Enjoying the violence of war: Association with posttraumatic symptomatology in U.S. combat Veterans

Abstract: Objective: Engaging in war-related violence can have a devastating impact on military personnel, with research suggesting that injuring or killing others can contribute to posttraumatic stress disorder (PTSD), depression, and moral injury. However, there is also evidence that perpetrating violence in war can become pleasurable to a substantial number of combatants and that developing this "appetitive" form of aggression can diminish PTSD severity. Secondary analyses were conducted on data from a study of moral injury in U.S., Iraq, and Afghanistan combat veterans, to examine the impact of recognizing that one enjoyed war-related violence on outcomes of PTSD, depression, and trauma-related guilt. METHOD: Three multiple regression models evaluated the impact of endorsing the item, "I came to realize during the war that I enjoyed violence" on PTSD, depression, and trauma-related guilt, after controlling for age, gender, and combat exposure. Results: Results indicated that enjoying violence was positively associated with PTSD, β (SE) = 15.86 (3.02), p < .001, depression, β (SE) = 5.41 (0.98), p < .001, and guilt, β (SE) = 0.20 (0.08), p < .05. Enjoying violence moderated the relationship between combat exposure and PTSD symptoms, β (SE) = -0.28 (0.15), p < .05, such that there was a decrease in the strength of the relationship between combat exposure and PTSD in the presence of endorsing having enjoyed violence. Conclusions: Implications for understanding the impact of combat experiences on postdeployment adjustment, and for applying this understanding to effectively treating posttraumatic symptomatology, are discussed.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    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.