The Mental Health and Substance Misuse Needs of Male ex-Armed Forces Personnel in Prison
Abstract: Ex-armed forces personnel constitute the largest known occupational group in prison but there is little evidence regarding their mental health, or substance misuse, needs. A total of 105 participants were interviewed and measures assessing symptoms of common mental health (CMH) problems and substance misuse were completed along with a review of their health care records. Forty (38%) participants screened for current CMH problems (CCMH) and high levels of dual symptomology and alcohol misuse were assessed. Thirty-nine (37%) had a mental health diagnosis recorded, most commonly for post-traumatic stress disorder (PTSD), depression and personality disorder. Those who screened for a CCMH problem were more likely to have pre-service vulnerability to negative health outcomes and those with dual symptomology were more likely to have experienced deployment during their service. Findings suggest the mental health needs of this group are similar to the general prison population. Potentially higher prevalences of PTSD and alcohol misuse may direct service provision.
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.