IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v16y2019i18p3334-d265828.html
   My bibliography  Save this article

Provincial Dietary Intake Study (PDIS): Prevalence and Sociodemographic Determinants of the Double Burden of Malnutrition in A Representative Sample of 1 to Under 10-Year-Old Children from Two Urbanized and Economically Active Provinces in South Africa

Author

Listed:
  • Marjanne Senekal

    (Division Human Nutrition, University of Cape Town, UCT Medical Campus, Anzio Road, Anatomy, Building, Observatory 7925, Cape Town, South Africa)

  • Johanna H Nel

    (Department of Logistics, Stellenbosch University, Stellenbosch, 7600, South Africa)

  • Sonia Malczyk

    (Division Human Nutrition, University of Cape Town, UCT Medical Campus, Anzio Road, Anatomy, Building, Observatory 7925, Cape Town, South Africa)

  • Linda Drummond

    (Division Human Nutrition, University of Cape Town, UCT Medical Campus, Anzio Road, Anatomy, Building, Observatory 7925, Cape Town, South Africa)

  • Janetta Harbron

    (Division Human Nutrition, University of Cape Town, UCT Medical Campus, Anzio Road, Anatomy, Building, Observatory 7925, Cape Town, South Africa)

  • Nelia P Steyn

    (Division Human Nutrition, University of Cape Town, UCT Medical Campus, Anzio Road, Anatomy, Building, Observatory 7925, Cape Town, South Africa)

Abstract

The objective of this study was to determine the prevalence and socio-demographic predictors of malnutrition in two urbanized economically active provinces (Gauteng N = 733, Western Cape N = 593) in South Africa. A multistage stratified cluster random sampling design was applied. Fieldworkers visited homes, measured children aged 1-<10-years old (N = 1326) and administered a questionnaire (mother/primary caregiver). In under-five year old children (N = 674) 21.6% were stunted [height-for-age z-score < −2 SD], 5.6 % underweight [weight-for-age z-score < −2 SD], 10.3% overweight (body mass index-for-age z-score) (BAZ)> +2 SD ≤ +3 SD] and 7.0% obese (BAZ > +3 SD). In 5–<10-year olds (N = 626) 6.7% were stunted, 6.8% underweight, 13.4% overweight and 6.8% obese. Stunting and overweight in the same child was present in 5.7% under-five year olds and 1.7% in 5–<10-year olds. Multiple logistic regression analyses identified having a mother with a post-grade 12 qualification (OR = 0.34) and having an obese mother (OR 0.46) as protectors and being in the under-five age group (OR = 3.73) as a risk factor for stunting. Being in the under-five age group was also a risk factor for a BAZ > 1 (OR 2.39), while being in the third wealth quintile was protective (OR = 0.62). Results indicate that stunting and overweight/obesity are still present at concerning levels, especially in the under-five age group.

Suggested Citation

  • Marjanne Senekal & Johanna H Nel & Sonia Malczyk & Linda Drummond & Janetta Harbron & Nelia P Steyn, 2019. "Provincial Dietary Intake Study (PDIS): Prevalence and Sociodemographic Determinants of the Double Burden of Malnutrition in A Representative Sample of 1 to Under 10-Year-Old Children from Two Urbaniz," IJERPH, MDPI, vol. 16(18), pages 1-27, September.
  • Handle: RePEc:gam:jijerp:v:16:y:2019:i:18:p:3334-:d:265828
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/16/18/3334/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/16/18/3334/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Deon Filmer & Lant Pritchett, 2001. "Estimating Wealth Effects Without Expenditure Data—Or Tears: An Application To Educational Enrollments In States Of India," Demography, Springer;Population Association of America (PAA), vol. 38(1), pages 115-132, February.
    2. Daniela Casale, 2016. "Analysing the links between child health and education outcomes: Evidence from NIDS Waves 1 – 4," SALDRU Working Papers 179, Southern Africa Labour and Development Research Unit, University of Cape Town.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nelia P. Steyn & Johanna H. Nel & Linda Drummond & Sonia Malczyk & Marjanne Senekal, 2022. "Has Food Security and Nutritional Status Improved in Children 1–<10 Years in Two Provinces of South Africa between 1999 (National Food Consumption Survey) and 2018 (Provincial Dietary Intake Study (PD," IJERPH, MDPI, vol. 19(3), pages 1-21, January.
    2. Nelia P. Steyn & Johanna H. Nel & Sonia Malczyk & Linda Drummond & Marjanne Senekal, 2020. "Provincial Dietary Intake Study (PDIS): Energy and Macronutrient Intakes of Children in a Representative/Random Sample of 1–<10-Year-Old Children in Two Economically Active and Urbanized Provinces in ," IJERPH, MDPI, vol. 17(5), pages 1-37, March.
    3. Marjanne Senekal & Johanna Nel & Sonia Malczyk & Linda Drummond & Nelia P. Steyn, 2020. "Provincial Dietary Intake Study (PDIS): Micronutrient Intakes of Children in a Representative/Random Sample of 1- to <10-Year-Old Children in Two Economically Active and Urbanized Provinces in South A," IJERPH, MDPI, vol. 17(16), pages 1-27, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Diane Coffey & Ashwini Deshpande & Jeffrey Hammer & Dean Spears, 2019. "Local Social Inequality, Economic Inequality, and Disparities in Child Height in India," Demography, Springer;Population Association of America (PAA), vol. 56(4), pages 1427-1452, August.
    2. Angus Deaton & Jean Dreze, 2008. "Nutrition in India: Facts and Interpretations," Working Papers 1071, Princeton University, Woodrow Wilson School of Public and International Affairs, Research Program in Development Studies..
    3. Cornelie Nienaber-Rousseau & Olusola F. Sotunde & Patricia O. Ukegbu & P. Hermanus Myburgh & Hattie H. Wright & Lize Havemann-Nel & Sarah J. Moss & Iolanthé M. Kruger & H. Salomé Kruger, 2017. "Socio-Demographic and Lifestyle Factors Predict 5-Year Changes in Adiposity among a Group of Black South African Adults," IJERPH, MDPI, vol. 14(9), pages 1-16, September.
    4. Kumar, Kaushalendra & Shukla, Ankita & Singh, Abhishek & Ram, Faujdar & Kowal, Paul, 2016. "Association between wealth and health among older adults in rural China and India," The Journal of the Economics of Ageing, Elsevier, vol. 7(C), pages 43-52.
    5. Winters, P. & Kafle, K. & Benfica, R., 2018. "IFAD RESEARCH SERIES 21 - Does relative deprivation induce migration? Evidence from sub-Saharan Africa," IFAD Research Series 280070, International Fund for Agricultural Development (IFAD).
    6. Langyintuo, Augustine S. & Mungoma, Catherine, 2008. "The effect of household wealth on the adoption of improved maize varieties in Zambia," Food Policy, Elsevier, vol. 33(6), pages 550-559, December.
    7. Ravi Prakash & Abhishek Singh, 2014. "Who Marries Whom? Changing Mate Selection Preferences in Urban India and Emerging Implications on Social Institutions," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 33(2), pages 205-227, April.
    8. Samikshya Poudel & Timothy Dobbins & Husna Razee & Blessing Akombi-Inyang, 2023. "Adolescent Pregnancy in South Asia: A Pooled Analysis of Demographic and Health Surveys," IJERPH, MDPI, vol. 20(12), pages 1-14, June.
    9. Tuccio, Michele & Wahba, Jackline & Hamdouch, Bachir, 2016. "International Migration: Driver of Political and Social Change?," IZA Discussion Papers 9794, Institute of Labor Economics (IZA).
    10. Pritchett, Lant & Sumarto, Sudarno & Suryahadi, Asep, 2001. "Targeted Programs in an Economic Crisis: Empirical Findings from Indonesia’s Experience," MPRA Paper 58727, University Library of Munich, Germany.
    11. Derek Headey & David Stifel & Liangzhi You & Zhe Guo, 2018. "Remoteness, urbanization, and child nutrition in sub‐Saharan Africa," Agricultural Economics, International Association of Agricultural Economists, vol. 49(6), pages 765-775, November.
    12. Barik, Debasis & Desai, Sonalde & Vanneman, Reeve, 2018. "Economic Status and Adult Mortality in India: Is the Relationship Sensitive to Choice of Indicators?," World Development, Elsevier, vol. 103(C), pages 176-187.
    13. Laetitia Duval & François-Charles Wolff, 2016. "Emigration intentions of Roma: evidence from Central and South-East Europe," Post-Communist Economies, Taylor & Francis Journals, vol. 28(1), pages 87-107, January.
    14. Antonia Grohmann & Lukas Menkhoff & Helke Seitz, 2022. "The Effect of Personalized Feedback on Small Enterprises’ Finances in Uganda," Economic Development and Cultural Change, University of Chicago Press, vol. 70(3), pages 1197-1227.
    15. Paschalis Arvanitidis & Athina Economou & Christos Kollias, 2016. "Terrorism’s effects on social capital in European countries," Public Choice, Springer, vol. 169(3), pages 231-250, December.
    16. Christopher H. Herbst & Monique Vledder & Karen Campbell & Mirja Sjöblom & Agnes Soucat, 2011. "The Human Resources for Health Crisis in Zambia : An Outcome of Health Worker Entry, Exit, and Performance within the National Health Labor Market," World Bank Publications - Books, The World Bank Group, number 5938, December.
    17. Janina Isabel Steinert & Lucie Dale Cluver & G. J. Melendez-Torres & Sebastian Vollmer, 2018. "One Size Fits All? The Validity of a Composite Poverty Index Across Urban and Rural Households in South Africa," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 136(1), pages 51-72, February.
    18. Pulkit Sharma & Achut Manandhar & Patrick Thomson & Jacob Katuva & Robert Hope & David A. Clifton, 2019. "Combining Multi-Modal Statistics for Welfare Prediction Using Deep Learning," Sustainability, MDPI, vol. 11(22), pages 1-15, November.
    19. Janz, Teresa & Augsburg, Britta & Gassmann, Franziska & Nimeh, Zina, 2023. "Leaving no one behind: Urban poverty traps in Sub-Saharan Africa," World Development, Elsevier, vol. 172(C).
    20. Wang, Fa, 2017. "Maximum likelihood estimation and inference for high dimensional nonlinear factor models with application to factor-augmented regressions," MPRA Paper 93484, University Library of Munich, Germany, revised 19 May 2019.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:16:y:2019:i:18:p:3334-:d:265828. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.