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Association between county-level risk groups and COVID-19 outcomes in the United States: a socioecological study
BACKGROUND: Geographic heterogeneity in COVID-19 outcomes in the United States is well-documented and has been linked with factors at the county level, including sociodemographic and health factors. Whether an integrated measure of place-based risk can classify counties at high risk for COVID-19 out...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8756413/ https://www.ncbi.nlm.nih.gov/pubmed/35027022 http://dx.doi.org/10.1186/s12889-021-12469-y |
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author | Khan, Sadiya S. Krefman, Amy E. McCabe, Megan E. Petito, Lucia C. Yang, Xiaoyun Kershaw, Kiarri N. Pool, Lindsay R. Allen, Norrina B. |
author_facet | Khan, Sadiya S. Krefman, Amy E. McCabe, Megan E. Petito, Lucia C. Yang, Xiaoyun Kershaw, Kiarri N. Pool, Lindsay R. Allen, Norrina B. |
author_sort | Khan, Sadiya S. |
collection | PubMed |
description | BACKGROUND: Geographic heterogeneity in COVID-19 outcomes in the United States is well-documented and has been linked with factors at the county level, including sociodemographic and health factors. Whether an integrated measure of place-based risk can classify counties at high risk for COVID-19 outcomes is not known. METHODS: We conducted an ecological nationwide analysis of 2,701 US counties from 1/21/20 to 2/17/21. County-level characteristics across multiple domains, including demographic, socioeconomic, healthcare access, physical environment, and health factor prevalence were harmonized and linked from a variety of sources. We performed latent class analysis to identify distinct groups of counties based on multiple sociodemographic, health, and environmental domains and examined the association with COVID-19 cases and deaths per 100,000 population. RESULTS: Analysis of 25.9 million COVID-19 cases and 481,238 COVID-19 deaths revealed large between-county differences with widespread geographic dispersion, with the gap in cumulative cases and death rates between counties in the 90(th) and 10(th) percentile of 6,581 and 291 per 100,000, respectively. Counties from rural areas tended to cluster together compared with urban areas and were further stratified by social determinants of health factors that reflected high and low social vulnerability. Highest rates of cumulative COVID-19 cases (9,557 [2,520]) and deaths (210 [97]) per 100,000 occurred in the cluster comprised of rural disadvantaged counties. CONCLUSIONS: County-level COVID-19 cases and deaths had substantial disparities with heterogeneous geographic spread across the US. The approach to county-level risk characterization used in this study has the potential to provide novel insights into communicable disease patterns and disparities at the local level. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-021-12469-y. |
format | Online Article Text |
id | pubmed-8756413 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-87564132022-01-13 Association between county-level risk groups and COVID-19 outcomes in the United States: a socioecological study Khan, Sadiya S. Krefman, Amy E. McCabe, Megan E. Petito, Lucia C. Yang, Xiaoyun Kershaw, Kiarri N. Pool, Lindsay R. Allen, Norrina B. BMC Public Health Research BACKGROUND: Geographic heterogeneity in COVID-19 outcomes in the United States is well-documented and has been linked with factors at the county level, including sociodemographic and health factors. Whether an integrated measure of place-based risk can classify counties at high risk for COVID-19 outcomes is not known. METHODS: We conducted an ecological nationwide analysis of 2,701 US counties from 1/21/20 to 2/17/21. County-level characteristics across multiple domains, including demographic, socioeconomic, healthcare access, physical environment, and health factor prevalence were harmonized and linked from a variety of sources. We performed latent class analysis to identify distinct groups of counties based on multiple sociodemographic, health, and environmental domains and examined the association with COVID-19 cases and deaths per 100,000 population. RESULTS: Analysis of 25.9 million COVID-19 cases and 481,238 COVID-19 deaths revealed large between-county differences with widespread geographic dispersion, with the gap in cumulative cases and death rates between counties in the 90(th) and 10(th) percentile of 6,581 and 291 per 100,000, respectively. Counties from rural areas tended to cluster together compared with urban areas and were further stratified by social determinants of health factors that reflected high and low social vulnerability. Highest rates of cumulative COVID-19 cases (9,557 [2,520]) and deaths (210 [97]) per 100,000 occurred in the cluster comprised of rural disadvantaged counties. CONCLUSIONS: County-level COVID-19 cases and deaths had substantial disparities with heterogeneous geographic spread across the US. The approach to county-level risk characterization used in this study has the potential to provide novel insights into communicable disease patterns and disparities at the local level. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-021-12469-y. BioMed Central 2022-01-13 /pmc/articles/PMC8756413/ /pubmed/35027022 http://dx.doi.org/10.1186/s12889-021-12469-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Khan, Sadiya S. Krefman, Amy E. McCabe, Megan E. Petito, Lucia C. Yang, Xiaoyun Kershaw, Kiarri N. Pool, Lindsay R. Allen, Norrina B. Association between county-level risk groups and COVID-19 outcomes in the United States: a socioecological study |
title | Association between county-level risk groups and COVID-19 outcomes in the United States: a socioecological study |
title_full | Association between county-level risk groups and COVID-19 outcomes in the United States: a socioecological study |
title_fullStr | Association between county-level risk groups and COVID-19 outcomes in the United States: a socioecological study |
title_full_unstemmed | Association between county-level risk groups and COVID-19 outcomes in the United States: a socioecological study |
title_short | Association between county-level risk groups and COVID-19 outcomes in the United States: a socioecological study |
title_sort | association between county-level risk groups and covid-19 outcomes in the united states: a socioecological study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8756413/ https://www.ncbi.nlm.nih.gov/pubmed/35027022 http://dx.doi.org/10.1186/s12889-021-12469-y |
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