Geostatistical modelling of child undernutrition in developing countries using remote-sensed data: evidence from Bangladesh and Ghana demographic and health surveys

Abstract

Childhood chronic undernutrition, known as stunting, remains a critical public health problem globally. Unfortunately while the global stunting prevalence has been declining over time, as a result of concerted public health efforts, there are areas (notably in sub-Saharan Africa and South Asia) where progress has stagnated. These regions are also resource-poor, and monitoring progress in the fight against chronic undernutrition can be problematic. We propose geostatistical modelling using data from existing demographic surveys supplemented by remote-sensed information to provide improved estimates of childhood stunting, accounting for spatial and non-spatial differences across regions. We use two study areas-Bangladesh and Ghana-and our results, in the form of prevalence maps, identify communities for targeted intervention. For Bangladesh, the maps show that all districts in the south-eastern region are identified to have greater risk of stunting, while in Ghana the greater northern region had the highest prevalence of stunting. In countries like Bangladesh and Ghana with limited resources, these maps can be useful diagnostic tools for health planning, decision making and implementation.