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.

COVID-19 vaccine hesitancy cannot fully explain disparities in vaccination coverage across the contiguous United States

“…Both standardized and unstandardized coefficients were estimated in this study. Following the general rules of SEM [20] , [25] , we assumed exogenous variables covaried with each other, and there existed unobserved factors affecting all endogenous variables. Meanwhile, when estimating unstandardized path coefficients, the variances of all locally exogenous variables were considered.…”

“…Another concern is the modifiable area unit problem (MAUP), which postulates that different aggregation units may lead to different modeling results [17] . A county-level analysis may gloss over disparities at a more localized level [20] . However, due to the inaccessibility of finer-grained variables, county-level analysis is the best study that could be conducted at this time.…”

“…Besides the insufficient vaccination coverage, another well-documented issue is the inequity in COVID-19 vaccination coverage [7] , [10] , [24] , [39] , [48] . Given that all US residents aged over 12 years have been eligible to receive vaccines since May 2021, and there is currently an excess of vaccines in supply, the roots of inequity in vaccination coverage should be traced to other structural barriers, such as socioeconomic privilege [4] , [10] , [32] , political ideology [48] , racial disparity [1] , [20] , and vaccine hesitancy [26] , [46] . Broadly, studies claimed that communities with lower vaccine hesitancy, higher socioeconomic statuses (e.g.…”

O racismo estrutural e as mortes de negros por COVID-19

“…A pandemia não gerou desigualdades na saúde, mas apenas expôs e exacerbou as desigualdades de saúde e socioeconômicas existentes de longa data (Phiri et al, 2021). Uma vez que as principais recomendações antes do período de vacinas eram a permanência em casa, fato este revelado ser uma manifestação de privilégio econômico (Hu et al, 2021). Como se não bastasse isso, a população negra diante do cenário atual da pandemia pelo novo coronavírus, vem sofrendo o peso da cor da pele nos números estatísticos desiguais sobre infecções e morte pela COVID-19 no Brasil e no mundo, esta penalização de grupos vulneráveis estamentado pelo racismo estrutural relaciona-se diretamente à policrise sanitária, moral, política e de fluxos migratórios na sociedade .…”

A fairness assessment of mobility-based COVID-19 case prediction models

“…The interactions between human mobility and epidemic spread have been studied unprecedentedly during the COVID-19 pandemic [1][2][3][4][5][6][7][8] . With these efforts, nonpharmaceutical interventions (such as national lockdowns) have been evaluated for their effectiveness and socio-economic impact on different groups 9-11 , models have been developed to predict disease spatial diffusion 12, 13 , and scenarios have been modeled to assess their outcomes [14][15][16][17] . Studies have demonstrated that mobility data are a meaningful proxy measure of social distancing 18 , affect viral spreading 19,20 , and are useful for predicting the spread of COVID-19 [21][22][23] .…”