Evidence map›Paper›PMID 39794726›Full record

ArticleBMC public health2025

Spatial-temporal distribution and evolution of medical and health talents in China.

Lei Zhang, Jie Tang, Qianqian Zhou, Yu Song, Dayong Zhang

Abstract read
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Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

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6citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Lei ZhangSchool of Economy and Management, Zhejiang Sci-Tech University, Hangzhou, 310018, China.
Jie TangSchool of Economy and Management, Zhejiang Sci-Tech University, Hangzhou, 310018, China.
Qianqian ZhouSchool of Economy and Management, Zhejiang Sci-Tech University, Hangzhou, 310018, China.
Yu SongThe XIPU Institution, Xi'an Jiaotong-Livepool University, Suzhou, 215123, China.
Dayong ZhangSchool of Medicine, Hangzhou City University, Hangzhou, 310015, China. zhangdy@hzcu.edu.cn.

Funding

China Postdoctoral Science Foundation 2020M681811National Natural Science Foundation of China 72174182
6 · The paper itself

Abstract

backgroundIn the context of public health emergencies, the presence of medical and health talents (MHT) is critically important for support in any country or region. This study aims to analyze the spatial and temporal distributions and evolution of MHT in China and propose strategies and recommendations for promoting a balanced distribution.

methodsThis research used data from 31 provinces in China to construct a multidimensional index system for measuring the agglomeration level of MHT. The indices include talent agglomeration density (TAD), talent agglomeration scale (TAS), talent agglomeration intensity (TAI), and talent agglomeration equilibrium (TAE). Using provincial data from the years 1982, 1990, 2000, 2010, and 2020, a spatiotemporal analysis of the MHT agglomeration levels was conducted. Furthermore, the regional dynamic distribution of MHT was analyzed using kernel density estimation diagrams. The spatial autocorrelation of MHT was assessed through global and local Moran's I, and the spatial gap and decomposition of MHT were analyzed using the Dagum Gini coefficient.

resultsFrom the temporal level, the TAD and TAI of MHT showed an increasing trend over the studied period, whereas TAS decreased and TAE first increased and then decreased from 1982 to 2020. At the spatial level, the TAD, TAS, TAI, and TAE of MHT exhibited varied patterns among the eastern, central, and western regions of China, showing significant geographical disparities, generally demarcated by the Hu Huanyong Line. The regional dynamic distribution level of MHT in the country and the three regions were expanding. Spatial autocorrelation analysis using global and local Moran's I for TAD, TAS, TAI, and TAE demonstrated significant regional differences. The Dagum Gini coefficient of TAD, TAS, TAI, and TAE revealed divergent trends in regional disparities, with overall declines in disparities for TAD and TAI, a slight increase for TAS, and fluctuating patterns for TAE.

conclusionsFrom a temporal perspective, the overall number of MHT in China has been increasing annually at the national and provincial levels. From the spatial perspective, TAD, TAS, TAI, and TAE exhibit significant differences among the three regions. Kernel analysis reveals that the distribution differences are gradually expanding in national level and varying in regional level. Moreover, the global and local Moran's I indices reveal varying spatial autocorrelation for TAD, TAS, TAI, and TAE. The Dagum Gini coefficients of TAD, TAS, TAI, and TAE show different patterns of decomposition.

Indexed as

Health WorkforceChinaHumansSpatio-Temporal AnalysisMedical and health talentsRegional differencesSpatial-temporal distributionTalent agglomeration densityTalent agglomeration equilibriumTalent agglomeration intensityTalent agglomeration scale

Identifiers

PMID39794726
PMCPMC11720572

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