The short-term association between environmental variables and mortality: evidence from Europe

Abstract Using fine-grained, publicly available data, this article studies the short-term association between environmental factors, i.e. weather and air pollution characteristics, and weekly mortality rates in small European regions. Hereto, we develop a mortality modelling framework where a baseline model captures a region-specific, seasonal trend observed within the historical weekly mortality rates. Using a machine learning algorithm, we then explain deviations from this baseline using features constructed from environmental data that capture anomalies and extreme events. We illustrate our proposed modelling framework through a case study on more than 550 NUTS 3 regions (Nomenclature of Territorial Units for Statistics, level 3) in 20 European countries. We show that temperature-related features are most influential in explaining mortality deviations from the baseline over short time periods. Furthermore, we find that environmental features prove particularly beneficial in southern regions for explaining elevated levels of mortality, and we observe evidence of a harvesting effect related to heat waves.

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Publication Details

Journal
Journal of the Royal Statistical Society Series A (Statistics in Society)
Published
2025-05-23
DOI
https://doi.org/10.1093/jrsssa/qnaf052
Citations
4
Primary Topic
Climate Change and Health Impacts
Type
article
Field-Weighted Citation Impact
4.80

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article

The short-term association between environmental variables and mortality: evidence from Europe

Katrien Antonio, Torsten Kleinow, Jens Robben
4 citations
Journal of the Royal Statistical Society Series A (Statistics in Society)
Climate Change and Health Impacts
4.80
article

The short-term association between environmental variables and mortality: evidence from Europe

Katrien Antonio, Torsten Kleinow, Jens Robben
article en
4 citations

Abstract

Abstract Using fine-grained, publicly available data, this article studies the short-term association between environmental factors, i.e. weather and air pollution characteristics, and weekly mortality rates in small European regions. Hereto, we develop a mortality modelling framework where a baseline model captures a region-specific, seasonal trend observed within the historical weekly mortality rates. Using a machine learning algorithm, we then explain deviations from this baseline using features constructed from environmental data that capture anomalies and extreme events. We illustrate our proposed modelling framework through a case study on more than 550 NUTS 3 regions (Nomenclature of Territorial Units for Statistics, level 3) in 20 European countries. We show that temperature-related features are most influential in explaining mortality deviations from the baseline over short time periods. Furthermore, we find that environmental features prove particularly beneficial in southern regions for explaining elevated levels of mortality, and we observe evidence of a harvesting effect related to heat waves.

Journal of the Royal Statistical Society Series A (Statistics in Society)
Amsterdam University of Applied Sciences (NL), University of Amsterdam (NL), KU Leuven (BE)
Fonds De La Recherche Scientifique - FNRS, Fonds Wetenschappelijk Onderzoek
Openalex Percentile: Top 5%
Climate Change and Health Impacts
4.80
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