Characterizing school-linked and non-school linked SARS-CoV-2 cases in children and households: a retrospective cohort study using linked population-level administrative data from Manitoba, Canada
Bibliographic record
Abstract
ObjectiveTo identify directionality of SARS-CoV-2 cases within households and schools, and characterize school, household, and neighbourhood factors associated with school and non-school linked cases. ApproachUsing linked administrative data housed at the Manitoba Centre for Health Policy, a cohort of 449,425 individuals from 126,670 households with school-aged children enrolled in Manitoba public schools between September 1, 2020-June 30, 2021 was created. Using population-based SARS-CoV-2 laboratory test results, education enrolment and household composition data, index and transmission cases were counted and categorized at an individual and household level as school or non-school linked. Logistic regression models examined school, household and neighbourhood factors associated with school- and non-school linked households. Results143280 individuals were tested for SARS-CoV-2 during the study period; 14712 individuals (10.3%) tested positive. Amongst positive cases, 1695 (11.5%) were school-linked; the remaining 13017 (88.5%) were non-school linked. 13.7% of SARS-CoV-2 positive households had ≥1 case linked to school; 86.3% did not. Preliminary results found higher incomes quintiles had lower odds of being a school-linked household, as did living outside the Winnipeg region. Having more children in a grade and more household members resulted in higher odds of being a school-linked transmission household. No associations were observed for having a child under age five in the household. ConclusionsTaken together, these data suggest schools were not a large source of SARS-CoV-2 cases in Manitoba and factors associated with school-linked households largely mirrored broader community transmission patterns. ImplicationsThese findings may influence public health approaches to future pandemic control.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".