title: Interpolation between spatial frameworks: an application of process convolution to estimating neighbourhood disease prevalence.
abstract: Health data may be collected across one spatial framework (e.g. health provider agencies), but contrasts in health over another spatial framework (neighbourhoods) may be of policy interest. In the UK, population prevalence totals for chronic diseases are provided for populations served by general practitioner practices, but not for neighbourhoods (small areas of circa 1500 people), raising the question whether data for one framework can be used to provide spatially interpolated estimates of disease prevalence for the other. A discrete process convolution is applied to this end and has advantages when there are a relatively large number of area units in one or other framework. Additionally, the interpolation is modified to take account of the observed neighbourhood indicators (e.g. hospitalisation rates) of neighbourhood disease prevalence. These are reflective indicators of neighbourhood prevalence viewed as a latent construct. An illustrative application is to prevalence of psychosis in northeast London, containing 190 general practitioner practices and 562 neighbourhoods, including an assessment of sensitivity to kernel choice (e.g. normal vs exponential). This application illustrates how a zero-inflated Poisson can be used as the likelihood model for a reflective indicator.
authors.affiliation: Department of Geography and Centre for Statistics, Queen Mary University of London, UK.
authors.forename: Peter
authors.initials: P
authors.lastname: Congdon
mesh_terms: D001499:Bayes Theorem; D056808:Biostatistics; D002908:Chronic Disease; D058006:General Practice; D006801:Humans; D016013:Likelihood Functions; D008131:London / Q000453:epidemiology; D015233:Models, Statistical; D016011:Normal Distribution; D016012:Poisson Distribution; D015995:Prevalence; D011618:Psychotic Disorders / Q000453:epidemiology; D011634:Public Health / Q000706:statistics & numerical data*; D012111:Residence Characteristics; D017062:Small-Area Analysis; D006113:United Kingdom / Q000453:epidemiology