Describing interaction effect between lagged rainfalls on malaria: an epidemiological study in south-west China.
Describing interaction effect between lagged rainfalls on malaria: an epidemiological study in south-west China.
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描述滞后降雨对疟疾的相互作用影响：中国西南部的流行病学研究
DOI：
10.1186/s12936-017-1706-2
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发表时间：
2017-01-31
期刊：
影响因子：
3
通讯作者：
Zhao X
中科院分区：
文献类型：
--
作者：
Wu Y;Qiao Z;Wang N;Yu H;Feng Z;Li X;Zhao X
在讨论气象因子与疟疾的关系时，以往的研究主要集中在不同气候因子之间的相互作用，而在很大程度上忽略了一个特定气候预测器在不同滞后期可能存在的相互作用。在本研究中，通过探索滞后降雨及其对疟疾流行的影响的相互作用来调查这一问题，这是这些气象变量的一个典型例子。采用变系数分布滞后非线性模型对2004 - 2009年西南地区30个县区疟疾病例和3个气候变量的周数据进行了分析。第6周、第9周和第12周滞后的相关模式会因第4周滞后的不同降雨量而有所不同。不同降水水平下的非线性降水模式各不相同。在第4周滞后的低雨量期，降雨的增加可能促进疟疾的传播。然而，对于第四周滞后的高降雨量，有证据表明，过量降雨降低了疟疾的风险。本研究首次报道了滞后降雨对疟疾的相互作用，强调了在相关研究中整合滞后预测因子之间的相互作用的重要性，有助于更好地了解和预测疟疾的传播。本文的在线版本（doi:10.1186/s12936-017-1706-2）包含补充材料，可供授权用户使用。
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. The online version of this article (doi:10.1186/s12936-017-1706-2) contains supplementary material, which is available to authorized users.