title: Point pattern analysis with spatially varying covariate effects, applied to the study of cerebrovascular deaths.
abstract: This article proposes a modeling approach for handling spatial heterogeneity present in the study of the geographical pattern of deaths due to cerebrovascular disease.The framework involvesa point pattern analysis with components exhibiting spatial variation. Preliminary studies indicate that mortality of this disease and the effect of relevant covariates do not exhibit uniform geographic distribution. Our model extends a previously proposed model in the literature that uses spatial and non-spatial variables by allowing for spatial variation of the effect of non-spatial covariates. A number of relative risk indicators are derived by comparing different covariate levels, different geographic locations, or both. The methodology is applied to the study of the geographical death pattern of cerebrovascular deaths in the city of Rio de Janeiro. The results compare well against existing alternatives, including fixed covariate effects. Our model is able to capture and highlight important data information that would not be noticed otherwise, providing information that is required for appropriate health decision-making.
authors.affiliation: Universidade Federal Fluminense; Universidade Federal do Rio de Janeiro.
authors.forename: Jony Arrais
authors.initials: JA
authors.lastname: Pinto Junior
authors.forename: Dani
authors.initials: D
authors.lastname: Gamerman
authors.forename: Marina Silva
authors.initials: MS
authors.lastname: Paez
authors.forename: Regina Helena
authors.initials: RH
authors.lastname: Fonseca Alves
mesh_terms: D056808:Biostatistics / Q000379:methods; D001938:Brazil / Q000453:epidemiology; D002561:Cerebrovascular Disorders / Q000401:mortality*; D006801:Humans; D015233:Models, Statistical; D015999:Multivariate Analysis; D016011:Normal Distribution; D016016:Proportional Hazards Models; D012044:Regression Analysis; D012306:Risk
keywords: Gaussian process; point pattern; spatially varying covariate effects