The effect of the COVID-19 pandemic on influenza-related hospitalization, intensive care admission and mortality in children in Canada: A population-based study
Bibliographic record
Abstract
Background: The COVID-19 pandemic resulted in unprecedented implementation of wide-ranging public health measures globally. During the pandemic, dramatic decreases in seasonal influenza virus detection have been reported worldwide. Information on the impact on paediatric influenza-related hospitalisations is limited. We describe influenza-related hospitalisation in children in Canada following the onset of the COVID-19 pandemic. Methods: Data on influenza-related hospitalisations, intensive care unit (ICU) admissions and in-hospital deaths in children across Canada were obtained from the Canadian Immunisation Monitoring Program, ACTive (IMPACT). This national active surveillance initiative comprises 90% of all tertiary care paediatric beds in Canada. The study period included eleven influenza seasons, from the 2010/2011 season until the 2020/2021 season inclusive. Time series modelling was used to compare the observed to predicted influenza-related hospitalisations following the COVID-19 pandemic. Results: < 0•0001). No paediatric influenza-related hospitalisations, ICU admission or deaths were reported for the 2020/2021 influenza season. Conclusions: We show complete absence of paediatric influenza infection-related hospitalisation in a Canadian National Surveillance Network during the 2020/2021 influenza season. This significant decrease is likely related in large part to non-pharmacological public health interventions implemented during the COVID-19 pandemic, although the potential role of viral interference is unknown. Funding: The Canadian Immunisation Monitoring Program, Active (IMPACT) influenza surveillance is a national surveillance initiative managed by the Canadian Paediatric Society and conducted by the IMPACT network of paediatric investigators on behalf of the Public Health Agency of Canada's Centre for Immunisation and Respiratory Infectious Diseases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".