This research Project investigates the best way to represent the spatial disparity of diseases, to enable decision makers the best access to localised spatial health data. The initial focus of the Project is on cancer, with the results then to be applied to other diseases.
Better management of disease will be achieved through the development and application of new ways of
- small area disease mapping
- modelling disease and potential risk factors
- visualising the results of these models linking this new information on the spatial and temporal distribution of disease and associated risk factors with health service utilisation
These research results will be more easily communicated through the visualisation tool developed in Project 4.4.1.
Based on research outcomes, the project is now well placed to communicate the developed methodology to a wider audience. The statistical approach applied to health modelling has derived results beyond those achieved with a traditional statistical approaches and has broad potential applications.
Su Kang has submitted her PhD thesis on Comparison of Spatial Modelling Using Point-process Data and Aerial Data. Two PhD papers have also been submitted for the Journal of Applied Statistics and Spatial and Spatio-temporal Epidemiology. The papers consider the comparison of models with respect to choice of spatial scale, for both grid and statistical local area (SLA) geographies and the recommendations for the choice of spatial scale and smoothness priors, for researchers dealing with spatial models and data.
Work on spatio-temporal survival models has continued with the aim of determining if improvements in survival across time are shared equally across small areas. The work focuses on the two cancers with evidence of spatial inequalities in survival where cancer stage information is available – breast and colorectal cancer. A paper has also been prepared on the spatio-temporal patterns for the underlying risk of developing lung cancer.