A PAN-EUROPEAN MULTI-CRITERIA RISK ASSESSMENT TOOL

Assessing the environmental burden on respiratory health requires integrating a complex set of variables—ranging from pollution levels to land use and fiscal measures—into a form that is scientifically robust, comparable across regions, and useful for policy decisions. Traditional methods of aggregating such data, particularly additive scoring models, present major limitations. They often cannot simultaneously handle quantitative and qualitative variables, may produce misleading results by assigning similar scores to very different risk profiles, and rely on arbitrary weighting systems that compromise objectivity.

Other existing approaches, such as DALYs (Disability-Adjusted Life Years) and QALYs (Quality-Adjusted Life Years), use multiplicative models based on only two variables (typically survival and utility/disability). These indicators, while widespread, have been the subject of extensive methodological and ethical critiques, as their foundational assumptions have been shown to be invalid in several contexts. Furthermore, conventional risk assessment models that estimate probabilities at national or regional levels based on disease incidence provide limited value for proactive health planning and environmental interventions.

In contrast, the methodology presented in the frame of the REMEDIA project introduces an innovative, multidimensional aggregation technique designed to generate a single synthetic exposome risk indicator for respiratory diseases. This method leverages comprehensive data—sourced, for example, from EUROSTAT—across diverse domains including pollutant emissions, land use, and environmental taxation. Each variable is treated as a distinct dimension in a high-dimensional mathematical space.

Using multidimensional projection techniques, these many variables are mapped onto a single optimized axis within a hyperplane. The projection is determined by identifying the direction that captures the maximum amount of meaningful information—assessed through an “inertia” metric, which quantifies how well the projection represents the variance in the original data. By minimizing inertia, the method ensures that the final indicator is statistically robust and highly representative of the underlying complexity.

This projection yields a normalized score between 0 and 100 for each country or region, representing its overall exposome-related risk for respiratory disease. Unlike traditional methods, this approach avoids the pitfalls of arbitrary scoring, ensures comparability, and respects the multidimensional nature of environmental risk factors.

The key advantage of this methodology is its capacity to transform complex, heterogeneous data into a single, actionable metric. For the first time, it allows for the creation of a pan-European risk map showing relative environmental vulnerability in terms of pulmonary health. Such a tool has profound implications for evidence-based decision-making, allowing policymakers to better target interventions, allocate resources, and monitor environmental health risks over time.

Target users: Decision makers, general public