EMD-regression for modelling multi-scale relationships, and application to weather-related cardiovascular mortality

Abdous

et al. 2021

IJERPH

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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.

Section: Discussionmentioning

confidence: 99%

Cardiovascular Health Peaks and Meteorological Conditions: A Quantile Regression Approach

Chiu

Abdous

et al. 2021

IJERPH

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“…The aggregation could also be helpful in studies dealing with exposures with less acute effects such as humidity on cardiovascular mortality during spring (e.g. Masselot et al, 2018). On the other hand, there is a loss of information in the aggregation process, especially concerning health peaks.…”

Section: Discussionmentioning

confidence: 99%

“…It has already been shown that temperature affects cardiovascular mortality and morbidity (e.g. Bayentin et al, 2010;Bustinza et al, 2013;Masselot et al, 2018). Therefore, in order to efficiently organize private and public health service and mitigate the effect of temperature on cardiovascular diseases, it is important to understand every aspect of the relationship.…”

Section: Application and Comparisonmentioning

confidence: 99%

Aggregating the response in time series regression models, applied to weather-related cardiovascular mortality

Masselot

Science of The Total Environment

Bélanger

et al. 2018

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In environmental epidemiology studies, health response data (e.g. hospitalization or mortality) are often noisy because of hospital organization and other social factors. The noise in the data can hide the true signal related to the exposure. The signal can be unveiled by performing a temporal aggregation on health data and then using it as the response in regression analysis. From aggregated series, a general methodology is introduced to account for the particularities of an aggregated response in a regression setting. This methodology can be used with usually applied regression models in weather-related health studies, such as generalized additive models (GAM) and distributed lag nonlinear models (DLNM). In particular, the residuals are modelled using an autoregressive-moving average (ARMA) model to account for the temporal dependence. The proposed methodology is illustrated by modelling the influence of temperature on cardiovascular mortality in Canada. A comparison with classical DLNMs is provided and several aggregation methods are compared. Results show that there is an increase in the fit quality when the response is aggregated, and that the estimated relationship focuses more on the outcome over several days than the classical DLNM. More precisely, among various investigated aggregation schemes, it was found that an aggregation with an asymmetric Epanechnikov kernel is more suited for studying the temperature-mortality relationship.

“…This extension allows each IMF to represent a narrow frequency band, aiding both interpretation and the subsequent regression model. In this study, two white noise variables are added with a standard deviation equal to 20% of the standard deviation of temperature time series, as recommended in previous studies [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

Data-Enhancement Strategies in Weather-Related Health Studies

Masselot

Ouarda

et al. 2022

IJERPH

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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.