Abstract

AbstractThis paper compares the growth and nutritional status of pre-school children of three states of India, namely, Jharkhand, Bihar and West Bengal using third National Family Health Survey (NFHS-3) data. The sample sizes of Jharkhand, Bihar and West Bengal are 951, 1,373 and 1,600, respectively. Data on socio-demographic background of the households such as sex composition, place of residence, religion, level of education of mothers, mother’s age groups, and wealth index of the family are taken to see the differential effects of these variables on the child health status.It has been found that the distributions of weight and height around the means remain remarkably stable over age in those three states. It has also been found that the rates of growth of mean weights and heights are far lower in Bihar and Jharkhand than in West Bengal and India. The low growth rates of the mean values during the first year for both weight and height translate to high rates of undernutrition and stunting. It is also seen that high rate of stunting and underweight in Jharkhand and Bihar starts from 9 months and onwards while in West Bengal and India it starts from 12 months and onwards. Percentage of undernourished children is the highest in Bihar followed by Jharkhand and West Bengal. Comparatively higher growth rate of nutritional status and the low intensity of under nutrition of children are found in the socio-economic groups of male gender, urban areas, other communities and of secondary and higher educated mothers. Another notable finding is seen that only in West Bengal, reduction of underweight is directly related to upward movement of literacy along with wealth index but in Jharkhand and Bihar, there is no impact of literacy on reducing underweight and only higher wealth index is responsible for reducing underweight and stunting.KeywordsWealth IndexNational Family Health SurveyChild Health StatusIndia FigureWealth Index HouseholdThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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