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A linear mixed model to estimate COVID‐19‐induced excess mortality

Author

Listed:
  • Johan Verbeeck
  • Christel Faes
  • Thomas Neyens
  • Niel Hens
  • Geert Verbeke
  • Patrick Deboosere
  • Geert Molenberghs

Abstract

The Corona Virus Disease (COVID‐19) pandemic has increased mortality in countries worldwide. To evaluate the impact of the pandemic on mortality, the use of excess mortality rather than reported COVID‐19 deaths has been suggested. Excess mortality, however, requires estimation of mortality under nonpandemic conditions. Although many methods exist to forecast mortality, they are either complex to apply, require many sources of information, ignore serial correlation, and/or are influenced by historical excess mortality. We propose a linear mixed model that is easy to apply, requires only historical mortality data, allows for serial correlation, and down‐weighs the influence of historical excess mortality. Appropriateness of the linear mixed model is evaluated with fit statistics and forecasting accuracy measures for Belgium and the Netherlands. Unlike the commonly used 5‐year weekly average, the linear mixed model is forecasting the year‐specific mortality, and as a result improves the estimation of excess mortality for Belgium and the Netherlands.

Suggested Citation

  • Johan Verbeeck & Christel Faes & Thomas Neyens & Niel Hens & Geert Verbeke & Patrick Deboosere & Geert Molenberghs, 2023. "A linear mixed model to estimate COVID‐19‐induced excess mortality," Biometrics, The International Biometric Society, vol. 79(1), pages 417-425, March.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:1:p:417-425
    DOI: 10.1111/biom.13578
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    References listed on IDEAS

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    3. Harvey, A C & Todd, P H J, 1983. "Forecasting Economic Time Series with Structural and Box-Jenkins Models: A Case Study: Response," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(4), pages 313-315, October.
    4. Bianca Cox & Françoise Wuillaume & Herman Oyen & Sophie Maes, 2010. "Monitoring of all-cause mortality in Belgium (Be-MOMO): a new and automated system for the early detection and quantification of the mortality impact of public health events," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 55(4), pages 251-259, August.
    5. David Morgan & Junya Ino & Gabriel Di Paolantonio & Fabrice Murtin, 2020. "Excess mortality: Measuring the direct and indirect impact of COVID-19," OECD Health Working Papers 122, OECD Publishing.
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