The impact of substance use on posttraumatic stress disorder symptoms and treatment discontinuation

Abstract: This study examined the impact of ongoing substance use during posttraumatic stress disorder (PTSD) and substance use disorder (SUD) treatment on PTSD symptoms and treatment discontinuation. The study represents a secondary analysis of U.S. military veterans (N = 183) who participated in a randomized clinical trial for the treatment of both PTSD and SUD. Veterans mostly identified as Black (53.8%) or White (41.9%) and male (92.4%). Substance use, PTSD symptoms, and treatment discontinuation were measured at 4-week intervals throughout treatment. Predictors were the percentage of days with alcohol, cannabis, and other substance use (primarily cocaine and opioids) and the average number of alcoholic drinks per drinking day. Outcomes were PTSD symptoms and treatment discontinuation at concurrent and prospective assessments. Multilevel models accounted for the nested structure of the longitudinal data. Alcohol, cannabis, and other substance use did not predict PTSD symptoms or treatment discontinuation prospectively. Concurrently, we observed that as a participant's percentage of drinking days increased by 34.7% (i.e., 1 standard deviation), PTSD symptoms during the same period were 0.07 standard deviations higher (i.e., 1 point on the PCL), B = 0.03, p = .033. No other substances were related to PTSD symptoms concurrently. The findings demonstrate that PTSD symptoms improved regardless of substance use during exposure-based PTSD and SUD treatment, and treatment discontinuation was not associated with substance use. This study suggests that substance use during treatment cannot directly explain the poorer treatment outcomes observed in the literature on comorbid PTSD/SUD compared to PTSD-only populations.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    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.