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

Kandala, Ngianga-Bakwin, Nnanatu, Chibuzor Christopher, Dukhi, Natisha, Sewpaul, Ronel, Davids, Adlai and Reddy, Sasiragha Priscilla (2021) Mapping the Burden of Hypertension in South Africa: A Comparative Analysis of the National 2012 SANHANES and the 2016 Demographic and Health Survey. International Journal of Environmental Research and Public Health, 18 (10). p. 5445. ISSN 1660-4601

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Official URL: https://doi.org/10.3390/ijerph18105445

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.

Item Type: Article
Uncontrolled Keywords: Bayesian geo-additive regression; spatial modelling; South Africa; KwaZulu-Natal; Mpumalanga
Subjects: B900 Others in Subjects allied to Medicine
L900 Others in Social studies
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Elena Carlaw
Date Deposited: 24 May 2021 10:37
Last Modified: 31 May 2021 14:37
URI: http://nrl.northumbria.ac.uk/id/eprint/46254

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