Evidence mapPaperPMID 42383009Full record

ArticleiScience2026

Urbanization compound pathways of global lung cancer incidence risk under proximal and distal interactions.

Ye Li, Yuanxiang Shi, Boxi Li, Fangqi Qu, Changchun Ye, Yongqiang Lai, Xiyu Zhang, Baoguo Shi

Abstract read
In one paragraph

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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Ye LiSchool of Public Administration, Hangzhou Normal University, Hangzhou, Zhejiang, China.
Yuanxiang ShiSchool of Public Administration, Hangzhou Normal University, Hangzhou, Zhejiang, China.
Boxi LiDepartment of Economics, School of Economics, Minzu University of China, Beijing, China.
Fangqi QuDepartment of Economics, School of Economics, Minzu University of China, Beijing, China.
Changchun YeSchool of Public Administration, Hangzhou Normal University, Hangzhou, Zhejiang, China.
Yongqiang LaiSchool of Medical Humanities and Management, Hangzhou Medical College, Hangzhou, Zhejiang, China.
Xiyu ZhangDepartment of Laboratorial Science and Technology, School of Public Health, Peking University, Beijing, China.
Baoguo ShiDepartment of Economics, School of Economics, Minzu University of China, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer burden varies across countries undergoing different stages of urbanization, but the interacting roles of behavioral and macro-environmental factors remain difficult to quantify. Using GBD 2021 and World Bank data from 187 countries between 1991 and 2021, we combined GWRF-SHAP to examine nonlinear and spatially heterogeneous interactions between urbanization, proximal behavioral factors, and distal socio-environmental contexts. Lung cancer incidence shifted from traditionally developed regions toward rapidly urbanizing areas. Urbanization-occupational exposure and urbanization-forest cover emerged as the dominant interaction pathways, ranking first in 103 and 33 countries, respectively. Air pollution tended to amplify risk in rapidly urbanizing countries, whereas forest-related interactions generally showed buffering patterns with regional exceptions. Consequently, when formulating geographically differentiated lung cancer prevention strategies, it is essential to consider the combined impact of occupational health management, pollution control, the transition to clean energy, and environmental planning.

Indexed as

cancercomputational molecular modellingenvironmental healthmachine learningpublic health

Identifiers

PMID42383009
PMCPMC13316285

What Socratic holds

Textmetadata
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 Socratic graph.