Excess mortality during the COVID-19 outbreak in Italy: a two-stage interrupted time-series analysis

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Abstract

Background

Italy was the first country outside China to experience the impact of the COVID-19 pandemic, which resulted in a significant health burden. This study presents an analysis of the excess mortality across the 107 Italian provinces, stratified by sex, age group and period of the outbreak.

Methods

The analysis was performed using a two-stage interrupted time-series design using daily mortality data for the period January 2015–May 2020. In the first stage, we performed province-level quasi-Poisson regression models, with smooth functions to define a baseline risk while accounting for trends and weather conditions and to flexibly estimate the variation in excess risk during the outbreak. Estimates were pooled in the second stage using a mixed-effects multivariate meta-analysis.

Results

In the period 15 February–15 May 2020, we estimated an excess of 47 490 [95% empirical confidence intervals (eCIs): 43 984 to 50 362] deaths in Italy, corresponding to an increase of 29.5% (95% eCI: 26.8 to 31.9%) from the expected mortality. The analysis indicates a strong geographical pattern, with the majority of excess deaths occurring in northern regions, where few provinces experienced increases up to 800% during the peak in late March. There were differences by sex, age and area both in the overall impact and in its temporal distribution.

Conclusion

This study offers a detailed picture of excess mortality during the first months of the COVID-19 pandemic in Italy. The strong geographical and temporal patterns can be related to the implementation of lockdown policies and multiple direct and indirect pathways in mortality risk.

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  1. SciScore for 10.1101/2020.07.22.20159632: (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: 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.
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    • No protocol registration statement was detected.

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