Evidence map›Paper›PMID 39734787›Full record

ArticleChina CDC weekly2024

Impact of Immunization Strategies on Rubella Incidence During Progress Toward Rubella Control and Elimination - China, 2004-2023.

Siyu Liu, Xia Xu, Qianqian Liu, Lin Tang, Xiaoqi Wang, Aodi Huang, Jiuhong Li, Yuexin Xiu, Hong Yang, Ning Wen and 3 more

Abstract read
In one paragraph

Article in China CDC weekly, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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. Article
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

13 authors.

Siyu LiuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Immunization Programme, Chinese Center for Disease Control and Prevention, Beijing, China.
Xia XuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Immunization Programme, Chinese Center for Disease Control and Prevention, Beijing, China.
Qianqian LiuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Immunization Programme, Chinese Center for Disease Control and Prevention, Beijing, China.
Lin TangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Immunization Programme, Chinese Center for Disease Control and Prevention, Beijing, China.
Xiaoqi WangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Immunization Programme, Chinese Center for Disease Control and Prevention, Beijing, China.
Aodi HuangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Immunization Programme, Chinese Center for Disease Control and Prevention, Beijing, China.
Jiuhong LiNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Immunization Programme, Chinese Center for Disease Control and Prevention, Beijing, China.
Yuexin XiuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Immunization Programme, Chinese Center for Disease Control and Prevention, Beijing, China.
Hong YangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Immunization Programme, Chinese Center for Disease Control and Prevention, Beijing, China.
Ning WenNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Immunization Programme, Chinese Center for Disease Control and Prevention, Beijing, China.
Lance E RodewaldNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Immunization Programme, Chinese Center for Disease Control and Prevention, Beijing, China.
Zundong YinNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Immunization Programme, Chinese Center for Disease Control and Prevention, Beijing, China.
Fuzhen WangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Immunization Programme, Chinese Center for Disease Control and Prevention, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Since rubella was incorporated into the national disease surveillance system in 2004, rubella incidence has changed dramatically. This study analyzed the impact of immunization strategies on the age-specific and sex-specific incidence of rubella in China from 2004 to 2023 to inform efforts to accelerate rubella elimination. Methods: Annual rubella-containing vaccine (RCV) coverage levels, reported rubella cases and incidence, and vaccination status of cases were obtained from the National Immunization Program Information Management System, the National Notifiable Diseases Reporting System, and the Measles Surveillance System, and used to describe temporal trends. Incidence trends and annual percent changes (APC) by age group and sex were estimated using joinpoint regression. Results: In 2008, RCV coverage was 51.5% for the first dose and 39.0% for the second dose, increasing to and stabilizing at 95% or above for both doses by 2012. There were 584,758 cases of rubella reported in China during 2004-2023. Joinpoint regression showed that rubella incidence initially increased, peaked in 2008, and then decreased (APC Conclusions: The decrease in rubella incidence is closely associated with vaccination. Nationwide inclusion of RCV significantly impacted on the prevention and control of rubella. Targeted measures to address immunization gaps and maintain high surveillance sensitivity are necessary to promote rubella elimination.

Indexed as

Congenital rubella syndromeIncidenceRubellaVaccination

Identifiers

PMID39734787
PMCPMC11673184

What OpenQuestion holds

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Registered trials

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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.