SARS-CoV-2 infection prevention and control measures in Belgian schools between December 2020 and June 2021 and their association with seroprevalence: a cross-sectional analysis of a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: To protect school-aged children from the potential consequences of a new viral infection, public health authorities recommended to implement infection prevention and control (IPC) measures in school settings. Few studies evaluated the implementation of these measures and their effect on SARS-CoV-2 infection rates among pupils and staff. The aim of this study was to describe the implementation of infection prevention and control (IPC) measures in Belgian schools and assess its relation to the prevalence of anti-SARS-CoV-2 antibodies among pupils and staff. METHODS: We conducted a prospective cohort study in a representative sample of primary and secondary schools in Belgium between December 2020 and June 2021. The implementation of IPC measures in schools was assessed using a questionnaire. Schools were classified according to their compliance with the implementation of IPC measures as 'poor', 'moderate' or 'thorough'. Saliva samples were collected from pupils and staff to determine the SARS-CoV-2 seroprevalence. To assess the association between the strength of implementation of IPC measures and SARS-CoV-2 seroprevalence among pupils and staff, we conducted a cross-sectional analysis using the data collected in December 2020/January 2021. RESULTS: A variety of IPC measures (ventilation, hygiene and physical distancing) was implemented by more than 60% of schools, with most attention placed on hygiene measures. In January 2021, poor implementation of IPC measures was associated with an increase in anti-SARS-CoV-2 antibody prevalence among pupils from 8.6% (95%CI: 4.5 - 16.6) to 16.7% (95%CI: 10.2 - 27.4) and staff from 11.5% (95%CI: 8.1 - 16.4) to 17.6% (95%CI: 11.5 - 27.0). This association was only statistically significant for the assessment of all IPC measures together in the population comprised of pupils and staff. CONCLUSIONS: Belgian schools were relatively compliant with recommended IPC measures at the school level. Higher SARS-CoV-2 seroprevalence among pupils and staff was found in schools with poor implementation of IPC measures, compared to schools with thorough implementation. TRIAL REGISTRATION: This trial is registered under the NCT04613817 ClinicalTrials.gov Identifier on November 3, 2020.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".