The emerging need for subnational estimation of demographic and health indicators in low- and middle-income countries (LMICs) is driving a move from design-based methods to spatial and spatio-temporal approaches. The latter are model-based and overcome data sparsity by borrowing strength across space, time and covariates and can, in principle, be leveraged to create yearly fine-scale pixel level maps based on household surveys. However, typical implementations of the model-based approaches do not fully acknowledge the complex survey design, and do not enjoy the theoretical consistency of design-based approaches. We describe how spatial and spatio-temporal methods are currently used for small area estimation in the context of LMICs, highlight the key challenges that need to be overcome, and discuss a new approach, which is methodologically closer in spirit to small area estimation. The main discussion points are demonstrated through two case studies: spatial analysis of vaccination coverage in Nigeria based on the 2018 Demographic and Health Surveys (DHS) survey, and spatio-temporal analysis of neonatal mortality in Malawi based on 2010 and 2015--2016 DHS surveys. We discuss our key findings both generally and with an emphasis on the implications for popular approaches undertaken by industrial producers of subnational prevalence estimates.
翻译:对中低收入国家人口和健康指标进行国家以下各级估计的新出现的需要正在推动从设计方法转向空间和时空方法,后者以模型为基础,通过在空间、时间和千差万别之间借取力量,克服了数据宽度,在空间、时间和千差万别之间相互借重,原则上可以用来根据住户调查制作年度微调像素水平地图。然而,基于模式的方法的典型实施并不完全承认复杂的调查设计,也不享受基于设计方法的理论一致性。我们描述了目前如何利用空间和时空方法在低空间和时空方法范围内进行小面积估计,强调需要克服的关键挑战,并讨论一种新的方法,在方法上与小面积估计更加接近。主要讨论要点通过两个案例研究得到证明:根据2018年人口和健康调查(DHS)调查对尼日利亚疫苗接种覆盖率的空间分析,以及根据2010年和2015-2016年国家人口和健康调查对马拉维新生儿死亡率的抽测。我们讨论了我们国家以下各级工业评估的主要结果,重点是生产者对2010年和2015-2016年人口健康调查进行的普遍影响。