Evidence map›Paper›PMID 35266107›Full record

ArticleHealth economics review2022

The comparison of catastrophic health expenditure and its inequality between urban and rural households in China.

Xian-Zhi Fu

Open access · goldAbstract read
In one paragraph

Article in Health economics review, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
6.2field-weighted citation impact, top 3% of its field
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

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3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.

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4 · The record

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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

1 author at 1 institution in 1 country.

Xian-Zhi FuSchool of Political Science and Public Administration, Wuhan University, Wuhan, 430072, Hubei, China. 1371810134@qq.com.ORCID http://orcid.org/0000-0003-2246-1564
Wuhan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn recent years, the goal of universal coverage of the basic medical insurance schemes has been basically achieved in China, but the heavy economic burden of diseases is still the main cause of poverty in many households. Exploring catastrophic health expenditure (CHE) and its inequality are highly important for forward-looking policymaking. This study aims to compare the incidence, intensity and inequality of CHE between urban and rural households in China.

methodsThis study was based on a national representative household survey-the China Family Panel Studies (CFPS)-that was conducted from 2012 to 2018. Concentration index (CI) was employed to measure the inequality of CHE incidence and overshoot, while the decomposition method of the CI was used to estimate the main influencing factors affecting inequality of CHE incidence.

resultsFrom 2012 to 2018, the CHE incidence of urban households increased from 11.01 to 11.88%, while the CHE incidence of rural households decreased from 18.42 to 18.31%. During the same period, the CI of CHE incidence for urban households decreased from - 0.1480 to - 0.1693, while that for rural households declined from - 0.1062 to - 0.1501. The major contribution to the pro-poor inequality in CHE incidence was associated with socioeconomic status, lagged CHE, receiving inpatient services, having elderly members, education of household head, and self-assessed health status of household head.

conclusionsRural households had higher risk of incurring CHE than urban households. The strong pro-poor inequality for CHE incidence and overshoot could be found in both two groups. The problem of poverty due to illness was more severe among low-income groups in rural areas than in urban areas. The relevant policy interventions should further focus on encouraging the development of supplementary medical insurance and increasing the reimbursement rate for hospitalization expenses in the medical assistance system.

Indexed as

Catastrophic health expenditureConcentration indexDecompositionInequality

Identifiers

PMID35266107
PMCPMC8908628
OpenAlexW4220819204

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.