Evidence map›Paper›PMID 40123712›Full record

ArticleInfection and drug resistance2025

Cross-District Transmission of Tuberculosis in a High-Mobility City in China: Implications for Regional Collaboration in Infectious Disease Control.

Chuangyue Hong, Jinjin Ge, Jing Gui, Xiaoling Che, Yilin Li, Zhipeng Zhuo, Mingzhen Li, Feng Wang, Weiguo Tan, Zhiguang Zhao

Abstract read
In one paragraph

Article in Infection and drug resistance, 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. 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

10 authors.

Chuangyue Hong *Department of Tuberculosis Prevention and Control, Shenzhen Center for Chronic Disease Control; Shenzhen Institute of Pulmonology, Shenzhen, 518020, People's Republic of China.
Jinjin Ge *National Clinical Research Center for Infectious Diseases, Shenzhen Clinical Research Center for Tuberculosis, Shenzhen Third People's Hospital, Shenzhen, 518112, People's Republic of China.
Jing GuiDepartment of Tuberculosis Prevention and Control, Shenzhen Center for Chronic Disease Control; Shenzhen Institute of Pulmonology, Shenzhen, 518020, People's Republic of China.
Xiaoling CheDepartment of Tuberculosis Prevention and Control, Shenzhen Center for Chronic Disease Control; Shenzhen Institute of Pulmonology, Shenzhen, 518020, People's Republic of China.
Yilin LiDepartment of Tuberculosis Prevention and Control, Shenzhen Center for Chronic Disease Control; Shenzhen Institute of Pulmonology, Shenzhen, 518020, People's Republic of China.
Zhipeng ZhuoDepartment of Tuberculosis Prevention and Control, Shenzhen Center for Chronic Disease Control; Shenzhen Institute of Pulmonology, Shenzhen, 518020, People's Republic of China.
Mingzhen LiDepartment of Tuberculosis Prevention and Control, Shenzhen Center for Chronic Disease Control; Shenzhen Institute of Pulmonology, Shenzhen, 518020, People's Republic of China.
Feng WangDepartment of Tuberculosis Prevention and Control, Shenzhen Center for Chronic Disease Control; Shenzhen Institute of Pulmonology, Shenzhen, 518020, People's Republic of China.ORCID 0000-0003-2096-0239
Weiguo TanDepartment of Tuberculosis Prevention and Control, Shenzhen Center for Chronic Disease Control; Shenzhen Institute of Pulmonology, Shenzhen, 518020, People's Republic of China.
Zhiguang ZhaoDepartment of Tuberculosis Prevention and Control, Shenzhen Center for Chronic Disease Control; Shenzhen Institute of Pulmonology, Shenzhen, 518020, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aims to elucidate the transmission dynamics of tuberculosis in a Chinese city with high population mobility and to identify the associated risk factors. Methods: We included the data from ten city-level surveillance sites in Shenzhen between 2018 and 2023. Genomic clusters were defined as having a genomic distance of 12 single nucleotide polymorphisms based on whole-genome sequencing. Cross-district clusters were characterized as clusters containing patients from at least two districts, indicating cross-district transmission. Risk factors for clustering were identified using logistic regression. Results: Of the 2,519 enrolled patients, 263 (10.4%) were grouped into 119 genomic clusters. Notably, 52.1% (62/119) of these clusters were cross-district clusters. We analyzed the data from Shenzhen's 10 districts separately and compared the results with a citywide combined analysis, finding that the combined analysis revealed significantly higher clustering rates across all districts (P<0.001). Furthermore, the risk of cross-district transmission was 3.41 times higher (95% CI: 1.49-7.80) among internal migrants than among residents. Multivariable logistic regression analysis identified significant risk factors for TB transmission, including age under 25 years (OR=3.07, 95% CI: 1.17-8.03), age 25-44 years (OR=2.86, 95% CI: 1.13-7.23), and drug-resistant TB (OR=1.57, 95% CI: 1.15-2.13). Conclusion: Cross-district transmission is a key factor in the spread of tuberculosis in cities with high population mobility. TB control institutions at all levels must transcend regional boundaries and enhance collaboration to achieve more effective tuberculosis control.

Indexed as

cross-district transmissionMycobacterium tuberculosisrisk factorswhole-genome sequencing

Identifiers

PMID40123712
PMCPMC11930267

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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 Socratic graph.