Multilevel Spatial Modelling of Chronic Child Undernutrition in Kenya

Authors: Kevin Mwenda*, University of California, Santa Barbara
Topics: Africa, Spatial Analysis & Modeling, Population Geography
Keywords: Kenya, stunting, spatial statistics, hot-spot
Session Type: Paper
Day: 4/13/2018
Start / End Time: 5:20 PM / 7:00 PM
Room: Gallier A, Sheraton, 4th Floor
Presentation File: No File Uploaded

Despite valiant efforts towards addressing food insecurity that have seen undernutrition decline globally, many sub-Saharan countries still struggle to feed their people. Undernutrition, a global health problem with effects particularly devastating in Sub-Saharan Africa, causes those affected to suffer from weakened immune systems, increased susceptibility to diseases, and higher mortality rates. Chronic undernutrition remains a significant obstacle to children under five years old in Kenya, where approximately 1 in 4 children are stunted, meaning that they have height-for-age Z-scores less than minus two standard deviations below the median of a reference height-for-age standard. Chronic child undernutrition in the form of stunting varies within and between various regional scales. Several studies have examined stunting prevalence by conducting trend analyses and analyzed socioeconomic, demographic and cultural factors associated with childhood stunting by using standard logistic regressions. However, no high-resolution spatial analysis has been conducted to identify hotspots of stunting in Kenya. Furthermore, no multilevel analyses have been conducted to analyze independent predictors of stunting in Kenya. The objective of this paper is to identify and quantify socioeconomic, demographic, cultural, climatic and environmental factors associated with childhood stunting in Kenya using a multilevel multivariate logistic regression and high-resolution spatial modelling approach.

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