Cardiovascular Health Peaks and Meteorological Conditions: A Quantile Regression Approach

Yohann Moanahere Chiu, Fateh Chebana, Belkacem Abdous, Diane Bélanger and Pierre Gosselin
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Yohann Moanahere Chiu: Faculty of Pharmacy, Laval University, 1050 Avenue de la Médecine, Quebec, QC G1V 0A6, Canada
Fateh Chebana: Centre Eau Terre Environnement, Institut National de la Recherche Scientifique, 490 Rue de la Couronne, Quebec, QC G1K 9A9, Canada
Belkacem Abdous: Department of Social and Preventive Medicine, Faculty of Medicine, Laval University, 1050 Avenue de la Médecine, Quebec, QC G1V 0A6, Canada
Diane Bélanger: Centre Eau Terre Environnement, Institut National de la Recherche Scientifique, 490 Rue de la Couronne, Quebec, QC G1K 9A9, Canada
Pierre Gosselin: TEIAC Unity, Quebec National Institute of Public Health, 945 Avenue Wolfe, Quebec, QC G1V 5B3, Canada

IJERPH, 2021, vol. 18, issue 24, 1-14

Abstract: Cardiovascular morbidity and mortality are influenced by meteorological conditions, such as temperature or snowfall. Relationships between cardiovascular health and meteorological conditions are usually studied based on specific meteorological events or means. However, those studies bring little to no insight into health peaks and unusual events far from the mean, such as a day with an unusually high number of hospitalizations. Health peaks represent a heavy burden for the public health system; they are, however, usually studied specifically when they occur (e.g., the European 2003 heatwave). Specific analyses are needed, using appropriate statistical tools. Quantile regression can provide such analysis by focusing not only on the conditional median, but on different conditional quantiles of the dependent variable. In particular, high quantiles of a health issue can be treated as health peaks. In this study, quantile regression is used to model the relationships between conditional quantiles of cardiovascular variables and meteorological variables in Montreal (Canada), focusing on health peaks. Results show that meteorological impacts are not constant throughout the conditional quantiles. They are stronger in health peaks compared to quantiles around the median. Results also show that temperature is the main significant variable. This study highlights the fact that classical statistical methods are not appropriate when health peaks are of interest. Quantile regression allows for more precise estimations for health peaks, which could lead to refined public health warnings.

Keywords: quantile regression; cardiovascular diseases; health peaks; meteorological conditions; environmental health; heatwaves (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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