Symptom Severity, Self-efficacy and Treatment-Seeking for Mental Health Among US Iraq/Afghanistan Military Veterans
Abstract: Military veterans have high rates of mental health problems, yet the majority do not seek treatment. Understanding treatment-seeking in this population is important. This study investigated if symptom severity and self-efficacy are associated with treatment-seeking among US Iraq/Afghanistan veterans. Survey data from 525 veterans meeting clinical criteria for PTSD and depression were included of which, 54.4% had sought treatment in the past 12 months. Multivariate logistic regression analysis indicated that high symptom severity was associated with treatment seeking, whereas high self-efficacy was associated with a decreased likelihood to seek treatment. Self-efficacy could be an underlying mechanism of treatment seeking decisions.
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.