A Bayesian localised conditional auto-regressive model for estimating the health effects of air pollution

TL;DR

This paper introduces a Bayesian localised conditional autoregressive model that better captures spatial heterogeneity in disease data, improving estimates of air pollution's health effects compared to traditional models.

Contribution

The paper develops a novel Bayesian localised CAR model that captures both smooth and abrupt spatial changes, enhancing estimation accuracy in ecological health studies.

Findings

The localised model outperforms traditional models in simulations.

Application to Glasgow data shows significant health effects of air pollution.

The model reveals spatial heterogeneity in health effects.

Abstract

Estimation of the long-term health effects of air pollution is a challenging task, especially when modelling small-area disease incidence data in an ecological study design. The challenge comes from the unobserved underlying spatial correlation structure in these data, which is accounted for using random effects modelled by a globally smooth conditional autoregressive model. These smooth random effects confound the effects of air pollution, which are also globally smooth. To avoid this collinearity a Bayesian localised conditional autoregressive model is developed for the random effects. This localised model is flexible spatially, in the sense that it is not only able to model step changes in the random effects surface, but also is able to capture areas of spatial smoothness in the study region. This methodological development allows us to improve the estimation performance of the covariate effects, compared to using traditional conditional auto-regressive models. These results are established using a simulation study, and are then illustrated with our motivating study on air pollution and respiratory ill health in Greater Glasgow, Scotland in 2010. The model shows substantial health effects of particulate matter air pollution and income deprivation, whose effects have been consistently attenuated by the currently available globally smooth models.