Heterogeneity and Risk of Bias in Studies Examining Risk Factors for Severe Illness and Death in COVID-19: A Systematic Review and Meta-Analysis

  1. Abraham Degarege
  2. Zaeema Naveed
  3. Josiane Kabayundo
  4. David Brett-Major

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Abstract

This systematic review and meta-analysis synthesized the evidence on the impacts of demographics and comorbidities on the clinical outcomes of COVID-19, as well as the sources of the heterogeneity and publication bias of the relevant studies. Two authors independently searched the literature from PubMed, Embase, Cochrane library, and CINAHL on 18 May 2021; removed duplicates; screened the titles, abstracts, and full texts by using criteria; and extracted data from the eligible articles. The variations among the studies were examined by using Cochrane, Q.; I2, and meta-regression. Out of 11,975 articles that were obtained from the databases and screened, 559 studies were abstracted, and then, where appropriate, were analyzed by meta-analysis (n = 542). COVID-19-related severe illness, admission to the ICU, and death were significantly correlated with comorbidities, male sex, and an age older than 60 or 65 years, although high heterogeneity was present in the pooled estimates. The study design, the study country, the sample size, and the year of publication contributed to this. There was publication bias among the studies that compared the odds of COVID-19-related deaths, severe illness, and admission to the ICU on the basis of the comorbidity status. While an older age and chronic diseases were shown to increase the risk of developing severe illness, admission to the ICU, and death among the COVID-19 patients in our analysis, a marked heterogeneity was present when linking the specific risks with the outcomes.

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  1. Version published to 10.3390/pathogens11050563
  2. ScreenIT

    SciScore for 10.1101/2020.12.03.20243659: (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
    Search methods for identification of studies: An article search was conducted in parallel in PubMed, Embase, Cochrane Library, and CINAHL.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Embase
    suggested: (EMBASE, RRID:SCR_001650)
    Cochrane Library
    suggested: (Cochrane Library, RRID:SCR_013000)

    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:
    Lack of association of some chronic diseases (e.g., kidney disease) and smoking with COVID-19 outcomes could be due to limitations in the original studies. The role of chronic kidney disease was assessed among comparatively few participants, which could explain its discordance with the results among cardiovascular equivalents. Potential ascertainment bias in the original studies could also partly explain the lack of association of chronic respiratory disease with severe illness though the disease showed to have a role in death and admission to ICU. Similarly, lack of correlation of smoking with clinical outcomes in the COVID-19 patients could be due the watering effect of the chronic disease outcomes. If most of the patients with cardiopulmonary diseases were smokers, exacerbated by ascertainment bias (they did not ask enough, or misinterpreted prior smoking and current smoking from never smoking), could miss the distinction of tobacco use. Unfortunately, most studies didn’t control chronic disease as a potential confounder when examining the role of smoking on COVID-19 related outcomes. These findings have a number of public health and research implications that will help in the management of high-risk COVID-19 patients to mitigate the progression of the disease and associated death. Based on our findings, special strategies are warranted to prevent SARS-CoV-2 infection and manage COVID-19 cases with underlying comorbidities, particularly older age males’ patients. For examp...

    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 10.1101/2020.12.03.20243659 on medRxiv