Evidence map›Paper›PMID 41000308›Full record

ArticleFrontiers in public health2025

Feasibility of ending tuberculosis in Shangrao City through active intervention measures: a mathematical study.

Mingshu Xu, Yue He, Qiao Liu, Qiuping Chen, Zeyu Zhao, Zheng Xu, Chongfei Shu, Jun Xia, Yuyan Yang, Laurent Gavotte and 5 more

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Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Mingshu Xu *Shangrao Centre for Disease Control and Prevention, Shangrao City, China.
Yue He *State Key Laboratory of Vaccines for Infectious Disease, Xiang An Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Intergration in Vaccine Research, School of Public Health, Xiamen University, Xiamen City, China.
Qiao Liu *State Key Laboratory of Vaccines for Infectious Disease, Xiang An Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Intergration in Vaccine Research, School of Public Health, Xiamen University, Xiamen City, China.
Qiuping ChenState Key Laboratory of Vaccines for Infectious Disease, Xiang An Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Intergration in Vaccine Research, School of Public Health, Xiamen University, Xiamen City, China.
Zeyu ZhaoState Key Laboratory of Vaccines for Infectious Disease, Xiang An Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Intergration in Vaccine Research, School of Public Health, Xiamen University, Xiamen City, China.
Zheng XuShangrao Centre for Disease Control and Prevention, Shangrao City, China.
Chongfei ShuShangrao Centre for Disease Control and Prevention, Shangrao City, China.
Jun XiaShangrao Centre for Disease Control and Prevention, Shangrao City, China.
Yuyan YangShangrao Centre for Disease Control and Prevention, Shangrao City, China.
Laurent GavotteEspace-Dev, Université de Montpellier, Montpellier, France.
Roger FrutosCIRAD, URM 17, Intertryp, Montpellier, France.
Huiming YeDepartment of Laboratory Medicine, Fujian Key Clinical Specialty of Laboratory Medicine, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen City, China.
Yanhua SuState Key Laboratory of Vaccines for Infectious Disease, Xiang An Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Intergration in Vaccine Research, School of Public Health, Xiamen University, Xiamen City, China.
Xiaolan WangShangrao People's Hospital, Shangrao City, China.
Zhen LiuShangrao Centre for Disease Control and Prevention, Shangrao City, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: China faces significant challenges in ending tuberculosis (TB). Active case finding (ACF) and TB preventive therapy (TPT) have proven to be critical measures in reducing TB incidence. This study uses a transmission dynamics model to identify the optimal intervention strategies for achieving WHO's TB elimination targets in Shangrao City. The findings guide targeted TB control efforts in similar settings. Methods: To account for COVID-19 pandemic disruptions, we first used a seasonal autoregressive integrated moving average (SARIMA) model to predict and substitute the reported TB incidence during 2020-2023. Subsequently, we developed an age-stratified dynamic transmission model using surveillance data from Shangrao City's Infectious Diseases Reporting System (IDRS) between 2008 and 2023 to evaluate tuberculosis transmission patterns across age groups. The model assessed the effectiveness of key interventions including active case finding (ACF), latent tuberculosis infection (LTBI) screening, and tuberculosis preventive treatment (TPT). Results: The model fit well with the reported data (R Conclusion: The current TB control efforts face significant challenges, with a considerable gap remaining in achieving TB elimination targets. Combining ACF with TPT presents a promising strategy to reach these goals. Older tuberculosis (TB) patients constitute a high-risk population, and effective prevention and treatment in this group are critical to achieving future TB elimination goals. To reduce the risk of recurrence and reinfection, enhanced follow-up monitoring of older patients should be prioritised alongside targeted health education interventions tailored to high-risk groups.

Indexed as

TuberculosisAdolescentAdultAgedAntitubercular AgentsChildChild, PreschoolChinaCOVID-19Feasibility StudiesFemaleHumansIncidenceMaleMass ScreeningMiddle AgedAntitubercular Agentsactive case findingdynamic modelend tuberculosistuberculosistuberculosis preventive therapy

Identifiers

PMID41000308
PMCPMC12457300

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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.