SARS-CoV-2 seroprevalence worldwide: a systematic review and meta-analysis

  1. Ali Rostami
  2. Mahdi Sepidarkish
  3. Mariska M.G. Leeflang
  4. Seyed Mohammad Riahi
  5. Malihe Nourollahpour Shiadeh
  6. Sahar Esfandyari
  7. Ali H. Mokdad
  8. Peter J. Hotez
  9. Robin B. Gasser

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Abstract

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  1. ScreenIT

    SciScore for 10.1101/2020.08.31.20185017: (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

    Antibodies
    SentencesResources
    We performed a systematic literature search in PubMed, Scopus, Embase, medRxiv and bioRxiv on August, 2020 using the following terms: “SARS-CoV-2”, “COVID-19”, “coronavirus”, “antibody”, “ELISA”, “seroprevalence”, and “population”.
    “SARS-CoV-2”, “COVID-19”, “coronavirus”, “antibody”, “ELISA”, “seroprevalence”,
    suggested: None
    Included were all peer-reviewed population-based studies, preprints, and research reports (published in English) which reported the prevalence of anti-SARS-CoV-2 serum antibodies in a ‘general’ population.
    anti-SARS-CoV-2
    suggested: None
    Software and Algorithms
    SentencesResources
    We performed a systematic literature search in PubMed, Scopus, Embase, medRxiv and bioRxiv on August, 2020 using the following terms: “SARS-CoV-2”, “COVID-19”, “coronavirus”, “antibody”, “ELISA”, “seroprevalence”, and “population”.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Embase
    suggested: (EMBASE, RRID:SCR_001650)
    bioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)
    Additional related articles were retrieved from Google Scholar and manual review of included papers.
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    Data extraction and quality assessment: After the screening of published articles for eligibility, a specific form in Microsoft Excel (version 2016; Microsoft Corporation, Redmond, WA) was used to extract relevant data and information from each eligible study.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    To assess the effect of these variables on seroprevalence, we performed random effects meta-regression analyses using the metareg STATA command [37]
    STATA
    suggested: (Stata, RRID:SCR_012763)

    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:
    As the present study represents a first “snap shot” of seroprevalence based on a critical evaluation of published information, it has a number of limitations: First, one limitation is the lack of published, population-based studies from many countries across the globe at this relatively early phase of the pandemic, and some of the studies included here lacked data on sex and age of subjects tested. We hope that these limitations can be addressed over the next months and years, so that future estimates will be more representative of the situation worldwide, so that conclusions might be drawn regarding endemic stability and instability in particular countries and regions. Second, different serological methods/assays (with varying sensitivities and specificities) were employed in different studies, which will have some effect on the accuracy of our global estimate, although subgroup analyses were undertaken to assess a potential effect of the serological methods used. Third, pooled analyses showed significant heterogeneity. As such heterogeneity was expected in meta-analyses of global prevalence estimates [67-69], we explored possible sources of heterogeneity, including geographic region and diagnostic methods. However, we did not find the source of this heterogeneity. This study reinforces that SARS-CoV-2 infection is a major global health threat and very rapidly-spreading communicable disease, with as the global seroprevalence rising to 3.38% only months after the commencement...

    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 10.1016/j.cmi.2020.10.020
  3. Version published to 10.1101/2020.08.31.20185017v1 on medRxiv