Journal
NATURE MEDICINE
Volume 27, Issue 2, Pages 264-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41591-020-01191-8
Keywords
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Funding
- Bloomberg Distinguished Professorship endowment
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The newly developed COVID-19 risk calculator for the US population provides estimates of risk for individuals and communities, incorporating socioeconomic factors, demography, pre-existing conditions, and mortality rates by age and ethnicity. By applying various risk factor distribution data to the model, it is possible to predict the COVID-19 risk for different cities and counties for the elderly population aged 65 and older. Validation analyses show that the model is well calibrated for the US population, and it can identify populations at higher risk of experiencing a disproportionately large number of deaths.
A new risk calculator calibrated specifically for the US population provides estimates of risk for COVID-19 for individuals and at the community level, using socioeconomic factors, demography, pre-existing conditions and mortality rates by age and ethnicity. Reducing COVID-19 burden for populations will require equitable and effective risk-based allocations of scarce preventive resources, including vaccinations(1). 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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