Assessing Climate-Driven Mortality Risk: A Stochastic Approach with Distributed Lag Non-Linear Models
Jiacheng Min, Han Li, Thomas Nagler, Shuanming Li

TL;DR
This paper introduces a novel stochastic modeling approach combining distributed lag non-linear models to assess climate-driven mortality risks, enabling detailed projections of future mortality patterns under climate change scenarios.
Contribution
It presents a new integrated model that explicitly incorporates climate effects into mortality risk assessment and offers a calibration method to separate climate-driven from non-climate-driven mortality risks.
Findings
Model effectively captures climate effects on mortality across regions.
Projections indicate increased summer mortality and decreased winter mortality.
Long-term mortality may rise under high emission scenarios.
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…
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Taxonomy
TopicsInsurance, Mortality, Demography, Risk Management · Global Health Care Issues
