Geographical Variation of Incidence of Chronic Obstructive Pulmonary Disease in Manitoba, Canada

Abstract

Abstract:We aimed to study the geographic variation in the incidence of COPD. We used health survey data (weighted to the population level) to identify 56,944 cases of COPD in Manitoba, Canada from 2001 to 2010. We used five cluster detection procedures, circular spatial scan statistic (CSS), flexible spatial scan statistic (FSS), Bayesian disease mapping (BYM), maximum likelihood estimation (MLE), and local indicator of spatial association (LISA). Our results showed that there are some regions in southern Manitoba that are potential clusters of COPD cases. The FSS method identified more regions than the CSS and LISA methods and the BYM and MLE methods identified similar regions as potential clusters. Most of the regions identified by the MLE and BYM methods were also identified by the FSS method and most of the regions identified by the CSS method were also identified by most of the other methods. The CSS, FSS and LISA methods identify potential clusters but are not able to control for confounders at the same time. However, the BYM and MLE methods can simultaneously identify potential clusters and control for possible confounders. Overall, we recommend using the BYM and MLE methods for cluster detection in areas with similar population and structure of regions as those in Manitoba.

Remote Sensing and Geospatial Technologies in Public Health

“…However, the techniques of cluster identification can be quite diverse, resulting in varied results. Mahmoud Torabi and Katie Galloway from the University of Manitoba demonstrate five different cluster detection procedures to study geographic variation in the incidence of Chronic Obstructive Pulmonary Disease (COPD) in Manitoba, Canada [11]. In their paper, "Geographical Variation of Incidence of Chronic Obstructive Pulmonary Disease in Manitoba, Canada", Torabi and Galloway discuss the methods, advantages and disadvantages, and the results of the various procedures, which provides a good reference for studying regional cluster analysis for other disease incidences.…”