Changes in hospitalizations and emergency department respiratory viral diagnosis trends before and during the COVID-19 pandemic in Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION: Population-level surveillance systems have demonstrated reduced transmission of non-SARS-CoV-2 respiratory viruses during the COVID-19 pandemic. In this study, we examined whether this reduction translated to reduced hospital admissions and emergency department (ED) visits associated with influenza, respiratory syncytial virus (RSV), human metapneumovirus, human parainfluenza virus, adenovirus, rhinovirus/enterovirus, and common cold coronavirus in Ontario. METHODS: Hospital admissions were identified from the Discharge Abstract Database and exclude elective surgical admissions and non-emergency medical admissions (January 2017-March 2022). Emergency department (ED) visits were identified from the National Ambulatory Care Reporting System. International Classification of Diseases (ICD-10) codes were used to classify hospital visits by virus type (January 2017-May 2022). RESULTS: At the onset of the COVID-19 pandemic, hospitalizations for all viruses were reduced to near-trough levels. Hospitalizations and ED visits for influenza (9,127/year and 23,061/year, respectively) were nearly absent throughout the pandemic (two influenza seasons; April 2020-March 2022). Hospitalizations and ED visits for RSV (3,765/year and 736/year, respectively) were absent for the first RSV season during the pandemic, but returned for the 2021/2022 season. This resurgence of hospitalizations for RSV occurred earlier in the season than expected, was more likely among younger infants (age ≤6 months), more likely among older children (aged 6.1-24 months), and less likely to comprise of patients residing in higher areas of ethnic diversity (p<0.0001). CONCLUSION: During the COVID-19 pandemic, there was a reduced the burden of other respiratory infections on patients and hospitals. The epidemiology of respiratory viruses in the 2022/23 season remains to be seen.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".