Spatiotemporal clusters of Kawasaki disease in South Korea from 2008 to 2017: A municipal-level ecological study

Abstract

IntroductionAs the etiology of Kawasaki disease (KD) remains unknown, identifying spatiotemporal clusters with proper stratification of KD could provide further evidence for investigating the triggers of KD. However, spatiotemporal distributions of KD with sex stratification have never been reported. Therefore, we aimed to analyze the spatiotemporal patterns of KD by sex in South Korea.MethodsWe extracted epidemiologic week (Epiweek)-based KD cases in patients <5 years of age (ICD-10-CM code: M303) from 2008 to 2017 national health insurance service data at the 250 municipal level. To determine whether spatial autocorrelation and persistent municipal-level clusters exist, year- and sex-stratified global Moran's I statistics, Getis-Ord Gi* statistics, and emerging hotspot analysis on KD incidence were conducted.ResultsA total of 72,510 KD cases were reported between 2008 and 2017 (male-to-female ratio = 1.40:1). Incidence has increased since 2008, with the highest incidence in 2016 (396.8 per 100,000 population). KD had seasonality of winter and summer but different by sex. Positive spatial autocorrelation was consistently reported in every stratum, with the 2011–2014 period having the strongest index value (total sex I = 0.286, p < 0.001; male I = 0.242, p < 0.001; female I = 0.213, p < 0.001). Hot spots were consistently detected in the northern parts, and cold spots were in the southern part for 9 years in both sexes. The emerging hot spot analysis showed new, consecutive, and sporadic hot spots on the northwestern and eastern coasts and new and sporadic cold spots in the southwestern part. However, the distribution and proportion of hot or cold spot types differed according to sex.DiscussionThe spatiotemporal features of KD had limits to concluding that only infectious triggers result in KD occurrence. Therefore, our findings support the notion that KD is a syndrome with multiple factors, including infectious, genetic, and environmental factors, that are associated with sex differences.