Decreased sound tolerance associated with blast exposure
Abstract: Current research on blast and other injuries sustained by United States Service members and Veterans of the Iraq and Afghanistan Wars reveals a multitude of auditory complaints linked to exposures experienced during these conflicts. Among these complaints is decreased sound tolerance, which refers to a class of auditory-related problems including physical and/or psychological reactions to aspects of everyday sounds. Limited attention has been given to the possible relationship between blast exposure and decreased sound tolerance in Service members and Veterans, which is the purpose of this report. Baseline data were gathered and analyzed from 426 Service members (n = 181) and Veterans (n = 245) who participated in the Noise Outcomes in Servicemembers Epidemiology (NOISE) Study. Logistic regression analyses were performed to generate odds ratios (ORs) with 95% confidence intervals (CIs) for each group, adjusted for age and sex. Of those who reported blast exposure, 33% of Service members (adjusted OR = 1.4; CI = 0.7–2.8) and 48% of Veterans (adjusted OR = 1.9; CI = 1.1–3.3) reported decreased sound tolerance. Among Service members and Veterans who did not report blast exposure, 28% and 34% respectively, also reported decreased sound tolerance. Overall, blast exposure increased the likelihood of participants reporting decreased sound tolerance. The strength of this association was significant in Veterans.
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.