Evaluation of geospatial methods to generate subnational HIV prevalence estimates for local level planning.
Evaluation of geospatial methods to generate subnational HIV prevalence estimates for local level planning.
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DOI：
10.1097/qad.0000000000001075
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发表时间：
2016-06-01
期刊：
影响因子：
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通讯作者：
Subnational Estimates Working Group of the HIV Modelling Consortium
中科院分区：
文献类型：
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作者：
Subnational Estimates Working Group of the HIV Modelling Consortium
补充数字内容可在文本中有证据表明，在艾滋病毒流行的实质性国家以下的变化。然而，强有力的艾滋病毒空间数据往往只能在高地理聚合水平上获得，而不能在决策所需的更精细分辨率上获得。因此，利用现有数据提供当地艾滋病毒流行率估计数的空间分析方法可能是有用的。这种方法已经存在，但在应用于艾滋病毒时尚未进行正式比较。使用了六种备选方法----包括联合国艾滋病毒/艾滋病联合规划署用于绘制地图的方法和适用于其他疾病的贝叶斯地质统计方法----利用住户调查的分组数据绘制了三个国家的艾滋病毒流行率地图和国家以下各级估计数。采用了两种方法来评估预测的准确性：内部验证，即保留一定比例的输入数据（测试数据集），以质疑预测;以及与前些年住户调查的具体地点数据进行比较。每种方法都可以在未采样的位置生成有用的准确预测流行率，预测误差的大小在不同方法中相似。然而，贝叶斯地统计方法始终在各国和验证程序中提供最强的统计性能。现有的方法可能能够提供比目前数据所允许的更精细的空间尺度上的艾滋病毒流行率估计数。所揭示的国家以下各级的差异可纳入规划，以确保对这一流行病的空间特征作出反应。贝叶斯地统计方法是一个很有前途的战略，整合艾滋病毒数据，以产生强大的本地估计。
Supplemental Digital Content is available in the text There is evidence of substantial subnational variation in the HIV epidemic. However, robust spatial HIV data are often only available at high levels of geographic aggregation and not at the finer resolution needed for decision making. Therefore, spatial analysis methods that leverage available data to provide local estimates of HIV prevalence may be useful. Such methods exist but have not been formally compared when applied to HIV. Six candidate methods – including those used by the Joint United Nations Programme on HIV/AIDS to generate maps and a Bayesian geostatistical approach applied to other diseases – were used to generate maps and subnational estimates of HIV prevalence across three countries using cluster level data from household surveys. Two approaches were used to assess the accuracy of predictions: internal validation, whereby a proportion of input data is held back (test dataset) to challenge predictions; and comparison with location-specific data from household surveys in earlier years. Each of the methods can generate usefully accurate predictions of prevalence at unsampled locations, with the magnitude of the error in predictions similar across approaches. However, the Bayesian geostatistical approach consistently gave marginally the strongest statistical performance across countries and validation procedures. Available methods may be able to furnish estimates of HIV prevalence at finer spatial scales than the data currently allow. The subnational variation revealed can be integrated into planning to ensure responsiveness to the spatial features of the epidemic. The Bayesian geostatistical approach is a promising strategy for integrating HIV data to generate robust local estimates.