Malaria risk in Nigeria: Bayesian geostatistical modelling of 2010 malaria indicator survey data.
Malaria risk in Nigeria: Bayesian geostatistical modelling of 2010 malaria indicator survey data.
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DOI：
10.1186/s12936-015-0683-6
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发表时间：
2015-04-14
期刊：
影响因子：
3
通讯作者：
Vounatsou P
中科院分区：
文献类型：
--
作者：
Adigun AB;Gajere EN;Oresanya O;Vounatsou P
2010年，国家疟疾控制计划在减少疟疾合作伙伴的支持下实施了具有全国代表性的疟疾指标调查（MIS），收集了疟疾负担和控制干预措施的相关数据。对 MIS 数据进行分析，以生成疟疾风险的当代平滑地图，并在控制环境/气候、人口和社会经济特征后评估控制干预措施对寄生虫血症风险的影响。根据观察到的寄生虫流行率数据拟合贝叶斯地统计逻辑回归模型。通过在地统计模型中应用贝叶斯变量选择来确定寄生虫血症的重要环境/气候风险因素。采用最佳模型在 4 平方公里分辨率的网格上预测疾病风险。进行验证以评估模型的预测性能。在调整环境、社会经济和人口因素后，得出了控制干预覆盖范围的各种衡量标准，以估计干预措施对寄生虫血症风险的影响。归一化植被指数和降雨量被确定为疟疾风险的重要环境/气候预测因子。人口调整后的风险估计范围从拉各斯州的 6.46% 到博尔诺州的 43.33%。干预措施似乎对疟疾风险没有重要影响。随着社会经济地位的提高以及生活在农村地区增加疟疾寄生虫检测呈阳性的几率，寄生虫血症的几率似乎呈下降趋势。年龄较大的儿童感染疟疾的风险也较高。制作的地图和寄生虫病儿童的估计提供了该国当前寄生虫流行情况的重要概况。控制活动将发现它是确定干预优先领域的有用工具。本文的在线版本 (doi:10.1186/s12936-015-0683-6) 包含补充材料，可供授权用户使用。
In 2010, the National Malaria Control Programme with the support of Roll Back Malaria partners implemented a nationally representative Malaria Indicator Survey (MIS), which assembled malaria burden and control intervention related data. The MIS data were analysed to produce a contemporary smooth map of malaria risk and evaluate the control interventions effects on parasitaemia risk after controlling for environmental/climatic, demographic and socioeconomic characteristics. A Bayesian geostatistical logistic regression model was fitted on the observed parasitological prevalence data. Important environmental/climatic risk factors of parasitaemia were identified by applying Bayesian variable selection within geostatistical model. The best model was employed to predict the disease risk over a grid of 4 km2 resolution. Validation was carried out to assess model predictive performance. Various measures of control intervention coverage were derived to estimate the effects of interventions on parasitaemia risk after adjusting for environmental, socioeconomic and demographic factors. Normalized difference vegetation index and rainfall were identified as important environmental/climatic predictors of malaria risk. The population adjusted risk estimates ranges from 6.46% in Lagos state to 43.33% in Borno. Interventions appear to not have important effect on malaria risk. The odds of parasitaemia appears to be on downward trend with improved socioeconomic status and living in rural areas increases the odds of testing positive to malaria parasites. Older children also have elevated risk of malaria infection. The produced maps and estimates of parasitaemic children give an important synoptic view of current parasite prevalence in the country. Control activities will find it a useful tool in identifying priority areas for intervention. The online version of this article (doi:10.1186/s12936-015-0683-6) contains supplementary material, which is available to authorized users.
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影响因子：
3
作者：
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通讯作者：
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