Associations Between Nutritional Indicators Using Geoadditive Latent Variable Models with Application to Child Malnutrition in Nigeria

Khatab, Khaled

doi:10.5772/27792

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…The present study applies a distributional bivariate probit model to explore the association between acute and chronic malnutrition and other factors (16) . The inclusion of structured additive predictor into the model permits linking spatial and other covariates to the means and correlations between the undernutrition indicators (17,18) . The categorical predictors under the study were assumed to have a linear effect; the effects of metrical covariates were considered non-linear and modelled non-parametrically.…”

Section: Introductionmentioning

confidence: 99%

Spatial distribution of stunting and wasting in 6–59 months children in Nepal: analysis using a Bayesian distributional bivariate probit model

Vatsa

Ghimire²,

Sapkota³

et al. 2023

J Nutr Sci

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The combined burden of stunting and wasting in children under five years is a serious public health concern. The present study aimed to estimate the joint burden of stunting and wasting among children aged 6–59 months and explore its spatial variation across Nepal. The 2016 Nepal Demographic and Health Survey data was used to study acute and chronic childhood malnutrition. A Bayesian distributional bivariate probit geoadditive model was designed to study the linear association and geographical variation of stunting and wasting among 6–59 months, children. Child-related factors such as low birth weight, fever in the last 2 weeks preceding the survey and fourth or greater birth order were associated with a higher likelihood of stunting. The likelihood of a child being stunted was significantly less in the wealthiest households, having improved toilet facilities, and if mothers were overweight. Children from severely food insecure households were significantly more likely, and children from poorer households were significantly less likely to suffer both acute and chronic malnutrition simultaneously. Results from spatial effect showed that children from Lumbini and Karnali had a higher burden of stunting, and the likelihood that achild would have been wasted was significantly higher in Madhesh and Province 1. Immediate nutritional efforts are vital in low-income and severely food insecure households to lessen the risk of stunting and wasting in under children. Disproportionate geographic variations in stunting and wasting warrant sub-regional-specific nutrition intervention to achieve nutrition targets and reduce the burden of childhood malnutrition across the country.

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Section: Introductionmentioning

confidence: 99%

Spatial distribution of stunting and wasting in 6–59 months children in Nepal: analysis using a Bayesian distributional bivariate probit model

Vatsa

Ghimire²,

Sapkota³

et al. 2023

J Nutr Sci

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“…Adebayo (2003) considered an adaptive Bayesian spline approach in the study of determinants of childhood malnutrition in Zambia and reported nonlinear relationship between metrical variables particularly age and the malnutrition indicators: stunting, wasting and underweight that were considered. Among other notable works on childhood malnutrition are Klasen (1996), Kandala et al (2001), Kandala et al (2009), Khatab (2012), andYadav et al (2015).…”

Section: Introductionmentioning

confidence: 99%

A Bivariate Analysis of the Spatial Distributions of Stunting and Wasting Among Children Under-Five in Nigeria

Adebayo¹,

Gayawan

2021

Journal of Development Policy and Practice

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Stunting and wasting are major malnutrition issues among children under five years of age and have continued to remain unacceptably high in Nigeria leading to high rates of child morbidity and mortality. Evidence-based strategies are required by government and non-governmental agencies to mitigate the suffering of these children, and this could be realised when the association between the determinants and the geographical distributions are fully understood. Using data from four waves of the Nigerian Demographic and Health Survey, we employed a distributional bivariate probit model to examine the geographical distributions of the levels and linear association between acute and chronic malnutrition in Nigeria after accounting for possible observed determinants. Bayesian inference was based on Markov chain Monte Carlo simulation. The findings reveal substantial spatial variations in stunting and wasting among under-five children in Nigeria, indicating a north–south divide. The findings show negative linear association between the two malnutrition indicators among children in some northern fringe states but positive for Akwa Ibom, Ebonyi and Anambra. The correlation also peaks around age 20 months indicating that during the first 2 years of life, the children have an increasing likelihood of suffering from stunting and wasting.

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“…Kandala et al 9 combined data from Malawi, Tanzania, and Zambia and used geoadditive Gaussian models to explain the spatial pattern of malnutrition in those countries. Similar models were used in the case of India by Yadav et al 10 and Yadav et al 11 A geoadditive latent variables approach was used in the case of Nigeria by Khatab 12 and Fahrmeir and Khatab, 13 while Adebayo 14 used adaptive Bayesian spline to estimate nonlinear effects. In those setups, Bayesian modeling of spatial components have been mostly based on Markov random fields, 15 while nonlinear effects are often modeled by Bayesian P-splines.…”

Section: Introductionmentioning

confidence: 99%

Modeling the spatial variability in the spread and correlation of childhood malnutrition in Nigeria

Gayawan

Adebayo

Waldmann³

2019

Statistics in Medicine

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The average nutritional status of children in Nigeria is, just as in most developing countries, still in an alarmingly bad condition. Prior studies have shown that this status relies on a series of different influences and can be measured by three anthropometric variables for stunting, wasting, and underweight. Different regression modeling techniques have been adopted over the years to explain the determinants and spatial clustering. Those indicators, however, show patterns that are not necessarily full filling requirements for ordinary regression models for the mean and are correlated among each other, a fact that has until now been ignored by most studies. Methods to model outcomes in the light of both, the whole distribution of and the correlation between two or more outcomes based on a set of covariates, have lately been developed. The aim of this paper is to make use of those methods to explain the underlying spatial structure in malnutrition in Nigeria. The study brings to limelight the pattern of spread as well as the interwoven relationships among childhood malnutrition indicators that would have otherwise remained unknown in Nigeria.

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Associations Between Nutritional Indicators Using Geoadditive Latent Variable Models with Application to Child Malnutrition in Nigeria

Cited by 3 publications

References 17 publications

Spatial distribution of stunting and wasting in 6–59 months children in Nepal: analysis using a Bayesian distributional bivariate probit model

Spatial distribution of stunting and wasting in 6–59 months children in Nepal: analysis using a Bayesian distributional bivariate probit model

A Bivariate Analysis of the Spatial Distributions of Stunting and Wasting Among Children Under-Five in Nigeria

Modeling the spatial variability in the spread and correlation of childhood malnutrition in Nigeria

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