The INLA approach for disease mapping and health risk assessment

Disease mapping is an essential statistical tool for studying the spatial pattern of the disease-specific mortality or hospitalization with respect to a population. Moreover, when relevant information about risk factors are available, it is possible to assess the impact on human health of environmental hazards. In this work the Integrated Nested Laplace Approximation (INLA) is introduced as an alternative to Markov Chain Monte Carlo for Bayesian inference with hierarchical latent Gaussian models. We describe how to implement a two-step procedure for assessing exposure and disease risk by modeling both geostatistical and areal data.

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