Evidence map›Paper›PMID 29125837›Full record

ArticlePloS one2017

Differences exist across insurance schemes in China post-consolidation.

Yang Li, Yinjun Zhao, Danhui Yi, Xiaojun Wang, Yan Jiang, Yu Wang, Xinchun Liu, Shuangge Ma

Abstract read
In one paragraph

Article in PloS one, 2017. 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

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

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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

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

8 authors.

Yang LiSchool of Statistics, Renmin University of China, Beijing, China.
Yinjun ZhaoSchool of Public Health, Yale University, New Haven, CT, United States of America.
Danhui YiSchool of Statistics, Renmin University of China, Beijing, China.
Xiaojun WangSchool of Statistics, Renmin University of China, Beijing, China.
Yan JiangSchool of Statistics, Renmin University of China, Beijing, China.
Yu WangSchool of Statistics, Renmin University of China, Beijing, China.
Xinchun LiuSchool of Public Health, Yale University, New Haven, CT, United States of America.
Shuangge MaSchool of Statistics, Renmin University of China, Beijing, China.ORCID http://orcid.org/0000-0001-9001-4999

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn China, the basic insurance system consists of three schemes: the UEBMI (Urban Employee Basic Medical Insurance), URBMI (Urban Resident Basic Medical Insurance), and NCMS (New Cooperative Medical Scheme), across which significant differences have been observed. Since 2009, the central government has been experimenting with consolidating these schemes in selected areas. This study examines whether differences still exist across schemes after the consolidation.

methodsA survey was conducted in the city of Suzhou, collecting data on subjects 45 years old and above with at least one inpatient or outpatient treatment during a period of twelve months. Analysis on 583 subjects was performed comparing subjects' characteristics across insurance schemes. A resampling-based method was applied to compute the predicted gross medical cost, OOP (out-of-pocket) cost, and insurance reimbursement rate.

resultsSubjects under different insurance schemes differ in multiple aspects. For inpatient treatments, subjects under the URBMI have the highest observed and predicted gross and OOP costs, while those under the UEBMI have the lowest. For outpatient treatments, subjects under the UEBMI and URBMI have comparable costs, while those under the NCMS have much lower costs. Subjects under the NCMS also have a much lower reimbursement rate.

conclusionsDifferences still exist across schemes in medical costs and insurance reimbursement rate post-consolidation. Further investigations are needed to identify the causes, and interventions are needed to eliminate such differences.

Indexed as

Insurance, HealthChinaFemaleHumansMale

Identifiers

PMID29125837
PMCPMC5681283

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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.