Evidence map›Paper›PMID 42271393›Full record

ArticleBMC health services research2026

Regional differences in the level of healthcare in China.

Hongrui Cui, Qingli Tan, Xiaoshan Liu

Abstract read
In one paragraph

Article in BMC health services research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Hongrui CuiSchool of Pharmaceutical Business, Guangdong Pharmaceutical University, Zhongshan, China.
Qingli TanSchool of Pharmaceutical Business, Guangdong Pharmaceutical University, Zhongshan, China. tanql@vip.163.com.
Xiaoshan LiuSchool of Pharmaceutical Business, Guangdong Pharmaceutical University, Zhongshan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesLarge inequities exist between different regions in China in terms of access to healthcare. However, there is limited quantitative research on these regional disparities. Therefore, we aimed to analyse the differences in the levels of healthcare in various regions of China, and to provide a reference for healthcare reform aimed at optimising the allocation of medical resources in China.

methodsWe collected and organised healthcare-related data from China's provinces, autonomous regions, and municipalities directly under the Central Government from 2012 to 2021 and selected 18 indicators related to medical services for the year 2021. We used factor analysis, cluster analysis, and the Theil and Atkinson index to quantitatively study the medical service levels of China's regions.

resultsThere was a large discrepancy between regions in terms of the level of healthcare, with the lowest level of healthcare in economically underdeveloped regions. The distribution of medical resources in various regions of China is uneven, with a lack of medical resources in the western and central regions and underutilisation of medical resources in easten region.

conclusionsTo rectify the imbalance in the level of healthcare services across China, health reform must focus on enhancing regional economic development. Strengthen the capacity of grassroots healthcare services and reduce inequalities in medical resource allocation. Moreover, future policies should focus more on addressing regional disparities in healthcare funding and urban-rural gaps in medical resource allocation.

Indexed as

Delivery of Health CareHealthcare DisparitiesHealth Services AccessibilityChinaHealth Care ReformHumansHealthcareMedical resourcesResource allocationUtilisation efficiency

Identifiers

PMID42271393
PMCPMC13479549

What OpenQuestion holds

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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.