Evidence map›Paper›PMID 34858912›Full record

Trial reportFrontiers in public health2021

Impact of Catastrophic Health Expenditures on Chinese Household Consumption.

Ning Wei, Wenhao Huang, Lü-Lin Zhou

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

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

16 citing papers in PubMed.

  1. Article
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  9. Prevalence and Determinants of Catastrophic Healthcare Expenditures in Iran From 2013 to 2019.Journal of preventive medicine and public health = Yebang Uihakhoe chi · 2024
    Article
  10. Article
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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

3 authors.

Ning WeiSchool of Management, Jiangsu University, Zhenjiang, China.
Wenhao HuangSchool of Management, Xuzhou Medical University, Xuzhou, China.
Lü-Lin ZhouSchool of Management, Jiangsu University, Zhenjiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

China has built a social medical insurance system that covers the entire population so as to reduce the impact of diseases on individuals and families. Although the decline in the incidence of catastrophic health expenditures (CHEs) in China is encouraging, this issue remains important. On the basis of considering selectivity bias and heterogeneity, we applied propensity score matching (PSM) to analyze the 2018 data from the China Family Panel Studies. We assigned CHE households and non-CHE households to the treatment group and the control group, respectively, and used non-random data to simulate a randomized trial to investigate the impact of CHE on household consumption in China. The results of this study indicate that, when the threshold is set at 40%, the consumption of households experiencing CHEs (CHE household) is significantly lower than that of households not experiencing CHEs (non-CHE households) and that CHEs have a significant negative impact on other household consumption and a significant impact on the household property and debt. This effect still exists when the threshold is set lower, with household essential consumption most affected. The occurrence of CHEs leads to a reduction in household consumption and a significantly worsening financial situation for the CHE households, impacting the basic quality of life of the families. Therefore, it is necessary to further reform the medical and health system to reduce the high medical expenses.

Indexed as

Catastrophic IllnessHealth ExpendituresChinaHumansQuality of LifeCHEChinaconsumptionhouseholdpropensity score matching

Identifiers

PMID34858912
PMCPMC8631299

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.