IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i1p359-d475265.html
   My bibliography  Save this article

Bivariate Joint Spatial Modeling to Identify Shared Risk Patterns of Hypertension and Diabetes in South Africa: Evidence from WHO SAGE South Africa Wave 2

Author

Listed:
  • Glory Chidumwa

    (Division of Epidemiology and Biostatistics, School of Public Health, University of the Witwatersrand, Johannesburg 2198, South Africa)

  • Innocent Maposa

    (Division of Epidemiology and Biostatistics, School of Public Health, University of the Witwatersrand, Johannesburg 2198, South Africa)

  • Paul Kowal

    (World Health Organization SAGE, CH-1211 Geneva, Switzerland
    Research Institute for Health Sciences, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Lisa K. Micklesfield

    (South African Medical Research Council/Wits Developmental Pathways for Health Research Unit, School of Clinical Medicine, University of the Witwatersrand, Johannesburg 2198, South Africa)

  • Lisa J. Ware

    (South African Medical Research Council/Wits Developmental Pathways for Health Research Unit, School of Clinical Medicine, University of the Witwatersrand, Johannesburg 2198, South Africa
    DSI-NRF Centre of Excellence in Human Development, University of the Witwatersrand, Johannesburg 2198, South Africa)

Abstract

Recent studies have suggested the common co-occurrence of hypertension and diabetes in South Africa. Given that hypertension and diabetes are known to share common socio-demographic, anthropometric and lifestyle risk factors, the aim of this study was to jointly model the shared and disease-specific geographical variation of hypertension and diabetes. The current analysis used the Study on Global Ageing and Adult Health (SAGE) South Africa Wave 2 (2014/15) data collected from 2761 participants. Of the 2761 adults (median age = 56 years), 641 (23.2%) had high blood pressure on measurement and 338 (12.3%) reported being diagnosed with diabetes. The shared component has distinct spatial patterns with higher values of odds in the eastern districts of Kwa-Zulu Natal and central Gauteng province. The shared component may represent unmeasured health behavior characteristics or the social determinants of health in our population. Our study further showed how a shared component (latent and unmeasured health behavior characteristics or the social determinants of health) is distributed across South Africa among the older adult population. Further research using similar shared joint models may focus on extending these models for multiple diseases with ecological factors and also incorporating sampling weights in the spatial analyses.

Suggested Citation

  • Glory Chidumwa & Innocent Maposa & Paul Kowal & Lisa K. Micklesfield & Lisa J. Ware, 2021. "Bivariate Joint Spatial Modeling to Identify Shared Risk Patterns of Hypertension and Diabetes in South Africa: Evidence from WHO SAGE South Africa Wave 2," IJERPH, MDPI, vol. 18(1), pages 1-12, January.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:1:p:359-:d:475265
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/1/359/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/1/359/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Joshua A. Salomon & Christopher J. L. Murray, 2002. "The Epidemiologic Transition Revisited: Compositional Models for Causes of Death by Age and Sex," Population and Development Review, The Population Council, Inc., vol. 28(2), pages 205-228, June.
    2. Colin D Mathers & Dejan Loncar, 2006. "Projections of Global Mortality and Burden of Disease from 2002 to 2030," PLOS Medicine, Public Library of Science, vol. 3(11), pages 1-20, November.
    3. Leonhard Knorr‐Held & Nicola G. Best, 2001. "A shared component model for detecting joint and selective clustering of two diseases," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 73-85.
    4. Ian H. Langford & Alistair H. Leyland & Jon Rasbash & Harvey Goldstein, 1999. "Multilevel Modelling of the Geographical Distributions of Diseases," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(2), pages 253-268.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    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. Ngianga-Bakwin Kandala & Samuel O.M. Manda & William W. Tigbe & Henry Mwambi & Saverio Stranges, 2014. "Geographic distribution of cardiovascular comorbidities in South Africa: a national cross-sectional analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(6), pages 1203-1216, June.
    2. Ngianga-Bakwin Kandala & Chibuzor Christopher Nnanatu & Natisha Dukhi & Ronel Sewpaul & Adlai Davids & Sasiragha Priscilla Reddy, 2021. "Mapping the Burden of Hypertension in South Africa: A Comparative Analysis of the National 2012 SANHANES and the 2016 Demographic and Health Survey," IJERPH, MDPI, vol. 18(10), pages 1-18, May.
    3. G. Guindon, 2014. "The impact of tobacco prices on smoking onset in Vietnam: duration analyses of retrospective data," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 15(1), pages 19-39, January.
    4. Hermann Pythagore Pierre Donfouet & Shukri F. Mohamed & Eric Malin, 2021. "Socioeconomic inequality in tobacco use in Kenya: a concentration analysis," International Journal of Health Economics and Management, Springer, vol. 21(2), pages 247-269, June.
    5. Ngianga-Bakwin Kandala & Saverio Stranges, 2014. "Geographic Variation of Overweight and Obesity among Women in Nigeria: A Case for Nutritional Transition in Sub-Saharan Africa," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-11, June.
    6. 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.
    7. 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..
    8. Jia Lu & Shabana Jamani & Joseph Benjamen & Eric Agbata & Olivia Magwood & Kevin Pottie, 2020. "Global Mental Health and Services for Migrants in Primary Care Settings in High-Income Countries: A Scoping Review," IJERPH, MDPI, vol. 17(22), pages 1-28, November.
    9. Matthijs van den Berg & Filip Smit & Theo Vos & Pieter H M van Baal, 2011. "Cost-Effectiveness of Opportunistic Screening and Minimal Contact Psychotherapy to Prevent Depression in Primary Care Patients," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-7, August.
    10. 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.
    11. Ide, Hiroo & Mollahaliloglu, Salih, 2009. "How firms set prices for medical materials: A multi-country study," Health Policy, Elsevier, vol. 92(1), pages 73-78, September.
    12. 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.
    13. 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).
    14. 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.
    15. 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.
    16. 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.
    17. Tuccio, Michele & Wahba, Jackline & Hamdouch, Bachir, 2016. "International Migration: Driver of Political and Social Change?," IZA Discussion Papers 9794, Institute of Labor Economics (IZA).
    18. 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.
    19. 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.
    20. 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.

    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:18:y:2021:i:1:p:359-:d:475265. 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.