Evidence map›Paper›PMID 37200630›Full record

ArticleGeoHealth2023

Spatial Accessibility Evaluation and Location Optimization of Primary Healthcare in China: A Case Study of Shenzhen.

Liutong Chen, Huatang Zeng, Liqun Wu, Qiannan Tian, Ning Zhang, Rongxin He, Hao Xue, Junyao Zheng, Jinlin Liu, Fengchao Liang and 1 more

Open access · goldAbstract read
In one paragraph

Article in GeoHealth, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
12.5field-weighted citation impact, top 1% of its field
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

16 citing papers in PubMed, 32 citations in OpenAlex.

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  11. Spatial accessibility and equity of primary healthcare in Zhejiang, China.International journal for equity in health · 2024
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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

11 authors at 6 institutions in 2 countries.

Liutong ChenSchool of Public Health and Emergency Management Southern University of Science and Technology Shenzhen China.
Huatang ZengShenzhen Health Development Research and Data Management Center Shenzhen China.
Liqun WuShenzhen Health Development Research and Data Management Center Shenzhen China.
Qiannan TianShenzhen Health Development Research and Data Management Center Shenzhen China.
Ning ZhangSchool of Public Policy and Administration Xi'an Jiaotong University Xi'an China.
Rongxin HeVanke School of Public Health Tsinghua University Beijing China.
Hao XueStanford Center on China's Economy and Institutions Stanford University Stanford CA USA.ORCID https://orcid.org/0000-0002-9683-5541
Junyao ZhengChina Institute for Urban Governance Shanghai Jiao Tong University Shanghai China.
Jinlin LiuSchool of Public Policy and Administration Northwestern Polytechnical University Xi'an China.
Fengchao LiangSchool of Public Health and Emergency Management Southern University of Science and Technology Shenzhen China.ORCID https://orcid.org/0000-0003-2088-0722
Bin ZhuSchool of Public Health and Emergency Management Southern University of Science and Technology Shenzhen China.ORCID https://orcid.org/0000-0002-0091-6356
Southern University of Science and Technology · CNHoover Institution · USNorthwestern Polytechnical University · CNShanghai Jiao Tong University · CNVanke (China) · CNXi'an Jiaotong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The unbalanced allocation of healthcare resources is a major challenge that hinders access to healthcare. Taking Shenzhen as an example, this study aimed to enhance equity in obtaining healthcare services, through measuring and visualizing the spatial accessibility of community healthcare centers (CHC), and optimizing CHC geospatial allocation. We used the number of health technicians per 10,000 to represent the CHC's service capacity, combined with resident points and census data to calculate the population the CHC needs to carry, and then analyzed the accessibility based on the Gaussian two-step floating catchment area method. In 2020, five regions in Shenzhen had better spatial accessibility scores: Nanshan (0.250), Luohu (0.246), Futian (0.244), Dapeng (0.226), and Yantian (0.196). The spatial accessibility of CHCs shows a gradual decrease from the city center to the edge, which is affected by economic and topographic factors. With the support of the maximal covering location problem model, we selected up to 567 candidate locations for the new CHC, which could improve Shenzhen's accessibility score from 0.189 to 0.361 and increase the coverage population by 63.46% within a 15-min impedance. By introducing spatial techniques and maps, this study provides (a) new evidence for promoting equitable access to primary healthcare services in Shenzhen and (b) a foundation for improving the accessibility of public service facilities in other areas.

Indexed as

Gaussian two‐step floating catchment arealocation optimizationprimary health carespatial accessibility

Identifiers

PMID37200630
PMCPMC10187614
OpenAlexW4376956364

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.