Use of space-time models to investigate the stability of patterns of disease.
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.
Abellan
Juan Jose
JJ
Small Area Health Statistics Unit, Department of Epidemiology and Public Health, Imperial College London, London, United Kingdom. j.abellan@imperial.ac.uk
Richardson
Sylvia
S
Best
Nicky
N
D001499:Bayes Theorem; D003198:Computer Simulation; D000013:Congenital Abnormalities / Q000453:epidemiology*; D004739:England / Q000453:epidemiology; D040362:Geographic Information Systems; D006801:Humans; D018570:Risk Assessment / Q000379:methods; D012959:Socioeconomic Factors; D013029:Space-Time Clustering