- Overview
The series “Tour d’horizon en trois questions” highlights research in all its forms and takes an informed look at current events.
Professor Fateh Chebana, an expert in data science applied to the environment and environmental health. Photo: Josée Lecompte
How can research help governments, municipalities and organizations better support the population when extreme weather events happen? Through data science!
This is one of the many topics being studied by Professor Fateh Chebana, an expert in data science applied to the environment and environmental health.
Affiliated with the Institut national de la recherche scientifique (INRS) since 2010, Professor Chebana is particularly interested in howdata science, a relatively recent scientific field at the intersection of statistics and computer science, can help prevent or lessen the negative effects of extreme heat or cold.
This high-level multidisciplinary work is the subject of collaboration between the INRS scientific team and its many partners, including Jérémie Boudreault (INRS), Céline Campagna (Ouranos) and Éric Lavigne (Health Canada).
As the mercury climbs this summer, the safety risks associated with extreme heat are very much in the spotlight. Professor Chebana explains how he and his team are putting science at the service of public health.
Why is it important to assess and predict the health costs associated with extreme weather events like heatwaves? Who benefits from this kind of research?
Extreme weather events have impacts in many areas, including transportation, tourism, agriculture and human health. In the latter case, they affect the population as a whole, and certain more vulnerable subpopulations in particular. Their major impacts include increases in the numbers of emergency room visits, hospitalizations, deaths, calls to 811, and ambulance needs.
These all have financial consequences for our social system. Our team is working on three major cost categories: direct healthcare costs, such as hospitalizations or staff wages; the indirect costs of absenteeism, meaning lost work hours; and intangible costs, which include our society’s willingness to pay to reduce the loss of human life, or decreased activity during heatwaves.
With data science, we can prepare for many eventualities from an economic, societal, human, material and logistical viewpoint. In the short term, assessing these costs can help guide decision-makers in allocating the necessary funds and avoid ad hoc responses to danger. Over the long term, cost forecasts for various scenarios and assumptions can guide health authority investments in prevention and adaptation measures, such as accessible green spaces, effective resource management and efficient alert systems. Here is a simple example: If a city like Montréal knows the volume and direct cost of the ambulance services that will be needed during a heat wave, it can decide to offer free public transportation to clear roads as a life-saving measure.
Each summer in Quebec, an average of 36,000 emergency room visits and 7,200 ambulance trips are attributed to extreme heat. If global temperatures rise by 2 °C, these figures could increase by more than 50% and 60%, respectively. Extreme heat has unsuspected effects on the healthcare system in Quebec. According to our conservative estimates, each year we would reach nearly $32 million dollars in direct costs, about $10 million in indirect costs, and $8.4 billion in intangible costs! If nothing is done, we may well imagine that these costs will have a catastrophic impact on our society. We must be forewarned and ready to act.
With extreme temperature and population health challenges now affecting all countries, have there been any national or international collaborative efforts in the field of applied data science?
Research in the field at INRS began about 15 years ago. To date, alongside our participation in international conferences and the development of ties with scientists from around the world, this research has mainly focused on provincial collaborations. We have started moving toward collaboration with federal agencies, including Health Canada and Environment and Climate Change Canada, as well as with British Columbia and Ontario. In addition, our academic programs allow us to recruit international students, including researcher Pierre Masselot, who is now a professor in London and with whom we are working today.
The current scientific exchanges are in their early stages, and one of the next steps will be to compare and exchange best practices (methods, perspectives and findings) in order to lead to more concrete collaborations.
Various challenges may help explain the current state of international collaboration and the path it has taken. Firstly, data science is a fairly recent field of study (going back to 2010, 2015, and even 2020) that is still in development, although its components, at the intersection of mathematics and computer science, have been around for much longer. Environmental health is also a relatively young discipline that began to attract significant attention with the major heatwave that Europe experienced in 2003. All things considered, these are both new fields!
Furthermore, even though extreme temperatures affect all countries, the responses and realities are specific to each government, municipality and location. Comparisons aren’t always possible.
What complicates matters the most is the collection of health data (on mortality, hospitalizations, etc.), as access to such data is protected by privacy concerns and various ethics protocols. Diverse realities and interests in each jurisdiction also affect research. Indeed, not all territories or countries wish to invest in science and data collection. But despite these challenges, international collaboration is being built!
How can data science provide an effective scientific argument to raise awareness of these issues among the public and policymakers?
Climate change will amplify the frequency, intensity and duration of heatwaves. Measures to address the health and financial impacts include alert systems, efforts to combat heat islands, and the protection of vulnerable populations with targeted messages, door-to-door outreach, and the opening of air-conditioned public spaces.
Data science can help build objective scientific arguments to assist decision-making and raise awareness among the public as well as policymakers. This enables us to consider sophisticated approaches and models that are more realistic, more representative and more precise. This topic is quite demanding in terms of the climate, health, and economic data it requires. The more data we have, the more accurate and localized cost estimates will be, making the economic argument all the more compelling.
Finally, we must remember that the arguments evolve depending on the audience. Economic arguments complement others, such as those that consider well-being or environmental protection, in order to convince and engage a greater share of people. The words and reasoning will vary depending on whether one is addressing people from the community sector or the political, economic or scientific worlds. With data science, we can answer the same question from several angles without foregoing scientific rigour.
Decision-making bodies today are very interested in the economic side of things. What will the financial costs of heatwaves be for a hospital, a city, or a ministry? We can use data science to meet the need for budgeting knowledge. With our team’s recent work at the provincial level, the government will have enough economic arguments to prepare for each summer period and to implement mitigation and adaptation measures to address climate change. In this regard, we should keep one interesting figure in mind: An amount invested to adapt to extreme temperatures can generate direct or indirect benefits that are up to 15 times greater. As we can see, it is clearly a worthwhile investment for avoiding high costs and, more importantly, serious consequences for society.
