Bayesian spatio-temporal modeling for identifying unusual and unstable trends in mammography utilization

Duncan, Earl, White, Nicole, & Mengersen, Kerrie (2016) Bayesian spatio-temporal modeling for identifying unusual and unstable trends in mammography utilization. BMJ Open, 6(5), e010253.

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Abstract

Objectives: To compare two Bayesian models capable of identifying unusual and unstable temporal patterns in spatio-temporal 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 to the overall common temporal trend. SLAs which did follow 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. Analysis of these models has provided interesting insights into the temporal trends of mammography screening counts, and has revealed several possible avenues for further research, such as extending the models to allow for multiple common temporal trends and accounting for additional spatio-temporal heterogeneity.

Impact and interest:

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ID Code: 95745
Item Type: Journal Article
Refereed: Yes
Keywords: Mammography, Public health, Bayesian statistics, Spatio-temporal modelling, Unusual trends
DOI: 10.1136/bmjopen-2015-010253
ISSN: 2044-6055
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)
Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Biostatistics (010402)
Divisions: Current > Research Centres > ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS)
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Funding:
  • CRCSI/4.4.2
Copyright Owner: Copyright 2016 by the BMJ Publishing Group Ltd.
Deposited On: 24 May 2016 23:38
Last Modified: 28 Aug 2016 15:46

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