Relationship of social determinants of health with symptom severity among Veterans and non-Veterans with probable posttraumatic stress disorder or depression
Abstract: Social determinants of health (SDoH) refer to the conditions in the environments in which people live that affect health outcomes and risks. SDoH may provide proximal, actionable targets for interventions. This study examined how SDoH are associated with posttraumatic stress disorder (PTSD) and depression symptoms among Veterans and non-Veterans with probable PTSD or depression. Four multiple regressions were conducted. Two multiple regressions with Veterans examined the impact of SDoH on PTSD symptoms and on depression symptoms. Two multiple regressions with non-Veterans examined the impact of SDoH on PTSD symptoms and on depression symptoms. Independent variables included demographic characteristics, adverse experiences (in childhood and adulthood), and SDoH (discrimination, education, employment, economic instability, homelessness, justice involvement, and social support). Correlates that were statistically significant (p < 0.05) and clinically meaningful (rpart >|0.10|) were interpreted. For Veterans, lower social support (rpart = − 0.14) and unemployment (rpart = 0.12) were associated with greater PTSD symptoms. Among non-Veterans, greater economic instability (rpart = 0.19) was associated with greater PTSD symptoms. In the depression models, lower social support (rpart = − 0.23) and greater economic instability (rpart = 0.12) were associated with greater depression for Veterans, while only lower social support was associated with greater depression for non-Veterans (rpart = − 0.14). Among Veterans and non-Veterans with probable PTSD or depression, SDoH were associated with PTSD and depression symptoms, particularly social support, economic instability, and employment. Beyond direct treatment of mental health symptoms, addressing social support and economic factors such as instability and employment in the context of PTSD and depression are potential intervention targets that would benefit from future research.
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.