Brief report: Synthetic cannabinoid use among military personnel
Abstract: Background and Objectives: Synthetic cannabinoids (SCs) may function as a marijuana alternative for soldiers subject to frequent drug screens, yet no study has interviewed past military users of SCs. Methods: Veterans participating in eight US veterans treatment courts were interviewed (n = 318; response rate = 54.9%). Thematic analyses were completed. Results: Sixty-five veterans (21.3%) reported SC use. Three major themes were identified: SCs were not a suitable marijuana replacement, the experience was unpleasant/problematic, and curiosity, sometimes paired with the perception of safely eluding drug screens, facilitated use. Conclusion and Scientific Significance: While members of the military experimented with SCs, habitual use of SCs within the Armed Forces does not appear widespread. The perception that SCs are excluded from all urinalyses may contribute to experimentation, but the unpleasant experience generally discourages recurrent use.
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.