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Methods for modelling excess mortality across England during the COVID-19 pandemic

Lookup NU author(s): Emeritus Professor Paul BurtonORCiD

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Abstract

© The Author(s) 2021.Excess mortality is an important measure of the scale of the coronavirus-2019 pandemic. It includes both deaths caused directly by the pandemic, and deaths caused by the unintended consequences of containment such as delays to accessing care or postponements of healthcare provision in the population. In 2020 and 2021, in England, multiple groups have produced measures of excess mortality during the pandemic. This paper describes the data and methods used in five different approaches to estimating excess mortality and compares their estimates. The fundamental principles of estimating excess mortality are described, as well as the key commonalities and differences between five approaches. Two of these are based on the date of registration: a quasi-Poisson model with offset and a 5-year average; and three are based on date of occurrence: a Poisson model without offset, the European monitoring of excess mortality model and a synthetic controls model. Comparisons between estimates of excess mortality are made for the period March 2020 through March 2021 and for the two waves of the pandemic that occur within that time-period. Model estimates are strikingly similar during the first wave of the pandemic though larger differences are observed during the second wave. Models that adjusted for reduced circulation of winter infection produced higher estimates of excess compared with those that did not. Models that do not adjust for reduced circulation of winter infection captured the effect of reduced winter illness as a result of mobility restrictions during the period. None of the estimates captured mortality displacement and therefore may underestimate excess at the current time, though the extent to which this has occurred is not yet identified. Models use different approaches to address variation in data availability and stakeholder requirements of the measure. Variation between estimates reflects differences in the date of interest, population denominators and parameters in the model relating to seasonality and trend.

Publication metadata

Author(s): Barnard S, Chiavenna C, Fox S, Charlett A, Waller Z, Andrews N, Goldblatt P, Burton P, De Angelis D

Publication type: Article

Publication status: Published

Journal: Statistical Methods in Medical Research

Year: 2022

Volume: 31

Issue: 9

Pages: 1790-1802

Print publication date: 01/09/2022

Online publication date: 23/10/2021

Acceptance date: 02/04/2018

ISSN (print): 0962-2802

ISSN (electronic): 1477-0334

Publisher: Sage Publications Ltd

URL: https://doi.org/10.1177/09622802211046384

DOI: 10.1177/09622802211046384

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