Anger mediates the relationship between posttraumatic stress disorder and suicidal ideation in veterans
Abstract: Background: Theoretical models and cross-sectional empirical studies of suicide indicate that anger is a factor that may help explain the association between posttraumatic stress disorder (PTSD) and suicide, but to date no longitudinal studies have examined this relationship. The current study used longitudinal data to examine whether changes in anger mediated the association between changes in PTSD symptomatology and suicidal ideation (SI). Methods: Post 9/11-era veterans (N = 298) were assessed at baseline, 6-months, and 12-month time points on PTSD symptoms, anger, and SI. Analyses covaried for age, sex, and depressive symptoms. Multilevel structural equation modeling was used to examine the three waves of data. Results: The effect of change in PTSD symptoms on SI was reduced from B = 0.02 (p = .008) to B = −0.01 (p = .67) when change in anger was added to the model. Moreover, the indirect effect of changes in PTSD symptoms on suicidal ideation via changes in anger was significant, B = 0.02, p = .034. The model explained 31.1% of the within-person variance in SI. Limitations: Focus on predicting SI rather than suicidal behavior. Sample was primarily male. Conclusions: Findings suggest that the association between PTSD and SI is accounted for, in part, by anger. This study further highlights the importance of anger as a risk factor for veteran suicide. Additional research on clinical interventions to reduce anger among veterans with PTSD may be useful in reducing suicide risk.
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.