School opening associated with lower test-adjusted COVID-19 case rates in children
Bibliographic record
Abstract
BACKGROUND: There is conflicting evidence from prior studies on the relationship between in-person schooling and transmission of SARS-CoV-2 among children. This may be due to multiple confounders in estimating this relationship, including the decision to close schools, community rates of infection, and rates of testing. METHODS: Regression-based observational study to estimate the relationship between school openings and COVID-19 case rates among children, while accounting for potential confounders including community case rates, mitigations in schools, and rates of testing among schoolchildren. The setting is US school districts in the Fall of 2021, from 3 weeks prior through 12 weeks after school opening, using restricted data obtained from the Centers for Disease Control and Prevention. Data were available for school districts in 2592 counties, containing 86% of the US population. RESULTS: School openings were associated with a brief rise in cases among children relative to adults, with a peak of 39.3 [37.7, 40.9] additional cases per 100 000 per week. However, children were tested at higher rates when schools were in session. After adjusting for testing rates, case rates among children were significantly lower after schools reopened by 4.7 cases per 100 000 compared with over summer break. CONCLUSION: School reopening in the USA in the 2021-22 academic year was accompanied by an increase in SARS-CoV-2 testing in children and a brief rise in pediatric cases. When testing rates are accounted for, school reopening was associated with a decrease in COVID-19 cases among children relative to adults. A lower threshold for testing in the school setting may be an important confounder in studies of SARS-CoV-2 transmission.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".