Developing a spatial-statistical model and map of historical malaria prevalence in Botswana using a staged variable selection procedure
Developing a spatial-statistical model and map of historical malaria prevalence in Botswana using a staged variable selection procedure
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DOI：
10.1186/1476-072x-6-44
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发表时间：
2007-09-24
影响因子：
4.9
通讯作者：
Kleinschmidt, Immo
中科院分区：
文献类型：
--
作者：
Craig, Marlies H.;Sharp, Brian L.;Kleinschmidt, Immo
背景资料：近年来已经绘制了几幅疟疾风险地图，其中许多地图来自MARA（非洲疟疾风险地图）项目整理的感染流行率数据，并使用各种环境数据集作为预测因素。由于数据中的过度拟合、混杂和非独立性导致的分析问题，变量选择是一个主要障碍。在贝叶斯空间框架中测试和比较每个解释变量的组合对大多数研究人员来说仍然是不可行的。本研究的目的是开发一个疟疾风险地图，使用一个系统的和切实可行的变量选择过程的空间分析和映射历史疟疾risk in Botswana.Results：从8个环境数据主题的50个潜在的解释变量中，42个显着相关的疟疾流行率在单变量logistic回归和排列的赤池信息标准。那些与相同环境主题的高级亲属相关的人暂时被排除在外。其余14名候选人进行了排名的选择频率后，运行自动逐步选择程序的1000个自助样本从数据。非空间的多变量模型，通过逐步纳入选择频率的顺序。然后使用进一步的逐步bootstrap程序重新评估先前排除的变量是否纳入，从而排除另一个变量。最后，贝叶斯地质统计模型，使用马尔可夫链蒙特卡罗模拟拟合的数据，导致在三个预测变量，即夏季降雨量，年平均温度和海拔高度的最终模型。在考虑空间相关性后，每一项都与疟疾流行率独立且显著相关。该模型被用来预测疟疾流行率在未观察到的位置，产生一个平滑的风险地图为整个national.Conclusion：我们已经产生了一个高度合理的和简约的模型，历史的疟疾风险博茨瓦纳从点参考数据从1961/2疟疾感染的流行率调查1-14岁的儿童。在从50个潜在变量的列表开始后，我们通过应用系统的和可重复的阶段性变量选择程序（包括空间分析），以三个高度合理的预测因子结束，该程序适用于其他环境决定的传染病。所有这些都是使用通用统计软件完成的。
Background: Several malaria risk maps have been developed in recent years, many from the prevalence of infection data collated by the MARA (Mapping Malaria Risk in Africa) project, and using various environmental data sets as predictors. Variable selection is a major obstacle due to analytical problems caused by over-fitting, confounding and non-independence in the data. Testing and comparing every combination of explanatory variables in a Bayesian spatial framework remains unfeasible for most researchers. The aim of this study was to develop a malaria risk map using a systematic and practicable variable selection process for spatial analysis and mapping of historical malaria risk in Botswana.Results: Of 50 potential explanatory variables from eight environmental data themes, 42 were significantly associated with malaria prevalence in univariate logistic regression and were ranked by the Akaike Information Criterion. Those correlated with higher-ranking relatives of the same environmental theme, were temporarily excluded. The remaining 14 candidates were ranked by selection frequency after running automated step-wise selection procedures on 1000 bootstrap samples drawn from the data. A non-spatial multiple-variable model was developed through step-wise inclusion in order of selection frequency. Previously excluded variables were then re-evaluated for inclusion, using further step-wise bootstrap procedures, resulting in the exclusion of another variable. Finally a Bayesian geo-statistical model using Markov Chain Monte Carlo simulation was fitted to the data, resulting in a final model of three predictor variables, namely summer rainfall, mean annual temperature and altitude. Each was independently and significantly associated with malaria prevalence after allowing for spatial correlation. This model was used to predict malaria prevalence at unobserved locations, producing a smooth risk map for the whole country.Conclusion: We have produced a highly plausible and parsimonious model of historical malaria risk for Botswana from point-referenced data from a 1961/2 prevalence survey of malaria infection in 1-14 year old children. After starting with a list of 50 potential variables we ended with three highly plausible predictors, by applying a systematic and repeatable staged variable selection procedure that included a spatial analysis, which has application for other environmentally determined infectious diseases. All this was accomplished using general-purpose statistical software.