Analyzing the Effects of Feeding Practices, WASH, and District-Level Spatial Covariates on Malnutrition Among Indian Children: Aiming for SDGs 6 and 3
64th ISI World Statistics Congress - Ottawa, Canada
Format: CPS Abstract
Keywords: "bayesian, public-health, sdgs, spatial
Session: CPS 67 - Sustainable development goals II
Tuesday 18 July 5:30 p.m. - 6:30 p.m. (Canada/Eastern)
The UN sustainable development goals (SDGs) 6 and 3 address affordable and safe drinking water, proper sanitation and hygiene (WASH) practises, and supporting healthy lifestyles and well-being at all ages. Previous studies have shown that inadequate drinking water, sanitation, and hygiene (WASH) practises, as well as improper feeding practises, are the major causes of childhood malnutrition in low and middle-income countries.
As per the National Family Health Survey (NFHS)-V, the prevalence of nutritional status-stunting, wasting, and overweight, among Indian children were found to be quite high and geographically varied. Despite the efforts of Indian government to improve WASH facilities across the country, the prevalence of malnutrition has increased in many Indian states since the previous NFHS. Therefore, to look for the significant factors behind the issue this study incorporates feeding practises among children as an explanatory variable, as well as the WASH indicators- drinking water source, time to collect drinking water, person who usually collects drinking water, toilet facility, and child stool disposal, handwashing facility, childhood disease, and some other socioeconomic and demographic maternal and household characteristics, along with district-level spatial covariates. The study employs a Bayesian structured additive regression model to examine the influence of these explanatory variables on childhood nutritional status. The model allows for the simultaneous investigation of linear and non-linear covariate effects, as well as the incorporation of spatial variables on a single platform. For this study, NFHS-V 2019-21 data (KIDS file) collected from www.DHSprogram.com is considered and analysed using the software package BayesX via the R interface.