Background: Salmonella enterica serotype Enteritidis (S. Enteritidis) remains a major foodborne pathogen in North America yet studies examining the spatial epidemiology of salmonellosis in urban environments are lacking. Our ecological study combined a number of spatial statistical methods with a geographic information system to assess area-level heterogeneity of S. Enteritidis infection rates in the city of Toronto.
Methods: Data on S. Enteritidis infections between January 1, 2007 and December 31, 2009 were obtained from Ontario's surveillance system, and were grouped and analyzed at the forward sortation area (FSA)-level (an area signified by the first three characters of the postal code). Incidence rates were directly standardized using the FSA-level age- and sex-based standard population. A spatial empirical Bayes method was used to smooth the standardized incidence rates (SIRs). Global clustering of FSAs with high or low non-smoothed SIRs was evaluated using the Getis-Ord G method. Local clustering of FSAs with high, low, or dissimilar non-smoothed SIRs was assessed using the Getis-Ord Gi* and the Local Moran's I methods.
Results: Spatial heterogeneity of S. Enteritidis infection rates was detected across the city of Toronto. The non-smoothed FSA-level SIRs ranged from 0 to 16.9 infections per 100,000 person-years (mean = 6.6), whereas the smoothed SIRs ranged from 2.9 to 11.1 (mean = 6.3). The global Getis-Ord G method showed significant (p ≤ 0.05) maximum spatial clustering of FSAs with high SIRs at 3.3 km. The local Getis-Ord Gi* method identified eight FSAs with significantly high SIRs and one FSA with a significantly low SIR. The Local Moran's I method detected five FSAs with significantly high-high SIRs, one FSA with a significantly low-low SIR, and four significant outlier FSAs (one high-low, and three low-high).
Conclusions: Salmonella Enteritidis infection rates clustered globally at a small distance band, suggesting clustering of high SIRs in small distinct areas. This finding was supported by the local cluster analyses, where distinct FSAs with high SIRs, mainly in downtown Toronto, were detected. These areas should be evaluated by future studies to identify risk factors of disease in order to implement targeted prevention and control programs. We demonstrated the usefulness of combining several spatial statistical techniques with a geographic information system to detect geographical areas of interest for further study, and to evaluate spatial processes that influenced S. Enteritidis infection rates. Our study methodology could be applied to other foodborne disease surveillance data.