Quantifying changes in respiratory syncytial virus-associated hospitalizations among children in Texas during COVID-19 pandemic using records from 2006 to 2021.

Aim: To quantify changes on RSV- associated hospitalizations during COVID-19 pandemic, among children four years of age or younger at the state and county levels of Texas using routinely acquired hospital admission records. Methods: We used the Texas Public Use Data Files (PUDF) of the Department of State Human Services (DSHS) to obtain hospital admissions and healthcare outcomes from 2006 to 2021. We used the 2006-2019 period to estimate a long-term temporal trend and predict expected values for 2020-2021. Actual and predicted values were used to quantify changes in seasonal trends of the number of hospital admissions and mean length of hospital stay. Additionally, we calculated hospitalization rates and assessed their similarity to rates reported in the RSV Hospitalization Surveillance Network (RSV-NET). Results: An unusually low number of hospitalizations in 2020 was followed by an unusual peak in the third quarter of 2021. Hospital admissions in 2021 were approximately twice those in a typical year. The mean length of hospital stay typically followed a seasonal trend before COVID-19, but increased by a factor of ∼6.5 during the pandemic. Spatial distribution of hospitalization rates revealed localized healthcare infrastructure overburdens during COVID-19. RSV associated hospitalization rates were, on average, two times higher than those of RSV-NET. Conclusion: Hospital admission data can be used to estimate long-term temporal and spatial trends and quantify changes during events that exacerbate healthcare systems, such as pandemics. Using the mean difference between hospital rates calculated with hospital admissions and hospital rates obtained from RSV-NET, we speculate that state-level hospitalization rates for 2022 could be at least twice those observed in the two previous years, and the highest in the last 17 years.

Pilot study and cumulative risk framework to advance long-haul driver health.

Commercial drivers are essential to the economic recovery, yet their work exposes them to many health and safety hazards. Research to improve driver health should be designed with an understanding of both the complex occupational environment and the risk management context. We present results from a small pilot study of driver health concerns and behaviors to illustrate concepts and frameworks from human health risk assessment and management that may assist in the design and translation of driver and other worker health research. The pilot study surveyed 18 long-haul truck drivers at a truck stop using an instrument adapted from the International Physical Activity Questionnaire and a transient community needs assessment developed for the US Antarctic Program Recreation and Wellness Survey. Respondents' characteristics and health concerns reflect existing literature: mostly male of older age with musculoskeletal and chronic health conditions. The two most common barriers to physical activity were lack of time and physical limitations. Applying cumulative risk assessment and risk-based decision-making frameworks, we suggest that preventive health management opportunities can be improved for these transient workers through actions of employers, truck stop owners and their communities. Considering lessons learned in implementing the pilot, cumulative risk assessment, and risk-based decision making in research design can facilitate holistic research considering co-exposures, risk factors and mitigators across multiple domains of health to inform worker protection.

Prevalence of anxiety and depression in South Asia during COVID-19: A systematic review and meta-analysis.

INTRODUCTION: The COVID-19 pandemic has impacted biopsychosocial health and wellbeing globally. Pre-pandemic studies suggest a high prevalence of common mental disorders, including anxiety and depression in South Asian countries, which may aggravate during this pandemic. This systematic meta-analytic review was conducted to estimate the pooled prevalence of anxiety and depression in South Asian countries during the COVID-19 pandemic. METHOD: We systematically searched for cross-sectional studies on eight major bibliographic databases and additional sources up to October 12, 2020, that reported the prevalence of anxiety or depression in any of the eight South Asian countries. A random-effects model was used to calculate the pooled proportion of anxiety and depression. RESULTS: A total of 35 studies representing 41,402 participants were included in this review. The pooled prevalence of anxiety in 31 studies with a pooled sample of 28,877 was 41.3% (95% confidence interval [CI]: 34.7-48.1, I 2 = 99.18%). Moreover, the pooled prevalence of depression was 34.1% (95% CI: 28.9-39.4, I 2 = 99%) among 37,437 participants in 28 studies. Among the South Asian countries, India had a higher number of studies, whereas Bangladesh and Pakistan had a higher pooled prevalence of anxiety and depression. No studies were identified from Afghanistan, Bhutan, and Maldives. Studies in this review had high heterogeneity, high publication bias confirmed by Egger's test, and varying prevalence rates across sub-groups. CONCLUSION: South Asian countries have high prevalence rates of anxiety and depression, suggesting a heavy psychosocial burden during this pandemic. Clinical and public mental health interventions should be prioritized alongside improving the social determinants of mental health in these countries. Lastly, a low number of studies with high heterogeneity requires further research exploring the psychosocial epidemiology during COVID-19, which may inform better mental health policymaking and practice in South Asia.