Effects of school-level and area-level socio-economic factors on elementary school student COVID-19 infections: a population-based observational study
Bibliographic record
Abstract
OBJECTIVES: To estimate the variability of the cumulative incidence of SARS-CoV-2 infections among elementary school students attributable to individual schools and/or their geographic areas, and to ascertain whether socio-economic characteristics of school populations and/or geographic areas may be predictive of this variability. DESIGN: Population-based observational study of SARS-CoV-2 infections among elementary school children. SETTING: 3994 publicly funded elementary schools in 491 forward sortation areas (designated geographic unit based on first three characters of Canadian postal code), Ontario, Canada, September 2020 to April 2021. PARTICIPANTS: All students attending publicly funded elementary schools with a positive molecular test for SARS-CoV-2 reported by the Ontario Ministry of Education. MAIN OUTCOME MEASURES: Cumulative incidence of laboratory-confirmed elementary school student SARS-CoV-2 infections in Ontario, 2020-21 school year. RESULTS: A multilevel modelling approach was used to estimate the effects of socio-economic factors at the school and area levels on the cumulative incidence of elementary school student SARS-CoV-2 infections. At the school level (level 1), the proportion of the student body from low-income households was positively associated with cumulative incidence (β=0.083, p<0.001). At the area level (level 2), all dimensions of marginalisation were significantly related to cumulative incidence. Ethnic concentration (β=0.454, p<0.001), residential instability (β=0.356, p<0.001) and material deprivation (β=0.212, p<0.001) were positively related, while dependency (β=-0.204, p<0.001) was negatively related. Area-related marginalisation variables explained 57.6% of area variability in cumulative incidence. School-related variables explained 1.2% of school variability in cumulative incidence. CONCLUSIONS: The socio-economic characteristics of the geographic area of schools were more important in accounting for the cumulative incidence of SARS-CoV-2 elementary school student infections than individual school characteristics. Schools in marginalised areas should be prioritised for infection prevention measures and education continuity and recovery plans.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".