Aggression and violent behavior in the military: Self-reported conflict tactics in a sample of service members and veterans seeking treatment for posttraumatic stress disorder
Abstract: Irritability, angry outbursts, and aggression are common among individuals with posttraumatic stress disorder (PTSD). Although aggression can be a problem among many individuals with PTSD, research suggests that the relationship between PTSD and aggression might be particularly relevant among military/veteran populations as compared to civilians. The current study examined psychological and physical aggression in a large sample of treatment-seeking military service members and veterans (N = 1434) enrolled in nine PTSD clinical trials. A baseline assessment using a modified version of the Revised Conflict Tactics Scales evaluated aggression toward others in the past month. The results indicated that psychological aggression was more prevalent than physical aggression among military personnel with PTSD. Overall, 84.7% reported engaging in weekly psychological aggression, and 11.4% reported weekly physical aggression. Shouting at someone, insulting someone, and stomping off during a disagreement were the most frequent forms of psychological aggression endorsed. The findings provide a detailed account of the point prevalence and nature of various self-reported aggressive behaviors in military personnel with PTSD.
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.