Evidence mapPaperPMID 41316939Full record

ArticleInquiry : a journal of medical care organization, provision and financing

Health Services Accessibility and Self-Reported Health Among Chinese Middle-Aged and Older Adults: Propensity Score Matching and Mixed-Effects Ordinal Logit Analysis.

Heling Ai, Ariel Shensa, Faina Linkov

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In one paragraph

Article in Inquiry : a journal of medical care organization, provision and financing. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Heling AiShanghai University of Traditional Chinese Medicine, China.ORCID 0009-0008-0268-6612
Ariel ShensaDepartment of Health, Exercise & Applied Science, Duquesne University, Pittsburgh, PA, USA.ORCID 0000-0002-6620-217X
Faina LinkovDepartment of Health, Exercise & Applied Science, Duquesne University, Pittsburgh, PA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study evaluates the impact of unmet healthcare needs on self-reported health among middle-aged and older Chinese adults (≥45 years), with a focus on urban-rural disparities. Using 2011, 2013, and 2015 CHARLS data, we applied propensity score matching and mixed-effects ordinal logit models, with interaction effects regression for urban-rural differences. We analyzed the impact of unmet healthcare needs-defined as instances where individuals perceived a need for medical care but did not receive it-on self-reported health, which was measured using a 5-point Likert scale. Unmet outpatient care alone did not significantly affect self-reported health but had a negative impact when financial barriers were present (AOR = 0.44, 95% CI = [0.24, 0.82]). Unmet inpatient care significantly decreased self-reported health (AOR = 0.65, 95% CI = [0.57, 0.74]), with financial barriers worsening the effect (AOR = 0.25, 95% CI = [0.15, 0.42]). The negative impact of unmet inpatient care was significant only for rural residents (AOR = 0.67, 95% CI = [0.50, 0.89]). Unmet healthcare needs, particularly due to financial barriers, significantly harm self-reported health, with rural populations more affected, highlighting the need for targeted reforms in both financial protection and healthcare system delivery.

Indexed as

Health Services AccessibilityHealth StatusAgedChinaEast Asian PeopleFemaleHealthcare DisparitiesHealth Services Needs and DemandHumansMaleMiddle AgedPropensity ScoreRural PopulationSelf ReportUrban Populationfinancial barriershealth services accessibilitypropensity score matchingself-reported healthurban-rural health disparities

Identifiers

PMID41316939
PMCPMC12665022

What Socratic holds

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LicenceCC BY-NC
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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 Socratic graph.