Abstract
As of August 2020, the United States is the global epicenter of the COVID-19 pandemic. Emerging data suggests that "essential" workers, who are disproportionately more likely to be racial/ethnic minorities and immigrants, bear a disproportionate degree of risk. We used publicly available data to build a series of spatial autoregressive models assessing county level associations between COVID-19 mortality and (1) percentage of individuals engaged in farm work, (2) percentage of households without a fluent, adult English-speaker, (3) percentage of uninsured individuals under the age of 65, and (4) percentage of individuals living at or below the federal poverty line. We further adjusted these models for total population, population density, and number of days since the first reported case in a given county. We found that across all counties that had reported a case of COVID-19 as of July 12, 2020 (n = 3024), a higher percentage of farmworkers, a higher percentage of residents living in poverty, higher density, higher population, and a higher percentage of residents over the age of 65 were all independently and significantly associated with a higher number of deaths in a county. In urban counties (n = 115), a higher percentage of farmworkers, higher density, and larger population were all associated with a higher number of deaths, while lower rates of insurance coverage in a county was independently associated with fewer deaths. In non-urban counties (n = 2909), these same patterns held true, with higher percentages of residents living in poverty and senior residents also significantly associated with more deaths. Taken together, our findings suggest that farm workers may face unique risks of contracting and dying from COVID-19, and that these risks are independent of poverty, insurance, or linguistic accessibility of COVID-19 health campaigns.
Highlights
A novel coronavirus, SARS-CoV-2, is causing a global pandemic of COVID-19 respiratory disease
Per the New York Times (NYT): The data is the product of dozens of journalists working across several time zones to monitor news conferences, analyze data releases and seek clarification from public officials on how they categorize cases. . .At times, cases have disappeared from a local government database, or officials have moved a patient first identified in one state or county to another, often with no explanation
We used spatial autoregression models to assess the role of select social determinants of health as risk factors and drivers of the COVID-19 pandemic across the United States and by region
Summary
A novel coronavirus, SARS-CoV-2, is causing a global pandemic of COVID-19 respiratory disease. This pandemic has resulted in nearly 22 million cases and over 800,000 deaths since early January [1]. As of August 17 2020, the United States has more cases than any other nation in the world, with just over 5.4 million cases and 170,000 deaths [1]. Preliminary data indicates that existing health inequities in the United States are likely linked to COVID-19 morbidity and mortality [2].
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