Hospital burden of influenza, respiratory syncytial virus, and other respiratory viruses in Canada, seasons 2010/2011 to 2018/2019
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to develop a model to estimate the hospitalization burden attributable to influenza, respiratory syncytial virus (RSV), enterovirus (EV), human metapneumovirus (HMPV), human parainfluenza virus (HPIV), and other respiratory viruses (OV) in Canada. METHODS: A Poisson regression model was developed using respiratory hospitalization administrative data for the seasons 2010/2011 to 2018/2019. RESULTS: The estimated average seasonal number of respiratory hospitalizations attributable to influenza was 15,000 in Canada (rate 43.4 hospitalizations per 100,000 population [95%CI 40.9, 46.0]), and 13,000 (rate 36.3 hospitalizations per 100,000 population [95%CI 29.2, 43.4]) for RSV. The estimated average seasonal numbers of hospitalizations attributable to EV, HMPV, HPIV, and OV were 6000 (rate 16.2 hospitalizations per 100,000 population [95%CI 10.7, 21.8]), 4000 (rate 12.4 hospitalizations per 100,000 population [95%CI 7.1, 17.6]), 2000 (rate 5.9 hospitalizations per 100,000 population [95%CI 2.0, 9.8]), and 3000 (rate 8.9 hospitalizations per 100,000 population [95%CI 0.04, 17.7]), respectively. CONCLUSION: This study provided updated and new Canadian estimates for hospitalizations attributable to influenza, RSV, EV, HMPV, and HPIV for 2010/2011 to 2018/2019 surveillance seasons. These estimates are important given the emergence of SARS-CoV-2 and the ongoing circulation of seasonal respiratory viruses. Routine burden estimation is pivotal in supporting the implementation and evaluation of public health programs focused at mitigating the impacts of respiratory viruses. Although multiple external factors are at play, this study indicates that influenza and RSV attributable hospitalizations were persisting and generally increasing in Canada in recent years preceding the COVID-19 pandemic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".