Nirsevimab Against Hospitalizations and Emergency Department Visits for Lower Respiratory Tract Infection in Infants
Bibliographic record
Abstract
Importance: Nirsevimab, a long-acting monoclonal antibody available since 2023, has demonstrated effectiveness in preventing lower respiratory tract infection (LRTI) caused by respiratory syncytial virus (RSV) in clinical trials and postlicensure settings. However, its broader real-world associations with respiratory-related outcomes in infants remain unclear, and characterizing these associations is essential to inform pediatric immunization policy. Objective: To evaluate the real-world association of nirsevimab with LRTI-related hospitalizations and emergency department (ED) visits in infants. Data Sources: MEDLINE, Embase, Web of Science, Scopus, Global Health, and medRxiv databases were systematically searched for observational studies published between January 1, 2023, and June 20, 2025. Data analysis was performed between January 1, 2025, and June 20, 2025. Study Selection: Postlicensure observational studies reporting original data on the effectiveness of nirsevimab immunization programs in infants and children aged 24 months or younger in routine clinical settings were eligible for inclusion. Data Extraction and Synthesis: Two reviewers independently extracted data and assessed study quality using the Critical Appraisal Checklist of the Joanna Briggs Institute. Random-effects meta-analysis was conducted to estimate pooled odds ratios (ORs) and 95% confidence intervals. Main Outcomes and Measures: Primary outcomes were all-cause LRTI-related hospitalization, all-cause hospitalization, all-cause LRTI-related ED visit, and RSV-LRTI-related ED visit. Results: Of 1752 records screened, 15 studies met inclusion criteria; 11 studies were from 5 countries included in the meta-analysis, comprising 236 764 infants and children in the nirsevimab group and 27 522 in the control group. Compared with controls, nirsevimab was associated with lower odds of all-cause LRTI-related hospitalization (OR, 0.38; 95% CI, 0.28-0.53), all-cause LRTI-related ED visits (OR, 0.52; 95% CI, 0.37-0.73), and RSV-LRTI-related ED visits (OR, 0.24; 95% CI, 0.13-0.47). No significant difference was observed in all-cause hospitalizations (OR, 0.56; 95% CI, 0.14-2.20) between the nirsevimab and control groups. Conclusions and Relevance: In this meta-analysis, nirsevimab was associated with reduced LRTI-related hospitalizations and ED visits in infants and young children. These findings support nirsevimab's potential to reduce respiratory-related morbidity in young children and health care utilization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".