Hazardous Duty: Investigating Resistance to Police at the Point of Arrest Among Incarcerated Military Veterans
Abstract: The link between military service and crime has been a subject of investigation for several decades. Although research has examined the likelihood of arrest, incarceration, and recidivism across military cohorts, relatively little is known about the circumstances surrounding police contact and suspect behavior at the exact moment of arrest. This is a critical oversight given that what transpires during an arrest can have a marked impact on downstream criminal justice outcomes, including access to diversionary programming like veterans treatment courts. Using a nationally representative survey of prison inmates, this study analyzes veteran and nonveteran self-reports of their arrest controlling for a host of relevant demographic, mental health, and criminal history variables. Findings indicate that veterans are significantly less likely to resist the police at arrest. These results provide further support to the sentiment that military culture and training can have a lasting behavioral influence on those who experience it.
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.