Use of Anthropometric Measures to Determine Child Malnutrition in Pakistan

Dr. Sehar Saleem; Dr. Seyab Yasin; Mrs Maria Aslam; Dr. Atif Khan Jadoon; Miss Kiran Sarfraz

Authors

Dr. Sehar Saleem Lecturer, Department of Statistics, Lahore College for Women University, Lahore, Pakistan
Dr. Seyab Yasin Department of Economics and Quantitative Methods, Dr Hasan Murad School of Management (HSM), University of Management and Technology, Lahore, Pakistan
Mrs Maria Aslam Department of Statistics, Lahore College for Women University, Lahore, Pakistan
Dr. Atif Khan Jadoon Department of Economics, University of the Punjab, Lahore, Pakistan
Miss Kiran Sarfraz Department of Statistics, Lahore College for Women University, Lahore, Pakistan

Abstract

Background: Child malnutrition has been a substantial and consistent public health issue worldwide for many years, significantly affecting child mortality and morbidity that is severely obstructive in children's survival and growth. Objective: This study aims to assess how socioeconomic and demographic factors, as well as community health facilities, are related to child malnutrition among children aged less than five years in Punjab, Pakistan. Method: Multiple Indicator Cluster Survey (MICS) 2017-18 is conducted by the Bureau of Statistics, Pakistan, including men and women aged 15-49 years and children less than five years. Quantile Regression (QR) and Ordinary Least Squares (OLS) are used on anthropometric indicators to identify the factors contributing to child malnutrition in Pakistan. Results: A child's age, birth order, area of residence, sex, division, wealth index quintile, parental age, and education were significant determinants of child malnutrition. Female children are more likely to have higher stunting than male. Wealth index quantiles revealed the highest wasting in the poorer group compared to the richest. Conclusion: The OLS estimates can be deceptive when predicting the impact of factors at the lower end of the anthropometric indicator distribution. QR is a better approach than OLS to assess anthropometric distribution.

Keywords: Malnutrition, Anthropometric indicators, Quantile Regression, Ordinary Least Squares