Socio-demographic Determinants of Child Malnutrition Age 0-5 years in Bangladesh: NB and ZINB Approaches

TL;DR

This study analyzes socio-demographic factors affecting child malnutrition in Bangladesh using NB and ZINB models, highlighting the importance of appropriate statistical methods for over-dispersed count data.

Contribution

It introduces the application of NB and ZINB models to analyze household-level malnutrition counts, addressing over-dispersion and excess zeros in the data.

Findings

Negative binomial model outperforms ZINB in this context.

Over-dispersion is significant in malnutrition count data.

Socio-demographic factors are significantly associated with malnutrition counts.

Abstract

Although child malnutrition is improving over the world in the last couple of decades, still now it is concerning issue among the developing countries including Bangladesh. In general, malnutrition is a dichotomous response variable fitted with logistic regression model. But in this study, counting number of malnourished children in each household is defined as response variable. UNICEF with co-operating Bangladesh Bureau of Statistics (BBS) conducted Multiple Indicator Cluster Survey (MICS) covering 64000 households in Bangladesh by using two stage stratified sampling technique, where 21000 households have children age 0-5 years. We use bivariate analysis figuring out significant association between target and socio-demographic predictor variables. Then Negative binomial regression model is used over poisson regression model due to arising over-dispersion problem ($variance > mean$). Zero inflated negative binomial model also is applied for the excess of zeros in the target variable. Considering standard error and significant level of individual factors NB model provides better result as compare to ZINB.