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Combining school-catchment area models with geostatistical models for analysing school survey data from low-resource settings: Inferential benefits and limitations
Spatial Statistics ( IF 2.3 ) Pub Date : 2022-06-21 , DOI: 10.1016/j.spasta.2022.100679
Peter M Macharia 1, 2 , Nicolas Ray 3, 4 , Caroline W Gitonga 2 , Robert W Snow 2, 5 , Emanuele Giorgi 1
Affiliation  

School-based sampling has been used to inform targeted responses for malaria and neglected tropical diseases. Standard geostatistical methods for mapping disease prevalence use the school location to model spatial correlation, which is questionable since exposure to the disease is more likely to occur in the residential location. In this paper, we propose to overcome the limitations of standard geostatistical methods by introducing a modelling framework that accounts for the uncertainty in the location of the residence of the students. By using cost distance and cost allocation models to define spatial accessibility and in absence of any information on the travel mode of students to school, we consider three school catchment area models that assume walking only, walking and bicycling and, walking and motorized transport. We illustrate the use of this approach using two case studies of malaria in Kenya and compare it with the standard approach that uses the school locations to build geostatistical models. We argue that the proposed modelling framework presents several inferential benefits, such as the ability to combine data from multiple surveys some of which may also record the residence location, and to deal with ecological bias when estimating the effects of malaria risk factors. However, our results show that invalid assumptions on the modes of travel to school can worsen the predictive performance of geostatistical models. Future research in this area should focus on collecting information on the modes of transportation to school which can then be used to better parametrize the catchment area models.



中文翻译:

将学区模型与地统计模型相结合,用于分析来自低资源环境的学校调查数据:推断的好处和限制

以学校为基础的抽样已被用于为疟疾和被忽视的热带病的针对性应对提供信息。绘制疾病流行率的标准地质统计学方法使用学校位置来模拟空间相关性,这是有问题的,因为在住宅位置更有可能发生疾病。在本文中,我们建议通过引入一个考虑学生住所位置不确定性的建模框架来克服标准地统计方法的局限性。通过使用成本距离和成本分配模型来定义空间可达性,并且在没有任何关于学生上学出行方式的信息的情况下,我们考虑了三个学校学区模型,假设仅步行、步行和骑自行车以及步行和机动交通。我们使用肯尼亚的两个疟疾案例研究来说明这种方法的使用,并将其与使用学校位置建立地统计模型的标准方法进行比较。我们认为,所提出的建模框架具有几个推论优势,例如能够结合来自多个调查的数据,其中一些调查还可能记录居住位置,以及在估计疟疾风险因素的影响时处理生态偏差。然而,我们的结果表明,对上学旅行方式的无效假设会恶化地统计模型的预测性能。该领域的未来研究应侧重于收集有关上学交通方式的信息,然后可以将其用于更好地对集水区模型进行参数化。

更新日期:2022-06-21
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