TY - JOUR
T1 - A county-level susceptibility index and coronavirus disease 2019 mortality in the United States
T2 - A socioecological study
AU - Khan, Sadiya S.
AU - McCabe, Megan E.
AU - Krefman, Amy E.
AU - Petito, Lucia Catherine
AU - Yang, Xiaoyun
AU - Kershaw, Kiarri N.
AU - Pool, Lindsay R
AU - Allen, Norrina B.
N1 - Publisher Copyright:
The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/7/6
Y1 - 2020/7/6
N2 - As of June 2020, the United States (US) has experienced the highest number of deaths related to coronavirus disease 2019 (Covid-19) in the world, but significant geographic heterogeneity exists at the county-level. Therefore, we sought to classify counties in the United States across multiple domains utilizing a socioecological framework and examine the association between these county-level groups and Covid-19 mortality. We harmonized and linked county-level sociodemographic, health, and environmental metrics associated with increased susceptibility for Covid-19 mortality. Latent class analysis defined a county-level susceptibility index (CSI) based on these metrics (n=2701 counties). Next, we used linear regression models to estimate the associations of the CSI and Covid-19 deaths per capita and initial mortality doubling time (as of 6/2/20), adjusted for days since first Covid-19 case. We identified 4 groups classified by the CSI with distinct sociodemographic, health, and environmental profiles and widespread geographic dispersion. Covid-19 deaths per capita were significantly higher in the group consisting of rural, vulnerable counties (55.8 [95% CI 50.3-61.2] deaths per 100,000) compared with the group with diverse, urban counties (32.2 [27.3-37.0]) at similar points in the outbreak (76 days since first case). Our findings can inform equitable resource allocation for Covid-19 to allow targeted public health preparedness and response in vulnerable counties.
AB - As of June 2020, the United States (US) has experienced the highest number of deaths related to coronavirus disease 2019 (Covid-19) in the world, but significant geographic heterogeneity exists at the county-level. Therefore, we sought to classify counties in the United States across multiple domains utilizing a socioecological framework and examine the association between these county-level groups and Covid-19 mortality. We harmonized and linked county-level sociodemographic, health, and environmental metrics associated with increased susceptibility for Covid-19 mortality. Latent class analysis defined a county-level susceptibility index (CSI) based on these metrics (n=2701 counties). Next, we used linear regression models to estimate the associations of the CSI and Covid-19 deaths per capita and initial mortality doubling time (as of 6/2/20), adjusted for days since first Covid-19 case. We identified 4 groups classified by the CSI with distinct sociodemographic, health, and environmental profiles and widespread geographic dispersion. Covid-19 deaths per capita were significantly higher in the group consisting of rural, vulnerable counties (55.8 [95% CI 50.3-61.2] deaths per 100,000) compared with the group with diverse, urban counties (32.2 [27.3-37.0]) at similar points in the outbreak (76 days since first case). Our findings can inform equitable resource allocation for Covid-19 to allow targeted public health preparedness and response in vulnerable counties.
KW - Communicable diseases
KW - County-level health
KW - Covid-19
KW - Pandemic
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U2 - 10.1101/2020.07.04.20146084
DO - 10.1101/2020.07.04.20146084
M3 - Article
AN - SCOPUS:85098614161
JO - Free Radical Biology and Medicine
JF - Free Radical Biology and Medicine
SN - 0891-5849
ER -