Evidence map›Paper›PMID 30466451›Full record

ArticleInternational journal for equity in health2018

The inequality of inpatient care net benefit under integration of urban-rural medical insurance systems in China.

Xue Yang, Mingsheng Chen, Jinglin Du, Zhonghua Wang

Abstract read
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Article in International journal for equity in health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

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24citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

24 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Xue YangSchool of Health Policy & Management, Nanjing Medical University, 101Longmian Avenue, Jiangning District, Nanjing, 211166, People's Republic of China.
Mingsheng ChenSchool of Health Policy & Management, Nanjing Medical University, 101Longmian Avenue, Jiangning District, Nanjing, 211166, People's Republic of China.
Jinglin DuSchool of Health Policy & Management, Nanjing Medical University, 101Longmian Avenue, Jiangning District, Nanjing, 211166, People's Republic of China.
Zhonghua WangSchool of Health Policy & Management, Nanjing Medical University, 101Longmian Avenue, Jiangning District, Nanjing, 211166, People's Republic of China. wzh04@njmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChina has recently made efforts to integrate urban and rural basic medical insurance systems in order to ensure both urban and rural enrollees obtain unified benefits. However, whether the distribution of government healthcare subsides has become more equitable remains unknown. The purpose of this study was to analyze determinants of and inequality in net inpatient care benefits under the integration of urban-rural medical insurance systems in China.

methodsData were obtained from a nationally representative household survey, the Fifth National Health Services Survey (2013), conducted in Anhui province. A multiple regression model and concentration index (CI) was used to estimate related factors and inequality of inpatient care net benefits.

resultsFindings indicated that individuals received more inpatient care benefits when urban and rural social healthcare systems were integrated. Factors associated with net benefits included gender, age, marital status, retirement, educational level, history of chronic diseases, health status, willingness to seek inpatient care and per capita income. The rich were found to disproportionately benefit from inpatient care, and the CI of net benefits for integrated insurance enrollees was the lowest among all three available health insurance schemes. These findings indicate that the recent unification of urban-rural social health insurances reduces inequality in net benefits from government subsidies. Some socioeconomic factors, such as per capita income, 60 years of age and over, history of chronic disease and high educational level positively influence inequality.

conclusionIn China, accelerating the integration of urban and rural medical insurance systems is an effective way to increase equity of benefit in urban and rural areas. Strategies aimed at reducing inpatient benefit inequality must address socioeconomic factors influencing healthcare outcomes.

Indexed as

Socioeconomic FactorsAdultChinaChronic DiseaseFamily CharacteristicsFemaleFinancing, GovernmentHealthcare DisparitiesHumansIncomeInpatientsInsurance CoverageInsurance, HealthMaleMiddle AgedNational Health ProgramsBenefit distributionConcentration indexHealth insurance systemInequality

Identifiers

PMID30466451
PMCPMC6251195

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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.