Health care utilization and costs following Kawasaki disease
Bibliographic record
Abstract
Objectives: Kawasaki disease (KD) is a common childhood vasculitis with increasing incidence in Canada. Acute KD hospitalizations are associated with high health care costs. However, there is minimal health care utilization data following initial hospitalization. Our objective was to determine rates of health care utilization and costs following KD diagnosis. Methods: We used population-based health administrative databases to identify all children (0 to 18 years) hospitalized for KD in Ontario between 1995 and 2018. Each case was matched to 100 nonexposed comparators by age, sex, and index year. Follow-up continued until death or March 2019. Our primary outcomes were rates of hospitalization, emergency department (ED), and outpatient physician visits. Our secondary outcomes were sector-specific and total health care costs. Results: We compared 4,597 KD cases to 459,700 matched comparators. KD cases had higher rates of hospitalization (adjusted rate ratio 2.07, 95%CI 2.00 to 2.15), outpatient visits (1.30, 95%CI 1.28 to 1.33), and ED visits (1.22, 95%CI 1.18 to 1.26) throughout follow-up. Within 1 year post-discharge, 717 (15.6%) KD cases were re-hospitalized, 4,587 (99.8%) had ≥1 outpatient physician visit and 1,695 (45.5%) had ≥1 ED visit. KD cases had higher composite health care costs post-discharge (e.g., median cost within 1 year: $2466 CAD [KD cases] versus $234 [comparators]). Total health care costs for KD cases, respectively, were $13.9 million within 1 year post-discharge and $54.8 million throughout follow-up (versus $2.2 million and $23.9 million for an equivalent number of comparators). Conclusions: Following diagnosis, KD cases had higher rates of health care utilization and costs versus nonexposed children. The rising incidence and costs associated with KD could place a significant burden on health care systems.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".