Sensitivity Analysis of Excess Mortality due to the COVID‐19 Pandemic
Estimating excess mortality is challenging. The metric depends on the expected mortality level, which can differ based on given choices, such as the method and the time series length used to estimate the baseline. However, these choices are often arbitrary, and are not subject to any sensitivity ana...
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Published in | Population and development review Vol. 48; no. 2; pp. 279 - 302 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Blackwell Publishing Ltd
01.06.2022
John Wiley and Sons Inc |
Subjects | |
Online Access | Get full text |
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Summary: | Estimating excess mortality is challenging. The metric depends on the expected mortality level, which can differ based on given choices, such as the method and the time series length used to estimate the baseline. However, these choices are often arbitrary, and are not subject to any sensitivity analysis. We bring to light the importance of carefully choosing the inputs and methods used to estimate excess mortality. Drawing on data from 26 countries, we investigate how sensitive excess mortality is to the choice of the mortality index, the number of years included in the reference period, the method, and the time unit of the death series. We employ two mortality indices, three reference periods, two data time units, and four methods for estimating the baseline. We show that excess mortality estimates can vary substantially when these factors are changed, and that the largest variations stem from the choice of the mortality index and the method. We also find that the magnitude of the variation in excess mortality is country‐specific, resulting in cross‐country rankings changes. Finally, based on our findings, we provide guidelines for estimating excess mortality. |
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Bibliography: | Marília R. Nepomuceno, Ainhoa Alustiza Galarza, Laboratory of Demographic Data, Max Planck Institute for Demographic Research, Rostock, Germany. Ilya Klimkin, National Research University Higher School of Economics, Moscow, Russia. Dmitri A. Jdanov, Vladimir M. Shkolnikov, Laboratory of Demographic Data, Max Planck Institute for Demographic Research, Rostock, Germany. National Research University Higher School of Economics, Moscow, Russia. Email nepomuceno@demogr.mpg.de ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Marília R. Nepomuceno, Ainhoa Alustiza Galarza, Laboratory of Demographic Data, Max Planck Institute for Demographic Research, Rostock, Germany. Ilya Klimkin, National Research University Higher School of Economics, Moscow, Russia. Dmitri A. Jdanov, Vladimir M. Shkolnikov, Laboratory of Demographic Data, Max Planck Institute for Demographic Research, Rostock, Germany. National Research University Higher School of Economics, Moscow, Russia. Email: nepomuceno@demogr.mpg.de |
ISSN: | 0098-7921 1728-4457 |
DOI: | 10.1111/padr.12475 |