Analysis of Alcohol Use and Alcohol Use Disorder Trends in U.S. Active-Duty Service Women

Abstract: Introduction: Alcohol use (AU) and disorders (AUDs) have been increasing among women over the past decade, with the largest increases among women of child-bearing age. Unprecedented stressors during the COVID-19 pandemic may have impacted AU for women with and without children. Little is known about how these trends are impacting women in the military. Methods: Cross-sectional study of active-duty service women (ADSW) in the U.S. Army, Air Force, Navy, and Marine Corps during fiscal years (FY) 2016-2021. We report the prevalence of AU and AUD diagnoses by FY, before/during the COVID-19 pandemic (2016-2019; 2020-2021, respectively), and by parental status. Log-binomial and logistic regressions examined associations of demographics, military, and family structure characteristics, with AU and AUD, during pre-COVID-19 and COVID-19 timeframes. Results: We identified 281,567 ADSW in the pre-COVID-19 period and 237,327 ADSW in the during COVID-19 period. The prevalence of AU was lower during the COVID-19 period (47.9%) than during the pre-COVID-19 period (63.0%); similarly, the prevalence of AUD was lower during the COVID-19 period (2.7%) than during the pre-COVID period (4.0%). ADSW with children had larger percentage decreases during the COVID-19 period. ADSW with children had a consistently lower prevalence and odds of AUD compared with ADSW without children in the pre- and during COVID-19 periods. Conclusion: Decreasing trends in AU and AUD among ADSW were unexpected. However, the prevalence of AU and AUD may not have been accurately captured during the COVID-19 period due to reductions in access to care. Continued postpandemic comparison of AU/AUD among women by parental status and demographic factors may guide targeted health efforts.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record 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.

    Mental Health Peer reviewed