Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data

  • Su Yun Kang | suyun602@gmail.com Mathematical Sciences School, Queensland University of Technology, Brisbane; Cooperative Research Centre Programme for Spatial Information, Melbourne, Australia.
  • Susanna M. Cramb Mathematical Sciences School, Queensland University of Technology, Brisbane; Viertel Centre for Research in Cancer Control, Cancer Council Queensland, Brisbane, Australia.
  • Nicole M. White Mathematical Sciences School, Queensland University of Technology, Brisbane; Cooperative Research Centre Programme for Spatial Information, Melbourne, Australia.
  • Stephen J. Ball School of Nursing, Midwifery and Paramedicine, Faculty of Health Sciences, Curtin University, Perth, Australia.
  • Kerrie L. Mengersen Mathematical Sciences School, Queensland University of Technology, Brisbane; Cooperative Research Centre Programme for Spatial Information, Melbourne, Australia.

Abstract

Disease maps are effective tools for explaining and predicting patterns of disease outcomes across geographical space, identifying areas of potentially elevated risk, and formulating and validating aetiological hypotheses for a disease. Bayesian models have become a standard approach to disease mapping in recent decades. This article aims to provide a basic understanding of the key concepts involved in Bayesian disease mapping methods for areal data. It is anticipated that this will help in interpretation of published maps, and provide a useful starting point for anyone interested in running disease mapping methods for areal data. The article provides detailed motivation and descriptions on disease mapping methods by explaining the concepts, defining the technical terms, and illustrating the utility of disease mapping for epidemiological research by demonstrating various ways of visualising model outputs using a case study. The target audience includes spatial scientists in health and other fields, policy or decision makers, health geographers, spatial analysts, public health professionals, and epidemiologists.

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Published
2016-05-31
Section
Original Articles
Keywords:
Areal data, Bayesian mapping, Disease mapping, Spatial information, Visualisation
Statistics
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How to Cite
Kang, S. Y., Cramb, S., White, N., Ball, S., & Mengersen, K. (2016). Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data. Geospatial Health, 11(2). https://doi.org/10.4081/gh.2016.428