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


Autoria(s): Kang, Su Yun; Cramb, Susanna; White, Nicole; Ball, Stephen J.; Mengersen, Kerrie
Data(s)

2016

Resumo

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.

Formato

application/pdf

Identificador

http://eprints.qut.edu.au/92908/

Publicador

Universita degli Studi di Napoli Federico II

Relação

http://eprints.qut.edu.au/92908/1/Kang%20IN%20PRESS%20Making%20the%20most%20of%20spatial%20information%20in%20health.pdf

Kang, Su Yun, Cramb, Susanna, White, Nicole, Ball, Stephen J., & Mengersen, Kerrie (2016) Making the most of spatial information in health: A tutorial in Bayesian disease mapping for areal data. Geospatial Health. (In Press)

Direitos

Copyright 2016 PAGEPress

Fonte

ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS); School of Mathematical Sciences; Science & Engineering Faculty

Palavras-Chave #010402 Biostatistics #111706 Epidemiology #areal data #Bayesian #disease mapping #spatial #visualisation
Tipo

Journal Article