Evidence map›Paper›PMID 40382476›Full record

ArticleScientific reports2025

Spatiotemporal effects of urban micro-scale built environment on cardiovascular diseases.

Jinlong Liang, Shuguang Deng, Heping Yang, Shuyan Zhu, Rui Zheng

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

5 authors.

Jinlong LiangSchool of Geographical and Planning, Nanning Normal University, No.508 Xinning Road, Wuming District, Nanning, 530100, Guangxi, China.
Shuguang DengSchool of Geographical and Planning, Nanning Normal University, No.508 Xinning Road, Wuming District, Nanning, 530100, Guangxi, China. dsgsos@163.com.
Heping YangNeurovascular Intervention Center, Guangxi Ethnic Hospital, Nanning, 530022, Guangxi, China.
Shuyan ZhuSchool of Geographical and Planning, Nanning Normal University, No.508 Xinning Road, Wuming District, Nanning, 530100, Guangxi, China.
Rui ZhengSchool of Geographical and Planning, Nanning Normal University, No.508 Xinning Road, Wuming District, Nanning, 530100, Guangxi, China.

Funding

The General Project of Humanities and Social Sciences Research of the Ministry of Education 20YJA630011
6 · The paper itself

Abstract

Cardiovascular disease (CVD) has become a significant threat to the health of urban populations, and the urban built environment, as a key determinant of cardiovascular health, affects residents through various dimensions including physical activity, urban pollution, mental health, and dietary habits. However, existing research predominantly focuses on macro-level geographic scales, with limited exploration of the potential impact of intra-urban microenvironments on CVD. This study focuses on the central area of Nanning, China, as the case study area, employing methods such as global spatial autocorrelation analysis, emerging spatiotemporal hotspot analysis, and spatiotemporal geographically weighted regression (GTWR) analysis to comprehensively examine the spatiotemporal associations between CVD and built environment elements. The results reveal that CVD and built environment elements exhibit significant spatial clustering and correlations, with all variables demonstrating spatial clustering patterns. Six built environment factors-parks and squares, transportation facilities, life services, sports and leisure, medical care, and Catering and food-are spatially associated with disease incidence. The influence of built environment factors on CVD varies and exhibits pronounced spatiotemporal heterogeneity, with the greatest coefficient fluctuation observed for parks and squares, and the smallest for catering services. Parks and squares generally contribute positively to cardiovascular health by lowering disease risk across most areas, although in the central zone, dense population and heavy traffic lead to a positive association with disease incidence. Fortunately, this adverse impact has been gradually mitigated through ongoing improvements in urban green space planning; transportation facilities increases disease risk due to associated noise and air pollution, with particularly strong effects observed in the central region. However, the implementation of green transportation initiatives has effectively mitigated this negative impact; life services show a positive association with CVD, but their diverse types and spatially balanced distribution render their impact relatively minor; sports and leisure are associated with reduced disease risk in the central part of the study area, but in the northeast and northwest, they exhibit a positive association due to population dispersion. As residents' usage habits become more consistent, the associated impacts are gradually stabilizing; medical care help reduce disease risk in the central and eastern regions, but show a positive correlation in the northern area due to patient overflow and referral patterns. With the more equitable distribution of healthcare resources, this relationship is gradually stabilizing; catering and food are positively associated with CVD, but the effect is relatively small and spatially balanced, likely due to their widespread and uniform distribution. These findings offer valuable case-based evidence for urban planning and public health policymaking, thereby contributing to the construction and advancement of healthy cities.

Indexed as

Built EnvironmentCardiovascular DiseasesChinaCitiesHumansSpatio-Temporal AnalysisUrban PopulationBuilt environmentCardiovascular diseasesSpatiotemporal epidemiologySpatiotemporal evolution

Identifiers

PMID40382476
PMCPMC12085694

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.