War trauma impacts in ukrainian combat and civilian populations: Moral injury and associated mental health symptoms
Abstract: This is the first study to compare active-duty soldiers and student civilian samples during the first three months of the Ukrainian-Russian war in relation to moral injury and its association with PTSD, anxiety and depression. A total of 350 participants, including 191 active-duty soldiers of the Ukrainian Armed Force (UAF), who were on the frontline during the full-scale invasion of Russian troops in February 2022, and 159 students from different HEIs in Volyn oblast, were recruited into the study through their attendance at the Ukrainian Psychotrauma Center. Prior to the in-person group-intervention program of psychosocial support for military and civil populations at the Ukrainian Psychotrauma Center, moral injury, PTSD, depression, and anxiety were assessed. Results showed significantly higher moral injury, PTSD, depression, and anxiety scores in civilian students, with a two-way ANOVA indicating a significant impact of female gender in civilians only. A hierarchical regression indicated that moral injury is a predictor of PTSD symptoms in both active-duty and civilian student groups. However, previous family trauma of genocide is associated with PTSD symptoms in active soldiers only. The findings of the current study could contribute insights for clinical practice for combatants and civilians during the current war.
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.