title: Bayesian spatio-temporal analysis of joint patterns of male and female lung cancer risks in Yorkshire (UK).
abstract: Recent advances in disease mapping have focused first on including the time dimension, thus giving rise to spatio-temporal analysis of the variation of disease risk and, secondly, on carrying out joint analysis of two diseases that share common environmental risk factors and are, therefore, related. Here, we try to combine both issues and present a joint analysis of the spatio-temporal variation of the risks of two related diseases processes-male and female lung cancer incidence-in a region of England. To do so, we use a Bayesian hierarchical model that splits the risk of disease into two spatio-temporal components: a shared component and a specific component that calibrates the differential between the two diseases.
authors.affiliation: Best Small Area Health Statistics Unit (SAHSU), Department of Epidemiology and Public Health, Imperial College London, UK. sylvia.richardson@imperial.ac.uk
authors.forename: Sylvia
authors.initials: S
authors.lastname: Richardson
authors.forename: Juan J
authors.initials: JJ
authors.lastname: Abellan
authors.forename: Nicky
authors.initials: N
authors.lastname: Best
mesh_terms: D001499:Bayes Theorem; D004739:England / Q000453:epidemiology; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000453:epidemiology*; D008297:Male; D015233:Models, Statistical; D011159:Population Surveillance / Q000379:methods*; D012306:Risk; D012737:Sex Factors; D013029:Space-Time Clustering