A cold-health watch and warning system, applied to the province of Quebec (Canada)

Yan, Bixun; Chebana, Fateh; Masselot, Pierre; Campagna, Céline; Gosselin, Pierre; Ouarda, Taha B. M. J.; Lavigne, Éric

doi:10.1016/j.scitotenv.2020.140188

Cited by 8 publications

(6 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…influenza for cold but not for heat). Indeed, Yan et al [44] focused on developing a cold system applied to the province of Quebec [44] where the temperature thresholds are considered constant over the winter season. Therefore, a similar system with evolving cold temperature thresholds : Excludes Laurentides, *: represents α 0 and α 1 of Tmin, n.a: there is no α 2 in the case of the present study Fig.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, a rule to determine the preliminary threshold based on the heat wave characteristics was introduced to distinguish heat-related mortality episodes from artifacts. Splines and distributed lag non-linear model (DLNM) were also applied to calculate the expected excess mortality as well as the lags (between the heat wave and the impact on mortality), respectively, as in [43][44][45]. The method includes four steps as detailed below: First, we proceed to the division of the database into monthly.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A heat-health watch and warning system with extended season and evolving thresholds

et al. 2021

Self Cite

View full text Add to dashboard Cite

Background Many countries have developed heat-health watch and warning systems (HHWWS) or early-warning systems to mitigate the health consequences of extreme heat events. HHWWS usually focuses on the four hottest months of the year and imposes the same threshold over these months. However, according to climate projections, the warm season is expected to extend and/or shift. Some studies demonstrated that health impacts of heat waves are more severe when the human body is not acclimatized to the heat. In order to adapt those systems to potential heat waves occurring outside the hottest months of the season, this study proposes specific health-based monthly heat indicators and thresholds over an extended season from April to October in the northern hemisphere. Methods The proposed approach, an adoption and extension of the HHWWS methodology currently implemented in Quebec (Canada). The latter is developed and applied to the Greater Montreal area (current population 4.3 million) based on historical health and meteorological data over the years. This approach consists of determining excess mortality episodes and then choosing monthly indicators and thresholds that may involve excess mortality. Results We obtain thresholds for the maximum and minimum temperature couple (in °C) that range from (respectively, 23 and 12) in April, to (32 and 21) in July and back to (25 and 13) in October. The resulting HHWWS is flexible, with health-related thresholds taking into account the seasonality and the monthly variability of temperatures over an extended summer season. Conclusions This adaptive and more realistic system has the potential to prevent, by data-driven health alerts, heat-related mortality outside the typical July–August months of heat waves. The proposed methodology is general and can be applied to other regions and situations based on their characteristics.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A heat-health watch and warning system with extended season and evolving thresholds

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although the present paper focuses on the heat wave issue, the possibility of tuning algorithms for better flexibility of the proposed methods allows their application to other issues. For instance, there is a growing interest in developing public health action plans and thresholds related to cold spells (Bustinza & Lebel, 2013; Yan et al, 2020). Thresholds for waterborne diseases and extreme precipitations (e.g.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Approaches to Identify Thresholds in a Heat-Health Warning System Context

Masselot

Chebana

Campagna

et al. 2021

Journal of the Royal Statistical Society Series A: Statistics in Society

Self Cite

View full text Add to dashboard Cite

During the last two decades, a number of countries or cities established heat-health warning systems in order to alert public health authorities when some heat indicator exceeds a predetermined threshold. Different methods were considered to establish thresholds all over the world, each with its own strengths and weaknesses.The common ground is that current methods are based on exposure-response function estimates that can fail in many situations. The present paper aims at proposing several data-driven methods to establish thresholds using historical data of health issues and environmental indicators. The proposed methods are model-based regression trees (MOB), multivariate adaptive regression splines (MARS), the patient rule-induction method (PRIM) and adaptive index models (AIM). These methods focus on finding relevant splits in the association between indicators and the health outcome but do it in different fashions. A simulation study and a realworld case study hereby compare the discussed methods. Results show that proposed methods are better at predicting adverse days than current thresholds and benchmark methods. The results nonetheless suggest that PRIM is overall the more reliable method with low variability of results according to the scenario or case.

show abstract

“…Those variables have an impact on CVD occurrence [ 7 , 8 , 9 ]. Furthermore, besides meteorological variables, atmospheric pollution variables should also be included in future research [ 68 , 69 ].…”

Section: Discussionmentioning

confidence: 99%

Cardiovascular Health Peaks and Meteorological Conditions: A Quantile Regression Approach

Chiu

Chebana

Abdous

et al. 2021

IJERPH

Self Cite

View full text Add to dashboard Cite

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.

show abstract

A cold-health watch and warning system, applied to the province of Quebec (Canada)

Cited by 8 publications

References 69 publications

A heat-health watch and warning system with extended season and evolving thresholds

A heat-health watch and warning system with extended season and evolving thresholds

Machine Learning Approaches to Identify Thresholds in a Heat-Health Warning System Context

Cardiovascular Health Peaks and Meteorological Conditions: A Quantile Regression Approach

Contact Info

Product

Resources

About