Centre for Geographic Medicine, KEMRI - University of Oxford, Kenyatta National Hospital Grounds, Nairobi, Kenya.
BMC Infect Dis. 2009 Nov 20;9:180. doi: 10.1186/1471-2334-9-180.
To design an effective strategy for the control of malaria requires a map of infection and disease risks to select appropriate suites of interventions. Advances in model based geo-statistics and malaria parasite prevalence data assemblies provide unique opportunities to redefine national Plasmodium falciparum risk distributions. Here we present a new map of malaria risk for Kenya in 2009.
Plasmodium falciparum parasite rate data were assembled from cross-sectional community based surveys undertaken from 1975 to 2009. Details recorded for each survey included the month and year of the survey, sample size, positivity and the age ranges of sampled population. Data were corrected to a standard age-range of two to less than 10 years (PfPR2-10) and each survey location was geo-positioned using national and on-line digital settlement maps. Ecological and climate covariates were matched to each PfPR2-10 survey location and examined separately and in combination for relationships to PfPR2-10. Significant covariates were then included in a Bayesian geostatistical spatial-temporal framework to predict continuous and categorical maps of mean PfPR2-10 at a 1 x 1 km resolution across Kenya for the year 2009. Model hold-out data were used to test the predictive accuracy of the mapped surfaces and distributions of the posterior uncertainty were mapped.
A total of 2,682 estimates of PfPR2-10 from surveys undertaken at 2,095 sites between 1975 and 2009 were selected for inclusion in the geo-statistical modeling. The covariates selected for prediction were urbanization; maximum temperature; precipitation; enhanced vegetation index; and distance to main water bodies. The final Bayesian geo-statistical model had a high predictive accuracy with mean error of -0.15% PfPR2-10; mean absolute error of 0.38% PfPR2-10; and linear correlation between observed and predicted PfPR2-10 of 0.81. The majority of Kenya's 2009 population (35.2 million, 86.3%) reside in areas where predicted PfPR2-10 is less than 5%; conversely in 2009 only 4.3 million people (10.6%) lived in areas where PfPR2-10 was predicted to be > or =40% and were largely located around the shores of Lake Victoria.
Model based geo-statistical methods can be used to interpolate malaria risks in Kenya with precision and our model shows that the majority of Kenyans live in areas of very low P. falciparum risk. As malaria interventions go to scale effectively tracking epidemiological changes of risk demands a rigorous effort to document infection prevalence in time and space to remodel risks and redefine intervention priorities over the next 10-15 years.
为设计疟疾控制的有效策略，需要绘制感染和疾病风险地图，以选择合适的干预措施组合。基于模型的地统计学和疟原虫流行率数据汇总技术的进步，为重新定义全国间日疟原虫风险分布提供了独特的机会。本文呈现了肯尼亚 2009 年疟疾风险的新地图。
从 1975 年至 2009 年开展的基于社区的横断面调查中汇总间日疟原虫寄生虫率数据。每个调查记录的详细信息包括调查的月份和年份、样本量、阳性率以及抽样人群的年龄范围。数据经过校正，采用标准年龄范围（2 岁以下至不到 10 岁），并使用国家和在线数字居民点地图为每个调查地点定位。将生态和气候协变量与每个 PfPR2-10 调查地点匹配，并分别和联合检查与 PfPR2-10 的关系。然后将显著的协变量纳入贝叶斯地统计学时空框架，以预测肯尼亚 2009 年 1x1km 分辨率的连续和分类 PfPR2-10 均值地图。使用保留数据检验映射表面的预测准确性，并绘制后验不确定性分布。
共纳入 1975 年至 2009 年间 2095 个地点的 2682 项 PfPR2-10 调查数据进行地统计学建模。选择用于预测的协变量包括城市化程度、最高温度、降水、增强植被指数和到主要水体的距离。最终的贝叶斯地统计学模型具有较高的预测准确性，平均误差为 -0.15%PfPR2-10；平均绝对误差为 0.38%PfPR2-10；观测值与预测 PfPR2-10 之间的线性相关性为 0.81。肯尼亚 2009 年的大部分人口（3520 万人，占 86.3%）居住在预测 PfPR2-10 小于 5%的地区；相反，2009 年仅 430 万人（占 10.6%）居住在预测 PfPR2-10 大于等于 40%的地区，这些地区主要位于维多利亚湖周围。
基于模型的地统计学方法可用于精确插值肯尼亚的疟疾风险，我们的模型表明，肯尼亚的大多数人生活在极低的间日疟原虫风险地区。随着疟疾干预措施的大规模实施，有效跟踪风险的流行病学变化需要严格努力，及时记录感染流行率，以在未来 10-15 年内对风险进行建模并重新定义干预重点。
