Environmental epidemiology of Kawasaki disease: Linking disease etiology, pathogenesis and global distribution

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.