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Association Between Income Inequality and County-Level COVID-19 Cases and Deaths in the US

Educational Objective
To identify the key insights or developments described in this article
Key Points

Question  How does the association between county-level income inequality, measured by the Gini coefficient, and COVID-19 cases and deaths change over time?

Findings  This ecological cohort study found that there was a positive correlation between Gini coefficients and county-level COVID-19 cases and deaths during the study period. The association between income inequality and COVID-19 cases and deaths varied over time and was strongest in the summer months of 2020.

Meaning  The findings suggest that, during the COVID-19 pandemic, areas of higher income inequality may serve as effective targets for interventions to mitigate the spread of SARS-CoV-2.

Abstract

Importance  Socioeconomically marginalized communities have been disproportionately affected by the COVID-19 pandemic. Income inequality may be a risk factor for SARS-CoV-2 infection and death from COVID-19.

Objective  To evaluate the association between county-level income inequality and COVID-19 cases and deaths from March 2020 through February 2021 in bimonthly time epochs.

Design, Setting, and Participants  This ecological cohort study used longitudinal data on county-level COVID-19 cases and deaths from March 1, 2020, through February 28, 2021, in 3220 counties from all 50 states, Puerto Rico, and the District of Columbia.

Main Outcomes and Measures  County-level daily COVID-19 case and death data from March 1, 2020, through February 28, 2021, were extracted from the COVID-19 Data Repository by the Center for Systems Science and Engineering at Johns Hopkins University in Baltimore, Maryland.

Exposure  The Gini coefficient, a measure of unequal income distribution (presented as a value between 0 and 1, where 0 represents a perfectly equal geographical region where all income is equally shared and 1 represents a perfectly unequal society where all income is earned by 1 individual), and other county-level data were obtained primarily from the 2014 to 2018 American Community Survey 5-year estimates. Covariates included median proportions of poverty, age, race/ethnicity, crowding given by occupancy per room, urbanicity and rurality, educational level, number of physicians per 100 000 individuals, state, and mask use at the county level.

Results  As of February 28, 2021, on average, each county recorded a median of 8891 cases of COVID-19 per 100 000 individuals (interquartile range, 6935-10 666 cases per 100 000 individuals) and 156 deaths per 100 000 individuals (interquartile range, 94-228 deaths per 100 000 individuals). The median county-level Gini coefficient was 0.44 (interquartile range, 0.42-0.47). There was a positive correlation between Gini coefficients and county-level COVID-19 cases (Spearman ρ = 0.052; P < .001) and deaths (Spearman ρ = 0.134; P < .001) during the study period. This association varied over time; each 0.05-unit increase in Gini coefficient was associated with an adjusted relative risk of COVID-19 deaths: 1.25 (95% CI, 1.17-1.33) in March and April 2020, 1.20 (95% CI, 1.13-1.28) in May and June 2020, 1.46 (95% CI, 1.37-1.55) in July and August 2020, 1.04 (95% CI, 0.98-1.10) in September and October 2020, 0.76 (95% CI, 0.72-0.81) in November and December 2020, and 1.02 (95% CI, 0.96-1.07) in January and February 2021 (P < .001 for interaction). The adjusted association of the Gini coefficient with COVID-19 cases also reached a peak in July and August 2020 (relative risk, 1.28 [95% CI, 1.22-1.33]).

Conclusions and Relevance  This study suggests that income inequality within US counties was associated with more cases and deaths due to COVID-19 in the summer months of 2020. The COVID-19 pandemic has highlighted the vast disparities that exist in health outcomes owing to income inequality in the US. Targeted interventions should be focused on areas of income inequality to both flatten the curve and lessen the burden of inequality.

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Article Information

Accepted for Publication: March 12, 2021.

Published: May 3, 2021. doi:10.1001/jamanetworkopen.2021.8799

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Tan AX et al. JAMA Network Open.

Corresponding Author: Michelle C. Odden, PhD, Department of Epidemiology and Population Health, Stanford University, 259 Campus Dr, HRP Redwood Building, Room T259, Stanford, CA 94305 (modden@stanford.edu).

Author Contributions: Ms Tan had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Tan, Abdel Magid, Odden.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Tan, Hinman, Abdel Magid.

Critical revision of the manuscript for important intellectual content: Tan, Abdel Magid, Nelson, Odden.

Statistical analysis: Tan, Nelson.

Administrative, technical, or material support: Tan, Hinman.

Supervision: Abdel Magid, Odden.

Conflict of Interest Disclosures: Dr Nelson reported receiving grants from the National Institutes of Health and National MS Society and serving as a compensated consultant for Acumen Inc. Dr Odden reported receiving grants from the National Institutes of Health and serving as a consultant for Cricket Health Inc outside the submitted work. No other disclosures were reported.

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