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Sensitivity Analysis of Excess Mortality due to the Covid-19 Pandemic

Bibliographic Data

ID4120836
AuthorsMarília R Nepomuceno (0000-0002-5687-5776), Ilya Klimkin (0000-0003-0200-2467), Dmitri A Jdanov (0000-0003-4633-2011), Ainhoa Alustiza‐galarza (0000-0001-6608-4815), Vladimir M Shkolnikov (0000-0003-2259-5423)
Year2022
Volume48
Issue2
Pages279-302
Publication date2022-06-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenuePopulation and Development Review (JOURNAL)
Journal identifiersISSN: 0098-7921 • E-ISSN: 1728-4457
PublisherWiley (PUBLISHER • GB)
DOI10.1111/padr.12475
PMID35600716
OpenAlexW4214810733
LanguageEN
Citations received24
References cited32

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

Baseline (sea · Biology · Coronavirus disease 2019 (COVID-19 · Disease · Econometrics · Economics · Excess mortality · Index (typography · Metric (unit · Mortality rate · Operations management · Pandemic · Sensitivity (control systems · Statistics · Climate Change and Health Impacts · Computer Science · Demography · Engineering · Global Health Care Issues · Insurance, Mortality, Demography, Risk Management · Internal Medicine · Mathematics · Medicine

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Unique citing works24
Citations per year6
Citation span2022 - 2026 (5)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 23
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