Evidence map›Paper›PMID 34644313›Full record

ArticlePloS one2021

The efficiency of provincial government health care expenditure after China's new health care reform.

Xuesong Guo, Jun Zhang, Zhiwei Xu, Xin Cong, Zhenli Zhu

Abstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

14 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

5 authors.

Xuesong GuoSchool of Public Policy and Administration of Xi'an Jiaotong University, Xi'an, Shanxi, China.ORCID 0000-0003-0969-3299
Jun ZhangSchool of Public Policy and Administration of Xi'an Jiaotong University, Xi'an, Shanxi, China.
Zhiwei XuDepartment of the Quality and Management of the Medical, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, Shandong, China.
Xin CongDepartment of Communist Youth League of the Medical, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, Shandong, China.
Zhenli ZhuDepartment of Education, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveWe aim to estimate the total factor productivity and analyze factors related to the Chinese government's health care expenditure in each of its provinces after its implementation of new health care reform in the period after 2009. MATERIALS AND

methodsWe use the Malmquist DEA model to measure efficiency and apply the Tobit regression to explore factors that influence the efficiency of government health care expenditure. Data are taken from the China statistics yearbook (2004-2020).

resultsWe find that the average TFP of China's 31 provincial health care expenditure was lower than 1 in the period 2009-2019. We note that the average TFP was much higher after new health care reform was implemented, and note this in the eastern, central and western regions. But per capita GDP, population density and new health care reform implementation are found to have a statistically significant impact on the technical efficiency of the provincial government's health care expenditure (P<0.05); meanwhile, region, education, urbanization and per capita provincial government health care expenditure are not found to have a statistically significant impact.

conclusionAlthough the implementation of the new medical reform has improved the efficiency of the government's health expenditure, it is remains low in 31 provinces in China. In addition, the government should consider per capita GDP, population density and other factors when coordinating the allocation of health care input. SIGNIFICANCE: This study systematically analyzes the efficiency and influencing factors of the Chinese government's health expenditure after it introduced new health care reforms. The results show that China's new medical reform will help to improve the government's health expenditure. The Chinese government can continue to adhere to the new medical reform policy, and should pay attention to demographic and economic factors when implementing the policy.

Indexed as

GovernmentHealth ExpendituresChinaHealth Care ReformRegression Analysis

Identifiers

PMID34644313
PMCPMC8513862

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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.