_:n299b451a1b6c48cf87e27db12d75df1fxb38
<http://schema.org/description>
"We present a novel approach for the analysis of multivariate case-control\ngeoreferenced data using Bayesian inference in the context of disease mapping,\nwhere the spatial distribution of different types of cancers is analyzed.\nExtending other methodology in point pattern analysis, we propose a\nlog-Gaussian Cox process for point pattern of cases and the controls, which\naccounts for risk factors, such as exposure to pollution sources, and includes\na term to measure spatial residual variation.\n  For each disease, its intensity is modeled on a baseline spatial effect\n(estimated from both controls and cases), a disease-specific spatial term and\nthe effects on covariates that account for risk factors. By fitting these\nmodels the effect of the covariates on the set of cases can be assessed, and\nthe residual spatial terms can be easily compared to detect areas of high risk\nnot explained by the covariates.\n  Three different types of effects to model exposure to pollution sources are\nconsidered. First of all, a fixed effect on the distance to the source. Next,\nsmooth terms on the distance are used to model non-linear effects by means of a\ndiscrete random walk of order one and a Gaussian process in one dimension with\na Matérn covariance.\n  Models are fit using the integrated nested Laplace approximation (INLA) so\nthat the spatial terms are approximated using an approach based on solving\nStochastic Partial Differential Equations (SPDE). Finally, this new framework\nis applied to a dataset of three different types of cancer and a set of\ncontrols from Alcalá de Henares (Madrid, Spain). Covariates available include\nthe distance to several polluting industries and socioeconomic indicators. Our\nfindings point to a possible risk increase due to the proximity to some of\nthese industries."
<https://api.deepai.org/profile/virgilio-gomez-rubio>