Data-Enhancement Strategies in Weather-Related Health Studies.

Pierre Masselot,Pierre Gosselin,Taha B M J Ouarda,Fateh Chebana,Diane Bélanger

doi:10.3390/ijerph19020906

Abstract

Although the relationship between weather and health is widely studied, there are still gaps in this knowledge. The present paper proposes data transformation as a way to address these gaps and discusses four different strategies designed to study particular aspects of a weather–health relationship, including (i) temporally aggregating the series, (ii) decomposing the different time scales of the data by empirical model decomposition, (iii) disaggregating the exposure series by considering the whole daily temperature curve as a single function, and (iv) considering the whole year of data as a single, continuous function. These four strategies allow studying non-conventional aspects of the mortality-temperature relationship by retrieving non-dominant time scale from data and allow to study the impact of the time of occurrence of particular event. A real-world case study of temperature-related cardiovascular mortality in the city of Montreal, Canada illustrates that these strategies can shed new lights on the relationship and outlines their strengths and weaknesses. A cross-validation comparison shows that the flexibility of functional regression used in strategies (iii) and (iv) allows a good fit of temperature-related mortality. These strategies can help understanding more accurately climate-related health.

Highlights

Received: 23 November 2021During recent years, the relationship between weather and human health has abundantly been studied
For strategies one to three (AG, EMDR, and FY), the temperature series are provided by Environment Canada and correspond to the spatial mean of several weather stations scattered throughout the metropolitan community of Montreal (MCM) territory
relative risk (RR) significantly below one are associated to the intrinsic mode functions (IMF) of periodicity of roughly 100 days that shows higher amplitude during winter and to the seasonality showing that mortality is usually lower in summer being around 80% the average of winter, confirming the overall impact of cold is more important than heat

Summary

Introduction

Received: 23 November 2021During recent years, the relationship between weather and human health has abundantly been studied. While extreme heat-related mortality and its projected increase seem to be widely accepted [14], the evolution of winter-related mortality is less clear [5,15] Another example is the question of the impact of humidity and its role as a confounder in weather-related health studies, which is still open [16]. Quantifying physiological adaptation for forecasting purpose is an important challenge [19] Uncovering these areas, as well as others, may be crucial for an efficient anticipation of climate change impacts on population health [20]

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