Female Veterans: Socioeconomic Characteristics and Disability Patterns Among Social Security Beneficiaries
Abstract: In 2020, approximately 2 million women were veterans of military service. Female veterans constitute a growing proportion of Social Security beneficiaries. Using American Community Survey data for the period 2015-2019, we present a detailed study of the socioeconomic characteristics of female veterans, focusing on Social Security beneficiaries. We assess and compare the employment, earnings, income, and disability status of female veterans, female nonveterans, and male veterans. Female veterans were more likely than female nonveterans to have a college degree and, among those employed, to have higher median earnings. Female veterans younger than 62 were more likely than female nonveterans to be Social Security beneficiaries. Among all female beneficiaries, veterans were more likely than nonveterans to report having one or more functional limitations. More than half of female veteran beneficiaries aged 25-54 reported having a service-connected disability.
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.