The impact of substance use disorders on treatment engagement among justice-involved veterans with posttraumatic stress disorder
Abstract: Veterans involved with the criminal justice system represent a particularly vulnerable population who experience high rates of both posttraumatic stress disorder (PTSD) and substance use disorders (SUD). This study sought to investigate whether having co-occurring SUD is a barrier to PTSD treatment. This is a retrospective observational study of a national sample of justice-involved veterans served by the Veterans Health Administration (VA) Veterans Justice Outreach (VJO) program who had a diagnosis of PTSD (N = 27,857). Mixed effects logistic regression models with a random effect for facility (N = 141 medical centers) were utilized to estimate the odds of receiving each type of PTSD treatment as a function of having a SUD diagnosis. Results indicate that a majority of veterans with PTSD served by VJO have a SUD diagnosis (73%), and having a co-occurring SUD was associated with higher odds of receiving PTSD treatment, after adjusting for demographic differences. Although not without limitations, these results suggest that among justice-involved veterans enrolled in VHA with PTSD, having a SUD comorbidity is not a barrier to PTSD treatment and may in fact facilitate access to PTSD treatment.
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.