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Life sciences · Review
Health Sa Gesondheid · September 25, 2026
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Background: Hypertension is a major cause of preventable cardiovascular morbidity and mortality and remains a significant public health challenge in South Africa. Most existing risk prediction models were derived from high-income populations and may not perform well in low- and middle-income settings. Aim: To synthesise pooled evidence on risk factors of hypertension in South Africa and use the findings to inform the development of a clinical prediction model. Setting: South Africa. Methods: A systematic review and meta-analysis were conducted using studies retrieved from PubMed, Scopus, Academic Search Complete, MEDLINE, Web of Science and African Journals (SABINET) from 1990 to March 2025. Titles, abstracts and full texts were screened independently by two reviewers. Random-effects meta-analysis was used to pool odds ratios for eligible risk factors. Certainty of evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation (GRADE), while predictor selection for model development was guided by risk weight and geotemporal trend. Results: Fifty-four studies, including cross-sectional, case–control and cohort designs, were included. The studies covered multiple South African provinces, with 15 national studies spanning all nine provinces. Strong pooled associations were observed for dyslipidaemia (odds ratio [OR] 3.79, 95% confidence interval [CI] 1.84–7.80), age > 50 years (OR 3.49, 95% CI 1.82–6.70), diabetes (OR 2.57, 95% CI 1.86–3.53), obesity (OR 2.35, 95% CI 1.89–2.91) and gold mine dust exposure (OR 2.35, 95% CI 1.70–3.25). These findings informed the final prediction model. Conclusion: Age, metabolic factors, environmental exposures and selected social determinants were important predictors of hypertension. The resulting model may support early screening in primary care, although external validation is required. Contribution: This study synthesises South African evidence on hypertension risk factors and translates the findings into a context-specific clinical prediction model.