🤖 AI Summary
In low- and middle-income countries (LMICs), high-resolution subnational mapping of health indicators—such as vaccination coverage—is hindered by sparse household survey data, complex sampling designs, and the absence of reliable population denominators. To address this, we propose a novel integrated framework combining small-area estimation (SAE) with model-based geostatistics (MBG). Our approach jointly incorporates survey weights, spatial random effects, and geographic covariates at the unit level and calibrates estimates to administrative-level totals using Demographic and Health Surveys (DHS) data. This is the first method to systematically bridge design-based and model-based paradigms, ensuring both theoretical consistency and spatial smoothness. Applied to Nigeria’s 2018 DHS data, our pixel-level maps demonstrate substantially improved estimation accuracy and more realistic uncertainty quantification. The framework provides WHO, UNICEF, and other stakeholders with a verifiable, scalable alternative for subnational health mapping.
📝 Abstract
The emerging need for subnational estimation of demographic and health indicators in lowand 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.
