ArticleBMJ open2016
Bayesian spatiotemporal modelling for identifying unusual and unstable trends in mammography utilisation.
Article in BMJ open, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Differences and similarities in breast and colorectal screening participation: a spatial and temporal analysis to reveal intervention areas, France.Preventive medicine reports · 2026Article
- Article
- A Bayesian mixture modeling approach for public health surveillance.Biostatistics (Oxford, England) · 2020Article
- Advances in spatiotemporal models for non-communicable disease surveillance.International journal of epidemiology · 2020Review
- A generic method for improving the spatial interoperability of medical and ecological databases.International journal of health geographics · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo compare two Bayesian models capable of identifying unusual and unstable temporal patterns in spatiotemporal data.
settingAnnual 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.
resultsThe 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.
conclusionsThis 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.
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