Evidence map›Paper›PMID 42732324›Full record

ArticleGeoHealth2026

Association of Environmental Factors and Aging Levels Under Different Urbanization Levels in China.

Zehua Zheng, Yiting Liu, Jiawei Zang, Huaiyue Xu, Kailai Lu, Jie Ban, Qing Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in GeoHealth, 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

7 authors.

Zehua ZhengChina CDC Key Laboratory of Environment and Population Health National Institute of Environmental Health Chinese Center for Disease Control and Prevention Beijing China.ORCID https://orcid.org/0009-0005-4765-6079
Yiting LiuChina CDC Key Laboratory of Environment and Population Health National Institute of Environmental Health Chinese Center for Disease Control and Prevention Beijing China.
Jiawei ZangChina CDC Key Laboratory of Environment and Population Health National Institute of Environmental Health Chinese Center for Disease Control and Prevention Beijing China.
Huaiyue XuChina CDC Key Laboratory of Environment and Population Health National Institute of Environmental Health Chinese Center for Disease Control and Prevention Beijing China.
Kailai LuChina CDC Key Laboratory of Environment and Population Health National Institute of Environmental Health Chinese Center for Disease Control and Prevention Beijing China.
Jie BanChina CDC Key Laboratory of Environment and Population Health National Institute of Environmental Health Chinese Center for Disease Control and Prevention Beijing China.ORCID https://orcid.org/0000-0001-5865-0573
Qing WangChina CDC Key Laboratory of Environment and Population Health National Institute of Environmental Health Chinese Center for Disease Control and Prevention Beijing China.ORCID https://orcid.org/0000-0002-7031-6049

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In China, the processes of population aging and urbanization are persistently accelerating. To gain insights into the association of environmental factors and aging levels under different urbanization levels, this study examined how these factors correlate with aging levels across varying degrees of urbanization in China. County-level data encompassing aging levels and 22 pertinent factors were collated for the years 2010 and 2020. Aging levels were quantified by the proportions of elderly population aged 65+ and 85+ within the total population. The counties were categorized into three urbanization levels: high, medium, and low. Fixed effect models and random forest models were established to scrutinize the impacts and significance of independent variables on aging levels under different urbanization levels. Notably, the aging levels across all urbanization levels rose by a factor of 1.5-2.1 from 2010 to 2020. The results of the two models indicate that there are urbanization gradient differences in the impact factors: in low-urbanization areas, resident savings and PM

Indexed as

aging levelenvironmental and socioeconomic factorsurbanization level

Identifiers

PMID42732324
PMCPMC13570655

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.