Race/ethnicity and gender differences in mental health diagnoses among Iraq and Afghanistan Veterans
Abstract: Veterans who served in Operation Enduring Freedom (OEF; predominantly in Afghanistan) and Operations Iraqi Freedom and New Dawn (OIF and OND; predominantly in Iraq) and are enrolled in the VA are comprised of a growing cohort of women and higher proportions of racial/ethnic minorities than civilians. To compare rates of mental health disorders by race/ethnicity and gender for this diverse cohort, we conducted a retrospective analysis of existing records from OEF/OIF/OND veterans who were seen at the VA 10/7/01-8/1/2013 (N=792,663). We found that race/ethnicity was related to diagnoses of mental health disorders. Asian/Pacific Islanders (A/PIs) were diagnosed with all disorders at lower rates than whites, and American Indian/Alaska Native (AI/AN) males were diagnosed with most disorders at higher rates than white males. Research is needed to identify contributing factors to differential rates of diagnoses based on race/ethnicity and gender. A/PIs and AI/ANs have unique patterns of mental health diagnoses indicating they should be considered separately to present a comprehensive picture of veteran 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.