Alcohol Abuse Disorder Prevalence and its Distribution Across Portugal

Abstract

Disease mapping is linked to two other scientific areas: small area estimation and ecological-spatial regression. This paper reviews similarities and differences among them. Bayesian hierarchical models are typically used in this context, using a combination of covariate data and a set of spatial random effects to represent the risk surface. The random effects are typically modeled by a conditional autoregressive prior distribution, and a number of alternative specifications have been proposed in the literature. The four models assessed here are applied to a study on alcohol abuse in Portugal, using data collected by the World Mental Health Survey Initiative.

Keywords

• alcohol abuse
• Bayesian hierarchical models
• disease mapping
• generalized linear models
• small area estimation