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-19...
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doaj-7c706f6d635047cc9b4cacf4e5236f782021-07-21T16:00:57ZengBMJ Publishing GroupBMJ Open2044-60552020-09-0110910.1136/bmjopen-2020-039886US-county level variation in intersecting individual, household and community characteristics relevant to COVID-19 and planning an equitable response: a cross-sectional analysisMathew V Kiang0Nancy Krieger1Caroline O Buckee2Jarvis T Chen3Taylor Chin4Rebecca Kahn5Ruoran Li6Satchit Balsari71 Center for Population Health Sciences, Stanford University, Palo Alto, California, USA Department of Social and Behavioral Sciences, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA3 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA 2 Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA Epidemiology, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USAEpidemiology, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USAEpidemiology, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USAEmergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USAObjectives 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.https://bmjopen.bmj.com/content/10/9/e039886.full |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mathew V Kiang Nancy Krieger Caroline O Buckee Jarvis T Chen Taylor Chin Rebecca Kahn Ruoran Li Satchit Balsari |
spellingShingle |
Mathew V Kiang Nancy Krieger Caroline O Buckee Jarvis T Chen Taylor Chin Rebecca Kahn Ruoran Li Satchit Balsari US-county level variation in intersecting individual, household and community characteristics relevant to COVID-19 and planning an equitable response: a cross-sectional analysis BMJ Open |
author_facet |
Mathew V Kiang Nancy Krieger Caroline O Buckee Jarvis T Chen Taylor Chin Rebecca Kahn Ruoran Li Satchit Balsari |
author_sort |
Mathew V Kiang |
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_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_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_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 |
publisher |
BMJ Publishing Group |
series |
BMJ Open |
issn |
2044-6055 |
publishDate |
2020-09-01 |
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. |
url |
https://bmjopen.bmj.com/content/10/9/e039886.full |
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