Income inequality tied to more COVID-19 cases, deaths

Credit: Drazen Zigic / iStock

A study yesterday in JAMA Network Open identifies an association between US county-level income inequality and higher rates of COVID-19 infection and death in summer 2020.

The ecological cohort study, by Stanford University researchers, analyzed coronavirus case and death data from Johns Hopkins University on 3,220 counties in 50 states, Puerto Rico, and Washington, DC, from Mar 1, 2020, to Feb 28, 2021. 

County income data, obtained mostly from the 2014 through 2018 American Community Survey, were used to estimate the Gini coefficient, which measures unequal income. Coefficient scores range from 0 to 1, with 0 representing perfect income equality and 1 representing perfect income inequality, in which one person earns all the income. 

The researchers accounted for potential confounding factors such as crowded living conditions, urban versus rural residence, and measures of poverty (eg, housing situation, education level, and healthcare availability). 

Weak positive correlation over summer

By the end of the study, counties recorded a median of 8,891 COVID-19 cases and 156 deaths per 100,000 people. The median within-county Gini coefficient was 0.44, and there was a weak positive correlation between the coefficient and county coronavirus cases and deaths. 

The association, however, was inverse for cases in September to December and for deaths in November to December, which the authors said could be due to increased social mixing at those times, relaxed infection mitigation measures, "pandemic fatigue," a return to in-person K-12 and college education, and increased travel and gatherings during Thanksgiving and Christmas. 

"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," the authors wrote. "However, this hypothesis remains speculative, and future studies using GPS (Global Ppositioning System) patterns during this era may better elucidate social distancing behavior stratified by income inequality."

The association varied over time, with each 0.05 increase in Gini coefficient was tied to an adjusted relative risk (aRR) of coronavirus deaths. In March and April 2020, the coefficient was 1.25, while it was 1.20 in May and June, 1.46 in July and August, 1.04 in September and October, 0.76 in November and December, and 1.02 in January and February 2021. The adjusted association between Gini coefficient and COVID-19 cases also peaked in July and August (aRR, 1.28). 

After adjustment, for each 0.05 rise in Gini coefficient, the aRR of COVID-19 cases was 1.18 for March and April 2020, 1.23 for May and June, 1.28 for July and August, 0.90 for September and October, 0.85 for November and December, and 1.02 for January and February 2021. 

Crowded households, public-facing jobs

The authors noted that income inequality may increase the odds of infection for lower-income people, who often need to continue working in order to live in an area also inhabited by much wealthier people. 

Disadvantaged people are also more likely to live in crowded households and work in jobs that require interaction with the public, such as childcare, eldercare, cleaning, janitorial, and restaurant jobs. In contrast, counties with more income equality may better be able to put in place public health mitigation strategies.

They also said that within-county income inequality is more representative of how residents live in those areas than state-level data, and many public health measures are issued at the county level, making counties a more relevant target for policy change.

"The COVID-19 pandemic has highlighted the vast disparities that exist in health outcomes owing to income inequality in the US," the researchers wrote. "Targeted interventions should be focused on areas of income inequality to both flatten the curve and lessen the burden of inequality."

Such interventions, they said, could include the distribution of personal protective equipment, more COVID-19 testing, further guidance on nonpharmaceutical interventions, educational campaigns, and work to boost vaccine acceptance among high-risk groups.

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