A Ridge-Based Multivariate Logistic Modelling for Assessing Factors Associated with Multifactorial Determinants of Under Five Child Malnutrition Outcomes in Mozambique.

Adelino Benedito Nhancale; Fredrick Onyango; Joyce Otieno

doi:10.36948/ijfmr.2025.v07i03.45002

A Ridge-Based Multivariate Logistic Modelling for Assessing Factors Associated with Multifactorial Determinants of Under Five Child Malnutrition Outcomes in Mozambique.

Author(s)	Mr. Adelino Benedito Nhancale, Prof. Dr. Fredrick Onyango, Dr. Joyce Otieno
Country	Mozambique
Abstract	In Mozambique, individual and integrated programmes targeting the healthcare, sanitation, socioeconomic sectors, and nutrition still leave child malnutrition, specifically within the under-five category, as a critical public health challenge. This study sought to discern the most important predictors of child malnutrition using DHS-aligned variables designed to guide focused strategies. A Ridge Logistic Regression model was conducted to analyse data from the nationally representative Mozambique Demographic and Health Survey (MDHS2022) to predict a binary composite malnutrition outcome—defined by the presence of at least one of three conditions: stunting, underweight, or wasting, or anthropometric Z-scores (< -2 SD). Independent variables included child-specific and household demographic, maternal, and reproductive health, education, socioeconomic status, water, and sanitation. Even though the model was fitted based on a binary composite outcome, classification metrics were retained, ROC curves, precision-recall curves, and goodness-of-fit diagnostics performed separately for each type of malnutrition. The findings showed the best model performance for wasting (AUC = 0.99; Accuracy = 0.9317), followed by underweight (AUC = 0.86) and stunting (AUC = 0.63). Other significant factors included male gender, child age 24-35 and 48-59 months, low birthweight, rural dwelling, poor sanitation, inadequate shelter, lower BMI z-score, and low standing. Acute environmental factors were associated with wasting and showed high model stability, while underweight reflected a mix of acute and chronic drivers with moderate predictive performance. Stunting proved harder to classify due to its association with structural deprivation. In any case, utilising Ridge Logistic Regression revealed underlying issues affixed to each form of malnutrition in conjunction with DHS-aligned predictors and advanced the understanding of distinct risk factors, which affirmed the model’s utility—even with respect to wasting—for policy and intervention strategy formulation guided by empirical data.
Keywords	Child malnutrition, Ridge Logistic Regression, DHS, Mozambique, stunting, underweight, wasting, public health
Field	Mathematics > Statistics
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-06-04
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.45002
Short DOI	https://doi.org/g9m7kn

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A Ridge-Based Multivariate Logistic Modelling for Assessing Factors Associated with Multifactorial Determinants of Under Five Child Malnutrition Outcomes in Mozambique.

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