Identifying factors associated with child malnutrition in Ghana: a cross-sectional study using Bayesian multilevel ordinal logistic regression approach

Abstract

Objective In developing countries, malnutrition is a noteworthy concern related to the well-being of people, and this study aimed to determine the factors that affect malnutrition among children below 5 years in Ghana.Design The study used a secondary data source, specifically the Ghanaian Multiple Indicator Cluster Survey Six (MICS 6), which was conducted by the Ghana Statistical Service in 2017–2018. The MICS data are hierarchical, as children are categorised within households, and households are further grouped within a higher cluster, violating the independence assumption that must be addressed in the analyses. This study used a Bayesian multilevel ordinal logistic regression to model, identify and analyse the factors linked to child malnutrition in Ghana.Setting The setting of the study was the household level across the previous 10 administrative regions in Ghana.Participants Data for 8875 children under 5 years were used for the study. The data were gathered from households in all 10 administrative regions of Ghana using a sampling procedure consisting of stratification and random selection to ensure national representation.Results The results showed that the Northern Region of Ghana had the highest occurrence rate of severe and moderate malnutrition, and factors such as the count of children’s books or picture books, whether the child experienced fever in the last 2 weeks, age and sex of the child, and the child’s household wealth index quintile were strongly linked to malnutrition among Ghanaian children.Conclusion These findings underscore the intricate interplay of factors contributing to child nutrition in Ghana and suggest that addressing malnutrition necessitates a comprehensive approach that considers factors such as access to healthcare and reading materials, household wealth, and other social and environmental factors.