Spatio-temporal analysis of the role of climate in inter-annual variation of malaria incidence in Zimbabwe.
Spatio-temporal analysis of the role of climate in inter-annual variation of malaria incidence in Zimbabwe.
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DOI：
10.1186/1476-072x-5-20
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发表时间：
2006-05-15
影响因子：
4.9
通讯作者：
Smith T
中科院分区：
文献类型：
--
作者：
Mabaso ML;Vounatsou P;Midzi S;Da Silva J;Smith T
在流行区的边缘，气候是疟疾发病率年际变化的主要决定因素。这种关联的时空效应的定量描述对于可操作的疟疾早期预警系统（MEWS）和疟疾控制的发展具有实际意义。我们使用贝叶斯负二项式模型对 1988 年至 1999 年津巴布韦地区一级疟疾年发病率与选定气候协变量之间的关系进行时空分析。疟疾发病时间和强度存在相当大的年际差异。平均气温、降雨量和蒸气压的年平均值是年发病率增加的强有力的正向预测因素，而最高和最低温度则具有相反的影响。我们的建模方法根据未测量的时空变化风险因素进行了调整，结果表明，虽然疟疾发病率的逐年变化主要由气候驱动，但所产生的空间风险模式可能在很大程度上受到其他风险因素的影响，除了分别在极端潮湿和干燥条件发生后的高风险年份和低风险年份。我们的模型揭示了一种空间变化的风险模式，该模式不仅仅归因于气候。我们假设，只有以极端气候条件为特征的年份对于开发基于气候的 MEWS 和划定易受气候驱动的流行病影响的地区可能很重要。然而，本研究确定的气候风险因素的预测价值仍需评估。
On the fringes of endemic zones climate is a major determinant of inter-annual variation in malaria incidence. Quantitative description of the space-time effect of this association has practical implications for the development of operational malaria early warning system (MEWS) and malaria control. We used Bayesian negative binomial models for spatio-temporal analysis of the relationship between annual malaria incidence and selected climatic covariates at a district level in Zimbabwe from 1988–1999. Considerable inter-annual variations were observed in the timing and intensity of malaria incidence. Annual mean values of average temperature, rainfall and vapour pressure were strong positive predictors of increased annual incidence whereas maximum and minimum temperature had the opposite effects. Our modelling approach adjusted for unmeasured space-time varying risk factors and showed that while year to year variation in malaria incidence is driven mainly by climate, the resultant spatial risk pattern may to large extent be influenced by other risk factors except during high and low risk years following the occurrence of extremely wet and dry conditions, respectively. Our model revealed a spatially varying risk pattern that is not attributable only to climate. We postulate that only years characterized by extreme climatic conditions may be important for developing climate based MEWS and for delineating areas prone to climate driven epidemics. However, the predictive value of climatic risk factors identified in this study still needs to be evaluated.