| Use of Space–Time Models to Investigate the Stability of Patterns of Disease Juan Jose Abellan,1,2 Sylvia Richardson,3 and Nicky Best3 1Small Area Health Statistics Unit, Department of Epidemiology and Public Health, Imperial College London, London, United Kingdom; 2CIBER Epidemiología y Salud Pública, Spain; 3Department of Epidemiology and Public Health, Imperial College London, London, United Kingdom Abstract
Background: The use of Bayesian hierarchical spatial models has become widespread in disease mapping and ecologic studies of health–environment associations. In this type of study, the data are typically aggregated over an extensive time period, thus neglecting the time dimension. The output of purely spatial disease mapping studies is therefore the average spatial pattern of risk over the period analyzed, but the results do not inform about, for example, whether a high average risk was sustained over time or changed over time. Objective: We investigated how including the time dimension in disease-mapping models strengthens the epidemiologic interpretation of the overall pattern of risk. Methods: We discuss a class of Bayesian hierarchical models that simultaneously characterize and estimate the stable spatial and temporal patterns as well as departures from these stable components. We show how useful rules for classifying areas as stable can be constructed based on the posterior distribution of the space–time interactions. We carry out a simulation study to investigate the sensitivity and specificity of the decision rules we propose, and we illustrate our approach in a case study of congenital anomalies in England. Results: Our results confirm that extending hierarchical disease-mapping models to models that simultaneously consider space and time leads to a number of benefits in terms of interpretation and potential for detection of localized excesses. Key words: Bayesian hierarchical models, congenital anomalies, disease mapping, mixture models, space–time interactions, stable disease patterns. Environ Health Perspect 116:1111–1119 (2008) . doi:10.1289/ehp.10814 available via http://dx.doi.org/ [Online 25 April 2008]
This article is part of the mini-monograph on spatial epidemiology. Address correspondence to J.J. Abellan, Small Area Health Statistics Unit, Department of Epidemiology and Public Health, Imperial College London, St. Mary's Campus, School of Medicine, Norfolk Place, London W2 1PG, UK. Telephone: 44-0-20-7594-3302. Fax: 44-0-20-7402-2150. E-mail: j.abellan@imperial.ac.uk Supplemental Material is available online at http://www.ehponline.org/members/2008/10814/suppl.pdf We thank M. Toledano and C. Keshishian for comments on the congenital anomalies data, K. de Hoogh for help building the variable-size grid square, and N. Cressie and A. Lewin for insightful discussions about the mixture model and classification rules. The Small Area Health Statistics Unit is funded by a grant from the Department of Health and the Department for Environment, Food and Rural Affairs. J.J.A. acknowledges partial support from grant MTM2004-03290 from the Spanish Ministry of Education and Science. The authors declare they have no competing financial interests. Received 28 August 2007 ; accepted 25 April 2008.
Correction In the manuscript originally published online, Figure 4C was incorrect ; it has been corrected here.
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