Explore the Possible Impact of BCG Vaccination Policy on the Morbidity, Mortality, and Recovery Rates due to COVID-19 Infection

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Abstract

BACKGROUND

The Coronavirus Disease-19 (COVID-19) is the new form of an acute infectious respiratory disease and has quickly spread over most continents in the world. Recently, it has been shown that Bacille Calmette-Guerin (BCG) might protect against COVID-19. This study aims to investigate the possible correlation between BCG vaccination and morbidity/mortality/recovery rate associated with COVID-19 infection.

METHODS

Data of COVID-19 confirmed cases, deaths, recoveries, and population were obtained from https://www.worldometers.info/coronavirus/ (Accessed on 12 June, 2020). To have meaningful comparisons among countries’ mortality and recovery rates, we only choose those countries with COVID-19 infected cases at least 200. The Poisson regression and logistic regression were used to explore the relationship between BCG vaccination and morbidity, mortality and recovery rates.

RESULTS

Among those 158 countries with at least 200 COVID-19 infected cases, there were 141 countries with BCG vaccination information available. The adjusted rates ratio of COVID-19 confirmed cases for Current BCG vaccination vs. non-Current BCG vaccination was 0.339 (with 95% CI= (0.338,0.340)). Moreover, the adjusted odds ratio (OR) of death and recovery after coronavirus infected for Current BCG vaccination vs. non-Current BCG vaccination were 0.258 (with 95% CI= (0.254,0.261)) and 2.151 (with 95% CI= (2.140,2.163)), respectively.

CONCLUSIONS

That data in this study show the BCG might provide the protection against COVID-19, with consequent less COVID-19 infection and deaths and more rapid recovery. BCG vaccine might bridge the gap before the disease-specific vaccine is developed, but this hypothesis needs to be further tested in rigorous randomized clinical trials.

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  1. SciScore for 10.1101/2020.06.14.20131268: (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
    All analyses were done by using the SPSS v26.0 software (SPSS Inc., Chicago, IL, USA).
    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: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04327206RecruitingBCG Vaccination to Protect Healthcare Workers Against COVID-…
    NCT04328441Active, not recruitingReducing Health Care Workers Absenteeism in Covid-19 Pandemi…
    NCT04379336RecruitingBCG Vaccination for Healthcare Workers in COVID-19 Pandemic
    NCT04384549RecruitingEfficacy of BCG Vaccination in the Prevention of COVID19 Via…
    NCT04348370RecruitingBCG Vaccine for Health Care Workers as Defense Against COVID…
    NCT04369794RecruitingCOVID-19: BCG As Therapeutic Vaccine, Transmission Limitatio…
    NCT04347876RecruitingOutcome of COVID-19 Cases Based on Tuberculin Test: Can Prev…


    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.