The evolution of PTSD symptoms in serving and ex-serving personnel of the UK armed forces from 2004 to 16: A longitudinal examination
Abstract: Whilst most military personnel do not develop Post-Traumatic Stress Disorder (PTSD), ex-serving personnel exhibit higher levels compared to those in the military. The heterogeneity of symptom development for serving and ex-serving personnel has not yet been compared in the UK Armed Forces (UK AF). Latent class growth modelling was employed to estimate the trajectories of PTSD symptoms from three waves of data from the PTSD Checklist (PCL-C) from a UK AF sample (N = 7357). Regression mixture models were conducted to investigate covariates of class membership. Five trajectory classes were identified. Most of the sample reported no-low symptoms (71.3%). Of those reporting probable PTSD during the 12 year-period, 4.6% showed improvements, 4.9% worsened, and 1.8% displayed chronic symptoms. A class with subthreshold elevated symptoms (17.3%) was also identified. Trajectories of serving and ex-serving personnel were not substantially different, but more ex-serving personnel were symptomatic and those with chronic symptoms worsened over time. Chronic disorder was associated with lower rank, experiencing violent combat, and proximity to wounding/death on deployment. Worsening symptoms were associated with childhood stress/violence, lower rank, not being in a relationship, inconsistent post-deployment social support, proximity to wounding/death, and voluntary, or medical discharge. The present study found most UKAF personnel did not report PTSD symptoms between 2004 and 16 but, among those experiencing probable PTSD, more participants reported deteriorating/persistent symptoms than who improved. PTSD-onset was related to adversities across childhood and deployment, and lack of social support. Findings underscore the importance of addressing the through-life contributors of PTSD in order to prevent ingrained disorder.
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.