Recruitment location influences bias and uncertainty in SARS-CoV-2 seroprevalence estimates

  1. Tyler S. Brown
  2. Pablo Martinez de Salazar Munoz
  3. Abhishek Bhatia
  4. Bridget Bunda
  5. Ellen K. Williams
  6. David Bor
  7. James S. Miller
  8. Amir M. Mohareb
  9. Julia Thierauf
  10. Wenxin Yang
  11. Julian Villalba
  12. Vivek Naranbai
  13. Wilfredo Garcia Beltran
  14. Tyler E. Miller
  15. Doug Kress
  16. Kristen Stelljes
  17. Keith Johnson
  18. Daniel B. Larremore
  19. Jochen Lennerz
  20. A. John Iafrate
  21. Satchit Balsari
  22. Caroline O. Buckee
  23. Yonatan H. Grad

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Abstract

The initial phase of the COVID-19 pandemic in the US was marked by limited diagnostic testing, resulting in the need for seroprevalence studies to estimate cumulative incidence and define epidemic dynamics. In lieu of systematic representational surveillance, venue-based sampling was often used to rapidly estimate a community’s seroprevalence. However, biases and uncertainty due to site selection and use of convenience samples are poorly understood. Using data from a SARS-CoV-2 serosurveillance study we performed in Somerville, Massachusetts, we found that the uncertainty in seroprevalence estimates depends on how well sampling intensity matches the known or expected geographic distribution of seropositive individuals in the study area. We use GPS-estimated foot traffic to measure and account for these sources of bias. Our results demonstrated that study-site selection informed by mobility patterns can markedly improve seroprevalence estimates. Such data should be used in the design and interpretation of venue-based serosurveillance studies.

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    SciScore for 10.1101/2021.02.03.21251011: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study was designated minimal risk human subjects research and approved by institutional review boards at Massachusetts General Hospital and the Harvard T.H. Chan School of Public Health (Protocol number: 2020P001081).
    RandomizationIn brief, this model randomly draws participants from a simple synthetic population, stratified by age and location, according to a sampling strategy, which specifies the number of participants (in this case, equal to the number of serological tests performed) in each age-location group.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Within this small sample, with important limitations discussed below, we observed 11-13% cumulative incidence of prior COVID-19 infection (adjusted for test performance). Certain forms of convenience sampling may be better suited than structured surveys for reaching epidemiologically important demographics during the COVID-19 pandemic. For example, lower-wage or frontline workers who are at higher risk of SARS-CoV-2 exposure [15, 16, 14] may be less likely to participate if recruited using conventional survey outreach methods (e.g., mail or phone contact) due to constraints on their time [17, 18] and lack of incentives [17]. Convenience sampling at highly-visited community locations, including essential business, may be an attractive alternative to structured sampling in this important population, similar to venue-based sampling approaches developed to study so-called “hidden populations” [19]. Ultimately, the design of SARS-CoV-2 serosurveillance studies must balance these potential benefits against the biases and limitations on generalizability that are inherent to convenience sampling. Geographic heterogeneity in SARS-CoV-2 cumulative incidence within cities has been repeatedly observed [13, 20, 21], with areas of lower socioeconomic status hit hardest by COVID-19. In our study, conducted in an area with clear geographic differences in the cumulative incidence of PCR-confirmed SARS-CoV-2 infections between wards, convenience sampling resulted in an under-sampling of the co...

    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

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  2. Version published to 10.1101/2021.02.03.21251011 on medRxiv