Periodic Reporting for period 2 - EARLY-ADAPT (Signs of Early Adaptation to Climate Change)
Periodo di rendicontazione: 2022-08-01 al 2024-01-31
EARLY-ADAPT (“Signs of Early Adaptation to Climate Change”) is a European Research Council (ERC) Consolidator Grant (CoG), whose overarching aim is to jointly analyse the environmental and socioeconomic drivers of recent trends in public health.
Societal Challenge: Environmental factors kill hundreds of thousands of Europeans every year. Climate change is an additional threat for public health, and adaptation an essential strategy of increasing importance. Societies are devoting efforts to adaptation, but evidence of effectiveness is still lacking.
Hypothesis and Aim: The driving hypothesis of EARLY-ADAPT is that European societies are starting to adapt to climate change, but the effectiveness of early adaptation is heterogeneous between populations and through time. The project aims to detect, understand and quantify the drivers and inequalities of human adaptation between countries, regions, cities and social groups.
Methodological Approach: EARLY-ADAPT is integrating multiple health outcomes and environmental and socioeconomic factors to perform a numerically-intensive, epidemiological analysis between daily spatiotemporal datasets. The project is using different layers of data to analyse the scales and spatiotemporal heterogeneity of the drivers of early adaptation.
Research Plan: After creating a homogeneous, continental-wide database of human health in Europe, EARLY-ADAPT is modelling the relationship between health and the environment, and quantifying the modifying effect of the societal factors. The final aim of the project is to perform a predictability analysis to determine the most realistic adaptation scenarios for the projections of future health.
Impact in Science: The continental-wide, multi-factor, multi-scale framework of EARLY-ADAPT aims to connect a range of disciplines to reveal the drivers, and the inequalities, of the early adaptation response to climate change.
Societal Challenge: Environmental factors kill hundreds of thousands of Europeans every year. Climate change is an additional threat for public health, and adaptation an essential strategy of increasing importance. Societies are devoting efforts to adaptation, but evidence of effectiveness is still lacking.
Hypothesis and Aim: The driving hypothesis of EARLY-ADAPT is that European societies are starting to adapt to climate change, but the effectiveness of early adaptation is heterogeneous between populations and through time. The project aims to detect, understand and quantify the drivers and inequalities of human adaptation between countries, regions, cities and social groups.
Methodological Approach: EARLY-ADAPT is integrating multiple health outcomes and environmental and socioeconomic factors to perform a numerically-intensive, epidemiological analysis between daily spatiotemporal datasets. The project is using different layers of data to analyse the scales and spatiotemporal heterogeneity of the drivers of early adaptation.
Research Plan: After creating a homogeneous, continental-wide database of human health in Europe, EARLY-ADAPT is modelling the relationship between health and the environment, and quantifying the modifying effect of the societal factors. The final aim of the project is to perform a predictability analysis to determine the most realistic adaptation scenarios for the projections of future health.
Impact in Science: The continental-wide, multi-factor, multi-scale framework of EARLY-ADAPT aims to connect a range of disciplines to reveal the drivers, and the inequalities, of the early adaptation response to climate change.
During the first 36 months of the project, EARLY-ADAPT’s team performed the following work:
* We set up and recruited the team.
* We created the project website.
* We created the database of the project, with 125 health datasets from various national agencies for statistics, 112 of which from European countries.
* We collected and pre-processed environmental data to estimate the exposures, including climate variables and air pollution concentrations.
* We used machine learning techniques to transform data from ground-level stations, satellites and climate and air quality reanalyses into daily, high-resolution estimates of the main air pollutants in Europe.
* We designed and implemented harmonisation protocols to integrate health and environmental data.
* We designed protocols and generated subroutines to automatically integrate and post-process new datasets, or any eventual change in the format of the existing ones.
* We used epidemiological models to estimate the risk associations between exposures and health variables at continental, regional and local scales.
* We used these epidemiological models to estimate the heat-related mortality burden of recent record-breaking summers in Europe, showing the differences between countries and sex and age groups, and analysing the role of early adaptation to climate change.
* We applied detection and attribution techniques to quantify the contribution of climate change on the mortality burden caused by environmental variables.
* We analysed the role of societal ageing in explaining the changes in adaptation.
* We additionally developed Spatial Bayesian models for the small-area epidemiological analysis.
* We developed predictive models to assess the predictability of heat-cold-health early warning systems.
* We published 25 scientific papers, mostly in high-impact journals like The Lancet, Nature Medicine, The Lancet Public Health, The Lancet Regional Health - Europe, or Nature Communications.
* We showed the results in scientific conferences and the media, including interviews in CNN, BBC or The New York Times.
* We set up and recruited the team.
* We created the project website.
* We created the database of the project, with 125 health datasets from various national agencies for statistics, 112 of which from European countries.
* We collected and pre-processed environmental data to estimate the exposures, including climate variables and air pollution concentrations.
* We used machine learning techniques to transform data from ground-level stations, satellites and climate and air quality reanalyses into daily, high-resolution estimates of the main air pollutants in Europe.
* We designed and implemented harmonisation protocols to integrate health and environmental data.
* We designed protocols and generated subroutines to automatically integrate and post-process new datasets, or any eventual change in the format of the existing ones.
* We used epidemiological models to estimate the risk associations between exposures and health variables at continental, regional and local scales.
* We used these epidemiological models to estimate the heat-related mortality burden of recent record-breaking summers in Europe, showing the differences between countries and sex and age groups, and analysing the role of early adaptation to climate change.
* We applied detection and attribution techniques to quantify the contribution of climate change on the mortality burden caused by environmental variables.
* We analysed the role of societal ageing in explaining the changes in adaptation.
* We additionally developed Spatial Bayesian models for the small-area epidemiological analysis.
* We developed predictive models to assess the predictability of heat-cold-health early warning systems.
* We published 25 scientific papers, mostly in high-impact journals like The Lancet, Nature Medicine, The Lancet Public Health, The Lancet Regional Health - Europe, or Nature Communications.
* We showed the results in scientific conferences and the media, including interviews in CNN, BBC or The New York Times.
Part of the work done during the first 36 months of the project has focused on the creation of the project database, the largest, format-homogeneous mortality dataset available to date in Europe, together with the best available climate, air pollution, socioeconomic and demographic data. Towards this aim, we designed and implemented harmonisation protocols for a successful integration of data. Most of the health datasets were protected under third party intellectual property rights, and therefore we interacted with different institutions to understand the legal terms of the collected data, as well as the implementation of additional data access protocols to protect it. The initial database is being periodically updated through the acquisition of new datasets or the download of real time products (e.g. weather and climate forecasts and projections). This has required the design of protocols and the generation of subroutines to automatically integrate and post-process the new data. Overall, the database is ready and currently being analysed.
One of the most important breakthroughs of the project was the estimation of the heat-related mortality burden of the record-breaking summer of 2022 in Europe, which was published in Nature Medicine. We estimated 61,672 heat-related deaths in Europe between 30 May and 4 September 2022. Italy (18,010 deaths), Spain (11,324) and Germany (8,173) had the highest summer heat-related mortality numbers, while Italy (295 deaths per million), Greece (280), Spain (237) and Portugal (211) had the highest heat-related mortality rates. Relative to population, we estimated 56% more heat-related deaths in women than men, with higher rates in men aged 0–64 (+41%) and 65–79 (+14%) years, and in women aged 80+ years (+27%). In a subsequent study, currently under review, we estimated the heat-related mortality burden of the summer of 2023, and quantified the mortality burden that would have occurred without the role of present-century adaptation. This line of research received widespread media attention, with over a thousand news pieces dedicated to its findings and implications, including interviews in CNN, BBC or The New York Times, being ranked by CarbonBrief in the top 3 of climate papers in 2023 for news and social media attention.
In the upcoming months, we will explore the major determinants of human mortality across a range of relevant scales and disciplines, including several environmental, socioeconomic and demographic factors. Among them, we have already published a paper on the role of ageing, a key factor in the European context. This forthcoming research is going to require an integrated analysis through spatial scales, which compares, at each layer, and between layers, the extent to which populations are reducing their vulnerability.
One of the most important breakthroughs of the project was the estimation of the heat-related mortality burden of the record-breaking summer of 2022 in Europe, which was published in Nature Medicine. We estimated 61,672 heat-related deaths in Europe between 30 May and 4 September 2022. Italy (18,010 deaths), Spain (11,324) and Germany (8,173) had the highest summer heat-related mortality numbers, while Italy (295 deaths per million), Greece (280), Spain (237) and Portugal (211) had the highest heat-related mortality rates. Relative to population, we estimated 56% more heat-related deaths in women than men, with higher rates in men aged 0–64 (+41%) and 65–79 (+14%) years, and in women aged 80+ years (+27%). In a subsequent study, currently under review, we estimated the heat-related mortality burden of the summer of 2023, and quantified the mortality burden that would have occurred without the role of present-century adaptation. This line of research received widespread media attention, with over a thousand news pieces dedicated to its findings and implications, including interviews in CNN, BBC or The New York Times, being ranked by CarbonBrief in the top 3 of climate papers in 2023 for news and social media attention.
In the upcoming months, we will explore the major determinants of human mortality across a range of relevant scales and disciplines, including several environmental, socioeconomic and demographic factors. Among them, we have already published a paper on the role of ageing, a key factor in the European context. This forthcoming research is going to require an integrated analysis through spatial scales, which compares, at each layer, and between layers, the extent to which populations are reducing their vulnerability.