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Comparison of methods for predicting COVID-19-related death in the general population using the OpenSAFELY platform

This paper evaluate different methods for predicting COVID-19-related death using the OpenSAFELY platform

Diagnostic and Prognostic Research, 2022



Obtaining accurate estimates of the risk of COVID-19-related death in the general population is challenging in the context of changing levels of circulating infection.


We propose a modelling approach to predict 28-day COVID-19-related death which explicitly accounts for COVID-19 infection prevalence using a series of sub-studies from new landmark times incorporating time-updating proxy measures of COVID-19 infection prevalence. This was compared with an approach ignoring infection prevalence.

The target population was adults registered at a general practice in England in March 2020. The outcome was 28-day COVID-19-related death. Predictors included demographic characteristics and comorbidities. Three proxies of local infection prevalence were used: model-based estimates, rate of COVID-19-related attendances in emergency care, and rate of suspected COVID-19 cases in primary care.

We used data within the TPP SystmOne electronic health record system linked to Office for National Statistics mortality data, using the OpenSAFELY platform, working on behalf of NHS England.

Prediction models were developed in case-cohort samples with a 100-day follow-up. Validation was undertaken in 28-day cohorts from the target population. We considered predictive performance (discrimination and calibration) in geographical and temporal subsets of data not used in developing the risk prediction models. Simple models were contrasted to models including a full range of predictors.


Prediction models were developed on 11,972,947 individuals, of whom 7999 experienced COVID-19-related death. All models discriminated well between individuals who did and did not experience the outcome, including simple models adjusting only for basic demographics and number of comorbidities: C-statistics 0.92–0.94. However, absolute risk estimates were substantially miscalibrated when infection prevalence was not explicitly modelled.


Our proposed models allow absolute risk estimation in the context of changing infection prevalence but predictive performance is sensitive to the proxy for infection prevalence. Simple models can provide excellent discrimination and may simplify implementation of risk prediction tools.

  • Elizabeth Williamson,
  • John Tazare,
  • Krishnan Bhaskaran,
  • Helen McDonald,
  • Alex Walker,
  • Laurie Tomlinson,
  • Kevin Wing,
  • Seb Bacon,
  • Chris Bates,
  • Helen Curtis,
  • Harriet Forbes,
  • Caroline Minassian,
  • Caroline Morton,
  • Emily Nightingale,
  • Amir Mehrkar,
  • Dave Evans,
  • Brian Nicholson,
  • David Leon,
  • Peter Inglesby,
  • Brian MacKenna,
  • Nicholas Davies,
  • Nick DeVito,
  • Henry Drysdale,
  • Jonathan Cockburn,
  • Will Hulme,
  • Jess Morley,
  • Ian Douglas,
  • Christopher Rentsch,
  • Rohini Mathur,
  • Angel Wong,
  • Anna Schultze,
  • Richard Croker,
  • John Parry,
  • Frank Hester,
  • Sam Harper,
  • Richard Grieve,
  • David Harrison,
  • Ewout Steyerberg,
  • Rosalind Eggo,
  • Karla Diaz-Ordaz,
  • Ruth Keogh,
  • Stephen Evans,
  • Liam Smeeth,
  • Ben Goldacre
The OpenSAFELY Collaborative., Williamson, E.J., Tazare, J. et al. Comparison of methods for predicting COVID-19-related death in the general population using the OpenSAFELY platform. Diagn Progn Res 6, 6 (2022).