Risks and Benefits of Antibiotics vs. COVID-19 Morbidity and Mortality

  1. Hisaya Tanioka
  2. Sayaka Tanioka

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Abstract

Objectives

The purpose is to analyze the potential association of each antibiotic consumption rate and use ratio with COVID-19 morbidity and mortality, and to investigate the efficacy and safe use of antibiotics against COVID-19.

Design

Retrospective statistical analysis study of antibiotic use compared with COVID-19 morbidity and mortality.

Methods

Each antibiotic defined daily dose (DDD) per 1000 inhabitants per day as each antibiotic consumption rate was available in the official reports and each antibacterial use ratio data was calculated from them. Coronavirus Disease data were obtained from the WHO Coronavirus Disease Dashboard. The relationships between the sum of DDD, each antibacterial DDD, each antibiotic use ratio, and COVID-19 morbidity and mortality were examined. The statistical correlation was calculated by univariate linear regression analysis and expressed by Pearson’s correlation coefficient.

Results

The sum of DDD had no statistical correlation with mortality and morbidity. Cephalosporins were a negative correlation with them. Penicillins had a weak positive correlation with them. Macrolides, quinolones, and sulfonates showed a slightly negative correlation tendency with mortality.

Conclusions

Cephalosporins may affect less COVID-19 morbidity and mortality. Penicillins suggest to accelerate them. The combination of cephalosporins with macrolides or quinolones may be a helpful treatment. The difference in antibiotic use between Japan and EU/EEA countries will suggest an explanation for the reduction in morbidity and mortality caused by COVID-19.

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  1. ScreenIT

    SciScore for 10.1101/2020.10.15.20213603: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: Setting and samples: This single-center study was obtained by the institutional ethics review board, and this study was the retrospective cohort study using public data.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data were analyzed statistical software available through Microsoft Excel 2013 (Microsoft Corporation Redmond, Washington).
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    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 10.1101/2020.10.15.20213603v1 on medRxiv