Diane Bélanger

Health impacts of the July 2010 heat wave in Québec, Canada

BackgroundOne of the consequences of climate change is the increased frequency and intensity of heat waves which can cause serious health impacts. In Québec, July 2010 was marked by an unprecedented heat wave in recent history. The purpose of this study is to estimate certain health impacts of this heat wave.MethodsThe crude daily death and emergency department admission rates during the heat wave were analyzed in relation to comparison periods using 95% confidence intervals.ResultsDuring the heat wave, the crude daily rates showed a significant increase of 33% for deaths and 4% for emergency department admissions in relation to comparison periods. No displacement of mortality was observed over a 60-day horizon.ConclusionsThe all-cause death indicator seems to be sufficiently sensitive and specific for surveillance of exceedences of critical temperature thresholds, which makes it useful for a heat health-watch system. Many public health actions combined with the increased use of air conditioning in recent decades have contributed to a marked reduction in mortality during heat waves. However, an important residual risk remains, which needs to be more vigorously addressed by public health authorities in light of the expected increase in the frequency and severity of heat waves and the aging of the population.

An open source web application for the surveillance and prevention of the impacts on public health of extreme meteorological events: the SUPREME system

BackgroundEvery year, many deaths or health problems are directly linked to heat waves. Consequently, numerous jurisdictions around the world have developed intervention plans that are employed during extreme heat events; beyond their emergency sections, these plans generally include preventive measures to be implemented each year. Over the last five years, local and regional information systems have been implemented in a few Canadian cities for surveillance purposes. However, until recently, no such systems existed at the provincial level. In the context of the Government of Quebecs 2006-2012 Action Plan on Climate Change, a real-time integrated system for the surveillance and monitoring of extreme heat events has been implemented on a provincial level. The system is a component of a broader approach that would also monitor the public health impacts of all types of extreme meteorological events.ResultsAfter conducting a detailed needs analysis, the Quebec National Institute for Public Health developed and implemented an integrated web application leveraging open source software for the real-time Surveillance and Prevention of the impacts of Extreme Meteorological Events on public health, called the SUPREME system. Its first field use involved heat waves. This decision-support system is based on open source software and is composed of four modules: (1) data acquisition and integration, (2) risk analysis and alerts, (3), cartographic application, and (4) information dissemination - climate change and health portal. The system is available to health specialists through a secure web information portal and provides access to weather forecasts, historic and real-time indicators (including deaths and hospital admissions), alerts and various cartographic data used for conducting prevention activities and launching emergency measures.ConclusionsThe SUPREME system was implemented and used during the summer of 2010. It served as an important decision-making tool during the July 2010 heat wave in the province of Quebec, Canada. Planned improvements for 2011 include the integration of data related to other risk factors for other extreme events to the system. The next steps will be to provide access to the application to other groups of specialists that are involved in the prevention, monitoring, or analysis of extreme meteorological events and their effects on community health and well-being.

Neighbourhood and dwelling characteristics associated with the self-reported adverse health effects of heat in most deprived urban areas: A cross-sectional study in 9 cities.

Dwelling and neighbourhood characteristics associated with the prevalence of self-reported heat-induced adverse health effects are not well known. We interviewed 3485 people in the most disadvantaged neighbourhoods of the nine largest cities in Québec, Canada. The prevalence of heat-induced adverse health effects was 46%, out of which one fourth led to medical consultation. Multivariate analyses showed that dissatisfaction with the summer dwelling temperature, which refers to home heat exposure, and perception that the neighbourhood is polluted due to traffic, were determinant, even after adjusting for current health status. These risk indicators can be used to identify subgroups at high risk and as priority-setting criteria for urban renewal programs for the hotter climate to come.

Perceived adverse health effects of heat and their determinants in deprived neighbourhoods: a cross-sectional survey of nine cities in Canada

This study identifies several characteristics of individuals who report their physical and/or mental health as being adversely affected by summertime heat and humidity, within the most disadvantaged neighbourhoods of the nine largest cities of Québec (Canada). The study is cross-sectional by stratified representative sample; 3485 people were interviewed in their residence. The prevalence of reported impacts was 46%, mostly physical health. Female gender and long-term medical leave are two impact risk indicators in people <65 years of age. Low income and air conditioning at home are risk indicators at all ages. Results for having ≥2 diagnoses of chronic diseases, particularly for people self-describing as in poor health (odds ratio, OR<65 = 5.6; OR≥65 = 4.2), and perceiving daily stress, are independent of age. The prevalence of reported heat-related health impacts is thus very high in those inner cities, with notable differences according to age, stress levels and long-term medical leave, previously unmentioned in the literature. Finally, the total number of pre-existing medical conditions seems to be a preponderant risk factor. This study complements the epidemiologic studies based on mortality or severe morbidity and shows that the heat-related burden of disease appears very important in those communities, affecting several subgroups differentially.

An adaptation index to high summer heat associated with adverse health impacts in deprived neighborhoods

Socially and materially disadvantaged urban areas present a group of factors strongly correlated with high heat and humidity adverse health effects, particularly in densely populated cities where the heat island effect extends over large areas. This paper presents an adaptation index to high summer heat whose validity was tested by correlating it with self-reported adverse health impacts to heat. The data comes from a 2011 cross-sectional study conducted in the most deprived areas in 9 cities of 100,000 or more inhabitants in Quebec (Canada). In total, 3,485 people were interviewed at home. An index of various behavioral adaptations was developed using a Multiple Correspondence Analysis. This individual-level adaptation index summarizes a range of 14 easy-to-use and energy-efficient solutions for cooling off or protecting oneself against the sun, both at home and in other places, whether indoors or out. In addition, it shows that adaptation to heat goes beyond air conditioning in the home. People who experience adverse effects of heat on their health tend to adopt more of the behaviors measured by the index than those perceiving little or none, regardless of their age group or presence of air conditioning at home. Monitoring and improving this index over time and in several populations and contexts would establish a significant milestone for adaptation in health promotion and prevention.

A multilevel analysis to explain self-reported adverse health effects and adaptation to urban heat: a cross-sectional survey in the deprived areas of 9 Canadian cities

BackgroundThis study identifies the characteristics and perceptions related to the individual, the dwelling and the neighbourhood of residence associated with the prevalence of self-reported adverse health impacts and an adaptation index when it is very hot and humid in summer in the most disadvantaged sectors of the nine most populous cities of Québec, Canada, in 2011.Methods The study uses a cross-sectional design and a stratified representative sample; 3485 people (individual-level) were interviewed in their residence. They lived in 1647 buildings (building-level) in 87 most materially and socially disadvantaged census dissemination areas (DA-level). Multilevel analysis was used to perform 3-level models nested one in the other to examine individual impacts as well as the adaptation index.ResultsFor the prevalence of impacts, which is 46 %, the logistic model includes 13 individual-level indicators (including air conditioning and the adaptation index) and 1 building-level indicator. For the adaptation index, with values ranging from -3 to +3, the linear model has 10 individual-level indicators, 1 building-level indicator and 2 DA-level indicators. Of all these indicators, 9 were associated to the prevalence of impacts only and 8 to the adaptation index only.ConclusionThis 3-level analysis shows the differential importance of the characteristics of residents, buildings and their surroundings on self-reported adverse health impacts and on adaptation (other than air conditioning) under hot and humid summer conditions. It also identifies indicators specific to impacts or adaptation. People with negative health impacts from heat rely more on adaptation strategies while low physical activity and good dwelling/building insulation lead to lower adaptation. Better neighbourhood walkability favors adaptations other than air conditioning. Thus, adaptation to heat in these neighbourhoods seems reactive rather than preventive. These first multi-level insights pave the way for the development of a theoretical framework of the process from heat exposure to impacts and adaptation for research, surveillance and public health interventions at all relevant levels.

Mortality and morbidity peaks modeling: An extreme value theory approach:

Hospitalizations and deaths belong to the most studied health variables in public health. Those variables are usually analyzed through mean events and trends, based on the whole dataset. However, this approach is not appropriate to comprehend health outcome peaks which are unusual events that strongly impact the health care network (e.g. overflow in hospital emergency rooms). Peaks can also be of interest in etiological research, for instance when analyzing relationships with extreme exposures (meteorological conditions, air pollution, social stress, etc.). Therefore, this paper aims at modeling health variables exclusively through the peaks, which is rarely done except over short periods. Establishing a rigorous and general methodology to identify peaks is another goal of this study. To this end, the extreme value theory appears adequate with statistical tools for selecting and modeling peaks. Selection and analysis for deaths and hospitalizations peaks using extreme value theory have not been applied in public health yet. Therefore, this study also has an exploratory goal. A declustering procedure is applied to the raw data in order to meet extreme value theory requirements. The application is done on hospitalization and death peaks for cardiovascular diseases, in the Montreal and Quebec metropolitan communities (Canada) for the period 1981–2011. The peak return levels are obtained from the modeling and can be useful in hospital management or planning future capacity needs for health care facilities, for example. This paper focuses on one class of diseases in two cities, but the methodology can be applied to any other health peaks series anywhere, as it is data driven.

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

In a number of environmental studies, relationships between nat4ural processes are often assessed through regression analyses, using time series data. Such data are often multi-scale and non-stationary, leading to a poor accuracy of the resulting regression models and therefore to results with moderate reliability. To deal with this issue, the present paper introduces the EMD-regression methodology consisting in applying the empirical mode decomposition (EMD) algorithm on data series and then using the resulting components in regression models. The proposed methodology presents a number of advantages. First, it accounts of the issues of non-stationarity associated to the data series. Second, this approach acts as a scan for the relationship between a response variable and the predictors at different time scales, providing new insights about this relationship. To illustrate the proposed methodology it is applied to study the relationship between weather and cardiovascular mortality in Montreal, Canada. The results shed new knowledge concerning the studied relationship. For instance, they show that the humidity can cause excess mortality at the monthly time scale, which is a scale not visible in classical models. A comparison is also conducted with state of the art methods which are the generalized additive models and distributed lag models, both widely used in weather-related health studies. The comparison shows that EMD-regression achieves better prediction performances and provides more details than classical models concerning the relationship.

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

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.