CMTool is a new climate service to help public health agencies make decisions ahead of heat waves and cold spells in Europe
CMTool provides multi-lead (three month, one month and one week) probabilistic forecasts of mortality risk ahead of the peak winter and summer seasons.
Negative impacts on human health from climate change are already observable in all European countries and beyond. They serve as a compelling rationale to support the rapid strengthening of climate services to support policy development and action in protecting people’s health from climate change.
The current and potential climate-related health risks include direct effects that mostly occur through changes in extreme weather events, and indirect effects that are mainly induced by changes in major environment, social and economic determinants of health and well-being. Specifically, heat waves are increasing in frequency, intensity and duration, and cold spells continue to persist, both endangering fundamental health determinants and increasing mortality and morbidity.
Temperature-related illness and death is putting strain on public health systems; strengthening health systems and building capacity is crucial to providing climate-resilient healthcare and to protecting the health of millions of European citizens. Health service delivery needs to be assured at all times, particularly when challenged during times of crisis, such as during summer and winter emergencies. Climate forecasts would allow for better short-to-medium-term resource management within health systems and would help authorities prepare and respond ahead of heat waves and cold spells.
Heat–health action plans (HHAP) and cold weather plans (CWP) depend on reliable early-warning systems to allow for long-term planning (e.g. energy use, urban design, health workforce management), as well as timely activation. This helps authorities prepare and respond to emergency situations and thus reduce excess morbidity and mortality due to temperature extremes.
Over the last few years, the quality of seasonal forecasts for average temperature conditions and precipitation totals have significantly improved, with our capacity to anticipate mean temperatures over land being higher in spring both in North America and in Europe. A new generation of climate prediction models are being developed to improve forecast quality in the near future, and these forecasts are currently being included in a regional framework of developing climate services in Europe.
As part of EUPORIAS, we are working closely with the World Health Organization (WHO) Regional Office for Europe to develop a climate-driven mortality model to provide probabilistic predictions of exceeding emergency mortality thresholds for heat wave and cold spell scenarios. The predictions are based on temperature forecasts (1–3 months ahead) to support decision making for the preparedness of health services and protection of vulnerable communities ahead of future extreme temperature events.
We believe that better information on future extreme temperature related mortality will support public health agencies in making better decisions on health care provisions and therefore help to justify the additional resources involved (medical staff, beds) and avoid unnecessary loss of human life.
Within the broader perspective of the Global Framework for Climate Services (GFCS), WHO and the World Meteorological Organization (WMO) have been working together to foster the development of climate services to protect health, one of the five priority areas of the GFCS. This WHO–WMO collaboration, together with engagement of their Member States shares the common with EUPORIAS aim to bridge the gap between available climate information and public health concerns. The CMTOOL prototype case study illustrates such a potential for climate service application in Europe.
To formulate the model, daily mortality data corresponding to 187 regions across 16 countries in Europe were obtained from 1998–2003. Data were aggregated to 54 larger regions in Europe, defined according to similarities in population structure and climate. Location-specific average mortality rates, at given temperature intervals over the time period, were modelled to account for the increased mortality observed during both high and low temperature extremes and differing comfort temperatures between regions. Model parameters are estimated in a Bayesian framework, in order to generate probabilistic simulations of mortality across Europe for time periods of interest.
By replacing observed temperature data in the model with forecast temperature from state-of-the-art European forecasting systems, which are being developed in the EUPORIAS project, probabilistic mortality predictions could potentially be made several months ahead of imminent heat waves and cold spells.
Via interviews and participation in high level policy meetings we have engaged with stakeholders from the national level (head of department of health) to the local level (medical doctors) to establish their needs and preferred format for visualising probabilistic predictions of mortality risk.
Ultimately, we hope to extend this climate-mortality prediction tool to a wider geographical domain, beyond Europe and to extend the model to account for heat and cold stress in animals, as part of a integrated ‘one health’ approach.CMTool