Context: Environmental health risks arise from interacting exposures, population vulnerability, and existing disease burden, yet these domains are often assessed separately. Objective: This paper proposes the Cumulative Environmental–Health Exposure Index (IEC-ES), a transparent composite framework designed to support territorial prioritization of public-health and environmental interventions. Framework: IEC-ES combines three dimensions environmental exposure (E), human vulnerability (V), and health burden (H). Indicators are selected for relevance, availability, comparability, and actionability, converted to a common 0-2 risk scale, aggregated within dimensions, and combined using explicit weights that sum to one. Equal weighting can be used as a baseline, while context-specific weights may be tested through sensitivity analysis. Illustrative application: In a generic urban example, harmonized 0–2 normalization yields an IEC-ES of approximately 1.25, corresponding to moderate risk. In a sanitation-focused comparison, a high-risk neighborhood (E=1.80, V=2.00, H=2.00) reaches 1.93, whereas a second neighborhood (E=1.00, V=1.00, H=0.75) reaches 0.92 and is therefore classified as moderate risk. Conclusion: IEC-ES offers a simple and interpretable structure for integrating heterogeneous environmental, social, and health information into a decision-support score. It should be used with component-level interpretation, transparent documentation of indicator choices, uncertainty analysis, and empirical validation before routine policy deployment.
Context: Environmental health risks arise from interacting exposures, population vulnerability, and existing disease burden, yet these domains are often assessed separately. Objective: This paper proposes the Cumulative Environmental–Health Exposure Index (IEC-ES), a transparent composite framework designed to support territorial prioritization of public-health and environmental interventions. Framework: IEC-ES combines three dimensions environmental exposure (E), human vulnerability (V), and health burden (H). Indicators are selected for relevance, availability, comparability, and actionability, converted to a common 0-2 risk scale, aggregated within dimensions, and combined using explicit weights that sum to one. Equal weighting can be used as a baseline, while context-specific weights may be tested through sensitivity analysis. Illustrative application: In a generic urban example, harmonized 0–2 normalization yields an IEC-ES of approximately 1.25, corresponding to moderate risk. In a sanitation-focused comparison, a high-risk neighborhood (E=1.80, V=2.00, H=2.00) reaches 1.93, whereas a second neighborhood (E=1.00, V=1.00, H=0.75) reaches 0.92 and is therefore classified as moderate risk. Conclusion: IEC-ES offers a simple and interpretable structure for integrating heterogeneous environmental, social, and health information into a decision-support score. It should be used with component-level interpretation, transparent documentation of indicator choices, uncertainty analysis, and empirical validation before routine policy deployment.