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a toolkit for dynaMic health Impact analysiS to predicT disability-Related costs in the Aging population based on three case studies of steeL-industry exposed areas in europe

MISTRAL

Start Date
End Date
Total Funding
€ 3 619 635
Funding Programme
European Countries Involved

The environment is one of the most crucial determinants of health. The Global Burden of Disease report estimates an emerging impact in terms of disability and reducing the quality of life worldwide, particularly for the aging populations. One of the root causes of this decline is likely to derive from the interaction of socio-environmental risk factors and sub-clinical conditions and the consequent increase of the primary non-communicable disease (dementia, COPD, cerebrovascular and chronic ischemic heart diseases). The multi-dimensional nature causal pathways of these interactions are still mostly unknown. In this complex scenario, where the relationship between exposure and outcomes is so different and multifaceted, the Health Impact Assessment (HIA) process is the standard tool that provides an overview of the matter, from the screening of health risk factors to the introduction of new health policies and the monitoring of effects. A complete digital approach for HIA that could dynamically adapt to the variability of the determinants and their interaction is still poorly investigated. Artificial Intelligence algorithms offer innovative and high-performance possibilities for HIA implementations, improving elaboration and resizing of complex information and data. This proposal aims to develop a technological toolkit for dynamic, intelligent HIA toolkit to predict the health impact of health-related features, forecasting the trajectories of disability and quality of life reduction. This method will use environmental, socio-economic, geographical, and clinical characteristics, managed and elaborated with a federated learning architecture. The generated models will be adjusted for lifestyle and individual conditions data sourced from large population-based digital surveys. The models will be trained and validated on three different exposures to the steel plants' pollution: Taranto in southern Italy, Rybnik in Poland, and Flanders in Belgium.

Project partners

ISTITUTO SUPERIORE DI SANITA (IT); UNIVERSITEIT HASSELT (BE); WINGS ICT SOLUTIONS TECHNOLOGIES PLIROFORIKIS KAI EPIKOINONION ANONYMI ETAIREIA (EL); AKADEMIA GORNICZO-HUTNICZA IM. STANISLAWA STASZICA W KRAKOWIE (PL); POLITECNICO DI BARI (IT); PUBLIC HEALTH
ENVIRONMENT AND SOCIAL EQUITY - PLANET (IT); NUROMEDIA GMBH (DE); UBITEL INGENIERIA SL (ES); UNIVERSITA DEGLI STUDI DI BARI ALDO MORO (IT); UNIVERSITY OF SUFFOLK LTD (UK); THE CHANCELLOR
MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD (UK); AZIENDA SANITARIA LOCALE TARANTO (IT)

 
Acknowledgement
Alzheimer Europe's database on research projects was developed as part of the 2020 Work Plan which received funding under an operating grant from the European Union’s Health Programme (2014–2020).