Adverse Childhood Experiences, Military Adversities, and Adult Health Outcomes Among Female Veterans in the UK
Abstract: Adverse childhood experiences (ACEs) are well-documented risk factors for poor outcomes in adulthood, including worse physical and mental health. A higher prevalence of ACEs has been reported in military populations compared with the general population. Although there is a body of literature exploring childhood adversities in military populations, research focusing on the female Veteran population in the United Kingdom is limited. Data were collected through a cross-sectional, self-report survey. The survey was completed by female army Veterans recruited via a female military association. The response rate was approximately 45%, and the efective sample for this study consisted of 750 female UK army Veterans. Participant histories of ACEs, military adversities, and current mental and physical health difculties were assessed. The most frequently reported ACEs were emotional abuse, physical abuse, and feeling unloved by family. Experiencing childhood adversities was most strongly associated with mental health difculties such as posttraumatic stress disorder and military adversities such as emotional bullying, sexual harassment, and sexual assault during military service. This study provides insight into the prevalence rates of ACEs in a largely under-researched population and into the relationship between military adversities and adult health outcomes. Further research is needed to better understand the unique needs of female Veterans in the United Kingdom and how they compare with those of their male counterparts and women in the UK general population.
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.