Risk factors for suicide among veterans living with and without HIV: a nested case-control study
Abstract: The rate of suicide among people with HIV (PWH) remains elevated compared to the general population. The aim of the study was to examine the association between a broad range of risk factors, HIV-specific risk factors, and suicide. We conducted a nested case-control study using data from the Veterans Aging Cohort Study (VACS) between 2006 and 2015. The risk of suicide was estimated using conditional logistic regression and models were stratified by HIV status. Most risk factors associated with suicide were similar between PWH and people without HIV; these included affective disorders, use of benzodiazepines, and mental health treatment. Among PWH, HIV-specific risk factors were not associated with suicide. A multiplicative interaction was observed between a diagnosis of HIV and a previous suicide attempt. Among PWH, a high prevalence of psychiatric, substance use disorders and multimorbidity contribute to the risk of suicide.
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.