Bayesian spatiotemporal modelling for identifying unusual and unstable trends in mammography utilisation

OBJECTIVES: To compare two Bayesian models capable of identifying unusual and unstable temporal patterns in spatiotemporal data. SETTING: Annual counts of mammography screening users from each statistical local area (SLA) in Brisbane, Australia, recorded between 1997 and 2008 inclusive. PRIMARY OUTCOME MEASURES: Mammography screening counts. RESULTS: The temporal trends of 91 SLAs (58%) were dissimilar from the overall common temporal trend. SLAs that followed the common temporal trend also tended to have stable temporal trends. SLAs with unstable temporal trends tended to be situated farther from the city and farther from mammography screening facilities. CONCLUSIONS: This paper demonstrates the usefulness of the two models in identifying unusual and unstable temporal trends, and the synergy obtained when both models are applied to the same data set. An analysis of these models has provided interesting insights into the temporal trends of mammography screening counts and has shown several possible avenues for further research, such as extending the models to allow for multiple common temporal trends and accounting for additional spatiotemporal heterogeneity.