Current challenges of severe acute respiratory syndrome coronavirus 2 seroprevalence studies among blood donors: A scoping review

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

Background and Objectives

Blood donors are increasingly being recognized as an informative resource for surveillance. We aimed to review severe acute respiratory syndrome coronavirus 2 seroprevalence studies conducted among blood donors to investigate methodological biases and provide guidance for future research.

Materials and Methods

We conducted a scoping review of peer‐reviewed and preprint publications between January 2020 and January 2021. Two reviewers used standardized forms to extract seroprevalence estimates and data on methodology pertaining to population sampling, periodicity, assay characteristics, and antibody kinetics. National data on cumulative incidence and social distancing policies were extracted from publicly available sources and summarized.

Results

Thirty‐three studies representing 1,323,307 blood donations from 20 countries worldwide were included (sample sizes ranged from 22 to 953,926 donations). The majority of the studies (79%) reported seroprevalence rates <10% (ranging from 0% to 76% [after adjusting for waning antibodies]). Overall, less than 1 in 5 studies reported standardized seroprevalence rates to reflect the demographics of the general population. Stratification by age and sex were most common (64% of studies), followed by region (48%). A total of 52% of studies reported seroprevalence at a single time point. Overall, 27 unique assay combinations were identified, 55% of studies used a single assay and only 39% adjusted seroprevalence rates for imperfect test characteristics. Among the nationally representative studies, case detection was most underrepresented in Kenya (1:1264).

Conclusion

By the end of 2020, seroprevalence rates were far from reaching herd immunity. In addition to differences in community transmission and diverse public health policies, study designs and methodology were likely contributing factors to seroprevalence heterogeneity.

Article activity feed

  1. SciScore for 10.1101/2021.05.13.21257177: (What is this?)

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

    Table 1: Rigor

    EthicsField Sample Permit: Data were entered into Microsoft Excel using a standardized form, which included: the full reference, region, data of sample collection, data necessary to calculate unadjusted SARS-CoV-2 seroprevalence (the number of samples tested and reactivity).
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Two reviewers (SS and SU) independently searched articles using the search engines PubMed and medRxiv for SARS-CoV-2 seroprevalence studies in English among blood donor populations from January 1, 2020 to January 19, 2021.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Data were entered into Microsoft Excel using a standardized form, which included: the full reference, region, data of sample collection, data necessary to calculate unadjusted SARS-CoV-2 seroprevalence (the number of samples tested and reactivity).
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    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 review has limitations. It is possible seroprevalence estimates from blood donors were conducted but have yet to be published or possibly never intended to be published as research articles instead reported directly to public health authorities. For example, in Denmark, the Netherlands and England weekly seroprevalence reports were generated from blood operators to inform COVID-19 modelling. Therefore, it is possible our sample is biased towards blood operators and countries who had sufficient resources to prepare findings in the form of a manuscript. We attempted to evaluate the association between non-pharmaceutical interventions and seroprevalence, but this introduced several limitations. First, policies in most countries varied at every-level of government and the policy index uses the most stringent level. This can over represent social distancing policies in countries like the United States were there were significant variations in policies at the federal, state, counties, and individual cities. The stringency policy index weighs each policy equally which may not reflect the impact of the individual policies on incidence. Finally, even though we lagged our seroprevalence rates by two weeks, there is still a possibility of reverse causality (countries that had more cases adopted stricter policies). Considering these limitations, we believe the policy stringency index or other descriptions of policy measures can still provide context to the studies, but associations ...

    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

    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.