title: Surveillance of individual level disease maps.
abstract: Methods for the production of individual (address) level disease maps are often retrospective; they estimate a map of the average relative risk of disease over a study period. However, recently, epidemiologists have started to look at weekly or monthly reports of disease and assess them for any change in the distribution of relative risk. For example, in the United States of America, the Centre for Disease Control and Prevention now routinely collects information on over 50 notifiable diseases every week. In this paper we present a method for the detection of a sudden change in the geographical distribution of the disease in a prospective study. The method is based on an estimate of the directional derivative of the conditional probability of a case, given either a case or control has occurred. It is based on standard kernel approaches to nonparametric regression and it is readily applied in any standard statistical software package. Two simulated examples of sudden clustering around a fixed point are provided.
authors.affiliation: School of Medicine, Health Policy and Practice, University of East Anglia, Norwich, UK. allan.clark@uea.ac.uk
authors.forename: Allan B
authors.initials: AB
authors.lastname: Clark
authors.forename: Andrew B
authors.initials: AB
authors.lastname: Lawson
mesh_terms: D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D011159:Population Surveillance / Q000379:methods*; D011446:Prospective Studies; D012044:Regression Analysis; D012306:Risk; D013029:Space-Time Clustering; D018709:Statistics, Nonparametric