Evidence map›Paper›PMID 36636036›Full record

ArticleRisk management and healthcare policy2023

Risk Characteristics of Catastrophic Health Expenditure in Multidimensional Borderline Poor Households in China.

Xiaocang Xu, Xiuquan Huang

Open access · goldAbstract read
In one paragraph

Article in Risk management and healthcare policy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.9field-weighted citation impact, top 9% 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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

10 citing papers in PubMed, 10 citations in OpenAlex.

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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

2 authors at 2 institutions in 2 countries.

Xiaocang XuSchool of Economics and Management, Huzhou University, Huzhou, 313000, People's Republic of China.
Xiuquan HuangFaculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, 999078, People's Republic of China.
Huzhou University · CNMacao Polytechnic University · MO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Ending poverty and realizing common prosperity are the essential requirements for the localization of Marxism in China. The ongoing COVID-19 pandemic in the post-poverty era further aggravates the risk of catastrophic health expenditure for borderline poor households and increases the uncertainty of returning to poverty due to illness, potentially undermining decades of hard-won efforts to eradicate poverty in China. Methods: Based on the latest data released by China Health and Retirement Longitudinal Survey (CHARLS) database, this paper uses the Logit model to empirically explore the risk characteristics of catastrophic health expenditure of multidimensional borderline poor households based on identifying multidimensional borderline poor households. Results: The results show that factors such as family income level, child support, and medical insurance have different impacts on catastrophic health expenditure, and the risk of catastrophic health expenditure of multidimensional borderline poor households is much higher than that of non- multidimensional borderline poor households, and there is a certain difference between urban and rural areas. Discussion: The government should strengthen and improve the social security system and health service system, such as medical insurance, and more resources should be allocated to multidimensional borderline poor households, especially in rural areas.

Indexed as

catastrophic health expenditurecommon prosperityinsurance systemmultidimensional borderline poor householdsurban-rural differences

Identifiers

PMID36636036
PMCPMC9830416
OpenAlexW4313559123

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.