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Pitfalls and solutions in case fatality risk estimation – A multi-country analysis on the effects of demographics, surveillance, time lags between case reports and deaths and healthcare system capacity on COVID-19 CFR estimates

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  • Patrizio Vanella
  • Christian Wiessner
  • Anja Holz
  • Gérard Krause
  • Annika Möhl
  • Sarah Wiegel
  • Berit Lange
  • Heiko Becher

Abstract

Across European countries, there have been large differences in COVID-19 case fatality risk (CFR) estimates, and considerable variation in these estimates over time. CFR estimates vary depending on both the method used for estimation and countryspecific characteristics. While crude methods simply use cumulative total numbers of cases and deaths, the CFR can be influenced by the demographic characteristics of the cases, the case detection rates, the time lags between the reporting of infections and deaths and infrastructure characteristics, such as healthcare capacities. We use publicly available weekly data for 11 European countries on the COVID- 19 case and death numbers by age group for the year 2020. Moreover, we use data on national weekly test rates to adjust the case numbers, and to investigate the effects of different time lags between the reporting of cases and deaths on the estimation of CFRs. Finally, we describe the association between case fatality rates and the demand for hospital and intensive care unit beds for COVID-19 cases, while taking into account national bed capacities. The crude CFR estimates differ considerably across the investigated countries. In the crude international CFR time series, the differences are smaller when adjusting for the demographics of the cases. Differences in testing policies significantly affect the CFR estimates as well. However, the question of precisely how these testing procedures should be adjusted requires further investigation. Lag adjustments of CFRs do not lead to improvements in estimates of COVID-19 CFRs, and no connection between hospital capacities and CFRs can be found for the countries included in our study.

Suggested Citation

  • Patrizio Vanella & Christian Wiessner & Anja Holz & Gérard Krause & Annika Möhl & Sarah Wiegel & Berit Lange & Heiko Becher, 2022. "Pitfalls and solutions in case fatality risk estimation – A multi-country analysis on the effects of demographics, surveillance, time lags between case reports and deaths and healthcare system capacit," Vienna Yearbook of Population Research, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna, vol. 20(1), pages 167-193.
  • Handle: RePEc:vid:yearbk:v:20:y:2022:i:1:oid:0x003d3ca1
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    1. Nicholas G. Reich & Justin Lessler & Derek A. T. Cummings & Ron Brookmeyer, 2012. "Estimating Absolute and Relative Case Fatality Ratios from Infectious Disease Surveillance Data," Biometrics, The International Biometric Society, vol. 68(2), pages 598-606, June.
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    Cited by:

    1. Florian Dorn & Sahamoddin Khailaie & Marc Stoeckli & Sebastian C. Binder & Tanmay Mitra & Berit Lange & Stefan Lautenbacher & Andreas Peichl & Patrizio Vanella & Timo Wollmershäuser & Clemens Fuest & , 2023. "The common interests of health protection and the economy: evidence from scenario calculations of COVID-19 containment policies," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 24(1), pages 67-74, February.
    2. Patrizio Vanella & Christina Benita Wilke & Doris Söhnlein, 2022. "Prevalence and Economic Costs of Absenteeism in an Aging Population—A Quasi-Stochastic Projection for Germany," Forecasting, MDPI, vol. 4(1), pages 1-23, March.
    3. Dorn, Florian & Lange, Berit & Braml, Martin & Gstrein, David & Nyirenda, John L.Z. & Vanella, Patrizio & Winter, Joachim & Fuest, Clemens & Krause, Gérard, 2023. "The challenge of estimating the direct and indirect effects of COVID-19 interventions – Toward an integrated economic and epidemiological approach," Economics & Human Biology, Elsevier, vol. 49(C).

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