Estimated hospitalizations attributed to norovirus and rotavirus infection in Canada, 2006–2010
Bibliographic record
Abstract
Enteric viruses including norovirus and rotavirus are leading causes of gastroenteritis in Canada. However, only a small number of clinical cases are actually tested for these pathogens leading to systematic underestimation of attributed hospitalizations in administrative databases. The objective of this analysis was to estimate the number of hospitalizations due to norovirus and rotavirus in Canada. Hospitalization records for acute gastroenteritis-associated discharges at all acute-care hospitals in Canada between 2006 and 2011 were analysed. Cause-unspecified gastroenteritis hospitalizations were modelled using age-specific negative binomial models with cause-specified gastroenteritis admissions as predictors. The coefficients from the models were used to estimate the number of norovirus and rotavirus admissions. The total annual hospitalizations for rotavirus were estimated to be between 4500 and 10 000. Total annual hospitalizations for norovirus were estimated to be between 4000 and 11 000. The mean total annual cost associated with these hospitalizations was estimated to be at least $16 million for rotavirus and $21 million for norovirus (all figures in Canadian dollars). This study is the first comprehensive analysis of norovirus and rotavirus hospitalizations in Canada. These estimates provide a more complete assessment of the burden and economic costs of these pathogens to the Canadian healthcare system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".