An investigation of the impact of various geographical scales for the specification of spatial dependence

Abstract

Ecological studies are based on characteristics of groups of individuals, which are common in various disciplines including epidemiology. It is of great interest for epidemiologists to study the geographical variation of a disease by accounting for the positive spatial dependence between neighbouring areas. However, the choice of scale of the spatial correlation requires much attention. In view of a lack of studies in this area, this study aims to investigate the impact of differing definitions of geographical scales using a multilevel model. We propose a new approach – the grid-based partitions and compare it with the popular census region approach. Unexplained geographical variation is accounted for via area-specific unstructured random effects and spatially structured random effects specified as an intrinsic conditional autoregressive process. Using grid-based modelling of random effects in contrast to the census region approach, we illustrate conditions where improvements are observed in the estimation of the linear predictor, random effects, parameters, and the identification of the distribution of residual risk and the aggregate risk in a study region. The study has found that grid-based modelling is a valuable approach for spatially sparse data while the statistical local area-based and grid-based approaches perform equally well for spatially dense data.

Keywords:

• Bayesian hierarchical models
• ecological fallacy
• grid-based partitions
• integrated nested Laplace approximation
• intrinsic conditional autoregression
• spatial epidemiology

AMS Subject Classification::

• 62H11
• 91B72

Acknowledgements

The work was supported by the Cooperative Research Centre for Spatial Information, whose activities are funded by the Australian Commonwealth's Cooperative Research Centres Programme. Peter Baade is supported by an NHMRC Career Development Fellowship (#1005334). The authors would like to thank Cancer Council Queensland for supplying the data for analysis, Susanna Cramb for assisting with the data and two anonymous reviewers for their helpful comments.