ArticleBMC health services research2025
The spatial effects and influencing factors of inter-provincial health resource allocation efficiency in China.
Article in BMC health services research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Does Provincial Pooling of the Resident Medical Insurance Fund Improve the Efficiency of Medical Resource Allocation of China?Healthcare (Basel, Switzerland) · 2026Article
- Developing an integrated dementia care model using service blueprinting: process visualization and failure point analysis.BMC health services research · 2026Article
- Spatiotemporal heterogeneity and socioeconomic determinants of syphilis incidence in Xining, Northwest China: a geospatial analysis.BMC infectious diseases · 2026Article
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8 authors.
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Abstract
backgroundChinese government has been adjusting its strategies in response to rapid domestic and international changes to devise effective health policies and enhance public health. However, with the rapid socio-economic development and the COVID-19 outbreak, many researchers have identified issues of inefficiency and uneven distribution in health resource allocation within China. Therefore, how to scientifically allocate and efficiently use Chinese health resources has become an urgent issue.
methodsThe super-efficiency SBM model and global Malmquist model were used to measure and dynamically monitor the health resource allocation efficiency of 31 provinces in China from 2008 to 2020. Moran's I was applied to test the spatial autocorrelation of the efficiency, and the spatial Dubin model was constructed to analyze influencing factors. All data were collected from the China Health Statistical Yearbook and China Statistical Yearbook from 2008 to 2020.
resultsThe super-efficiency SBM model revealed an average health resource allocation efficiency score of 0.632. The average Malmquist productivity index for the same period was 1.090, indicating a generally positive growth. Moran's I test showed a notable spatial autocorrelation in efficiency distribution. And the regression results of the spatial Dubin model showed that the efficiency was affected by the dependency ratio, illiteracy rate, per capita disposable income, per capita public health budget expenditure, number of medical insurance participants, and the balance of medical insurance fund revenue and expenditure. DISCUSSIONS: The results revealed that China's health resource allocation exhibited low efficiency and regional disparities, primarily driven by uneven regional development. From 2008 to 2020, overall productivity increased by 9%, which were predominantly attributable to technological advancements. Under conditions of strong spatial autocorrelation, the efficiency of health resource allocation was shaped by multiple factors operating through distinct spatial channels.
conclusionGiven the challenges of health resource allocation efficiency in China, it is vital to implement targeted strategies. These include strengthening policy support for inefficiency regions, relying on technological progress and scientific management, fostering cross-regional collaboration to leverage spatial effects, and considering multiple factors for rational health resource allocation to ensure the sustainability of the health services.
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