Systematic Review of Criminal and Legal Involvement After Substance Use and Mental Health Treatment Among Veterans: Building Toward Needed Research
Abstract: Evidence indicates that substance use and mental health treatment is often associated with reduced criminal activity. The present systematic review examined this association among military veterans, and aimed to provide a comprehensive summary of needed research to further contribute to reduced criminal activity among veterans. This systematic review was derived from a scoping review that mapped existing research on justice-involved veterans’ health. For the current systematic review, a subset of 20 publications was selected that addressed the question of whether criminal activity declines among veterans treated for substance use and mental health disorders. Generally, veterans improved on criminal outcomes from pre- to post-treatment for opioid use, other substance use, or mental health conditions, and more sustained treatment was associated with better outcomes. This occurred despite high rates of criminal involvement among veterans prior to entering treatment. Needed are substance use and mental health treatment studies that include women justice-involved veterans, follow criminally-active veterans for longer periods of time, and use validated and reliable measures of criminal activity with fully transparent statistical procedures. Future randomized trials should evaluate new treatments against evidence-based treatments (versus no-treatment control conditions). Subsequent studies should examine how to link veterans to effective treatments, facilitate sustained treatment engagement, and ensure the availability of effective treatments, and examine mechanisms (mediators and moderators) that explain the association of treatment with reduced criminal activity among veterans. Best practices are needed for reducing criminal activity among the minority of justice-involved veterans who do not have diagnosed substance use and/or mental health disorders.
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.