TY - JOUR
T1 - Individual and community-level risk for COVID-19 mortality in the United States
AU - Jin, Jin
AU - Agarwala, Neha
AU - Kundu, Prosenjit
AU - Harvey, Benjamin
AU - Zhang, Yuqi
AU - Wallace, Eliza
AU - Chatterjee, Nilanjan
N1 - Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc.
PY - 2021/2
Y1 - 2021/2
N2 - Reducing COVID-19 burden for populations will require equitable and effective risk-based allocations of scarce preventive resources, including vaccinations1. To aid in this effort, we developed a general population risk calculator for COVID-19 mortality based on various sociodemographic factors and pre-existing conditions for the US population, combining information from the UK-based OpenSAFELY study with mortality rates by age and ethnicity across US states. We tailored the tool to produce absolute risk estimates in future time frames by incorporating information on pandemic dynamics at the community level. We applied the model to data on risk factor distribution from a variety of sources to project risk for the general adult population across 477 US cities and for the Medicare population aged 65 years and older across 3,113 US counties, respectively. Validation analyses using 54,444 deaths from 7 June to 1 October 2020 show that the model is well calibrated for the US population. Projections show that the model can identify relatively small fractions of the population (for example 4.3%) that might experience a disproportionately large number of deaths (for example 48.7%), but there is wide variation in risk across communities. We provide a web-based risk calculator and interactive maps for viewing community-level risks.
AB - Reducing COVID-19 burden for populations will require equitable and effective risk-based allocations of scarce preventive resources, including vaccinations1. To aid in this effort, we developed a general population risk calculator for COVID-19 mortality based on various sociodemographic factors and pre-existing conditions for the US population, combining information from the UK-based OpenSAFELY study with mortality rates by age and ethnicity across US states. We tailored the tool to produce absolute risk estimates in future time frames by incorporating information on pandemic dynamics at the community level. We applied the model to data on risk factor distribution from a variety of sources to project risk for the general adult population across 477 US cities and for the Medicare population aged 65 years and older across 3,113 US counties, respectively. Validation analyses using 54,444 deaths from 7 June to 1 October 2020 show that the model is well calibrated for the US population. Projections show that the model can identify relatively small fractions of the population (for example 4.3%) that might experience a disproportionately large number of deaths (for example 48.7%), but there is wide variation in risk across communities. We provide a web-based risk calculator and interactive maps for viewing community-level risks.
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U2 - 10.1038/s41591-020-01191-8
DO - 10.1038/s41591-020-01191-8
M3 - Article
C2 - 33311702
AN - SCOPUS:85101461066
SN - 1078-8956
VL - 27
SP - 264
EP - 269
JO - Nature medicine
JF - Nature medicine
IS - 2
ER -