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

Abstract

Background: In India, it has been observed that the prevalence of stunting among under-five children decreased, but the prevalence is still alarmingly high. In previous studies, traditional (linear and logistic) regression analyses were used, and these analyses were limited to encapsulated cross-distribution variations. Our study's objective was to examine how the different determinants are heterogenous in various percentiles. Methods: This article examined the change in the stunting distribution of children and examined the relationships between the key covariate's trends and patterns in stunting among children aged <3 years over a period of 24 years. Four successive rounds of the National Family Health Survey data 1992-93, 1998-99, 2005-06, and 2015-16 were used for analysis. The final study included 206579 children aged <3 years (N= 106136 male, 100443 female). To explain and analyze differences in the stunting distribution, the lambda-mu-sigma (LMS) method was used. Trends in stunting distribution over time were analysed using separate sex-stratified quantile regression (QR). The selected socioeconomic, demographic and other predictors considered for this analysis. Results: The quantile regressions have clearly indicated that mothers who have higher than primary level education were beneficial to decrease child malnutrition at the lower end of the distribution. The age, birth order, mother's body-mass-index (BMI) and wealth, among others, were some more determining factors for HAZ. Results of selected quantile regression estimated at 5th, 10th, 25th, 50th, 75th, 90th, and 95th quantiles. The wealth index was a highly negative association with lower quantiles compared to upper quantiles in stunting However, in the age classification, as the age increases, there was a negative association in the upper quantiles of stunting. Small size at birth was having a negative association in all the quantiles of stunting. Conclusions: The outcome of various covariates working differently across the stunting distribution was suggested by quantile regression. The major discrepancies in different aspects were underlined by socioeconomic and demographic aspects of India. The heterogeneity of this effect was shown using quantile regression.