Evidence map›Paper›PMID 39346593›Full record

ArticleFrontiers in public health2024

GIS-based assessment of spatial and temporal disparities of urban health index in Shenzhen, China.

Duan Yongheng, Xie Shan, Liu Fei, Tang Jinglin, Gong Liyue, Liu Xiaoying, Wen Tingxiao, Wang Hongrui

Abstract read
In one paragraph

Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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.

Duan Yongheng *Shenzhen Health Development Research and Data Management Center, Shenzhen, Guangdong, China.
Xie Shan *Shenzhen Health Development Research and Data Management Center, Shenzhen, Guangdong, China.
Liu FeiShenzhen Health Development Research and Data Management Center, Shenzhen, Guangdong, China.
Tang JinglinNational Defense Technology Strategic Research Think Tank of National University of Defense Technology, Changsha, Hunan, China.
Gong LiyueSchool of Life Sciences, Central South University, Changsha, China.
Liu XiaoyingLibrary of Central South University, Changsha, China.
Wen TingxiaoSchool of Life Sciences, Central South University, Changsha, China.
Wang HongruiSchool of Life Sciences, Central South University, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To explore the inter-regional health index at the city level to contribute to the reduction of health inequalities. Methods: Employed the health determinant model to select indicators for the urban health index of Shenzhen City. Utilized principal component analysis, the weights of these indicators are determined to construct the said health index. Subsequently, the global Moran's index and local Moran's index are utilized to investigate the geographical spatial distribution of the urban health index across various administrative districts within Shenzhen. Results: The level of urban health index in Shenzhen exhibits spatial clustering and demonstrates a positive spatial correlation (2017, Moran's Conclusion: Our study reveals the urban health index in a relatively high-income (Shenzhen) in a developing country. Certain spatially correlated areas in Shenzhen present opportunities for the government to address health disparities through regional connectivity.

Indexed as

Geographic Information SystemsHealth Status DisparitiesUrban HealthChinaCitiesHumansSocioeconomic FactorsSpatio-Temporal Analysisgeographic in information system (GIS)health inequalitieshighincome cityMoran’s Ispatial autocorrelation analysisurban health index

Identifiers

PMID39346593
PMCPMC11430086

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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.