Application of three focused cluster detection methods to study geographic variation in the incidence of multiple sclerosis in Manitoba, Canada.

BACKGROUND: Macroscopic geographic variation in the incidence and prevalence of MS is well-recognized. Microscopic geographic variation in the distribution of MS is also recognized, but less well-studied. Most studies have focused on prevalent cases of MS, although studies of variation in disease incidence are more relevant for developing etiologic hypotheses. We aimed to study geographic variation in the incidence of MS using three different methods.

METHODS: We used population-based administrative (health claims) data to identify 2,290 incident cases of MS in the province of Manitoba, Canada from 1990 to 2006. We applied three focused cluster-detection procedures, including the circular spatial scan statistic (CSS), flexible spatial scan statistic (FSS), and Bayesian disease mapping (BYM), to the dataset.

RESULTS: The CSS and FSS methods identified 30 and 26 regions as potential clusters, respectively, although the regions identified differed slightly due to the non-circular shape of some regions in Manitoba. The BYM approach identified 37 regions as potential clusters, again with some differences as compared to the other two methods. Twelve regions were identified as potential clusters by all three methods. All methods identified the western part of the city of Winnipeg as a significant cluster. Using the BYM approach, the incidence of MS was highest among areas of higher socioeconomic status.

CONCLUSIONS: Two methods CSS and FSS only capture geographical variations and are not able to control for confounders at the same time which may lead to mis-identification of clusters. However, the BYM method can simultaneously identify geographical variations and control for possible confounders.