Disparities in COVID-19 Mortality Rates: Implications for Rural Health Policy and Preparedness.

CONTEXT: It is well established that rural communities face geographic and socioeconomic challenges linked to higher rates of health disparities across the United States, though the coronavirus disease 2019 (COVID-19) impact on rural communities is less certain. OBJECTIVE: To understand the COVID-19 pandemic's impact on rural communities in Tennessee, investigate differences in rural-urban mortality rates after controlling for confounding variables, and inform state pandemic response policy. DESIGN: A cross-sectional analysis of cumulative COVID-19 morality rates. SETTING/PARTICIPANTS: Tennessee county-level COVID-19 mortality data from March 1, 2020, to January 31, 2021, were matched with county-level sociodemographic and health data from public datasets: Agency for Healthcare Research and Quality Social Determinants of Health, PLACES: Local Data for Better Health County Data, and the US Census Bureau. County status was defined using the 2013 National Center for Health Statistics Urban-Rural Classification. MAIN OUTCOME MEASURES: A negative binomial regression model estimated adjusted incidence rate ratio and 95% confidence intervals (CI) for rural compared with urban mortality. Unadjusted rate ratios and rate differences for COVID-19 mortality in rural versus urban counties were compared with those for influenza and pneumonia and all-cause mortality over the past 5 years. RESULTS: During the study period, 9650 COVID-19 deaths occurred across 42 urban and 53 rural counties. Controlling for county-level sociodemographic characteristics, health care access, and comorbidities, incidence rate ratio was 1.13 (95% CI, 1.00-1.28, P < .05) for rural as compared with urban deaths. Unadjusted COVID-19 mortality risk difference between rural and urban counties was greater (61.85, 95% CI, 54.31-69.31) than 5-year influenza and pneumonia rural-urban risk difference (12.57, 95% CI, 11.16-13.00) during 2015-2019. CONCLUSIONS: COVID-19 mortality rates were greater for populations living in Tennessee's rural as compared with urban counties during the study period. This differential impact must be considered in public health decision making to mitigate COVID-19.

How Stable Is Early Academic Performance? Using Cluster Analysis to Classify Low Achievement and EF.

We explored whether and how cognitive measures of executive function (EF) can be used to help classify academic performance in Kindergarten and first grade using nonparametric cluster analysis. We found that EF measures were useful in classifying low-reading performance in both grades, but mathematics performance could be grouped into low, average, and high groups without the use of EF tasks. Membership in the high-performing groups was more stable through first grade than membership in the low or average groups, and certain Kindergarten EF tasks differentially predicted first-grade reading and mathematics cluster membership. Our results suggest a stronger link between EF deficits and low performance than between EF strengths and high performance. We highlight the importance of simultaneously using academic and cognitive skills to classify achievement, particularly since existing classification schemes have been largely based on arbitrary cutoffs using limited academic measures.