Burden of Illness Associated With Respiratory Syncytial Virus, Influenza, and Coronavirus Disease 2019 in Infants and Young Children in Ontario, Canada, 2018–2023: A Population-Based Canadian Immunization Research Network Study
Bibliographic record
Abstract
Background: As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transitions to endemicity and respiratory syncytial virus (RSV) and influenza reestablish their seasonal circulation patterns, understanding their comparative burden on infants and children can guide health system responses and funding priorities. Methods: This was a population-based cohort study of children aged <5 years in Ontario, Canada, from September 2018 to August 2023 using linked health administrative databases. Results: Seasonal cohorts comprised 731 838 to 763 660 children. RSV- and influenza-related hospital admission rates immediately decreased in March 2020. In 2021-2022, RSV-related admissions rebounded but remained 8%-11% lower than prepandemic seasons, whereas 2022-2023 RSV-related admissions increased 105%-113% versus prepandemic seasons and peak admissions occurred in November versus December. Influenza did not rebound until 2022-2023, when admissions were 28%-37% higher than prepandemic seasons. Coronavirus disease 2019 (COVID-19)-related admissions remained low until 2021-2022 and were lower than RSV-related admissions across all age groups during prepandemic and pandemic seasons. By contrast, 2021-2022 COVID-19-related admissions exceeded prepandemic influenza-related admissions by 30%-40% but decreased by 45% in 2022-2023 and were lower than prepandemic influenza-related admissions, except among infants aged <12 months who remained at highest risk. There was no distinct seasonal pattern for COVID-19-related admissions during the study. Conclusions: RSV remains a major cause of childhood hospitalization. Although COVID-19 appears to be receding, its disease burden as it transitions to endemicity remains to be established. The unprecedented peaks in RSV-related hospital admissions during 2022-2023, together with the return of influenza and ongoing SARS-CoV-2 transmission, underscore the need to strengthen systems for real-time surveillance and to proactively prepare for critical healthcare scenarios.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".