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Structured Additive Quantile Regression for Assessing the Determinants of Childhood Anemia in Rwanda

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  • Faustin Habyarimana

    (School of Mathematics, Statistics and Computer Sciences, University of KwaZulu-Natal, Pietermaritzburg Campus, Private Bag X01, Scottsville 3209, South Africa)

  • Temesgen Zewotir

    (School of Mathematics, Statistics and Computer Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4000, South Africa)

  • Shaun Ramroop

    (School of Mathematics, Statistics and Computer Sciences, University of KwaZulu-Natal, Pietermaritzburg Campus, Private Bag X01, Scottsville 3209, South Africa)

Abstract

Childhood anemia is among the most significant health problems faced by public health departments in developing countries. This study aims at assessing the determinants and possible spatial effects associated with childhood anemia in Rwanda. The 2014/2015 Rwanda Demographic and Health Survey (RDHS) data was used. The analysis was done using the structured spatial additive quantile regression model. The findings of this study revealed that the child’s age; the duration of breastfeeding; gender of the child; the nutritional status of the child (whether underweight and/or wasting); whether the child had a fever; had a cough in the two weeks prior to the survey or not; whether the child received vitamin A supplementation in the six weeks before the survey or not; the household wealth index; literacy of the mother; mother’s anemia status; mother’s age at the birth are all significant factors associated with childhood anemia in Rwanda. Furthermore, significant structured spatial location effects on childhood anemia was found.

Suggested Citation

  • Faustin Habyarimana & Temesgen Zewotir & Shaun Ramroop, 2017. "Structured Additive Quantile Regression for Assessing the Determinants of Childhood Anemia in Rwanda," IJERPH, MDPI, vol. 14(6), pages 1-15, June.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:6:p:652-:d:101835
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    References listed on IDEAS

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    1. Oscar Ngesa & Henry Mwambi, 2014. "Prevalence and Risk Factors of Anaemia among Children Aged between 6 Months and 14 Years in Kenya," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-10, November.
    2. 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.
    3. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    4. 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.
    5. Yue, Yu Ryan & Rue, Håvard, 2011. "Bayesian inference for additive mixed quantile regression models," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 84-96, January.
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    Cited by:

    1. Bin Xu, 2022. "How to Efficiently Reduce the Carbon Intensity of the Heavy Industry in China? Using Quantile Regression Approach," IJERPH, MDPI, vol. 19(19), pages 1-24, October.
    2. Shristi Sharma & Bipin Kumar Acharya & Qian Wu, 2022. "Spatial Variations and Determinants of Anemia among Under-five Children in Nepal, DHS (2006–2016)," IJERPH, MDPI, vol. 19(14), pages 1-13, July.

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