Abstract:
Background: Under-five malnutrition is a major public health issue contributing to mortality and morbidity,
especially in developing countries like Ghana where the rates remain unacceptably high. Identification of critical risk
factors of under-five malnutrition using appropriate and advanced statistical methods can help formulate
appropriate health programmes and policies aimed at achieving the United Nations SDG Goal 2 target 2. This study
attempts to develop a simultaneous quantile regression, an in-depth statistical model to identify critical risk factors
of under-five severe chronic malnutrition (severe stunting).
Methods: Based on the nationally representative data from the 2014 Ghana Demographic and Health Survey,
height-for-age z-score (HAZ) was estimated. Multivariable simultaneous quantile regression modelling was
employed to identify critical risk factors for severe stunting based on HAZ (a measure of chronic malnutrition in
populations). Quantiles of HAZ with focus on severe stunting were modelled and the impact of the risk factors
determined. Significant test of the difference between slopes at different selected quantiles of severe stunting and
other quantiles were performed. A quantile regression plots of slopes were developed to visually examine the
impact of the risk factors across these quantiles.
Results: Data on a total of 2716 children were analysed out of which 144 (5.3%) were severely stunted. The models
identified child level factors such as type of birth, sex, age, place of delivery and size at birth as significant risk
factors of under-five severe stunting. Maternal and household level factors identified as significant predictors of
under-five severe stunting were maternal age and education, maternal national health insurance status, household
wealth status, and number of children under-five in households. Highly significant differences exist in the slopes
between 0.1 and 0.9 quantiles. The quantile regression plots for the selected quantiles from 0.1 to 0.9 showed
substantial differences in the impact of the covariates across the quantiles of HAZ considered.
Conclusion: Critical risk factors that can aid formulation of child nutrition and health policies and interventions that
will improve child nutritional outcomes and survival were identified. Modelling under-five severe stunting using
multivariable simultaneous quantile regression models could be beneficial to addressing the under-five severe
stunting.
