↓ Skip to main content

Comparing the effects of China’s three basic health insurance schemes on the equity of health-related quality of life: using the method of coarsened exact matching

Overview of attention for article published in Health and Quality of Life Outcomes, March 2018
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

Mentioned by

blogs
1 blog
twitter
1 X user
facebook
2 Facebook pages
reddit
1 Redditor

Citations

dimensions_citation
63 Dimensions

Readers on

mendeley
114 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Comparing the effects of China’s three basic health insurance schemes on the equity of health-related quality of life: using the method of coarsened exact matching
Published in
Health and Quality of Life Outcomes, March 2018
DOI 10.1186/s12955-018-0868-0
Pubmed ID
Authors

Min Su, Zhongliang Zhou, Yafei Si, Xiaolin Wei, Yongjian Xu, Xiaojing Fan, Gang Chen

Abstract

China has three basic health insurance schemes: Urban Employee Basic Medical Insurance (UEBMI), Urban Resident Basic Medical Insurance (URBMI) and New Rural Cooperative Medical Scheme (NRCMS). This study aimed to compare the equity of health-related quality of life (HRQoL) of residents under any two of the schemes. Using data from the 5th National Health Services Survey of Shaanxi Province, China, coarsened exact matching method was employed to control confounding factors. We included a matched sample of 6802 respondents between UEBMI and URBMI, 34,169 respondents between UEBMI and NRCMS, and 36,928 respondents between URBMI and NRCMS. HRQoL was measured by EQ-5D-3L based on the Chinese-specific value set. Concentration index was adopted to assess health inequality and was decomposed into its contributing factors to explain health inequality. After matching, the horizontal inequity indexes were 0.0036 and 0.0045 in UEBMI and URBMI, 0.0035 and 0.0058 in UEBMI and NRCMS, and 0.0053 and 0.0052 in URBMI and NRCMS respectively, which were mainly explained by age, educational and economic statuses. The findings demonstrated the pro-rich health inequity was much higher for the rural scheme than that for the urban ones. This study highlights the need to consolidate all three schemes by administrating uniformly, merging funds pooling and benefit packages. Based on the contributing factors, strategies aim to facilitate health conditions of the elderly, narrow economic gap, and reduce educational inequity, are essential. This study will provide evidence-based strategies on consolidating the fragmented health schemes towards reducing health inequity in both China and other developing countries.

Timeline

Login to access the full chart related to this output.

If you don’t have an account, click here to discover Explorer

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 114 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 114 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 15 13%
Student > Ph. D. Student 11 10%
Researcher 7 6%
Student > Doctoral Student 6 5%
Student > Bachelor 4 4%
Other 20 18%
Unknown 51 45%
Readers by discipline Count As %
Social Sciences 14 12%
Nursing and Health Professions 11 10%
Medicine and Dentistry 9 8%
Business, Management and Accounting 6 5%
Agricultural and Biological Sciences 5 4%
Other 14 12%
Unknown 55 48%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 March 2018.
All research outputs
#3,779,440
of 23,026,672 outputs
Outputs from Health and Quality of Life Outcomes
#362
of 2,187 outputs
Outputs of similar age
#75,856
of 332,611 outputs
Outputs of similar age from Health and Quality of Life Outcomes
#23
of 50 outputs
Altmetric has tracked 23,026,672 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,187 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 82% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 332,611 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 50 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.