Patterns and correlates of racial/ethnic disparities in posttraumatic stress disorder screening among recently separated veterans.
Abstract: Despite the high prevalence of posttraumatic stress disorder (PTSD) among military veterans, there is a lack of knowledge about racial/ethnic differences. The current study describes patterns and correlates of PTSD screening across race/ethnicity and gender in a sample of 9420 veterans recently separated from the military. Veterans who identified as White (n = 6222), Hispanic/Latinx (n = 1313), Black (n = 1027), Asian/Hawaiian/Pacific Islander (n = 420) and multiracial (n = 438) were included. Trauma exposure and PTSD were assessed with the Primary Care PTSD Screen for DSM-5. Contextual factors examined included the intensity of ongoing stressful events, perceived social support, and sociodemographic variables (e.g., income). Weighted analyses were conducted to account for differential sample response rates. Regression analyses examining correlates of racial/ethnic differences in PTSD screening were stratified by gender. Among men and women, positive PTSD screening rates were significantly elevated among Black, multiracial, and Hispanic/Latinx veterans compared with White veterans. Sociodemographics, trauma exposure, stress and social support accounted for elevated positive screening rates among all racial/ethnic groups except Black men and multiracial women. Findings suggest that Black, Hispanic/Latinx and multiracial veterans may be at higher risk for PTSD shortly following separation from the military. Contextual factors examined explain the excess risk among some, but not all, subgroups. Further specifying disparities in PTSD diagnostic rates and risk factors will enable targeted and tailored intervention among veteran subgroups.
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.