The graph above details per capita Covid-19 vaccination rates and the Covid-19 Impact Score, developed at the F. Marie Hall Institute of Rural and Community Health, Department of Research, Reporting, and Data Management at Texas Tech University Health Sciences Center. The Covid-19 Impact Score details the level of fatality and case burden the virus contributed to a community. This analysis was specifically interested in rural and urban counties which were further segmented by 2020 federal election voting results.

With the recent FDA approval of the Pfizer Covid-19 vaccine in August 2021 and the current widespread availability of Covid-19 vaccines in general, the lack of access to Covid-19 vaccines has decreased tremendously. Though many claimed that a lack of FDA approval was the major reason for not becoming vaccinated, the FDA approval of one of the Covid-19 vaccines did little to boost vaccination rates. Some experts believe that the FDA approval was just one of a myriad of reasons for the widespread vaccine hesitancy, which ultimately affects the Covid-19 impact rates.

One theory is that the difference in Covid-19 impact and vaccination rates may be in part ideologically driven, which concurs with previous research on this burgeoning topic, in which the perception of one’s vulnerability and the threat of the virus was strongly related to one’s political ideology.

Regarding the analysis in this article, it was predicted that there would be a rural and urban difference in the vaccination rates and Covid-19 Impact Score, as access to care and resources are harder to come by in rural areas. This hypothesis proved to be correct as shown in the Covid-19 Impact graphs across rural and urban counties. Access to care does play a role, but when another layer, county voting results, was added to the investigation intriguing trends panned out.

When reviewing both urban and rural counties segmented across voting results, it was observed that both provided similar trends across their political preferences. Stated simply, the rural-urban divide contributes to the Covid-19 impact across counties, but so does politicization. If politicization had little to no effect on vaccination rates or the Covid-19 Impact Score, there would be very little divergence between counties that preferred differing presidential candidates.

The first-row graphs depict the per capita rate of fully vaccinated individuals across all United States counties. Both rural and urban counties show similar trends, and both sets of counties experience similar vaccination growth rates until March 2021. Counties where the democratic candidate, Joseph R. Biden, Jr., won in the 2020 United States presidential election had vaccination rates that continued to climb. In comparison, counties where the republican candidate, Donald J. Trump, won saw a slowing of their vaccination rates.

The second-row graphs show the Covid-19 Impact Score by county rurality and by a county’s presidential candidate winner. Rural counties have less healthcare and financial resources than urban counties so it was expected that they would bear a harder brunt against the virus’ effects on their denizens. Both rural and urban republican and democratic counties trended closely with each other until February 2021 when they started to diverge. One factor contributing to the impact divergence likely comes from the introduction of the Covid-19 vaccines for the American public starting in January 2021.

Methodology: Data sources for this analysis were publicly available. All data was analyzed at the county level. COVID-19 deaths and cases were collected from The New York Times’ GitHub repository. The range of data was from March 2020 to August 2021. County population was collected from the County Health Rankings & Roadmaps, a program of the University of Wisconsin Population Health Institute. The U.S. Department of Agriculture, Economic Research Service provided the Rural-Urban Continuum codes, which was used to divide counties into rural or urban. Vaccination data was collected from the Centers of Disease Control and Prevention. 2020 general election data was collected from the Massachusetts Institute of Technology’s Election Data + Science Lab.

COVID-19 confirmed cases and deaths were calculated at a per capita rate, i.e., a per 100k rate, for all counties. The data was then normalized on a 0 to 1 scale. These per 100k rates were then weighted to create the COVID-19 Impact Score. For this analysis, cases per capita were weighted at 20% and deaths per capita at 80%. These two weighted scores were then added together to create the composite, COVID-19 Impact Score.