Spatial and neighborhood data in the collaborative cohort of cohorts for COVID-19 Research (C4R)

Neighborhood factors, encompassing social, built, and natural environments, may explain geographic differences in the impact of COVID-19 pandemic on populations. Data from pre-existing national, population-based cohorts could be leveraged to better understand how pre-existing conditions (both individual and neighborhood) contribute to risk factor development and disease progression. We catalogued spatial and neighborhood data in the Collaborative Cohort of Cohorts for COVID-19 Research (C4R), comprising 14 diverse US cohorts (>50,000 participants). The C4R sample is generally spatially and socially representative of the overall nation, with C4R’s calculated spatial coverage representing 28% of US land area and 52% of the total US population. However, C4R (vs. non C4R) areas were more urban, wealthy, with more foreign-born residents, and less car-dependent with lower proportion employed and green. Twelve cohorts collected neighborhood characteristics – most commonly social environment data on neighborhood socioeconomic status– based on participants’ addresses. The most common built environment measures were related to food access, followed by other destination-based measures such as walkability. Natural environment data were available in the fewest cohorts, with emphasis on air quality or greenspace. This work provides clarity on available neighborhood and spatial data and facilitates future harmonization of data from C4R cohorts. Ultimately, this may enable future longitudinal and comparative analyses of neighborhood influences on COVID-19.

Authors

Publication Details

Journal
PLoS ONE
Published
2026-07-22
DOI
https://doi.org/10.1371/journal.pone.0352170
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
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article

Spatial and neighborhood data in the collaborative cohort of cohorts for COVID-19 Research (C4R)

Nicole L. Spartano, Jason P. Block, Earle C. Chambers, Sharrelle Barber et al.
PLoS ONE
COVID-19 epidemiological studies
article

Spatial and neighborhood data in the collaborative cohort of cohorts for COVID-19 Research (C4R)

Nicole L. Spartano, Jason P. Block, Earle C. Chambers, Sharrelle Barber, Kiarri N. Kershaw, Cameron J. Reimer, Lori A. Bateman, Yvonne L. Michael, Mitchell S.V. Elkind, Stephen T. Dickinson, Marcia Pescador Jimenez, Tatjana Rundek, Norrina B. Allen, Carmen R. Isasi, Gabriela R. Oates, Alka M. Kanaya, Leslie A. McClure, Daniel A. Rodrı́guez, Jana A. Hirsch, Katherine Lawin, Elizabeth C. Oelsner, Russell G. Buhr, Joel D. Kaufman, Yu Zhang, Talea Cornelius, Lilah M. Besser, Sandra S. Albrecht, Ana V. Diez Roux, Linda C. Gallo, Namratha R. Kandula, Suzanne E. Judd, Brenda R. Phillips, Jessica A. Reese, Sharon Stein Merkin, Joyce Lee, Hoda S Abdel Magid, Anna M. Kucharska‐Newton, James Crooks, Jose Gutierrez, Lauren B. Beach, Pallavi Balte, Gina S. Lovasi, Steven Melly, Erin R. Kulick, Mario Sims, Stephen T. Francisco, Penny Gordon-Larsen, Dave Mauger, Elizabeth A. Regan, Scott C. Brown, Michael Schembri, Peter James, Carla Wilson
article en

Abstract

Neighborhood factors, encompassing social, built, and natural environments, may explain geographic differences in the impact of COVID-19 pandemic on populations. Data from pre-existing national, population-based cohorts could be leveraged to better understand how pre-existing conditions (both individual and neighborhood) contribute to risk factor development and disease progression. We catalogued spatial and neighborhood data in the Collaborative Cohort of Cohorts for COVID-19 Research (C4R), comprising 14 diverse US cohorts (>50,000 participants). The C4R sample is generally spatially and socially representative of the overall nation, with C4R’s calculated spatial coverage representing 28% of US land area and 52% of the total US population. However, C4R (vs. non C4R) areas were more urban, wealthy, with more foreign-born residents, and less car-dependent with lower proportion employed and green. Twelve cohorts collected neighborhood characteristics – most commonly social environment data on neighborhood socioeconomic status– based on participants’ addresses. The most common built environment measures were related to food access, followed by other destination-based measures such as walkability. Natural environment data were available in the fewest cohorts, with emphasis on air quality or greenspace. This work provides clarity on available neighborhood and spatial data and facilitates future harmonization of data from C4R cohorts. Ultimately, this may enable future longitudinal and comparative analyses of neighborhood influences on COVID-19.

PLoS ONEVol. 21(7)
Openalex Percentile: Top 9%
COVID-19 epidemiological studies
DOIOpen Access PDFOpenAlex
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