Identifying US Counties with High Cumulative COVID-19 Burden and Their Characteristics
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Identifying areas with high COVID-19 burden and their characteristics can help improve vaccine distribution and uptake, reduce burdens on health care systems, and allow for better allocation of public health intervention resources. Synthesizing data from various government and nonprofit institutions of 3,142 United States (US) counties as of 12/21/2020, we studied county-level characteristics that are associated with cumulative case and death rates using regression analyses. Our results showed counties that are more rural, counties with more White/non-White segregation, and counties with higher percentages of people of color, in poverty, with no high school diploma, and with medical comorbidities such as diabetes and hypertension are associated with higher cumulative COVID-19 case and death rates. We identify the hardest hit counties in US using model-estimated case and death rates, which provide more reliable estimates of cumulative COVID-19 burdens than those using raw observed county-specific rates. Identification of counties with high disease burdens and understanding the characteristics of these counties can help inform policies to improve vaccine distribution, deployment and uptake, prevent overwhelming health care systems, and enhance testing access, personal protection equipment access, and other resource allocation efforts, all of which can help save more lives for vulnerable communities.
Significance statement
We found counties that are more rural, counties with more White/non-White segregation, and counties with higher percentages of people of color, in poverty, with no high school diploma, and with medical comorbidities such as diabetes and hypertension are associated with higher cumulative COVID-19 case and death rates. We also identified individual counties with high cumulative COVID-19 burden. Identification of counties with high disease burdens and understanding the characteristics of these counties can help inform policies to improve vaccine distribution, deployment and uptake, prevent overwhelming health care systems, and enhance testing access, personal protection equipment access, and other resource allocation efforts, all of which can help save more lives for vulnerable communities.
Article activity feed
-
-
SciScore for 10.1101/2020.12.02.20234989: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Prevalence rates for several comorbidities were obtained from County Health Rankings & Roadmap. Roadmapsuggested: (Roadmap, RRID:SCR_017207)The following packages were used in formatting results and creating plots: ggplot2, usmap, gridExtra, tidyverse, plyr. ggplot2suggested: (ggplot2, RRID:SCR_014601)Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Therefore, these limitations are less relevant to our primary study goals. Our analyses of weekly COVID-19 case and death rates also …
SciScore for 10.1101/2020.12.02.20234989: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Prevalence rates for several comorbidities were obtained from County Health Rankings & Roadmap. Roadmapsuggested: (Roadmap, RRID:SCR_017207)The following packages were used in formatting results and creating plots: ggplot2, usmap, gridExtra, tidyverse, plyr. ggplot2suggested: (ggplot2, RRID:SCR_014601)Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Therefore, these limitations are less relevant to our primary study goals. Our analyses of weekly COVID-19 case and death rates also support how COVID-19 has disproportionately affected various vulnerable populations at different times. The early stages of the pandemic in the Spring had disproportionately affected racially diverse but socioeconomically disadvantaged urban areas. More recently in the Fall, predominately white, rural, socioeconomically disadvantaged areas have been hit harder. Most research on the COVID-19 pandemic has focused on urban areas, and more studies of rural areas with individual-level data are needed to better characterize the experience of these diverse 46 million individuals (5, 41). Next steps to improving our model-based estimates can include obtaining data at finer geographic resolution such as at the US census tract or zip code level. Additional variables such as neighborhood testing rates, percentage of healthcare workers, percentage of essential workers, exposure to infected individuals within households and in communities, personal protective equipment access, use of public transportation, utilization rates of available healthcare facilities/resources, access to living resources (such as a lack of access to clean water in many households of American Indian/Alaskan Native communities), health literacy, occupation and work conditions, housing conditions, basic living resources, and access to COVID-19 treatments may further improve modeling acc...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
-