Evidence map›Paper›PMID 42022153›Full record

ArticleThe Lancet regional health. Western Pacific2026

Real-world effectiveness of nirsevimab against RSV hospitalisation in infants during a season with low coverage and delayed circulation in Beijing, China: a population-based retrospective cohort study.

Juan Li, Luodan Suo, Lai Wei, Zhiqiang Cao, Feng Lu, Hui Xu, Jiayao Luo, Ying Shen, Pei Gao, Dan Wu and 5 more

Abstract read
In one paragraph

Article in The Lancet regional health. Western Pacific, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Juan LiBeijing Key Laboratory of Surveillance, Early Warning and Pathogen Research on Emerging Infectious Diseases, Beijing Center for Disease Prevention and Control, Beijing 100013, China.
Luodan SuoBeijing Key Laboratory of Surveillance, Early Warning and Pathogen Research on Emerging Infectious Diseases, Beijing Center for Disease Prevention and Control, Beijing 100013, China.
Lai WeiBeijing Key Laboratory of Surveillance, Early Warning and Pathogen Research on Emerging Infectious Diseases, Beijing Center for Disease Prevention and Control, Beijing 100013, China.
Zhiqiang CaoBeijing Key Laboratory of Surveillance, Early Warning and Pathogen Research on Emerging Infectious Diseases, Beijing Center for Disease Prevention and Control, Beijing 100013, China.
Feng LuBeijing Municipal Health Big Data and Policy Research Center (Beijing Institute of Hospital Management), Beijing 101117, China.
Hui XuBeijing Key Laboratory of Surveillance, Early Warning and Pathogen Research on Emerging Infectious Diseases, Beijing Center for Disease Prevention and Control, Beijing 100013, China.
Jiayao LuoBeijing Key Laboratory of Surveillance, Early Warning and Pathogen Research on Emerging Infectious Diseases, Beijing Center for Disease Prevention and Control, Beijing 100013, China.
Ying ShenBeijing Key Laboratory of Surveillance, Early Warning and Pathogen Research on Emerging Infectious Diseases, Beijing Center for Disease Prevention and Control, Beijing 100013, China.
Pei GaoBeijing Key Laboratory of Surveillance, Early Warning and Pathogen Research on Emerging Infectious Diseases, Beijing Center for Disease Prevention and Control, Beijing 100013, China.
Dan WuBeijing Key Laboratory of Surveillance, Early Warning and Pathogen Research on Emerging Infectious Diseases, Beijing Center for Disease Prevention and Control, Beijing 100013, China.
Zhaomin FengBeijing Key Laboratory of Surveillance, Early Warning and Pathogen Research on Emerging Infectious Diseases, Beijing Center for Disease Prevention and Control, Beijing 100013, China.
Rui YuBeijing Key Laboratory of Surveillance, Early Warning and Pathogen Research on Emerging Infectious Diseases, Beijing Center for Disease Prevention and Control, Beijing 100013, China.
Moning GuoBeijing Municipal Health Big Data and Policy Research Center (Beijing Institute of Hospital Management), Beijing 101117, China.
Quanyi WangBeijing Key Laboratory of Surveillance, Early Warning and Pathogen Research on Emerging Infectious Diseases, Beijing Center for Disease Prevention and Control, Beijing 100013, China.
Peng YangBeijing Key Laboratory of Surveillance, Early Warning and Pathogen Research on Emerging Infectious Diseases, Beijing Center for Disease Prevention and Control, Beijing 100013, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Nirsevimab was first deployed in Beijing through a pilot voluntary, self-pay scheme during the 2024-2025 epidemic season, following its approval in China in December 2023. We aimed to evaluate the real-world effectiveness of nirsevimab against RSV-associated lower respiratory tract infection (LRTI) hospitalisation in Beijing, China. Methods: We conducted a population-based retrospective cohort study using linked, individual-level data from the Beijing Immunisation Information System and Hospitalisation Information System. The study population comprised all infants born between April 1, 2024, and March 31, 2025, in 24 designated pilot maternity hospitals. The primary endpoint was RSV-associated LRTI hospitalisation. City-wide virological surveillance was used to define the population-level exposure risk period. Nirsevimab effectiveness was estimated using Bayesian Poisson regression in a propensity score-matched cohort, with three additional models (Firth penalized Poisson, Bayesian Cox, and Firth penalized Cox) as sensitivity analyses. Findings: Among 44,791 eligible infants, 1166 (2.6%) received nirsevimab. In the propensity score-matched cohort (1129 infants per group), the incidence rates of RSV-associated LRTI hospitalisation were 18.8 vs. 2.3 per 1000 person-years in non-recipients and recipients, respectively. Bayesian Poisson regression demonstrated 83.3% effectiveness (95% CrI: 33.3-97.5%; posterior probability: 0.995). Sensitivity analyses yielded consistent estimates (82.8-84.5%). Analyses in the full cohort produced directionally consistent estimates. Interpretation: This first evidence of nirsevimab's effectiveness in China validates its value in a novel geographical and implementation context, supporting its inclusion as a recommended preventive option. Real-time virological surveillance is essential to optimise immunisation timing amid evolving RSV epidemiology. Funding: Capital's Funds for Health Improvement and Research (2026-1G-3012); Beijing Research Center for Respiratory Infectious Diseases (BJRID2024-002); Beijing Municipal Health Commission's Funds for the High-qualified Public Health Professionals Development Project (Discipline Core-03-36); Capital's Funds for Health Improvement and Research (2024-2-30117).

Indexed as

Cohort studyEffectivenessHospitalisationInfantsNirsevimabRespiratory syncytial virusRespiratory syncytial virus monoclonal antibody

Identifiers

PMID42022153
PMCPMC13098450

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.