Modelling the Spatially Varying Non-Linear Effects of Heat Exposure
Xinyi Chen, Marta Blangiardo, Connor Gascoigne, and Garyfallos Konstantinoudis

TL;DR
This paper introduces a Bayesian framework combining non-linear effects and spatial modeling to analyze heat-related mortality disparities across small regions in Switzerland, accounting for uncertainties and multiple influencing factors.
Contribution
The study develops a novel Bayesian approach integrating non-linear functions with the BYM2 model to assess spatially varying heat effects on mortality.
Findings
Higher heat-related mortality in northern Switzerland
Lower minimum mortality temperatures in mountainous regions
Population age, green space, and temperature vulnerabilities drive disparities
Abstract
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 1km1km 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).…
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