Excess deaths in Spain during the first year of the COVID–19 pandemic outbreak from age/sex–adjusted death rates

This article has been Reviewed by the following groups

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Abstract

Assess the impact of the illness designated COVID–19 during the first year of pandemic outbreak in Spain through age/sex–specific death rates.

S tudy design

Age/sex–specific weeekly deaths in Spain were retrieved from Eurostat. Spanish resident population was obtained from the National Statistics Office.

M ethods

Generalized linear Poisson regressions were used to compute the contrafactual expected rates after one year (52 weeks or 364 days) of the pandemic onset. From this one–year age/sex–specific and age/sex–adjusted mortality excess rates were deduced.

R esults

For the past continued 13 years one–year age/sex–adjusted death rates had not been as high as the rate observed on February 28th, 2021.

The excess death rate was estimated as 1.790×10 −3 (95 % confidence interval, 1.773×10 −3 to 1.808×10 −3 ; P −score = 20.2 % and z −score = 11.4) with an unbiased standard deviation of the residuals equal to 157×10 −6 . This made 84 849 excess deaths (84 008 to 85 690). Sex disaggregation resulted in 44 887 (44 470 to 45 303) male excess deaths and 39 947 (39 524 to 40 371) female excess deaths.

C onclusion

With 73 571 COVID–19 deaths and 9772 COVID–19 suspected deaths that occurred in nursing homes during the spring of 2020 it is only 1496 excess deaths (1.8 %, a z −score of 0.2) that remains unattributed.

The infection rate during the first year of the pandemic is estimated in 16 % of population after comparing the ENE–COVID seroprevalence, the excess deaths at the end of the spring 2020 and the excess deaths at the end of the first year of the pandemic.

Article activity feed

  1. SciScore for 10.1101/2020.07.22.20159707: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.


    Results from OddPub: Thank you for sharing your code.


    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.