An integrated approach for risk profiling and spatial prediction of Schistosoma mansoni-hookworm coinfection
An integrated approach for risk profiling and spatial prediction of Schistosoma mansoni-hookworm coinfection
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DOI：
10.1073/pnas.0601559103
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发表时间：
2006-05-02
影响因子：
11.1
通讯作者：
Utzinger, J
中科院分区：
文献类型：
--
作者：
Raso, G;Vounatsou, P;Utzinger, J
多物种寄生虫感染在发展中世界很普遍，但用于控制的资源却很稀缺。我们提出了一个综合的方法，与曼氏血吸虫和钩虫科特迪瓦西部的合并感染的风险分析和空间预测。我们的方法结合了人口，环境和社会经济数据;将它们纳入地理信息系统;并采用空间统计。人口统计学和社会经济学数据来自教育登记处和对学童进行的问卷调查。环境数据来自遥感卫星图像和数字化地面地图。通过两种不同的诊断方法从粪便检查中获得的寄生虫学数据作为结局指标。贝叶斯变异函数模型用于评估风险因素和空间变异。曼氏-钩虫合并感染与人口、环境和社会经济变量的关系。在3，578名有完整数据记录的学龄儿童中，有680名（19.0%）发现合并感染。钩虫或沙门氏菌单一感染的流行率。mansoni分别为24.3%和24.1%。多项贝叶斯空间分布模型显示，年龄、性别、社会经济地位和海拔高度是S.曼氏-钩虫混合感染我们的结论是，我们的综合方法，采用多种数据源，地理信息系统和遥感技术，贝叶斯空间统计，是一个强大的工具，风险分析和空间预测的S。曼氏-钩虫混合感染更一般地说，这种方法有利于风险绘图和预测其他寄生虫组合和多重寄生，因此可以指导综合疾病控制计划在资源有限的设置。
Multiple-species parasitic infections are pervasive in the developing world, yet resources for their control are scarce. We present an integrated approach for risk profiling and spatial prediction of coinfection with Schistosoma mansoni and hookworm for western Cote d'Ivoire. Our approach combines demographic, environmental, and socioeconomic data; incorporates them into a geographic information system; and employs spatial statistics. Demographic and socioeconomic data were obtained from education registries and from a questionnaire administered to schoolchildren. Environmental data were derived from remotely sensed satellite images and digitized ground maps. Parasitologic data, obtained from fecal examination by using two different diagnostic approaches, served as the outcome measure. Bayesian variogram models were used to assess risk factors and spatial variation of S. mansoni-hookworm coinfection in relation to demographic, environmental, and socioeconomic variables. Coinfections were found in 680 of 3,578 schoolchildren (19.0%) with complete data records. The prevalence of monoinfections with either hookworm or S. mansoni was 24.3% and 24.1%, respectively. Multinomial Bayesian spatial models showed that age, sex, socioeconomic status, and elevation were good predictors for the spatial distribution of S. mansoni-hookworm coinfection. We conclude that our integrated approach, employing a diversity of data sources, geographic information system and remote sensing technologies, and Bayesian spatial statistics, is a powerful tool for risk profiling and spatial prediction of S. mansoni-hookworm coinfection. More generally, this approach facilitates risk mapping and prediction of other parasite combinations and multiparasitism, and hence can guide integrated disease control programs in resource-constrained settings.