Developing the atlas of cancer in Queensland : methodological issues
Data(s) |
2011
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Resumo |
Background: Achieving health equity has been identified as a major challenge, both internationally and within Australia. Inequalities in cancer outcomes are well documented, and must be quantified before they can be addressed. One method of portraying geographical variation in data uses maps. Recently we have produced thematic maps showing the geographical variation in cancer incidence and survival across Queensland, Australia. This article documents the decisions and rationale used in producing these maps, with the aim to assist others in producing chronic disease atlases. Methods: Bayesian hierarchical models were used to produce the estimates. Justification for the cancers chosen, geographical areas used, modelling method, outcome measures mapped, production of the adjacency matrix, assessment of convergence, sensitivity analyses performed and determination of significant geographical variation is provided. Conclusions: Although careful consideration of many issues is required, chronic disease atlases are a useful tool for assessing and quantifying geographical inequalities. In addition they help focus research efforts to investigate why the observed inequalities exist, which in turn inform advocacy, policy, support and education programs designed to reduce these inequalities. |
Formato |
application/pdf |
Identificador | |
Publicador |
BioMed Central |
Relação |
http://eprints.qut.edu.au/44105/1/methods_final_qut_ep.pdf DOI:10.1186/1476-072X-10-9 Cramb, Susanna, Mengersen, Kerrie, & Baade, Peter (2011) Developing the atlas of cancer in Queensland : methodological issues. International Journal of Health Geographics, 10, p. 9. http://purl.org/au-research/grants/ARC/LP100100570 |
Direitos |
2011 Cramb et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Fonte |
Faculty of Science and Technology; Mathematical Sciences |
Palavras-Chave | #010401 Applied Statistics #111706 Epidemiology #cancer #bayesian hierarchical models #incidence #relative survival #queensland |
Tipo |
Journal Article |