A multivariate model for spatio-temporal health outcomes with an application to suicide mortality

This article considers models for multivariate mortality outcomes (e.g., bivariate, trivariate, or higher dimensional) observed over a set Of areas and through time. The model outlined here allows for spatially structured and white noise errors and for their intercorrelation. It also includes possible temporal continuity in such types of error via structured temporal effects. An extension to spatially varying regression effects is considered, as well as the option Of nonparametric specification of priors for spatial residuals and regression effects. Allowing for spatially correlated intercepts or regression effects may alter inferences regarding the changing impact on mortality of socioeconomic or environmental predictors. The modeling framework is illustrated by an application to male and female suicide mortality in London,focusing on the impact on suicide of deprivation and social fragmentation ("anomie") in the 33 London boroughs during three periods: 1979-83, 1984-88 and 1989-93. Suicide trends by age group are also considered and show considerable differences in the trends in impacts of deprivation and social fragmentation.