Abstract:
BACKGROUND: Childhood stunting is a major indicator of child malnutrition and a focus area of Global Nutrition Targets for 2025 and Sustainable Development Goals. Risk factors for childhood stunting are well studied and well known
and could be used in a risk prediction model for assessing whether a child is stunted or not. However, the selection
of child stunting predictor variables is a critical step in the development and performance of any such prediction
model. This paper compares the performance of child stunting diagnostic predictive models based on predictor variables selected using a set of variable selection methods.
METHODS: Firstly, we conducted a subjective review of the literature to identify determinants of child stunting in SubSaharan Africa. Secondly, a multivariate logistic regression model of child stunting was fitted using the identified
predictors on stunting data among children aged 0–59 months in the Malawi Demographic Health Survey (MDHS
2015–16) data. Thirdly, several reduced multivariable logistic regression models were ftted depending on the predictor variables selected using seven variable selection algorithms, namely backward, forward, stepwise, random forest,
Least Absolute Shrinkage and Selection Operator (LASSO), and judgmental. Lastly, for each reduced model, a diagnostic predictive model for the childhood stunting risk score, defned as the child propensity score based on derived
coefcients, was calculated for each child. The prediction risk models were assessed using discrimination measures,
including area under-receiver operator curve (AUROC), sensitivity and specifcity.
RESULTS: The review identifed 68 predictor variables of child stunting, of which 27 were available in the MDHS
2016–16 data. The common risk factors selected by all the variable selection models include household wealth index,
age of the child, household size, type of birth (singleton/multiple births), and birth weight. The best cut-of point
on the child stunting risk prediction model was 0.37 based on risk factors determined by the judgmental variable
selection method. The model’s accuracy was estimated with an AUROC value of 64% (95% CI: 60%-67%) in the test
data. For children residing in urban areas, the corresponding AUROC was AUC=67% (95% CI: 58–76%), as opposed
to those in rural areas, AUC=63% (95% CI: 59–67%).
CONCLUUSION: The derived child stunting diagnostic prediction model could be useful as a first screening tool to identify children more likely to be stunted. The identified children could then receive necessary nutritional interventions.
