenting for service in the US Air Force: an epidemiological surveillance study
Abstract: U.S. surveys demonstrate recent decreases in the prevalence of alcohol use and binge drinking among young adults. The current study aims to determine whether similar trends are evident in a similarly aged cohort of service members in the US Air Force to inform ongoing prevention efforts. Participants were 103,240 Air Force personnel in entry-level training between 2016 and 2019. Participants anonymously completed the AUDIT (Alcohol Use Disorder Identification Test) regarding their pre-service drinking. Logistic regression analyses and the Cochran-Armitage test were conducted to measure population trends over the study duration with stratification by age (<21 vs. ≥21) and evaluation of specific alcohol behaviours. Between 2016 and 2019, the proportion of young service members endorsing any alcohol use significantly decreased for both the <21 group (i.e. from 38.9% to 32.6%) and the ≥21 group (i.e. from 80.6% to 77.5%). Among those who endorsed drinking, a decrease over time in binge use was also observed from 46.6% to 37.8% for the <21 group and from 34.2% to 27.5% for the ≥21 group. Responses to other specific alcohol risk items and total AUDIT scores also demonstrated decreases. Binge use and risky drinking remained disproportionately common among those under the legal drinking age. It is encouraging to observe a shift toward abstinence and decreased binge use among this population of young military recruits. However, given the risk for many adverse health and legal consequences in this population, more work is needed to prevent problematic drinking, especially among those under the legal drinking age.
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.