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Bayesian Spatial Modeling of Anemia among Children under 5 Years in Guinea

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  • Thierno Souleymane Barry

    (Mathematics (Statistics Option) Program, Pan African University Institute for Basic Sciences, Technology and Innovation (PAUISTI), Nairobi 62000-00200, Kenya)

  • Oscar Ngesa

    (Department of Mathematics and Physical Sciences, Taita Taveta University, Voi 635-80300, Kenya)

  • Nelson Owuor Onyango

    (School of Mathematics, College of Biology and Physical Sciences, University of Nairobi, Nairobi 30197, Kenya)

  • Henry Mwambi

    (School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban 4041, South Africa)

Abstract

Anemia is a major public health problem in Africa, affecting an increasing number of children under five years. Guinea is one of the most affected countries. In 2018, the prevalence rate in Guinea was 75% for children under five years. This study sought to identify the factors associated with anemia and to map spatial variation of anemia across the eight (8) regions in Guinea for children under five years, which can provide guidance for control programs for the reduction of the disease. Data from the Guinea Multiple Indicator Cluster Survey (MICS5) 2016 was used for this study. A total of 2609 children under five years who had full covariate information were used in the analysis. Spatial binomial logistic regression methodology was undertaken via Bayesian estimation based on Markov chain Monte Carlo (MCMC) using WinBUGS software version 1.4. The findings in this study revealed that 77% of children under five years in Guinea had anemia, and the prevalences in the regions ranged from 70.32% (Conakry) to 83.60% (NZerekore) across the country. After adjusting for non-spatial and spatial random effects in the model, older children (48–59 months) (OR: 0.47, CI [0.29 0.70]) were less likely to be anemic compared to those who are younger (0–11 months). Children whose mothers had completed secondary school or above had a 33% reduced risk of anemia (OR: 0.67, CI [0.49 0.90]), and children from household heads from the Kissi ethnic group are less likely to have anemia than their counterparts whose leaders are from Soussou (OR: 0.48, CI [0.23 0.92]).

Suggested Citation

  • Thierno Souleymane Barry & Oscar Ngesa & Nelson Owuor Onyango & Henry Mwambi, 2021. "Bayesian Spatial Modeling of Anemia among Children under 5 Years in Guinea," IJERPH, MDPI, vol. 18(12), pages 1-18, June.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:12:p:6447-:d:574896
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    1. Yong Tu & Shi‐Ming Yu & Hua Sun, 2004. "Transaction‐Based Office Price Indexes: A Spatiotemporal Modeling Approach," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 32(2), pages 297-328, June.
    2. Flores, G. & Bauchner, H. & Feinstein, A.R. & Nguyen, U.-S.D.T., 1999. "The impact of ethnicity, family income, and parental education on children's health and use of health services," American Journal of Public Health, American Public Health Association, vol. 89(7), pages 1066-1071.
    3. Riccardo Borgoni & Francesco Billari, 2003. "Bayesian spatial analysis of demographic survey data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 8(3), pages 61-92.
    4. J. Law, 2009. "Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology by LAWSON, A. B," Biometrics, The International Biometric Society, vol. 65(2), pages 661-662, June.
    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. 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.
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