Magnitude and time-course of excess mortality during COVID-19 outbreak: population-based empirical evidence from highly impacted provinces in northern Italy

  1. Sara Conti
  2. Pietro Ferrara
  3. Giampiero Mazzaglia
  4. Marco I. D’Orso
  5. Roberta Ciampichini
  6. Carla Fornari
  7. Fabiana Madotto
  8. Michele Magoni
  9. Giuseppe Sampietro
  10. Andrea Silenzi
  11. Claudio V. Sileo
  12. Alberto Zucchi
  13. Giancarlo Cesana
  14. Lamberto Manzoli
  15. Lorenzo G. Mantovani

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Abstract

Background

The real impact of SARS-CoV-2 on overall mortality remains uncertain and surveillance reports attributed to COVID-19 a limited amount of deaths during the outbreak. Aim of this study is to assess the excess mortality (EM) during COVID-19 outbreak in highly impacted areas of northern Italy.

Methods

We analyzed data on deaths occurred in the first four months of 2020 in health protection agencies (HPA) of Bergamo and Brescia (Lombardy), building a time-series of daily number of deaths and predicting the daily standardized mortality ratio (SMR) and cumulative number of excess deaths (ED) through a Poisson generalized additive model of the observed counts in 2020, using 2019 data as a reference.

Results

We estimated 5740 (95% Credible Set (CS): 5552–5936) ED in the HPA of Bergamo and 3703 (95% CS: 3535 – 3877) in Brescia, corresponding to 2.55 (95% CS: 2.50–2.61) and 1.93 (95% CS: 1.89–1.98) folds increase in the number of deaths. The ED wave started a few days later in Brescia, but the daily estimated SMR peaked at the end of March in both HPAs, roughly two weeks after the introduction of lock-down measures, with significantly higher estimates in Bergamo (9.4, 95% CI: 9.1–9.7).

Conclusion

EM was significantly larger than that officially attributed to COVID-19, disclosing its hidden burden likely due to indirect effects on health system. Time-series analyses highlighted the impact of lockdown restrictions, with a lower EM in the HPA where there was a smaller delay between the epidemic outbreak and their enforcement.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.10.20150565: (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
    All analyses were carried out with statistical software SAS version 9.4 (SAS Institute, Cary, NC, USA) and R version 4.0.0 (R Project for Statistical Computing, www.R-project.org).
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)
    R Project for Statistical
    suggested: (R Project for Statistical Computing, RRID:SCR_001905)

    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: We detected the following sentences addressing limitations in the study:
    Some limitations should also be considered in interpreting the results. First, while accuracy is preserved, the precision of the estimated excess deaths is affected by the average number of daily events, decreasing in the subgroups that have low daily frequencies of death (e.g., the youngest age class). Second, the reference level is computed based on 2019 only: using a longer time-window could define the reference level better. However, trends in mortality within these areas proved to be very stable during the last years [26]. In conclusion, we documented a significant increase of the overall mortality during the first months of 2020, particularly March and April, indicating that COVID-19 outbreak had a substantially larger impact than what emerges from official estimates. Time-series analyses suggest that the national and local restrictions had a massive effect, determining a considerable reduction of COVID-19 burden. Furthermore, this study may serve as model for country-based estimations of overall (direct and indirect) impact of the COVID-19 epidemic on population mortality.

    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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  2. Version published to 10.1101/2020.07.10.20150565v1 on medRxiv
  3. Version published to 10.1183/23120541.00458-2020