Evidence map›Paper›PMID 41213917›Full record

ArticleNature communications2025

Prognostic value of an abnormal chest X-ray result in predicting the development of tuberculosis.

Ping Zhu, Xinyi Chen, Jie Pan, Wei Wang, Xiaogang Hao, Bingjun Xu, Bingdong Zhan, Chunfu Fang, Ye Shen, Kui Liu and 5 more

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
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.

Ping Zhu *Department of Tuberculosis Control and Prevention, Quzhou City Center for Disease Control and Prevention, Quzhou, Zhejiang, China.ORCID http://orcid.org/0000-0003-1129-2478
Xinyi Chen *Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China.
Jie Pan *Department of Tuberculosis Control and Prevention, Quzhou City Center for Disease Control and Prevention, Quzhou, Zhejiang, China.
Wei Wang *Department of Tuberculosis Control and Prevention, Quzhou City Center for Disease Control and Prevention, Quzhou, Zhejiang, China.
Xiaogang Hao *Department of Tuberculosis Control and Prevention, Quzhou City Center for Disease Control and Prevention, Quzhou, Zhejiang, China.
Bingjun XuDepartment of Tuberculosis Control and Prevention, Quzhou City Center for Disease Control and Prevention, Quzhou, Zhejiang, China.
Bingdong ZhanDepartment of Tuberculosis Control and Prevention, Quzhou City Center for Disease Control and Prevention, Quzhou, Zhejiang, China.ORCID http://orcid.org/0000-0003-3916-1306
Chunfu FangDepartment of Tuberculosis Control and Prevention, Quzhou City Center for Disease Control and Prevention, Quzhou, Zhejiang, China.
Ye ShenDepartment of Epidemiology and Biostatistics, University of Georgia College of Public Health, Athens, GA, USA.ORCID http://orcid.org/0000-0002-6662-0048
Kui LiuDepartment of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China.
Xiaowei QiuDepartment of Radiology, Hangzhou Red Cross Hospital, Hangzhou, Zhejiang, China.
Wei WangDepartment of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China.
Bin ChenDepartment of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China. bchen@cdc.zj.cn.ORCID http://orcid.org/0000-0002-3761-0465
Jianmin JiangDepartment of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China. jmjiang@cdc.zj.cn.ORCID http://orcid.org/0000-0003-3563-4006
Leonardo MartínezDepartment of Epidemiology, School of Public Health, Boston University, Boston, MA, USA.ORCID http://orcid.org/0000-0003-0954-8489

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although tuberculosis is preventable, whom to prioritize for preventive interventions is debated. As part of a follow-up to a mass tuberculosis screening, we aimed to evaluate tuberculosis incidence among persons with distinct baseline X-ray results. From March to October 2020, we implemented a mass tuberculosis screening intervention among elderly persons in eastern China. Participants were followed for incident tuberculosis until October 2022. Tuberculosis incidence over two years among participants with differing baseline chest X-ray results were assessed. Physical examinations and X-rays were administered to all participants. Among persons suspected of tuberculosis, computed tomography, culture or GeneXpert MTB/RIF was performed. Among 183,808 participants, 27,796 had a baseline abnormal X-ray. The annualized incidence per 100,000 person-years was 1,525 (95% CI, 989-2,243), 278 (202-373), 325 (218-466), and 61 (50-75) among participants with X-rays suggestive of active, stable, uncertain and normal, respectively. Among persons with X-ray findings suggestive of tuberculosis, the increased hazard was 16.7 (95% CI, 11.9-23.4). These results demonstrate the risk of developing tuberculosis among persons with an abnormal chest X-ray was very high. Combining mass screening with preventive interventions based on X-ray findings should be explored as a combination intervention package to reduce tuberculosis in high-risk populations.

Indexed as

Radiography, ThoracicTuberculosisTuberculosis, PulmonaryAgedAged, 80 and overChinaFemaleHumansIncidenceMaleMass ScreeningMiddle AgedMycobacterium tuberculosisPrognosisTomography, X-Ray Computed

Identifiers

PMID41213917
PMCPMC12603200

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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.