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.
| Original language | English |
|---|---|
| Pages (from-to) | 79-101 |
| Number of pages | 23 |
| Journal | REVSTAT: Statistical Journal |
| Volume | 13 |
| Issue number | 1 |
| Publication status | Published - Mar 2015 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Alcohol abuse
- Bayesian hierarchical models
- Disease mapping
- Generalized linear models
- Small area estimation
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