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Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes

BACKGROUND: The SARS-CoV-2 pandemic has disproportionately affected racial and ethnic minority communities across the United States. We sought to disentangle individual and census tract-level sociodemographic and economic factors associated with these disparities. METHODS AND FINDINGS: All adults te...

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Autores principales: Cromer, Sara J., Lakhani, Chirag M., Wexler, Deborah J, Burnett-Bowie, Sherri-Ann M., Udler, Miriam, Patel, Chirag J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7536884/
https://www.ncbi.nlm.nih.gov/pubmed/33024982
http://dx.doi.org/10.1101/2020.09.30.20201830
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author Cromer, Sara J.
Lakhani, Chirag M.
Wexler, Deborah J
Burnett-Bowie, Sherri-Ann M.
Udler, Miriam
Patel, Chirag J.
author_facet Cromer, Sara J.
Lakhani, Chirag M.
Wexler, Deborah J
Burnett-Bowie, Sherri-Ann M.
Udler, Miriam
Patel, Chirag J.
author_sort Cromer, Sara J.
collection PubMed
description BACKGROUND: The SARS-CoV-2 pandemic has disproportionately affected racial and ethnic minority communities across the United States. We sought to disentangle individual and census tract-level sociodemographic and economic factors associated with these disparities. METHODS AND FINDINGS: All adults tested for SARS-CoV-2 between February 1 and June 21, 2020 were geocoded to a census tract based on their address; hospital employees and individuals with invalid addresses were excluded. Individual (age, sex, race/ethnicity, preferred language, insurance) and census tract-level (demographics, insurance, income, education, employment, occupation, household crowding and occupancy, built home environment, and transportation) variables were analyzed using linear mixed models predicting infection, hospitalization, and death from SARS-CoV-2. Among 57,865 individuals, per capita testing rates, individual (older age, male sex, non-White race, non-English preferred language, and non-private insurance), and census tract-level (increased population density, higher household occupancy, and lower education) measures were associated with likelihood of infection. Among those infected, individual age, sex, race, language, and insurance, and census tract-level measures of lower education, more multi-family homes, and extreme household crowding were associated with increased likelihood of hospitalization, while higher per capita testing rates were associated with decreased likelihood. Only individual-level variables (older age, male sex, Medicare insurance) were associated with increased mortality among those hospitalized. CONCLUSIONS: This study of the first wave of the SARS-CoV-2 pandemic in a major U.S. city presents the cascade of outcomes following SARS-CoV-2 infection within a large, multi-ethnic cohort. SARS-CoV-2 infection and hospitalization rates, but not death rates among those hospitalized, are related to census tract-level socioeconomic characteristics including lower educational attainment and higher household crowding and occupancy, but not neighborhood measures of race, independent of individual factors.
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spelling pubmed-75368842020-10-07 Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes Cromer, Sara J. Lakhani, Chirag M. Wexler, Deborah J Burnett-Bowie, Sherri-Ann M. Udler, Miriam Patel, Chirag J. medRxiv Article BACKGROUND: The SARS-CoV-2 pandemic has disproportionately affected racial and ethnic minority communities across the United States. We sought to disentangle individual and census tract-level sociodemographic and economic factors associated with these disparities. METHODS AND FINDINGS: All adults tested for SARS-CoV-2 between February 1 and June 21, 2020 were geocoded to a census tract based on their address; hospital employees and individuals with invalid addresses were excluded. Individual (age, sex, race/ethnicity, preferred language, insurance) and census tract-level (demographics, insurance, income, education, employment, occupation, household crowding and occupancy, built home environment, and transportation) variables were analyzed using linear mixed models predicting infection, hospitalization, and death from SARS-CoV-2. Among 57,865 individuals, per capita testing rates, individual (older age, male sex, non-White race, non-English preferred language, and non-private insurance), and census tract-level (increased population density, higher household occupancy, and lower education) measures were associated with likelihood of infection. Among those infected, individual age, sex, race, language, and insurance, and census tract-level measures of lower education, more multi-family homes, and extreme household crowding were associated with increased likelihood of hospitalization, while higher per capita testing rates were associated with decreased likelihood. Only individual-level variables (older age, male sex, Medicare insurance) were associated with increased mortality among those hospitalized. CONCLUSIONS: This study of the first wave of the SARS-CoV-2 pandemic in a major U.S. city presents the cascade of outcomes following SARS-CoV-2 infection within a large, multi-ethnic cohort. SARS-CoV-2 infection and hospitalization rates, but not death rates among those hospitalized, are related to census tract-level socioeconomic characteristics including lower educational attainment and higher household crowding and occupancy, but not neighborhood measures of race, independent of individual factors. Cold Spring Harbor Laboratory 2020-09-30 /pmc/articles/PMC7536884/ /pubmed/33024982 http://dx.doi.org/10.1101/2020.09.30.20201830 Text en http://creativecommons.org/licenses/by/4.0/It is made available under a CC-BY 4.0 International license (http://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Cromer, Sara J.
Lakhani, Chirag M.
Wexler, Deborah J
Burnett-Bowie, Sherri-Ann M.
Udler, Miriam
Patel, Chirag J.
Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes
title Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes
title_full Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes
title_fullStr Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes
title_full_unstemmed Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes
title_short Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes
title_sort geospatial analysis of individual and community-level socioeconomic factors impacting sars-cov-2 prevalence and outcomes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7536884/
https://www.ncbi.nlm.nih.gov/pubmed/33024982
http://dx.doi.org/10.1101/2020.09.30.20201830
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