Evidence map›Paper›PMID 39210361›Full record

ArticleBMC health services research2024

Cross-provincial inpatient mobility patterns and their determinants in China.

Jintao Yang, Bin Yan, Shenggen Fan, Zhenggang Ni, Xiao Yan, Gexin Xiao

Abstract read
In one paragraph

Article in BMC health services research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 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

6 authors.

Jintao YangCollege of Economics and Management, China Agricultural University, Beijing, 100083, China.
Bin YanState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), Beijing, 100101, China.
Shenggen FanCollege of Economics and Management, China Agricultural University, Beijing, 100083, China.
Zhenggang NiNational Institute of Hospital Administration, National Health Commission of the People's Republic of China (NHC), Building No.3, Courtyard 6, Shouti South Road, Haidian District, Beijing, 100044, China.
Xiao YanSchool of Big Data Science, Hebei Finance University, Baoding, 071051, China.
Gexin XiaoNational Institute of Hospital Administration, National Health Commission of the People's Republic of China (NHC), Building No.3, Courtyard 6, Shouti South Road, Haidian District, Beijing, 100044, China. biocomputer@126.com.

Funding

National Funding Postdoctoral Fellowship Program of CPSF GZC20233022National Natural Science Foundation of China 82090054,72061147002
6 · The paper itself

Abstract

backgroundThe incongruity between the regional supply and demand of healthcare services is a persistent challenge both globally and in China. Patient mobility plays a pivotal role in addressing this issue. This study aims to delineate the cross-provincial inpatient mobility network (CIMN) in China and identify the underlying factors influencing this CIMN.

methodsWe established China's CIMN by applying a spatial transfer matrix, utilizing the flow information from 5,994,624 cross-provincial inpatients in 2019, and identified the primary demand and supply provinces for healthcare services. Subsequently, we employed GeoDetector to analyze the impact of 10 influencing factors-including medical resources, medical quality, and medical expenses-on the spatial patterns of CIMN.

findingsBeijing, Shanghai, Zhejiang, and Jiangsu provinces are the preferred medical destinations for cross-provincial inpatients, while Anhui, Henan, Hebei, and Jiangsu provinces are the main sources for cross-provincial inpatients. Patient flow between provinces decreases with distance. The spatial distribution of medical resources, medical quality, and medical expenses account for 87%, 73%, and 56% of the formation of CIMN, respectively. Additionally, interactions between these factors enhance explanatory power, suggesting that considering their interactions can more effectively optimize medical resources and services.

conclusionsThe analysis of CIMN reveals the supply and demand patterns of healthcare services, providing insights into the inequality characteristics of healthcare access. Furthermore, understanding the driving factors and their interactions offers essential evidence for optimizing healthcare services.

Indexed as

InpatientsAdultChinaFemaleHealth Services AccessibilityHealth Services Needs and DemandHumansMaleMiddle AgedPatient TransferHealthcare efficiencyHealthcare flowsPatient mobilitySpatial stratified heterogeneity

Identifiers

PMID39210361
PMCPMC11363524

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.