The Effects of Adverse Childhood Experiences and Warfare Exposure on Military Sexual Trauma Among Veterans
Abstract: Military sexual trauma (MST) is a pervasive problem; this study examined the relationship of the precursory traumas of adverse childhood experiences (ACEs) and warfare exposure with MST. Post-9/11 veterans were surveyed at 3 months and at 24 to 30 months post-military separation. Female veterans who experienced at least 1 ACE but no warfare exposure were significantly more likely to receive unwanted sexual attention. Veterans (males and females) experiencing three or more ACEs but no warfare exposure were significantly more likely to receive unwanted sexual attention and contact. Experiencing only warfare exposure was not related to unwanted sexual attention or contact for females; however, a significant interaction was found between combined warfare exposure, ACEs, and MST for males and females. Veterans who reported warfare exposure and one to two or three or more ACEs were more likely to report unwanted sexual attention and/or contact. Exploration of individual ACEs revealed a significant relationship between childhood sexual abuse and unwanted sexual contact. For females, witnessing domestic violence predicted unwanted sexual contact. There was also a significant interaction between childhood sexual abuse and warfare exposure. Females who experienced both childhood sexual abuse and warfare exposure were significantly more likely to receive unwanted sexual attention and unwanted sexual contact. Albeit a small sample, males who experienced both were also significantly more likely to receive unwanted sexual attention. The findings reveal that precursory traumatic experiences in childhood and the interaction of ACEs and warfare exposure during military service can increase the likelihood of unwanted sexual attention and contact. This research further substantiates the need for screening efforts. It also demonstrates the importance of practitioners engaging in trauma-informed care principles and practices to address the residual effects of previous experiences during sexual trauma or mental health treatment efforts.
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.