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 area-level approaches to unit-level methods. The latter are model-based and overcome data sparsity by borrowing strength across covariates and space and can, in principle, be leveraged to create 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 methods are currently used for prevalence mapping in the context of LMICs, highlight the key challenges that need to be overcome, and propose a new approach, which is methodologically closer in spirit to small area estimation. The main discussion points are demonstrated through a case study of vaccination coverage in Nigeria based on 2018 Demographic and Health Surveys (DHS) data. 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一级估计的新出现的需要正在推动从基于设计的地区一级方法转向单位一级方法,后者以模型为基础,通过在千差万别和空间之间借用实力克服了数据宽度,在原则上可以用来根据住户调查制作精细的象素水平地图,然而,基于模型的方法的典型实施并不完全承认复杂的调查设计,也不享受基于设计的方法的理论一致性。我们描述了目前如何在基于LMIC范围内使用空间方法绘制流行情况图,强调了需要克服的主要挑战,并提出了在方法上更接近小地区估计的新方法。主要讨论要点通过根据2018年人口和健康调查(人口和健康调查)数据对尼日利亚疫苗接种范围进行的个案研究得到证明。我们一般地讨论了我们的主要结论,并强调了国家以下各级流行估计的工业生产者对流行方法的影响。