Ethnoracial differences in treatment-seeking veterans with substance use disorders and co-occurring PTSD: Presenting characteristics and response to integrated exposure-based treatment
Abstract: Objective: Substance use disorders (SUD) and posttraumatic stress disorder (PTSD) frequently co-occur. While previous research has examined ethnoracial differences among individuals with either SUD or PTSD, little research to date has focused on individuals with co-occurring SUD/PTSD. The current study addresses this gap in the literature. Method: Participants were 79 military veterans (91% male; 38% African American [AA] and 62% White) with current SUD/PTSD who were randomized to receive Concurrent Treatment of PTSD and Substance Use Disorders using Prolonged Exposure (COPE) or Relapse Prevention (RP). Primary outcomes included substance use and self-reported and clinician-rated PTSD symptoms. Results: At baseline, AA participants were significantly older, reported greater substance and alcohol use, and tended to report higher PTSD severity than White participants. AA participants evidenced greater decreases in substance and alcohol use during treatment, but greater increases in substance and alcohol use during follow-up as compared to White participants. All participants decreased alcohol consumption during treatment; however, AA participants in the COPE condition and White participants in the RP condition evidenced the steepest decreases in average number of drinks per drinking day (DDD) during treatment. Additionally, White participants receiving RP reported greater increases in DDD during followup compared to AA participants. Conclusion: Overall, integrated treatment for co-occurring SUD/PTSD was effective for both AA and White participants; however, some important differences emerged by ethnoracial group. Findings suggest that greater attention to race and ethnicity is warranted to better understand the needs of diverse patients with SUD/PTSD and to optimize treatment outcomes.
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.