Do racial and ethnic disparities in following stay-at-home orders influence COVID-19 health outcomes? A mediation analysis approach

Abstract

Racial/ethnic disparities are among the top-selective underlying determinants associated with the disproportional impact of the COVID-19 pandemic on human mobility and health outcomes. This study jointly examined county-level racial/ethnic differences in compliance with stay-at-home orders and COVID-19 health outcomes during 2020, leveraging two-year geo-tracking data of mobile devices across ~4.4 million point-of-interests (POIs) in the contiguous United States. Through a set of structural equation modeling, this study quantified how racial/ethnic differences in following stay-at-home orders could mediate COVID-19 health outcomes, controlling for state effects, socioeconomics, demographics, occupation, and partisanship. Results showed that counties with higher Asian populations decreased most in their travel, both in terms of reducing their overall POIs' visiting and increasing their staying home percentage. Moreover, counties with higher White populations experienced the lowest infection rate, while counties with higher African American populations presented the highest case-fatality ratio. Additionally, control variables, particularly partisanship, median household income, percentage of elders, and urbanization, significantly accounted for the county differences in human mobility and COVID-19 health outcomes. Mediation analyses further revealed that human mobility only statistically influenced infection rate but not case-fatality ratio, and such mediation effects varied substantially among racial/ethnic compositions. Last, robustness check of racial gradient at census block group level documented consistent associations but greater magnitude. Taken together, these findings suggest that US residents' responses to COVID-19 are subject to an entrenched and consequential racial/ethnic divide.

Fig 1. Conceptual diagrams for mediation analysis.

c_EM means the variance of the error term of mediators; c_EO means the variance of the error term of outcomes; c_C means the variance of controls; c_P means the variance of predictors; r_CP refers to the covariance matrix between control variables and predictors.

Fig 2. Spatial distribution of human mobility and COVID-19 health outcomes in the contiguous US.

Each panel represents one metric from (a) average staying home (%) from 1 March to 31 December 2020 compared to 2019 baseline, (b) average visit change (%) from 1 March to 31 December 2020, (c) cumulative cases/100,000 by the end of 2020, and (d) cumulative deaths/100 cases by the end of 2020.

Fig 3. Temporal evolution of human mobility and COVID outcomes.

Each row depicts a metric from (a) weekly percentage change in POI visits compared to 2019 baseline, (b) weekly percentage of residents staying home, (c) weekly new confirmed cases/100,000, and (d) weekly new deaths/100 cases. Each column represents a racial group, with each curve denoting one quintile. Sample comprises 3,108 contiguous US counties. Y-axis limits are shared by each row individually.

Fig 4. Standardized path diagrams.

To clearly show the most important structure, we only depicted the paths with 1) P-value < 0.05, 2) standardized direct effect greater than 0.1, or 3) standardized covariate greater than 0.3. Double-headed dotted arrows represent the covariates between exogenous variables. Solid arrows represent the direct effect. The width of the arrow is proportional to the magnitude of standardized path coefficients. Red refers to a positive estimation while green represents a negative one. Similar diagrams can be found in S2 Fig with visit change (%) as mediator.