Evidence map›Paper›PMID 38178099›Full record

ArticleBMC health services research2024

Dynamic evolution and spatial difference of public health service supply in economically developed provinces of China: typical evidence from Guangdong Province.

Yan-Ting He, Yue-Chi Zhang, Rang-Ke Wu, Wen Huang, Ruo-Nan Wang, Luo-Xuan He, Bei Li, Yi-Li Zhang

Abstract read
In one paragraph

Article in BMC health services research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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.

Yan-Ting He *School of Health Management, Southern Medical University, Guangzhou510515, China.
Yue-Chi ZhangSchool of Social & Political Sciences, College of Social Sciences, University of Glasgow, Glasgow, UK.
Rang-Ke WuSchool of Foreign Studies, Southern Medical University, Guangzhou510515, China.
Wen HuangThe Fifth Affiliate Hospital of Southern Medical University, Guangzhou510515, China.
Ruo-Nan WangSchool of Health Management, Southern Medical University, Guangzhou510515, China.
Luo-Xuan HeSchool of Health Management, Southern Medical University, Guangzhou510515, China.
Bei Li *School of Health Management, Southern Medical University, Guangzhou510515, China. 45510307@qq.com.
Yi-Li Zhang *School of Health Management, Southern Medical University, Guangzhou510515, China. zhangyi1223882953@163.com.

Funding

Guangdong Philosophy and Social Sciences Planning 2020 Project "Research on Vulnerability Measurement and Prevention and Control Capacity Improvement of Community Public Health Emergency Response System in Pearl River Delta Cities" GD20CGL23Guangdong Provincial Key Laboratory of Philosophy and Social Sciences: Key Laboratory of Public Health Policy Research and Evaluation 2015WSY0010The National Project Cultivation Program of the School of Health Management, Southern Medical University 2022RFT002
6 · The paper itself

Abstract

objectiveThe outbreak of the COVID-19 pandemic has drawn attention from all sectors of society to the level of public health services. This study aims to investigate the level of public health service supply in the four major regions of Guangdong Province, providing a basis for optimizing health resource allocation.

methodsThis article uses the entropy method and panel data of 21 prefecture-level cities in Guangdong Province from 2005 to 2021 to construct the evaluation index system of public health service supply and calculate its supply index. On this basis, the standard deviation ellipse method, kernel density estimation, and Markov chain are used to analyze the spatiotemporal evolution trend of the public health service supply level in Guangdong Province. The Dagum Gini coefficient and panel regression model are further used to analyze the relative differences and the key influencing factors of difference formation. Finally, the threshold effect model is used to explore the action mechanism of the key factors.

resultsOverall, the level of public health service supply in Guangdong Province is on an upward trend. Among them, polarization and gradient effects are observed in the Pearl River Delta and Eastern Guangdong regions; the balance of public health service supply in Western Guangdong and Northern Mountainous areas has improved. During the observation period, the level of public health services in Guangdong Province shifted towards a higher level with a smaller probability of leapfrogging transition, and regions with a high level of supply demonstrated a positive spillover effect. The overall difference, intra-regional difference and inter-regional difference in the level of public health service supply in Guangdong Province during the observation period showed different evolutionary trends, and spatial differences still exist. These differences are more significantly positively affected by factors such as the level of regional economic development, the degree of fiscal decentralization, and the urbanization rate. Under different economic development threshold values, the degree of fiscal decentralization and urbanization rate both have a double threshold effect on the role of public health service supply level.

conclusionThe overall level of public health service supply in Guangdong Province has improved, but spatial differences still exist. Key factors influencing these differences include the level of regional economic development, the degree of fiscal decentralization, and the urbanization rate, all of which exhibit threshold effects. It is suggested that, in view of the actual situation of each region, efforts should be made to build and maintain their own advantages, enhance the spatial linkage of public health service supply, and consider the threshold effects of key factors in order to optimize the allocation of health resources.

Indexed as

PandemicsUrbanizationChinaCitiesHealth ServicesHumansDagum Gini coefficientDynamic evolutionKernel density estimationMarkov chainPanel regression modelPublic health servicesSpatial differencesStandard deviation ellipse method

Identifiers

PMID38178099
PMCPMC10768127

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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.