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The need for rigorous and timely health and demographic summaries hasprovided the impetus for an explosion in geographic studies, with a commonapproach being the production of pixel-level maps, particularly in low andmiddle income countries. In this context, household surveys are a major sourceof data, usually with a two-stage cluster design with stratification by regionand urbanicity. Accurate estimates are of crucial interest for precision publichealth policy interventions, but many current studies take a cavalier approachto acknowledging the sampling design, while presenting results at a finegeographic scale. In this paper we investigate the extent to which accountingfor sample design can affect predictions at the aggregate level, which isusually the target of inference. We describe a simulation study in whichrealistic sampling frames are created for Kenya, based on population anddemographic information, with a survey design that mimics a Demographic HealthSurvey (DHS). We compare the predictive performance of various commonly-usedmodels. We also describe a cluster level model with a discrete spatialsmoothing prior that has not been previously used, but provides reliableinference. We find that including stratification and cluster level randomeffects can improve predictive performance. Spatially smoothed direct(weighted) estimates were robust to priors and survey design. Continuousspatial models performed well in the presence of fine scale variation; however,these models require the most "hand holding". Subsequently, we examine how themodels perform on real data; specifically we model the prevalence of secondaryeducation for women aged 20-29 using data from the 2014 Kenya DHS.

Design- and Model-Based Approaches to Small-Area Estimation in a Low and Middle Income Country Context: Comparisons and Recommendations