It is well known that mortality has a seasonal structure, where rates often peak in winter and are lowest during summer. Deviations from this seasonal structure may occur for several reasons, for example, due to extreme environmental conditions, such as heat waves, cold spells, and high air pollution. From a public health perspective, it is important to understand what drives such excess mortality and how we can properly quantify its extent, especially in the context of climate change. From an actuarial perspective, understanding these short-term mortality dynamics is essential in a life insurance context for the estimation of incurred-but-not-reported death benefit payments, pricing short-term life-contingent insurance products, and accurately assessing short-term fluctuations in mortality risk. Using machine learning techniques, we examine the drivers of short-term excess mortality among older adults and shed light on the impact of extreme weather conditions and air pollution.
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