Augmenting disease maps: a Bayesian meta-analysis approach

Farzana Jahan; Earl W. Duncan; Susanna M. Cramb; Peter D. Baade; Kerrie L. Mengersen

doi:10.1098/rsos.192151

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Augmenting disease maps: a Bayesian meta-analysis approach

Journal article

Open access

Peer reviewed

Augmenting disease maps: a Bayesian meta-analysis approach

Farzana Jahan, Earl W. Duncan, Susanna M. Cramb, Peter D. Baade and Kerrie L. Mengersen

Royal Society Open Science, Vol.7(8), Art. 192151

2020

DOI: https://doi.org/10.1098/rsos.192151

PMID: 32968502

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Abstract

Cancer Atlas

Cancer Incidence

Disease Atlas

Geographical Patterns

Mathematics

Online Estimates

Small Area Estimates

Spatial statistics

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, Australia
Susanna M. Cramb (Author) - Cancer Council Queensland, Brisbane, Queensland, Australia
Peter D. Baade (Author) - Cancer Council Queensland, Brisbane, Queensland, Australia
Kerrie 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
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