Children on the Homefront: The Experience of Children From Military Families
Abstract: OBJECTIVE: Although studies have begun to explore the impact of the current wars on child well-being, none have examined how children are doing across social, emotional, and academic domains. In this study, we describe the health and well-being of children from military families from the perspectives of the child and nondeployed parent. We also assessed the experience of deployment for children and how it varies according to deployment length and military service component. PARTICIPANTS AND METHODS: Data from a computer-assisted telephone interview with military children, aged 11 to 17 years, and nondeployed caregivers (n = 1507) were used to assess child well-being and difficulties with deployment. Multivariate regression analyses assessed the association between family characteristics, deployment histories, and child outcomes. RESULTS: After controlling for family and service-member characteristics, children in this study had more emotional difficulties compared with national samples. Older youth and girls of all ages reported significantly more school-, family-, and peer-related difficulties with parental deployment (P < .01). Length of parental deployment and poorer nondeployed caregiver mental health were significantly associated with a greater number of challenges for children both during deployment and deployed-parent reintegration (P < .01). Family characteristics (eg, living in rented housing) were also associated with difficulties with deployment. CONCLUSIONS: Families that experienced more total months of parental deployment may benefit from targeted support to deal with stressors that emerge over time. Also, families in which caregivers experience poorer mental health may benefit from programs that support the caregiver and child.
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.