Spatial distribution of various forms of malnutrition among reproductive age women in Nepal: A Bayesian geoadditive...

Addressing both the under-and over-nutritional status of women is an eminent challenge for developing countries like Nepal. This paper examined a critical analysis of factors associated with various forms of malnutrition using Bayesian geoadditive quantile regression approach and assessed spatial variations of malnutrition among Nepalese women using Asian cut-off values. Data drawn from the 2016 Nepal Demographic and Health Survey was utilized to assess the spatial distributions of underweight, overweight and obesity at the provincial level. Spatial and nonlinear components were estimated using Markov random fields and Bayesian P-splines, respectively. The analysis of 4,338 women confirmed that women living in extremely urbanized areas and in Province 1, Province 3, and Province 4 were more likely to be overweight/obese. Similarly, the likelihood of being underweight was prominently high among women residing in rural municipality and women residing in Province 2 and Province 7. Women from the richest and richer quintiles, and with primary education were more likely to be obese. Furthermore, currently-working women and women having access to protected water source were less likely to be obese while improved toilet and access to electricity facility were associated with obesity. Women with access to newspaper and radio were less prone to obesity. Inconsistent distribution of under- and over-nutrition existed in Nepal, given that the high prevalence of overweight/obesity among women living in metropolitan and undernutrition among rural women. Specific intervention measures, addressing location-specific nutrition issues are urgent. Rigorous implementation of strategies incorporated in the national nutrition plan is called for to curb the burden of overweight/obesity. Involving mass media to promote healthier lifestyle and nutritious food could be advantageous at the population level, especially in rural municipalities.