Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data
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 mapp...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
PAGEPress Publications
2016-05-01
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| Series: | Geospatial Health |
| Subjects: | |
| Online Access: | http://www.geospatialhealth.net/index.php/gh/article/view/428 |
| _version_ | 1818115016781463552 |
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| author | Su Yun Kang Susanna M. Cramb Nicole M. White Stephen J. Ball Kerrie L. Mengersen |
| author_facet | Su Yun Kang Susanna M. Cramb Nicole M. White Stephen J. Ball Kerrie L. Mengersen |
| author_sort | Su Yun Kang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T03:59:55Z |
| format | Article |
| id | doaj.art-5fc81080b18e4fc89a04113bb0596126 |
| institution | Directory Open Access Journal |
| issn | 1827-1987 1970-7096 |
| language | English |
| last_indexed | 2024-12-11T03:59:55Z |
| publishDate | 2016-05-01 |
| publisher | PAGEPress Publications |
| record_format | Article |
| series | Geospatial Health |
| spelling | doaj.art-5fc81080b18e4fc89a04113bb05961262022-12-22T01:21:40ZengPAGEPress PublicationsGeospatial Health1827-19871970-70962016-05-0111210.4081/gh.2016.428379Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal dataSu Yun Kang0Susanna M. Cramb1Nicole M. White2Stephen J. Ball3Kerrie L. Mengersen4Mathematical Sciences School, Queensland University of Technology, Brisbane; Cooperative Research Centre Programme for Spatial Information, MelbourneMathematical Sciences School, Queensland University of Technology, Brisbane; Viertel Centre for Research in Cancer Control, Cancer Council Queensland, BrisbaneMathematical Sciences School, Queensland University of Technology, Brisbane; Cooperative Research Centre Programme for Spatial Information, MelbourneSchool of Nursing, Midwifery and Paramedicine, Faculty of Health Sciences, Curtin University, PerthMathematical Sciences School, Queensland University of Technology, Brisbane; Cooperative Research Centre Programme for Spatial Information, MelbourneDisease 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.http://www.geospatialhealth.net/index.php/gh/article/view/428Areal dataBayesian mappingDisease mappingSpatial informationVisualisation |
| spellingShingle | Su Yun Kang Susanna M. Cramb Nicole M. White Stephen J. Ball Kerrie L. Mengersen Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data Geospatial Health Areal data Bayesian mapping Disease mapping Spatial information Visualisation |
| title | Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data |
| title_full | Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data |
| title_fullStr | Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data |
| title_full_unstemmed | Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data |
| title_short | Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data |
| title_sort | making the most of spatial information in health a tutorial in bayesian disease mapping for areal data |
| topic | Areal data Bayesian mapping Disease mapping Spatial information Visualisation |
| url | http://www.geospatialhealth.net/index.php/gh/article/view/428 |
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