United States County-level COVID-19 Death Rates and Case Fatality Rates Vary by Region and Urban Status

Ahmed, Rashid; Williamson, M.; Hamid, Muhammad Akhter; Ashraf, N

doi:10.3390/healthcare8030330

Cited by 28 publications

(31 citation statements)

References 28 publications

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“…Our study highlights a concerning disparity between urban and rural counties, consistent with several other studies, wherein urban counties experience higher incidence rates, yet rural counties experience higher case fatality rates [2, 22]. Moreover, our study is the first to identify Hispanic & African American populations, elderly populations, and large nursing homes as major contributing factors to this urban-rural disparity in Texas.…”

Section: Discussionsupporting

confidence: 89%

A Multiple Linear Regression Analysis of Rural-Urban COVID-19 Risk Disparities in Texas

Luo¹,

Zhong

Sun

et al. 2021

Preprint

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As the number of COVID-19 cases in the U.S. rises, the differential impact of the pandemic in urban and rural regions becomes more pronounced, and the major factors relating to this difference remain unclear. Using the 254 counties of Texas as units of analysis, we utilized multiple linear regression to investigate the influence of 83 county-level predictor variables including race demographics, age demographics, healthcare and financial status, and prevalence of and mortality rate from COVID-19 risk factors on the incidence rate and case fatality rate from COVID-19 in Texas on September 15, 2020. Here, we report that urban counties experience, on average, 41.1% higher incidence rates from COVID-19 than rural counties and 34.7% lower case fatality rates. Through comparisons between our models, we found that this difference was largely attributable to four major predictor variables: namely, the proportion of elderly residents, African American residents, and Hispanic residents, and the presence of large nursing homes. According to our models, counties with high incidence rates of COVID-19 are predicted to have high proportions of African American residents and Hispanic residents coupled with low proportions of elderly residents. Furthermore, we found that counties with the highest case fatality rates are predicted to have high proportions of elderly residents, obese residents, and Hispanic residents, coupled with low proportions of residents ages 20-39 and residents who report smoking cigarettes. In our study, major variables and their effects on COVID-19 risk are quantified, highlighting the most vulnerable populations and regions of Texas.

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Section: Discussionsupporting

confidence: 89%

A Multiple Linear Regression Analysis of Rural-Urban COVID-19 Risk Disparities in Texas

Luo¹,

Zhong

Sun

et al. 2021

Preprint

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“…These and other regional patterns [28,29] may not hold over a longer study time-period. Urban-rural disparities explored in earlier research found increased mortality rates [30] and higher prevalence rates of COVID-19 infection in urban counties [12]. Another study found higher standardized cases and fatalities in rural counties in just one US state [13], while others found severe negative impacts of the pandemic on rural unemployment and economic wellbeing [10].…”

Section: Socioeconomic and Spatial Disparities Of The Covid-19 Pandemicmentioning

confidence: 89%

Spatial Disparities of COVID-19 Cases and Fatalities in United States Counties

Jackson

Derakhshan

Blackwood

et al. 2021

IJERPH

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This paper examines the spatial and temporal trends in county-level COVID-19 cases and fatalities in the United States during the first year of the pandemic (January 2020–January 2021). Statistical and geospatial analyses highlight greater impacts in the Great Plains, Southwestern and Southern regions based on cases and fatalities per 100,000 population. Significant case and fatality spatial clusters were most prevalent between November 2020 and January 2021. Distinct urban–rural differences in COVID-19 experiences uncovered higher rural cases and fatalities per 100,000 population and fewer government mitigation actions enacted in rural counties. High levels of social vulnerability and the absence of mitigation policies were significantly associated with higher fatalities, while existing community resilience had more influential spatial explanatory power. Using differences in percentage unemployment changes between 2019 and 2020 as a proxy for pre-emergent recovery revealed urban counties were hit harder in the early months of the pandemic, corresponding with imposed government mitigation policies. This longitudinal, place-based study confirms some early urban–rural patterns initially observed in the pandemic, as well as the disparate COVID-19 experiences among socially vulnerable populations. The results are critical in identifying geographic disparities in COVID-19 exposures and outcomes and providing the evidentiary basis for targeting pandemic recovery.

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“…ZCTAs are generalized representations of ZCs that have been assigned to census blocks and representative of the geographic locations of populated areas [ 25 ]. ZCTA boundaries were constructed utilizing shapefiles (.shp) and geodatabases (.gdb) from TIGER/Line Shapefiles [ 25 , 27 ]. In addition, the TIGER/Line Database was utilized to stratify county areas including/surrounding the ZCs as the following: urban cluster areas (2,500-<50,000 population), urbanized areas (50,000–99,999 population), and metropolitan areas (≥100,000 population).…”

Section: Methodsmentioning

confidence: 99%

COVID-19 fatalities by zip codes and socioeconomic indicators across various U.S. regions

Sen-Crowe

Lin

Alfaro

et al. 2021

Annals of Medicine &Amp; Surgery

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Background There is a paucity of literature addressing COVID-19 case-fatality ratios (CFR) by zip code (ZC). We aim to analyze trends in COVID-19 CFR, population density, and socioeconomic status (SES) indicators (unemployment, median household income) to identify ZCs heavily burdened by COVID-19. Methods Cross-sectional study to investigate the US prevalence of COVD-19 fatalities by ZC and SES. CFRs were calculated from state/county Departments of Health. Inclusion criteria were counties that reported cases/deaths by ZC and a CFR≥2%. This study was reported in line with the STROCSS criteria. Results 609/1,853 ZCs, spanning 327 counties in 7 states had CFRs ≥2%. A significant positive correlation was found between the CFR and median household income (Pearson correlation:0.107; 95% CI [289.1,1937.9]; p < 0.001). No significant correlations exist between the CFR, and population/mi (Sen-Crowe et al., 2020) [2] or unemployment rate. Significant associations exist between the CFR and young males and elderly females without public insurance. CFR was inversely associated with persons aged <44 and individuals aged ≥65. The percentage of nursing homes (NHs) within cities residing within high CFR ZCs range from 8.7% to 67.6%. Conclusion Significant positive association was found between the CFR and median household income. Population/mi (Sen-Crowe et al., 2020) [2] and unemployment rates, did not correlate to CFR. NHs were heavily distributed in high CFR zip codes. We recommend the targeted vaccination of zip codes with a large proportion of long-term care facilities. Finally, we recommend for improved screening and safety guidelines for vulnerable populations (e.g nursing home residents) and established protocols for when there is evidence of substantial infectious spread.

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United States County-level COVID-19 Death Rates and Case Fatality Rates Vary by Region and Urban Status

Cited by 28 publications

References 28 publications

A Multiple Linear Regression Analysis of Rural-Urban COVID-19 Risk Disparities in Texas

A Multiple Linear Regression Analysis of Rural-Urban COVID-19 Risk Disparities in Texas

Spatial Disparities of COVID-19 Cases and Fatalities in United States Counties

COVID-19 fatalities by zip codes and socioeconomic indicators across various U.S. regions

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