Childhood adversities and post-military-service incarceration in a male UK Armed Forces Veteran sample from Northern Ireland
Abstract: Exposure to childhood adversity confers increased risk for a range of adverse outcomes, including involvement with the criminal justice system. Military Veterans are known to experience a disproportionate rate of adversities compared with the general population. Few studies have investigated the relationship between childhood adversities and post-military-service incarceration among Veterans. This study examined patterns of early adversity in a Veteran sample (N = 695) from Northern Ireland using latent class analysis. Logistic regression analysis was then used to investigate associations between various socio-demographic covariates and latent class membership as predictors of post-military-service incarceration. Four classes were identified: a baseline class, a chaotic home class, a physical and psychological abuse class, and a multi-adversity class. Regression analysis identified that the multiadversity class was associated with significantly increased odds of post-military-service incarceration (odds ratio = 4.08; 95% confidence interval, 1.45-11.50, p < 0.01) when controlling for both age and alcohol use. Interventions designed to aid adaptation and integration of Veterans into civilian life should be trauma informed, and interventions for individuals with a history of multi-adversity exposure should be considered in that context.
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.