Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published January 23, 2015 | Published
Journal Article Open

Evaluating the Performance of a Climate-Driven Mortality Model during Heat Waves and Cold Spells in Europe


The impact of climate change on human health is a serious concern. In particular, changes in the frequency and intensity of heat waves and cold spells are of high relevance in terms of mortality and morbidity. This demonstrates the urgent need for reliable early-warning systems to help authorities prepare and respond to emergency situations. In this study, we evaluate the performance of a climate-driven mortality model to provide probabilistic predictions of exceeding emergency mortality thresholds for heat wave and cold spell scenarios. Daily mortality data corresponding to 187 NUTS2 regions across 16 countries in Europe were obtained from 1998–2003. Data were aggregated to 54 larger regions in Europe, defined according to similarities in population structure and climate. Location-specific average mortality rates, at given temperature intervals over the time period, were modelled to account for the increased mortality observed during both high and low temperature extremes and differing comfort temperatures between regions. Model parameters were estimated in a Bayesian framework, in order to generate probabilistic simulations of mortality across Europe for time periods of interest. For the heat wave scenario (1–15 August 2003), the model was successfully able to anticipate the occurrence or non-occurrence of mortality rates exceeding the emergency threshold (75th percentile of the mortality distribution) for 89% of the 54 regions, given a probability decision threshold of 70%. For the cold spell scenario (1–15 January 2003), mortality events in 69% of the regions were correctly anticipated with a probability decision threshold of 70%. By using a more conservative decision threshold of 30%, this proportion increased to 87%. Overall, the model performed better for the heat wave scenario. By replacing observed temperature data in the model with forecast temperature, from state-of-the-art European forecasting systems, probabilistic mortality predictions could potentially be made several months ahead of imminent heat waves and cold spells.

Additional Information

© 2015 by the authors; licensee MDPI Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). Received: 31 October 2014. Accepted: 19 January 2015. Published: 23 January 2015. The research leading to these results has received funding from the EUPORIAS project (grant agreement No. 308291) funded by the European Commission's Seventh Framework Research Programme. The mortality data collection was supported by the EU Community Action Programme for Public Health (grant agreement No. 2005114). Joan Ballester gratefully acknowledges funding from the European Commission through a Marie Curie International Outgoing Fellowship of the 7th Framework Programme for Research (project MEMENTO from the FP7-PEOPLE-2011-IOF call). Rachel Lowe is grateful to the STEPHI project, Daniel Simpson and Harvard Rue for valuable training and technical support in using the R-INLA package. Academic Editors: Kristie L. Ebi and Jeremy Hess (This article belongs to the Special Issue Extreme Weather-Related Morbidity and Mortality: Risks and Responses)

Attached Files

Published - ijerph-12-01279.pdf


Files (3.5 MB)
Name Size Download all
3.5 MB Preview Download

Additional details

August 22, 2023
August 22, 2023