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In low- and middle-income countries, household surveys are the most reliabledata source to examine health and demographic indicators at the subnationallevel, an exercise in small area estimation. Model-based unit-level models arefavored in producing the subnational estimates at fine scale, such as theadmin-2 level. Typically, the surveys employ stratified two-stage clustersampling with strata consisting of an urban/rural designation crossed withadministrative regions. To avoid bias and increase predictive precision, thestratification should be acknowledged in the analysis. To move from the clusterto the area requires an aggregation step in which the prevalence surface isaveraged with respect to population density. This requires estimating apartition of the study area into its urban and rural components, and to do thiswe experiment with a variety of classification algorithms, including logisticregression, Bayesian additive regression trees and gradient boosted trees.Pixel-level covariate surfaces are used to improve prediction. We estimatespatial HIV prevalence in women of age 15-49 in Malawi using thestratification/aggregation method we propose.

Modeling Urban/Rural Fractions in Low- and Middle-Income Countries