Evidence map›Paper›PMID 39027631›Full record

ArticleChina CDC weekly2024

The Epidemiological Characteristics and Spatiotemporal Clustering of Measles - China, 2005-2022.

Qianqian Liu, Jiuhong Li, Siyu Liu, Lin Tang, Xiaoqi Wang, Aodi Huang, Xia Xu, 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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

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

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: In recent years, the incidence of measles in China has consistently remained below 1 per 100,000 population, yet the disease has not been eliminated. This study aims to comprehensively analyze the epidemiological characteristics of measles from 2005 to 2022, identify high-risk populations and areas, and propose targeted interventions. Methods: We utilized data from the China Disease Prevention and Control Information System for our comprehensive analysis. Spatial autocorrelation was employed to examine the spatial clustering of measles, while spatiotemporal scanning analysis was used to detect spatiotemporal clustering to describe measles epidemiology during the study period. Results: Between 2005 and 2022, 732,218 measles cases were reported in China. Overall, the incidence of measles exhibited a downward trend, particularly during the periods of 2008-2011 and 2015-2022. In 2022, the incidence rate reached its historical low at 0.039 per 100,000 population. Measles predominantly affects young children. Since 2017, global spatial clustering has diminished, although hotspot areas persist in the western provinces. Spatial-temporal scanning identified a high-incidence cluster from 2005 to 2008, comprising 15 provinces in the western, central, and northern regions of China. Conversely, from 2016 to 2022, a low-incidence cluster was detected in the southern and central provinces. Conclusions: China has made significant progress in measles prevention and control. The recent low incidence and absence of substantial spatiotemporal clustering indicate that China is nearing measles elimination. However, there is a continuing need to enhance prevention and control efforts among very young children and in historic incidence hotspots in western provinces. Additionally, improving the diagnosis of vaccine-associated rash illnesses is essential.

Indexed as

Epidemiological CharacteristicsMeaslesSpatiotemporal

Identifiers

PMID39027631
PMCPMC11252050

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.