Rapid estimation of excess mortality in times of COVID-19 in Portugal - Beyond reported deaths

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Abstract

Background: One month after the first COVID-19 infection was recorded, Portugal counted 18 051 cases and 599 deaths from COVID-19. To understand the overall impact on mortality of the pandemic of COVID-19, we estimated the excess mortality registered in Portugal during the first month of the epidemic, from March 16 until April 14 using two different methods. Methods: We compared the observed and expected daily deaths (historical average number from daily death registrations in the past 10 years) and used 2 standard deviations confidence limit for all-cause mortality by age and specific mortality cause, considering the last 6 years. An adapted ARIMA model was also tested to validate the estimated number of all-cause deaths during the study period. Results: Between March 16 and April 14, there was an excess of 1,255 all-cause deaths, 14% more than expected. The number of daily deaths often surpassed the 2 standard deviations confidence limit. The excess mortality occurred mostly in people aged 75+. Forty-nine percent (49%) of the estimated excess deaths were registered as due to COVID-19, The other 51% registered as other natural causes. Conclusion: Even though Portugal took early containment measures against COVID-19, and the population complied massively with those measures, there was significant excess mortality during the first month of the pandemic, mostly among people aged 75+. Only half of the excess mortality was registered as directly due do COVID-19.

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  1. SciScore for 10.1101/2020.05.14.20100909: (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

    Software and Algorithms
    SentencesResources
    The model was adjusted with the SPSS program, with a determination coefficient of R2 = 0.801, Ljung-Box test not statistically significant, Autocorrelation Function and Partial Autocorrelation Function not statistically significant in the residues, and estimates of the auto parameters - statistically significant regression and moving averages (p <0.01).
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


    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.

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