A demographic adjustment to improve measurement of COVID-19 severity at the developing stage of the pandemic

  1. Simona Bignami-Van Assche
  2. Daniela Ghio

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

The need for accurate statistics has never been felt so deeply as the novel COVID-19 pathogen spreads around the world and quantifying its severity is a primary clinical and public health issue. In Italy, the magnitude and increasing trend of the case-fatality risk (CFR) is fueling the already high levels of public alarm. In this paper, we highlight that the widely used crude CFR is an inaccurate measure of the disease severity since the pandemic is still unfolding. With the goal to improve its comparability over time and across countries at this stage, we then propose a demographic adjustment of the CFR that addresses the bias arising from differential case ascertainment by age. When applied to publicly released data for Italy, we show that until March 16 our adjusted CFR was similar to that of Wuhan – the most affected Chinese region, where COVID-19 has now been contained. This indicates that our adjusted CFR improves its comparability over time, making an important tool to chart the course of the COVID-19 pandemic across countries. Since March 16, the Italian COVID-19 outbreak has entered a new phase, with the northern and southern regions following different trajectories. As a result, our adjusted CFR has been increasing between March 16 and March 20. Data at the subnational level are needed to correctly assess the disease severity in the country at this stage.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.03.23.20040998: (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: 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.

    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 10.1101/2020.03.23.20040998v1 on medRxiv