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

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  • Samson B. Adebayo
  • Ezra Gayawan

Abstract

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.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:jodepp:v:7:y:2022:i:1:p:31-52
    DOI: 10.1177/24551333211051433
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    References listed on IDEAS

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    1. Senayit Seyoum, 2018. "Analysis of Prevalence of Malaria and Anemia Using Bivariate Probit Model," Annals of Data Science, Springer, vol. 5(2), pages 301-312, June.
    2. Samson B. Adebayo, 2003. "Modelling childhood malnutrition in Zambia: an adaptive Bayesian splines approach," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 12(2), pages 227-241, December.
    3. 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.
    4. 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.
    5. Stephan Klasen, 1996. "Nutrition, health and mortality in sub‐Saharan Africa: Is there a gender bias?," Journal of Development Studies, Taylor & Francis Journals, vol. 32(6), pages 913-932.
    6. Brezger, Andreas & Lang, Stefan, 2006. "Generalized structured additive regression based on Bayesian P-splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 967-991, February.
    7. R. A. Rigby & D. M. Stasinopoulos, 2005. "Generalized additive models for location, scale and shape," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 507-554, June.
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