Evidence map›Paper›PMID 37779492›Full record

ArticleGlobal health action2023

Effects of health poverty alleviation project from the perspective of vulnerability to poverty: evidence from five Chinese prefectures.

Yan Wei, Zhaochi Zhang, Mingjian Zhang

Abstract read
In one paragraph

Article in Global health action, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 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

3 authors.

Yan WeiSchool of Statistics, Xi'an University of Finance and Economics, Xi'an, China.ORCID 0000-0003-0883-9778
Zhaochi ZhangSchool of Statistics, Xi'an University of Finance and Economics, Xi'an, China.ORCID 0009-0005-4997-8194
Mingjian ZhangSchool of Statistics, Xi'an University of Finance and Economics, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe Health Poverty Alleviation Project (HPAP) has received widespread attention as a primary means of preventing poverty caused by illness. However, further evidence is required to confirm the effects of HPAP.

objectiveThis study examines the effectiveness and mechanisms of action of HPAP using data from a special survey conducted in five Chinese prefectures in 2018-2019.

methodThis study uses a three-step feasible generalised least-squares method to measure the farm households' vulnerability to poverty. Hierarchical linear regression and propensity score matching were employed to assess the poverty-reduction effects of HPAP. A mediating effects model was used to test how these policies alleviated poverty.

resultsThe mean vulnerability to poverty among farm households was 0.367, with 11.89% experiencing both poverty and vulnerability, particularly in areas of deep poverty. This study has found that HPAP significantly reduces poverty and is more effective in reducing the vulnerability of non-poor farm households than poor farm households. Additionally, the results suggest that improving human capital stock and reducing medical expenditure are the two pathways through which HPAP can alleviate farm households' vulnerability to poverty.

conclusionsThis study suggests that the vulnerability to poverty perspective should be incorporated into poverty alleviation policy formulation. HPAP enhances differentiation and precision. Thus, a long-term mechanism of HPAP should be developed.

Indexed as

FarmersPovertyChinaEast Asian PeopleFamily CharacteristicsHealth ExpendituresHumansChinaHealth Poverty Alleviation Projecthierarchical linear regressionpropensity score matchingvulnerability to poverty

Identifiers

PMID37779492
PMCPMC10547439

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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.