Nirsevimab effectiveness, number needed to immunize and impact on severe RSV outcomes in preterm, high-risk and healthy-term infants, Quebec, Canada
Bibliographic record
Abstract
ABSTRACT Background During the 2024-25 season, a universal infant nirsevimab program was publicly funded in Quebec, Canada. We estimated effectiveness, number-needed-to-immunize (NNI) and impact against severe respiratory syncytial virus (RSV) outcomes. Methods We conducted a test-negative study among nirsevimab-eligible children RSV-tested during ER consultation or hospitalization between October 1 st , 2024 and March 3 st , 2025. Eligible children were healthy-term and born during the RSV season (at-birth group) or <6 months old on October 1 st (catch-up group), preterm, with high-risk conditions or living in remote regions. We estimated adjusted effectiveness by eligible group and further used 2023-24 respiratory hospitalization rates, RSV-positivity from a systematic hospital-based surveillance network, and coverage estimates to derive the NNI and tally of averted hospitalizations and ICU admissions. Results Effectiveness analyses included 3,172 ER consultations (668 RSV-positive) and 1,758 hospitalizations (549 RSV-positive). Nirsevimab effectiveness against ER consultation, hospitalization and ICU admission was 86% (95%CI:82-90), 89% (95%CI:84-92) and 88% (95%CI:58-97), respectively. Effectiveness exceeded 80% for all eligible groups. We estimate 41 at-birth and 58 catch-up immunizations needed to avert one RSV-associated hospitalization between October and March. Applying Quebec catch-up coverage (64%) and timing (November launch), we estimate more than half of RSV-associated hospitalizations and ICU admissions were prevented, potentially increasing to more than three-quarters if catch-up coverage reached 90% by October 1 st . Conclusions Nirsevimab is highly effective and could more substantially impact the overall burden of RSV hospitalization and ICU admission through broad and timely administration to healthy-term and high-risk infants. To inform optimal nirsevimab timing, the durability of late-season protection warrants further investigation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".