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Detailed epidemiologic examination of the distribution of Kawasaki disease (KD) cases could help elucidate the etiology and pathogenesis of this puzzling condition. Location of residence at KD admission was obtained for patients diagnosed in Canada (excluding Quebec) between March 2004 and March 2015. We identified 4,839 patients, 164 of whom (3.4%) developed a coronary artery aneurysm (CAA). A spatiotemporal clustering analysis was performed to determine whether non-random clusters emerged in the distributions of KD and CAA cases. A high-incidence KD cluster occurred in Toronto, ON, between October 2004 and May 2005 (116 cases; relative risk (RR) = 3.43; p

Background We have previously documented an increase in the incidence of Kawasaki disease (KD) in Ontario followed by a stabilization from 1995 to 2006. We sought to validate the estimation of incidence of KD using administrative data and to describe the epidemiology of KD across Canada from 2004 to 2014. Methods We queried the Canadian Hospital Discharge Database for hospital admissions associated with a discharge diagnosis of KD. The data set was manually curated and estimates of incidence were compared with those obtained from the retrospective triennial surveillances of KD performed in 2007 and 2010. Results The average number of cases per year identified through administrative data was 245 ± 45 vs 229 ± 33 from retrospective surveillance. This overestimation, representing 7 ± 6%, is similar to the historical percentage of patients originally diagnosed with KD in whom the diagnosis is subsequently excluded. The annual incidence of KD in Canada was 19.6, 6.4, and 1.3 cases per 100,000 children younger than 5 years, 5-9 years, and 10-14 years old, respectively, with important regional and seasonal differences. The incidence remained stable over the study period in the youngest age group but increased in both older age categories. Coronary artery aneurysms affected 3.5% of all patients, and 0.8% experienced associated major cardiac complications. Conclusions Reliance on administrative data to determine incidence of KD is feasible and accurate with manual curation of the data. The incidence of KD in Canada seems to have plateaued for younger children. Differences in annual incidence observed between provinces remain to be explained, and might reflect genetic or environmental differences.

Background The pathogenesis of Kawasaki disease (KD) is commonly ascribed to an exaggerated immunologic response to an unidentified environmental or infectious trigger in susceptible children. A comprehensive framework linking epidemiological data and global distribution of KD has not yet been proposed. Methods and findings Patients with KD (n = 81) were enrolled within 6 weeks of diagnosis along with control subjects (n = 87). All completed an extensive epidemiological questionnaire. Geographic localization software characterized the subjects’ neighborhood. KD incidence was compared to atmospheric biological particles counts and winds patterns. These data were used to create a comprehensive risk framework for KD, which we tested against published data on the global distribution. Compared to controls, patients with KD were more likely to be of Asian ancestry and were more likely to live in an environment with low exposure to environmental allergens. Higher atmospheric counts of biological particles other than fungus/spores were associated with a temporal reduction in incidence of KD. Finally, westerly winds were associated with increased fungal particles in the atmosphere and increased incidence of KD over the Greater Toronto Area. Our proposed framework was able to explain approximately 80% of the variation in the global distribution of KD. The main limitations of the study are that the majority of data used in this study are limited to the Canadian context and our proposed disease framework is theoretical and circumstantial rather than the result of a single simulation. Conclusions Our proposed etiologic framework incorporates the 1) proportion of population that are genetically susceptible; 2) modulation of risk, determined by habitual exposure to environmental allergens, seasonal variations of atmospheric biological particles and contact with infectious diseases; and 3) exposure to the putative trigger. Future modelling of individual risk and global distribution will be strengthened by taking into consideration all of these non-traditional elements.