Untangling factors associated with country-specific COVID-19 incidence, mortality and case fatality rates during the first quarter of 2020

  1. Lev Shagam

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Abstract

At early stages of the COVID-19 pandemic which we are experiencing, the publicly reported incidence, mortality and case fatality rates (CFR) vary significantly between countries. Here we aim to untangle factors that are associated with the differences during the first quarter of the year 2020. Number of performed COVID-19 tests has a strong correlation with country-specific incidence (p < 2 × 10 −16 ) and mortality rate (p = 5.1 × 10 −8 ). Using multivariate linear regression we show that incidence and mortality rates correlate significantly with GDP per capita (p = 2.6 × 10 −15 and 7.0 × 10 −4 , respectively), country-specific duration of the outbreak (2.6 × 10 −4 and 0.0019), fraction of citizens over 65 years old (p = 0.0049 and 3.8 × 10 −4 ) and level of press freedom (p = 0.021 and 0.019) which cumulatively explain 80% of variability of incidence and more than 60% of variability of mortality of the disease during the period analyzed. Country hemisphere demonstrated significant correlation only with mortality (p = 0.17 and 0.036) whereas population density (p = 0.94 and p = 0.75) and latitude (p = 0.61 and 0.059) did not reach significance in our model. Case fatality rate is shown to rise as the outbreak progresses (p=0.028). We rank countries by COVID-19 mortality corrected for incidence and the factors that were shown to affect it, and by CFR corrected for outbreak duration, yielding very similar results. Among the countries where the outbreak started after the 15th of February and with at least 1000 registered patients during the period analyzed, the lowest corrected CFR are seen in Israel, South Africa and Chile. The ranking results should be considered with caution as they do not consider all confounding factors or data reporting biases.

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    SciScore for 10.1101/2020.04.22.20075580: (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

    Software and Algorithms
    SentencesResources
    Calculations were performed in R version 3.6.0 using packages stats, tibble and data.table, visualization was done using package ggplot2.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    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:
    Exploration of them during the course of the outbreak faces certain limitations based on data availability as well as standardization problems, however is crucial to timely understand spread and effect of the virus. Being a likely issue for current analysis, the anticipated data bias has also been addressed here as a phenomenon that we are aiming to untangle. Here we show a number of correlations for the epidemiological parameters with variables characterizing economical, demographic, healthcare and political organization of the 156 countries (or overseas territories) analyzed. Incidence, mortality and case fatality rates are expectedly correlated with duration of the COVID-19 outbreak. Other correlations demonstrated here are for incidence and mortality of the infection (CFR was herein analyzed only for 39 countries that fulfil the requirements on number of people affected and outbreak start date which substantially decreases sensitivity compared to incidence and mortality where sample sizes are much bigger). Correlation with fraction of citizens over 65 years old is concordant with the previous findings of case fatality rate dependence on patient age (Onder, Rezza, and Brusaferro 2020). The spotted correlation of GDP per capita with incidence and mortality requires further investigation on its origins. One of the factors contributing could be higher mobility of population in ‘rich’ countries which led to quicker spread of the virus. Another one influencing the reported COVI...

    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

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  2. Version published to 10.1101/2020.04.22.20075580v1 on medRxiv