A Decomposition Analysis of Inequality in Malnutrition Among Under Five Children in India: Findings from National Family Health Survey-5

A Decomposition Analysis of Inequality in Malnutrition Among Under Five Children in India: Findings from National Family Health Survey-5 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Decomposition Analysis of Inequality in Malnutrition Among Under Five Children in India: Findings from National Family Health Survey-5 Naresh Chandra Kabdwal, Niraj Kumar Yadav, Kh. Jitenkumar Singh, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4443583/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Background: Nutrition is essential for good health and human development, particularly during the early stages of life. Nutritional status of children is essential for their development and growth at early stage of lives, with implications extending into adult life. Malnutrition is recognized to be the biggest single threat to the public health worldwide, particularly in developing countries. Nutritional status can be determined anthropometrically which is the outcome of complex interactions between socio-economic and biological variables. This study aimed to measure malnutrition inequality among underfive children in India and we have used a regression-based decomposition analysis to see the impact of socioeconomic determinants on this inequality. Methods: The study utilizes data from the National Family Health Survey (NFHS-5) conducted between 2019 and 2021. To examine the magnitude of socioeconomic inequality in child malnutrition, the concentration indices for stunting, underweight, and wasting are employed. Furthermore, decomposition analysis of the concentration indices is conducted to understand the role of socio-economic factors in child-hood malnutrition inequality. Results: The obtained concentration indices of stunting, underweight, and wasting were respectively -0.126, -0.138, and -0.064. Socioeconomic inequality in underweight and stunting was statistically significant, with children in the lowest socioeconomic quintile bearing a higher burden of malnutrition; however, no socio-economic gradient was observed in wasting. Urban children in the lowest quintiles experienced a higher burden of stunting than rural children, according to the concentration index value derived from the area of residence. Furthermore, socioeconomic status significantly influenced malnutrition inequality, with approximately 36% of the inequality in stunting, 44% in underweight, and over 68% in wasting attributed to family socioeconomic status. Conclusion: The average reduction in the national-level malnutrition indices reflects the impact of malnutrition among children in low-income families. To effectively address this issue, the government and policymakers must implement direct and targeted actions aimed at eliminating current inequities in the socio-economic determinants linked with malnutrition. By prioritizing interventions that specifically target vulnerable populations and address the underlying socioeconomic factors contributing to malnutrition, policymakers can work towards achieving equitable access to nutrition and improving the health outcomes of all children. Malnutrition Inequality Socioeconomic factors Decomposition NFHS-5 Figures Figure 1 Introduction Nutrition plays an important role in good health and human development. If humans consume a balanced diet, malnutrition can be avoided. Malnutrition is characterized by deficits, surpluses, and unbalanced calorie, protein, and nutrient intakes. It encompasses both under and over-nutrition. Despite significant growths in child's health and the overall decline in the rate of malnutrition worldwide, in developing countries continue to bear the significant burden of malnutrition, posing a persistent challenge to public health [ 3 ]. Globally, an estimated 156 million children under the age of five years are stunted, and approximately 50 million children in this age group suffer from wasting. This burden of malnutrition is not evenly distributed across countries; with the majority of affected children, approximately 145 million children are stunted and 48 million children are wasted, residing in African and Asian countries [30]. Furthermore, within countries, there exist socioeconomic inequalities, with children from lower socio-economic groups bearing a greater impact of malnutrition. Children's nutritional status is influenced by various socio-economic factors, including their mother's nutritional status and educational background, household wealth, demographic variables and residential area [1, 4, 15]. In India, the concentration indices and decomposition analysis were employed to examine socioeconomic inequalities in child mortality at both the state and national levels. Malnutrition in children under five years is a significant public health issue across many developing nations, and India is not an exception [23]. Child malnutrition manifests in various forms, which include stunting (height/length-for-age more than 2 standard deviations below the median), wasting (weight-for-height/length more than 2 standard deviations below the median), and underweight (weight-for-age more than 2 standard deviations below the median). Approximately 32% of children under five years are stunted, 3.5% are severely wasted, and 20.2% are underweight, in developing countries [ 2 ]. In India, the prevalence rate of stunting has decreased from 38.4–35.72%, while wasting has seen a slight decrease from 21–18.77%, and underweight prevalence has declined from 35.8–31.17% between time periods of NFHS-4 and NFHS-5 [ 12 ]. The central, northern, and eastern regions of India tend to be the centers of malnutrition [37]. A study carried out in India revealed that the malnutrition of spatial clustering was evident in geographical areas characterized by high poverty rates, low levels of women's education, and below-average BMI levels among women [17]. Malnutrition arises primarily from immediate causes such as lack of care, inadequate nutrition intake, and disease onset, compounded by underlying factors like inadequate education and unhealthy environments [ 6 ]. Studies conducted in countries have also highlighted significant disparities in child growth, other than India. For instance, research in Kenya's urban areas revealed a substantial gap in nutritional status between the rich and poor [ 9 ]. This inequality was attributed to factors such as poor housing conditions and limited access to clean water, food, and healthcare services. Additionally, findings suggest that children from impoverished households experience undernourishment not solely due to economic constraints but also because of reduced maternal healthcare utilization and inadequate parental education, compounded by maternal health issues [ 5 ]. Despite the prevalence of stunting, wasting, and underweight among children under five in India, there is a limited number of studies examining growth faltering as measured by z-scores [ 10 ]. Research indicates that weight gain begins shortly after birth, with a decline in weight-for-age z-score (WAZ) observed during the first three months, followed by a slower rate of decline [18]. A comparative analysis of the NFHS-1 and NFHS-3 data aimed to give insights into the growth patterns of children aged 0–3 years at the national level [18]. Early childhood growth has been linked to educational outcomes in later years, with stunting associated with cognitive deficits and educational challenges in late adolescence. However, it's important to note that children’s educational performance has been influenced by various kinds of socio-demographic factors, particularly their mother’s education and the condition of the environment at home, which may confound the relationship [11, 34]. The Global Nutrition Report 2021 shows that India is home to 34.7% of the world's undernourished children. Malnutrition is thought to be the root cause of about half of all under-five infant fatalities in India [14]. The nutritional status of children must be better because today's citizens will be tomorrow's future. India's 102nd ranking in the Global Hunger Index (GHI) [13] underscores the severe levels of undernutrition within the country. Furthermore, various literatures have highlighted the existence of a nutritional poverty problem in India [16]. Despite this recognition, there is a notable lack of studies focusing on examining the socioeconomic variation of malnutrition among children across the country. As a result, the purpose of this study aims to address this gap by measuring malnutrition inequality among children under five in India and exploring the effect of socio-economic determinants on these disparities. Materials and Methods Data The data utilized for this study were obtained from the National Family Health Survey-5 (NFHS-5) conducted during 2019–21. NFHS-5 aimed to gather crucial information on health, family welfare, and emerging issues such as the nutritional status of children under the age of five at national, state, and district levels. The survey collected demographic, health, and socioeconomic data from 133,920 households, comprising 47,199 urban and 85,721 rural households. This nationally representative dataset offers vital insights into health, family welfare, and emerging concerns across the country. Moreover, NFHS is part of the broader Demographic and Health Surveys (DHS), which are nationally representative household surveys providing comprehensive data for monitoring and evaluating various indicators related to population, health, and nutrition. Overall, this study analyzed data from a total of 232,920 children, whose accurate and complete information on age, sex, weight, and height, as well as the characteristics of their mothers and their families socioeconomic status, was collected. The objective was to examine socioeconomic inequalities in malnutrition among children and identify variables influencing them. The data included anthropometric measurements of children's weight, height, and underweight levels. Detailed information related to the NFHS-5 design, protocols, and tools can be found in the NFHS-5 national report [ 12 ], and all essential information is publicly available on http://rchiips.org/NFHS . Measuring malnutrition Anthropometric markers, such as weight-for-age (WAZ), height-for-age (HAZ), and weight-for-height (WHZ) z-scores, are frequently employed to assess children's health status, particularly regarding malnutrition. In this study, the WHO's growth chart [36] was utilized to calculate z-scores. Children with z-score values for weight-for-age (WAZ), height-for-age (HAZ), and weight-for-height (WHZ) less than − 2SD were classified as underweight, stunting, and wasted, respectively [35]. Predictor variables The study categorized predictors into four distinct groups: 1) Child-related factors, encompassing age (0 to 59 months), sex of the child (female and male), child health outcomes, and birth order of the child; 2) Maternal factors, encompassing education levels (No education, Primary, Secondary, and Higher) and age categories (15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49 years); 3) Residential factors, comprising urban, rural, and national levels; and 4) Socioeconomic factors, encompassing religion (Muslim, Hindu, Christian, and Others), and wealth status (Poorer, Poorest, Middle, Richest, and Richer). The wealth status variable was derived from survey data, using wealth quintile as a proxy for income of household to evaluate socioeconomic status. Initially, household assets, including items such as televisions, bicycles, or cars, along with housing characteristics such as the source of flooring materials, drinking water, and toilet facilities were organized and categorized. Subsequently, the wealth quintile score for each household was computed using principal component analysis, a statistical method. Finally, the wealth quintile scores were used to ascertain the socioeconomic ranking of each household, dividing them into five quintiles: Poorer, Poorest, Middle, Richer, and Richest. Detailed information regarding the method for estimating the wealth-asset quintile can be found elsewhere. Statistical analysis Concentration Index (CI) Income-related inequality in wasting, stunting, and underweight was assessed using the concentration index (CI), with the wealth score serving for the socioeconomic variables and the binary outcomes indicating wasting, stunting, and underweight. The CI quantifies the extent of inequality by measuring the deviation of the concentration curve from the line of equality, ranging from − 1 to + 1. A value of 0 indicates no socioeconomic inequality, while positive values denote pro-rich inequality, and vice versa. Higher values signify greater socioeconomic inequality. Wagstaff's decomposition analysis was employed to further dissect the CI, revealing the effects of individual factors to income related inequality [32]. The concentration index (CI) will be used to assess the level of socioeconomic inequality among children with stunting, underweight, and wasting. Following is the CI formula: $$c= \frac{2}{n.{\mu }}\left({\sum }_{k=1}^{n}{y}_{k} {R}_{k}\right)-1$$ 1 Where, n denotes the sample size, µ denotes the average of \({y}_{k}\) , \({y}_{k}\) denotes the value of the underweight, wasting and stunting indices of the k th individual, and R k denotes the relative rank of socio-economic status of the k th individual. The formula will be used to calculate the concentration index (CI) of underweight, wasting and stunting. To investigate the effect of socioeconomic factors on child malnutrition inequality, the concentration index will be decomposed. This decomposition method requires a linear regression model. Therefore, stunting and underweight will be treated as continuous dependent variables, and multiple linear regressions will be conducted to calculate the coefficients between child malnutrition and socioeconomic factors. For continuous outcome variables, a linear regression model connecting to the outcome variable \(y\) to the set of \({i}^{th}\) determinant of malnutrition \(\left({x}_{ik}\right)\) expressed as: $${ y}_{k}=\alpha +{\sum }_{i}{ \beta }_{i}{x}_{ik}+{ϵ}_{k}$$ 2 Where, \({\beta }_{i}\) denotes the coefficient of independent variable and the error term is \({ϵ}_{k}\) . Equation ( 2 ) replaces Eq. ( 1 ) with an equal amount of \({y}_{k}\) , \({y}_{k}\) of the concentration index will be $$c=\sum _{i}\left(\frac{{\beta }_{i}\stackrel{-}{{x}_{i}}}{\mu }\right){c}_{i}+\frac{{GC}_{ϵ}}{\mu }$$ 3 Where \(\stackrel{-}{{ x}_{i}}\) denotes the mean of \({ x}_{i}\) and for \({x}_{i},{ c}_{i}\) denotes a concentration index and \(\) \({GC}_{ϵ}\) denotes the generalized concentration index for the error term ( ɛ ). Elasticity \(\left(\frac{{\beta }_{i}\stackrel{-}{{x}_{i}}}{\mu }\right)\) is a part of the first (deterministic component) of Eq. ( 3 ), which express the explanatory variables the impact malnutrition, and in socio-economic groups \({c}_{i}\) denotes the inequality of determinant variables in the distribution. In non-deterministic component (second part) related to the malnutrition inequality in the socio-economic groups, that are unable to clarified by contributors \(\left(\frac{{GC}_{\in }}{\mu }\right)\) . The multivariate linear regression model will include socioeconomic characteristics such as socioeconomic status, residential area, household size, mother's age at birth (years), mother's education sex, age (0–5 years) and birth-order of children. All of the statistical analyses were carried out using Stata 14.1 (Stata Corp, USA). Results Table 1 presents the distribution of children underfive years in India by selected background characteristics, providing descriptive statistics of both the explanatory and outcome variables. The average growth among children, measured through z-scores for height-for-age (HAZ), weight-for-age (WAZ), and weight-for-height (WHZ), was found to be 0.36, 0.31, and 0.19, respectively. Boys slightly outnumbered girls, accounting for 51.80% of the dataset compared to 48.2% for girls. The average age of the children in the dataset was 29.6 months, with ages ranging from 0 to 59 months. Furthermore, a substantial percentage of the children (20.30%) resided in urban areas, while the majority (79.70%) lived in rural areas. Table 1 Characteristics of a dataset Background Characteristic Mean (Standard Deviation) Number (percentage) Mini-mum Maxim-um Child health outcome (z-score) Weight for height 0.19 (0.39) 0 1 Height for age 0.36 (0.48) 0 1 Weight for age 0.31 (0.46) 0 1 Sex of children Female 112,255(48.2) Male (reference) 120,665(51.8) Children age ( in months) 29.6 (17.43) 0 59 Birth order of children 2.15 (1.35) 0 16 Mother’s education No education (reference) 51210(21.99) Primary 4,215(12.91) Secondary 119864(51.5) Higher 31765(13.6) Mother’s age at birth (in years) 15–19(reference) 5,461(2.34) 20–24 66,485(28.54) 25–29 92,448(39.69) 30–34 45,587(19.57) 35–39 17,614(7.56) 40–44 4,196(1.82) 45–49 1,129(0.48) Place of residence Urban(reference) Rural 47199(20.3) 85721(79.7) Wealth quintile Poorest (reference) 63,406(27.22) Poorer 54,463(23.38) Middle 45,083(19.36) Richer 39,094(16.78) Richest 30,874(13.26) Number of Household member 6.25(2.61) 1 35 The majority of mothers in the dataset have attained secondary education (51.5%), while a notable portion have no formal education (21.99%), with smaller proportions having primary education (12.91%) or higher education (13.6%). Based on the data, the most common age range for mothers at the birth of their children is between 25 and 29 years, representing 39.69% of the dataset, with smaller proportions falling into other age brackets. In terms of wealth distribution, the largest proportion of individuals in the dataset belong to the poorest quintile, comprising 27.22% of the sample, followed by the poorer quintile at 23.38%, with decreasing percentages observed in the middle, richer, and richest quintiles. Additionally, the average family size was 6.25 individuals, and the distribution of children in this study was nearly uniform across economic quintiles. Table 2 Concentration index and Prevalence rate of under five child malnutrition, NFHS-5 Variable Stunting Wasting Under-weight Prevalence (%) CI Prevalence (%) CI Prevalence (%) CI National 35.72 -0.126 18.77 -0.064 31.17 -0.138 Urban 29.89 -0.148 17.64 -0.053 26.08 -0.159 Rural 37.20 -0.129 19.05 -0.080 15.22 -0.146 Table 2 presents the concentration indices and prevalence rates for stunting, wasting, and underweight among children, disaggregated by national, urban, and rural areas. The prevalence of stunting among Indian children was observed to be higher than that of wasting and underweight. Similarly, this pattern of malnutrition index prevalence was consistent across different residential areas. At the national level, the obtained concentration indices for stunting, wasting, and underweight were − 0.126, -0.064, and − 0.138, respectively. Stunting affects 35.72% of children nationally, with a concentration index of -0.126, indicating a higher concentration among disadvantaged groups. Underweight prevalence is 31.17% nationally, with a similar concentration trend. Wasting, affecting 18.77% of children nationally, exhibits a lower concentration index, suggesting a more equitable distribution across socioeconomic groups. The concentration index values indicated that the lowest quintiles experienced a greater burden of stunting in urban children compared to rural children. Conversely, wasting and underweight concentration indices were somewhat higher in rural areas compared to urban areas. The urban and rural breakdowns revealed differing prevalence rates and concentration indices across the various indicators. Figure 1 illustrates the prevalence of child’s malnutrition, including stunting, wasting, and underweight, at rural, urban, and national levels in India from 2019 to 2021. The findings indicate that all three indices of child growth were more pronounced among both urban and rural populations. Moreover, while the prevalence of stunting decreased among both urban and rural children during the period, underweight prevalence also declined among these groups. However, wasting increased among both urban and rural children over the same period. These trends were consistent at the national level in India. Additionally, this section presents the findings of the decomposition of socioeconomic inequality and regression analysis in underweight and stunting. Notably, due to the non-significance of the concentration index of wasting, its decomposition results were not included. Table 3 Stunting (height-for-age) scores: Determinants and decomposition study of the contribution of several explanatory variables, NFHS-5, 2019–21. Background Characteristic Elasticity Coefficient CIs Absolute Contribution to CI Contribution Percentage Sex of children Female -0.0229 -0.105 -0.0075 0.0002 -0.0009 Birth order of children 0.1298 0.698 -0.097 -0.0159 0.0629 Children age (in months) 0.1624 0.521 -0.0043 -0.0007 -0.0035 Mothers education Primary 0.0589 0.069 0.2714 0.0159 -0.0799 Secondary -0.0878 -0.257 -0.1372 -0.1206 0.0603 Higher 0.2679 -0.429 -0.6089 -0.1631 0.8159 Mothers' age (in years) at birth 20–24 -0.0053 -0.092 -0.0929 0.0005 -0.0025 25–29 -0.0339 -0.248 0.0596 -0.002 0.0101 30–34 -0.0302 -0.389 0.0845 -0.0026 0.0128 35–39 -0.0138 -0.486 0.0119 -0.0002 0.0008 40–44 -0.0043 -0.539 -0.1039 0.0005 0.0022 45–49 -0.001 -0.639 -0.2808 0.0003 -0.0014 Place of residence Rural -0.0351 -0.056 -0.6039 0.0212 -0.1058 Wealth-quintile Poorer -0.0233 -0.181 -0.3717 0.0087 -0.0433 Middle -0.0404 -0.325 0.1513 -0.0061 0.0306 Richer -0.0639 -0.541 0.6143 -0.0393 0.1965 Richest -0.0732 -0.745 1 -0.0732 0.3659 Constant -0.349 Table 3 presents coefficients, elasticity’s, concentration indices (CIs), and absolute contributions to CI for several key variables associated with child malnutrition. Female children show a negative association with malnutrition, as indicated by their coefficient of -0.105, elasticity of -0.0229, and CI of -0.0075. A child's age exhibits a positive association, with a coefficient of 0.521, an elasticity of 0.1624, and a CI of -0.0043. The birth order of the child demonstrates a positive association with a coefficient of 0.698 and a CI of -0.0970. Mother's education level shows nuanced effects: primary education has a coefficient of 0.069, secondary education has a coefficient of -0.257, and higher education has a coefficient of -0.429. Mother's age at birth presents mixed effects across different age brackets, with varying coefficients, elasticity’s, and CIs. Rural residence is positively associated with malnutrition, with a coefficient of -0.056, an elasticity of -0.0351, and a CI of -0.6039. Wealth quintiles exhibit a gradient effect, with coefficients ranging from − 0.181 for the poorer quintile to -0.745 for the richest quintile. Table 4 Wasting (weight-for-age) scores: Determinants and decomposition study of the contribution of several explanatory variables, NFHS-5, 2019–21. Background Characteristic Elasticity Coefficient CIs Absolute Contribution to CI Contribution Percentage Sex of children Female -0. 0402 -0.112 -0.0075 0. 0003 -0. 0038 Birth order of children -0. 0078 -0.209 -0.097 0. 0007 -0. 0096 Children age (months) -0. 2645 -0.548 -0.0043 0. 0011 -0. 0144 Mothers education Primary 0.0467 -0.109 0.2713 0.0127 -0.1595 Secondary 0.0312 -0.131 -0.1372 -0.0043 0.0539 Higher 0.1092 -0.237 -0.6089 -0.0665 0.8371 Mothers' age (in years) at birth 20–24 -0.0341 -0.053 -0.0929 0.0031 -0.0398 25–29 -0.0237 -0.022 0.0596 -0.0014 -0.0178 30–34 -0.0152 -0.05 0.0845 -0.0013 -0.0162 35–39 -0.0038 -0.045 0.0119 -0.0001 0.0006 40–44 -0.0012 -0.076 -0.1039 0.0001 -0.0015 45–49 0.0006 0.141 -0.2808 -0.0002 0.0023 Place of residence Rural -0.0691 0.098 -0.6039 0.0417 -0.5251 Wealth-quintile Poorer -0.0255 -0.151 -0.3717 0.0095 -0.0119 Middle -0.0393 -0.214 0.1513 -0.0059 0.075 Richer -0.0456 -0.295 0.6143 -0.0280 0.3524 Richest -0.5434 -0.439 1.0000 -0.0543 0.6839 Constant -0.466 Table 4 provides coefficients, elasticity’s, concentration indices (CIs), absolute contributions to CI, and contribution percentages for various variables related to child malnutrition. Female sex shows a coefficient of -0.112, indicating a negative association with malnutrition, with an elasticity of -0.0402 and a CI of -0.0075, contributing − 0.0038 to the overall CI. The child's age exhibits a larger coefficient of -0.548, suggesting a stronger negative association with malnutrition, accompanied by an elasticity of -0.2645 and a CI of -0.0043, contributing − 0.0144. The birth order of the child has a smaller coefficient of -0.209, indicating a less pronounced negative association, with an elasticity of -0.0078 and a CI of -0.0970, contributing − 0.0096. Mother's education level presents varying effects: primary education has a negative coefficient of -0.109, secondary education has a negative coefficient of -0.131, and higher education has a larger negative coefficient of -0.237, each contributing differently to the overall CI. Mother's age at birth displays diverse effects across age groups, with varying coefficients and contributions. Rural residence is associated with a positive CI, while wealth quintiles exhibit gradients, with negative CIs for the poorer quintiles and positive CIs for the richer quintiles. Table 5 Underweight (weight-for-height) scores: Determinants and decomposition study of the contribution of several explanatory variables, NFHS-5, 2019-21. Background Characteristic Elasticity Coefficient CIs Absolute Contribution to CI Contribution Percentage Sex of children Female -0. 0267 -0.115 -0 0074 -0.0002 -0.0009 Birth order of child 0.0958 0.412 -0.097 -0.0093 0.0449 Children age (in months) 0. 1376 0.607 -0.0043 -0.0006 -0.0029 Mothers education Primary 0.0664 -0.153 0.2714 0.0180 -0.0871 Secondary 0.0765 -0.289 -0.1372 -0.0105 0.0508 Higher 0.2452 -0.497 -0.6089 -0.1494 0.7222 Mothers' age (in years) at birth 20–24 -0.0404 -0.155 -0.0929 0.0038 -0.0181 25–29 -0.0591 -0.246 0.0596 -0.0035 0.017 30–34 -0.0393 -0.384 0.0845 -0.0033 0.0161 35–39 -0.0166 -0.495 0.0119 -0.0002 0.0009 40–44 -0.0055 -0.603 -0.1039 0.0006 -0.0028 45–49 -0.0006 -490 -0.2808 0.0002 0.0002 Place of residence Rural -0.0536 -0.958 -0.6039 0.0323 -0.1564 Wealth-quintile Poorer -0.0342 -0.247 -0.3717 0.0127 0.0615 Middle -0.0557 -0.391 0.1513 -0.0084 0.0408 Richer -0.0772 -0.579 0.6143 -0.0474 0.2293 Richest -0.9181 -0.829 1 -0.9181 0.4439 Constant -0.441 Table 5 outlines coefficients, elasticity’s, concentration indices (CIs), absolute contributions to CI, and contribution percentages for various factors affecting child malnutrition. Female sex exhibits a coefficient of -0.115, indicating a negative association with malnutrition, with an elasticity of -0.0267 and a CI of -0.0074, contributing − 0.0009 to the overall CI. Conversely, a child's age presents a coefficient of 0.607, suggesting a positive association, with an elasticity of 0.1376 and a CI of -0.0043, contributing − 0.0029. The birth order of the child demonstrates a positive association with a coefficient of 0.412, an elasticity of 0.0958, and a CI of -0.0970, contributing − 0.0449. Regarding a mother's education level, primary education exhibits a coefficient of -0.153, secondary education has a coefficient of -0.289, and higher education displays a larger coefficient of -0.497, each contributing differently to the overall CI. Mother's age at birth shows varied effects across age groups, with varying coefficients and contributions. Additionally, rural residence is associated with a negative CI, while wealth quintiles display gradients, with negative CIs for poorer quintiles and positive CIs for richer quintiles. Tables 3 , 4 , and 5 present the outcomes of decomposition analysis, which include elasticity, coefficient, concentration index, contribution, and absolute contribution percentage of all determinants to socioeconomic inequality in malnutrition. Generally, socioeconomic characteristics more prevalent among lower quintiles (with more negative concentration indices) led to a greater improvement in malnutrition inequality. Moreover, factors associated with higher elasticity were positively related to socioeconomic disparities in malnutrition. However, a significant portion of the socioeconomic disparity in malnutrition among children under the age of five could be determined by socioeconomic level. Approximately 36% of stunting inequalities, 68% of wasting inequality, and 44% of underweight inequality were attributable to socioeconomic status. Maternal education level was another factor contributing to the inequality in stunting, wasting, and underweight (7.99% in stunting, 15.95% in wasting, and 8.71% in underweight). Disparities in residential areas and the average number of child deliveries influenced the remaining inequality in underfive child malnutrition. Discussion Despite recent economic progress, India has not been able to provide better outcomes for child growth. The outcomes also indicate the presence of socioeconomic clustering, which further widens the gap in achieving growth among children. The purpose of this study was to identify the factors that contribute to wealth inequality in stunting, wasting, and underweight among children under the age of five in both urban and rural India. Unfortunately, there is limited information available to comprehend the timing of growth faltering in Indian children underfive years. The few studies conducted on this subject have identified growth faltering using the National Centre for Health Statistics (NCHS) reference growth standards [18]. This study stands out from previous research in measuring growth faltering by utilizing WHO standards, providing a more comprehensive understanding of this phenomenon compared to the NCHS methodology [25]. Maternal factors continue to play a significant role in contributing to inequality in child malnutrition. Stunting, defined as height-for-age more than − 2 standard deviations below the median, reflects chronic undernutrition in children. Maternal factors, including age, first birth age, education, parity, and anemia, accounted for over half of the inequality observed in stunting among children in recent years. Studies conducted in India have consistently found associations between maternal factors, particularly maternal education, and stunting. This trend is not unique to India but has been observed in both developed and developing countries [ 7 ]. Similarly, underweight, defined as weight-for-age more than − 2 standard deviations below the median, reflects both past and present undernutrition. Maternal factors also emerged as the primary contributors to inequality among underweight children [20]. Research employing decomposition analysis has highlighted the role of maternal education in improving weight-for-age z-scores among Indian children [22, 27]. Wasting, defined as weight-for-height more than − 2 standard deviations below the median, is a measure of acute undernutrition. Although child wasting inequality has decreased, the prevalence of child wasting has increased, particularly in countries such as India and Sri Lanka [29]. Maternal factors were found to be the highest contributors to inequality in child wasting during recent years. This study goes beyond previous research efforts by aiming to provide a comprehensive understanding of the nutritional condition of children across different socioeconomic categories. Its objectives include monitoring the disparity in stunting, wasting, and underweight, as well as determining the factors connected to this disparity. To achieve these goals, the study utilizes data from the National Family Health Survey-5 (NFHS-5) and employs decomposition analysis of the concentration index. This approach allows for a detailed examination of the socioeconomic factors contributing to disparities in child malnutrition. A higher concentration of malnutrition among disadvantaged socioeconomic groups, as shown by the concentration indices for stunting, wasting, and underweight, were found to be -0.126, -0.064, and − 0.138, respectively. Decomposition analysis revealed that maternal education and household economic status were the primary factors contributing to socioeconomic inequalities in malnutrition. These findings align with similar studies conducted in other countries, suggesting that despite reductions in malnutrition rates, socioeconomic disparities persist and may even be widening [1, 21]. Our study's decomposition results showed that the primary cause of disparities in child malnutrition was the economic status of households. Approximately 36% of the stunting inequality, 68% of the wasting inequality, and 44% of the underweight inequality were attributed to disparities in the economic status of families. These findings of this study are consistent with earlier research, which also highlighted household economic status as a key determinant of malnutrition inequality. Compared to access to healthcare, promoting socioeconomic status has been shown to have a more significant impact on health indicators and outcomes, particularly in improving nutritional status and reducing child mortality [19, 8, 33]. Household economic status directly influences anthropometric outcomes and children's nutrition by enabling access to food and indirectly influencing other factors. The decomposition analysis also highlighted that maternal education is the second significant variable contributing to inequality in stunting, underweight and wasting. Children of mothers with higher levels of education were at a lower risk of experiencing growth disorders compared to those with less educated mothers. Our study found that 8% of the stunting inequality, 16% of the wasting inequality, and 9% of the underweight inequality were attributed to differences in maternal levels of education. Mother’s education emerged as a crucial factor influencing inequalities in child malnutrition. Parental education, particularly the education of mothers, plays a vital role in promoting the health of children. Mothers who are educated are more likely to seek healthcare and engage in activities that enhance their nutritional status and their children's health [26]. The third determinant contributing to malnutrition inequality is living in rural areas. Studies across 47 developing countries have highlighted significant disparities in children's nutritional conditions in urban and rural areas [31]. Further examination of the socioeconomic factors impacting children's nutritional status revealed notable differences between urban and rural children. This disparity stems from variations in key factors; children residing in cities typically experience more favorable circumstances [28]. In general, rural households exhibit lower levels of education, improved sanitation facilities, less availability of safe drinking water, and less availability of healthcare facilities in relation to their urban counterparts. However, initiatives like the ''National Program for Improving Nutritional Status of Children'' have struggled to effectively address the social determinants impacting the children's nutritional status and to address disparities in nutritional outcomes, particularly among impoverished families [24]. Therefore, it is imperative for policymakers to implement targeted strategies to address malnutrition inequalities based on socioeconomic determinants as well as comprehensive interventions aimed at reducing the overall prevalence of malnutrition, particularly stunting. Conclusion The study provides valuable insights into the growth patterns and inequalities in child malnutrition among underfive in India. Several key findings emerge from this analysis. Firstly, maternal factors, particularly mother's education, were found to be the primary contributors to inequality in socioeconomic status in stunting and underweight among children in both urban and rural areas during the period of 2019–21. Secondly, maternal factors also played a significant role in explaining wasting inequalities among children. These findings underscore the importance of focusing on improving maternal education, especially in rural and slum areas, to address malnutrition among children. Children from economically disadvantaged families are disproportionately affected by malnutrition, particularly stunting, highlighting the need for targeted interventions. Moreover, addressing maternal education can potentially alleviate this burden by improving knowledge and awareness among mothers, thereby positively impacting child nutrition outcomes. Prioritizing nutrition-sensitive and nutrition-specific interventions in both urban and rural areas, particularly targeting the urban and rural poor residing in low-income slums, is crucial. Addressing the rich-poor inequality in malnutrition necessitates integrating and converging nutrition interventions with poverty reduction policies for children in urban and rural areas. Expanding the scope of existing programs like Integrated Child Development Services (ICDS) to include mass education on health and nutrition, with a focus on pregnant and lactating mothers, is essential. This expansion should also emphasize improving maternal education, which plays a significant role in addressing malnutrition disparities. For meaningful progress in combating this public health issue among children, direct and targeted efforts are required from the government and policymakers to eliminate existing socioeconomic inequalities associated with malnutrition. Declarations Ethics approval and consent to participate : Not applicable Consent for publication : Not applicable Availability of data and materials : NFHS data is a nationally representative data set which available freely in public domain Competing interests : The authors declare that they have no competing interests. Funding: Authors did not receive any funding to carry out this research. Authors' contributions : All authors contributed to the study conception and design. Author 1 and 2 wrote the main manuscript text, and author 2 prepared the tables and figures, author 1 and 3 helped to decide the aims and objective and author 4 helped to running the program for making tables. All the authors reviewed the manuscript. Acknowledgements: Not Applicable References Ahmed S, Hasan MM, Ahmed W, Chowdhury MA. Socio-economic Inequity of Malnutrition among under-five Children and Women at Reproductive Age in Bangladesh. J Nutr Health. 2013;1:13-7. Amini RM, Rashidian A, Khosravi A, Arab M, Abbasian E, Morasae EK. Changes in socio-economic inequality in neonatal mortality in Iran between 1995-2000 and 2005-2010: an Oaxaca decomposition analysis. International journal of health policy and management. 2017 Apr;6(4):219. Black RE, Allen LH, Bhutta ZA, Caulfield LE, De Onis M, Ezzati M, Mathers C, Rivera J. Maternal and child undernutrition: global and regional exposures and health consequences. The lancet. 2008 Jan 19;371(9608):243-60. Chalasani S. Understanding wealth-based inequalities in child health in India: a decomposition approach. Social science & medicine. 2012 Dec 1;75(12):2160-9. Chauhan BG, Chauhan S, Chaurasia H. Decomposing the gap in child malnutrition between poor and non-poor in Sierra Leone. Journal of Public Health. 2019 Feb 6;27:119-27. Correia LL, Silva AC, Campos JS, Andrade FM, Machado MM, Lindsay AC, Leite ÁJ, Rocha HA, Cunha AJ. Prevalence and determinants of child undernutrition and stunting in semiarid region of Brazil. Revista de saude publica. 2014;48:19-28. Deshpande A, Ramachandran R. Which Indian children are short and why? Social identity, Childhood Malnutrition and Cognitive Outcomes. 2020 Mar 2. Dollar D, Kraay A. Growth is Good for the Poor. Journal of economic growth. 2002 Sep;7:195-225. Fotso JC, Madise N, Baschieri A, Cleland J, Zulu E, Mutua MK, Essendi H. Child growth in urban deprived settings: does household poverty status matter? At which stage of child development?. Health & place. 2012 Mar 1;18(2):375-84. Headey D, Menon P, Nguyen P. The timing of growth faltering in India has changed significantly over 1992–2016, with variations in prenatal and postnatal improvement (P10-005-19). Current developments in nutrition. 2019 Jun 1;3:nzz034-P10. Holme AR, Blair PS, Emond AM. Psychosocial and educational outcomes of weight faltering in infancy in ALSPAC. BMJ open. 2013 Jul 1;3(7):e002863. IIPS, & ICF. National Family Health Survey (NFHS-5): 2019-21 India. Mumbai: International Institute for Population Sciences (IIPS). 2021. Index GH. Global, Regional, and National Trends-Global Hunger Index-peer-reviewed annual publication designed to comprehensively measure and track hunger at the global, regional, and country levels. Global Hunger Index. Global Hunger Index. 2019. Index GH. Global, Regional, and National Trends-Global Hunger Index-peer-reviewed annual publication designed to comprehensively measure and track hunger at the global, regional, and country levels. Global Hunger Index. Global Hunger Index. 2021. Jayawardena P. Socio-economic determinants and inequalities in childhood malnutrition in Sri Lanka. Well-Being and Social Policy Journal. 2012;8(1):1-22. Jha R, Gaiha R, Sharma A. Poverty nutrition trap in rural India. Australian National University Trade and Development Working Paper. 2005 Mar.. Khan J, Mohanty SK. Spatial heterogeneity and correlates of child malnutrition in districts of India. BMC public health. 2018 Dec;18:1-3. Mamidi RS, Shidhaye P, Radhakrishna KV, Babu JJ, Sudhershan Reddy P. Pattern of growth faltering and recovery in under-5 children in India using WHO growth standards—a study on First and Third National Family Health Survey. Indian pediatrics. 2011 Nov;48:855-60. Marianne F, Danny L, Wodon QT, Yepes T. Achieving the millennium development goals: the role of infrastructure. Available at SSRN 636582. 2003 Nov. Nie P, Rammohan A, Gwozdz W, Sousa-Poza A. Changes in child nutrition in India: a decomposition approach. International journal of environmental research and public health. 2019 May;16(10):1815. O'Donnell O, Wagstaff A, Van Doorslaer E, Lindelow M. Analyzing health equity using household survey data: a guide to techniques and their implementation. World Bank Publications; 2007 Nov 2. Prakash M, Jain K. Inequalities among malnourished children in India: A decomposition analysis from 1992-2006. International Journal of Social Economics. 2016 Jun 13;43(6):643-59. Sahu SK, Kumar SG, Bhat BV, Premarajan KC, Sarkar S, Roy G, Joseph N. Malnutrition among under-five children in India and strategies for control. Journal of natural science, biology, and medicine. 2015 Jan;6(1):18. Salvucci V. Determinants and trends of socioeconomic inequality in child malnutrition: the case of Mozambique, 1996–2011. Journal of international development. 2016 Aug; 28 (6):857-75. Shrimpton R, Victora CG, de Onis M, Lima RC, Blossner M, Clugston G. Worldwide timing of growth faltering: implications for nutritional interventions. Pediatrics. 2001 May 1; 107 (5):e75-5. Singh L, Rai RK, Singh PK. Assessing the utilization of maternal and child health care among married adolescent women: evidence from India. Journal of biosocial science. 2012 Jan;44(1):1-26. Singh S, Srivastava S, Upadhyay AK. Socio-economic inequality in malnutrition among children in India: an analysis of 640 districts from National Family Health Survey (2015–16). International journal for equity in health. 2019 Dec;18:1-9. Smith LC, Ruel MT, Ndiaye A. Why is child malnutrition lower in urban than in rural areas? Evidence from 36 developing countries. World development. 2005 Aug 1;33(8):1285-305. United Nations Children's Fund, World Health Organization, The World Bank. UNICEF-WHO-World Bank joint child malnutrition estimates. Levels & Trends in Child Malnutrition. 2012. United Nations Children's Fund, World Health Organization, The World Bank. UNICEF-WHO-World Bank joint child malnutrition estimates. Levels & Trends in Child Malnutrition. 2016. Van de Poel E, O’Donnell O, Van Doorslaer E. Are urban children really healthier? Evidence from 47 developing countries. Social science & medicine. 2007 Nov 1;65(10):1986-2003. Wagstaff A. Socioeconomic inequalities in child mortality: comparisons across nine developing countries. Bulletin of the World Health Organization. 2000;78:19-29. Wagstaff A, Watanabe N. Socioeconomic inequalities in child malnutrition in the developing world. World Bank Policy Research Working Paper. 2000 Sep 30(2434). Walker SP, Chang SM, Powell CA, Simonoff E, Grantham-McGregor SM. Early childhood stunting is associated with poor psychological functioning in late adolescence and effects are reduced by psychosocial stimulation. The Journal of nutrition. 2007 Nov 1;137(11):2464-9. WHO Multicentre Growth Reference Study Group, de Onis M. WHO Child Growth Standards based on length/height, weight and age. Acta paediatrica. 2006 Apr;95:76-85. WHO Working Group. Use and interpretation of anthropometric indicators of nutritional status. Bulletin of the World health organization. 1986;64(6):929. Yadav A, Ladusingh L, Gayawan E. Does a geographical context explain regional variation in child malnutrition in India?. Journal of Public Health. 2015 Oct;23:277-87. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 31 Oct, 2024 Reviews received at journal 16 Oct, 2024 Reviews received at journal 15 Oct, 2024 Reviews received at journal 13 Oct, 2024 Reviewers agreed at journal 26 Sep, 2024 Reviewers agreed at journal 26 Sep, 2024 Reviewers agreed at journal 26 Sep, 2024 Reviewers agreed at journal 26 Sep, 2024 Reviewers agreed at journal 04 Sep, 2024 Reviewers agreed at journal 02 Jul, 2024 Reviewers agreed at journal 11 Jun, 2024 Reviewers invited by journal 06 Jun, 2024 Editor invited by journal 27 May, 2024 Editor assigned by journal 27 May, 2024 Submission checks completed at journal 21 May, 2024 First submitted to journal 19 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4443583","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":308076642,"identity":"1b8d027f-4437-4f42-915e-0f2de2012d52","order_by":0,"name":"Naresh Chandra Kabdwal","email":"","orcid":"","institution":"Banasthali Vidyapith","correspondingAuthor":false,"prefix":"","firstName":"Naresh","middleName":"Chandra","lastName":"Kabdwal","suffix":""},{"id":308076643,"identity":"c09acced-f272-4b95-9877-09e1bd89f60c","order_by":1,"name":"Niraj Kumar Yadav","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYDACZiCWbDjAwM/e//ABkM3DR7QWyZ4zzAYgLWxE2cQI1GJww4dNAsQhqMWcnffhB8sddxI33OA9Vvk1x06GjYH54aMbeLRYNrMbS0ieeZY483Zf2m3ZbclAh7EZG+fg0WJwmI1BQrLtcGLfnQNmtyW3MQO18LBJE9DC/AOkpeFGglmx5LZ6orSwgW2ZcCPHjPHjtsPEabGQPHPYeGbPsWRpxm3HediYCfnl/DHm25I7Dsv2szcf/PhzW7U9P3vzw8f4tIAAswSMwQMmCSgHAcYPMMYPIlSPglEwCkbByAMAHcpLp2bLdsAAAAAASUVORK5CYII=","orcid":"","institution":"Banasthali Vidyapith","correspondingAuthor":true,"prefix":"","firstName":"Niraj","middleName":"Kumar","lastName":"Yadav","suffix":""},{"id":308076644,"identity":"af86b440-229b-4be2-8448-e36ba0b79940","order_by":2,"name":"Kh. Jitenkumar Singh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Kh.","middleName":"Jitenkumar","lastName":"Singh","suffix":""},{"id":308076645,"identity":"40ce37ef-92c8-4f76-afa2-61d706e6c380","order_by":3,"name":"Jitendra Yadav","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jitendra","middleName":"","lastName":"Yadav","suffix":""}],"badges":[],"createdAt":"2024-05-19 08:53:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4443583/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4443583/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57941317,"identity":"50c36382-6932-4e59-a337-1ce8dec9a04b","added_by":"auto","created_at":"2024-06-07 18:56:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":180368,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMalnutrition among children under five years in India, 2019-21\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4443583/v1/c94c6a656dc05469c03cd85e.png"},{"id":57943216,"identity":"6e4c7cfb-c5ca-4daf-8552-b6c699e99ae9","added_by":"auto","created_at":"2024-06-07 19:04:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1026995,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4443583/v1/861b07dc-b898-4130-be52-24d1a9c17c01.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Decomposition Analysis of Inequality in Malnutrition Among Under Five Children in India: Findings from National Family Health Survey-5","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNutrition plays an important role in good health and human development. If humans consume a balanced diet, malnutrition can be avoided. Malnutrition is characterized by deficits, surpluses, and unbalanced calorie, protein, and nutrient intakes. It encompasses both under and over-nutrition.\u003c/p\u003e \u003cp\u003eDespite significant growths in child's health and the overall decline in the rate of malnutrition worldwide, in developing countries continue to bear the significant burden of malnutrition, posing a persistent challenge to public health [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Globally, an estimated 156\u0026nbsp;million children under the age of five years are stunted, and approximately 50\u0026nbsp;million children in this age group suffer from wasting. This burden of malnutrition is not evenly distributed across countries; with the majority of affected children, approximately 145\u0026nbsp;million children are stunted and 48\u0026nbsp;million children are wasted, residing in African and Asian countries [30]. Furthermore, within countries, there exist socioeconomic inequalities, with children from lower socio-economic groups bearing a greater impact of malnutrition.\u003c/p\u003e \u003cp\u003eChildren's nutritional status is influenced by various socio-economic factors, including their mother's nutritional status and educational background, household wealth, demographic variables and residential area [1, 4, 15]. In India, the concentration indices and decomposition analysis were employed to examine socioeconomic inequalities in child mortality at both the state and national levels. Malnutrition in children under five years is a significant public health issue across many developing nations, and India is not an exception [23].\u003c/p\u003e \u003cp\u003eChild malnutrition manifests in various forms, which include stunting (height/length-for-age more than 2 standard deviations below the median), wasting (weight-for-height/length more than 2 standard deviations below the median), and underweight (weight-for-age more than 2 standard deviations below the median). Approximately 32% of children under five years are stunted, 3.5% are severely wasted, and 20.2% are underweight, in developing countries [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In India, the prevalence rate of stunting has decreased from 38.4\u0026ndash;35.72%, while wasting has seen a slight decrease from 21\u0026ndash;18.77%, and underweight prevalence has declined from 35.8\u0026ndash;31.17% between time periods of NFHS-4 and NFHS-5 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The central, northern, and eastern regions of India tend to be the centers of malnutrition [37]. A study carried out in India revealed that the malnutrition of spatial clustering was evident in geographical areas characterized by high poverty rates, low levels of women's education, and below-average BMI levels among women [17].\u003c/p\u003e \u003cp\u003eMalnutrition arises primarily from immediate causes such as lack of care, inadequate nutrition intake, and disease onset, compounded by underlying factors like inadequate education and unhealthy environments [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Studies conducted in countries have also highlighted significant disparities in child growth, other than India. For instance, research in Kenya's urban areas revealed a substantial gap in nutritional status between the rich and poor [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This inequality was attributed to factors such as poor housing conditions and limited access to clean water, food, and healthcare services. Additionally, findings suggest that children from impoverished households experience undernourishment not solely due to economic constraints but also because of reduced maternal healthcare utilization and inadequate parental education, compounded by maternal health issues [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the prevalence of stunting, wasting, and underweight among children under five in India, there is a limited number of studies examining growth faltering as measured by z-scores [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Research indicates that weight gain begins shortly after birth, with a decline in weight-for-age z-score (WAZ) observed during the first three months, followed by a slower rate of decline [18]. A comparative analysis of the NFHS-1 and NFHS-3 data aimed to give insights into the growth patterns of children aged 0\u0026ndash;3 years at the national level [18]. Early childhood growth has been linked to educational outcomes in later years, with stunting associated with cognitive deficits and educational challenges in late adolescence. However, it's important to note that children\u0026rsquo;s educational performance has been influenced by various kinds of socio-demographic factors, particularly their mother\u0026rsquo;s education and the condition of the environment at home, which may confound the relationship [11, 34].\u003c/p\u003e \u003cp\u003eThe Global Nutrition Report 2021 shows that India is home to 34.7% of the world's undernourished children. Malnutrition is thought to be the root cause of about half of all under-five infant fatalities in India [14]. The nutritional status of children must be better because today's citizens will be tomorrow's future.\u003c/p\u003e \u003cp\u003eIndia's 102nd ranking in the Global Hunger Index (GHI) [13] underscores the severe levels of undernutrition within the country. Furthermore, various literatures have highlighted the existence of a nutritional poverty problem in India [16]. Despite this recognition, there is a notable lack of studies focusing on examining the socioeconomic variation of malnutrition among children across the country. As a result, the purpose of this study aims to address this gap by measuring malnutrition inequality among children under five in India and exploring the effect of socio-economic determinants on these disparities.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData\u003c/h2\u003e \u003cp\u003eThe data utilized for this study were obtained from the National Family Health Survey-5 (NFHS-5) conducted during 2019\u0026ndash;21. NFHS-5 aimed to gather crucial information on health, family welfare, and emerging issues such as the nutritional status of children under the age of five at national, state, and district levels. The survey collected demographic, health, and socioeconomic data from 133,920 households, comprising 47,199 urban and 85,721 rural households. This nationally representative dataset offers vital insights into health, family welfare, and emerging concerns across the country. Moreover, NFHS is part of the broader Demographic and Health Surveys (DHS), which are nationally representative household surveys providing comprehensive data for monitoring and evaluating various indicators related to population, health, and nutrition.\u003c/p\u003e \u003cp\u003eOverall, this study analyzed data from a total of 232,920 children, whose accurate and complete information on age, sex, weight, and height, as well as the characteristics of their mothers and their families socioeconomic status, was collected. The objective was to examine socioeconomic inequalities in malnutrition among children and identify variables influencing them. The data included anthropometric measurements of children's weight, height, and underweight levels. Detailed information related to the NFHS-5 design, protocols, and tools can be found in the NFHS-5 national report [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and all essential information is publicly available on \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rchiips.org/NFHS\u003c/span\u003e\u003cspan address=\"http://rchiips.org/NFHS\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMeasuring malnutrition\u003c/h2\u003e \u003cp\u003eAnthropometric markers, such as weight-for-age (WAZ), height-for-age (HAZ), and weight-for-height (WHZ) z-scores, are frequently employed to assess children's health status, particularly regarding malnutrition. In this study, the WHO's growth chart [36] was utilized to calculate z-scores. Children with z-score values for weight-for-age (WAZ), height-for-age (HAZ), and weight-for-height (WHZ) less than \u0026minus;\u0026thinsp;2SD were classified as underweight, stunting, and wasted, respectively [35].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePredictor variables\u003c/h2\u003e \u003cp\u003eThe study categorized predictors into four distinct groups: 1) Child-related factors, encompassing age (0 to 59 months), sex of the child (female and male), child health outcomes, and birth order of the child; 2) Maternal factors, encompassing education levels (No education, Primary, Secondary, and Higher) and age categories (15\u0026ndash;19, 20\u0026ndash;24, 25\u0026ndash;29, 30\u0026ndash;34, 35\u0026ndash;39, 40\u0026ndash;44, and 45\u0026ndash;49 years); 3) Residential factors, comprising urban, rural, and national levels; and 4) Socioeconomic factors, encompassing religion (Muslim, Hindu, Christian, and Others), and wealth status (Poorer, Poorest, Middle, Richest, and Richer).\u003c/p\u003e \u003cp\u003eThe wealth status variable was derived from survey data, using wealth quintile as a proxy for income of household to evaluate socioeconomic status. Initially, household assets, including items such as televisions, bicycles, or cars, along with housing characteristics such as the source of flooring materials, drinking water, and toilet facilities were organized and categorized. Subsequently, the wealth quintile score for each household was computed using principal component analysis, a statistical method. Finally, the wealth quintile scores were used to ascertain the socioeconomic ranking of each household, dividing them into five quintiles: Poorer, Poorest, Middle, Richer, and Richest. Detailed information regarding the method for estimating the wealth-asset quintile can be found elsewhere.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eConcentration Index (CI)\u003c/h2\u003e \u003cp\u003eIncome-related inequality in wasting, stunting, and underweight was assessed using the concentration index (CI), with the wealth score serving for the socioeconomic variables and the binary outcomes indicating wasting, stunting, and underweight. The CI quantifies the extent of inequality by measuring the deviation of the concentration curve from the line of equality, ranging from \u0026minus;\u0026thinsp;1 to +\u0026thinsp;1. A value of 0 indicates no socioeconomic inequality, while positive values denote pro-rich inequality, and vice versa. Higher values signify greater socioeconomic inequality. Wagstaff's decomposition analysis was employed to further dissect the CI, revealing the effects of individual factors to income related inequality [32].\u003c/p\u003e \u003cp\u003eThe concentration index (CI) will be used to assess the level of socioeconomic inequality among children with stunting, underweight, and wasting. Following is the CI formula:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$c= \\frac{2}{n.{\\mu }}\\left({\\sum }_{k=1}^{n}{y}_{k} {R}_{k}\\right)-1$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, n denotes the sample size, \u003cem\u003e\u0026micro;\u003c/em\u003e denotes the average of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y}_{k}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y}_{k}\\)\u003c/span\u003e\u003c/span\u003e denotes the value of the underweight, wasting and stunting indices of the \u003cem\u003ek\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e individual, and \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e denotes the relative rank of socio-economic status of the \u003cem\u003ek\u003c/em\u003e\u003csup\u003eth\u003c/sup\u003e individual. The formula will be used to calculate the concentration index (CI) of underweight, wasting and stunting.\u003c/p\u003e \u003cp\u003eTo investigate the effect of socioeconomic factors on child malnutrition inequality, the concentration index will be decomposed. This decomposition method requires a linear regression model. Therefore, stunting and underweight will be treated as continuous dependent variables, and multiple linear regressions will be conducted to calculate the coefficients between child malnutrition and socioeconomic factors.\u003c/p\u003e \u003cp\u003eFor continuous outcome variables, a linear regression model connecting to the outcome variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(y\\)\u003c/span\u003e\u003c/span\u003e to the set of\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({i}^{th}\\)\u003c/span\u003e\u003c/span\u003e determinant of malnutrition \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left({x}_{ik}\\right)\\)\u003c/span\u003e\u003c/span\u003e expressed as:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${ y}_{k}=\\alpha +{\\sum }_{i}{ \\beta }_{i}{x}_{ik}+{ϵ}_{k}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{i}\\)\u003c/span\u003e\u003c/span\u003e denotes the coefficient of independent variable and the error term is\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ϵ}_{k}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eEquation (\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) replaces Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) with an equal amount of\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y}_{k}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y}_{k}\\)\u003c/span\u003e\u003c/span\u003eof the concentration index will be\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$c=\\sum _{i}\\left(\\frac{{\\beta }_{i}\\stackrel{-}{{x}_{i}}}{\\mu }\\right){c}_{i}+\\frac{{GC}_{ϵ}}{\\mu }$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{-}{{ x}_{i}}\\)\u003c/span\u003e\u003c/span\u003e denotes the mean of\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ x}_{i}\\)\u003c/span\u003e\u003c/span\u003e and for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{i},{ c}_{i}\\)\u003c/span\u003e\u003c/span\u003edenotes a concentration index and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({GC}_{ϵ}\\)\u003c/span\u003e\u003c/span\u003e denotes the generalized concentration index for the error term (\u003cem\u003eɛ\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eElasticity\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(\\frac{{\\beta }_{i}\\stackrel{-}{{x}_{i}}}{\\mu }\\right)\\)\u003c/span\u003e\u003c/span\u003e is a part of the first (deterministic component) of Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which express the explanatory variables the impact malnutrition, and in socio-economic groups \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({c}_{i}\\)\u003c/span\u003e\u003c/span\u003e denotes the inequality of determinant variables in the distribution. In non-deterministic component (second part) related to the malnutrition inequality in the socio-economic groups, that are unable to clarified by contributors\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(\\frac{{GC}_{\\in }}{\\mu }\\right)\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe multivariate linear regression model will include socioeconomic characteristics such as socioeconomic status, residential area, household size, mother's age at birth (years), mother's education sex, age (0\u0026ndash;5 years) and birth-order of children.\u003c/p\u003e \u003cp\u003eAll of the statistical analyses were carried out using Stata 14.1 (Stata Corp, USA).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the distribution of children underfive years in India by selected background characteristics, providing descriptive statistics of both the explanatory and outcome variables. The average growth among children, measured through z-scores for height-for-age (HAZ), weight-for-age (WAZ), and weight-for-height (WHZ), was found to be 0.36, 0.31, and 0.19, respectively. Boys slightly outnumbered girls, accounting for 51.80% of the dataset compared to 48.2% for girls. The average age of the children in the dataset was 29.6 months, with ages ranging from 0 to 59 months. Furthermore, a substantial percentage of the children (20.30%) resided in urban areas, while the majority (79.70%) lived in rural areas.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of a dataset\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBackground Characteristic\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e(Standard Deviation)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eNumber (percentage)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMini-mum\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eMaxim-um\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild health outcome (z-score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight for height\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19 (0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight for age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.36 (0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight for age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.31 (0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex of children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112,255(48.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120,665(51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildren age ( in months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.6 (17.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth order of children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.15 (1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother\u0026rsquo;s education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51210(21.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,215(12.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119864(51.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31765(13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother\u0026rsquo;s age at birth (in years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;19(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,461(2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66,485(28.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92,448(39.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45,587(19.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,614(7.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,196(1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,129(0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban(reference)\u003c/p\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47199(20.3)\u003c/p\u003e \u003cp\u003e85721(79.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63,406(27.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54,463(23.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45,083(19.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39,094(16.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30,874(13.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Household member\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.25(2.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe majority of mothers in the dataset have attained secondary education (51.5%), while a notable portion have no formal education (21.99%), with smaller proportions having primary education (12.91%) or higher education (13.6%). Based on the data, the most common age range for mothers at the birth of their children is between 25 and 29 years, representing 39.69% of the dataset, with smaller proportions falling into other age brackets. In terms of wealth distribution, the largest proportion of individuals in the dataset belong to the poorest quintile, comprising 27.22% of the sample, followed by the poorer quintile at 23.38%, with decreasing percentages observed in the middle, richer, and richest quintiles. Additionally, the average family size was 6.25 individuals, and the distribution of children in this study was nearly uniform across economic quintiles.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConcentration index and Prevalence rate of under five child malnutrition, NFHS-5\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eVariable\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eStunting\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003eWasting\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003eUnder-weight\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePrevalence (%)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePrevalence (%)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ePrevalence (%)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the concentration indices and prevalence rates for stunting, wasting, and underweight among children, disaggregated by national, urban, and rural areas. The prevalence of stunting among Indian children was observed to be higher than that of wasting and underweight. Similarly, this pattern of malnutrition index prevalence was consistent across different residential areas. At the national level, the obtained concentration indices for stunting, wasting, and underweight were \u0026minus;\u0026thinsp;0.126, -0.064, and \u0026minus;\u0026thinsp;0.138, respectively. Stunting affects 35.72% of children nationally, with a concentration index of -0.126, indicating a higher concentration among disadvantaged groups. Underweight prevalence is 31.17% nationally, with a similar concentration trend. Wasting, affecting 18.77% of children nationally, exhibits a lower concentration index, suggesting a more equitable distribution across socioeconomic groups. The concentration index values indicated that the lowest quintiles experienced a greater burden of stunting in urban children compared to rural children. Conversely, wasting and underweight concentration indices were somewhat higher in rural areas compared to urban areas. The urban and rural breakdowns revealed differing prevalence rates and concentration indices across the various indicators.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the prevalence of child\u0026rsquo;s malnutrition, including stunting, wasting, and underweight, at rural, urban, and national levels in India from 2019 to 2021. The findings indicate that all three indices of child growth were more pronounced among both urban and rural populations. Moreover, while the prevalence of stunting decreased among both urban and rural children during the period, underweight prevalence also declined among these groups. However, wasting increased among both urban and rural children over the same period. These trends were consistent at the national level in India. Additionally, this section presents the findings of the decomposition of socioeconomic inequality and regression analysis in underweight and stunting. Notably, due to the non-significance of the concentration index of wasting, its decomposition results were not included.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStunting (height-for-age) scores: Determinants and decomposition study of the contribution of several explanatory variables, NFHS-5, 2019\u0026ndash;21.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBackground\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eCharacteristic\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eElasticity\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCoefficient\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCIs\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAbsolute Contribution to CI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eContribution Percentage\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex of children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth order of children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0629\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildren age (in months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMothers education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.1206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0603\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.6089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.1631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eMothers' age (in years) at birth\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.2808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.6039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth-quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.3717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0306\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents coefficients, elasticity\u0026rsquo;s, concentration indices (CIs), and absolute contributions to CI for several key variables associated with child malnutrition. Female children show a negative association with malnutrition, as indicated by their coefficient of -0.105, elasticity of -0.0229, and CI of -0.0075. A child's age exhibits a positive association, with a coefficient of 0.521, an elasticity of 0.1624, and a CI of -0.0043. The birth order of the child demonstrates a positive association with a coefficient of 0.698 and a CI of -0.0970. Mother's education level shows nuanced effects: primary education has a coefficient of 0.069, secondary education has a coefficient of -0.257, and higher education has a coefficient of -0.429. Mother's age at birth presents mixed effects across different age brackets, with varying coefficients, elasticity\u0026rsquo;s, and CIs. Rural residence is positively associated with malnutrition, with a coefficient of -0.056, an elasticity of -0.0351, and a CI of -0.6039. Wealth quintiles exhibit a gradient effect, with coefficients ranging from \u0026minus;\u0026thinsp;0.181 for the poorer quintile to -0.745 for the richest quintile.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWasting (weight-for-age) scores: Determinants and decomposition study of the contribution of several explanatory variables, NFHS-5, 2019\u0026ndash;21.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBackground Characteristic\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eElasticity\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCoefficient\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCIs\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAbsolute Contribution to CI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eContribution Percentage\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSex of children\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0. 0402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0. 0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0. 0038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBirth order of children\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0. 0078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0. 0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0. 0096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChildren age (months)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0. 2645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0. 0011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0. 0144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMothers education\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrimary\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSecondary\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0539\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHigher\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.6089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eMothers' age (in years) at birth\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e20\u0026ndash;24\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e25\u0026ndash;29\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e30\u0026ndash;34\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e35\u0026ndash;39\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e40\u0026ndash;44\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e45\u0026ndash;49\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.2808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePlace of residence\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRural\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.6039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.5251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWealth-quintile\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePoorer\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.3717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMiddle\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRicher\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRichest\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.5434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6839\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides coefficients, elasticity\u0026rsquo;s, concentration indices (CIs), absolute contributions to CI, and contribution percentages for various variables related to child malnutrition. Female sex shows a coefficient of -0.112, indicating a negative association with malnutrition, with an elasticity of -0.0402 and a CI of -0.0075, contributing \u0026minus;\u0026thinsp;0.0038 to the overall CI. The child's age exhibits a larger coefficient of -0.548, suggesting a stronger negative association with malnutrition, accompanied by an elasticity of -0.2645 and a CI of -0.0043, contributing \u0026minus;\u0026thinsp;0.0144. The birth order of the child has a smaller coefficient of -0.209, indicating a less pronounced negative association, with an elasticity of -0.0078 and a CI of -0.0970, contributing \u0026minus;\u0026thinsp;0.0096. Mother's education level presents varying effects: primary education has a negative coefficient of -0.109, secondary education has a negative coefficient of -0.131, and higher education has a larger negative coefficient of -0.237, each contributing differently to the overall CI. Mother's age at birth displays diverse effects across age groups, with varying coefficients and contributions. Rural residence is associated with a positive CI, while wealth quintiles exhibit gradients, with negative CIs for the poorer quintiles and positive CIs for the richer quintiles.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnderweight (weight-for-height) scores: Determinants and decomposition study of the contribution of several explanatory variables, NFHS-5, 2019-21.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBackground Characteristic\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eElasticity\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCoefficient\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCIs\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAbsolute Contribution to CI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eContribution Percentage\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSex of children\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0. 0267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0 0074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBirth order of child\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChildren age (in months)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0. 1376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMothers education\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrimary\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSecondary\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0508\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHigher\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.6089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.1494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eMothers' age (in years) at birth\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e20\u0026ndash;24\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e25\u0026ndash;29\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e30\u0026ndash;34\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e35\u0026ndash;39\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e40\u0026ndash;44\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e45\u0026ndash;49\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.2808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePlace of residence\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRural\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.6039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWealth-quintile\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePoorer\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.3717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0615\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMiddle\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0408\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRicher\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRichest\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.9181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.9181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e outlines coefficients, elasticity\u0026rsquo;s, concentration indices (CIs), absolute contributions to CI, and contribution percentages for various factors affecting child malnutrition. Female sex exhibits a coefficient of -0.115, indicating a negative association with malnutrition, with an elasticity of -0.0267 and a CI of -0.0074, contributing \u0026minus;\u0026thinsp;0.0009 to the overall CI. Conversely, a child's age presents a coefficient of 0.607, suggesting a positive association, with an elasticity of 0.1376 and a CI of -0.0043, contributing \u0026minus;\u0026thinsp;0.0029. The birth order of the child demonstrates a positive association with a coefficient of 0.412, an elasticity of 0.0958, and a CI of -0.0970, contributing \u0026minus;\u0026thinsp;0.0449. Regarding a mother's education level, primary education exhibits a coefficient of -0.153, secondary education has a coefficient of -0.289, and higher education displays a larger coefficient of -0.497, each contributing differently to the overall CI. Mother's age at birth shows varied effects across age groups, with varying coefficients and contributions. Additionally, rural residence is associated with a negative CI, while wealth quintiles display gradients, with negative CIs for poorer quintiles and positive CIs for richer quintiles.\u003c/p\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e present the outcomes of decomposition analysis, which include elasticity, coefficient, concentration index, contribution, and absolute contribution percentage of all determinants to socioeconomic inequality in malnutrition. Generally, socioeconomic characteristics more prevalent among lower quintiles (with more negative concentration indices) led to a greater improvement in malnutrition inequality. Moreover, factors associated with higher elasticity were positively related to socioeconomic disparities in malnutrition. However, a significant portion of the socioeconomic disparity in malnutrition among children under the age of five could be determined by socioeconomic level. Approximately 36% of stunting inequalities, 68% of wasting inequality, and 44% of underweight inequality were attributable to socioeconomic status. Maternal education level was another factor contributing to the inequality in stunting, wasting, and underweight (7.99% in stunting, 15.95% in wasting, and 8.71% in underweight). Disparities in residential areas and the average number of child deliveries influenced the remaining inequality in underfive child malnutrition.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite recent economic progress, India has not been able to provide better outcomes for child growth. The outcomes also indicate the presence of socioeconomic clustering, which further widens the gap in achieving growth among children. The purpose of this study was to identify the factors that contribute to wealth inequality in stunting, wasting, and underweight among children under the age of five in both urban and rural India.\u003c/p\u003e \u003cp\u003eUnfortunately, there is limited information available to comprehend the timing of growth faltering in Indian children underfive years. The few studies conducted on this subject have identified growth faltering using the National Centre for Health Statistics (NCHS) reference growth standards [18]. This study stands out from previous research in measuring growth faltering by utilizing WHO standards, providing a more comprehensive understanding of this phenomenon compared to the NCHS methodology [25].\u003c/p\u003e \u003cp\u003eMaternal factors continue to play a significant role in contributing to inequality in child malnutrition. Stunting, defined as height-for-age more than \u0026minus;\u0026thinsp;2 standard deviations below the median, reflects chronic undernutrition in children. Maternal factors, including age, first birth age, education, parity, and anemia, accounted for over half of the inequality observed in stunting among children in recent years. Studies conducted in India have consistently found associations between maternal factors, particularly maternal education, and stunting. This trend is not unique to India but has been observed in both developed and developing countries [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSimilarly, underweight, defined as weight-for-age more than \u0026minus;\u0026thinsp;2 standard deviations below the median, reflects both past and present undernutrition. Maternal factors also emerged as the primary contributors to inequality among underweight children [20]. Research employing decomposition analysis has highlighted the role of maternal education in improving weight-for-age z-scores among Indian children [22, 27].\u003c/p\u003e \u003cp\u003eWasting, defined as weight-for-height more than \u0026minus;\u0026thinsp;2 standard deviations below the median, is a measure of acute undernutrition. Although child wasting inequality has decreased, the prevalence of child wasting has increased, particularly in countries such as India and Sri Lanka [29]. Maternal factors were found to be the highest contributors to inequality in child wasting during recent years.\u003c/p\u003e \u003cp\u003eThis study goes beyond previous research efforts by aiming to provide a comprehensive understanding of the nutritional condition of children across different socioeconomic categories. Its objectives include monitoring the disparity in stunting, wasting, and underweight, as well as determining the factors connected to this disparity. To achieve these goals, the study utilizes data from the National Family Health Survey-5 (NFHS-5) and employs decomposition analysis of the concentration index. This approach allows for a detailed examination of the socioeconomic factors contributing to disparities in child malnutrition.\u003c/p\u003e \u003cp\u003eA higher concentration of malnutrition among disadvantaged socioeconomic groups, as shown by the concentration indices for stunting, wasting, and underweight, were found to be -0.126, -0.064, and \u0026minus;\u0026thinsp;0.138, respectively. Decomposition analysis revealed that maternal education and household economic status were the primary factors contributing to socioeconomic inequalities in malnutrition. These findings align with similar studies conducted in other countries, suggesting that despite reductions in malnutrition rates, socioeconomic disparities persist and may even be widening [1, 21].\u003c/p\u003e \u003cp\u003eOur study's decomposition results showed that the primary cause of disparities in child malnutrition was the economic status of households. Approximately 36% of the stunting inequality, 68% of the wasting inequality, and 44% of the underweight inequality were attributed to disparities in the economic status of families. These findings of this study are consistent with earlier research, which also highlighted household economic status as a key determinant of malnutrition inequality. Compared to access to healthcare, promoting socioeconomic status has been shown to have a more significant impact on health indicators and outcomes, particularly in improving nutritional status and reducing child mortality [19, 8, 33]. Household economic status directly influences anthropometric outcomes and children's nutrition by enabling access to food and indirectly influencing other factors.\u003c/p\u003e \u003cp\u003eThe decomposition analysis also highlighted that maternal education is the second significant variable contributing to inequality in stunting, underweight and wasting. Children of mothers with higher levels of education were at a lower risk of experiencing growth disorders compared to those with less educated mothers. Our study found that 8% of the stunting inequality, 16% of the wasting inequality, and 9% of the underweight inequality were attributed to differences in maternal levels of education. Mother\u0026rsquo;s education emerged as a crucial factor influencing inequalities in child malnutrition. Parental education, particularly the education of mothers, plays a vital role in promoting the health of children. Mothers who are educated are more likely to seek healthcare and engage in activities that enhance their nutritional status and their children's health [26].\u003c/p\u003e \u003cp\u003eThe third determinant contributing to malnutrition inequality is living in rural areas. Studies across 47 developing countries have highlighted significant disparities in children's nutritional conditions in urban and rural areas [31]. Further examination of the socioeconomic factors impacting children's nutritional status revealed notable differences between urban and rural children. This disparity stems from variations in key factors; children residing in cities typically experience more favorable circumstances [28]. In general, rural households exhibit lower levels of education, improved sanitation facilities, less availability of safe drinking water, and less availability of healthcare facilities in relation to their urban counterparts.\u003c/p\u003e \u003cp\u003eHowever, initiatives like the ''National Program for Improving Nutritional Status of Children'' have struggled to effectively address the social determinants impacting the children's nutritional status and to address disparities in nutritional outcomes, particularly among impoverished families [24]. Therefore, it is imperative for policymakers to implement targeted strategies to address malnutrition inequalities based on socioeconomic determinants as well as comprehensive interventions aimed at reducing the overall prevalence of malnutrition, particularly stunting.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study provides valuable insights into the growth patterns and inequalities in child malnutrition among underfive in India. Several key findings emerge from this analysis. Firstly, maternal factors, particularly mother's education, were found to be the primary contributors to inequality in socioeconomic status in stunting and underweight among children in both urban and rural areas during the period of 2019\u0026ndash;21. Secondly, maternal factors also played a significant role in explaining wasting inequalities among children. These findings underscore the importance of focusing on improving maternal education, especially in rural and slum areas, to address malnutrition among children. Children from economically disadvantaged families are disproportionately affected by malnutrition, particularly stunting, highlighting the need for targeted interventions. Moreover, addressing maternal education can potentially alleviate this burden by improving knowledge and awareness among mothers, thereby positively impacting child nutrition outcomes.\u003c/p\u003e \u003cp\u003ePrioritizing nutrition-sensitive and nutrition-specific interventions in both urban and rural areas, particularly targeting the urban and rural poor residing in low-income slums, is crucial. Addressing the rich-poor inequality in malnutrition necessitates integrating and converging nutrition interventions with poverty reduction policies for children in urban and rural areas. Expanding the scope of existing programs like Integrated Child Development Services (ICDS) to include mass education on health and nutrition, with a focus on pregnant and lactating mothers, is essential. This expansion should also emphasize improving maternal education, which plays a significant role in addressing malnutrition disparities. For meaningful progress in combating this public health issue among children, direct and targeted efforts are required from the government and policymakers to eliminate existing socioeconomic inequalities associated with malnutrition.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e:\u0026nbsp;Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e NFHS data is a nationally representative data set which available freely in public domain\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e Authors did not receive any funding to carry out this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eAll authors contributed to the study conception and design. Author 1 and 2 wrote the main manuscript text, and author 2 prepared the tables and figures, author 1 and 3 helped to decide the aims and objective and author 4 helped to running the program for making tables. All the authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e Not Applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmed S, Hasan MM, Ahmed W, Chowdhury MA. Socio-economic Inequity of Malnutrition among under-five Children and Women at Reproductive Age in Bangladesh. J Nutr Health. 2013;1:13-7.\u003c/li\u003e\n\u003cli\u003eAmini RM, Rashidian A, Khosravi A, Arab M, Abbasian E, Morasae EK. Changes in socio-economic inequality in neonatal mortality in Iran between 1995-2000 and 2005-2010: an Oaxaca decomposition analysis. International journal of health policy and management. 2017 Apr;6(4):219.\u003c/li\u003e\n\u003cli\u003eBlack RE, Allen LH, Bhutta ZA, Caulfield LE, De Onis M, Ezzati M, Mathers C, Rivera J. Maternal and child undernutrition: global and regional exposures and health consequences. The lancet. 2008 Jan 19;371(9608):243-60.\u003c/li\u003e\n\u003cli\u003eChalasani S. Understanding wealth-based inequalities in child health in India: a decomposition approach. Social science \u0026amp; medicine. 2012 Dec 1;75(12):2160-9.\u003c/li\u003e\n\u003cli\u003eChauhan BG, Chauhan S, Chaurasia H. Decomposing the gap in child malnutrition between poor and non-poor in Sierra Leone. Journal of Public Health. 2019 Feb 6;27:119-27.\u003c/li\u003e\n\u003cli\u003eCorreia LL, Silva AC, Campos JS, Andrade FM, Machado MM, Lindsay AC, Leite \u0026Aacute;J, Rocha HA, Cunha AJ. Prevalence and determinants of child undernutrition and stunting in semiarid region of Brazil. Revista de saude publica. 2014;48:19-28.\u003c/li\u003e\n\u003cli\u003eDeshpande A, Ramachandran R. Which Indian children are short and why? Social identity, Childhood Malnutrition and Cognitive Outcomes. 2020 Mar 2.\u003c/li\u003e\n\u003cli\u003eDollar D, Kraay A. Growth is Good for the Poor. Journal of economic growth. 2002 Sep;7:195-225.\u003c/li\u003e\n\u003cli\u003eFotso JC, Madise N, Baschieri A, Cleland J, Zulu E, Mutua MK, Essendi H. Child growth in urban deprived settings: does household poverty status matter? At which stage of child development?. Health \u0026amp; place. 2012 Mar 1;18(2):375-84.\u003c/li\u003e\n\u003cli\u003eHeadey D, Menon P, Nguyen P. The timing of growth faltering in India has changed significantly over 1992\u0026ndash;2016, with variations in prenatal and postnatal improvement (P10-005-19). Current developments in nutrition. 2019 Jun 1;3:nzz034-P10.\u003c/li\u003e\n\u003cli\u003eHolme AR, Blair PS, Emond AM. Psychosocial and educational outcomes of weight faltering in infancy in ALSPAC. BMJ open. 2013 Jul 1;3(7):e002863.\u003c/li\u003e\n\u003cli\u003eIIPS, \u0026amp; ICF. National Family Health Survey (NFHS-5): 2019-21 India. Mumbai: International Institute for Population Sciences (IIPS). 2021.\u003c/li\u003e\n\u003cli\u003eIndex GH. Global, Regional, and National Trends-Global Hunger Index-peer-reviewed annual publication designed to comprehensively measure and track hunger at the global, regional, and country levels. Global Hunger Index. Global Hunger Index. 2019.\u003c/li\u003e\n\u003cli\u003eIndex GH. Global, Regional, and National Trends-Global Hunger Index-peer-reviewed annual publication designed to comprehensively measure and track hunger at the global, regional, and country levels. Global Hunger Index. Global Hunger Index. 2021.\u003c/li\u003e\n\u003cli\u003eJayawardena P. Socio-economic determinants and inequalities in childhood malnutrition in Sri Lanka. Well-Being and Social Policy Journal. 2012;8(1):1-22.\u003c/li\u003e\n\u003cli\u003eJha R, Gaiha R, Sharma A. Poverty nutrition trap in rural India. Australian National University Trade and Development Working Paper. 2005 Mar..\u003c/li\u003e\n\u003cli\u003eKhan J, Mohanty SK. Spatial heterogeneity and correlates of child malnutrition in districts of India. BMC public health. 2018 Dec;18:1-3.\u003c/li\u003e\n\u003cli\u003eMamidi RS, Shidhaye P, Radhakrishna KV, Babu JJ, Sudhershan Reddy P. Pattern of growth faltering and recovery in under-5 children in India using WHO growth standards\u0026mdash;a study on First and Third National Family Health Survey. Indian pediatrics. 2011 Nov;48:855-60.\u003c/li\u003e\n\u003cli\u003eMarianne F, Danny L, Wodon QT, Yepes T. Achieving the millennium development goals: the role of infrastructure. Available at SSRN 636582. 2003 Nov.\u003c/li\u003e\n\u003cli\u003eNie P, Rammohan A, Gwozdz W, Sousa-Poza A. Changes in child nutrition in India: a decomposition approach. International journal of environmental research and public health. 2019 May;16(10):1815.\u003c/li\u003e\n\u003cli\u003eO'Donnell O, Wagstaff A, Van Doorslaer E, Lindelow M. Analyzing health equity using household survey data: a guide to techniques and their implementation. World Bank Publications; 2007 Nov 2.\u003c/li\u003e\n\u003cli\u003ePrakash M, Jain K. Inequalities among malnourished children in India: A decomposition analysis from 1992-2006. International Journal of Social Economics. 2016 Jun 13;43(6):643-59.\u003c/li\u003e\n\u003cli\u003eSahu SK, Kumar SG, Bhat BV, Premarajan KC, Sarkar S, Roy G, Joseph N. Malnutrition among under-five children in India and strategies for control. Journal of natural science, biology, and medicine. 2015 Jan;6(1):18.\u003c/li\u003e\n\u003cli\u003eSalvucci V. Determinants and trends of socioeconomic inequality in child malnutrition: the case of Mozambique, 1996\u0026ndash;2011. Journal of international development. 2016 Aug; 28 (6):857-75.\u003c/li\u003e\n\u003cli\u003eShrimpton R, Victora CG, de Onis M, Lima RC, Blossner M, Clugston G. Worldwide timing of growth faltering: implications for nutritional interventions. Pediatrics. 2001 May 1; 107 (5):e75-5.\u003c/li\u003e\n\u003cli\u003eSingh L, Rai RK, Singh PK. Assessing the utilization of maternal and child health care among married adolescent women: evidence from India. Journal of biosocial science. 2012 Jan;44(1):1-26.\u003c/li\u003e\n\u003cli\u003eSingh S, Srivastava S, Upadhyay AK. Socio-economic inequality in malnutrition among children in India: an analysis of 640 districts from National Family Health Survey (2015\u0026ndash;16). International journal for equity in health. 2019 Dec;18:1-9.\u003c/li\u003e\n\u003cli\u003eSmith LC, Ruel MT, Ndiaye A. Why is child malnutrition lower in urban than in rural areas? Evidence from 36 developing countries. World development. 2005 Aug 1;33(8):1285-305.\u003c/li\u003e\n\u003cli\u003eUnited Nations Children's Fund, World Health Organization, The World Bank. UNICEF-WHO-World Bank joint child malnutrition estimates. Levels \u0026amp; Trends in Child Malnutrition. 2012.\u003c/li\u003e\n\u003cli\u003eUnited Nations Children's Fund, World Health Organization, The World Bank. UNICEF-WHO-World Bank joint child malnutrition estimates. Levels \u0026amp; Trends in Child Malnutrition. 2016.\u003c/li\u003e\n\u003cli\u003eVan de Poel E, O\u0026rsquo;Donnell O, Van Doorslaer E. Are urban children really healthier? Evidence from 47 developing countries. Social science \u0026amp; medicine. 2007 Nov 1;65(10):1986-2003.\u003c/li\u003e\n\u003cli\u003eWagstaff A. Socioeconomic inequalities in child mortality: comparisons across nine developing countries. Bulletin of the World Health Organization. 2000;78:19-29.\u003c/li\u003e\n\u003cli\u003eWagstaff A, Watanabe N. Socioeconomic inequalities in child malnutrition in the developing world. World Bank Policy Research Working Paper. 2000 Sep 30(2434).\u003c/li\u003e\n\u003cli\u003eWalker SP, Chang SM, Powell CA, Simonoff E, Grantham-McGregor SM. Early childhood stunting is associated with poor psychological functioning in late adolescence and effects are reduced by psychosocial stimulation. The Journal of nutrition. 2007 Nov 1;137(11):2464-9.\u003c/li\u003e\n\u003cli\u003eWHO Multicentre Growth Reference Study Group, de Onis M. WHO Child Growth Standards based on length/height, weight and age. Acta paediatrica. 2006 Apr;95:76-85.\u003c/li\u003e\n\u003cli\u003eWHO Working Group. Use and interpretation of anthropometric indicators of nutritional status. Bulletin of the World health organization. 1986;64(6):929.\u003c/li\u003e\n\u003cli\u003eYadav A, Ladusingh L, Gayawan E. Does a geographical context explain regional variation in child malnutrition in India?. Journal of Public Health. 2015 Oct;23:277-87.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Malnutrition, Inequality, Socioeconomic factors, Decomposition, NFHS-5","lastPublishedDoi":"10.21203/rs.3.rs-4443583/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4443583/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eNutrition is essential for good health and human development, particularly during the early stages of life. Nutritional status of children is essential for their development and growth at early stage of lives, with implications extending into adult life. Malnutrition is recognized to be the biggest single threat to the public health worldwide, particularly in developing countries. Nutritional status can be determined anthropometrically which is the outcome of complex interactions between socio-economic and biological variables. This study aimed to measure malnutrition inequality among underfive children in India and we have used a regression-based decomposition analysis to see the impact of socioeconomic determinants on this inequality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe study utilizes data from the National Family Health Survey (NFHS-5) conducted between 2019 and 2021. To examine the magnitude of socioeconomic inequality in child malnutrition, the concentration indices for stunting, underweight, and wasting are employed. Furthermore, decomposition analysis of the concentration indices is conducted to understand the role of socio-economic factors in child-hood malnutrition inequality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe obtained concentration indices of stunting, underweight, and wasting were respectively -0.126, -0.138, and -0.064. Socioeconomic inequality in underweight and stunting was statistically significant, with children in the lowest socioeconomic quintile bearing a higher burden of malnutrition; however, no socio-economic gradient was observed in wasting. Urban children in the lowest quintiles experienced a higher burden of stunting than rural children, according to the concentration index value derived from the area of residence. Furthermore, socioeconomic status significantly influenced malnutrition inequality, with approximately 36% of the inequality in stunting, 44% in underweight, and over 68% in wasting attributed to family socioeconomic status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe average reduction in the national-level malnutrition indices reflects the impact of malnutrition among children in low-income families. To effectively address this issue, the government and policymakers must implement direct and targeted actions aimed at eliminating current inequities in the socio-economic determinants linked with malnutrition. By prioritizing interventions that specifically target vulnerable populations and address the underlying socioeconomic factors contributing to malnutrition, policymakers can work towards achieving equitable access to nutrition and improving the health outcomes of all children.\u003c/p\u003e","manuscriptTitle":"A Decomposition Analysis of Inequality in Malnutrition Among Under Five Children in India: Findings from National Family Health Survey-5","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 18:56:38","doi":"10.21203/rs.3.rs-4443583/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-31T13:11:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-16T06:56:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-15T15:59:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-13T14:27:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177000371059789355709418351378043656999","date":"2024-09-26T17:58:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309705054551585689614407134384733599276","date":"2024-09-26T09:21:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87979252865664368482701539130705993988","date":"2024-09-26T09:10:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134863648104497470158181446776013272001","date":"2024-09-26T08:01:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315530849792585283011355541818001540998","date":"2024-09-04T13:25:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"222700703553919104101005628792493759645","date":"2024-07-02T08:33:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253772118203415386032767356948861364628","date":"2024-06-12T02:27:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-06T08:00:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-27T07:01:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-27T06:48:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-21T14:26:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-05-19T08:44:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1a9e5ba2-7e7a-4ca7-a06c-3134ed3a04c9","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T07:08:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-07 18:56:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4443583","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4443583","identity":"rs-4443583","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}