COVID-19 surveillance in the Flemish school system: development of systematic data collection within the public health school system and descriptive analysis of cases reported between October 2020 and June 2021.


Journal

BMC public health
ISSN: 1471-2458
Titre abrégé: BMC Public Health
Pays: England
ID NLM: 100968562

Informations de publication

Date de publication:
15 10 2022
Historique:
received: 21 12 2021
accepted: 21 09 2022
entrez: 15 10 2022
pubmed: 16 10 2022
medline: 19 10 2022
Statut: epublish

Résumé

The age-specific distribution of SARS-CoV-2 cases in schools is not well described. Reported statistics reflect the intensity of community transmission while being shaped by biases from age-dependent testing regimes, as well as effective age-specific interventions. A case surveillance system was introduced within the Flemish school and health-prevention network during the 2020-2021 school year. We present epidemiological data of in-school reported cases in pre-, primary and secondary schools identified by the case surveillance system, in conjunction with test data and community cases from October 2020 to June 2021. We describe the development of the surveillance system and provide the number of reported cases and standardized rates per grade over time. We calculated absolute and relative differences in case incidence according to school grade (primary: grades 1-6, and secondary: grades 7-12) using grades 7-8 as a comparator, relating them to non-pharmaceutical infection prevention interventions. Cumulative population incidences (IP) stratified by age, province and socioeconomic status (SES) of the school population are presented with their 95% confidence intervals (CI). A total of 59,996 COVID-19 cases were reported in the school surveillance system, with the highest population adjusted IP in grade 11-12 of 7.39% (95%CI 7.24-7.53) and ranging from 2.23% to 6.25% from pre-school through grade 10. Age-specific reductions in mask introduction and in-person teaching were temporally associated with decreased case incidence, while lower pupil SES was associated with an increase in cumulative cases (excess 2,739/100,000 pupils compared to highest SES tertile). Community testing volumes varied more for children compared to adults, with overall higher child test-positivity. Holidays influence capturing of cases by the system, however efficiency increased to above 75% after further automation and integration in existing structures. We demonstrate that effective integration of case surveillance within an electronic school health system is feasible, provides valuable data regarding the evolution of an epidemic among schoolchildren, and is an integral component of public health surveillance and pandemic preparedness. The relationship towards community transmission needs careful evaluation because of age-different testing regimens. In the Flemish region, case incidence within schools exhibited an age gradient that was mitigated through grade-specific interventions, though differences by SES remain.

Sections du résumé

BACKGROUND
The age-specific distribution of SARS-CoV-2 cases in schools is not well described. Reported statistics reflect the intensity of community transmission while being shaped by biases from age-dependent testing regimes, as well as effective age-specific interventions. A case surveillance system was introduced within the Flemish school and health-prevention network during the 2020-2021 school year. We present epidemiological data of in-school reported cases in pre-, primary and secondary schools identified by the case surveillance system, in conjunction with test data and community cases from October 2020 to June 2021.
METHODS
We describe the development of the surveillance system and provide the number of reported cases and standardized rates per grade over time. We calculated absolute and relative differences in case incidence according to school grade (primary: grades 1-6, and secondary: grades 7-12) using grades 7-8 as a comparator, relating them to non-pharmaceutical infection prevention interventions. Cumulative population incidences (IP) stratified by age, province and socioeconomic status (SES) of the school population are presented with their 95% confidence intervals (CI).
RESULTS
A total of 59,996 COVID-19 cases were reported in the school surveillance system, with the highest population adjusted IP in grade 11-12 of 7.39% (95%CI 7.24-7.53) and ranging from 2.23% to 6.25% from pre-school through grade 10. Age-specific reductions in mask introduction and in-person teaching were temporally associated with decreased case incidence, while lower pupil SES was associated with an increase in cumulative cases (excess 2,739/100,000 pupils compared to highest SES tertile). Community testing volumes varied more for children compared to adults, with overall higher child test-positivity. Holidays influence capturing of cases by the system, however efficiency increased to above 75% after further automation and integration in existing structures.
CONCLUSION
We demonstrate that effective integration of case surveillance within an electronic school health system is feasible, provides valuable data regarding the evolution of an epidemic among schoolchildren, and is an integral component of public health surveillance and pandemic preparedness. The relationship towards community transmission needs careful evaluation because of age-different testing regimens. In the Flemish region, case incidence within schools exhibited an age gradient that was mitigated through grade-specific interventions, though differences by SES remain.

Identifiants

pubmed: 36243679
doi: 10.1186/s12889-022-14250-1
pii: 10.1186/s12889-022-14250-1
pmc: PMC9568939
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1921

Informations de copyright

© 2022. The Author(s).

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Auteurs

Joanna Merckx (J)

Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, 2001 McGill Street, Suite 1200, Montreal, QC, H3A 1G1, Canada. joanna-trees.merckx@mcgill.ca.

Jonas Crèvecoeur (J)

I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium.

Kristiaan Proesmans (K)

Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium.

Naïma Hammami (N)

Agency for Care and Health, Infection Prevention and Control, Flemish Community, Brussels, Belgium.

Hilde Denys (H)

Department Onderwijs, Brussels, Belgium.

Niel Hens (N)

I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium.
Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium.

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Classifications MeSH