Advocating for minority Veterans in the United States: Principles for equitable public policy
Abstract: In advocating for minority Veterans in the United States, a primary issue has been confronting policy approaches that seek to maintain existing institutional structures and processes and add minority Veterans in after the fact. This approach foregrounds the needs and experiences of majority Veterans in designing and implementing policy at the expense of the unique needs of minority Veterans, creating barriers and perpetuating harms through initiatives that are often well intentioned. Drawing on work with the U.S. Senate and House Veterans’ Aff airs Committees of the 115th, 116th, and 117th Congresses, as well as with the U.S. Department of Veterans Affairs, the authors present principles for equitable Veteran public policy grounded in intersectionality theory. In addition to presenting these principles, the authors discuss the ways they communicate their framework to, and negotiate with, policy-makers in the context of advocating for minority Veterans. The authors’ work addresses the tension inherent in certain formulations of intersectionality theory that treat identity categories as stable and universal. In complicating this notion of identity and the way it is operationalized in Veteran policy, the authors demonstrate how a renewed notion of identity categories, grounded in intersectionality theory, can guide policy work to benefit minority 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.