The risks of malaria infection in Kenya in 2009.
The risks of malaria infection in Kenya in 2009.
复制标题
DOI：
10.1186/1471-2334-9-180
复制
发表时间：
2009-11-20
影响因子：
3.7
通讯作者：
Snow RW
中科院分区：
文献类型：
--
作者：
Noor AM;Gething PW;Alegana VA;Patil AP;Hay SI;Muchiri E;Juma E;Snow RW
为制定有效的疟疾控制战略，需要绘制感染和疾病风险地图，以选择适当的干预措施。基于模型的地理统计和疟疾寄生虫流行数据汇编方面的进展为重新定义国家恶性疟原虫风险分布提供了独特的机会。在这里，我们展示了2009年肯尼亚疟疾风险的新地图。恶性疟原虫寄生率数据收集自1975年至2009年进行的基于社区的横断面调查。每次统计调查的详细资料包括统计调查的月份和年份、样本数目、积极程度及抽样人口的年龄组别。数据被更正为2至10岁以下的标准年龄范围(PfPR2-10)，并使用国家和在线数字定居点地图对每个调查地点进行了地理定位。生态和气候协变量与每个PfPR2-10调查地点相匹配，并分别和组合检查与PfPR2-10的关系。然后，将重要的协变量纳入贝叶斯地统计时空框架，以预测2009年肯尼亚境内1×1公里分辨率的平均PfPR2-10的连续和分类地图。使用模型坚持数据来检验映射表面的预测精度，并绘制了后验不确定性的分布。从1975至2009年间在2 095个地点进行的调查中选择了2 682个关于PfPR2-10的估计数，以纳入地质统计模型。选择的预测协变量是城市化、最高温度、降水量、增强的植被指数和与主要水体的距离。最终的贝叶斯地统计模型具有较高的预测精度，平均误差为-0.15%PfPR2-10，平均绝对误差为0.38%PfPR2-10，实测值与预测值的线性相关系数为0.81。肯尼亚2009年人口的大部分(3,520万，86.3%)居住在预测的≥2-10低于5%的地区；相反，2009年只有430万人(10.6%)生活在预测PfPR2-10为PfPR2-10的40%的地区，并且主要分布在维多利亚湖沿岸。基于模型的地理统计方法可以用来精确地估计肯尼亚的疟疾风险，我们的模型表明，大多数肯尼亚人生活在恶性疟原虫风险非常低的地区。随着疟疾干预措施的规模扩大，有效跟踪风险的流行病学变化需要作出严格的努力，在时间和空间上记录感染流行情况，以重塑风险，并在未来10-15年重新确定干预优先事项。
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 × 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 ≥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.
登录
查看更多内容
影响因子：
56.3
作者：
Hay, Simon I.;Smith, David L.;Snow, Robert W.
通讯作者：
Snow, Robert W.
影响因子：
4.9
作者：
Kazembe, Lawrence N;Kleinschmidt, Immo;Holtz, Timothy H;Sharp, Brian L
通讯作者：
Sharp, Brian L
影响因子：
4.9
作者：
Craig, Marlies H.;Sharp, Brian L.;Kleinschmidt, Immo
通讯作者：
Kleinschmidt, Immo
影响因子：
15.8
作者：
Guerra, Carlos A.;Gikandi, Priscilla W.;Tatem, Andrew J.;Noor, Abdisalan M.;Smith, Dave L.;Hay, Simon I.;Snow, Robert W.
通讯作者：
Snow, Robert W.
影响因子：
3.3
作者：
Craig, MH;Kleinschmidt, I;Sharp, BL
通讯作者：
Sharp, BL