• 1.

    Sen A, 1999. Development as Freedom. Oxford: Oxford University Press.

  • 2.

    Svedberg P, 1996. Gender bias in Sub-Saharan Africa: reply and further evidence. Journal of Development Studies 32: 933943.

  • 3.

    United Nations Children's Fund, 1998. The State of the World's Children. New York: UNICEF.

  • 4.

    Demographic and Health Survey (EDHS) for Egypt, 2003. MEASURE DHS (Demographic and Health Surveys). Calverton, MD: EDHS.

  • 5.

    Fahrmeir L, Kneib T, Lang S, 2004. Penalized structured additive regression of space-time data: a Bayesian perspective. Statistica Sinica 14: 731761.

    • Search Google Scholar
    • Export Citation
  • 6.

    Fahrmeir L, Lang S, 2001. Bayesian inference for generalized additive mixed models based on Markov random field priors. Applied Statistics (JRSS C) 50: 201220.

    • Search Google Scholar
    • Export Citation
  • 7.

    Adebayo SB, 2003. Semiparametric Bayesian regression for multivariate responses. PhD Thesis, Hieronymus Verlag, Munich, Germany.

  • 8.

    Kandala NB, Lang S, Klasen S, Fahrmeir L, 2001. Semiparametric analysis of the socio-demographic determinants of undernutrition in two African countries. Research in Official Statistics 4: 81100.

    • Search Google Scholar
    • Export Citation
  • 9.

    Khatab K, 2007. Analysis of childhood diseases and malnutrition in developing countries of Africa. PhD thesis, Dr. Hut Verlag, Munich, Germany.

    • Search Google Scholar
    • Export Citation
  • 10.

    Kandala NB, Ji C, Stallard N, Stranges S, Cappuccio FP, 2007. Spatial analysis of risk factors for childhood morbidity in Nigeria. Journal of Tropical Medicine 77: 770778.

    • Search Google Scholar
    • Export Citation
  • 11.

    Kandala NB, Magadi MA, Madise NJ, 2006. An investigation of district spatial variations of childhood diarrhoea and fever morbidity in Malawi. Social Science and Medicine 62: 11381152.

    • Search Google Scholar
    • Export Citation
  • 12.

    Khatab K, Fahrmeir L, 2009. Analysis of childhood morbidity with geoadditive probit and latent variable model: a case study for Egypt. Am J Trop Med Hyg 81: 114128.

    • Search Google Scholar
    • Export Citation
  • 13.

    Brezger A, Kneib T, Lang S, 2005. BayesX. Software for Bayesian Inference based on Markov Chain Monte Carlo Simulation Techniques verison 2. Available at: http://www.stat.uni-muenchen.de/~BayesX.

    • Search Google Scholar
    • Export Citation
  • 14.

    Raach AW, 2005. A Bayesian semiparametric latent variable model for binary, ordinal and continuous response. Dissertation. Available at: http://epub.ub.uni-muenchen.de/11000/1/tr066.pdf.

    • Search Google Scholar
    • Export Citation
  • 15.

    Besag J, York Y, Mollie A, 1991. Bayesian image restoration with two applications in spatial statistics (with discussion). Ann Inst Statist Math 43: 159.

    • Search Google Scholar
    • Export Citation
  • 16.

    Brezger A, Lang S, 2005. Generalized structured additive regression based on Bayesian P-splines. Computational Statistics and Data Analysis 50: 967991.

    • Search Google Scholar
    • Export Citation
  • 17.

    Fahrmeir L, Raach A, 2007. A Bayesian semiparametric latent variable model for mixed responses. Psychometrika 72: 327346.

  • 18.

    Klasen S, 1996. Nutrition, health, and mortality in sub-Saharan Africa: is there a gender bias? Journal of Development Studies 32: 913932.

    • Search Google Scholar
    • Export Citation
  • 19.

    Borooah V, 2002. The Role of Maternal Literacy in Reducing the Risk of Child Malnutrition in India. International Centre for Economic Research, ICER Working Papers N. 31-2002.

    • Search Google Scholar
    • Export Citation
  • 20.

    Kandala NB, 2002. Spatial modelling of socio-economic and demographic determinants of childhood undernutrition and mortality in Africa. PhD Thesis, Shaker Verlag, Munich, Germany.

    • Search Google Scholar
    • Export Citation
  • 21.

    Gibson J, 2001. Literacy and Intra-household Externalities. J World Development 29: 155166.

  • 22.

    Lavy V, Strauss J, Thomas D, de Vreyer P, 1996. Quality of health care, survival and health outcomes in Ghana. Journal of Health Economics 15: 33357.

    • Search Google Scholar
    • Export Citation
  • 23.

    African Nutrition Chartbooks, 1996. Nutrition of Infants and Young Children in Mali. Calverton, MD: Macro International Inc.

  • 24.

    World Health Organization, 1999. Infant and Young Child Growth: The WHO Multicentre Growth Reference Study. Executive Board: Implementation of Resolutions and Decisions EB105/Inf.doc/1.Geneva:WHO.

    • Search Google Scholar
    • Export Citation
  • 25.

    Alderman H, Behrman JR, Lavy V, Menon R, 1997. Child Nutrition, Child Health and School Enrollment: A Longitudinal Analysis. Policy Research Working Paper 1700. The World Bank, south Asia.

    • Search Google Scholar
    • Export Citation
  • 26.

    Toroitich-Ruto C, 1998. The correlates of low nutritional status among young school-going children in Kenya. International Journal of Health Education, UK 36: 120126.

    • Search Google Scholar
    • Export Citation

 

 

 

 

Childhood Malnutrition in Egypt using Geoadditive Gaussian and Latent Variable Models

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  • 1 Institute of Occupational and Social Medicine, Medical School, Rheinisch-Westfälische Technische Hochschule Aachen (RWTH) University, Aachen, Germany

Major progress has been made over the last 30 years in reducing the prevalence of malnutrition amongst children less than 5 years of age in developing countries. However, approximately 27% of children under the age of 5 in these countries are still malnourished. This work focuses on the childhood malnutrition in one of the biggest developing countries, Egypt. This study examined the association between bio-demographic and socioeconomic determinants and the malnutrition problem in children less than 5 years of age using the 2003 Demographic and Health survey data for Egypt. In the first step, we use separate geoadditive Gaussian models with the continuous response variables stunting (height-for-age), underweight (weight-for-age), and wasting (weight-for-height) as indicators of nutritional status in our case study. In a second step, based on the results of the first step, we apply the geoadditive Gaussian latent variable model for continuous indicators in which the 3 measurements of the malnutrition status of children are assumed as indicators for the latent variable “nutritional status”.

Author Notes

*Address correspondence to Khaled Khatab, Institute of Occupational and Social Medicine, Medical School, RWTH Aachen University, Germany, Pauwelsstraße 30, 52074 Aachen. E-mails: kkhatab@ukaachen.de or khaledkhatab314@yahoo.com

Author's address: Khaled Khatab, Institute of Occupational and Social Medicine, Medical School, RWTH Aachen University, Germany, Pauwelsstraße 30, 52074 Aachen, E-mails: kkhatab@ukaachen.de or khaledkhatab314@yahoo.com.

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