Serological evidence of human infection with SARS-CoV-2: a systematic review and meta-analysis

  1. Xinhua Chen
  2. Zhiyuan Chen
  3. Andrew S Azman
  4. Xiaowei Deng
  5. Ruijia Sun
  6. Zeyao Zhao
  7. Nan Zheng
  8. Xinghui Chen
  9. Wanying Lu
  10. Tingyu Zhuang
  11. Juan Yang
  12. Cecile Viboud
  13. Marco Ajelli
  14. Daniel T Leung
  15. Hongjie Yu

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Abstract

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  1. Version published to 10.1016/s2214-109x(21)00026-7
  2. ScreenIT

    SciScore for 10.1101/2020.09.11.20192773: (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
    Data Sources and Searches: According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline (http://www.prisma-statement.org/) (11), we performed a systematic literature review from three peer-reviewed databases (PubMed, Embase and Web of Science) and four preprint severs (medRxiv, bioRxiv, SSRN and Wellcome) with predefined search terms (Appendix Table 1).
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Embase
    suggested: (EMBASE, RRID:SCR_001650)
    bioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)

    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:
    Our study has several limitations. First, although we have performed meta-regression and subgroup analysis to explore heterogeneity of varied seroprevalence for different populations, there are still some factors that we have not taken into account, so that some heterogeneity cannot be well explained quantitatively. Second, misclassification bias may occur due to the limited information on exposures for the study populations, especially for the “Poorly-defined” populations. For some key information (e.g., the use of PPE for healthcare workers) that cannot be extracted from the original articles, we have tried to contact the authors, but the response rate was low. Third, our findings involving cumulative incidence of confirmed cases are limited by the inherently heterogeneous incidence across locations, due to the diversity of testing strategy, case tracing and identification, and public health interventions. Fourth, only raw data without correcting for assay performance or sampling design were used in the analysis to ensure comparability within studies. Fifth, the seroprevalence of IgG was possibly overestimated due to the inability to isolate IgG-based seroprevalence from total antibodies-based seroprevalence in some studies. Lastly, our infection-to-case estimates did not account for age-specific differences in exposure risk and symptom severity. In conclusion, the overall quality of the existing seroprevalence studies of SARS-CoV-2 is low and international efforts to stand...

    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 10.1101/2020.09.11.20192773 on medRxiv