Some Interpolation Estimators in Environmental Risk Assessment for Spatially Misaligned Health Data |
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Authors: | Email author" target="_blank">Fedele?P?GrecoEmail author Andrew?B?Lawson Daniela?Cocchi Tom?Temples |
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Institution: | (1) University of Bologna, Bologna, Italy;(2) University of South Carolina, Columbia, SC, USA |
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Abstract: | Ecological regression studies are widely used in geographical epidemiology to assess the relationships between health hazard
and putative risk factors. Very often, health data are measured at an aggregate level because of confidentiality restrictions,
while putative risk factors are measured on a different grid, i.e., independent (exposure) variable and response (counts)
variable are spatially misaligned. To perform a regression of risk on exposure, one needs to realign the spatial support of
the variables. Bayesian hierarchical models constitute a natural approach to the problem because of their ability to model
the exposure field and the relationship between exposure and relative risk at different levels of the hierarchy, taking proper
account of the variability induced by the covariate estimation. In the current paper, we propose two fully Bayesian solutions
to the problem. The first one is based on the kernel-smoothing technique, while the second one is built on the tessellation
of the study region. We illustrate our methods by assessing the relationship between exposure to uranium in drinkable waters
and cancer incidence, in South Carolina (USA). |
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Keywords: | CAR model Bayesian hierarchical models health hazard Kernel smoothing spatially misaligned data Voronoi tessellation |
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