County-Level Sociodemographic Factors Associated with COVID-19 Incidence and Mortality in North and South Carolina.

BACKGROUND There is limited research regarding associations between county-level factors and COVID-19 incidence and mortality. While the Carolinas are geographically connected, they are not homogeneous, with statewide political and intra-state socioeconomic differences leading to heterogeneous spread between and within states.METHODS Infection and mortality data from Johns Hopkins University during the 7 months since the first reported case in the Carolinas was combined with county-level socioeconomic/demographic factors. Time series imputations were performed whenever county-level reported infections were implausible. Multivariate Poisson regression models were fitted to extract incidence (infection and mortality) rate ratios by county-level factor. State-level differences in filtered trends were also calculated. Geospatial maps and Kaplan-Meier curves were constructed stratifying by median county-level factor. Differences between North and South Carolina were identified.RESULTS Incidence and mortality rates were lower in North Carolina than South Carolina. Statistically significant higher incidence and mortality rates were associated with counties in both states with higher proportions of Black/African American populations and those without health insurance aged < 65 years. Counties with larger populations aged ≥ 75 years were associated with increased mortality (but decreased incidence) rates.LIMITATIONS COVID-19 data contained multiple inconsistencies, so imputation was needed, and covariate-based data was not synchronous and potentially insufficient in granularity given the epidemiology of the disease. County-level analyses imply within-county homogeneity, an assumption increasingly breached by larger counties.CONCLUSION While statewide interventions were initially implemented, inter-county racial/ethnic and socioeconomic variability points to the need for more heterogeneous interventions, including policies, as populations within particular counties may be at higher risk.

Dynamics of Covid-19 mortality and social determinants of health: a spatiotemporal analysis of exceedance probabilities.

PURPOSE: To determine the association of social factors with Covid-19 mortality and identify high-risk clusters. METHODS: Data on Covid-19 deaths across 3,108 contiguous U.S. counties from the Johns Hopkins University and social determinants of health (SDoH) data from the County Health Ranking and the Bureau of Labor Statistics were fitted to Bayesian semi-parametric spatiotemporal Negative Binomial models, and 95% credible intervals (CrI) of incidence rate ratios (IRR) were used to assess the associations. Exceedance probabilities were used for detecting clusters. RESULTS: As of October 31, 2020, the median mortality rate was 40.05 per 100, 000. The monthly urban mortality rates increased with unemployment (IRRadjusted:1.41, 95% CrI: 1.24, 1.60), percent Black population (IRRadjusted:1.05, 95% CrI: 1.04, 1.07), and residential segregation (IRRadjusted:1.03, 95% CrI: 1.02, 1.04). The rural monthly mortality rates increased with percent female population (IRRadjusted: 1.17, 95% CrI: 1.11, 1.24) and percent Black population (IRRadjusted:1.07 95% CrI:1.06, 1.08). Higher college education rates were associated with decreased mortality rates in rural and urban counties. The dynamics of exceedance probabilities detected the shifts of high-risk clusters from the Northeast to Southern and Midwestern counties. CONCLUSIONS: Spatiotemporal analyses enabled the inclusion of unobserved latent risk factors and aid in scientifically grounded decision-making at a granular level.

The Association of Social Determinants of Health With COVID-19 Mortality in Rural and Urban Counties.

PURPOSE: To identify the county-level effects of social determinants of health (SDoH) on COVID-19 (corona virus disease 2019) mortality rates by rural-urban residence and estimate county-level exceedance probabilities for detecting clusters. METHODS: The county-level data on COVID-19 death counts as of October 23, 2020, were obtained from the Johns Hopkins University. SDoH data were collected from the County Health Ranking and Roadmaps, the US Department of Agriculture, and the Bureau of Labor Statistics. Semiparametric negative binomial regressions with expected counts based on standardized mortality rates as offset variables were fitted using integrated Laplace approximation. Bayesian significance was assessed by 95% credible intervals (CrI) of risk ratios (RR). County-level mortality hotspots were identified by exceedance probabilities. FINDINGS: The COVID-19 mortality rates per 100,000 were 65.43 for the urban and 50.78 for the rural counties. Percent of Blacks, HIV, and diabetes rates were significantly associated with higher mortality in rural and urban counties, whereas the unemployment rate (adjusted RR = 1.479, CrI = 1.171, 1.867) and residential segregation (adjusted RR = 1.034, CrI = 1.019, 1.050) were associated with increased mortality in urban counties. Counties with a higher percentage of college or associate degrees had lower COVID-19 mortality rates. CONCLUSIONS: SDoH plays an important role in explaining differential COVID-19 mortality rates and should be considered for resource allocations and policy decisions on operational needs for businesses and schools at county levels.