Examining longitudinal disparities in COVID-19 prevalence in the U.S.: a county level growth rate perspective.

Background: The objectives of the present study are to understand the longitudinal variability in COVID-19 reported cases at the county level and to associate the observed rates of infection with the adoption and lifting of stay-home orders.Materials and Methods: The study uses the trajectory of the pandemic in a county and controls for social and economic risk factors, physical environment, and health behaviors to elucidate the social determinants contributing to the observed rates of infection.Results and conclusion: Results indicated that counties with higher percentages of young individuals, racial and ethnic minorities and, higher population densities experienced greater difficulty suppressing transmission.Except for Education and the Gini Index, all factors were influential on the rate of COVID-19 spread before and after stay-home orders. However, after lifting the orders, six of the factors were not influential on the rate of spread; these included: African-Americans, Population Density, Single Parent Households, Average Daily PM2.5, HIV Prevalence Rate, and Home Ownership. It was concluded that different factors from the ones controlling the initial spread of COVID-19 are at play after stay-home orders are lifted.KEY MESSAGESObserved rates of COVID-19 infection at the County level in the U.S. are not directly associated with adoption and lifting of stay-home orders.Disadvantages in sociodemographic determinants negatively influence the rate of COVID-19 spread.Counties with more young individuals, racial and ethnic minorities, and higher population densities have greater difficulty suppressing transmission.

Assessing COVID-19 risk, vulnerability and infection prevalence in communities.

BACKGROUND: The spread of coronavirus in the United States with nearly five and half million confirmed cases and over 170,000 deaths has strained public health and health care systems. While many have focused on clinical outcomes, less attention has been paid to vulnerability and risk of infection. In this study, we developed a planning tool that examines factors that affect vulnerability to COVID-19. METHODS: Across 46 variables, we defined five broad categories: 1) access to medical services, 2) underlying health conditions, 3) environmental exposures, 4) vulnerability to natural disasters, and 5) sociodemographic, behavioral, and lifestyle factors. The developed tool was validated by comparing the estimated overall vulnerability with the real-time reported normalized confirmed cases of COVID-19. ANALYSIS: A principal component analysis was undertaken to reduce the dimensions. In order to identify vulnerable census tracts, we conducted rank-based exceedance and K-means cluster analyses. RESULTS: All of the 5 vulnerability categories, as well as the overall vulnerability, showed significant (P-values <<0.05) and relatively strong correlations (0.203<ρ<0.57) with the normalized confirmed cases of COVID-19 at the census tract level. Our study showed a total of 722,357 (~17% of the County population) people, including 171,403 between the ages of 45-65 (~4% of County's population), and 76,719 seniors (~2% of County population), are at a higher risk based on the aforementioned categories. The exceedance and K-means cluster analysis demonstrated that census tracts in the northeastern, eastern, southeastern and northwestern regions of the County are at highest risk. CONCLUSION: Policymakers can use this planning tool to identify neighborhoods at high risk for becoming hot spots; efficiently match community resources with needs, and ensure that the most vulnerable have access to equipment, personnel, and medical interventions.