1. Epidemiology and Global Health
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Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset

  1. Ariel Karlinsky  Is a corresponding author
  2. Dmitry Kobak  Is a corresponding author
  1. Hebrew University, Israel
  2. University of Tübingen, Germany
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Cite this article as: eLife 2021;10:e69336 doi: 10.7554/eLife.69336

Abstract

Comparing the impact of the COVID-19 pandemic between countries or across time is difficult because the reported numbers of cases and deaths can be strongly affected by testing capacity and reporting policy. Excess mortality, defined as the increase in all-cause mortality relative to the expected mortality, is widely considered as a more objective indicator of the COVID-19 death toll. However, there has been no global, frequently-updated repository of the all-cause mortality data across countries. To fill this gap, we have collected weekly, monthly, or quarterly all-cause mortality data from 94 countries and territories, openly available as the regularly-updated World Mortality Dataset. We used this dataset to compute the excess mortality in each country during the COVID-19 pandemic. We found that in several worst-affected countries (Peru, Ecuador, Bolivia, Mexico) the excess mortality was above 50% of the expected annual mortality. At the same time, in several other countries (Australia, New Zealand) mortality during the pandemic was below the usual level, presumably due to social distancing measures decreasing the non-COVID infectious mortality. Furthermore, we found that while many countries have been reporting the COVID-19 deaths very accurately, some countries have been substantially underreporting their COVID-19 deaths (e.g. Nicaragua, Russia, Uzbekistan), sometimes by two orders of magnitude (Tajikistan). Our results highlight the importance of open and rapid all-cause mortality reporting for pandemic monitoring.

Data availability

Full data is publicly available at: https://github.com/akarlinsky/world_mortality

The following data sets were generated

Article and author information

Author details

  1. Ariel Karlinsky

    Economics, Hebrew University, Jerusalem, Israel
    For correspondence
    ariel.karlinsky@mail.huji.ac.il
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0966-5837
  2. Dmitry Kobak

    Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
    For correspondence
    dmitry.kobak@uni-tuebingen.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5639-7209

Funding

DK was supported by the Deutsche Forschungsgemeinschaft (BE5601/4-1 and the Cluster of Excellence ``Machine Learning --- New Perspectives for Science', EXC 2064, project number 390727645), the Federal Ministry of Education and Research (FKZ 01GQ1601 and 01IS18039A) and the National Institute of Mental Health of the National Institutes of Health under Award Number U19MH114830. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Reviewing Editor

  1. Marc Lipsitch, Harvard TH Chan School of Public Health, United States

Publication history

  1. Received: April 13, 2021
  2. Accepted: June 29, 2021
  3. Accepted Manuscript published: June 30, 2021 (version 1)
  4. Accepted Manuscript updated: July 7, 2021 (version 2)
  5. Version of Record published: August 3, 2021 (version 3)

Copyright

© 2021, Karlinsky & Kobak

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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Further reading

    1. Epidemiology and Global Health
    2. Medicine
    3. Microbiology and Infectious Disease
    Edited by Diane M Harper et al.
    Collection

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    Background:

    Monitoring malaria transmission is a critical component of efforts to achieve targets for elimination and eradication. Two commonly monitored metrics of transmission intensity are parasite prevalence (PR) and the entomological inoculation rate (EIR). Comparing the spatial and temporal variations in the PR and EIR of a given geographical region and modelling the relationship between the two metrics may provide a fuller picture of the malaria epidemiology of the region to inform control activities.

    Methods:

    Using geostatistical methods, we compare the spatial and temporal patterns of Plasmodium falciparum EIR and PR using data collected over 38 months in a rural area of Malawi. We then quantify the relationship between EIR and PR by using empirical and mechanistic statistical models.

    Results:

    Hotspots identified through the EIR and PR partly overlapped during high transmission seasons but not during low transmission seasons. The estimated relationship showed a 1-month delayed effect of EIR on PR such that at lower levels of EIR, increases in EIR are associated with rapid rise in PR, whereas at higher levels of EIR, changes in EIR do not translate into notable changes in PR.

    Conclusions:

    Our study emphasises the need for integrated malaria control strategies that combine vector and human host managements monitored by both entomological and parasitaemia indices.

    Funding:

    This work was supported by Stichting Dioraphte grant number 13050800.