Acute health care use among children during the first 2.5 years of the COVID-19 pandemic in Ontario, Canada: a population-based repeated cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The effects of the decline in health care use at the start of the COVID-19 pandemic on the health of children are unclear. We sought to estimate changes in rates of severe and potentially preventable health outcomes among children during the pandemic. METHODS: We conducted a repeated cross-sectional study of children aged 0-17 years using linked population health administrative and disease registry data from January 2017 through August 2022 in Ontario, Canada. We compared observed rates of emergency department visits and hospital admissions during the pandemic to predicted rates based on the 3 years preceding the pandemic. We evaluated outcomes among children and neonates overall, among children with chronic health conditions and among children with specific diseases sensitive to delays in care. RESULTS: All acute care use for children decreased immediately at the onset of the pandemic, reaching its lowest rate in April 2020 for emergency department visits (adjusted relative rate [RR] 0.28, 95% confidence interval [CI] 0.28-0.29) and hospital admissions (adjusted RR 0.43, 95% CI 0.42-0.44). These decreases were sustained until September 2021 and May 2022, respectively. During the pandemic overall, rates of all-cause mortality, admissions for ambulatory care-sensitive conditions, newborn readmissions or emergency department visits or hospital admissions among children with chronic health conditions did not exceed predicted rates. However, after declining significantly between March and May 2020, new presentations of diabetes mellitus increased significantly during most of 2021 (peak adjusted RR 1.49, 95% CI 1.28-1.74 in July 2021) and much of 2022. Among these children, presentations for diabetic ketoacidosis were significantly higher than expected during the pandemic overall (adjusted RR 1.14, 95% CI 1.00-1.30). We observed similar time trends for new presentations of cancer, but we observed no excess presentations of severe cancer overall (adjusted RR 0.91, 95% CI 0.62-1.34). INTERPRETATION: In the first 30 months of the pandemic, disruptions to care were associated with important delays in new diagnoses of diabetes but not with other acute presentations of select preventable conditions or with mortality. Mitigation strategies in future pandemics or other health system disruptions should include education campaigns around important symptoms in children that require medical attention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".