Clustering and longitudinal change in SARS-CoV-2 seroprevalence in school children in the canton of Zurich, Switzerland: prospective cohort study of 55 schools

  1. Agne Ulyte
  2. Thomas Radtke
  3. Irene A Abela
  4. Sarah R Haile
  5. Christoph Berger
  6. Michael Huber
  7. Merle Schanz
  8. Magdalena Schwarzmueller
  9. Alexandra Trkola
  10. Jan Fehr
  11. Milo A Puhan
  12. Susi Kriemler

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Abstract

Objectives

To examine longitudinal changes in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence and to determine the clustering of children who were seropositive within school classes in the canton of Zurich, Switzerland from June to November 2020.

Design

Prospective cohort study.

Setting

Switzerland had one of the highest second waves of the SARS-CoV-2 pandemic in Europe in autumn 2020. Keeping schools open provided a moderate to high exposure environment to study SARS-CoV-2 infections. Children from randomly selected schools and classes, stratified by district, were invited for serological testing of SARS-CoV-2. Parents completed questionnaires on sociodemographic and health related questions.

Participants

275 classes in 55 schools; 2603 children participated in June-July 2020 and 2552 in October-November 2020 (age range 6-16 years).

Main outcome measures

Serology of SARS-CoV-2 in June-July and October-November 2020, clustering of children who were seropositive within classes, and symptoms in children.

Results

In June-July, 74 of 2496 children with serological results were seropositive; in October-November, the number had increased to 173 of 2503. Overall SARS-CoV-2 seroprevalence was 2.4% (95% credible interval 1.4% to 3.6%) in the summer and 4.5% (3.2% to 6.0%) in late autumn in children who were not previously seropositive, leading to an estimated 7.8% (6.2% to 9.5%) of children who were ever seropositive. Seroprevalence varied across districts (in the autumn, 1.7-15.0%). No significant differences were found among lower, middle, and upper school levels (children aged 6-9 years, 9-13 years, and 12-16 years, respectively). Among the 2223 children who had serology tests at both testing rounds, 28/70 (40%) who were previously seropositive became seronegative, and 109/2153 (5%) who were previously seronegative became seropositive. Symptoms were reported for 22% of children who were seronegative and 29% of children who were newly seropositive since the summer. Between July and November 2020, the ratio of children diagnosed with SARS-CoV-2 infection to those who were seropositive was 1 to 8. At least one child who was newly seropositive was detected in 47 of 55 schools and in 90 of 275 classes. Among 130 classes with a high participation rate, no children who were seropositive were found in 73 (56%) classes, one or two children were seropositive in 50 (38%) classes, and at least three children were seropositive in 7 (5%) classes. Class level explained 24% and school level 8% of variance in seropositivity in the multilevel logistic regression models.

Conclusions

With schools open since August 2020 and some preventive measures in place, clustering of children who were seropositive occurred in only a few classes despite an increase in overall seroprevalence during a period of moderate to high transmission of SARS-CoV-2 in the community. Uncertainty remains as to whether these findings will change with the new variants of SARS-CoV-2 and dynamic levels of community transmission.

Trial registration

NCT04448717

Article activity feed

  1. Version published to 10.1136/bmj.n616
  2. ScreenIT

    SciScore for 10.1101/2020.12.19.20248513: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Antibodies
    SentencesResources
    Statistical analysis: Statistical analysis included descriptive statistics and Bayesian hierarchical modelling to estimate seroprevalence, accounting for the sensitivity and specificity of the SARS-CoV-2 antibody test, the hierarchical structure of cohort (individual and school levels), and post-stratification weights, which adjusted for the population-level grade level at school and geographic district [2].
    SARS-CoV-2
    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:
    Finally, a few ecological studies tried to estimate the overall effect of closing and opening schools on the development of the pandemic (in terms of diagnosed and reported cases and deaths) [29– 31], with major limitations of uncontrolled confounding, high level of aggregation (e.g., pooling school and university closures as one intervention, or analyzing aggregated outcomes on country-level) and potentially measuring the outcome in a population not exposed to the intervention. Finally, a stochastic modelling study of infection spread in schools have shown that some, although minimal, clustering of infections (outbreaks) is likely to happen even if major prevention and screening strategies are implemented [32]. In contrast to the mentioned retrospective and modelling studies, our study offers a prospective population level view, corresponding to school structure thanks to sampling on the school and class levels. In addition, having measured the baseline seroprevalence in June-July 2020, we were able to study the incidence of newly seropositive cases and their clustering in classes and schools in autumn. The study had a very high retention rate, with 89% of enrolled children retested in autumn. Together with the newly enrolled children from the same classes joining in autumn, the study had a high overall participation rate, especially given that it included venous blood sampling in children. High participation rate within a large proportion of classes allowed to study cluster...

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04448717RecruitingLongitudinal Study of Seroprevalence of SARS-CoV-2 Antibodie…

    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.

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  3. Version published to 10.1101/2020.12.19.20248513 on medRxiv