论文标题
在低收入国家和中等收入国家建模城市/农村分数
Modeling Urban/Rural Fractions in Low- and Middle-Income Countries
论文作者
论文摘要
在低收入和中等收入国家中，家庭调查是在次数估算中检查健康和人口统计指标的最可靠数据来源。基于模型的单位级模型在良好的规模（例如Admin-2级别）中产生次国估计值受到青睐。通常，调查采用分层的两阶段群集抽样，其地层由与行政区域交叉的城市/农村指定组成。为了避免偏见并提高预测精度，应在分析中确认分层。要从群集移动到该区域，需要一个聚集步骤，在该步骤中，相对于种群密度平均。这需要将研究区域的分区估计到其城市和农村组成部分中，为此，我们尝试了各种分类算法，包括逻辑回归，贝叶斯添加剂回归树和梯度增强的树木。像素级协变量表面用于改善预测。我们使用我们提出的分层/聚集方法来估计马拉维15-49岁妇女的空间HIV患病率。
In low- and middle-income countries, household surveys are the most reliable data source to examine health and demographic indicators at the subnational level, an exercise in small area estimation. Model-based unit-level models are favored in producing the subnational estimates at fine scale, such as the admin-2 level. Typically, the surveys employ stratified two-stage cluster sampling with strata consisting of an urban/rural designation crossed with administrative regions. To avoid bias and increase predictive precision, the stratification should be acknowledged in the analysis. To move from the cluster to the area requires an aggregation step in which the prevalence surface is averaged with respect to population density. This requires estimating a partition of the study area into its urban and rural components, and to do this we experiment with a variety of classification algorithms, including logistic regression, Bayesian additive regression trees and gradient boosted trees. Pixel-level covariate surfaces are used to improve prediction. We estimate spatial HIV prevalence in women of age 15-49 in Malawi using the stratification/aggregation method we propose.
