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Mapping the Burden of Hypertension in South Africa: A Comparative Analysis of the National 2012 SANHANES and the 2016 Demographic and Health Survey

Author

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  • Ngianga-Bakwin Kandala

    (Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
    Division of Epidemiology and Biostatistics, School of Public Health, University of the Witwatersrand, Braamfontein, Johannesburg 2000, South Africa)

  • Chibuzor Christopher Nnanatu

    (Department of Mathematics, Physics & Electrical Engineering (MPEE), Northumbria University, Newcastle NE 18 ST, UK
    Department of Statistics, Nnamdi Azikiwe University, Awka PMB 5025, Nigeria)

  • Natisha Dukhi

    (Health & Wellbeing, Human and Social Capabilities (HSC) Division, Human Sciences Research Council, Private Bag X9182, Cape Town 8000, South Africa)

  • Ronel Sewpaul

    (Health & Wellbeing, Human and Social Capabilities (HSC) Division, Human Sciences Research Council, Private Bag X9182, Cape Town 8000, South Africa)

  • Adlai Davids

    (Health & Wellbeing, Human and Social Capabilities (HSC) Division, Human Sciences Research Council, Private Bag X9182, Cape Town 8000, South Africa
    Faculty of Health Sciences, Nelson Mandela University, Port Elizabeth 6031, South Africa)

  • Sasiragha Priscilla Reddy

    (Health & Wellbeing, Human and Social Capabilities (HSC) Division, Human Sciences Research Council, Private Bag X9182, Cape Town 8000, South Africa
    Faculty of Health Sciences, Nelson Mandela University, Port Elizabeth 6031, South Africa)

Abstract

This study investigates the provincial variation in hypertension prevalence in South Africa in 2012 and 2016, adjusting for individual level demographic, behavioural and socio-economic variables, while allowing for spatial autocorrelation and adjusting simultaneously for the hierarchical data structure and risk factors. Data were analysed from participants aged ?15 years from the South African National Health and Nutrition Examination Survey (SANHANES) 2012 and the South African Demographic and Health Survey (DHS) 2016. Hypertension was defined as blood pressure ? 140/90 mmHg or self-reported health professional diagnosis or on antihypertensive medication. Bayesian geo-additive regression modelling investigated the association of various socio-economic factors on the prevalence of hypertension across South Africa’s nine provinces while controlling for the latent effects of geographical location. Hypertension prevalence was 38.4% in the SANHANES in 2012 and 48.2% in the DHS in 2016. The risk of hypertension was significantly high in KwaZulu-Natal and Mpumalanga in the 2016 DHS, despite being previously nonsignificant in the SANHANES 2012. In both survey years, hypertension was significantly higher among males, the coloured population group, urban participants and those with self-reported high blood cholesterol. The odds of hypertension increased non-linearly with age, body mass index (BMI), waist circumference. The findings can inform decision making regarding the allocation of public resources to the most affected areas of the population.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:10:p:5445-:d:557982
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    References listed on IDEAS

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    1. Ngianga-Bakwin Kandala & Gebrenegus Ghilagaber, 2006. "A Geo-Additive Bayesian Discrete-Time Survival Model and its Application to Spatial Analysis of Childhood Mortality in Malawi," Quality & Quantity: International Journal of Methodology, Springer, vol. 40(6), pages 935-957, December.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
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    1. Elelwani Malau & Irene Thifhelimbilu Ramavhoya & Melitah Molatelo Rasweswe, 2024. "Importance of Utilizing Non-Communicable Disease Screening Tools; Ward-Based Community Health Care Workers of South Africa Explain," IJERPH, MDPI, vol. 21(3), pages 1-14, February.
    2. Aynaz Lotfata & George Grekousis & Ruoyu Wang, 2023. "Using geographical random forest models to explore spatial patterns in the neighborhood determinants of hypertension prevalence across chicago, illinois, USA," Environment and Planning B, , vol. 50(9), pages 2376-2393, November.
    3. Nthai E. Ramoshaba & Mthetho Q. Fihla & Wenzile S. Mthethwa & Lisa Tshangela & Zuqaqambe M. Mampofu, 2022. "Neck Circumference and Blood Pressure Measurements among Walter Sisulu University Students," IJERPH, MDPI, vol. 19(22), pages 1-7, November.

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