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Public health
Original research
US-county level variation in intersecting individual, household and community characteristics relevant to COVID-19 and planning an equitable response: a cross-sectional analysis
  1. Taylor Chin1,
  2. Rebecca Kahn1,
  3. Ruoran Li1,
  4. Jarvis T Chen2,
  5. Nancy Krieger2,
  6. Caroline O Buckee1,
  7. Satchit Balsari3,4,
  8. Mathew V Kiang4,5,6
  1. 1Epidemiology, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
  2. 2Social and Behavioral Sciences, Harvard TH Chan School of Public Health, Boston, MA, United States
  3. 3Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
  4. 4FXB Center for Health and Human Rights, Harvard University, Cambridge, Massachusetts, USA
  5. 5Center for Population Health Sciences, Stanford University, Palo Alto, California, USA
  6. 6Epidemiology and Population Health, Stanford University, Stanford, California, USA
  1. Correspondence to Taylor Chin; taylorchin{at}g.harvard.edu

Abstract

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.

  • public health
  • epidemiology
  • health policy
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/.

https://doi.org/10.1136/bmjopen-2020-039886

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    Footnotes

    • TC and RK are joint first authors.

    • SB and MVK are joint senior authors.

    • Twitter @rebeccajk13, @ruoranepi, @mathewkiang

    • Contributors SB and MVK conceived and designed the project. TC, RK, RL and MVK acquired and analysed the data. NK contributed to framing the conceptualisation and discussion of the individual, household and community characteristics. All authors interpreted the results. TC and RK drafted the first version of the manuscript. All authors provided critical input for subsequent revisions. All authors approve of the final version to be published. All authors agree to be accountable for this work and ensure the accuracy and integrity of the work will be appropriately investigated and resolved.

    • Funding TC, RK and RL were supported in part by Award Number U54GM088558 from the US National Institute of General Medical Sciences. NK was supported in part by her American Cancer Society Clinical Research Professor Award. MVK was supported by the National Institute on Drug Abuse of the National Institutes of Health (K99DA051534).

    • Disclaimer Dissemination of the results to study participants and or patient organisation is not applicable. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences, National Institute on Drug Abuse, the National Institutes of Health, or other contributing agencies. The authors conducted the study independently, and the decision to submit the manuscript for publication was theirs alone.

    • Map disclaimer The depiction of boundaries on this map does not imply the expression of any opinion whatsoever on the part of BMJ (or any member of its group) concerning the legal status of any country, territory, jurisdiction or area or of its authorities. This map is provided without any warranty of any kind, either express or implied.

    • Competing interests None declared.

    • Patient consent for publication Not required.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement Data are available in a public, open access repository. We provide data and code to reproduce this paper at https://github.com/mkiang/county_preparedness/.