State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China.
Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen 361102, China.
Int J Environ Res Public Health. 2020 Feb 1;17(3):906. doi: 10.3390/ijerph17030906.
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 = 0.89, < 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.
预期寿命 (LE) 是衡量人口健康的综合而重要的指标。研究 LE 及其影响因素有助于改善健康状况。以前的研究既没有考虑 LE 的空间分层异质性，也没有探索其影响因素之间的相互作用。我们的研究基于中国 2010 年 31 个省的最新可用 LE 和社会环境因素数据。进行了描述性和空间自相关分析，以探索 LE 的空间特征。此外，使用地理探测器 (GeoDetector) 技术揭示了社会和环境因素及其相互作用对 LE 的影响及其达到 LE 最高水平的最佳范围。结果表明，LE 存在明显的空间分层异质性，LE 主要呈现出两种聚类类型（高高和低低），具有正自相关性。GeoDetector 的结果表明，每 10 万人中的大学生人数（NOCS）可以主要解释 LE 的空间分层异质性（决定系数 Power = 0.89，< 0.001）。随着社会和环境因素的离散化，我们发现出生率、总抚养比、每户居民人数和人均水资源等因素处于最小值时，LE 达到最高水平；相反，消费水平、人均 GDP、每 10 万人中的大学生人数、医疗保健支出和城市化率等因素处于最大值时，LE 达到最高水平。此外，任何两个因素对 LE 的相互作用都强于单个因素的影响。我们的研究表明，中国存在明显的 LE 空间分层异质性，这主要可以用 NOCS 来解释。
