Abstract
Disease mapping, the visualization of disease rates and the clustering of disease data are still one of the most interesting topics in geosciences. This is because of the nature of the data, which are often purely spatial with a rich descriptive part and which are easy to combine with other data (demographic, economic, etc.). This contribution aims to present the usage of empirical Bayesian methods in disease mapping and the subsequent creation of disease maps. Bayesian methods incorporate prior knowledge about the phenomenon (or underlying processes) to provide a more accurate and easily understandable description of the situation. Empirical Bayesian procedures are used for disease rates smoothing in the case of a choropleth map. They also help to identify local clusters of more/less affected areas. The main topic of the case study in this paper is the analysis of the spatial distribution of a disease called campylobacteriosis in the Czech Republic between the years 2008 and 2012 with the usage of global empirical Bayesian estimates based on binomial distribution and local empirical Bayesian estimates based on first order queen contiguity.
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Anselin L (1995) Local indicators of spatial association—LISA. Geogr Anal 27:93–115
Anselin L (2003) GeoDaTM 0.9 User’s Guide
Assuncao R, Reis E (1999) A new proposal to adjust Moran’s I for population density. Stat Med 2162:2147–2162
Bailey TC (2001) Spatial statistical methods in health. Cadernos Saúde Pública 17(5):1083–1098
Bailey TC, Gatrell AC (1995) Interactive spatial data analysis. Longman Scientific & Technical, Essex
Beale L, Abellan JJ, Hodgson S, Jarup L (2008) Methodologic issues and approaches to spatial epidemiology. Environ Health Perspect 116:1105–1110
Bell BS, Hoskins RE, Pickle LW, Wartenberg D (2006) Current practices in spatial analysis of cancer data: mapping health statistics to inform policymakers and the public. Int J Health Geogr 5:49
Bivand RS, Pebesma EJ, Gómez-Rubio V (2008) Applied spatial data analysis with R. Springer, New York
Clayton D, Bernardinelli L (1996) Bayesian methods for mapping disease risk. In: Elliott P, Cuzick J, English D, Stern R (eds) Geographical and environmental epidemiology: methods for small area studies. Oxford University Press, Oxford
Earickson R (2009) Medical geography. In: Kitchin R, Thrift N (eds) International encyclopedia of human geography. Elsevier, Oxford, pp 9–20
Elliott P, Wartenberg D (2004) Spatial epidemiology: current approaches and future challenges. Environ Health Perspect 112:998–1006
Gelfand A, Diggle P, Guttorp P, Fuentes M (2010) Handbook of spatial statistics. CRC, Boca Raton
Goldman D, Brender J (2000) Are standardized mortality ratios valid for public health data analysis? Stat Med 19:1081–1088
Griffith D, Arbia G (2010) Detecting negative spatial autocorrelation in georeferenced random variables. Int J Geogr Inf Sci 24:417–437
Haining R (1998) Spatial statistics and analysis of health data. GIS and health. Taylor and Francis, London, pp 29–47
Haining R (2004) Spatial data analysis: theory and practice. Cambridge University Press, Cambridge
Jarup L (2004) Health and environment information systems for exposure and disease mapping, and risk assessment. Environ Health Perspect 112:995–997
Koch T (2005) Cartographies of disease: maps, mapping and medicine. ESRI Press, Redlands
Last J, Abramson J (2001) A dictionary of epidemiology. Oxford University Press, New York
Lawson AB (2009) Bayesian disease mapping: hierarchical modeling in spatial epidemiology. CRC, Boca Raton
Lawson AB, Browne WJ, Vidal Rodeiro CL (2003) Disease mapping with WinBUGS and MLwiN. Wiley, Chichester
Marek L, Dvorský J, Pászto V, Tuček P (2013) On estimation of spatial clustering: case study of epidemiological data in the Olomouc Region, Czech Republic. VŠB – Technická univerzita Ostrava, Ostrava
Meade MS, Emch M (2010) Medical geography. The Guilford Press, New York
Moran P (1950) Notes on continuous stochastic phenomena. Biometrika 37:17–23
Openshaw S (1984) The modifiable areal unit problem. Geobooks, Norwhich
Pfeiffer D, Stevenson M, Robinson T, Rogers D (2008) Spatial analysis in epidemiology. Oxford University Press, Oxford
Richardson S et al (2004) Interpreting posterior relative risk estimates in disease-mapping studies. Environ Health Perspect 112(9):1016–1025
Rushton G (2003) Public health, GIS, and spatial analytic tools. Annu Rev Public Health 24:43–56
Scott LM, Janikas MV (2010) Spatial statistics in ArcGIS. In: Fischer MM, Getis A (eds) Handbook of applied spatial analysis. Springer, Heidelberg, pp 27–42
The Center for Food Security & Public Health (2013) Campylobacteriosis. http://www.cfsph.iastate.edu/FastFacts/pdfs/campylobacterosis_F.pdf
Tobler WR (1979) Cellular geography. In: Gale S, Olsson G (eds) Philosophy in geography. Reidel, Dordrecht, pp 379–386
Waller L (2005) Bayesian thinking in spatial statistics. Handbook Stat 25:589–618
Waller L (2009) Detection of clustering in spatial data. In: Fotheringham A, Rogerson P (eds) Sage handbook of spatial analysis. Sage, London
Waller LA, Gotway CA (2004) Applied spatial statistics for public health data. Wiley, New York
Acknowledgments
The authors gratefully acknowledge the support by the Operational Program Education for Competitiveness—European Social Fund (project CZ.1.07/2.3.00/20.0170 of the Ministry of Education, Youth and Sports of the Czech Republic).
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Marek, L., Pászto, V., Tuček, P. (2015). Bayesian Mapping of Medical Data. In: Brus, J., Vondrakova, A., Vozenilek, V. (eds) Modern Trends in Cartography. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-07926-4_37
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DOI: https://doi.org/10.1007/978-3-319-07926-4_37
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