Trends in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Seroprevalence in Massachusetts Estimated from Newborn Screening Specimens

  1. Kevin C Ma
  2. Jaime E Hale
  3. Yonatan H Grad
  4. Galit Alter
  5. Katherine Luzuriaga
  6. Roger B Eaton
  7. Stephanie Fischinger
  8. Devinder Kaur
  9. Robin Brody
  10. Sameed M Siddiqui
  11. Dylan Leach
  12. Catherine M Brown
  13. R Monina Klevens
  14. Lawrence Madoff
  15. Anne Marie Comeau

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Abstract

Background

Estimating the cumulative incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for setting public health policies. We leveraged deidentified Massachusetts newborn screening specimens as an accessible, retrospective source of maternal antibodies for estimating statewide seroprevalence in a nontest-seeking population.

Methods

We analyzed 72 117 newborn specimens collected from November 2019 through December 2020, representing 337 towns and cities across Massachusetts. Seroprevalence was estimated for the Massachusetts population after correcting for imperfect test specificity and nonrepresentative sampling using Bayesian multilevel regression and poststratification.

Results

Statewide seroprevalence was estimated to be 0.03% (90% credible interval [CI], 0.00–0.11) in November 2019 and rose to 1.47% (90% CI: 1.00–2.13) by May 2020, following sustained SARS-CoV-2 transmission in the spring. Seroprevalence plateaued from May onward, reaching 2.15% (90% CI: 1.56–2.98) in December 2020. Seroprevalence varied substantially by community and was particularly associated with community percent non-Hispanic Black (β = .024; 90% CI: 0.004–0.044); i.e., a 10% increase in community percent non-Hispanic Black was associated with 27% higher odds of seropositivity. Seroprevalence estimates had good concordance with reported case counts and wastewater surveillance for most of 2020, prior to the resurgence of transmission in winter.

Conclusions

Cumulative incidence of SARS-CoV-2 protective antibody in Massachusetts was low as of December 2020, indicating that a substantial fraction of the population was still susceptible. Maternal seroprevalence data from newborn screening can inform longitudinal trends and identify cities and towns at highest risk, particularly in settings where widespread diagnostic testing is unavailable.

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  1. Version published to 10.1093/cid/ciac158
  2. ScreenIT

    SciScore for 10.1101/2021.10.29.21265678: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The Institutional Review Boards of the Massachusetts Department of Public Health and the UMass Chan Medical School approved waivers of consent for the de-identified public health
    Consent: The Institutional Review Boards of the Massachusetts Department of Public Health and the UMass Chan Medical School approved waivers of consent for the de-identified public health
    Sex as a biological variableStudy population: Women who were residents of Massachusetts, gave birth in Massachusetts and whose infants’ dried blood spot (DBS) specimens had completed routine newborn screening met study surveillance inclusion criteria with the DBS specimens serving as surrogates for the women.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    The receptor binding domain (RBD) of the Spike protein used as ELISA Ag was expressed using HEK-293F cells and prepared as follows: a plasmid containing His-tagged RBD was transiently transfected in HEK-293F cells using PEI.
    HEK-293F
    suggested: RRID:CVCL_6642)
    Software and Algorithms
    SentencesResources
    Testing laboratories reported replicate OD and IgG concentration results for all well locations from specified plates to investigators at the New England Newborn Screening Program (NENSP)
    New England Newborn Screening Program
    suggested: None

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our findings are subject to at least several limitations and biases. As noted, selection bias could arise because fundamental risk differences between pregnant women and the general population are not accounted for in our statistical model. The direction and causes of this potential bias vary: pregnant women could have less exposure to SARS-CoV-2 due to behavioral choices, depending on sociodemographic characteristics, but increased biological susceptibility to infection due to immune weakening. Nonetheless, estimates from cohorts with clear selection biases, such as blood donors or healthy volunteers, can still meaningfully inform seroprevalence estimates in the general population [18, 24-26]. Second, misclassification bias can occur due to imperfect test sensitivity and specificity. We have estimated specificity using a pre-pandemic sample of DBS and have incorporated it into the statistical model; however, we do not have an estimate for sensitivity. Accounting for imperfect test sensitivity would be expected to shift the seroprevalence estimates higher and widen the credible intervals (Supplementary Figure 7), due in part to the uncertainty involved in measuring sensitivity itself. By assessing the distribution of maternal antibodies to SARS-CoV-2 statewide and over time, our study provides a strategy for the systematic evaluation and estimation of population-wide cumulative incidence of SARS-CoV-2. Prospective use of NBS-based cumulative incidence estimates of exposure fo...

    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.
    • Thank you for including a protocol registration statement.

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

    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 10.1101/2021.10.29.21265678v1 on medRxiv