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CC BY V4.0, Open Access
Journal article
Augmenting disease maps: a Bayesian meta-analysis approach
Royal Society Open Science, Vol.7(8), Art. 192151
2020
PMID: 32968502
Abstract
Analysis of spatial patterns of disease is a significant field of research. However, access to unit-level disease data can be difficult for privacy and other reasons. As a consequence, estimates of interest are often published at the small area level as disease maps. This motivates the development of methods for analysis of these ecological estimates directly. Such analyses can widen the scope of research by drawing more insights from published disease maps or atlases. The present study proposes a hierarchical Bayesian meta-analysis model that analyses the point and interval estimates from an online atlas. The proposed model is illustrated by modelling the published cancer incidence estimates available as part of the online Australian Cancer Atlas (ACA). The proposed model aims to reveal patterns of cancer incidence for the 20 cancers included in ACA in major cities, regional and remote areas. The model results are validated using the observed areal data created from unit-level data on cancer incidence in each of 2148 small areas. It is found that the meta-analysis models can generate similar patterns of cancer incidence based on urban/rural status of small areas compared with those already known or revealed by the analysis of observed data. The proposed approach can be generalized to other online disease maps and atlases.
Details
- Title
- Augmenting disease maps: a Bayesian meta-analysis approach
- Authors/Creators
- Farzana Jahan (Author)Earl W. Duncan (Author) - School of Mathematical Science, ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Queensland, AustraliaSusanna M. Cramb (Author) - Cancer Council Queensland, Brisbane, Queensland, AustraliaPeter D. Baade (Author) - Cancer Council Queensland, Brisbane, Queensland, AustraliaKerrie L. Mengersen (Author) - School of Mathematical Science, ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Queensland, Australia
- Publication Details
- Royal Society Open Science, Vol.7(8), Art. 192151
- Publisher
- The Royal Society
- Grants
- Bayesian learning for decision making in the big data era, 2019-06-14T18:04:43.803493, Australian Research Council (Australia, Canberra) - ARC
- Identifiers
- 991005575768407891
- Copyright
- © 2020 The Authors.
- Murdoch Affiliation
- College of Science, Technology, Engineering and Mathematics
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- Citation topics
- 1 Clinical & Life Sciences
- 1.228 Virology - Tropical Diseases
- 1.228.1878 Disease Mapping
- Web Of Science research areas
- Public, Environmental & Occupational Health
- ESI research areas
- Social Sciences, general