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

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. case and data from 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. 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. study suggest an association between county-level income inequality and COVID-19 cases and deaths. Targeted interventions implemented in a timely manner are of vital importance, especially as the United States turns a new corner with COVID-19 control and vaccine rollout. Targeted interventions should be focused on areas of income inequality to both flatten the curve and lessen the burden of inequality. Potential targeted interventions include the distribution of personal protective equipment, enhanced COVID-19 testing, providing further guidance on COVID-19 nonpharmaceutical interventions, educational campaigns, and finally, improving vaccine acceptance among those at highest risk of exposure.


Introduction
COVID-19, caused by SARS-CoV-2, has resulted in the largest pandemic in a century. The United States has been impacted significantly, accounting for 25% of COVID-19 cases and deaths from COVID-19 worldwide. 1 A significant body of evidence has shown that the prevalence of cases and deaths due to COVID-19 has been disproportionately higher among socially marginalized communities, exacerbated by health disparities. [2][3][4][5][6][7][8] One recent study by Liao and De Maio 9 found that an increase in a county's income inequality corresponded with an increase in COVID-19 incidence. Another study by Oronce et al 10 reported an association between increased state-level income inequality and COVID-19 cases. Income inequality may increase opportunities for infection, as the most disadvantaged individuals need to stay in the labor force to afford to live in a region that also includes much wealthier residents. 11 Moreover, individuals with lower incomes are more likely to reside in crowded housing and have public-facing jobs, such as service, child and elder care, and cleaning or janitorial services, which can increase the risk of exposure to SARS-CoV-2. 12 In this study, we sought to evaluate the correlation between county-level income inequality, measured by the Gini coefficient, and county-level COVID-19 case and death counts across the United States at different time epochs in 2020 and 2021. County-level income inequality reflects the lived experience of those residing in these regions better than state-level measures. Moreover, many public health orders are implemented at the county level, making this geographical unit relevant for policy. We hypothesized that counties with worse income inequality would have higher numbers of COVID-19 cases and deaths compared with those with more income equality and that the association between income inequality and COVID-19 cases would have strengthened over time, as those who reside in communities with more income equality would have a greater ability to implement risk mitigation strategies.

Methods
In this ecological cohort study, the number of cases and deaths from COVID-19 were extracted from March 1, 2020, to February 28, 2021, from the COVID-19 Data Repository by the Center for Systems Science and Engineering at Johns Hopkins University in Baltimore, Maryland. County-level data were obtained from the 2014 to 2018 American Community Survey 5-year estimates. 13 Health system data (physicians per 100 000 individuals) were obtained from Area Health Resources Files. 14 Selfreported data on mask use were obtained from New York Times estimates. 15 Our primary explanatory variable of interest was the Gini coefficient, 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. 16 Potential confounders at the county level were obtained from the 2014 to 2018 American Community Survey estimates, and included the following: poverty, age, race/ethnicity, crowding (given by occupancy per room), urbanicity and rurality, educational levels, number of physicians per 100 000 individuals, and mask use. We also included state as a fixed effect. To examine the time interaction between cases and deaths and Gini coefficients, noncumulative cases and deaths were split into time epochs spanning 2 months each beginning with the World Health Organization declaration of the pandemic: March and April 2020, May and June 2020, July and August 2020, September and October 2020, November

Statistical Analysis
We explored the associations between county-level Gini coefficients and county-level COVID-19 cases and deaths. Gini coefficients were transformed by dividing by 0.05 for easier interpretation in the models. 18 We used negative binomial regression to account for overdispersion in unadjusted and adjusted analyses. We used a likelihood ratio test to evaluate the interaction between county-level confounder. To account for county-level disease control policies, we adjusted for self-reported mask use (never, sometimes, frequently, and always). To assess for median poverty rate as an effect modifier, we also examined the association between COVID-19 cases and deaths and the interaction between income inequality and COVID-19 stratified by time epochs. All P values were from 2-sided tests, and results were deemed statistically significant at P < .05.

Results
As  To account for any differences observed in the association between income inequality and COVID-19 burden that might be modified by high or low median poverty rate, we examined the interaction between income inequality and COVID-19. We also looked at the interaction when stratified by time epochs. We found no significant interactions. We hypothesize that a potential mechanism explaining the association between COVID-19 cases and Gini coefficients being strongest in the summer months is that individuals with lower incomes in counties with greater income inequality may be at higher risk for COVID-19 infection owing to the economic pressure to remain in high-risk employment. Many who are at increased risk of COVID-19 cannot work from home. 19 Individuals with lower incomes tend to work in sectors that produce nontradable goods such as restaurants, hotels, or entertainment venues, which require person-toperson contact. 20 However, because this was an ecological study, we cannot make any inferences at the individual level.

Discussion
We observed a change in the direction of the association between income inequality and COVID-19 cases in September through December 2020 and deaths in November and December 2020. In these epochs, higher income inequality was associated with a lower rate of cases and deaths. We hypothesize that there may have been increased social mixing in these fall months, likely owing to a combination of factors including a policy shift from the White House away from risk mitigation strategies, increased individual risk-taking behavior (ie, "pandemic fatigue"), a return to in-person schooling and college education, and the Thanksgiving and Christmas holiday season with increased travel both within states and out of state. It is possible that the direct association of income inequality with COVID-19 cases and death was nullified by these factors, which led to an increase in cases and death. However, this hypothesis remains speculative, and future studies using GPS (Global Positioning System) patterns during this era may better elucidate social distancing behavior stratified by income inequality.

Limitations
This study has some limitations, including that it is an ecological design with a county-level outcome measure; as such, individual risk cannot be extrapolated from it. Furthermore, we did not account for concurrent changes in measures of income and employment owing to the time-lag availability of these measures in the American Community Survey. In addition, we did not pursue another form of analysis, such as a time-series analysis, because of the interest in evaluating month-to-month changes and because some counties had small numbers of cases and death. Last, the association between COVID-19 risk and income inequality may be stronger than estimated owing to the fact that some of the confounders included, such as crowded housing, may likely also be mediators on the pathway, which resulted in an attenuated risk.

Conclusions
The COVID-19 pandemic has highlighted the vast inequality across all socioeconomic levels in the