Space-time variation of malaria incidence in Yunnan province, China
Space-time variation of malaria incidence in Yunnan province, China
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DOI：
10.1186/1475-2875-8-180
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发表时间：
2009-07-31
期刊：
影响因子：
3
通讯作者：
Zhou, Hom Ning
中科院分区：
文献类型：
--
作者：
Clements, Archie C. A.;Barnett, Adrian G.;Zhou, Hom Ning
背景资料：了解疟疾发病率的时空变化提供了一个有效的疾病控制规划和monitoring.Methods的基础：1991年至2006年之间的128个县的间日疟原虫和恶性疟原虫疟疾的每月监测数据收集云南省，中国疟疾负担最重的省份之一。建立了县级发病率的贝叶斯泊松回归模型，包括降雨量、最高气温和时间趋势的影响。该模型还允许县级发病率和时间趋势的空间变化，并在6月至9月和前1月至2月的发病率之间的依赖性。6月至9月的发病率与前一年的1月至2月的发病率之间存在显著相关性。原始标准化发病率显示，在缅甸、老挝和越南接壤的一些县以及红河河谷的县，发病率很高。云南西南部和北方的一些县被确定为具有高发病率，而不是气候原因。发病率的总体趋势下降，但有显着的县之间的变化。结论：在夏季和前1月至2月的发病率之间的依赖性表明的作用intrinsichost病原体动态。根据1月至2月的发病率可以预测夏季高峰期的发病率，从而促进疟疾控制规划，提前几个月根据夏季疟疾负担的大小进行调整。县级时间趋势的异质性表明，疟疾负担的减少在全省分布不均匀。
Background: Understanding spatio-temporal variation in malaria incidence provides a basis for effective disease control planning and monitoring.Methods: Monthly surveillance data between 1991 and 2006 for Plasmodium vivax and Plasmodium falciparum malaria across 128 counties were assembled for Yunnan, a province of China with one of the highest burdens of malaria. County-level Bayesian Poisson regression models of incidence were constructed, with effects for rainfall, maximum temperature and temporal trend. The model also allowed for spatial variation in county-level incidence and temporal trend, and dependence between incidence in June-September and the preceding January-February.Results: Models revealed strong associations between malaria incidence and both rainfall and maximum temperature. There was a significant association between incidence in June-September and the preceding January-February. Raw standardised morbidity ratios showed a high incidence in some counties bordering Myanmar, Laos and Vietnam, and counties in the Red River valley. Clusters of counties in south-western and northern Yunnan were identified that had high incidence not explained by climate. The overall trend in incidence decreased, but there was significant variation between counties.Conclusion: Dependence between incidence in summer and the preceding January-February suggests a role of intrinsichost-pathogen dynamics. Incidence during the summer peak might be predictable based on incidence in January-February, facilitating malaria control planning, scaled months in advance to the magnitude of the summer malaria burden. Heterogeneities in county-level temporal trends suggest that reductions in the burden of malaria have been unevenly distributed throughout the province.