A classification of disease mapping methods.
This paper considers the underlying principles of depicting disease incidence on geographical maps and uses them to attempt a comparative classification of methods. After a discussion of the possibilities for incorporating time, we consider projection methods, some of which have been used to portray information in a manner supposed to be independent of population density. We then distinguish between non-parametric and model-based methods, including models for areal data using Bayesian ideas. Data in point form are also discussed and it is argued that the relative risk function provides a fundamental model useful for assessing different methods as a whole, some of which are known to be flawed and many of which are untested as regards their statistical properties.
Bithell
J F
JF
Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK. bithell@stats.ox.ac.uk
D001499:Bayes Theorem; D004812:Epidemiologic Methods; D006801:Humans; D015994:Incidence; D008377:Maps as Topic; D015233:Models, Statistical; D018570:Risk Assessment; D019371:Topography, Medical / Q000145:classification*