title: Statistical methods for the geographical analysis of rare diseases.
abstract: In this chapter we provide a summary of different methods for the detection of disease clusters. First of all, we give a summary of methods for computing estimates of the relative risk. These estimates provide smoothed values of the relative risks that can account for its spatial variation. Some methods for assessing spatial autocorrelation and general clustering are also discussed to test for significant spatial variation of the risk. In order to find the actual location of the clusters, scan methods are introduced. The spatial scan statistic is discussed as well as its extension by means of Generalised Linear Models that allows for the inclusion of covariates and cluster effects. In this context, zero-inflated models are introduced to account for the high number of zeros that appear when studying rare diseases. Finally, two applications of these methods are shown using data of Systemic Lupus Erythematosus in Spain and brain cancer in Navarre (Spain).
authors.affiliation: Departamento de Matemáticas, Universidad de Castilla-La Mancha, Escuela de Ingenieros Industriales, Albacete, Spain. Virgilio.Gomez@uclm.es
authors.forename: Virgilio
authors.initials: V
authors.lastname: Gómez-Rubio
authors.forename: Antonio
authors.initials: A
authors.lastname: López-Quílez
mesh_terms: D056808:Biostatistics; D001932:Brain Neoplasms / Q000453:epidemiology; D016000:Cluster Analysis; D006801:Humans; D016014:Linear Models; D008180:Lupus Erythematosus, Systemic / Q000401:mortality; D015233:Models, Statistical; D011159:Population Surveillance; D035583:Rare Diseases / Q000453:epidemiology*; D012984:Software; D013030:Spain / Q000453:epidemiology