Spatial Characteristics of Life Expectancy and Geographical Detection of Its Influencing Factors in China

Yafei Wu, Ke Hu, Yaofeng Han, Qilin Sheng and Ya Fang
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Yafei Wu: State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
Ke Hu: State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
Yaofeng Han: State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
Qilin Sheng: State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
Ya Fang: State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China

IJERPH, 2020, vol. 17, issue 3, 1-15

Abstract: Life expectancy (LE) is a comprehensive and important index for measuring population health. Research on LE and its influencing factors is helpful for health improvement. Previous studies have neither considered the spatial stratified heterogeneity of LE nor explored the interactions between its influencing factors. Our study was based on the latest available LE and social and environmental factors data of 31 provinces in 2010 in China. Descriptive and spatial autocorrelation analyses were performed to explore the spatial characteristics of LE. Furthermore, the Geographical Detector (GeoDetector) technique was used to reveal the impact of social and environmental factors and their interactions on LE as well as their optimal range for the maximum LE level. The results show that there existed obvious spatial stratified heterogeneity of LE, and LE mainly presented two clustering types (high–high and low–low) with positive autocorrelation. The results of GeoDetector showed that the number of college students per 100,000 persons (NOCS) could mainly explained the spatial stratified heterogeneity of LE (Power of Determinant ( PD ) = 0.89, p < 0.001). With the discretization of social and environmental factors, we found that LE reached the highest level with birth rate, total dependency ratio, number of residents per household and water resource per capita at their minimum range; conversely, LE reached the highest level with consumption level, GDP per capita, number of college students per 100,000 persons, medical care expenditure and urbanization rate at their maximum range. In addition, the interaction of any two factors on LE was stronger than the effect of a single factor. Our study suggests that there existed obvious spatial stratified heterogeneity of LE in China, which could mainly be explained by NOCS.

Keywords: life expectancy; spatial characteristics; social and environmental factors; spatial stratified heterogeneity; Geographical Detector (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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