Alcohol Consumption Levels and All-Cause Mortality Among Women Veterans and Non-Veterans Enrolled in the Women’s Health Initiative
Abstract: Purpose: To address research gaps regarding women Veterans’ alcohol consumption and mortality risk as compared to non-Veterans, the current study evaluated whether alcohol consumption amounts differed between women Veterans and non-Veterans, whether Veterans and non-Veterans within alcohol consumption groups differed on all-cause mortality, and whether Veteran status modified the association between alcohol consumption and all-cause mortality. Design and Methods: Six alcohol consumption groups were created using baseline data from the Women’s Health Initiative Program ( N = 145,521): lifelong abstainers, former drinkers, less than 1 drink/week (infrequent drinkers), 1–7 drinks/week (moderate drinkers), 8–14 drinks/week (moderately heavy drinkers), and 15 or more drinks/week (heavy drinkers). The proportions of Veteran and non-Veteran women within each alcohol consumption category were compared. Mortality rates within each alcohol consumption category were compared by Veteran status. Cox proportional hazard models, including a multiplicative interaction term for Veteran status, were fit to estimate adjusted mortality hazard (rate) ratios for each alcohol consumption category relative to a reference group of either lifelong abstainers or moderate drinkers. Results: Women Veterans were less likely to be lifelong abstainers than non-Veterans. Women Veterans who were former or moderate drinkers had higher age-adjusted mortality rates than did non-Veterans within these alcohol consumption categories. In the fully adjusted multivariate models, Veteran status did not modify the association between alcohol consumption category and mortality with either lifelong abstainers or moderate drinkers as referents. Implications: The results suggest that healthcare providers may counsel Veteran and non-Veteran women in similar ways regarding safe and less safe levels of alcohol consumption.
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.