Evidence map›Paper›PMID 41734927›Full record

ArticleBMJ open2026

Transmission dynamics and control of tuberculosis in high-altitude regions: a modelling study in Xizang, China.

Miaomiao Li, Ruyu Zheng, Luo Guo, Yuxuan Wu, Jingran Dong, Hongxia Jiang, Desheng Song, Zhuang Cui, Changping Li

Abstract read
In one paragraph

Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited 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

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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

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Miaomiao Li *Department of Epidemiology and Biostatistics, Tianjin Medical University, Tianjin, China.
Ruyu Zheng *Department of Epidemiology and Biostatistics, Tianjin Medical University, Tianjin, China.
Luo Guo *Department of Industrial Engineering, Tsinghua University, Beijing, China.
Yuxuan WuDepartment of Epidemiology and Biostatistics, Tianjin Medical University, Tianjin, China.
Jingran DongDepartment of Epidemiology and Biostatistics, Tianjin Medical University, Tianjin, China.
Hongxia JiangDepartment of Epidemiology and Biostatistics, Tianjin Medical University, Tianjin, China.
Desheng SongTianjin Medical University Optometry Technology Co., Ltd, Tianjin, People's Republic of China.
Zhuang CuiDepartment of Epidemiology and Biostatistics, Tianjin Medical University, Tianjin, China.ORCID http://orcid.org/0000-0001-6495-766X
Changping LiDepartment of Epidemiology and Biostatistics, Tianjin Medical University, Tianjin, China ChangpingLi417@126.com.ORCID http://orcid.org/0000-0003-4194-3470

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo estimate tuberculosis (TB) incidence trends in the high-altitude Xizang, China, and to explore the key intervention strategies on achieving the WHO 2030 TB control target.

designWe developed a susceptible-exposed-infectious-recovered transmission model using routinely reported TB surveillance data from 2004 to 2022. Scenario-based simulations were conducted to project future TB incidence under alternative intervention strategies. Model assumptions are as follows: (1) a stable population, (2) lifelong vaccine-induced immunity, (3) infectiousness of active TB cases, (4) relapse risk after recovery and (5) homogeneous mixing within the population.

settingSeven prefectures of Xizang Autonomous Region on the Tibetan Plateau, China.

participantsAn estimated population of approximately 3 million individuals residing in Xizang.

interventionsWe assessed the epidemiological impact of four interventions implemented independently: increasing vaccine efficacy rate, reducing transmission rates of susceptible individuals, decreasing progression rate from latent TB infection to active disease and reducing relapse rate among successfully treated patients, compared with continuation of current control measures.

resultsThe estimated basic reproduction number (

conclusionTargeted management of individuals with latent TB infection should be strengthened to substantially reduce TB transmission in high-altitude areas.

Indexed as

AltitudeTuberculosisBasic Reproduction NumberChinaHumansIncidencedynamical modelhigh-altitudeprevention and control measurestuberculosis

Identifiers

PMID41734927
PMCPMC12933775

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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.