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District‐level estimation of vaccination coverage: Discrete vs continuous spatial models
Statistics in Medicine ( IF 2 ) Pub Date : 2021-02-04 , DOI: 10.1002/sim.8897
C Edson Utazi 1, 2 , Kristine Nilsen 1 , Oliver Pannell 1 , Winfred Dotse-Gborgbortsi 1 , Andrew J Tatem 1
Affiliation  

Health and development indicators (HDIs) such as vaccination coverage are regularly measured in many low‐ and middle‐income countries using household surveys, often due to the unreliability or incompleteness of routine data collection systems. Recently, the development of model‐based approaches for producing subnational estimates of HDIs using survey data, particularly cluster‐level data, has been an active area of research. This is mostly driven by the increasing demand for estimates at certain administrative levels, for example, districts, at which many development goals are set and evaluated. In this study, we explore spatial modeling approaches for producing district‐level estimates of vaccination coverage. Specifically, we compare discrete spatial smoothing models which directly model district‐level data with continuous Gaussian process (GP) models that utilize geolocated cluster‐level data. We adopt a fully Bayesian framework, implemented using the INLA and SPDE approaches. We compare the predictive performance of the models by analyzing vaccination coverage using data from two Demographic and Health Surveys (DHS), namely the 2014 Kenya DHS and the 2015‐16 Malawi DHS. We find that the continuous GP models performed well, offering a credible alternative to traditional discrete spatial smoothing models. Our analysis also revealed that accounting for between‐cluster variation in the continuous GP models did not have any real effect on the district‐level estimates. Our results provide guidance to practitioners on the reliability of these model‐based approaches for producing estimates of vaccination coverage and other HDIs.

中文翻译:

疫苗接种覆盖率的地区级估计:离散与连续空间模型

许多低收入和中等收入国家使用家庭调查定期测量健康和发展指标 (HDI),例如疫苗接种覆盖率,这通常是由于常规数据收集系统不可靠或不完整。最近,使用调查数据,特别是集群级数据,开发基于模型的方法来生成地方 HDI 估计值,一直是一个活跃的研究领域。这主要是由于对某些行政级别的估算需求不断增加,例如在制定和评估许多发展目标的地区。在这项研究中,我们探索了空间建模方法,用于产生地区级疫苗接种覆盖率估计值。具体来说,我们将直接对地区级数据建模的离散空间平滑模型与利用地理定位集群级数据的连续高斯过程 (GP) 模型进行比较。我们采用完全贝叶斯框架,使用 INLA 和 SPDE 方法实现。我们使用来自两个人口和健康调查 (DHS) 的数据,即 2014 年肯尼亚 DHS 和 2015-16 年马拉维 DHS,通过分析疫苗接种覆盖率来比较模型的预测性能。我们发现连续 GP 模型表现良好,为传统的离散空间平滑模型提供了可靠的替代方案。我们的分析还表明,考虑连续 GP 模型中的集群间变化对地区级估计没有任何实际影响。
更新日期:2021-04-06
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