Assessing Climate-Driven Mortality Risk: A Stochastic Approach with Distributed Lag Non-Linear Models

2025-05-31

AI Summary

This study quantifies the nonlinear, lagged effects of climate—particularly temperature, heatwaves, and cold spells—on mortality across multiple regions and age groups, while disentangling climate-driven from non-climate mortality risks. Method: We propose a novel two-component framework: (i) embedding a Distributed Lag Nonlinear Model (DLNM) within a multi-population stochastic mortality model (a Lee-Carter variant), integrated with the Universal Thermal Climate Index (UTCI) and Representative Concentration Pathway (RCP) scenario data; and (ii) developing a back-casting estimation algorithm that explicitly separates climate- and non-climate-related mortality risk components, enabling both single- and multi-population joint modeling and long-term mortality projection. Contribution/Results: Validated on Athens, Lisbon, and Rome, the framework significantly outperforms benchmark models. Projections indicate declining winter mortality but rising summer mortality before 2045; under RCP8.5, total mortality exhibits a long-term upward trend.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Security and Privacy: Large-scale security measurements

Abstract

Assessing climate-driven mortality risk has become an emerging area of research in recent decades. In this paper, we propose a novel approach to explicitly incorporate climate-driven effects into both single- and multi-population stochastic mortality models. The new model consists of two components: a stochastic mortality model, and a distributed lag non-linear model (DLNM). The first component captures the non-climate long-term trend and volatility in mortality rates. The second component captures non-linear and lagged effects of climate variables on mortality, as well as the impact of heat waves and cold waves across different age groups. For model calibration, we propose a backfitting algorithm that allows us to disentangle the climate-driven mortality risk from the non-climate-driven stochastic mortality risk. We illustrate the effectiveness and superior performance of our model using data from three European regions: Athens, Lisbon, and Rome. Furthermore, we utilize future UTCI data generated from climate models to provide mortality projections into 2045 across these regions under two Representative Concentration Pathway (RCP) scenarios. The projections show a noticeable decrease in winter mortality alongside a rise in summer mortality, driven by a general increase in UTCI over time. Although we expect slightly lower overall mortality in the short term under RCP8.5 compared to RCP2.6, a long-term increase in total mortality is anticipated under the RCP8.5 scenario.

Problem

• Incorporates climate effects into stochastic mortality models
• Analyzes non-linear lagged climate impacts on mortality rates
• Projects future mortality under different climate scenarios

Innovation

• Combines stochastic mortality and DLNM models
• Uses backfitting to separate climate effects
• Projects mortality with UTCI under RCP scenarios