Population-Level Impact of Ontario’s Infant Rotavirus Immunization Program: Evidence of Direct and Indirect Effects
Bibliographic record
Abstract
OBJECTIVE: To evaluate the direct and indirect population impact of rotavirus (RV) immunization on hospitalizations and emergency department (ED) visits for acute gastroenteritis (AGE) in Ontario before and after the publicly-funded RV immunization program. METHODS: Administrative data was used to identify ED visits and hospitalizations for all Ontarians using ICD-10 codes. We used two outcome definitions: RV-specific AGE (RV-AGE) and codes representing RV-, other viral and cause unspecified AGE ("overall AGE"). The pre-program and public program periods were August 1, 2005 to July 31, 2011; and August 1, 2011 to March 31, 2013, respectively. A negative binominal regression model that included the effect of time was used to calculate rates and rate ratios (RRs) and 95% confidence intervals (CIs) for RV-AGE and overall AGE between periods, after adjusting for age, seasonality and secular trends. Analyses were conducted for all ages combined and age stratified. RESULTS: Relative to the pre-program period, the adjusted RRs for RV-AGE and overall AGE hospitalizations in the public program period were 0.29 (95%CI: 0.22-0.39) and 0.68 (95%CI: 0.62-0.75), respectively. Significant reductions in RV-AGE hospitalizations were noted overall and for the following age bands: < 12 months, 12-23 months, 24-35 months, 3-4 years, and 5-19 years. Significant declines in overall AGE hospitalizations were observed across all age bands, including older adults > = 65 years (RR 0.80, 95%CI: 0.72-0.90). The program was associated with adjusted RRs of 0.32 (95% CI: 0.20-0.52) for RV-AGE ED visits and 0.90 (95% CI: 0.85-0.96) for overall AGE ED visits. CONCLUSIONS: This large, population-based study provides evidence of the impact of RV vaccine in preventing hospitalizations and ED visits for RV-AGE and overall AGE, including herd effects.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".