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An application of density estimation to geographical epidemiology
Abstract
A relative risk function over a geographical region is defined and it is shown that it can be estimated effectively using kernel density estimation separately for the spatial distribution of disease cases and for a sample of controls. This procedure is demonstrated using data on childhood leukaemia in the vicinity of the Sellafield nuclear reprocessing plant in Cumbria, U.K. Various modifications to the method are proposed, including the use of an adaptive kernel. The final plot demonstrates a sharp peak at Sellafield and a reasonably smooth surface over the rest of the region, despite the small number of cases in the series.
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Citations
Cited by
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Gatrell, A. C., Bailey, T. C., Diggle, P. J., & Rowlingson, B. S. (1996) · Transactions of the Institute of British Geographers · doi:10.2307/622936 · Cited by 781
Geographically weighted Poisson regression for disease association mapping
Nakaya, T., Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2005) · Statistics in Medicine · doi:10.1002/sim.2129 · Cited by 565
Public Health, GIS, and Spatial Analytic Tools
Rushton, G. (2003) · Annual Review of Public Health · doi:10.1146/annurev.publhealth.24.012902.140843 · Cited by 320
Boer, M. M., Sadler, R. J., Wittkuhn, R. S., McCaw, L., & Grierson, P. F. (2009) · Forest Ecology and Management · doi:10.1016/j.foreco.2009.10.005 · Cited by 309
Metadata
- Authors
- Bithell, J. F.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 1990-06
- Volume
- 9
- Issue
- 6
- Pages
- 691-701
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2051974104
- Cited by
- 312
- References
- 0
- FWCI
- 0.95
- Type
- journal article
- Language
- en
- Open access
- closed
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