Health outcomes before and during the COVID-19 pandemic in caregivers of service members and Veterans with traumatic brain injury
Abstract: Purpose: To examine change in health-related quality of life (HRQOL) during the COVID-19 pandemic in caregivers of service members/veterans (SMVs) with traumatic brain injury (TBI), by comparing HRQOL during the first year of the pandemic to HRQOL 12 months pre-pandemic. Methods: Caregivers (N = 246) were classified into three COVID-19 Pandemic Impact groups based on impact ratings of the pandemic on HRQOL: No Impact (n = 50), Mild Impact (n = 117), and Moderate-Severe Impact (n = 79). Caregivers completed 19 measures across physical, social, caregiving, and economic HRQOL domains, and a measure of SMV Adjustment. T-scores were used to determine individual symptom trajectories for each measure as follows: Asymptomatic (pre + during < 60 T); Developed (pre < 60 + during ≥ 60 T); Improved (pre ≥ 60 T + during < 60 T); and Persistent (pre + during ≥ 60 T). Results: Using ANOVA, during the pandemic, the Moderate-Severe Impact group reported worse scores on 19 measures (d = 0.41–0.89) compared to the No Impact group and 18 measures (d = 0.31–0.62) compared to the Mild Impact group (d = 0.31–0.38). The Mild Impact group reported worse scores on two measures compared to the No Impact group (d = 0.42–0.43). Using the entire sample, the majority of HRQOL measures were classified as Asymptomatic (47.2–94.7%), followed by Persistent (2.4–27.2%). Few were classified as Developed (0.4–12.6%) or Improved (2.4–13.8%). Using repeated measures ANOVA, no meaningful effects sizes were found for mean scores on all measures completed pre-pandemic compared to during the pandemic (d ≤ 0.17). Conclusion: The vast majority of caregivers reported stability in HRQOL pre-pandemic compared to during the pandemic. The COVID-19 pandemic was not associated with a high prevalence of decline in caregiver HRQOL.
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.