MISTRAL 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

环境是健康的最重要决定因素之一。《全球疾病负担报告》估计，在残疾和降低全球生活质量方面，特别是对老龄化人口，正在出现影响。这一下降的根本原因之一可能是社会环境风险因素和亚临床病症的相互作用，以及随之而来的主要非传染性疾病（痴呆症、慢性阻塞性肺病、脑血管病和慢性缺血性心脏病）的增加。这些相互作用的多维性质因果途径仍然是未知的。在这种复杂的情况下，接触与结果之间的关系是如此不同和多方面，健康影响评估（HIA）过程是标准工具，提供了一个问题的概述，从健康风险因素的筛选到新的卫生政策的引入和效果的监测。一个完整的数字方法HIA，可以动态地适应变化的决定因素和它们的相互作用仍然是很差的调查。人工智能算法为HIA实施提供了创新和高性能的可能性，改善了复杂信息和数据的详细说明和分析。该提案旨在开发一个动态的、智能的HIA工具包技术工具包，以预测与健康相关的特征对健康的影响，预测残疾和生活质量下降的轨迹。该方法将使用环境，社会经济，地理和临床特征，并通过联邦学习架构进行管理和阐述。生成的模型将根据来自大型人口数字调查的生活方式和个人状况数据进行调整。这些模型将在三个不同的钢铁厂污染暴露下进行训练和验证：意大利南部的塔兰托、波兰的雷布尼克和比利时的弗兰德斯。

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.