基于时空地统计的极端气温与人群死亡暴露反应研究

Global climate change not only has a direct impact on human health, but also seriously influences the development of social and economic. In recent years, global climate change has led to a marked increase in the frequency of extreme temperature events, which have seriously affected population health. Therefore, it is necessary to study on the intrinsic correlation patterns of the impact of extreme temperature on population death, especially the characteristics of vulnerable populations affected by extreme temperature. However, the existing studies have a lack of attention on temperature variation on space and only consider on the one hand of the short exposure effect and nonlinear lag characteristics of extreme temperature. In order to deal with the above problems, this program combines spatial analysis technology with health research on climate change, and systematically analyzes the effects of extreme temperature on population death. First, the hybrid model of cross coupling cases and distributed lag nonlinear model is proposed to effectively solve the problem of the incompatible brief exposure effect and nonlinear hysteresis characteristics of extreme temperature; Then, spatio-temporal geo-statistical techniques are used to investigate on temperature variation of extreme temperature in space and the space-time model of “extreme temperature and population death” is constructed; Lastly, we analyzed the spatial heterogeneity of extreme temperature on population death and the characteristics of sensitive population in China. Research results will provide scientific and effective theoretical and technical support to effectively cope with the influence of climate change on population health.

全球气候变化不仅直接影响人类健康，还会对社会经济发展产生影响。近年来，全球气候变化导致极端气温事件的频率显著上升，而极端气温又严重影响了人群的健康。因此，有必要研究极端气温对人群死亡影响的内在关联模式，尤其是受到极端气温影响的脆弱人群特征。但现有的研究对于气温在空间上的变异缺乏重视，并且在极端气温的短暂暴露效应和非线性滞后特征方面仅考虑了一方面。本项目针对以上问题，将空间分析技术与气候变化健康研究相结合，系统开展极端气温事件与人群死亡影响的研究。首先构建耦合病例交叉和分布式滞后非线性模型的混合模型，有效弥补了极端气温在短暂暴露效应和非线性滞后特征不能兼容的问题；然后采用时空地统计技术研究气温在空间上的变异，建立极端气温－人群死亡时空模型；在此基础上分析极端气温对我国人群死亡的空间异质性以及敏感人群特征。研究成果将为有效应对气候变化对人群健康的影响提供科学有效的理论和技术支撑。

全球气候变化不仅直接影响人类健康，还会对社会经济发展产生影响。近年来，全球气候变化导致极端气温事件的频率显著上升，而极端气温又严重影响了人群的健康。因此，有必要研究极端气温对人群死亡影响的内在关联模式，尤其是受到极端气温影响的脆弱人群特征。但现有的研究对于气温在空间上的变异缺乏重视，并且在极端气温的短暂暴露效应和非线性滞后特征方面仅考虑了一方面。本项目针对以上问题，将空间分析技术与气候变化健康研究相结合，系统开展极端气温事件与人群死亡影响的研究。首先构建耦合病例交叉和分布式滞后非线性模型的混合模型，有效弥补了极端气温在短暂暴露效应和非线性滞后特征不能兼容的问题；然后研究气温在空间上的变异，建立极端气温－人群死亡时空模型；在此基础上分析极端气温对我国人群死亡的空间异质性以及敏感人群特征。研究成果将为有效应对气候变化对人群健康的影响提供科学有效的理论和技术支撑。