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Spatial Weighted Analysis of Malnutrition Among Children in Nigeria: A Bayesian Approach

Author

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  • Osafu Augustine Egbon

    (University of São Paulo
    Universidade Federal de São Carlos)

  • Omodolapo Somo-Aina

    (University of North Carolina Greensboro)

  • Ezra Gayawan

    (The Federal University of Technology)

Abstract

Research on malnutrition in children has undergone in-depth study across different disciplines ranging from health demography to statistics. However, the effect of carbon(IV) oxide ( $$\text{CO}_2$$ CO 2 ) pollution on malnutrition has not been adequately considered. While the number of industries emitting $$\text{CO}_2$$ CO 2 keeps increasing in Nigeria, the fight against malnutrition has not been successful. The $$\text{CO}_2$$ CO 2 generated by industrial activities is known to contribute to the decline in the nutrition content of crops and consequently increases the risk of malnutrition in the population. In order to account for the spatial impact, and identify susceptible areas in Nigeria, this study weighted the spatial variation using the average volume of $$\text{CO}_2$$ CO 2 emitted from 2001 to 2018. The Conditional Auto-Regressive (CAR) spatial model was adopted to model the spatial component in a Bayesian Hierarchical statistical model. Data were acquired from the Nigeria Demographic and Health Survey, and Mongabay databases. Adjusting for demographic and socioeconomic variables, the result shows that regions with a higher concentration of $$\text{CO}_2$$ CO 2 were at higher risk of malnutrition, compared to the regions with a lower concentration. However, the Northern region with a lower concentration of $$\text{CO}_2$$ CO 2 was consistently at higher risk of malnutrition than other regions.

Suggested Citation

  • Osafu Augustine Egbon & Omodolapo Somo-Aina & Ezra Gayawan, 2021. "Spatial Weighted Analysis of Malnutrition Among Children in Nigeria: A Bayesian Approach," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(3), pages 495-523, December.
    • Handle: RePEc:spr:stabio:v:13:y:2021:i:3:d:10.1007_s12561-021-09303-9
    DOI: 10.1007/s12561-021-09303-9
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    References listed on IDEAS

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    1. Benson, Todd & Amare, Mulubrhan & Oyeyemi, Motunrayo & Fadare, Olusegun, 2017. "Study of the determinants of chronic malnutrition in northern Nigeria: Qualitative evidence from Kebbi and Bauchi states:," NSSP working papers 44, International Food Policy Research Institute (IFPRI).
    2. Wenceslao González‐Manteiga & Rosa M. Crujeiras & Daniel Simpson & Finn Lindgren & Håvard Rue, 2012. "In order to make spatial statistics computationally feasible, we need to forget about the covariance function," Environmetrics, John Wiley & Sons, Ltd., vol. 23(1), pages 65-74, February.
    3. Amare, Mulubrhan & Benson, Todd & Fadare, Olusegun & Oyeyemi, Motunrayo, 2017. "Synopsis: Study of the determinants of chronic malnutrition in northern Nigeria: Quantitative evidence from the Nigeria Demographic and Health Surveys:," NSSP policy notes 44, International Food Policy Research Institute (IFPRI).
    4. Amare, Mulubrhan & Benson, Todd & Fadare, Olusegun & Oyeyemi, Motunrayo, 2017. "Study of the determinants of chronic malnutrition in northern Nigeria: Quantitative evidence from the Nigeria Demographic and Health Surveys:," NSSP working papers 45, International Food Policy Research Institute (IFPRI).
    5. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    6. Bolin, David & Lindström, Johan & Eklundh, Lars & Lindgren, Finn, 2009. "Fast estimation of spatially dependent temporal vegetation trends using Gaussian Markov random fields," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2885-2896, June.
    7. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    8. Rodrigues, E.C. & Assunção, R., 2012. "Bayesian spatial models with a mixture neighborhood structure," Journal of Multivariate Analysis, Elsevier, vol. 109(C), pages 88-102.
    9. Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
    10. Julian Besag & Debashis Mondal, 2005. "First-order intrinsic autoregressions and the de Wijs process," Biometrika, Biometrika Trust, vol. 92(4), pages 909-920, December.
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    Cited by:

    1. Samson B. Adebayo & Ezra Gayawan, 2022. "A Bivariate Analysis of the Spatial Distributions of Stunting and Wasting Among Children Under-Five in Nigeria," Journal of Development Policy and Practice, , vol. 7(1), pages 31-52, January.
    2. Olamide Seyi Orunmoluyi & Ezra Gayawan & Samuel Manda, 2022. "Spatial Co-Morbidity of Childhood Acute Respiratory Infection, Diarrhoea and Stunting in Nigeria," IJERPH, MDPI, vol. 19(3), pages 1-16, February.

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