Prevalence of, risk factors for, and consequences of posttraumatic stress disorder and other mental health problems in military populations deployed to Iraq and Afghanistan.
Abstract: This review summarizes the epidemiology of posttraumatic stress disorder (PTSD) and related mental health problems among persons who served in the armed forces during the Iraq and Afghanistan conflicts, as reflected in the literature published between 2009 and 2014. One-hundred and sixteen research studies are reviewed, most of which are among non-treatment-seeking US service members or treatment-seeking US veterans. Evidence is provided for demographic, military, and deployment-related risk factors for PTSD, though most derive from cross-sectional studies and few control for combat exposure, which is a primary risk factor for mental health problems in this cohort. Evidence is also provided linking PTSD with outcomes in the following domains: physical health, suicide, housing and homelessness, employment and economic well-being, social well-being, and aggression, violence, and criminality. Also included is evidence about the prevalence of mental health service use in this cohort. In many instances, the current suite of studies replicates findings observed in civilian samples, but new findings emerge of relevance to both military and civilian populations, such as the link between PTSD and suicide. Future research should make effort to control for combat exposure and use longitudinal study designs; promising areas for investigation are in non-treatment-seeking samples of US veterans and the role of social support in preventing or mitigating mental health problems in this group.
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.