This PhD thesis investigates the relationship between temperature and mortality at different spatial and temporal scales, addressing a crucial issue in the context of rising temperatures due to anthropogenic climate change. The thesis is structured around three research areas that bring innovation to the field of environmental epidemiology. First, it introduces spatial Bayesian distributed lag non-linear models (SB-DLNMs), a novel statistical approach for estimating lagged non-linear temperature-mortality associations in small areas. Second, it uses epidemiological models to transform weather forecasts, extending up to two weeks, into temperature-related impacts. Third, it transforms climate change projections up to the end of the century, also using these epidemiological models. The findings from this thesis have significant implications for decision-making, prevention strategies, and advancements in environmental epidemiology. SB-DLNMs demonstrate the ability to estimate reliable geographically varying risks in small areas, and the new framework shows potential beyond small-area analysis. By integrating weather and climate data across different temporal scales, the research supports the development of more effective heat-cold-health early warning systems, and provides insights into the health impacts under different climate change scenarios.
(Universitat Pompeu Fabra, 2024-11-20T14:26:01Z) Quijal-Zamorano, Marcos; Marí-Dell'Olmo, Marc; Ballester Claramunt, Joan; Universitat Pompeu Fabra. Departament de Medicina i Ciències de la Vida
