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Incorporating Spatial Statistics into Examining Equity in Health Workforce Distribution: An Empirical Analysis in the Chinese Context
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Incorporating Spatial Statistics into Examining Equity in Health Workforce Distribution: An Empirical Analysis in the Chinese Context
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Journal Article
Incorporating Spatial Statistics into Examining Equity in Health Workforce Distribution: An Empirical Analysis in the Chinese Context
2018
Overview
Existing measures of health equity bear limitations due to the shortcomings of traditional economic methods (i.e., the spatial location information is overlooked). To fill the void, this study investigates the equity in health workforce distribution in China by incorporating spatial statistics (spatial autocorrelation analysis) and traditional economic methods (Theil index). The results reveal that the total health workforce in China experienced rapid growth from 2004 to 2014. Meanwhile, the Theil indexes for China and its three regions (Western, Central and Eastern China) decreased continually during this period. The spatial autocorrelation analysis shows that the overall agglomeration level (measured by Global Moran’s I) of doctors and nurses dropped rapidly before and after the New Medical Reform, with the value for nurses turning negative. Additionally, the spatial clustering analysis (measured by Local Moran’s I) shows that the low–low cluster areas of doctors and nurses gradually reduced, with the former disappearing from north to south and the latter from east to west. On the basis of these analyses, this study suggests that strategies to promote an equitable distribution of the health workforce should focus on certain geographical areas (low–low and low–high cluster areas).
Publisher
MDPI AG,MDPI
