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US-county level variation in intersecting individual, household and community characteristics relevant to COVID-19 and planning an equitable response: a cross-sectional analysis

OBJECTIVES: To illustrate the intersections of, and intercounty variation in, individual, household and community factors that influence the impact of COVID-19 on US counties and their ability to respond. DESIGN: We identified key individual, household and community characteristics influencing COVID...

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Autores principales: Chin, Taylor, Kahn, Rebecca, Li, Ruoran, Chen, Jarvis T, Krieger, Nancy, Buckee, Caroline O, Balsari, Satchit, Kiang, Mathew V
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7467554/
https://www.ncbi.nlm.nih.gov/pubmed/32873684
http://dx.doi.org/10.1136/bmjopen-2020-039886
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author Chin, Taylor
Kahn, Rebecca
Li, Ruoran
Chen, Jarvis T
Krieger, Nancy
Buckee, Caroline O
Balsari, Satchit
Kiang, Mathew V
author_facet Chin, Taylor
Kahn, Rebecca
Li, Ruoran
Chen, Jarvis T
Krieger, Nancy
Buckee, Caroline O
Balsari, Satchit
Kiang, Mathew V
author_sort Chin, Taylor
collection PubMed
description OBJECTIVES: To illustrate the intersections of, and intercounty variation in, individual, household and community factors that influence the impact of COVID-19 on US counties and their ability to respond. DESIGN: We identified key individual, household and community characteristics influencing COVID-19 risks of infection and survival, guided by international experiences and consideration of epidemiological parameters of importance. Using publicly available data, we developed an open-access online tool that allows county-specific querying and mapping of risk factors. As an illustrative example, we assess the pairwise intersections of age (individual level), poverty (household level) and prevalence of group homes (community-level) in US counties. We also examine how these factors intersect with the proportion of the population that is people of colour (ie, not non-Hispanic white), a metric that reflects histories of US race relations. We defined ‘high’ risk counties as those above the 75th percentile. This threshold can be changed using the online tool. SETTING: US counties. PARTICIPANTS: Analyses are based on publicly available county-level data from the Area Health Resources Files, American Community Survey, Centers for Disease Control and Prevention Atlas file, National Center for Health Statistic and RWJF Community Health Rankings. RESULTS: Our findings demonstrate significant intercounty variation in the distribution of individual, household and community characteristics that affect risks of infection, severe disease or mortality from COVID-19. About 9% of counties, affecting 10 million residents, are in higher risk categories for both age and group quarters. About 14% of counties, affecting 31 million residents, have both high levels of poverty and a high proportion of people of colour. CONCLUSION: Federal and state governments will benefit from recognising high intrastate, intercounty variation in population risks and response capacity. Equitable responses to the pandemic require strategies to protect those in counties at highest risk of adverse COVID-19 outcomes and their social and economic impacts.
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spelling pubmed-74675542020-09-11 US-county level variation in intersecting individual, household and community characteristics relevant to COVID-19 and planning an equitable response: a cross-sectional analysis Chin, Taylor Kahn, Rebecca Li, Ruoran Chen, Jarvis T Krieger, Nancy Buckee, Caroline O Balsari, Satchit Kiang, Mathew V BMJ Open Public Health OBJECTIVES: To illustrate the intersections of, and intercounty variation in, individual, household and community factors that influence the impact of COVID-19 on US counties and their ability to respond. DESIGN: We identified key individual, household and community characteristics influencing COVID-19 risks of infection and survival, guided by international experiences and consideration of epidemiological parameters of importance. Using publicly available data, we developed an open-access online tool that allows county-specific querying and mapping of risk factors. As an illustrative example, we assess the pairwise intersections of age (individual level), poverty (household level) and prevalence of group homes (community-level) in US counties. We also examine how these factors intersect with the proportion of the population that is people of colour (ie, not non-Hispanic white), a metric that reflects histories of US race relations. We defined ‘high’ risk counties as those above the 75th percentile. This threshold can be changed using the online tool. SETTING: US counties. PARTICIPANTS: Analyses are based on publicly available county-level data from the Area Health Resources Files, American Community Survey, Centers for Disease Control and Prevention Atlas file, National Center for Health Statistic and RWJF Community Health Rankings. RESULTS: Our findings demonstrate significant intercounty variation in the distribution of individual, household and community characteristics that affect risks of infection, severe disease or mortality from COVID-19. About 9% of counties, affecting 10 million residents, are in higher risk categories for both age and group quarters. About 14% of counties, affecting 31 million residents, have both high levels of poverty and a high proportion of people of colour. CONCLUSION: Federal and state governments will benefit from recognising high intrastate, intercounty variation in population risks and response capacity. Equitable responses to the pandemic require strategies to protect those in counties at highest risk of adverse COVID-19 outcomes and their social and economic impacts. BMJ Publishing Group 2020-09-01 /pmc/articles/PMC7467554/ /pubmed/32873684 http://dx.doi.org/10.1136/bmjopen-2020-039886 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Public Health
Chin, Taylor
Kahn, Rebecca
Li, Ruoran
Chen, Jarvis T
Krieger, Nancy
Buckee, Caroline O
Balsari, Satchit
Kiang, Mathew V
US-county level variation in intersecting individual, household and community characteristics relevant to COVID-19 and planning an equitable response: a cross-sectional analysis
title US-county level variation in intersecting individual, household and community characteristics relevant to COVID-19 and planning an equitable response: a cross-sectional analysis
title_full US-county level variation in intersecting individual, household and community characteristics relevant to COVID-19 and planning an equitable response: a cross-sectional analysis
title_fullStr US-county level variation in intersecting individual, household and community characteristics relevant to COVID-19 and planning an equitable response: a cross-sectional analysis
title_full_unstemmed US-county level variation in intersecting individual, household and community characteristics relevant to COVID-19 and planning an equitable response: a cross-sectional analysis
title_short US-county level variation in intersecting individual, household and community characteristics relevant to COVID-19 and planning an equitable response: a cross-sectional analysis
title_sort us-county level variation in intersecting individual, household and community characteristics relevant to covid-19 and planning an equitable response: a cross-sectional analysis
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7467554/
https://www.ncbi.nlm.nih.gov/pubmed/32873684
http://dx.doi.org/10.1136/bmjopen-2020-039886
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