Emergency department utilization, admissions, and revisits in the United States (New York), Canada (Ontario), and New Zealand: A retrospective <scp>cross‐sectional</scp> analysis
Bibliographic record
Abstract
BACKGROUND: Emergency department (ED) utilization is a significant concern in many countries, but few population-based studies have compared ED use. Our objective was to compare ED utilization in New York (United States), Ontario (Canada), and New Zealand (NZ). METHODS: A retrospective cross-sectional analysis of all ED visits between January 1, 2016, and September 30, 2017, for adults ≥18 years using data from the State Emergency Department and Inpatient Databases (New York), the National Ambulatory Care Reporting System and Discharge Abstract Data (Ontario), and the National Non-Admitted Patient Collection and the National Minimum Data Set (New Zealand). Outcomes included age- and sex-standardized per-capita ED utilization (overall and stratified by neighborhood income), ED disposition, and ED revisit and hospitalization within 30 days of ED discharge. RESULTS: There were 10,998,371 ED visits in New York, 8,754,751 in Ontario, and 1,547,801 in New Zealand. Patients were older in Ontario (mean age 51.1 years) compared to New Zealand (50.3) and New York (48.7). Annual sex- and age-standardized per-capita ED utilization was higher in Ontario than New York or New Zealand (443.2 vs. 404.0 or 248.4 visits per 1000 population/year, respectively). In all countries, ED utilization was highest for residents of the lowest income quintile neighborhoods. The proportion of ED visits resulting in hospitalization was higher in New Zealand (34.5%) compared to New York (20.8%) and Ontario (12.8%). Thirty-day ED revisits were higher in Ontario (27.0%) than New Zealand (18.6%) or New York (21.4%). CONCLUSIONS: Patterns of ED utilization differed widely across three high-income countries. These differences highlight the varying approaches that our countries take with respect to urgent visits, suggest opportunities for shared learning through international comparisons, and raise important questions about optimal approaches for all countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".