Data analysis techniques: a tool for cumulative exposure assessment

摘要: Everyone is subject to environmental exposures from various sources, with negative health impacts (air, water and soil contamination, noise, etc.or positive effects (e.g. green space). Studies considering such complex settings in a global manner are rare. We propose use statistical factor cluster analyses create composite exposure index data-driven approach, view assess the burden experienced by populations. illustrate this approach large French metropolitan area. The study was carried out Great Lyon area (France, 1.2 M inhabitants) at census Block Group (BG) scale. used as indicators ambient air NO2 annual concentrations, noise levels proximity spaces, industrial plants, polluted sites road traffic. They were synthesized using Multiple Factor Analysis (MFA), technique without priori modeling, followed Hierarchical Clustering BG classes. first components of MFA explained, respectively, 30, 14, 11 9% total variance. five classes group: (1) particular type BGs population; (2) residential areas, less than average; (3) areas near midtown; (4) close industries; (5) midtown urban BGs, higher average spaces. Other numbers tested order variety clustering. present an techniques, which seem overlooked cumulative settings. Although it cannot be applied directly for risk or effect assessment, resulting can help identify hot spots exposure, prioritize policies compare across epidemiological framework.