Evidence map›Paper›PMID 37038241›Full record

ArticleArchives of public health = Archives belges de sante publique2023

Analysis of spatial pattern and influencing factors of private clinics in the main urban area of Guiyang in China from 2021 to 2022 based on multi-source data.

Wei Li, Fang-Juan Du, Ou Ruan

Abstract read
In one paragraph

Article in Archives of public health = Archives belges de sante publique, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Wei LiSchool of Geography and Environmental Sciences, School of Karst Science), Guizhou Normal University, Guiyang, 550001, Guizhou, China.
Fang-Juan DuSchool of Geography and Environmental Sciences, School of Karst Science), Guizhou Normal University, Guiyang, 550001, Guizhou, China. dfj521@gznu.edu.cn.
Ou RuanSchool of Geography and Environmental Sciences, School of Karst Science), Guizhou Normal University, Guiyang, 550001, Guizhou, China.

Funding

Guizhou Normal University 2018 academic new seedling cultivation and innovation exploration special project 2018-5769-21National Natural Science Foundation of China 41861029Science and Technology Support Project of Guizhou Science and Technology Department 2022-254
6 · The paper itself

Abstract

backgroundPrivate clinics are important places for residents to obtain daily medical care. However, previous researches mainly focused on public medical institutions but ignored the issue of systematic allocation of social medical resources such as clinics. It is critical to understand the private clinics distribution to analyze the rational allocation of medical resources and the spatial difference.

methodsBased on the field survey, land census, population density, and economic data from Guiyang, this study analyzes the spatial pattern of private clinics in the main urban area of Guiyang and the influencing factors by using spatial analysis methods such as kernel density, standard deviation ellipses, and geo-detector.

resultsThe private clinics in the main urban area of Guiyang are characterized by "inner dense, outer sparse dense," showing an overall spatial clustering feature of "four cores and two belts with many points" and "dense inside and sparse outside." Different types of private clinics have distinct spatial distribution characteristics and agglomeration forms. The growth of private clinics is closely linked to the population growth of mountainous cities. The most important factors influencing the spatial pattern of private clinics are residential land factors, followed by traffic factors and population density. The impact of economic, natural, and spatial factors is minimal. When using a geo-detector, the results of multi-factor interaction differ from those of single factors, and factor interactions have greater explanatory power than single factors in clinic distribution.

conclusionThis study investigates the geographic distribution and influencing variables of private clinics in typical mountain cities and identifies the causes of the current disparity in the distribution of healthcare resources. It is necessary to gradually develop the primary healthcare system in mountainous cities with legislation, counterpart support, and social resources. While ensuring equal access to health care for low-income people and mobile populations, various medical needs of community members should be fully considered and implemented as soon as possible.

Indexed as

Influence factorsMain urban area of GuiyangMultisource dataPrivate clinicsSpace pattern

Identifiers

PMID37038241
PMCPMC10084587

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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.