The impact of perceived everyday discrimination and income on racial and ethnic disparities in PTSD, depression, and anxiety among veterans
Abstract: Black and Hispanic/Latinx individuals experience a greater burden of mental health symptoms as compared to White individuals in the general population. Examination of ethnoracial disparities and mechanisms explaining these disparities among veterans is still in its nascence. The current study examined perceived everyday discrimination and income as parallel mediators of the association between race/ethnicity and PTSD, depression, and general anxiety symptoms in a sample of White, Black, and Hispanic/Latinx veterans stratified by gender. A random sample of 3,060 veterans living across the U.S. (oversampled for veterans living in high crime communities) completed a mail-based survey. Veterans completed self-report measures of perceived discrimination via the Everyday Discrimination Scale, PTSD symptoms via the Posttraumatic Stress Disorder Checklist-5, depressive symptoms via the Patient Health Questionnaire, and anxiety symptoms via the Generalized Anxiety Disorder Questionnaire. Models comparing Black vs. White veterans found that the significant effect of race on PTSD, depression, and anxiety symptoms was mediated by both perceived discrimination and income for both male and female veterans. Results were less consistent in models comparing Hispanic/Latinx vs. White veterans. Income, but not perceived discrimination, mediated the relationship between ethnicity/race and depression and anxiety symptoms, but only among women. Results suggest that discrimination and socioeconomic status are important mechanisms through which marginalized social status negatively impacts mental health.
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.