Longitudinal assessment of PTSD and illicit drug use among male and female OEF-OIF veterans
Abstract: Posttraumatic stress disorder (PTSD) and substance use share both directional (“self-medication”) and mutually-reinforcing associations over time. Research on gender differences regarding the co-occurrence of PTSD and substance use over time remains limited and largely focused on alcohol use; less is known regarding the co-occurrence of PTSD and illicit drug use, especially among veteran men vs. women. As the proportion of women in the military expands, we believe a greater focus on gender differences is warranted. We conducted a cross-lagged panel analysis of PTSD symptoms and drug use problems using two waves of data from a large, nationwide longitudinal registry of post-9/11 veterans. Participants included 608 men and 635 women (N = 1243; Mage = 42.3; 75.2% White) who completed self-report PTSD and drug use problem questionnaires at T1 and again at T2 15–37 months later. Veteran men reported more severe drug use and related problems overall, yet the cross-sectional correlation between PTSD and drug use problems was strongest among drug using veteran women. In our cross-lagged models, we found that PTSD symptoms predicted future drug use problems among veteran men, whereas drug use problems predicted future PTSD symptom severity among women. These results support the self-medication pathway among veteran men but not women, for whom drug use problems might prolong or exacerbate PTSD symptom severity over time. These results are consistent with some emerging evidence but also provide novel insight into functional associations governing the longitudinal course of PTSD and drug use problems for men vs. women.
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.