Dataset Information

Inequality in child undernutrition among urban population in India: a decomposition analysis.

ABSTRACT:

Background

With increasing urbanization in India, child growth among urban poor has emerged as a paramount public health concern amidst the continuously growing slum population and deteriorating quality of life. This study analyses child undernutrition among urban poor and non-poor and decomposes the contribution of various factors influencing socio-economic inequality. This paper uses data from two recent rounds of National Family Health Survey (NFHS-3&4) conducted during 2005-06 and 2015-16.

Methods

The concentration index (CI) and the concentration curve (CC) measure socio-economic inequality in child growth in terms of stunting, wasting, and underweight. Wagstaff decomposition further analyses key contributors in CI by segregating significant covariates into five groups-mother's factor, health-seeking factors, environmental factors, child factors, and socio-economic factors.

Results

The prevalence of child undernutrition was more pronounced among children from poor socio-economic strata. The concentration index decreased for stunting (-?0.186 to -?0.156), underweight (-?0.213 to -?0.162) and wasting (-?0.116 to -?0.045) from 2005 to 06 to 2015-16 respectively. The steepness in growth was more among urban poor than among urban non-poor in every age interval. Maternal education contributed about 19%, 29%, and 33% to the inequality in stunting, underweight and wasting, respectively during 2005-06. During 2005-06 as well as 2015-16, maternal factors (specifically mother's education) were the highest contributory factors in explaining rich-poor inequality in stunting as well as underweight. More than 85% of the economic inequality in stunting, underweight, and wasting among urban children were explained by maternal factors, environmental factors, and health-seeking factors.

Conclusion

All the nutrition-specific and nutrition-sensitive interventions in urban areas should be prioritized, focusing on urban poor, who are often clustered in low-income slums. Rich-poor inequality in child growth calls out for integration and convergence of nutrition interventions with policy interventions aimed at poverty reduction. There is also a need to expand the scope of the Integrated Child Development Services (ICDS) program to provide mass education regarding nutrition and health by making provisions of home visits of workers primarily focusing on pregnant and lactating mothers.

SUBMITTER: Singh SK

PROVIDER: S-EPMC7713021 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

Inequality in child undernutrition among urban population in India: a decomposition analysis.

Singh S K SK Srivastava Shobhit S Chauhan Shekhar S

BMC public health 20201203 1

<h4>Background</h4>With increasing urbanization in India, child growth among urban poor has emerged as a paramount public health concern amidst the continuously growing slum population and deteriorating quality of life. This study analyses child undernutrition among urban poor and non-poor and decomposes the contribution of various factors influencing socio-economic inequality. This paper uses data from two recent rounds of National Family Health Survey (NFHS-3&4) conducted during 2005-06 and 20 ...[more]

PMID: 33272222

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Project description:Background/objectiveChildren and women comprise vulnerable populations in terms of health and are gravely affected by the impact of economic inequalities through multi-dimensional channels. Urban areas are believed to have better socioeconomic and maternal and child health indicators than rural areas. This perception leads to the implementation of health policies ignorant of intra-urban health inequalities. Therefore, the objective of this study is to explain the pathways of economic inequalities in maternal and child health indicators among the urban population of India.MethodsUsing data from the third wave of the National Family Health Survey (NFHS, 2005-06), this study calculated relative contribution of socioeconomic factors to inequalities in key maternal and child health indicators such as antenatal check-ups (ANCs), institutional deliveries, proportion of children with complete immunization, proportion of underweight children, and Infant Mortality Rate (IMR). Along with regular CI estimates, this study applied widely used regression-based Inequality Decomposition model proposed by Wagstaff and colleagues.ResultsThe CI estimates show considerable economic inequalities in women with less than 3 ANCs (CI = -0.3501), institutional delivery (CI = -0.3214), children without fully immunization (CI = -0.18340), underweight children (CI = -0.19420), and infant deaths (CI = -0.15596). Results of the decomposition model reveal that illiteracy among women and her partner, poor economic status, and mass media exposure are the critical factors contributing to economic inequalities in maternal and child health indicators. The residuals in all the decomposition models are very less; this implies that the above mentioned factors explained maximum inequalities in maternal and child health of urban population in India.ConclusionFindings suggest that illiteracy among women and her partner, poor economic status, and mass media exposure are the critical pathways through which economic factors operate on inequalities in maternal and child health outcomes in urban India.

Project description:Background and objectivesStudies in low-and middle-income countries where nutrition transition is underway provides mixed evidence of double burden of maternal overnutrition and child undernutrition among mother-child pairs. Shifting dietary pattern and rapid increase in overweight/obesity among adults with persistent child undernutrition indicate that India is experiencing nutrition transition and double burden of malnutrition. Hence, the study explores the presence of and the factors associated with mother-child dyads of over- and undernutrition in India.Methods and materialsThe study uses National Family Health Survey 2015-16 data. The analytic sample consists of 28,817 weighted mother-child pairs where an overweight/obese mother is paired with an undernourished child. The nutritional status of children is defined according to WHO 2006 child growth standards as underweight (i.e., low weight-for-age), stunting (i.e., low height-for-age) and wasting (i.e., low weight-for-height). Maternal overweight/obesity (i.e., BMI ≥ 25 kg/m2) is defined using adult BMI criterion. Descriptive, bivariate, and adjusted multivariable logistic regression analysis are conducted.ResultsOf the overweight/obese mothers, 21.3%, 26.5%, and 14% have underweight, stunted, and wasted children respectively. In adjusted models, maternal short stature (aOR: 2.94, 95% CI: 2.30-3.75), age of child (aOR: 3.29, 95% CI: 2.76-3.92), and poorest wealth status (aOR: 2.01, 95% CI: 1.59-2.54) are significant predictors of overweight/obese mothers and stunted child pairs. Similarly, poor wealth status (aOR: 1.68, 95% CI:1.32-2.14), maternal stature (aOR: 2.70, 95% CI: 2.08-3.52), and child aged 2-5 years (aOR: 1.77, 95% CI:1.51-2.08) are also significantly associated with higher occurrence of overweight/obese mother and-underweight child pairs.ConclusionFindings of the study are consistent with the phase of nutrition transition and double burden of malnutrition. The paper concludes with suggestions to improve the socioeconomic condition, more strategic nutrition specific investments and policy interventions to eliminate all forms of malnutrition for achieving SDGs.

Project description:BackgroundEconomic growth is widely perceived as a major policy instrument in reducing childhood undernutrition in India. We assessed the association between changes in state per capita income and the risk of undernutrition among children in India.Methods and findingsData for this analysis came from three cross-sectional waves of the National Family Health Survey (NFHS) conducted in 1992-93, 1998-99, and 2005-06 in India. The sample sizes in the three waves were 33,816, 30,383, and 28,876 children, respectively. After excluding observations missing on the child anthropometric measures and the independent variables included in the study, the analytic sample size was 28,066, 26,121, and 23,139, respectively, with a pooled sample size of 77,326 children. The proportion of missing data was 12%-20%. The outcomes were underweight, stunting, and wasting, defined as more than two standard deviations below the World Health Organization-determined median scores by age and gender. We also examined severe underweight, severe stunting, and severe wasting. The main exposure of interest was per capita income at the state level at each survey period measured as per capita net state domestic product measured in 2008 prices. We estimated fixed and random effects logistic models that accounted for the clustering of the data. In models that did not account for survey-period effects, there appeared to be an inverse association between state economic growth and risk of undernutrition among children. However, in models accounting for data structure related to repeated cross-sectional design through survey period effects, state economic growth was not associated with the risk of underweight (OR 1.01, 95% CI 0.98, 1.04), stunting (OR 1.02, 95% CI 0.99, 1.05), and wasting (OR 0.99, 95% CI 0.96, 1.02). Adjustment for demographic and socioeconomic covariates did not alter these estimates. Similar patterns were observed for severe undernutrition outcomes.ConclusionsWe failed to find consistent evidence that economic growth leads to reduction in childhood undernutrition in India. Direct investments in appropriate health interventions may be necessary to reduce childhood undernutrition in India. Please see later in the article for the Editors' Summary.

Project description:ImportanceGeographic targeting of public health interventions is needed in resource-constrained developing countries.ObjectiveTo develop methods for estimating health and development indicators across micropolicy units, using assembly constituencies (ACs) in India as an example.Design, setting, and participantsThis cross-sectional study included children younger than 5 years who participated in the fourth National Family and Health Survey (NFHS-4), conducted between January 2015 and December 2016. Participants lived in 36 states and union territories and 640 districts in India. Children who had valid weight and height measures were selected for stunting, underweight, and wasting analysis, and children between age 6 and 59 months with valid blood hemoglobin concentration levels were included in the anemia analysis sample. The analysis was performed between February 1 and August 15, 2020.ExposuresA total of 3940 ACs were identified from the geographic location of primary sampling units in which the children's households were surveyed in NFHS-4.Main outcomes and measuresStunting, underweight, and wasting were defined according to the World Health Organization Child Growth Standards. Anemia was defined as blood hemoglobin concentration less than 11.0 g/dL.ResultsThe main analytic sample included 222 172 children (mean [SD] age, 30.03 [17.01] months; 114 902 [51.72%] boys) from 3940 ACs in the stunting, underweight, and wasting analysis and 215 593 children (mean [SD] age, 32.63 [15.47] months; 112 259 [52.07%] boys) from 3941 ACs in the anemia analysis. The burden of child undernutrition varied substantially across ACs: from 18.02% to 60.94% for stunting, with a median (IQR) of 35.56% (29.82%-42.42%); from 10.40% to 63.24% for underweight, with a median (IQR) of 32.82% (25.50%-40.96%); from 5.56% to 39.91% for wasting, with a median (IQR) of 19.91% (15.70%-24.27%); and from 18.63% to 83.05% for anemia, with a median (IQR) of 55.74% (48.41%-63.01%). The degree of inequality within states varied across states; those with high stunting, underweight, and wasting prevalence tended to have high levels of inequality. For example, Uttar Pradesh, Jharkhand, and Karnataka had high mean AC-level prevalence of child stunting (Uttar Pradesh, 45.29%; Jharkhand, 43.76%; Karnataka, 39.77%) and also large SDs (Uttar Pradesh, 6.90; Jharkhand, 6.02; Karnataka, 6.72). The Moran I indices ranged from 0.25 to 0.80, indicating varying levels of spatial autocorrelation in child undernutrition across the states in India. No substantial difference in AC-level child undernutrition prevalence was found after adjusting for possible random displacement of geographic location data.Conclusions and relevanceIn this cross-sectional study, substantial inequality in child undernutrition was found across ACs in India, suggesting the importance of considering local electoral units in designing targeted interventions. The methods presented in this paper can be further applied to measuring health and development indicators in small electoral units for enhanced geographic precision of public health data in developing countries.

Project description:In India, districts serve as central policy unit for program development, administration and implementation. The one-size-fits-all approach based on average prevalence estimates at the district level fails to capture the substantial small area variation. In addition to district average, heterogeneity within districts should be considered in policy design. The objective of this study was to quantify the extent of small area variation in child stunting, underweight and wasting across 36 states/Union Territories (UTs), 640 districts (and 543 PCs), and villages/blocks in India. We utilized the 4th round of Indian National Family Health Survey (NFHS-4) conducted in 2015-2016. The study population included 225,002 children aged 0-59 months whose height and weight information were available. Stunting was defined as height-for-age z-score below 2 SD from the World Health Organization child growth reference standards. Similarly, underweight and wasting were each defined as weight-for-age < -2 SD and weight-for-height < -2 SD from the age- and sex-specific medians. We adopted a four-level logistic regression model to partition the total variation in stunting, underweight and wasting. We computed precision-weighted prevalence of child anthropometric failures across districts and PCs as well as within-district/PC variation using standard deviation (SD) measures. For stunting, 56.4% (var: 0.237; SE: 0.008) of the total variation was attributed to villages/blocks, followed by 25.8% (var: 0.109; SE: 0.030) to states/UTs, and 17.7% (Var: 0.074; SE: 0.006) to districts. For underweight and wasting, villages/blocks accounted for 38.4% (var: 0.224; SE: 0.007) and 50% (var: 0.285; SE: 0.009), respectively, of the total contextual variance in India. Similar findings were shown in multilevel models incorporating PC as a geographical unit instead of districts. We found high positive correlations between mean prevalence and SD for stunting (r = 0.780, p < 0.001), underweight (r = 0.860, p < 0.001), and wasting (r = 0.857, p < 0.001) across all districts in India. A similar pattern of correlation was found for PCs. Within-district and within-PC variation are the primary source of variation for child malnutrition in India. Our results suggest the importance of considering heterogeneity within districts and PCs when planning and administering child nutrition policies.

Dataset Information

Inequality in child undernutrition among urban population in India: a decomposition analysis.

Background

Methods

Results

Conclusion

Publications

Inequality in child undernutrition among urban population in India: a decomposition analysis.

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