Evidence map›Paper›PMID 39604880›Full record

ArticleBMC infectious diseases2024

Transmissibility and control of tuberculosis in school outbreaks: a modeling study based on four outbreaks in China.

Qiao Liu, Meng Yang, Qiuping Chen, Chan Liu, Yue He, Laurent Gavotte, Zeyu Zhao, Yanhua Su, Roger Frutos, Kaiwei Luo and 1 more

Abstract read
In one paragraph

Article in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

11 authors.

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, People's Republic of China.
Meng Yang *Xiamen Tongan District Center for Disease Control and Prevention, Xiamen City, People's Republic of China.
Qiuping Chen *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, People's Republic of China.
Chan 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, People's Republic of China.
Yue HeState 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, People's Republic of China.
Laurent GavotteEspace-Dev, Université de Montpellier, Montpellier, France.
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, People's Republic of 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, People's Republic of China. suyanhua813@xmu.edu.cn.
Roger FrutosCIRAD, URM 17, Intertryp, Montpellier, France. frutossmt@gmail.com.
Kaiwei LuoHunan Provincial Center for Disease Control and Prevention, Hunan Academy of Preventive Medicine, Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Changsha City, People's Republic of China. 87616498@qq.com.
Tianmu 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, People's Republic of China. chentianmu@xmu.edu.cn.

Funding

Major Project of Guangzhou National Laboratory No. GZNL2024A01004Self-supporting Program of Guangzhou Laboratory No. SRPG22-007
6 · The paper itself

Abstract

backgroundThe elevated incidence of tuberculosis (TB) outbreaks in schools poses a significant challenge to prevention and control efforts in China. The commonality among most outbreaks is the failure to isolate patients at an early stage. Early isolation of TB cases is crucial for reducing the spread of TB within schools. This study aims to quantify the impact of different isolation proportions and durations on the attack rate of TB in schools. It explored the intervention effects of isolation measures in preventing and controlling TB in school settings. The goal is to provide insights that can serve as a reference for reducing the occurrence of TB outbreaks in schools.

methodsWe collected data from 4 school TB outbreaks. Susceptible-Exposed-Infected-Recovered (SEIR) model was used to fit the collected data and calculate transmissibility. Susceptible-Exposed-Infected-Quarantined-Recovered (SEIQR) model was employed to evaluate the effect of isolation. Effective reproduction numbers and cumulative incidence were used to quantify the transmissibility of TB.

resultsIn the 4 outbreaks, the majority of student cases were distributed in high grades of high school and universities, with a widespread occurrence of significant intervention delays. The median ascending reproduction value for the 4 outbreaks was 18.44 [interquartile range: 15.40-20.11]. Isolating 100% of the patients at the first month could reduce the number of cases by 99.47%, 87.99%, 96.48%, and 99.16%, respectively.

conclusionsThis study suggests that high schools and universities may represent significant high-risk environments for TB outbreaks. Early detection and isolation of cases are important strategies that can help reduce the risk of TB outbreaks, with observed case reductions of up to 99.47% when implemented promptly.

Indexed as

Disease OutbreaksSchoolsTuberculosisAdolescentChildChinaFemaleHumansIncidenceMaleSEIQR modelSEIR modelTransmissibilityTransmission dynamics modelTuberculosis outbreak

Identifiers

PMID39604880
PMCPMC11600741

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.