Estimated seroprevalence of SARS-CoV-2 antibodies among adults in Orange County, California

  1. Tim A. Bruckner
  2. Daniel M. Parker
  3. Scott M. Bartell
  4. Veronica M. Vieira
  5. Saahir Khan
  6. Andrew Noymer
  7. Emily Drum
  8. Bruce Albala
  9. Matthew Zahn
  10. Bernadette Boden-Albala

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Abstract

Clinic-based estimates of SARS-CoV-2 may considerably underestimate the total number of infections. Access to testing in the US has been heterogeneous and symptoms vary widely in infected persons. Public health surveillance efforts and metrics are therefore hampered by underreporting. We set out to provide a minimally biased estimate of SARS-CoV-2 seroprevalence among adults for a large and diverse county (Orange County, CA, population 3.2 million). We implemented a surveillance study that minimizes response bias by recruiting adults to answer a survey without knowledge of later being offered SARS-CoV-2 test. Several methodologies were used to retrieve a population-representative sample. Participants (n = 2979) visited one of 11 drive-thru test sites from July 10th to August 16th, 2020 (or received an in-home visit) to provide a finger pin-prick sample. We applied a robust SARS-CoV-2 Antigen Microarray technology, which has superior measurement validity relative to FDA-approved tests. Participants include a broad age, gender, racial/ethnic, and income representation. Adjusted seroprevalence of SARS-CoV-2 infection was 11.5% (95% CI: 10.5–12.4%). Formal bias analyses produced similar results. Prevalence was elevated among Hispanics (vs. other non-Hispanic: prevalence ratio [PR] = 1.47, 95% CI 1.22–1.78) and household income < $50,000 (vs. > $100,000: PR = 1.42, 95% CI: 1.14 to 1.79). Results from a diverse population using a highly specific and sensitive microarray indicate a SARS-CoV-2 seroprevalence of ~ 12 percent. This population-based seroprevalence is seven-fold greater than that using official County statistics. In this region, SARS-CoV-2 also disproportionately affects Hispanic and low-income adults.

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  1. ScreenIT

    SciScore for 10.1101/2020.10.07.20208660: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: We received human subjects approval from the UCI Institutional Review Board (HS# 2020-5952) and obtained informed consent from all study participants.
    Consent: We received human subjects approval from the UCI Institutional Review Board (HS# 2020-5952) and obtained informed consent from all study participants.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableBased on US Census-derived population distributions in OC of age, gender, race/ethnicity, and household income,11 we targeted recruitment to ensure adequate sample size of subjects from the following strata: age-by-gender (18 to 34 years, 35 to 54 years, 55 years or above; by male, female), race/ethnicity (Hispanic, Asian non-Hispanic, and other non-Hispanic including white and Black, and household income (<$50,000, $50,000-99,999, and $100,000 or above).

    Table 2: Resources

    Antibodies
    SentencesResources
    16 Unlike then available FDA authorized tests which detect antibodies against one or two antigens,17 the CoVAM quantitatively measures IgG and IgM antibodies against 12 antigens from SARS-CoV-2, including spike (S) as whole protein or separated domains including S1, S2, and receptor-binding domain (RBD), nucleocapsid protein (NP), and membrane (M) protein (Supplemental Material eFigure 1).
    IgM antibodies against 12
    suggested: (Novus Cat# H00054925-M12A, RRID:AB_2273576)
    nucleocapsid protein (NP)
    suggested: None
    The CoVAM also measures IgG and IgM antibodies against 53 antigens from other viruses that cause acute respiratory illness, including SARS-1, MERS, and the four seasonal coronaviruses, which can be used for assessment of antibody cross-reactivity.
    IgM antibodies against 53
    suggested: None
    From the 12 SARS-CoV-2 antigens on the array, a linear regression model was trained on positive and negative controls to determine optimal weighted combinations of reactive antigens to calculate composite SARS-CoV-2 IgG and IgM antibody levels that discriminate these two groups.
    IgM
    suggested: None
    We first calculated crude seroprevalence for SARS-CoV-2 antibodies (excluding household members of participants who were tested after study recruitment) by dividing the number of reactive cases by the total number of participants.
    SARS-CoV-2
    suggested: None
    Software and Algorithms
    SentencesResources
    The CoVAM also measures IgG and IgM antibodies against 53 antigens from other viruses that cause acute respiratory illness, including SARS-1, MERS, and the four seasonal coronaviruses, which can be used for assessment of antibody cross-reactivity.
    CoVAM
    suggested: None

    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:
    This updated estimate should inform the broader policy debate in the US regarding the relative benefits and limitations of various SARS-CoV-2 mitigation strategies.

    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 10.1038/s41598-021-82662-x
  3. Version published to 10.1101/2020.10.07.20208660v1 on medRxiv