Evidence map›Paper›PMID 40813650›Full record

ArticleBMC public health2025

Dengue situation in Chinese mainland, 2023.

Yujuan Yue, Xinchang Lun, Chunchun Zhao, Xiaobo Liu, Ning Zhao, Yuhong Guo, Xiuping Song, Liang Lu, Dongsheng Ren

Abstract read
In one paragraph

Article in BMC public health, 2025. 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. Review
  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

9 authors.

Yujuan YueNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, People's Republic of China. yueyujuan@icdc.cn.
Xinchang LunNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, People's Republic of China.
Chunchun ZhaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, People's Republic of China.
Xiaobo LiuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, People's Republic of China.
Ning ZhaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, People's Republic of China.
Yuhong GuoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, People's Republic of China.
Xiuping SongNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, People's Republic of China.
Liang LuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, People's Republic of China.
Dongsheng RenNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, People's Republic of China. Rendongsheng@icdc.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDengue fever outbreaks have occurred in Chinese mainland in recent years, especially in 2014 and 2019. In 2023, following the Corona Virus Disease 2019 pandemic, dengue fever has exhibited some new trends and characteristics.

methodsDescriptive epidemiological methods were used to analyse temporal, spatial and demographic distribution. Flow map and Sankey diagram were used to explore the flow of domestic and overseas dengue cases.

resultsIn 2023, a total of 14,585 indigenous cases, 3,632 domestic imported cases, and 1,579 overseas imported cases were identified in 10, 28, and 22 provinces, respectively among 31 provinces in Chinese mainland. Dengue fever exhibited seasonal characteristics, with peak periods occurring in late summer and early autumn. Most dengue cases were reported in Yunnan Province (70.4% of indigenous cases and 74.4% of overseas imported cases) and Guangdong Province (25.8% and 7.9%, respectively). Of the domestic imported cases, 91.5% were from the Yunnan Province. Additionally, only 30.6% of domestic imported cases were transmitted between provinces. Overseas imported cases were mainly from Myanmar (68.9%), Laos (8.4%), and Thailand (7.2%). Indigenous cases emerged from low to high latitudes during the months of the year, with further spatial growth in the western (Sichuan Province and Chongqing Municipality) and central (Hunan and Hubei provinces) regions. There was a significant increase in the number of domestic imported cases, and the proportion of farmer for dengue cases was substantially higher in 2013 than before.

conclusionsThe findings from the dengue fever analysis in 2023 can help in formulating targeted and strategic plans and implementing effective public health prevention and control measures.

Indexed as

DengueDisease OutbreaksAdolescentAdultChinaEast Asian PeopleFemaleHumansMaleMiddle AgedSeasonsDemographicDengue feverFlowSpatialTemporal

Identifiers

PMID40813650
PMCPMC12351840

What OpenQuestion holds

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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.