Impact of the first nirsevimab immunization campaign on RSV-related emergency department visits and hospitalizations in Québec, Canada
Bibliographic record
Abstract
Abstract Objectives During the 2024-2025 respiratory syncytial virus (RSV) season, a universal nirsevimab infant immunization program was implemented in the province of Québec. We evaluated its population-level impact on RSV-related emergency department (ED) visits and hospitalizations in infants, using both active surveillance and administrative health data. Methods Study population included all Québec children <18 years old. Incidence rates of RSV-confirmed hospitalizations (6 hospital-based active surveillance), RSV-associated hospitalizations and ED visits linked to a positive RSV test, and acute bronchiolitis ED visits (administrative databases) were measured for nirsevimab-eligible (0-5 months old) and non-eligible (6-11 months and 1-17 years old) age groups during the 2024-2025 season and compared to the previous seasons (varying lookback periods). Using difference-in-differences and observed versus expected approaches, pre-/post-intervention variations in 0-5-month-olds were adjusted for any time trends extending to non-targeted age groups. Results Decreases of 59% (95% CI: 49–70) and 66% (95% CI: 58–71) were observed in RSV-confirmed and RSV-associated hospitalizations, respectively. Acute bronchiolitis ED visits decreased by 35% (95% CI: 28–41), and RSV-associated ED visits, by 60% (95% CI: 56–65). This impact became noticeable approximately one week after expanding the campaign to all eligible infants. ED visits and hospitalizations were less frequent than expected (i.e. without nirsevimab) in 0-5-month-olds, across all indicators. Between 323 and 746 hospitalizations were possibly prevented. Conclusion The 2024-2025 nirsevimab campaign was associated with a two-thirds reduction in RSV-related hospitalizations and RSV-associated ED visits in Québec infants. ED visits for acute bronchiolitis were also reduced by a third.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".