Evidence map›Paper›PMID 34497795›Full record

ArticleFrontiers in public health2021

The Structural Characteristics of Economic Network and Efficiency of Health Care in China.

Kuang-Cheng Chai, Yang Yang, De-Cong Xie, Yang-Lu Ou, Ke-Chiun Chang, Xiao Han

Abstract read
In one paragraph

Article in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 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

6 authors.

Kuang-Cheng ChaiBusiness School, Guilin University of Electronic Technology, Guilin, China.
Yang YangBusiness School, Guilin University of Electronic Technology, Guilin, China.
De-Cong XieBusiness School, Guilin University of Electronic Technology, Guilin, China.
Yang-Lu OuBusiness School, Guilin University of Electronic Technology, Guilin, China.
Ke-Chiun ChangSchool of Economics and Management, Wuhan University, Wuhan, China.
Xiao HanInstitute of Central China Development, Wuhan University, Wuhan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the rapid development of the economy of China, the interactivity between provinces and the mobility of the population is increasing. Some patients who could have received the same treatment in their residential areas still choose to receive services in areas with higher economic development and concentrated high-quality medical resources, resulting in a huge waste of medical resources. Blindly increasing medical resources everywhere does not necessarily increase the output effectively. In this study, the data envelopment analysis (DEA) model, social network analysis (SNA), cluster analysis, and regression analysis are used to analyze the structural characteristics of the economic network structure and efficiency of health care in China. The results show that indegree and eigenvector centrality have a significant positive correlation with the efficiency of health care, and the clustering coefficient has a significant negative correlation with the efficiency of health care in China. This study uses a k-means algorithm to classify 31 provinces into three groups and extract their characteristics. As for the supply of health care resources, the government should command and dispatch the resources in the whole country through a top-down design based on the characteristics of each province.

Indexed as

Delivery of Health CareEfficiencyChinaEconomic DevelopmentHealth FacilitiesHumanscluster analysisefficiency of health caremedical resourcesnetwork structuresocial network analysis

Identifiers

PMID34497795
PMCPMC8419301

What OpenQuestion holds

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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.