Evidence map›Paper›PMID 38864022›Full record

ArticleFrontiers in public health2024

Identifying determinants of spatial agglomeration of healthcare resources by using spatial econometric methods: a longitudinal study in China.

Enhong Dong, Xiaoting Sun, Yueming Xi, Yijia Wang, Tao Wang, Weimin Gao

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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 6 papers, 1 of them a synthesis that pooled it.

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6citing papers in PubMed, 1 pooled it
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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

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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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

6 authors.

Enhong Dong *School of Nursing and Health Management, Shanghai University of Medicine and Health Science, Shanghai, China.
Xiaoting Sun *School of Nursing and Health Management, Shanghai University of Medicine and Health Science, Shanghai, China.
Yueming XiSchool of Public Health, University of Hangzhou Normal University, Hanzhou, China.
Yijia WangSchool of Basic Medicine, Capital Medical University, Beijing, China.
Tao WangShanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
Weimin GaoSchool of Nursing, Kunming Medical University, Kunming, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Healthcare resources are necessary for individuals to maintain their health. The Chinese government has implemented policies to optimize the allocation of healthcare resources and achieve the goal of equality in healthcare for the Chinese people since the implementation of the new medical reform in 2009. Given that no study has investigated regional differences from the perspective of healthcare resource agglomeration, this study aimed to investigate China's healthcare agglomeration from 2009 to 2017 in China and identify its determinants to provide theoretical evidence for the government to develop and implement scientific and rational healthcare policies. Methods: The study was conducted using 2009-2017 data to analyze health-resource agglomeration on institutions, beds, and workforce in China. An agglomeration index was applied to evaluate the degree of regional differences in healthcare resource allocation, and spatial econometric models were constructed to identify determinants of the spatial agglomeration of healthcare resources. Results: From 2009 to 2017, all the agglomeration indexes of healthcare exhibited a downward trend except for the number of institutions in China. Population density (PD), government health expenditures (GHE), urban resident's disposable income (URDI), geographical location (GL), and urbanization level (UL) all had positive significant effects on the agglomeration of beds, whereas both Conclusion: The agglomeration of healthcare resources was observed to remain at an ideal level in China from 2009 to 2017. According to the significant determinants, some corresponding targeted measures for the Chinese government and other developing countries should be fully developed to balance regional disparities in the agglomeration of healthcare resources across administrative regions.

Indexed as

Health ResourcesChinaHealth ExpendituresHumansLongitudinal StudiesModels, EconometricResource AllocationSpatial AnalysisagglomerationChinadeterminantshealthcare resourcesspatial econometric methods

Identifiers

PMID38864022
PMCPMC11165023

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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.