Spatial analysis and mapping of malaria risk in Malawi using point-referenced prevalence of infection data.
Spatial analysis and mapping of malaria risk in Malawi using point-referenced prevalence of infection data.
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DOI：
10.1186/1476-072x-5-41
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发表时间：
2006-09-20
影响因子：
4.9
通讯作者：
Sharp, Brian L
中科院分区：
文献类型：
--
作者：
Kazembe, Lawrence N;Kleinschmidt, Immo;Holtz, Timothy H;Sharp, Brian L
当前的疟疾控制举措旨在到 2010 年将疟疾负担减少一半。有效的控制需要基于证据的资源利用。通过地图描绘风险的空间模式是指导控制计划的重要工具。为此，利用经验数据进行了分析，以预测和绘制马拉维的疟疾风险图，目的是确定应重点关注的领域。 1-10 岁儿童的点参考感染流行率数据是从已发表的灰色文献和地理参考中收集的。基于模型的地统计方法用于分析和预测未观察到数据的地区的疟疾风险。模型中添加了地形和气候协变量，用于风险评估和改进的预测。贝叶斯方法用于模型拟合和预测。双变量模型显示疟疾风险与海拔、年最高气温、降雨量和潜在蒸散量 (PET) 显着相关。然而，在预测模型中，疟疾风险的空间分布与海拔相关，与最高温度和 PET 略有相关。由此产生的地图大致同意专家关于该国风险变化的意见，并进一步显示即使在地方层面也存在显着差异。高风险地区位于低洼湖岸地区，而低风险地区则位于该国的高地地区。该地图初步描述了马拉维疟疾风险的地理变化，可能有助于干预措施的选择和设计，这对于减轻马拉维的疟疾负担至关重要。
Current malaria control initiatives aim at reducing malaria burden by half by the year 2010. Effective control requires evidence-based utilisation of resources. Characterizing spatial patterns of risk, through maps, is an important tool to guide control programmes. To this end an analysis was carried out to predict and map malaria risk in Malawi using empirical data with the aim of identifying areas where greatest effort should be focussed. Point-referenced prevalence of infection data for children aged 1–10 years were collected from published and grey literature and geo-referenced. The model-based geostatistical methods were applied to analyze and predict malaria risk in areas where data were not observed. Topographical and climatic covariates were added in the model for risk assessment and improved prediction. A Bayesian approach was used for model fitting and prediction. Bivariate models showed a significant association of malaria risk with elevation, annual maximum temperature, rainfall and potential evapotranspiration (PET). However in the prediction model, the spatial distribution of malaria risk was associated with elevation, and marginally with maximum temperature and PET. The resulting map broadly agreed with expert opinion about the variation of risk in the country, and further showed marked variation even at local level. High risk areas were in the low-lying lake shore regions, while low risk was along the highlands in the country. The map provided an initial description of the geographic variation of malaria risk in Malawi, and might help in the choice and design of interventions, which is crucial for reducing the burden of malaria in Malawi.