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Article

Data-Enhancement Strategies in Weather-Related Health Studies

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Department of Public Health, Environments and Society, London School of Hygiene and Tropical Medicine (LSHTM), 15–17 Tavistock Place, London WC1H 9SH, UK
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Institut National de la Recherche Scientifique, INRS, Centre Eau Terre Environnement, 490 rue de la Couronne, Québec, QC G1K 9A9, Canada
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Institut National de Santé Publique du Québec, INSPQ, 945 av Wolfe, Québec, QC G1V 5B3, Canada
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Ouranos, Montréal, QC H3A 1B9, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Paul B. Tchounwou
Int. J. Environ. Res. Public Health 2022, 19(2), 906; https://doi.org/10.3390/ijerph19020906
Received: 23 November 2021 / Revised: 10 January 2022 / Accepted: 12 January 2022 / Published: 14 January 2022
(This article belongs to the Special Issue Statistical Methods in Environmental Epidemiology)
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. View Full-Text
Keywords: environment; epidemiology; time series; aggregation; empirical mode decomposition (EMD); functional regression; weather; health; Canada environment; epidemiology; time series; aggregation; empirical mode decomposition (EMD); functional regression; weather; health; Canada
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MDPI and ACS Style

Masselot, P.; Chebana, F.; Ouarda, T.B.M.J.; Bélanger, D.; Gosselin, P. Data-Enhancement Strategies in Weather-Related Health Studies. Int. J. Environ. Res. Public Health 2022, 19, 906. https://doi.org/10.3390/ijerph19020906

AMA Style

Masselot P, Chebana F, Ouarda TBMJ, Bélanger D, Gosselin P. Data-Enhancement Strategies in Weather-Related Health Studies. International Journal of Environmental Research and Public Health. 2022; 19(2):906. https://doi.org/10.3390/ijerph19020906

Chicago/Turabian Style

Masselot, Pierre, Fateh Chebana, Taha B.M.J. Ouarda, Diane Bélanger, and Pierre Gosselin. 2022. "Data-Enhancement Strategies in Weather-Related Health Studies" International Journal of Environmental Research and Public Health 19, no. 2: 906. https://doi.org/10.3390/ijerph19020906

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