Wu Yunyun, Qiao Zhijiao, Wang Nan, Yu Hongjie, Feng Zijian, Li Xiaosong, Zhao Xing
West China School of Public Health, Sichuan University, No. 17 Section 3, South Renmin Road, 610041, Chengdu, China.
Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China.
Malar J. 2017 Jan 31;16(1):53. doi: 10.1186/s12936-017-1706-2.
When discussing the relationship between meteorological factors and malaria, previous studies mainly focus on the interaction between different climatic factors, while the possible interaction within one particular climatic predictor at different lag periods has been largely neglected. In this study, this issue was investigated by exploring the interaction of lagged rainfalls and its impact on malaria epidemics, which is a typical example of those meteorological variables.
The weekly data of malaria cases and three climatic variables of 30 counties in southwest China from 2004 to 2009 were analysed with the varying coefficient-distributed lag non-linear model. The correlation patterns of the 6th, 9th and 12th week lags would vary over different rainfall levels at the 4th-week lag.
The non-linear patterns for rainfall at different rainfall levels are distinct from each other. In the low rainfall level at the 4th week lag, the increasing rainfall may promote the transmission of malaria. However, for the high rainfall level at the 4th week lag, evidence shows that the excessive rainfall decreases the risk of malaria.
This study reports for the first time that the interaction effect between lagged rainfalls on malaria exists, and highlights the importance of integrating the interaction between lagged predictors in relevant studies, which could help to better understand and predict malaria transmission.
在探讨气象因素与疟疾的关系时，以往研究主要聚焦于不同气候因素之间的相互作用，而一个特定气候预测因子在不同滞后时期内可能存在的相互作用在很大程度上被忽视了。在本研究中，通过探究滞后降雨量的相互作用及其对疟疾流行的影响来研究这一问题，滞后降雨量是这些气象变量中的一个典型例子。
运用变系数分布滞后非线性模型，分析了中国西南地区30个县2004年至2009年疟疾病例的每周数据以及三个气候变量。第4周滞后时不同降雨水平下第6、9和12周滞后的相关模式会有所不同。
不同降雨水平下的降雨非线性模式彼此不同。在第4周滞后的低降雨水平下，降雨增加可能会促进疟疾传播。然而，对于第4周滞后的高降雨水平，有证据表明降雨过多会降低疟疾风险。
本研究首次报道了滞后降雨量对疟疾存在相互作用效应，并强调了在相关研究中纳入滞后预测因子之间相互作用的重要性，这有助于更好地理解和预测疟疾传播。
