Evaluating temperature-mortality associations in Europe: A statistical analysis of long-term data on NUTS3-Level

Quantifying the relationship between temperature and mortality is essential for understanding climate-related health risks and informing public health interventions. This study employs a high-resolution spatiotemporal approach to assess temperature-related mortality across Europe by integrating epidemiological and meteorological datasets. Weekly mortality data for the period 2000–2022, sourced from Eurostat, include deaths categorized by age group and population. Meteorological data are obtained from the Copernicus European Regional Reanalysis (CERRA), which estimates hourly temperature at a 5.5 km spatial resolution. These datasets are subsequently aggregated at the NUTS3 level, to facilitate regional-scale analyses of temperature-mortality associations, incorporating both warm and cold seasons.

To capture the complex effects of temperature fluctuations on mortality, we employ Distributed Lag Non-Linear Models (DLNMs) within the Generalized Additive Models (GAMs) framework. This statistical approach enables the assessment of both immediate and lagged temperature effects while accounting for non-linear exposure-response relationships. By integrating high-resolution climate reanalysis data with epidemiological records, this study aims to generate more precise risk estimates that account for local climatic variations, demographic factors, and underlying health vulnerabilities. Our findings are expected to reveal significant spatial heterogeneity in temperature-related mortality risks across European regions, highlighting populations and areas disproportionately affected by temperature extremes. The methodological framework employed in this study advances current knowledge by leveraging high-resolution climate datasets, which enhance the precision of exposure assessment. Given the projected increase in extreme temperature events due to climate change, understanding their impact on mortality is crucial for guiding future public health interventions. This study emphasizes the importance of integrating climate data with epidemiological analysis to provide more accurate and localized mortality risk estimates. By offering insights into region-specific vulnerabilities, the findings will support the development of targeted public health policies and adaptive strategies.