Excess mortality during COVID-19 in five European countries and a critique of mortality data analysis

  1. J. Félix-Cardoso
  2. H. Vasconcelos
  3. P. Pereira Rodrigues
  4. R. Cruz-Correia

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

INTRODUCTION

The COVID-19 pandemic is an ongoing event disrupting lives, health systems, and economies worldwide. Clear data about the pandemic’s impact is lacking, namely regarding mortality. This work aims to study the impact of COVID-19 through the analysis of all-cause mortality data made available by different European countries, and to critique their mortality surveillance data.

METHODS

European countries that had publicly available data about the number of deaths per day/week were selected (England and Wales, France, Italy, Netherlands and Portugal). Two different methods were selected to estimate the excess mortality due to COVID19: (DEV) deviation from the expected value from homologue periods, and (RSTS) remainder after seasonal time series decomposition. We estimate total, age- and gender-specific excess mortality. Furthermore, we compare different policy responses to COVID-19.

RESULTS

Excess mortality was found in all 5 countries, ranging from 10.6% in Portugal (DEV) to 98.5% in Italy (DEV). Furthermore, excess mortality is higher than COVID-attributed deaths in all 5 countries.

DISCUSSION

The impact of COVID-19 on mortality appears to be larger than officially attributed deaths, in varying degrees in different countries. Comparisons between countries would be useful, but large disparities in mortality surveillance data could not be overcome. Unreliable data, and even a lack of cause-specific mortality data undermine the understanding of the impact of policy choices on both direct and indirect deaths during COVID-19. European countries should invest more on mortality surveillance systems to improve the publicly available data.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.04.28.20083147: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    This study has several limitations related to the quality of data or methods that are important to mention. As previously stated, data has many problems and this greatly limits its interpretations. For this reason, limitations cannot be overcome, and thus we chose to be conservative in estimating excess mortality, as one can confirm when comparing our results to other results, such as those mentioned in this paper. Regarding future work, we aim to include a regional analysis of each country and to add other countries. Data on causes of death would also help us understand collateral damages from the pandemic. Lastly, collecting data on hospital usage, especially A&E services, would provide good clues to whether excess deaths from non-COVID-19 causes could be avoided by strengthening other health services. ii. Main findings and recommendations: An excess of mortality was found in all studied countries in the period after the first COVID-19 attributed death, beyond those deaths directly confirmed as COVID-19. However, mortality surveillance systems in the five studied countries presented several data quality issues that hindered a more in-depth analysis, relevant both for pandemic and normal contexts. Therefore, as members of the international community of researchers that seek to work on European COVID-19 issues, the following recommendations are highly suggested:

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.04.28.20083147v1 on medRxiv