What is known?
Disease maps are an important tool to document how the burden of disease varies by small geographical area. The calculation of statistics reported in these maps often requires access to confidential data that cannot be shared publicly for further analyses. Developing statistical methods to enable other users to analyse the aggregated mapped data will enable a wider range of research questions to be investigated by researchers who do not have access to the original data.
What is new?
This study demonstrated that using a hierarchical Bayesian meta-analysis model was effective in modelling the published cancer incidence estimates available as part of the online Australian Cancer Atlas (ACA), and to generate similar patterns of cancer incidence across major cities, regional and remote areas that were obtained from using the unit record data.
What does this mean?
The modelling approach proposed in this study can be applied to published disease atlases to identify associations between different socio-demographic, economic, ecological or environmental factors on specific disease outcomes to answer research questions that may not have been included in the original Atlas scope.
Contact: Peter Baade
Reference: Jahan F, Duncan EW, Cramb SM, Baade PD, Mengersen KL. Augmenting Disease Maps: a Bayesian meta-analysis approach. Royal Society Open Science. 2020. doi: 10.1098/rsos.192151.