From the Division of High-Consequence Pathogens and Pathology, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia.
Department of Health Science, Saitama Prefectural University, Koshigaya, Saitama, Japan.
Pediatr Infect Dis J. 2018 Jun;37(6):526-530. doi: 10.1097/INF.0000000000001838.
Kawasaki disease (KD) is an acute febrile vasculitis, which primarily affects children. The etiology of KD is unknown; while certain characteristics of the disease suggest an infectious origin, genetic or environmental factors may also be important. Seasonal patterns of KD incidence are well documented, but it is unclear whether these patterns are caused by changes in climate or by other unknown seasonal effects.
The relationship between KD incidence and deviations from expected temperature and precipitation were analyzed using KD incidence data from Japanese nationwide epidemiologic surveys (1991-2004) and climate data from 136 weather stations of the Japan Meteorological Agency. Seven separate Poisson-distributed generalized linear regression models were run to examine the effects of temperature and precipitation on KD incidence in the same month as KD onset and the previous 1, 2, 3, 4, 5 and 6 months, controlling for geography as well as seasonal and long-term trends in KD incidence.
KD incidence was negatively associated with temperature in the previous 2, 3, 4 and 5 months and positively associated with precipitation in the previous 1 and 2 months. The model that best predicted variations in KD incidence used climate data from the previous 2 months. An increase in total monthly precipitation by 100 mm was associated with increased KD incidence (rate ratio [RR] 1.012, 95% confidence interval [CI]: 1.005-1.019), and an increase of monthly mean temperature by 1°C was associated with decreased KD incidence (RR 0.984, 95% CI: 0.978-0.990).
KD incidence was significantly affected by temperature and precipitation in previous months independent of other unknown seasonal factors. Climate data from the previous 2 months best predicted the variations in KD incidence. Although fairly minor, the effect of temperature and precipitation independent of season may provide additional clues to the etiology of KD.
川崎病(KD)是一种急性发热性血管炎,主要影响儿童。KD 的病因尚不清楚;虽然该病的某些特征表明其具有感染性起源,但遗传或环境因素也可能很重要。KD 发病率的季节性模式已有详细记录,但尚不清楚这些模式是由气候变化引起的,还是由其他未知的季节性影响引起的。
利用日本全国性流行病学调查(1991-2004 年)的 KD 发病率数据和日本气象厅 136 个气象站的气候数据,分析 KD 发病率与预期温度和降水偏差之间的关系。使用 7 个单独的泊松分布广义线性回归模型,在 KD 发病当月以及发病前 1、2、3、4、5 和 6 个月,考察温度和降水对 KD 发病率的影响,同时控制地理因素以及 KD 发病率的季节性和长期趋势。
KD 发病率与前 2、3、4 和 5 个月的温度呈负相关,与前 1 和 2 个月的降水呈正相关。预测 KD 发病率变化的最佳模型使用前 2 个月的气候数据。每月总降水量增加 100mm 与 KD 发病率增加相关(相对危险度[RR]1.012,95%置信区间[CI]:1.005-1.019),每月平均温度升高 1°C 与 KD 发病率降低相关(RR 0.984,95%CI:0.978-0.990)。
KD 发病率在前几个月受到温度和降水的显著影响,不受其他未知季节性因素的影响。前 2 个月的气候数据最能预测 KD 发病率的变化。尽管影响较小,但温度和降水独立于季节的影响可能为 KD 的病因提供更多线索。