Seroprevalence of Antibodies to SARS-CoV-2 in 10 Sites in the United States, March 23-May 12, 2020

  1. Fiona P. Havers
  2. Carrie Reed
  3. Travis Lim
  4. Joel M. Montgomery
  5. John D. Klena
  6. Aron J. Hall
  7. Alicia M. Fry
  8. Deborah L. Cannon
  9. Cheng-Feng Chiang
  10. Aridth Gibbons
  11. Inna Krapiunaya
  12. Maria Morales-Betoulle
  13. Katherine Roguski
  14. Mohammad Ata Ur Rasheed
  15. Brandi Freeman
  16. Sandra Lester
  17. Lisa Mills
  18. Darin S. Carroll
  19. S. Michele Owen
  20. Jeffrey A. Johnson
  21. Vera Semenova
  22. Carina Blackmore
  23. Debra Blog
  24. Shua J. Chai
  25. Angela Dunn
  26. Julie Hand
  27. Seema Jain
  28. Scott Lindquist
  29. Ruth Lynfield
  30. Scott Pritchard
  31. Theresa Sokol
  32. Lynn Sosa
  33. George Turabelidze
  34. Sharon M. Watkins
  35. John Wiesman
  36. Randall W. Williams
  37. Stephanie Yendell
  38. Jarad Schiffer
  39. Natalie J. Thornburg

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Abstract

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  1. ScreenIT

    SciScore for 10.1101/2020.06.25.20140384: (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
    R (Version 3.6.1) and Rstudio (version 1.2.1335) were used to perform statistical analysis.
    Rstudio
    suggested: (RStudio, RRID:SCR_000432)

    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:
    The study has limitations associated with the samples and with the tests used. The specimens were deidentified and originally collected for clinical purposes from persons seeking healthcare and were shared with CDC with minimal accompanying data. No data on recent symptomatic illness, underlying conditions, or possible COVID-19 exposures were available. For Lab B, in some sites it is possible that multiple specimens came from the same individual, potentially biasing results. Residual clinical specimens from screening or routine care are more likely to come from persons who require continued monitoring for chronic medical conditions despite the ongoing pandemic. These persons may not be representative of the general population, including in their healthcare seeking and social distancing behavior, immune response to infection, and disease exposure risk. Representativeness may vary as well by age group. Therefore, our seroprevalence estimates need to be confirmed by other studies, including serosurveys which use targeted sampling frames to enroll more representative populations.22 In addition, although overall sample size was large, there were a limited number of specimens in those <5 years of age, which limited our ability to estimate seroprevalence among young children. Furthermore, at this stage in the pandemic infections may not be evenly distributed even within these geographic sites, and seroprevalence estimates for large geographic sites (e.g., statewide for CT, MO, and U...

    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 10.1001/jamainternmed.2020.4130
  3. ScreenIT

    SciScore for 10.1101/2020.06.25.20140384: (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
    Key findings: Question: What proportion of persons in six U.S. sites had detectable antibodies to SARS-CoV-2 , March 23-May 3 , 2020 ?
    SARS-CoV-2 , March 23-May 3
    suggested: None
    Most specimens from each site had no evidence of antibody to SARS-CoV-2 .
    SARS-CoV-2
    suggested: None
    While humoral response kinetics to SARS-CoV-2 infection are not well understood, reactive IgA, IgM, and IgG antibodies have been detected as soon as one day after symptom onset.
    IgG antibodies
    suggested: None
    Software and Algorithms
    SentencesResources
    R ( Version 3.6.1 ) and Rstudio ( version 1.2.1335 ) were used to perform statistical analysis .
    Rstudio
    suggested: (RStudio, SCR_000432)

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

    • The study has limitations associated with the samples and with the tests used.
    • The specimens were

    Results from OddPub: We did not find a statement about open data. We also did not find a statement about open code. Researchers are encouraged to share open data when possible (see Nature blog).

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  4. Version published to 10.1101/2020.06.25.20140384v1 on medRxiv