Spatial Poisson Regression for Health and Exposure Data Measured at Disparate Resolutions

摘要: Abstract Ecological regression studies are widely used to examine relationships between disease rates for small geographical areas and exposure environmental risk factors. The raw data such studies, including cases, pollution concentrations, the reference population at risk, typically measured various levels of spatial aggregation but accumulated a common scale facilitate statistical analysis. In this traditional approach, heterogeneous distributions within aggregate may lead biased inference, whereas individual attributes as age, gender, smoking habits must either be summarized provide area-level covariate values or stratify This article presents analysis effect traffic on respiratory disorders in children. features disparate, nonnested scales, spatially varying covariates, latent fact...