Effect of the COVID-19 Pandemic on the Epidemiology of Kawasaki Disease in Canada
Bibliographic record
Abstract
Background COVID-19 substantially changed the epidemiology of Kawasaki disease (KD), with decreased incidence reported globally. We sought to determine its effect in Canada. Methods Pediatric admissions for KD (2004-2023) were identified through the Canadian Institute for Health Information. Clinical data for KD hospitalizations at The Hospital for Sick Children in Toronto (2016-June 2023) were manually reviewed. Results KD incidence was stable prior to the pandemic (22.5/100,000 0-4 year-old/year,p=0.19). There was a 30% reduction early pandemic (2020-2021) across all regions (15.8 cases/100,000 0-4 year-old) with blunting of traditional winter peaks. In the summer of 2022, an atypical peak in KD incidence was observed, aligning with the Delta wave. Epidemiology returned to normal patterns in 2023-2024 (+7% vs. pre-pandemic to 24.1 cases/100,000 0-4 year-old), with notable differences in Ontario and the Prairies. Patients diagnosed in the early pandemic were more likely to present with incomplete KD (odds ratio (95% confidence interval) OR:4.84(3.22-7.30),p=0.008), respiratory (OR:2.59(1.70-7.30),p=0.03) or abdominal symptoms (OR:4.84(3.15-7.44),p<.001). Those diagnosed in the late pandemic also presented with more incomplete KD (OR:1.76(1.16-2.66),p<0.001), respiratory (OR:1.70(1.05-2.75),p<0.001) or abdominal symptoms (OR:2.53(1.73-3.70),p<.001), albeit to a lesser extent, along with a shorter duration of fever before diagnosis (EST:-0.82(-1.52;-0.12) days, p=0.02) and higher odds of developing KD shock syndrome (OR:4.66(1.83-11.87),p=0.001). Throughout the pandemic, odds of developing giant coronary aneurysms or admission to the intensive care unit remained similar. Conclusions The pandemic saw the incidence of KD decrease significantly with disrupted seasonality. Pre-pandemic epidemiological patterns have returned. Patient presentations changed but outcomes remained stable compared to the pre-pandemic period.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".