Racial disparities in children tested for SARS-CoV-2 at pediatric emergency departments: A prospective cohort study
Bibliographic record
Abstract
Objectives: To evaluate the association between race and SARS-CoV-2 test positivity and outcomes in children. Study Design: Secondary analysis of a prospective cohort study recruiting children < 18 years, tested for SARS-CoV-2 between August 2020 and February 2022, in Canadian pediatric emergency departments. Race was self-reported by participants. The primary outcome was SARS-CoV-2 test positivity. Secondary outcomes were medical interventions and hospitalization within 14 days of index visit, and post-COVID condition (PCC) at 90-day follow-up. Associations were evaluated using multi-level logistic regression models. Results: Seven thousand and two-thirty three children underwent SARS-CoV-2 testing; median age was 2.0 years (IQR: 1.0-5.0), and 3366 (46.5%) were female. 1440 (19.9%) children tested positive for SARS-CoV-2, 776 (10.7%) were hospitalized, and 153 (13.2%) test-positive children experienced PCC. Compared to White children, most racial minority groups were more likely to test positive for SARS-CoV-2 (Middle Eastern aOR [95% CI] 2.62 [2.07, 3.32], Black aOR 2.36 [1.85, 3.03], Latin American aOR 2.23 [1.58, 3.15], South Asian aOR 2.17 [1.67, 2.82], Indigenous aOR 2.09 [1.29, 3.37], Southeast Asian aOR 1.82 [1.27, 2.62], Multiracial aOR 1.35 [1.07, 1.69], and had lower odds of medical interventions. Only Indigenous children were at higher odds of hospitalization than White children (aOR [95% CI]: 2.48 [1.03, 5.95]). Black children were less likely to report PCCs than White children (aOR 0.44 [0.22-0.86]). Conclusions: Racial disparities exist in SARS-CoV-2 test positivity and outcomes among Canadian children seeking emergency care. A better understanding of the factors contributing to these differences is needed to promote equitable health across the population.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".