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Prevalence and determinants of child malnutrition in Bangladesh: a comparative analysis of multilevel modeling

Afroza Jannat Suchana1, Syed Toukir Ahmed Noor1, Md Rafil Tazir Shah1

  • 1Department of Statistics, Shahjalal University of Science & Technology, Sylhet-3114, Bangladesh.

BMC Pediatrics
|January 31, 2026
PubMed

Insights

Child malnutrition in Bangladesh, affecting 31% of children under five, is linked to lower parental education and wealth. Improving maternal education and economic status is crucial for child health.

Area of Science:

  • Public Health
  • Pediatrics
  • Nutritional Epidemiology

Background:

  • Child malnutrition remains a significant public health concern in Bangladesh.
  • It profoundly impacts child development and overall health outcomes.
  • This study investigates malnutrition prevalence and its determinants in children under five.

Purpose of the Study:

  • To analyze the prevalence of stunting, wasting, and underweight in Bangladeshi children under five.
  • To identify key socio-economic, demographic, and health-related factors associated with child malnutrition.
  • To compare the performance of Generalized Linear Mixed Models (GLMM) and Generalized Estimating Equations (GEE) in analyzing malnutrition data.

Main Methods:

  • Utilized data from the Bangladesh Demographic and Health Survey (BDHS) 2017-18.
  • Employed Generalized Linear Mixed Models (GLMM) and Generalized Estimating Equations (GEE).
  • Examined associations between child malnutrition and factors like parental education, household wealth, maternal BMI, and geographic location.

Main Results:

  • Prevalence: 31% stunting, 22% underweight, 8% wasting.
  • Protective factors: Higher parental education and household wealth significantly reduced malnutrition risk (p < 0.05).
  • Geographic and maternal factors: Sylhet region showed higher odds of stunting and underweight; mothers with normal BMI had lower odds of all malnutrition types.

Conclusions:

  • Child malnutrition in Bangladesh is strongly associated with poor parental education, low economic status, and maternal health indicators.
  • GLMM showed superior predictive power (higher AUC) compared to GEE for analyzing malnutrition.
  • Policy recommendations include prioritizing maternal education, improving household economic conditions, and enhancing healthcare access.
Abstract

Keywords:
BangladeshGEEGLMMMalnutritionStuntingUnderweightWasting

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