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

Ngianga-Bakwin Kandala, Chibuzor Christopher Nnanatu, Natisha Dukhi, Ronel Sewpaul, Adlai Davids and Sasiragha Priscilla Reddy
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Ngianga-Bakwin Kandala: Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
Chibuzor Christopher Nnanatu: Department of Mathematics, Physics & Electrical Engineering (MPEE), Northumbria University, Newcastle NE 18 ST, UK
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
Sasiragha Priscilla Reddy: Health & Wellbeing, Human and Social Capabilities (HSC) Division, Human Sciences Research Council, Private Bag X9182, Cape Town 8000, South Africa

IJERPH, 2021, vol. 18, issue 10, 1-18

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.

Keywords: hypertension; Bayesian geo-additive regression; spatial modelling; South Africa; KwaZulu-Natal; Mpumalanga (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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