Approximate Bayesian inference for multivariate point pattern analysis in disease mapping

03/27/2019
by   Francisco Palmi-Perales, et al.
0

We present a novel approach for the analysis of multivariate case-control georeferenced data using Bayesian inference in the context of disease mapping, where the spatial distribution of different types of cancers is analyzed. Extending other methodology in point pattern analysis, we propose a log-Gaussian Cox process for point pattern of cases and the controls, which accounts for risk factors, such as exposure to pollution sources, and includes a term to measure spatial residual variation. For each disease, its intensity is modeled on a baseline spatial effect (estimated from both controls and cases), a disease-specific spatial term and the effects on covariates that account for risk factors. By fitting these models the effect of the covariates on the set of cases can be assessed, and the residual spatial terms can be easily compared to detect areas of high risk not explained by the covariates. Three different types of effects to model exposure to pollution sources are considered. First of all, a fixed effect on the distance to the source. Next, smooth terms on the distance are used to model non-linear effects by means of a discrete random walk of order one and a Gaussian process in one dimension with a Matérn covariance. Models are fit using the integrated nested Laplace approximation (INLA) so that the spatial terms are approximated using an approach based on solving Stochastic Partial Differential Equations (SPDE). Finally, this new framework is applied to a dataset of three different types of cancer and a set of controls from Alcalá de Henares (Madrid, Spain). Covariates available include the distance to several polluting industries and socioeconomic indicators. Our findings point to a possible risk increase due to the proximity to some of these industries.

READ FULL TEXT

page 5

page 6

research
06/14/2020

High-resolution Bayesian mapping of landslide hazard with unobserved trigger event

Statistical models for landslide hazard enable mapping of risk factors a...
research
10/13/2022

A Bayesian multivariate spatial approach for illness-death survival models

Illness-death models are a class of stochastic models inside the multi-s...
research
09/21/2021

A Bayesian hierarchical model for disease mapping that accounts for scaling and heavy-tailed latent effects

In disease mapping, the relative risk of a disease is commonly estimated...
research
02/02/2021

Bayesian analysis of population health data

The analysis of population-wide datasets can provide insight on the heal...
research
07/23/2018

Geostatistical modeling to capture seismic-shaking patterns from earthquake-induced landslides

In this paper, we investigate earthquake-induced landslides using a geos...
research
06/03/2022

A Bayesian modelling framework to quantify multiple sources of spatial variation for disease mapping

Spatial connectivity is an important consideration when modelling infect...

Please sign up or login with your details

Forgot password? Click here to reset