Modelling the spatially varying nonlinear effects of heat exposure
提出一个贝叶斯框架，结合非线性函数与空间模型，分析瑞士老年人热相关超额死亡率的空间不平等，发现人口年龄分布、绿地和高温暴露是主要驱动因素。
Abstract Exposure to high ambient temperatures is a significant driver of preventable mortality, with nonlinear health effects and elevated risks in specific regions. To capture this complexity and account for spatial dependencies across small areas, we propose a Bayesian framework that integrates nonlinear functions with the Besag, York, and Mollie model. Applying this framework to all-cause mortality data in Switzerland, we quantified spatial inequalities in heat-related mortality. We retrieved daily all-cause mortality at small areas (2,145 municipalities) for people older than 65 years from the Swiss Federal Office of Public Health and daily mean temperature at 1km×1km grid from the Swiss Federal Office of Meteorology. By fully propagating uncertainties, we derived key epidemiological metrics, including heat-related excess mortality and minimum mortality temperature (MMT). Heat-related excess mortality rates were higher in northern Switzerland, while lower MMTs were observed in mountainous regions. Further, we explored the role of the proportion of individuals older than 85 years, green space, average temperature, deprivation, urbanicity, air pollution, and language regions in explaining these discrepancies. We found that spatial disparities in heat-related excess mortality were primarily driven by population age distribution, green space, and vulnerabilities associated with elevated temperature exposure.