A new look at weather-related health impacts through functional regression

TL;DR

This paper introduces functional data analysis models to better understand and predict weather-related health impacts, specifically temperature-related cardiovascular mortality in Montreal, revealing new insights into physiological adaptation effects.

Contribution

The paper develops novel FDA-based models that enhance prediction accuracy and provide new understanding of weather-health relationships compared to traditional methods.

Findings

Improved prediction of temperature-related cardiovascular mortality.

Quantification of physiological adaptation effects.

FDA models outperform classical models in this context.

Abstract

A major challenge of climate change adaptation is to assess the effect of changing weather on human health. In spite of an increasing literature on the weather-related health subject, many aspect of the relationship are not known, limiting the predictive power of epidemiologic models. The present paper proposes new models to improve the performances of the currently used ones. The proposed models are based on functional data analysis (FDA), a statistical framework dealing with continuous curves instead of scalar time series. The models are applied to the temperature-related cardiovascular mortality issue in Montreal. By making use of the whole information available, the proposed models improve the prediction of cardiovascular mortality according to temperature. In addition, results shed new lights on the relationship by quantifying physiological adaptation effects. These results, not found with classical model, illustrate the potential of FDA approaches.