A two-stage approach to heat-mortality risk assessment comparing multiple exposure-to-temperature models: the case study in Lazio, Italy

2025-12-16

AI Summary

This study addresses temperature modeling uncertainty in estimating heat-related mortality in the Lazio region of Italy (2008–2022). Method: A two-stage analytical framework is developed: (1) comparing Bayesian quantile regression, Bayesian Gaussian regression, and ERA5-Land reanalysis data for municipal-scale temperature estimation; and (2) integrating individual cause-of-death records via case-crossover design and conditional Poisson regression to quantify temperature–mortality associations. Contribution/Results: The study demonstrates for the first time that temperature model selection substantially affects minimum mortality temperature (MMT) estimates—differences reach 2.3°C. It introduces a novel hierarchical (by sex and age) and multi-definition heatwave analytical framework, revealing significantly higher relative risks for females and individuals aged ≥80 years. These findings provide critical methodological guidance and empirical evidence for climate–health risk assessment, early-warning systems, and adaptation planning.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Comparing temperature models for heat-mortality risk assessment
• Evaluating spatiotemporal temperature reconstruction methods in Italy
• Analyzing temperature-mortality associations with Bayesian Poisson models

Innovation

• Two-stage approach comparing multiple temperature models
• Bayesian models with spatial interpolation for temperature reconstruction
• Case-crossover design using Bayesian conditional Poisson models