Determinants of malnutrition among under-five children in Bangladesh: a cross-sectional analytical study comparing
Mahmila Sanjana Mim1,2, Anamul Haque Sajib3, Jannatul Ferdous Nipa1
1Department of Statistics, University of Dhaka, Dhaka, 1000, Bangladesh.
Insights
The Proportional Odds Regression Model (POM) better analyzes child malnutrition in Bangladesh than Multinomial Logistic Regression (MLR). Key factors include mother's BMI, education, and household wealth.
Area of Science:
- Public Health
- Biostatistics
- Child Nutrition
Background:
- Child malnutrition is a major public health concern in Bangladesh.
- Identifying malnutrition determinants is crucial for effective interventions.
- This study compares statistical models for analyzing ordinal malnutrition data.
Purpose of the Study:
- To evaluate the suitability of statistical models for malnutrition data.
- To compare Multinomial Logistic Regression (MLR) and Proportional Odds Regression Model (POM).
- To identify key determinants of child malnutrition in Bangladesh.
Main Methods:
- Child nutritional status assessed using weight-for-age Z-scores (WAZ).
- Data categorized into severely undernourished, moderately undernourished, and nourished.
- MLR and POM applied; model fit compared using AIC and BIC.
Main Results:
- POM showed superior model fit (AIC: 8788.996, BIC: 9099.4353) over MLR (AIC: 8844.849, BIC: 9451.617).
- Significant malnutrition predictors identified: geographical division, child's sex, mother's BMI, mother's education, prenatal care, birth size, and household wealth.
Conclusions:
- POM effectively captures the ordinal structure of malnutrition categories, outperforming MLR.
- Findings highlight crucial determinants of child malnutrition in Bangladesh.
- Results provide guidance for targeted nutritional policies and development programs.
Background:
Malnutrition among children under five remains a pressing public health issue in Bangladesh. Identifying its determinants is critical for designing effective interventions. This study aims to evaluate the suitability of statistical models that account for the ordinal nature of malnutrition categories, comparing Multinomial Logistic Regression (MLR) and the Proportional Odds Regression Model (POM) using data from the sixth round of UNICEF's Multiple Indicator Cluster Survey (MICS).
Methods:
Child nutritional status was assessed using weight-for-age Z-scores (WAZ), categorized into severely undernourished, moderately undernourished, and nourished. MLR and POM were applied to model the relationship between malnutrition and various socio-demographic and health-related factors. Model performance was compared using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC).
Results:
POM demonstrated superior model fit (AIC: 8788.996, BIC: 9099.4353) compared to MLR (AIC: 8844.849, BIC: 9451.617). Significant predictors of malnutrition were identified through POM which included geographical division, child's sex, mother's BMI, mother's education, prenatal care, birth size, and household wealth index.
Conclusions:
The Proportional Odds Regression Model outperformed Multinomial Logistic Regression by effectively capturing the ordinal structure of malnutrition categories. These findings underscore key determinants of child malnutrition and offer valuable guidance for targeted nutritional policies and development programs in Bangladesh.
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