Increasing understanding of infants and young children in military families through focused research
Abstract: Until recently, the examination of the effects of combat deployment on military families have been largely limited to a small number of studies involving Vietnam veterans. While these studies help to demonstrate relationships between veterans’ psychological injuries and difficulties in child/family outcomes, much of this research was conducted years after the veteran’s return, potentially limiting the application of the studies to today’s sociopolitical landscape. In the past several years, additional studies have emerged that focus specifically on military families and children in the context of OEF/OIF. Although sparse, this emerging literature examines the reintegration experience of today’s military families struggling with the extraordinary stressors associated with combat deployment-related separation, physical injury, psychological injury, and uncertainty may place even the strongest families at risk for destabilization or compromised functioning.
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.