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

TL;DR

This paper introduces a Bayesian hierarchical model for disease mapping that accounts for heavy-tailed latent effects and outliers, improving detection and interpretation of disease risk variations across regions.

Contribution

It extends the BYM2 model by incorporating scale mixture structures to better identify outliers and heavy-tailed effects, with two novel prior specifications.

Findings

Proposed models outperform Congdon's in outlier detection and WAIC.

Simulation studies confirm improved efficiency in identifying outliers.

Application to Zika data identified 19 potential outliers in Rio de Janeiro.

Abstract

In disease mapping, the relative risk of a disease is commonly estimated across different areas within a region of interest. The number of cases in an area is often assumed to follow a Poisson distribution whose mean is decomposed as the product between an offset and the logarithm of the disease's relative risk. The log risk may be written as the sum of fixed effects and latent random effects. The BYM2 model decomposes each latent effect into a weighted sum of independent and spatial effects. We build on the BYM2 model to allow for heavy-tailed latent effects and accommodate potentially outlying risks, after accounting for the fixed effects. We assume a scale mixture structure wherein the variance of the latent process changes across areas and allows for outlier identification. We propose two prior specifications for this scale mixture parameter. These are compared through simulation studies and in the analysis of Zika cases from the first (2015-2016) epidemic in Rio de Janeiro city, Brazil. The simulation studies show that, in terms of the model assessment criterion WAIC and outlier detection, the two proposed parametrisations perform better than the model proposed by Congdon (2017) to capture outliers. In particular, the proposed parametrisations are more efficient, in terms of outlier detection, than Congdon's when outliers are neighbours. Our analysis of Zika cases finds 19 out of 160 districts of Rio as potential outliers, after accounting for the socio-development index. Our proposed model may help prioritise interventions and identify potential issues in the recording of cases.