Association of Posttraumatic Stress Disorder and Traumatic Brain Injury With Aggressive Driving in Iraq and Afghanistan Combat Veterans
Abstract: Purpose: Aggressive driving contributes to the high rates of postdeployment motor vehicle–related injury and death observed among veterans, and veterans cite problems with anger, aggressive driving, and road rage as being among their most pressing driving-related concerns. Both posttraumatic stress disorder (PTSD) and traumatic brain injury (TBI) have been associated with driving-related deficits in treatment-seeking samples of veterans, but the relative contribution of each of these conditions to problems with aggressive driving in the broader population of combat veterans is unclear. Method: χ2 and logistic regression analyses were used to examine the relative association of PTSD, TBI, and co-occurring PTSD and TBI to self-reported problems with road rage in a sample of 1,102 veterans living in the mid-Atlantic region of the United States who had served in Afghanistan or Iraq. Results: Results indicate that controlling for relevant demographic variables, PTSD without TBI (odds ratio = 3.44, p < .001), and PTSD with co-occurring TBI (odds ratio = 4.71, p < .001) were associated with an increased risk of road rage, but TBI without PTSD was not. Conclusions: Our findings suggest that PTSD, with or without comorbid TBI, may be associated with an increased risk of aggressive driving in veterans. Clinical implications for treating problems with road rage are discussed, including use of interventions targeting hostile interpretation bias and training in emotional and physiological arousal regulation skills.
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.