Modeling the Risk Factors of Undernutrition among Children below Five Years of Age in Uganda Using Generalized Structural Equation Models

Author:

Maniragaba Vallence Ngabo1,Atuhaire Leonard K.2,Rutayisire Pierre Claver1

Affiliation:

1. African Center of Excellence in Data Science, College of Business and Economic, University of Rwanda, Kigali P.O. Box 3248, Rwanda

2. School of Statistical Methods, College of Management Sciences, Makerere University, Kampala P.O. Box 7072, Uganda

Abstract

Introduction: The prevalence of undernutrition among children below five years of age, in Uganda and the world over, remains very high. About 45% of all global deaths among children below five years of age are attributed to undernutrition. A number of studies using different statistical approaches affirm this effect, yet some factors indicate the influence of other factors within the system. This study, therefore, uses a method that demonstrates how different variables feed into each other. Aim: The aim of this study was to establish the major factors associated with an increased likelihood of undernutrition and the paths showing how these risk factors influence undernutrition. Methods: Data from the Uganda Demographic and Health Survey (UDHS, 2016) were used for this study. A sample of 4530 children, whose age, height, and weight measurements were recorded, was considered for this study. Additionally, the study used generalized structural equation models to identify the multifaceted natures and paths of the risk factors that influence undernutrition among children below five years of age. The study relied on the UNICEF 2020 conceptual framework to identify and analyze the direct and indirect effects of these risk factors of undernutrition. Results: From the perspective of a male child, having a perceived small size at birth, a low birth weight, being breastfed for less than 6 months, having no formal education from mothers, limited income-generating opportunities, a low wealth status, and notable episodes of diarrhea were among the key factors associated with an increased likelihood of undernutrition. The identified paths were as follows: (i) Having no education, as this was associated with limited working opportunities and a low income, which increases the likelihood of low household wealth status, hence increasing the chances of undernutrition. (ii) Exposure to a rural setting was associated with an increased likelihood of undernutrition through association with poor and or low employment levels within the rural areas. (iii) A shorter duration of breastfeeding was associated with children in urban areas, resulting in an increased likelihood of undernutrition. (iv) Children aged between 6 and 47 months had a higher likelihood of undernutrition. Conclusions: An approach that addresses and recognizes all these factors at different levels, along the established paths, should be implemented to effectively reduce undernutrition among children below five years of age.

Funder

University of Rwanda, African Centre of Excellence in Data Science

Publisher

MDPI AG

Subject

Pediatrics, Perinatology and Child Health

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