We present a bivariate Bayesian space-time geostatistical model for exposure assessment and disease risk estimation. We use data from a panel study of 113 children on respiratory health in the high risk area of Valle del Mela (Sicily, IT). Data and gaseous pollutants were collected on 12 weeks in 21 locations 2007–2008. The model consists in an exposure model to predict pollutant concentrations at children’s residential addresses; a bivariate disease model. The original features are the joint specification of a spatial long-term effect and a spatiotemporal short-term effect of the pollutant concentrations and uncertainty propagation
Joint Analysis of Short and Long-Term Effects of Air Pollution
Biggeri Annibale;Catelan Dolores;Stoppa Giorgia;
2020
Abstract
We present a bivariate Bayesian space-time geostatistical model for exposure assessment and disease risk estimation. We use data from a panel study of 113 children on respiratory health in the high risk area of Valle del Mela (Sicily, IT). Data and gaseous pollutants were collected on 12 weeks in 21 locations 2007–2008. The model consists in an exposure model to predict pollutant concentrations at children’s residential addresses; a bivariate disease model. The original features are the joint specification of a spatial long-term effect and a spatiotemporal short-term effect of the pollutant concentrations and uncertainty propagation
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