Spatiotemporal analysis of the association between Kawasaki disease incidence and PM2.5 exposure: a nationwide database study in Japan

Abstract

Background Kawasaki disease (KD) is an acute vasculitis primarily affecting children. While some studies suggest a link between KD and PM2.5 exposure, findings remain inconsistent. This study aimed to perform spatiotemporal analysis to investigate the impact of monthly and annual exposure to PM2.5 and other air pollutants on the incidence of KD before and after the advent of the COVID-19 pandemic.Methods In this retrospective analysis, we used the Japanese administrative claims database to identify the incidence of KD in children under age 5 in 335 secondary medical care areas across Japan before (from July 2014 to December 2019) and during (from January 2020 to December 2021) the COVID-19 pandemic. For each of these periods, we developed hierarchical Bayesian models termed conditional autoregressive (CAR) models that can address the spatiotemporal clustering of KD to investigate the association between the monthly incidence of KD and exposure to PM2.5, NO, NO2 and SO2 over 1-month and 12-month durations. The pollution data were collected from publicly available data provided by the National Institute for Environmental Studies.Results In the before-pandemic and during-pandemic periods, 55 289 and 14 023 new cases of KD were identified, respectively. The CAR models revealed that only 12-month exposure to PM2.5 was consistently correlated with KD incidence, and each 1 µg/m3 increase in annual PM2.5 exposure corresponded to a 3%–10% rise in KD incidence. Consistent outcomes were observed in the age-stratified sensitivity analysis.Conclusions Annual exposure to PM2.5 was robustly linked with the onset of KD. Further research is needed to elucidate the underlying mechanism by which the spatiotemporal distribution of PM2.5 is associated with KD.