Psychometric properties of the self-injurious thoughts and behaviors interview–short form among U.S. active duty military Service members and Veterans
Abstract: We assessed the interrater reliability, convergent validity, and discriminant validity of the Self-Injurious Thoughts and Behaviors Interview-Short Form (SITBI-SF) in a sample of 1,944 active duty service members and veterans seeking services for posttraumatic stress disorder (PTSD) and related conditions. The SITBI-SF demonstrated high interrater reliability and good convergent and discriminant validity. The measurement properties of the SITBI-SF were comparable across service members and veterans. Approximately 8% of participants who denied a history of suicidal ideation on the SITBI-SF reported suicidal ideation on a separate self-report questionnaire (i.e., discordant responders). Discordant responders reported significantly higher levels of PTSD symptoms than those who denied suicidal ideation on both response formats. Findings suggest that the SITBI-SF is a reliable and valid interview-based measure of suicide-related thoughts and behaviors for use with military service members and veterans. Suicide risk assessment might be optimized if the SITBI-SF interview is combined with a self-report measure of related constructs.
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.