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a toolkit for dynaMic health Impact analysiS to predicT disability-Related costs...
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
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 popu...
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.
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Duración del proyecto: 47 meses
Fecha Inicio: 2023-01-01
Fecha Fin: 2026-12-31
Fecha Fin: 2026-12-31
Línea de financiación: concedida
El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-01-01
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
Presupuesto
El presupuesto total del proyecto asciende a
4M€
Líder del proyecto
ISTITUTO SUPERIORE DI SANITA
No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5