Paige John, Fuglstad Geir-Arne, Riebler Andrea, Wakefield Jon
Department of Mathematical Sciences, NTNU, Trondheim, Norway.
Department of Statistics and Biostatistics, University of Washington, Seattle, USA.
Spat Stat. 2022 Dec;52. doi: 10.1016/j.spasta.2022.100714. Epub 2022 Nov 9.
Spatial aggregation with respect to a population distribution involves estimating aggregate population quantities based on observations from individuals. In this context, a geostatistical workflow must account for three major sources of : aggregation weights, fine scale variation, and finite population variation. However, these sources of aggregation error are commonly ignored, and the population instead treated as a fixed population density surface. We improve common practice by introducing a allowing aggregation models to account for aggregation error simply and transparently. This preserves aggregate point estimates while increasing their uncertainties. We compare the proposed and the traditional approach using two simulation studies mimicking neonatal mortality rate (NMR) data from the 2014 Kenya Demographic and Health Survey. In the traditional approach, undercoverage/overcoverage of interval estimates depends arbitrarily on the aggregation grid resolution, while the new approach is resolution robust. Differences between the aggregation approaches increase as an area's population decreases, and are particularly large at the second administrative level and finer, but also at the first administrative level for some population quantities. These findings are consistent with those of an application to the true NMR data. We demonstrate in a sensitivity analysis that burden estimates and their uncertainties are not robust to changes in population density and census information, while prevalence estimates and uncertainties seem stable.
关于人口分布的空间聚集涉及根据个体观测值估计总体人口数量。在此背景下，地理统计工作流程必须考虑三个主要的聚集误差来源：聚集权重、精细尺度变化和有限总体变化。然而，这些聚集误差来源通常被忽略，总体反而被视为固定的人口密度表面。我们通过引入一种方法来改进常规做法，使聚集模型能够简单透明地考虑聚集误差。这在增加总体点估计不确定性的同时保留了这些估计值。我们使用两项模拟研究来比较所提出的方法和传统方法，这两项模拟研究模仿了2014年肯尼亚人口与健康调查中的新生儿死亡率（NMR）数据。在传统方法中，区间估计的覆盖不足/覆盖过度任意取决于聚集网格分辨率，而新方法具有分辨率稳健性。随着一个地区人口减少，聚集方法之间的差异会增大，在二级及更精细行政级别差异尤为明显，但对于某些人口数量，在一级行政级别差异也很大。这些发现与应用于真实NMR数据的结果一致。我们在敏感性分析中表明，负担估计及其不确定性对人口密度和人口普查信息的变化不具有稳健性，而患病率估计及其不确定性似乎较为稳定。
