Adversity during military service: the impact of military sexual trauma, emotional bullying and physical assault on the mental health and well-being of women veterans
Abstract: Despite making up about 11% of the UK military, there remains limited investigation on the impact of adversity women experience during their service in the UK military. Military adversity can result in a range of well-being difficulties that may persist following transition out of military. The present study therefore examined the prevalence and correlates of different types of military adversity (defined as sexual harassment, sexual assault, emotional bullying and physical assault) within a community sample of UK women veterans. Participants were recruited from a UK charity supporting women veterans. 750 women veterans completed an online survey collecting information on sociodemographic and military factors, military adversity, as well as mental health and well-being difficulties. Associations between variables were explored using multivariate logistic regressions. The findings indicate a high prevalence of military adversity (22.5% sexual harassment, 5.1% sexual assault, 22.7% emotional bullying and 3.3% physical assault). Younger women, those who held an officer rank during service and those who reported having a combat or combat support role during service were most at risk of military adversity. All types of adversity were significantly associated with probable post-traumatic stress disorder. Sexual harassment was additionally significantly associated with physical somatisation; sexual assault with alcohol difficulties; and emotional bullying with common mental health difficulties, low social support and loneliness. Conclusions: This study indicates that UK women veterans are at risk of a range of adverse experiences during military service and provides evidence of the impact of such adversities on mental health and wellbeing. Further research is required to better understand these relationships.
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.