Exposure to high ambient temperatures is a significant driver of preventable mortality, with non-linear 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 non-linear functions with the Besag, York, and Mollie (BYM2) 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$\times$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.

翻译：暴露于高环境温度是导致可预防性死亡的重要因素，其健康效应呈现非线性特征，并在特定区域表现出更高的风险。为捕捉这种复杂性并考虑小区域间的空间依赖性，我们提出了一个将非线性函数与Besag、York和 Mollie（BYM2）模型相结合的贝叶斯框架。将该框架应用于瑞士全因死亡率数据，我们量化了热相关死亡率的空间不平等性。我们从瑞士联邦公共卫生局获取了65岁以上人群在小区域（2145个市镇）的每日全因死亡率数据，并从瑞士联邦气象局获取了1km×1km网格的日平均温度数据。通过完全传播不确定性，我们推导出关键的流行病学指标，包括热相关超额死亡率和最低死亡率温度（MMT）。瑞士北部的热相关超额死亡率较高，而山区观测到较低的MMT。此外，我们探讨了85岁以上人口比例、绿地面积、平均温度、贫困程度、城市化水平、空气污染和语言区域在解释这些差异中的作用。我们发现，热相关超额死亡率的空间差异主要由人口年龄分布、绿地面积以及与高温暴露相关的脆弱性因素驱动。