Causes of alcohol-attributable death and associated years of potential life lost among LGB and non-LGB veteran men and women in Veterans Health Administration
Abstract: Alcohol use is a significant concern nationally and research now highlights higher rates of alcohol attributable death (AAD) and years of potential life lost (YPLL) among lesbian, gay, and bisexual (LGB) veterans compared to non-LGB veterans. In this study, we examined specific causes of AAD and associated YPLL between LGB and non-LGB veteran men and women to highlight needed outreach, prevention, and treatment strategies. Using data from the nationwide Veterans Health Administration electronic health record and National Death Index from 2014 to 2018, we examined the top ten ranked causes of AAD among LGB (n = 102,085) and non-LGB veteran (n = 5,300,521) men and women, as well as associated YPLL per AAD. We observed higher rates of AAD among men than women, but higher rates among LGB veterans relative to their same-sex non-LGB counterparts. We noted greater YPLL per AAD among LGB men and all women compared to non-LGB men, even when of similar or same rank in cause of death. Acute-cause AAD death (e.g., alcohol-related suicide, poisonings) was ranked higher among LGB men and all women. YPLL was greater for both acute- and chronic-cause AAD (e.g., liver disease) among LGB men and all women compared to non-LGB men. Causes of AAD differ between LGB and non-LGB men and women. The differences observed highlight disparities in acute- and chronic-cause AAD between groups help explain the higher number of YPLL per AAD that disfavor LGB men and women veterans, and essential next steps in primary and secondary prevention of hazardous drinking and mortality risk.
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.