Did Emergency Department Visits in Infants and Young Children Increase in the Last Decade?
Bibliographic record
Abstract
OBJECTIVES: The aims of the study were to measure overall trends and to identify leading causes for pediatric emergency department (ED) visits among children aged 0 to 4 years. METHODS: We conducted an 11-year population-based open cohort study using health administrative data from 2008 to 2018 in Ontario, Canada. All ED visits were extracted from the National Ambulatory Care Reporting System, along with the most responsible cause of each visit. Annual ED visit rates were calculated per 100 children in each year. Overall and disease-specific rates for all children were calculated and then stratified by sex and age groups. Relative percentage change in rates between 2008 and 2018 were calculated and compared using standardized differences (SDIFs). Statistical significance of time trends was tested using Poisson regression. RESULTS: This study included an average of 911,566 children from 2008 to 2018. All-cause ED visit rates increased by 28.2% from 2008 to 2018 (43.24-55.42 per 100, SDIF >0.1). Respiratory diseases were consistently the top cause of ED visits, and contributed to 1 in 3 ED visits in 2018. These respiratory conditions include asthma, asthma-related diseases (bronchiolitis, bronchitis, influenza, and pneumonia), and other respiratory diseases. Respiratory ED visit rates increased by 32.8% from 2008 to 2018 (11.51-15.28 per 100, SDIF <0.1), driven by a 46.4% (14.58-21.35 per 100, SDIF >0.1) increase among children younger than 1 year. There was a 78.0% increase in ED visits for bronchiolitis in infants (1.45-2.58 per 100, SDIF <0.1). CONCLUSIONS: Respiratory diseases like bronchiolitis among infants were the consistent leading cause for ED visits. All-cause ED visit rates among young children increased by 28.17% from 2008 to 2018.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".