Spatial Disease Cluster Detection: An Application to Childhood Asthma in Manitoba, Canada

Abstract

Cluster detection is an important part of spatial epidemiology because it may help suggest potential factors associated with disease and thus, guide further investigation of the nature of diseases. Many different methods have been proposed to test for disease clusters. The most popular methods for detecting spatial focused clusters are circular spatial scan statistic (CSS), flexible spatial scan statistic (FSS) and Bayesian disease mapping (BYM). The only latter approach is based on rigorous modeling approach. However, the Bayesian inference may depend on the choice of priors. We propose a frequentist approach, which yields to maximum likelihood estimation, to identify potential focused clusters. The proposed approach is based on the recent introduction of the method of data cloning. We can also provide the prediction (and prediction interval) for relative risk values. The advantages of data cloning approach are that the answers are independent of the choice of priors and non-estimable parameters are flagged automatically. We illustrate the proposed approach, and compare with aforementioned approaches, by analyzing a dataset of childhood asthma visits to hospital in the province of Manitoba, Canada, during 2000-2010. Our results showed that the potential clusters are mainly located in the north-central part of the province.