See the published version of this preprint at BMC Health Services Research.
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Learn more about In Review
Research article
Yun Zhang
The affiliated hospital of qingdao university
Corresponding Author
ORCiD: https://orcid.org/0000-0002-1358-6667
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Total Healthcare Expenditure (THE) has increased substantially in all countries. Since the health system reform and health policy environment differ from each country, it is necessary to analyze the motivations of THE in a specific country.
Methods: The objective of this study was to analyze the influential factors of Provincial THE (PTHE) per capita in China by using spatiotemporal panel data across 31 provinces (including provinces, autonomous regions, and municipalities, all called provinces in here) from 2009-2016 at the provincial and annual level. Generalized Estimating Equation (GEE) was used to identify the influential factors of PTHE per capita.
Results: The number of beds per 10,000 population explained most of the variation of PTHE per capita. The results also showed that health expenditure in China reacts more to mortality compared with the Gross Domestic Product (GDP) per capita. But mortality and Out-Of-Pocket Payments (OOP) as a percentage of THE were associated with PTHE per capita negatively. The rate of infectious diseases and THE as a percentage of GDP had no statistical significance. And the Proportion of the Population Aged 65 and Over (POP65) impact PTHE per capita positively. But the coefficient was small.
Conclusions: In response to these findings, we conclude that the impact of the increasing percentage of OOP in THE diminishes the PTHE. Furthermore, we find that both the "baseline" health level and health provision are positively correlated with PTHE, which outweighs the effect of GDP.
Keywords
Total Health Expenditure, Influential Factors, Panel Data, China
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CURRENT STATUS: Published
View the published article at BMC Health Services Research.
Version 3
Posted 19 Jun, 2020
No community comments so far
Reviewer #1 agreed
On 21 Jun, 2020
Review #1 received
Received 21 Jun, 2020
Editor assigned
On 18 Jun, 2020
Reviewers invited
Invitations sent on 18 Jun, 2020
Submission checks complete
On 17 Jun, 2020
Editor invited
On 17 Jun, 2020
No comments provided
Editorial decision: Minor revision
On 13 Jun, 2020
Review #2 received
Received 11 Jun, 2020
Review #1 received
Received 22 May, 2020
Reviewer #2 agreed
On 19 May, 2020
Editor assigned
On 15 May, 2020
Reviewers invited
Invitations sent on 15 May, 2020
Reviewer #1 agreed
On 15 May, 2020
Submission checks complete
On 14 May, 2020
Editor invited
On 14 May, 2020
No comments provided
Review #1 received
Received 13 Apr, 2020
Editorial decision: Major revision
On 13 Apr, 2020
Review #2 received
Received 05 Apr, 2020
Reviewer #2 agreed
On 09 Mar, 2020
Reviewer #1 agreed
On 06 Mar, 2020
Reviewers invited
Invitations sent on 04 Mar, 2020
Editor assigned
On 27 Feb, 2020
Submission checks complete
On 26 Feb, 2020
Editor invited
On 26 Feb, 2020
First submitted
On 18 Feb, 2020
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PDF Downloads: ...
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See the published version of this preprint at BMC Health Services Research.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Yun Zhang
The affiliated hospital of qingdao university
Corresponding Author
ORCiD: https://orcid.org/0000-0002-1358-6667
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Health Services Research.
Version 3
Posted 19 Jun, 2020
No community comments so far
Reviewer #1 agreed
On 21 Jun, 2020
Review #1 received
Received 21 Jun, 2020
Editor assigned
On 18 Jun, 2020
Reviewers invited
Invitations sent on 18 Jun, 2020
Submission checks complete
On 17 Jun, 2020
Editor invited
On 17 Jun, 2020
No comments provided
Editorial decision: Minor revision
On 13 Jun, 2020
Review #2 received
Received 11 Jun, 2020
Review #1 received
Received 22 May, 2020
Reviewer #2 agreed
On 19 May, 2020
Editor assigned
On 15 May, 2020
Reviewers invited
Invitations sent on 15 May, 2020
Reviewer #1 agreed
On 15 May, 2020
Submission checks complete
On 14 May, 2020
Editor invited
On 14 May, 2020
No comments provided
Review #1 received
Received 13 Apr, 2020
Editorial decision: Major revision
On 13 Apr, 2020
Review #2 received
Received 05 Apr, 2020
Reviewer #2 agreed
On 09 Mar, 2020
Reviewer #1 agreed
On 06 Mar, 2020
Reviewers invited
Invitations sent on 04 Mar, 2020
Editor assigned
On 27 Feb, 2020
Submission checks complete
On 26 Feb, 2020
Editor invited
On 26 Feb, 2020
First submitted
On 18 Feb, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Health Services Research.
Background: Total Healthcare Expenditure (THE) has increased substantially in all countries. Since the health system reform and health policy environment differ from each country, it is necessary to analyze the motivations of THE in a specific country.
Methods: The objective of this study was to analyze the influential factors of Provincial THE (PTHE) per capita in China by using spatiotemporal panel data across 31 provinces (including provinces, autonomous regions, and municipalities, all called provinces in here) from 2009-2016 at the provincial and annual level. Generalized Estimating Equation (GEE) was used to identify the influential factors of PTHE per capita.
Results: The number of beds per 10,000 population explained most of the variation of PTHE per capita. The results also showed that health expenditure in China reacts more to mortality compared with the Gross Domestic Product (GDP) per capita. But mortality and Out-Of-Pocket Payments (OOP) as a percentage of THE were associated with PTHE per capita negatively. The rate of infectious diseases and THE as a percentage of GDP had no statistical significance. And the Proportion of the Population Aged 65 and Over (POP65) impact PTHE per capita positively. But the coefficient was small.
Conclusions: In response to these findings, we conclude that the impact of the increasing percentage of OOP in THE diminishes the PTHE. Furthermore, we find that both the "baseline" health level and health provision are positively correlated with PTHE, which outweighs the effect of GDP.
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