Evidence map›Paper›PMID 42527553›Full record

Articlenpj health systems2026

Spatially heterogeneous and nonlinear factors influencing intercity patient mobility for chronic kidney disease: a nationwide study in China.

Chenghua Guo, Jingyi Wu, Qianlin Zuo, Pengfei Li, Luxia Zhang

Abstract read
In one paragraph

Article in npj health systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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

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

5 authors.

Chenghua Guo *School of Nursing, Peking University, Beijing, China.
Jingyi Wu *Advanced Institute of Information Technology, Peking University, Hangzhou, China.
Qianlin ZuoAdvanced Institute of Information Technology, Peking University, Hangzhou, China.
Pengfei LiSchool of Nursing, Peking University, Beijing, China. pfli@bjmu.edu.cn.
Luxia ZhangAdvanced Institute of Information Technology, Peking University, Hangzhou, China. zhanglx@bjmu.edu.cn.

Funding

National Natural Science Foundation of China 72125009National Natural Science Foundation of China 72474010
6 · The paper itself

Abstract

Uneven distribution of high-quality nephrology care in China has driven rising intercity patient mobility for chronic kidney disease (CKD). This study examined the spatial correlates of this mobility using over 4 million cross-city hospitalization records from 2014 to 2018. First, the Geodetector model was used to identify the key factors and their complex interactions driving patient inflows and outflows, including socioeconomic status, healthcare resource availability, and transportation accessibility. Then, multiscale geographically weighted regression (MGWR) was applied to explore geographical heterogeneity in the influence of these factors on intercity patient mobility. According to the Geodetector model, the leading correlates of patient outflows included hospital bed density, doctor density, and population growth rate, with evident nonlinear and synergistic associations. Patient inflows were mainly influenced by nephrology workforce availability and population structure. MGWR analysis revealed substantial spatial variation in the associations of general and nephrology-specific healthcare resources on intercity patient mobility, underscoring the complex interaction between healthcare capacity and geographic context. This study proposes a novel framework for understanding the spatial correlates of intercity CKD patient mobility in China and highlights the geographic heterogeneity of their associations. The findings support policies aimed at improving the equity and efficiency of CKD care across regions.

Identifiers

PMID42527553
PMCPMC13336799

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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.