COVID-19 Infections and Mortality in Florida Counties: Roles of Race, Ethnicity, Segregation, and 2020 Election Results

Abstract

Purpose This study investigates the association of racial and ethnic composition, segregation, and 2020 presidential election voting results with COVID-19 infections and deaths in Florida counties. Methods Florida county COVID-19 infection and death counts reported through March 2021 were supplemented with socioeconomic characteristics and 2020 presidential results to form the dataset employed in this ecological study. Poisson regression analysis measured the association of infection and mortality rates with county demographic and economic characteristics, then assessed the moderating role of county political preferences. Results Counties with higher proportions of Black residents experience disproportionately higher COVID-19 infection and mortality rates. Disparities are further inflated in counties with larger Republican vote shares. That voting effect extends to Hispanic population proportions and segregation, both of which are associated with higher COVID-19 infection and mortality rates in more Republican-leaning counties. Conclusions Communities challenged by pre-existing health disparities, segregation, and economic hardship before the pandemic bear disproportionate risk of COVID-19 infection and mortality. Factors associated with voter preference for the 2020 Republican presidential candidate compound those problems, worsening consequences for all county residents, suggesting deeper structural health challenges.

Characterization of persons with reported SARS-CoV-2 infection in the Oklahoma City tri-county area: Evidence from the first 12 months of transmission

“…SARS-CoV-2 infection and COVID-19 disease is known to disproportionately affect certain ethnic groups such as Hispanics or Latinos and African Americans 6 , 10 , 26 . We confirm this with our results showing that African American/Black persons with a positive SARS-CoV-2 test had approximately 3 times higher odds of hospitalization and 1.4 higher odds of dying compared to Non-Hispanic White persons.…”

“…For each confirmed case, we extracted the following: 1 date of notification in the system, 2 symptom onset date, 3 age, 4 gender, 5 ethnic group, 6 location (county, city/town, and zip code), 7 occupation, 8 presence of various symptoms at the time of testing, 9 information on chronic comorbidities and 10 whether the person was hospitalized and/or died following their diagnosis. All data on symptom onset, presence of symptoms, and presence of comorbidities were self-reported by the patient and covered symptoms/comorbidities as defined in a simple questionnaire without ICD codes.…”

“…The majority of these studies were based on data from large cities or selected population groups such as veterans, hospitalized patients or specific ethnic groups. Only a limited number of publications have used routinely collected surveillance data from smaller urban areas focusing on both hospitalized and non-hospitalized SARS-CoV-2 positive individuals 2 , 5 , 6 , 7 , 8 , 9 , 10 , 11 . In general, published evidence suggests that males are more likely to be infected, hospitalized and die from severe COVID-19 than females 12 , 13 .…”

Communities of Knowledge in Trouble

“…By all reputable scientific accounts, COVID vaccines have saved a lot of lives with almost no downsides. And yet there remains a community strongly opposed to them (Leonhardt, 2021), a group that, as a result, has suffered higher mortality over time (Bernet, 2022;Neelon et al, 2021;Sehgal et al, 2022;Wallace et al, 2022). In the remainder of this article, we review evidence of how partisan divergence in COVID responses unfolded in the United States and argue that this divergence is a product of how communities of knowledge work, not by processing information to arrive at a logical conclusion but through cues that hint at an understanding that individuals rarely achieve.…”

“…Gollwitzer et al (2020) measured aggregate movement data from cell phones as a function of county vote share in the 2016 election and found the extent to which people traveled outside of the home during the early months of COVID, when stay-at-home mandates were in effect, varied by county-level partisanship even when controlling for numerous factors that could explain away the relationship (for similar results, see Allcott et al, 2020; Barbalat & Franck, 2022; Barrios & Hochberg, 2021; Camobreco & He, 2022; Grossman et al, 2020; Im et al, 2021; Kavanagh et al, 2021). Once vaccines became available, uptake also followed partisan patterns (Leonhardt, 2021), and mortality eventually reflected these differences (Bernet, 2022; Neelon et al, 2021; Sehgal et al, 2022; Wallace et al, 2022).…”