  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.
