Comparing psychosocial functioning, suicide risk, and nonsuicidal self-injury between veterans with probable posttraumatic stress disorder and alcohol use disorder
Abstract: Background: Posttraumatic stress disorder (PTSD) and alcohol use disorder (AUD) are each common among Unites States (U.S.) military veterans and frequently co-occur (i.e., PTSD+AUD). Although comorbid PTSD+AUD is generally associated with worse outcomes relative to either diagnosis alone, some studies suggest the added burden of comorbid PTSD+AUD is greater relative to AUD-alone than to PTSD-alone. Furthermore, nonsuicidal self-injury (NSSI) is more common among veterans than previously thought but rarely measured as a veteran psychiatric health outcome. This study sought to replicate and extend previous work by comparing psychosocial functioning, suicide risk, and NSSI among veterans screening positive for PTSD, AUD, comorbid PTSD+AUD, and neither disorder. Methods: This study analyzed data from a national sample of N = 1046 U.S. veterans who had served during the Gulf War. Participants self-reported sociodemographic, functioning, and clinical information through a mailed survey. Results: Veterans with probable PTSD+AUD reported worse psychosocial functioning across multiple domains compared to veterans with probable AUD, but only worse functioning related to controlling violent behavior when compared to veterans with probable PTSD. Veterans with probable PTSD+AUD reported greater suicidal ideation and NSSI than veterans with probable AUD, but fewer prior suicide attempts than veterans with probable PTSD. Limitations: This study was cross-sectional, relied on self-report, did not verify clinical diagnoses, and may not generalize to veterans of other military conflicts. Conclusions: Findings underscore the adverse psychiatric and functional outcomes associated with PTSD and comorbid PTSD+AUD, such as NSSI, and highlight the importance of delivering evidence-based treatment to this veteran population.
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.