This study investigates how different spatiotemporal temperature models affect the estimation of heat-related mortality in Lazio, Italy (2008--2022). First, we compare three methods to reconstruct daily maximum temperature at the municipality level: 1. a Bayesian quantile regression model with spatial interpolation, 2. a Bayesian Gaussian regression model, 3. the gridded reanalysis data from ERA5-Land. Both Bayesian models are station-based and exhibit higher and more spatially variable temperatures compared to ERA5-Land. Then, using individual mortality data for cardiovascular and respiratory causes, we estimate temperature-mortality associations through Bayesian conditional Poisson models in a case-crossover design. Exposure is defined as the mean maximum temperature over the previous three days. Additional models include heatwave definitions combining different thresholds and durations. All models exhibit a marked increase in relative risk at high temperatures; however, the temperature of minimum risk varies significantly across methods. Stratified analyses reveal higher relative risk increases in females and the elderly (80+). Heatwave effects depend on the definitions used, but all methods capture an increased mortality risk associated with prolonged heat exposure. Results confirm the importance of temperature model choice in epidemiology and provide insights for early warning systems and climate-health adaptation strategies.

翻译：本研究探讨了不同时空温度模型如何影响意大利拉齐奥大区（2008–2022年）热相关死亡率的估计。首先，我们比较了三种在市级层面重建日最高温度的方法：1. 基于空间插值的贝叶斯分位数回归模型，2. 贝叶斯高斯回归模型，3. ERA5-Land的网格化再分析数据。两种贝叶斯模型均基于站点数据，与ERA5-Land相比，显示出更高且空间变异性更大的温度。随后，利用心血管和呼吸系统疾病导致的个体死亡率数据，我们通过病例交叉设计中的贝叶斯条件泊松模型估计了温度-死亡率关联。暴露定义为过去三天的平均最高温度。附加模型包括结合不同阈值和持续时间的酷热期定义。所有模型均显示在高温下相对风险显著增加；然而，最低风险对应的温度在不同方法间差异显著。分层分析表明，女性和老年人（80岁以上）的相对风险增加更高。酷热期效应取决于所采用的定义，但所有方法均捕捉到与长时间热暴露相关的死亡率风险升高。结果证实了温度模型选择在流行病学中的重要性，并为早期预警系统和气候-健康适应策略提供了见解。