The effect of the COVID-19 pandemic on hospital admissions and outpatient visits in Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION: The wave-over-wave effect of the COVID-19 pandemic on hospital visits for non-COVID-19-related diagnoses in Ontario, Canada remains unknown. METHODS: We compared the rates of acute care hospitalizations (Discharge Abstract Database), emergency department (ED) visits, and day surgery visits (National Ambulatory Care Reporting System) during the first five "waves" of Ontario's COVID-19 pandemic with prepandemic rates (since January 1, 2017) across a spectrum of diagnostic classifications. RESULTS: Patients admitted in the COVID-19 era were less likely to reside in long-term-care facilities (OR 0.68 [0.67-0.69]), more likely to reside in supportive housing (OR 1.66 [1.63-1.68]), arrive by ambulance (OR 1.20 [1.20-1.21]) or be admitted urgently (OR 1.10 [1.09-1.11]). Since the start of the COVID-19 pandemic (February 26, 2020), there were an estimated 124,987 fewer emergency admissions than expected based on prepandemic seasonal trends, representing reductions from baseline of 14% during Wave 1, 10.1% in Wave 2, 4.6% in Wave 3, 2.4% in Wave 4, and 10% in Wave 5. There were 27,616 fewer medical admissions to acute care, 82,193 fewer surgical admissions, 2,018,816 fewer ED visits, and 667,919 fewer day-surgery visits than expected. Volumes declined below expected rates for most diagnosis groups, with emergency admissions and ED visits associated with respiratory disorders exhibiting the greatest reduction; mental health and addictions was a notable exception, where admissions to acute care following Wave 2 increased above prepandemic levels. CONCLUSIONS: Hospital visits across all diagnostic categories and visit types were reduced at the onset of the COVID-19 pandemic in Ontario, followed by varying degrees of recovery.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".