Justin C Sandefur’s research while affiliated with Center for Global Development and other places

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Publications (1)


cIFRs, adjusted for health system capacity, by country income group (log scale). cIFRs, infection fatality rates conditional on age, sex and comorbidity; HICs, high-income countries; LICs, low-income countries; LMICs, lower middle-income countries; UMICs, upper middle-incomecountries.
Infection fatality ratio (IFR) by world region. Column 1 states total population in millions for each region. Column 2 reports population by 10-year age groups and by number of comorbidities (light grey: 0 comorbidity; dark grey: any comorbidity); the height of the graphs is proportional to the number of people in the most populous age group. Column 3 reports (a) regional IFRs calculated as an average of the IFRs conditional on age, sex and comorbidity weighted by the proportion of the population in each age, sex and comorbidity group and (b) regional IFRs adjusted for health system capacity (see Section Adjusting for differences in health system capacity).
Validation with independently estimated infection fatalityrates (IFRs). (A) Random sample studies, representative of large proportion of country’s population. (B) All studies included in Meyerowitz-Katz and Merone 17 or found through online search.
Predicted COVID-19 fatality rates based on age, sex, comorbidities and health system capacity
  • Literature Review
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September 2020

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116 Citations

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Justin C Sandefur

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Tessa Bold

Early reports suggest the fatality rate from COVID-19 varies greatly across countries, but non-random testing and incomplete vital registration systems render it impossible to directly estimate the infection fatality rate (IFR) in many low- and middle-income countries. To fill this gap, we estimate the adjustments required to extrapolate estimates of the IFR from high-income to lower-income regions. Accounting for differences in the distribution of age, sex and relevant comorbidities yields substantial differences in the predicted IFR across 21 world regions, ranging from 0.11% in Western Sub-Saharan Africa to 1.07% for high-income Asia Pacific. However, these predictions must be treated as lower bounds in low- and middle-income countries as they are grounded in fatality rates from countries with advanced health systems. To adjust for health system capacity, we incorporate regional differences in the relative odds of infection fatality from childhood respiratory syncytial virus. This adjustment greatly diminishes but does not entirely erase the demography-based advantage predicted in the lowest income settings, with regional estimates of the predicted COVID-19 IFR ranging from 0.37% in Western Sub-Saharan Africa to 1.45% for Eastern Europe.

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Citations (1)


... Once the initial calibration for mortality (number of COVID-19-related deaths) had been completed, with the model generating the expected number of deaths for England and adjusted to the full UK population, the number of incident cases was calibrated. As the relationship between incidence and mortality in the model was based on infection fatality rates estimated in a 2020 article by Ghisolfi et al. [36], once the mortality outputs had been calibrated, the modelled number of incident cases was expected to be consistent with the reported incidence data. Having confirmed that this was the case for England, with modelled estimates a close approximation of the observed incidence (8,651,622 modelled versus 8,651,710 reported), we proceeded to calibrate the model to generate the expected number of cases for the UK, taking account of the difference in population between England and the UK. ...

Reference:

Cost-Effectiveness of Introducing Nuvaxovid to COVID-19 Vaccination in the United Kingdom: A Dynamic Transmission Model
Predicted COVID-19 fatality rates based on age, sex, comorbidities and health system capacity