Affiliations: The DHS Program, ICF International, Rockville, MD, USA
Correspondence:
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Corresponding author: Benjamin K. Mayala, The DHS Program, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA. Tel.: +1 301 572 0507; Fax: +1 301 407 6501; E-mail: Ben.Mayala@icf.com.
Abstract: Over the last several years and within the framework of the Sustainable Development Goals, there has been a need to improve the measurement and understanding of local geographic patterns to support more decentralized decision-making and more efficient program implementation. This requires more disaggregated data that are not currently available in a nationally representative household survey. This study explores the potential of model-based geostatistics methodology to model DHS survey indicators. We implement a stacked ensemble modeling approach that combines multiple model algorithmic methods to increase predictive validity relative to a single modeling. The approach captures potentially complex interactions and non-linear effects among the geospatial covariates. Three submodels are fitted to six DHS indicator survey data using the geospatial covariates as exploratory predictors. The model prediction surfaces generated from the submodels are used as covariates in the final Bayesian geostatistical model, which is implemented through a stochastic partial differential equation approach in the integrated nested Laplace approximations. The proposed approach can help to inform the allocation of resources and program implementation in areas that need more attention. Countries can use this approach to model other DHS survey indicators at much smaller spatial scales.
