Evidence map›Paper›PMID 41376923›Full record

ArticleJournal of thoracic disease2025

The prevalence of chronic obstructive pulmonary disease in high-altitude areas of China: a systematic review and meta-analysis.

Shuna Wei, Xiaoju Liu

Abstract read
In one paragraph

Article in Journal of thoracic disease, 2025. 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

2 authors.

Shuna WeiThe First Clinical Medical College, Lanzhou University, Lanzhou, China.
Xiaoju LiuThe First Clinical Medical College, Lanzhou University, Lanzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: China is one of the countries with the heaviest burden of chronic obstructive pulmonary disease (COPD). In high-altitude areas of China (≥1,500 m), factors such as low oxygen levels, burning of biomass, and an aging population make the prevention and treatment of COPD more challenging. However, there is currently a lack of systematic epidemiological studies. This study aimed to assess the prevalence of COPD in high-altitude areas of China. Methods: The search was conducted in PubMed, Embase, Web of Science, Ovid, ProQuest, Scopus, The Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang, Weipu, and China Biology Medicine disc (CBM) from their inception to May 17, 2025. Studies were evaluated according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and registered in International Prospective Register of Systematic Reviews (PROSPERO) (Identifier: CRD420251063646). Results: A total of 6 cross-sectional studies from high-altitude areas of China (including 15,621 participants) were included. The COPD prevalence rate in high-altitude areas (≥1,500 m) of China was 10% [95% confidence interval (CI): 7-14%; P<0.001]. Subgroup analysis showed that the prevalence rate in people aged ≥50 years was significantly higher than that in those aged 40-49 years (17% Conclusions: The prevalence of COPD in high-altitude areas of China is 10%, which is closely related to aging and smoking. It is necessary to promote clean energy and strengthen health education for targeted intervention. In the future, priority should be given to conducting age-standardized estimates and multi-center studies to verify the results and provide a basis for COPD prevention and control strategies.

Indexed as

altitudeChinachronic obstructive pulmonary disease (COPD)epidemiologymeta-analysis

Identifiers

PMID41376923
PMCPMC12688621

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.