Risk factors and socioeconomic inequalities in undernutrition among children 0-59 months of age in India

Abstract

In the majority of low- and middle-income counties, child health-care dissimilarities are further aggravated by nutritional status (i.e., stunting, underweight, and wasting). In India, child malnutrition is the most important contributor to disease burden. The present study uses data from the 4th round of the National Family Health Survey (NFHS-4), conducted in 2015-2016. We considered anthropometric indicators of unit-level data of 2, 48, and 174 children aged 0-59 months. This study examines the socio-economic inequality in nutritional status and their determinants among under-5 year children. The factors considered in the analysis were categorized as child age in months, mother’s educational status, mother’s nutritional-status, type of caste, wealth index, birth order, and size of a child at birth. In this study, multivariate logistic regression and concentration index (CI) have been employed to explore the effect of various factors on the child’s nutritional status. The binary logistic analysis has demonstrated a significant association between child nutritional status and mother’s education, mother’s nutritional status, type of caste, wealth index, birth order, and size of a child at birth. The results show that the CI for stunting, underweight, and wasting were −0.14, −0.16, and −0.08. Therefore, these factors were significantly high in poorer households. Our study suggests that the nutrition-specific programs to encourage nutritional adequacy, diversity, reduces the nutritional burden, and growth of child’s in India.

Application of quantile regression to examine changes in the distribution of Height for Age (HAZ) of Indian children aged 0–36 months using four rounds of NFHS data

“…This article focuses on the discussion and the identification of stunting in low-and middleincome countries as a major public health issue. In India, according to the past research, socioeconomic (maternal education, poverty) and biological variables (age, birth weight and maternal BMI) have been proven to have an effect on a child's nutritional health [1,3,24,26,29]. Five quantiles of the socioeconomic variable wealth index have been taken into account in this study and the quantiles are (poorest, poorer, middle, richer, and richest).…”

“…This study elucidates the use of quantile regression approach to investigate the effects of demographic, socio-economic and health-related factors on HAZ at different percentiles [18]. The conventional (linear and logistic) regression methods that were employed in earlier studies, but these approaches were limited to capture cross-distribution changes [3,9,[26][27][28][29][30]. The analysis identified the child's age in months as one of the strongest determinants of the nutritional status.…”

“…The overall resistance-to diseases and future performance in school and at work was also associated with Malnutrition [1,2]. In this study we explore the risk factors for childhood malnutrition in India, which is one of world's fastest growing economies, and the second most populated country in the world [3,4]. Nutrition has been an important role in determining the health status, particularly in developing nations [5].…”

Explaining the poor-rich gap in anthropometric failure among children in India: An econometric analysis of the NFHS, 2021 and 2016

“…Few studies have examined the poor-rich gap in AF and its correlates in India ( Kanjilal et al, 2010 ; Karlsson et al, 2021 ; Khadse & Bansod, 2021 ; Kumar et al, 2015 ; Kumar & Paswan, 2021 ; Mokalla & Mendu, 2020 ; Mukhopadhyay & Chakraborty, 2020 ; Nguyen et al, 2021 ; Pathak & Singh, 2011 ; Shirisha et al, 2022 ; Singh et al, 2020 ; Subramanian et al, 2008 ; Subramanyam et al, 2010 ). However, the small number of studies exploring this issue are either outdated ( Kanjilal et al, 2010 ; Kumar et al, 2015 ; Pathak & Singh, 2011 ; Subramanian et al, 2008 ; Subramanyam et al, 2010 ) or haven't focused on identifying risk factors that contribute to the poor-rich gap in AF using data from all the states and regions of India ( Karlsson et al, 2021 ; Khadse & Bansod, 2021 ; Kumar & Paswan, 2021 ; Mokalla & Mendu, 2020 ; Mukhopadhyay & Chakraborty, 2020 ; Nguyen et al, 2021 ; Shirisha et al, 2022 ; Singh et al, 2020 ). One recent study ( Porwal et al, 2021 ) has documented the poor-rich gap in AF using the Comprehensive National Nutrition Survey (2016–18) data.…”

Application of Quantile Regression to Examine Changes in the Distribution of Stunting of Indian Children Aged 0-36 Months Using Four Rounds of NFHS Data.

“…In the world, among under-five children, about 3.5 million die each year due to child malnutrition. In developing countries, more than one-third of all child deaths under the age of five were mainly caused by child undernutrition [3]. Therefore, nutrition was a significant factor in the health sector, especially in developing countries [4].…”