High-dimensional order-free multivariate spatial disease mapping

TL;DR

This paper introduces a scalable Bayesian multivariate spatial disease mapping method that partitions large regions for efficient analysis, enabling joint modeling of multiple diseases with improved accuracy over traditional single-model approaches.

Contribution

The paper presents an order-free, scalable Bayesian approach using INLA and consensus Monte Carlo for multivariate spatial disease mapping in large datasets, overcoming computational challenges.

Findings

Efficient analysis of large spatial disease data sets.

Improved joint risk estimation for multiple diseases.

Successful application to cancer mortality in Spain.

Abstract

Despite the amount of research on disease mapping in recent years, the use of multivariate models for areal spatial data remains limited due to difficulties in implementation and computational burden. These problems are exacerbated when the number of small areas is very large. In this paper, we introduce an order-free multivariate scalable Bayesian modelling approach to smooth mortality (or incidence) risks of several diseases simultaneously. The proposal partitions the spatial domain into smaller subregions, fits multivariate models in each subdivision and obtains the posterior distribution of the relative risks across the entire spatial domain. The approach also provides posterior correlations among the spatial patterns of the diseases in each partition that are combined through a consensus Monte Carlo algorithm to obtain correlations for the whole study region. We implement the proposal using integrated nested Laplace approximations (INLA) in the R package bigDM and use it to jointly analyse colorectal, lung, and stomach cancer mortality data in Spanish municipalities. The new proposal permits the analysis of big data sets and provides better results than fitting a single multivariate model.