Impact of age, sex, comorbidities and clinical symptoms on the severity of COVID-19 cases: A meta-analysis with 55 studies and 10014 cases

  1. Md. Abdul Barek
  2. Md. Abdul Aziz
  3. Mohammad Safiqul Islam

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Abstract

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  1. Version published to 10.1016/j.heliyon.2020.e05684
  2. ScreenIT

    SciScore for 10.1101/2020.05.23.20110965: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationJadad scale was used for randomized controlled trials (RCTs) to determine the consistency of the sample [12].
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The relevant studies were systematically searched in PubMed, ScienceDirect, and SAGE database from January 1, 2020, to May 17, 2020. EndNote X 7.0 software records are used to exclude duplicates.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    SAGE
    suggested: (SAGE, RRID:SCR_009302)
    2.4 Statistical analysis: All analyses were performed by Microsoft Excel and Review Manager 5.3 (RevMan 5.3, The Cochrane Collaboration, Oxford, United Kingdom).
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    RevMan
    suggested: (RevMan, RRID:SCR_003581)
    Cochrane Collaboration
    suggested: None
    Heterogeneity in the forest plot was evaluated using both the Cochrane chi-square Q-test and I2 statistic.
    Cochrane chi-square
    suggested: None

    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:
    However, some limitations should be mentioned. Firstly, most of the studies included in this meta-analysis were not RCTs. Secondly, high heterogeneity statistics could be found due to the larger variations in sample size. Thirdly, reports being restricted to China and a few other countries and our aim to use this study’s results to forecast patients in general, including other countries and races. Fourthly, severity, morbidity, and follow-up of patients in different hospitals vary greatly. Without these limitations, this study analyzed the risk factors for progression to critical illness or death in COVID-19 patients to help to assess patient status and identify critical patients early. Paying close attention to these risk factors and personalized treatment regimens are needed to enhance the efficacy as well as reduce the risk of death.

    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.

  3. Version published to 10.1101/2020.05.23.20110965 on medRxiv