Lindsay S W, Thomas C J
Department of Biological Sciences, University of Durham, UK.
Trans R Soc Trop Med Hyg. 2000 Jan-Feb;94(1):37-45. doi: 10.1016/s0035-9203(00)90431-0.
Lymphatic filariasis remains a major public health problem in Africa and is 1 of the World Health Organization's 6 diseases targeted for global eradication. However, no detailed maps of the geographical distribution of this disease exist, making it difficult to target control activities and quantify the population at risk. We hypothesized that the distribution lymphatic filariasis is governed by climate. The climate at sites in Africa where surveys for lymphatic filariasis had taken place was characterized using computerized climate surfaces. Logistic regression analysis of the climate variables predicted with 76% accuracy whether sites had microfilaraemic patients or not. We used the logistic equation in a geographical information system to map risk of lymphatic filariasis infection across Africa, which compared favourably with expert opinion. Further validation with a quasi-independent data set showed that the model predicted correctly 88% of infected sites. A similar procedure was used to map risk of microfilaraemia in Egypt, where the dominant vector species differs from those in sub-Saharan Africa. By overlaying risk maps on a 1990 population grid, and adjusting for recent population increases, we estimate that around 420 million people will be exposed to this infection in Africa in the year 2000. This approach could be used to produce a sampling frame, based on estimated risk of microfilaraemia, for conducting filariasis surveys in countries that lack accurate distribution maps and thus save on costs.
淋巴丝虫病在非洲仍然是一个主要的公共卫生问题，是世界卫生组织全球根除目标的6种疾病之一。然而，目前尚无该疾病地理分布的详细地图，这使得难以确定控制活动的目标并对危险人群进行量化。我们假设淋巴丝虫病的分布受气候影响。利用计算机化气候表面对非洲进行淋巴丝虫病调查地点的气候进行了特征描述。对气候变量进行逻辑回归分析，预测地点是否有微丝蚴血症患者的准确率为76%。我们在地理信息系统中使用逻辑方程绘制了整个非洲淋巴丝虫病感染风险图，该图与专家意见相比表现良好。用一个准独立数据集进行的进一步验证表明，该模型正确预测了88%的感染地点。在埃及，主要病媒种类与撒哈拉以南非洲不同，我们采用类似程序绘制了微丝蚴血症风险图。通过将风险图叠加在1990年人口网格上，并对近期人口增长进行调整，我们估计2000年非洲约有4.2亿人将面临这种感染。这种方法可用于根据微丝蚴血症估计风险生成抽样框架，以便在缺乏准确分布图的国家进行丝虫病调查，从而节省成本。
