128 / 2019-11-04 23:41:00

Spatial characteristics of life expectancy and geographical detection of its influencing factors in China

life expectancy, influencing factors, spatial stratified heterogeneity, Geographical Detector

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 had found that LE was mainly affected by social environmental and biological factors. While these studies had neither considered the spatial stratified heterogeneity contained in the data nor explored the interactions between variables. Our study was based on the lateset available LE data and related social environmental factors data from Chinese Statistical Yearbook, including 31 provinces in 2010. Spatial autocorrelation analysis was performed to explore the spatial characteristics of LE as well as social environmental factors. Furthermore, we used the Geographical Detector (GeoDetector) technique to reveal the impact of social environmental factors and their interactions on LE as well as their optimal range for the maximum LE level. The results showed that the average LE showed a clear downward trend from the east to the west. From the results of spatial autocorrelation analysis, there existed obvious spatial aggregation for LE in china, and LE mainly presented two cluster types: high-high and low-low. We used the GeoDetector to explore the Power of Determinant (PD) of influencing factors. The results showed that number of college students per 100,000 persons (NOCS) had the highest influence on LE (PD = 0.89, p < 0.001); birth rate (BR), total dependency ratio (TDR), number of population per household (NOPPH), consumption level of urban residents (CLOUR), GDP per capital (GPC) and urban ratio (UR) had less influence on LE (0.45 ≤ PD ≤ 0.65, p < 0.05); water resource per capital (WRPC) and medical care expenditure of urban residents (MCEOUR) had the lowest influence on LE (PD < 0.4, p < 0.05). With the discretization of the social environmental factors, we found that the LE reached the highest level with BR, TDR, NOPPH and WRPC at the minimum range; conversely, LE reached the highest level with CLOUR, GPC, NOCS, MCEOUR and UR at the maximum range. In addition, the results showed the interaction of any two social environmental factors on LE was stronger than that of a single factor. The findings suggested that obvious spatial differences of LE existed in China and this pattern was influenced by various key social environmental factors.