Statistical challenges in estimating the long-term health impact of air pollution: spatial autocorrelation, collinearity and exposure measurement error Seminar

Estimation of the long-term health effects of air pollution is a challenging task, especially when modelling spatial small-area disease incidence data in an ecological study design. There are two main statistical challenges to be overcome in the modelling process, accurately estimating exposure to air pollution and adequately controlling for the unobserved spatial autocorrelation in the health data. The first of these arises because small area estimates of pollution concentrations are not available, and instead spatially sparse monitoring data (of high quality) has to be combined with spatially extensive modelled concentrations (potentially of lower quality). The second challenge is typically accounted for using spatially autocorrelated random effects modelled by a globally smooth conditional autoregressive model. However, the inclusion of 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 areas of spatial smoothness, but also it is able to capture step changes in the random effects surface. 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 by a motivating study on air pollution and respiratory ill health in the UK.