The impact of publicly funded rotavirus immunization programs on Canadian children
Bibliographic record
Abstract
BACKGROUND: In 2008, the National Advisory Committee on Immunization recommended routine rotavirus immunizations in healthy Canadian infants. Over the following seven years, eight provinces and two territories introduced the rotavirus vaccine into their publicly funded immunization programs. OBJECTIVE: Assess the burden of rotavirus infections before and after implementation of publicly funded immunization programs. METHODS: We analyzed laboratory-confirmed community cases of rotavirus reported to the National Enteric Surveillance Program and hospitalizations of children younger than three years old from 2007 to 2017 with rotavirus diagnosis-specific ICD-10 codes. Rates of illness were calculated for each province for the two years prior to and after implementation of public funding of the vaccine. The year of implementation was not included to accommodate the uptake period of the vaccine. Age-specific rates were assessed in jurisdictions where five years of data were available the year after the vaccine was publicly funded. The pre-post and difference-in-difference (DID) methodologies were applied to hospital discharge data to evaluate changes between the funding and non-funding jurisdictions. RESULTS: Community cases of laboratory-confirmed rotavirus infection reported to the National Enteric Surveillance Program declined by 54% between 2010 and 2017. Rates of hospital discharges decreased significantly among children in six provinces after the adoption of the rotavirus vaccine. Hospital discharge rates in Alberta, Manitoba, Ontario and Prince Edward Island dropped between 53% and 71%, and by 75% for British Columbia and Saskatchewan. CONCLUSION: Public funding of the rotavirus vaccine appeared to lead to significant reductions in laboratory-confirmed rotavirus cases reported to the National Enteric Surveillance Program and in the rates of rotavirus gastroenteritis-related hospital discharges.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".