Alcohol misuse among older military veterans: an intersectionality theory perspective
Abstract: Background Alcohol misuse among older adults is an emerging public health issue. Older veterans are particularly at risk of developing substance use dependency due to the enduring impacts of military service. The purpose of this study was to test the theory of intersectionality on alcohol misuse by veteran status and age, veteran status and sex, and veteran status and race. Methods: Combined data from the 2016, 2017, and 2018 Brief Risk Factor Resilience Survey (BRFSS) from the Centers for Disease Control and Prevention (CDC) were used in this cross-sectional study. The BRFSS is conducted annually with adults via landline or cellular telephones in all 50 states in the United States, as well as in the District of Columbia, Puerto Rico, and Guam. Alcohol misuse among individuals aged 65+ was examined by veteran status and the interaction between age, race, and sex using survey-weighted logistic regression models. Results: Results show no interaction between veteran status and age or sex. For the interaction between veteran status and race, significant disparities were found. Black/Other race veterans were significantly more likely to engage in binge drinking and heavy drinking compared to nonveterans of the same race, White veterans, and White nonveterans. Conclusion: Older veterans who are also Black, Indigenous and/or people of color (BIPOC) are at great risk of engaging in alcohol misuse due to the combined stressors from their intersectional identities. Interventions targeting this population should consider the historical, cultural, and systemic factors that contribute to a disproportionally higher rate of binge drinking and heavy drinking among BIPOC veterans.
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.