Seasonality and temporal clustering of Kawasaki syndrome

Epidemiology. 2005 Mar;16(2):220-5. doi: 10.1097/01.ede.0000152901.06689.d4.

Abstract

Background: The distribution of a syndrome in space and time may suggest clues to its etiology. The cause of Kawasaki syndrome, a systemic vasculitis of infants and children, is unknown, but an infectious etiology is suspected.

Methods: Seasonality and clustering of Kawasaki syndrome cases were studied in Japanese children with Kawasaki syndrome reported in nationwide surveys in Japan. Excluding the years that contained the 3 major nationwide epidemics, 84,829 cases during a 14-year period (1987-2000) were analyzed. To assess seasonality, we calculated mean monthly incidence during the study period for eastern and western Japan and for each of the 47 prefectures. To assess clustering, we compared the number of cases per day (daily incidence) with a simulated distribution (Monte Carlo analysis).

Results: Marked spatial and temporal patterns were noted in both the seasonality and deviations from the average number of Kawasaki syndrome cases in Japan. Seasonality was bimodal with peaks in January and June/July and a nadir in October. This pattern was consistent throughout Japan and during the entire 14-year period. Some years produced very high or low numbers of cases, but the overall variability was consistent throughout the entire country. Temporal clustering of Kawasaki syndrome cases was detected with nationwide outbreaks.

Conclusions: Kawasaki syndrome has a pronounced seasonality in Japan that is consistent throughout the length of the Japanese archipelago. Temporal clustering of cases combined with marked seasonality suggests an environmental trigger for this clinical syndrome.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Disease Outbreaks*
  • Health Surveys
  • Humans
  • Incidence
  • Infant, Newborn
  • Japan / epidemiology
  • Mucocutaneous Lymph Node Syndrome / epidemiology*
  • Seasons
  • Time Factors