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Social Deprivation and Rural Public Health in China: Exploring the Relationship Using Spatial Regression

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Abstract

Rural public health still faces serious challenges in China. These challenges in rural public health reduce peasants’ well-being and social satisfaction. Examining the social factors of rural public health helps improve the public health in rural areas. This study attempts to characterize the relationship between social deprivation and rural public health in China. In particular, 14 indicators are integrated for assessing social deprivation, which is described from five domains: income, employment, education, housing and demographic structure. The analytic hierarchy process, Delphi method, entropy method and coefficient variation method are selected as weight determining methods to evaluate the corresponding weights for the indicators of social deprivation. Then, the best models are selected from ordinary least squares regression models, spatial lag models and spatial error models according to the performances of these models. The results of assessing social deprivation indicate that the spatial distribution of social deprivation has great heterogeneity in rural China. In addition, the relative levels of social deprivation among 31 provinces that are estimated by different weight determination methods remain stable. Finally, the spatial regression models reveal that social deprivation is a positive contributor to the maternal mortality rate and child mortality rate, while social deprivation presents negative relationships with the proportion of healthy elders.

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Acknowledgements

This research received financial support from the National Natural Science Foundation of China (71874151 and 71403235), the Humanity and Social Science Foundation of Ministry of Education of China (18YJA630134), the Zhejiang Provincial Natural Science Foundation of China (LY18G030031).

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Correspondence to Heyuan You.

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You, H., Zhou, D., Wu, S. et al. Social Deprivation and Rural Public Health in China: Exploring the Relationship Using Spatial Regression. Soc Indic Res 147, 843–864 (2020). https://doi.org/10.1007/s11205-019-02183-z

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