Use of space-time models to investigate the stability of patterns of disease.

Environmental Health Perspectives
Juan Jose AbellanNicky Best

Abstract

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. We investigated how including the time dimension in disease-mapping models strengthens the epidemiologic interpretation of the overall pattern of risk. 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. Our results confirm that extending hierarch...Continue Reading

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Citations

Aug 19, 2008·Environmental Health Perspectives·Linda BealeLars Jarup
Apr 29, 2009·PLoS Neglected Tropical Diseases·Lars Eisen, Saul Lozano-Fuentes
Feb 23, 2013·Vector Borne and Zoonotic Diseases·Viktor ZöldiLászló Egyed
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Aug 9, 2016·Spatial and Spatio-temporal Epidemiology·Silvia LiveraniMarta Blangiardo
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Nov 25, 2020·BMC Pregnancy and Childbirth·Julius Nyerere Odhiambo, Benn Sartorius
Sep 27, 2020·International Journal of Health Geographics·Aswi AswiKerrie Mengersen

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