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

Abstract

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 (IRR_adjusted:1.41, 95% CrI: 1.24, 1.60), percent Black population (IRR_adjusted:1.05, 95% CrI: 1.04, 1.07), and residential segregation (IRR_adjusted:1.03, 95% CrI: 1.02, 1.04). The rural monthly mortality rates increased with percent female population (IRR_adjusted: 1.17, 95% CrI: 1.11, 1.24) and percent Black population (IRR_adjusted: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.

Keywords: Bayesian Analysis; Disparity; Education; Hotspots; Infectious Disease; Residential Segregation.