Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence: Navigating the absence of a gold standard

  1. Sahar Saeed
  2. Sheila F. O’Brien
  3. Kento Abe
  4. Qi-Long Yi
  5. Bhavisha Rathod
  6. Jenny Wang
  7. Mahya Fazel-Zarandi
  8. Ashleigh Tuite
  9. David Fisman
  10. Heidi Wood
  11. Karen Colwill
  12. Anne-Claude Gingras
  13. Steven J. Drews

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Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence studies bridge the gap left from case detection, to estimate the true burden of the COVID-19 pandemic. While multiple anti-SARS-CoV-2 immunoassays are available, no gold standard exists.

Methods

This serial cross-sectional study was conducted using plasma samples from 8999 healthy blood donors between April-September 2020. Each sample was tested by four assays: Abbott SARS-Cov-2 IgG assay, targeting nucleocapsid (Abbott-NP) and three in-house IgG ELISA assays (targeting spike glycoprotein, receptor binding domain, and nucleocapsid). Seroprevalence rates were compared using multiple composite reference standards and by a series of Bayesian Latent Class Models.

Result

We found 13 unique diagnostic phenotypes; only 32 samples (0.4%) were positive by all assays. None of the individual assays resulted in seroprevalence increasing monotonically over time. In contrast, by using the results from all assays, the Bayesian Latent Class Model with informative priors predicted seroprevalence increased from 0.7% (95% credible interval (95% CrI); 0.4, 1.0%) in April/May to 0.7% (95% CrI 0.5, 1.1%) in June/July to 0.9% (95% CrI 0.5, 1.3) in August/September. Assay characteristics varied over time. Overall Spike had the highest sensitivity (93.5% (95% CrI 88.7, 97.3%), while the sensitivity of the Abbott-NP assay waned from 77.3% (95% CrI 58.7, 92.5%) in April/May to 64.4% (95% CrI 45.6, 83.0) by August/September.

Discussion

Our results confirmed very low seroprevalence after the first wave in Canada. Given the dynamic nature of this pandemic, Bayesian Latent Class Models can be used to correct for imperfect test characteristics and waning IgG antibody signals.

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  1. Version published to 10.1371/journal.pone.0257743
  2. ScreenIT

    SciScore for 10.1101/2021.05.11.21256992: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The Research Ethics Board of the Canadian Blood Services and Lunenfeld-Tanenbaum Research Institute (LTRI) (REB study #20-0194-E) approved this study and exempted study-specific consent.
    Sex as a biological variablenot detected.
    RandomizationEach month 1500 deidentified samples were randomly selected by collection site.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Each sample was tested for SARS-CoV-2 IgG antibodies using four assays.
    SARS-CoV-2 IgG
    suggested: None
    Software and Algorithms
    SentencesResources
    The Abbott Architect SARS-Cov-2 IgG assay which targets the nucleocapsid antigen (Abbott-NP), (Abbott, Chicago IL) and three in-house IgG ELISA chemiluminescent assays recognizing distinct recombinant viral antigens: full length spike glycoprotein (Spike), spike glycoprotein receptor binding domain (RBD), and nucleocapsid (NP), were tested at the CBS laboratory in Ottawa and the Gingras laboratory [11,12] at the LTRI in Toronto, respectively.
    Abbott Architect
    suggested: (Abbott ARCHITECT i1000sr System, RRID:SCR_019328)
    Abbott
    suggested: (Abbott, RRID:SCR_010477)

    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 also has weaknesses. This study was conducted among blood donors, based on selection criteria to be allowed to donate blood donors may be healthy than the general population [32]. However, a recent study compared seroprevalence estimates from European blood donors to household surveys targeting the general population and found seroprevalence rates to be very similar [33]. Both CRS and LCA assume each of the assays is conditionally independent. It is possible this assumption may not hold, potentially biasing results. Assay performance is based on predefined thresholds. For the Abbott assay we used the manufacturer’s ≥1.4 cut off, but recent reports do suggest reducing the threshold to >0.8 to increase sensitivity and to account for waning antibody signals. However, the sensitivity and specificity was not available by the manufacture for us to evaluate this alternative threshold. All four assays only probed for IgG meaning that we did not measure IgM and IgA, which may provide some neutralizing capacity in some individuals as anti-SARS-CoV-2 IgG titers begin to rise. In other donors, different profiles of anti-SARS-COV-2 IgM, IgA and IgG may also provide different profiles of humoral protection. Finally, we did not assess for the SARS-CoV-2 neutralizing capacity of donor specimens nor the avidity of the IgG antibody responses in those donors. In conclusion, regardless of the analytical method we found at the end of the first COVID-19 wave, SARS-CoV-2 seroprevalence am...

    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.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  3. Version published to 10.1101/2021.05.11.21256992v1 on medRxiv