23 research outputs found
High-dimensional order-free multivariate spatial disease mapping
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 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 allows for the
analysis of large datasets and yields superior results compared to fitting a single multivariate model. Additionally, it facilitates
statistical inference through local homogeneous models, which may be more appropriate than a global homogeneous model
when dealing with a large number of areas.This work has been supported by the project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033. It has also been partially funded by the Public University of Navarra (project PJUPNA2001). Open Access funding provided by Universidad Pública de Navarra
Age- and sex-specific spatio-temporal patterns of colorectal cancer mortality in Spain (1975-2008)
Incluye 2 ficheros de datosIn this paper, space-time patterns of colorectal cancer (CRC) mortality risks are studied by sex and age group (50-69, ≥70) in Spanish provinces during the period 1975-2008. Space-time conditional autoregressive models are used to perform the statistical analyses. A pronounced increase in mortality risk has been observed in males for both age-groups. For males between 50 and 69 years of age, trends seem to stabilize from 2001 onward. In females, trends reflect a more stable pattern during the period in both age groups. However, for the 50-69 years group, risks take an upward trend in the period 2006-2008 after the slight decline observed in the second half of the period. This study offers interesting information regarding CRC mortality distribution among different Spanish provinces that could be used to improve prevention policies and resource allocation in different regions.This research has been supported by the Spanish Ministry of Science and Innovation (project MTM 2011-22664, which is co-funded by FEDER)