Effects of Social Support and Resilient Coping on Violent Behavior in Military Veterans
Abstract: Violence toward others has been identified as a serious post-deployment adjustment problem in a subset of Iraq and Afghanistan era veterans. The current study examines the intricate links between posttraumatic stress disorder (PTSD), commonly cited psychosocial risk and protective factors, and violent behavior using a national randomly selected longitudinal sample of Iraq and Afghanistan era U.S. veterans. A total of N=1090 veterans from 50 U.S. states and all U.S. military branches completed two waves of self-report survey data collection one year apart (retention rate=79%). History of severe violence at Wave 1 was the most substantial predictor of subsequent violence. In bivariate analyses high correlations were observed among risk and protective factors, and between risk and protective factors and severe violence at both time points. In multivariate analyses, baseline violence (OR=12.43, p<.001), baseline alcohol misuse (OR=1.06, p<.05), increases in PTSD symptoms between Waves 1 and 2 (OR=1.01, p<.05), and decreases in social support between Waves 1 and 2 (OR=.83, p<.05) were associated with increased risk for violence at Wave 2. Our findings suggest that rather than focusing specifically on PTSD symptoms, alcohol use, resilience or social support in isolation, it may be more useful to consider how these risk and protective factors work in combination to convey how military personnel and veterans are managing the transition from wartime military service to civilian life, and where it might be most effective to intervene.
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.