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. 2026 Sep 25;31:3385. doi: 10.4102/hsag.v31i0.3385

Risk factors of hypertension in South Africa: A systematic review and meta-analysis to inform the development of a clinical prediction model

Martins Nweke 1,✉, Julian Pillay 1
PMCID: PMC13636361  PMID: 42835999

Abstract

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.

Keywords: hypertension, risk factors, South Africa, systematic review, meta-analysis, prediction model, clinical prediction

Introduction

Hypertension remains one of the most important modifiable risk factors for cardiovascular disease and continues to contribute substantially to global morbidity and mortality (Forouzanfar et al. 2017:165–174; Global Burden of Disease [GBD] 2019 risk Factors Collaborators 2020:1223–1249; Roth et al. 2020:2982–3021). Elevated blood pressure is strongly associated with adverse outcomes such as stroke, coronary artery disease, heart failure and chronic kidney disease, making it a major target for prevention strategies worldwide (Forouzanfar et al. 2017:165–174; Roth et al. 2020:2982–3021). Global analyses from the Global Burden of Disease study consistently identify high systolic blood pressure among the leading contributors to disability-adjusted life years and premature mortality across both developed and developing regions (GBD 2019 Risk Factors Collaborators 2020:1223–1249). Despite improvements in detection and treatment in high-income countries, the global burden of hypertension remains high and continues to increase in many parts of the world (Mills, Stefanescu & He 2020:223–237; NCD Risk Factor Collaboration 2021:957–980).

The rise in hypertension prevalence has been particularly pronounced in low- and middle-income countries, including those in sub-Saharan Africa (Ataklte et al. 2015:291–298; Ibrahim & Damasceno 2012:611–619; NCD Risk Factor Collaboration 2021:957–980). Large pooled analyses and epidemiological investigations suggest that many countries in this region are undergoing rapid demographic and epidemiological transitions characterised by urbanisation, ageing populations and changes in lifestyle behaviours (Ibrahim & Damasceno 2012:611–619; Mills et al. 2020:223–237). As a result, hypertension prevalence has increased substantially while awareness, treatment and control rates remain comparatively low (Ataklte et al. 2015:291–298; Kayima et al. 2013:54–64). This disparity may be attributed to inadequate funding and structuring of healthcare systems in many low- and middle-income countries, particularly the limited preventive healthcare approaches that rely predominantly on biological or treatment-focused models rather than comprehensive population-level prevention strategies (Geldsetzer et al. 2019:652–662; Marmot et al. 2008:1661–1669).

South Africa reflects many of these regional patterns and faces a considerable burden of hypertension and cardiovascular disease (Addo, Smeeth & Leon 2007:1012–1018; Seedat, Rayner & Veriava 2014:288–294).

Hypertension is widely recognised as a major contributor to stroke and other cardiovascular conditions in the country, with prevalence estimates varying across provinces and population groups (Seedat et al. 2014:288–294). Several epidemiological studies conducted in African populations have identified multiple determinants associated with hypertension, including increasing age, obesity, diabetes, alcohol consumption, smoking, dietary patterns and socio-economic factors (Adeloye et al. 2015:230–242; Mills et al. 2020:223–237). Socio-economic determinants such as education, employment and income also influence cardiovascular risk through behavioural pathways and access to healthcare resources (Kaplan & Keil 1993:1973–1998; Stringhini et al. 2017:1229–1237).

A growing number of primary studies have examined hypertension risk factors using cross-sectional, case–control and cohort designs (Euser et al. 2009:214–217; Schlesselman 1982:1–354). However, these studies vary considerably in study design, sample size, measurement methods and statistical adjustments. Such heterogeneity makes it difficult to determine the relative importance of individual predictors when studies are interpreted independently. Systematic reviews and meta-analyses provide an important methodological approach for synthesising epidemiological evidence and generating more reliable pooled estimates of associations across heterogeneous studies (Munn et al. 2018:5; Page et al. 2021:71).

Globally, several systematic reviews and pooled analyses have examined hypertension prevalence and associated determinants across populations (GBD 2019 Risk Factors Collaborators 2020:1223–1249; Kayima et al. 2013:54–64; NCD Risk Factor Collaboration 2021:957–980). For example, the NCD Risk Factor Collaboration analysed global trends in hypertension prevalence using more than one thousand population-based studies, demonstrating major regional disparities in hypertension burden (NCD Risk Factor Collaboration 2021:957–980). Similarly, systematic reviews conducted in African populations have highlighted persistent gaps in hypertension awareness, treatment and control across the continent (Kayima et al. 2013:54–64). Although these investigations provide important insights into the global epidemiology of hypertension, relatively few systematic reviews have synthesised evidence specifically on hypertension risk factors within the South African context.

Beyond identifying individual risk factors, there is increasing interest in integrating epidemiological evidence into predictive frameworks that can guide prevention strategies and clinical decision-making. Clinical prediction models typically combine multiple demographic, behavioural and metabolic predictors to estimate an individual’s probability of developing disease. However, many widely used cardiovascular risk prediction models were developed using data from high-income populations and may not perform optimally in settings with different socio-economic, environmental and health system contexts.

Epidemiological theory suggests that chronic diseases such as hypertension arise from combinations of interacting component causes rather than single isolated exposures (Rothman 1976:587–592). Rothman’s causal pie model provides a useful framework for understanding how multiple risk factors combine to produce disease outcomes (Rothman 1976:587–592). Similarly, Hill’s principles of causation emphasise the importance of strength, consistency, biological plausibility and temporality when interpreting associations between exposures and disease outcomes (Hill 1965:295–300). Integrating these conceptual frameworks into evidence synthesis may therefore improve the identification and prioritisation of clinically meaningful predictors.

Systematic synthesis of hypertension risk factors in South Africa therefore provides an opportunity to strengthen the evidence base for risk prediction and prevention strategies within the country. Meta-analysis enables the combination of effect estimates from multiple studies, thereby improving statistical precision and allowing exploration of heterogeneity across populations and study designs (Page et al. 2021:71). When combined with structured evaluation of evidentiary certainty, such synthesis can help identify predictors that are both epidemiologically robust and clinically relevant. Therefore, this study aimed to synthesise pooled evidence on risk factors associated with hypertension in South Africa through a systematic review and meta-analysis and to use these findings to inform the development of a clinical prediction model. By integrating pooled epidemiological evidence with conceptual frameworks of causation and evidentiary certainty, the study seeks to contribute to improved identification of high-risk populations and support more effective hypertension prevention strategies in South Africa.

Research methods and design

This study was a systematic review and meta-analysis conducted to synthesise evidence on risk factors of hypertension in South Africa and to inform the development of a clinical prediction model. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines and was also informed by the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) framework for prediction model development (Collins et al. 2015:2; Page et al. 2021:2).

The protocol was registered prospectively in PROSPERO (Booth et al. 2012:148). The PROSPERO number ID: CRD420251026501.

Eligibility

Inclusion criteria

Studies were eligible if they met the following criteria:

  • included adults aged 18 years and above in South Africa.

  • investigated risk factors or predictors associated with hypertension.

  • defined hypertension as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg or current use of antihypertensive medication.

  • used cross-sectional, case–control, cohort or interventional designs that reported relevant associations between predictors and hypertension.

  • provided sufficient data to permit extraction or calculation of effect estimates for synthesis.

Exclusion criteria

Studies were excluded if they:

  • were conducted outside South Africa, unless South African data could be clearly separated.

  • focused on cardiovascular disease generally without a distinct analysis of hypertension.

  • reported no usable effect measure or insufficient data for synthesis.

  • were reviews, editorials, commentaries, conference abstracts or methodological papers without primary data.

  • duplicated the same dataset without providing additional relevant information, in which case the most suitable report was retained.

Search strategy

The search strategy was developed in consultation with an information specialist. Search terms were derived from key concepts related to hypertension, risk factors, prediction and South Africa. Both free-text terms and indexed subject headings were used where appropriate. Boolean operators such as AND and OR were applied to combine search concepts. The final search strategy was adapted to the syntax requirements of each database. The databases searched were PubMed, Scopus, Academic Search Complete, MEDLINE, Web of Science and African Journals (SABINET). Searches were conducted from January 1990 to March 2025. Where relevant, reference lists of included studies were also examined to identify additional eligible records.

Search terms

The search terms covered the following major concepts:

  • hypertension or high blood pressure.

  • risk factors, predictors, determinants, associated factors

  • South Africa.

  • systematic combinations relevant to epidemiological association studies.

  • A representative search strategy for PubMed is provided in the supplementary material.

Study selection

All records retrieved from the database searches were exported to a reference management system for storage and deduplication before screening. Duplicate records were removed prior to selection. Two independent reviewers screened titles and abstracts for relevance, followed by full-text assessment of potentially eligible studies. The reviewers were MN and a trained research assistant. Disagreements were resolved through discussion and consensus. Reasons for exclusion at the full-text stage were documented and presented in the PRISMA flow diagram.

Data extraction

For each eligible study, data were extracted using a predesigned extraction form. Extracted information included study design, year of publication, sample size, population characteristics, province or geographic setting, hypertension definition, predictor variables and effect estimates. Data extraction was undertaken by one reviewer and verified by a second reviewer to ensure consistency and accuracy.

Quality appraisal

Methodological quality of the included studies was assessed using the Newcastle-Ottawa Scale for cohort and case–control studies (Wells et al. 2000), with appropriate adaptation for cross-sectional studies. This tool was selected because the review included observational study designs commonly assessed using Newcastle-Ottawa criteria. Quality appraisal was conducted after final study selection, and the findings were summarised narratively and in tabular form.

Data analysis and presentation

Descriptive analysis was first undertaken to summarise the characteristics of the included studies, including the number of studies, study design distribution, publication years, provinces represented and notable predictors.

These descriptive findings are presented in Table 1. Meta-analysis was then conducted for eligible predictors using random-effects models based on log odds ratios and restricted maximum likelihood estimation. Between-study heterogeneity was assessed using I2 and τ2 statistics. Sensitivity analysis included exclusion of outliers and subgroup analysis by study design where appropriate. Small-study effects were assessed using Egger’s test when at least ten studies contributed to a pooled estimate. The synthesised pooled evidence was then used to inform the development of a clinical prediction model. Additional analyses included calculation of risk weight, geotemporal trend and predictor interaction density. Certainty of evidence was assessed using a modified GRADE framework and incorporated into the weighting process.

TABLE 1.

Characteristics of included studies on hypertension in South Africa and Africa.

Authors (year) Hypertension definition Age Sex Study design Sample size, populations and setting Province Race Notable predictors or associations
Chidumwa et al. (2024) ≥ 140/90 mmHg Adults ≥ 15 years Male and female Cross-sectional 18 024, community-based KwaZulu-Natal Black South Africans Age, BMI, alcohol
Chikafu and Chimbari (2021) ≥ 140/90 mmHg or antihypertensive medication Adults (≥ 18 years, mean: 45) Male and female Cross-sectional 131 adults, community-based KwaZulu-Natal Black South Africans Age, obesity, alcohol use, family history
Du Plessis et al. (2022) BP ≥ 140/90 mmHg (average of 3 measurements) Adults (≥ 20 years, mean: 43) Male and female Cross-sectional, household survey 1995, community-based North-West Black South Africans BMI, central obesity, smoking, alcohol, low education
Govender and Naidoo (2025) Self-reported or measured ≥ 140/90 mmHg Adults (≥ 18 years) Male and female Cross-sectional, health survey 400, hospital-based KwaZulu-Natal Black South Africans Older age, male sex, diabetes, obesity
Grimsrud et al. (2009) ≥ 140/90 mmHg or antihypertensive medication Adults ≥ 18 years Male and female Cross-sectional 4351, community-based National Mixed ancestry BMI, ART duration, age
Gupta et al. (2023) ≥ 140/90 mmHg (measured) 18–65 years (mean: 42) Male and female Cross-sectional, urban cohort 4799 National Nationally representative Age, obesity, inactivity, family history
Mabhida et al. (2022) ≥ 140/90 mmHg Adults ≥ 18 years Male and female Case-control 442, community-based Eastern Cape Black South Africans Age, BMI, genetics
Madela et al. (2023) ≥ 140/90 mmHg Adults ≥ 18 years Male and female Cross-sectional 7303, community-based KwaZulu-Natal Black South Africans Age, BMI, family history
Masilela et al. (2020) ≥ 140/90 mmHg Adults ≥ 18 years Male and female Cross-sectional 329, hospital-based Mpumalanga Black South Africans BMI, alcohol, smoking
Matshazi et al. (2021) ≥ 140/90 mmHg or antihypertensive use Adults (mean: 49) Male and female Cross-sectional, molecular and clinical 1456, community-based Western Cape Mixed ancestry and Black South Africans Age, sex, BMI, microRNA biomarkers
Monakali et al. (2018) ≥ 140/90 mmHg or on treatment Adults ≥ 18 years Male and female Cross-sectional 203, hospital-based Eastern Cape Black and mixed-race Obesity, age, female sex
Naidoo et al. (2019) Elevated BP defined by standardised measurement at age 23; linked to earlier BP trajectories (exact cut-off not explicitly stated in abstract) 23 years 49% male, 51% female Longitudinal birth cohort (BT20) 1540, community-based Gauteng Black South Africans Higher early-life linear growth ↑ risk; high childhood or adolescent BP trajectories → 4–5 increased adult risk
Nkeh et al. (2002) ≥ 140/90 mmHg Adults ≥ 18 years Male and female Case-control 576, hospital-based Gauteng Black South Africans Age, BMI, alcohol
Nkosi et al. (2021) ≥ 140/90 mmHg Adults ≥ 18 years Male and female Cross-sectional 2397, community-based Gauteng and North West Black South Africans Obesity, age, low physical activity
Ntlebi et al. (2020) ≥ 140/90 mmHg Adults ≥ 18 years Male and female Cross-sectional 361, community-based Gauteng Mixed BMI, age, diet
Ntuli et al. (2015) ≥ 140/90 mmHg Adults ≥ 18 years Male and female Cross-sectional 1281, community based Limpopo Black South Africans Age, diabetes, BMI
Owolabi et al. (2017) ≥ 140/90 mmHg Adults ≥ 18 years Male and female Cross-sectional 998, hospital-based Eastern Cape Black South Africans Age, BMI, male sex
Peer et al. (2025) ≥ 140/90 mmHg Adults ≥ 18 years Male and female Cross-sectional 460, community-based Western Cape Black South Africans Age, BMI, smoking
Peltzer and Phaswana-Mafuya (2013) ≥ 140/90 mmHg Adults ≥ 50 years Male and female Cross-sectional 3840, community-based National Black, Coloured, Indian, White Age, BMI, sex
Sharma et al. (2021) ≥ 140/90 mmHg Adults ≥ 18 years Male and female Cross-sectional 487, community survey Eastern Cape Black South Africans Age, BMI, alcohol
Sharma et al. (2024) ≥ 140/90 mmHg Adults ≥ 18 years Male and female Case-control 487, hospital-based Eastern Cape Black South Africans Age, BMI, WHR, Blood glucose
Steyn et al. (2008) ≥ 140/90 mmHg Adults ≥ 18 years Male and female Cross-sectional 12 952, community-based National Black, Coloured, Indian, White Age, BMI, low SES
Ware et al. (2019) ≥ 140/90 mmHg Adults ≥ 18 years Male and female Cross-sectional 1847, community-based National Black South Africans Age, BMI, male sex
Akpa et al. (2022) SBP ≥ 140 or DBP ≥ 90 mmHg, or previous diagnosis or antihypertensive use Mean 53.0 ± 10.7 years; adults ≥ 18 Male and female (45.9% male) Secondary data analysis of cross-sectional survey 16 445, community-based Multi-country (includes provinces across five African nations) Indigenous Africans Sufficient (OR 0.80) and high (OR 0.81) vegetable consumption protective
Ayo-Yusuf and Ayo-Yusuf (2008) BP ≥ 160/95 mmHg and/or antihypertensive use Adults 25–70 years Male and female (60.6% female) Secondary data analysis of cross-sectional survey 9098, community-based National Predominantly Black South Africans (75%) Complete edentulousness significantly associated with hypertension (OR 1.35, 95% CI 1.02–1.78)
Candy et al. (2000) DBP > 90 mmHg or 24-h ABPM > 85 mmHg (after washout) Adults; hypertensives from clinics, normotensives from community Male and female Case-control genetic association study 192, hospital-based Gauteng Black South Africans Arg16 homozygotes linked to lower BMI in hypertensives (p = 0.007); Glu27 polymorphism rarer (17%)
George et al. (2016) SBP ≥ 140 or DBP ≥ 90 mmHg, or antihypertensive use at follow-up Women, 29–53 years at baseline (mean 40 years), followed 8.3 years Female only Prospective cohort (baseline 2002–03, follow-up 2011–14) 478, institution-based Gauteng Black South African women Central adiposity (waist circumference, trunk fat) predicted hypertension more strongly than total adiposity
Lategan, Van den Berg and Walshet (2014) SBP ≥ 140 or DBP ≥ 90 mmHg, or on antihypertensive medication 25–64 years (adults) Male and female Cross-sectional (Assuring Health for All in Free State substudy) 339, community-based Free State Black South Africans (urban, transitioning community) Positive correlation between BP and BMI, WHtR, BAI; HIV inversely correlated with adiposity indices
Kandala et al. (2021) BP ≥ 140/90 mmHg or self-reported diagnosis or antihypertensive use ≥ 15 years Male and female Secondary analysis of cross-sectional surveys 18 231, community-based National (all 9 provinces) Black, Coloured, White, Indian or Asian South Africans Higher odds with male sex, coloured group, urban residence, high cholesterol, age, BMI, waist circumference
Magodoro et al. (2022) SBP ≥ 140 or DBP ≥ 90 mmHg, or antihypertensive use ≥ 15 years (median 36 years) Male and female Nationally representative cross-sectional survey (DHS 2016) 6420, community-based National Black, White, Coloured, Indian or Asian South Africans Female HIV-negatives had 5 mmHg higher SBP than HIV+; HIV not associated with worse cardiometabolic profile
Mphekgwana et al. (2020) SBP ≥ 140 and DBP ≥ 90 mmHg ≥ 18 years (adults 18–101 years) Male and female Cross-sectional (population-based) 2256, community-based Limpopo Black South Africans Semi-urban women: PA, family history, fruit intake, salty food, meat; rural women: WHR, diabetes, salty food; men: fried food, low fruit intake, WHR
Wandai et al. (2020) SBP > 140 or DBP > 90 mmHg, or antihypertensive medication use ≥ 15 years Male and female Panel survey multilevel analysis (NIDS waves 2008–2017) 93 787, community-based National (district-level analysis across nine provinces) Black, Coloured, White, Indian or Asian South Africans Age, BMI, race strong predictors; also influenced by alcohol, smoking, education, income, season
Nguyen et al. (2022) SBP ≥ 140 or DBP ≥ 90 mmHg, or self-reported diagnosis Women, 18–40 years Female only Cross-sectional (baseline of Rape Impact Cohort Evaluation study) 1742, hospital-based KwaZulu-Natal Black South African women Frequent NPSV, SH, physical and emotional IPV linked to hypertension; depression, PTSD, binge drinking mediated associations.
Ntlahla et al. (2021) SBP ≥ 140 or DBP ≥ 90 mmHg (pre-HTN: SBP 120–139 or DBP 80–89) 18–25 years (youth) Male and female Cross-sectional quantitative study 151, university community Eastern Cape South African youth (Black) Zonulin (gut permeability marker) higher in hypertensives; positively correlated with SBP in women, and obesity indices
Pisa et al. (2018) SBP ≥ 140 or DBP ≥ 90 mmHg, or on hypertension medication Middle-aged adults (mean 40–60 years) Male and female Cross-sectional, AWI-GEN Soweto site 2008, community-based Gauteng Urban Black South Africans All adiposity indices associated with hypertension; FM/FFSTM ratio strongest predictor; WC significant predictor in multivariate models
Patel et al. (2016) SBP ≥ 140 or DBP ≥ 90 mmHg, or on antihypertensive medication Adults 20–79 years Male and female Cross-sectional, multi-country (including South Africa) 1088, community-based Western Cape (Cape Town site) Multi-ethnic (focus on South Africans within global sample) BMI and WC both predicted hypertension; associations weakest in South Africa compared to other regions
Raphadu et al. (2023) Children and/or adolescents: NHLBI cut-offs by age/sex/height; Adults: SBP ≥ 130 or DBP ≥ 80 (2017 ACC/AHA) Tracked from childhood (5–12 years in 1999) to young adulthood (18–30 years in 2015) Male and female (164 women, 161 men) Longitudinal cohort (Ellisras Longitudinal Study) 325, school-based Limpopo Rural Black South Africans Sodium-to-potassium ratio stronger predictor than Na or K alone
Schutte et al. (2012) Optimal BP ≤120/80; hypertension ≥ 140/90 mmHg Adults ≥ 30 years Male and female Prospective cohort (PURE study, 2005–2010) 478, community-based North West Black South Africans Smoking, alcohol intake, waist circumference, γ-GT predicted HT; HIV infection inversely associated with BP rise
Solomons, Kruger and Puoane (2020) SBP ≥ 140 or DBP ≥ 90 mmHg Adults 32–81 years Male and female Secondary analysis of a cross-sectional study (PURE-Langa site, 2010) 454, community-based Western Cape Urban Black South Africans Poor adherence to dietary guidelines linked to higher BP; added sugar intake correlated with SBP in women
Wand et al. (2022) SBP ≥ 130 or DBP ≥ 80 mmHg, or diagnosis and medication (2017 ACC/AHA) ≥ 15 years (median age 32) Male and female Nationally representative cohort (SA-NIDS 2008–2017) 80 270, community-based National (nine provinces) Black South Africans Age, low education, obesity, high WC, smoking, alcohol, low exercise; urban residence predictive in men
Rudasingwa, Cho and Ishimwe (2024) SBP ≥ 140 or DBP ≥ 90 mmHg, or on antihypertensive treatment ≥ 15 years (adults) Male and female Secondary analysis of cross-sectional survey 3172, community-based National (with provincial and spatial hotspot analysis) South Africans (multi-province) Older age, unmarried, diabetes or pre-diabetes, increased risk; education protective; Limpopo and North West protective
Ntenda et al. (2022) Self-reported hypertension (doctor or nurse told respondent they have HTN) Women ≥ 20 years (mean 40.7 ± 18.4 years) Female only Secondary analysis of a cross-sectional survey (SADHS 2016) 6027, community-based National (all nine provinces analysed with provincial variation) Predominantly Black or African (84.5%), White (4.3%), Coloured or other (11.2%) Older age; never married; no insurance protection; Black or African ethnicity; overweight or obese perception; higher risk in Western Cape, Eastern Cape, Northern Cape, Free State, North West, Gauteng, Mpumalanga compared to Limpopo
Gass et al. (2010) Self-report Women, ≥ 18 years Female Cross-sectional 1229, mixed-community and hospital-based National Mixed None
Jamshidi, Khodayari Bavil and Egal (2019) HTN ≥ 130/80 mmHg Mean = 72.8 (8.66) Male and female Cross-sectional 275, elderly or faith-based day-care centre Gauteng Black Hb and ferritin
Matshazi et al. (2021) HTN ≥ 140/90 mmHg Mean = 42.6 years Male and female Secondary analysis of a cross-sectional study 881, community-based Western Cape N/A miR-novelchr1_36178 and miR-novel-chr15_18383
Mazenda, Katiyatiya and Sari (2025) Self-report N/A Male and female Cross-sectional 3278, community-based Gauteng N/A Protein-based diet
Modjadji et al. (2022) HTN ≥ 140/90 mmHg ≥ 60 years Male and female Cross-sectional 350, community-based Limpopo Black BMI, WC, WHtR
Motlhale and Ncayiyan (2019) HTN diagnosed by a health care provider Adults ≥ 18 years Male and female Cross-sectional 28 007, community-based Gauteng Mixed Migration
Sekgala et al. (2022) HTN ≥ 130/85 mmHg Males ≥ 20 years Males Cross-sectional 185, community-based Western Cape N/A Percentage of protein consumed
Nguyen et al. (2022) BP ≥ 140/90 mmHg or previous diagnosis Median age of 24 years All female Cross-sectional 1797, rape-exposed and non-rape-exposed participants KwaZulu-Natal Predominantly Black South African Childhood maltreatment (sexual, physical, emotional abuse, neglect)
Chidumwa et al. (2021) HTN ≥ 140/90 mmHg +/or antihypertensive Median 56 years Male and female Nested cross-sectional spatial modelling 2761, community-based National sample (all nine provinces; highest in KwaZulu-Natal and Gauteng) Mixed South African population (all major ethnic groups) Hypertension and diabetes co-occur; higher shared spatial risk in KwaZulu-Natal and central Gauteng
Jacobs et al. (2019) SBP ≥ 140 mmHg or DBP ≥ 90 mmHg, or self-reported antihypertensive medication use ≥ 30 years Men and women Cohort 2010, community-based North West Black South Africans PAI-1act, 4G/4G genotype, 5G/5G Genotype, WC
Naidoo, Fabian and Norris (2023) ≥ 140/90 mmHg 28 years 52% female Cross-sectional 933, community-based Gauteng province (Soweto) Predominantly Black South African Male, BMI, hyperuricaemia, albuminuria (in men), and self-reported history of gestational hypertension (in women).
Renta et al. (2022) Self-reported and measured hypertension; measured ≥ 140/90 mmHg Mean = 60.9 (13.1) rural and 59.9 (11.9) urban Male and female Nested cross-sectional, WHO-SAGE 4227, community-based National sample (all nine provinces) All provinces No rural/urban interaction; country differences in social capital-hypertension link
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WHO, World Health Organization; BMI, body mass index; BP, blood pressure; ART, antiretroviral therapy; OR, odds ratio; CI, confidence interval; HIV, human immunodeficiency virus; SABSSM, South African Social Attitudes Survey; WHR, waist-to-hip ratio; SES, socioeconomic status; SBP, systolic blood pressure; DBP, diastolic blood pressure; SIREN, Stroke Investigative Research and Educational Network; AWI, Africa Wits-INDEPTH Partnership for Genomic Studies; ABPM, ambulatory blood pressure monitoring; WHtR, waist-to-height ratio; BAI, body adiposity index; DHS, Demographic and Health Survey; NPSV, non-partner sexual violence; SH, sexual harassment; IPV, intimate partner violence; PTSD, post-traumatic stress disorder; FM/FFSTM, fat mass/fat-free soft tissue mass ratio; WC, waist circumference; NHLBI, National Heart, Lung, and Blood Institute; PURE, Prospective Urban and Rural Epidemiological Study; HT, hypertension; HTN, hypertension; Hb, haemoglobin.

Additional analysis

We calculated risk weight (RW), risk responsiveness, geotemporal trend and predictor interaction density (PI) as per these studies (Ibeneme et al. 2024:1402; Nweke & Mshunqane 2025:405; Nweke et al. 2025a, 2025b:282). To stabilise the risk estimate, we used the certainty of evidence as a global shrinkage factor to minimise overfitting (Steyerberg & Harrell 2016:245–247). Certainty was graded using a modified GRADE rubric (Page et al. 2021:n71), with weights of 1.0 (high), 0.75 (moderate), 0.5 (low) and 0.25 (very low). The certainty-adjusted RW was determined by multiplying Rw by the certainty weight. The critical risk threshold was established following (Ibeneme et al. 2024:1402; Nweke & Mshunqane 2025:405; Nweke et al. 2025a, 2025b:282).

Predictor governance

Predictors were managed using RW and evidence certainty. Pooled log odds ratios were mapped to a directed acyclic diagram to determine the predictor interaction density (PID). Predictors were retained only when both causal plausibility and evidentiary strength were present.

Ethical considerations

Ethical clearance to conduct this study was obtained from the Faculty of Health Sciences Research Ethics Committee of Durban University of Technology (No. IN-REC-025/25). Informed consent was not applicable because this review used data extracted from previously published studies and did not involve direct contact with human participants. The included primary studies had already undergone their respective ethical review processes as reported by their original authors.

Results

Search results and characteristics of studies

A total of 62 003 records were identified through database searching. After removal of 28 971 duplicates, 33 032 records remained for title and abstract screening. Of these, 32 939 were excluded. Ninety-seven reports were sought for retrieval, four could not be retrieved, and 91 full-text articles were assessed for eligibility. Thirty-five reports were excluded following the full-text review. Ultimately, 54 studies, comprising six cohort, three case–control and 45 cross-sectional studies, were included in the review and meta-analysis (Figure 1). Fifteen studies were national in scope and covered all nine provinces of South Africa. The included studies spanned multiple provinces and publication years, and together provided evidence on demographic, biological, behavioural, environmental and geographic predictors of hypertension. Study characteristics are presented in Table 1.

FIGURE 1.

FIGURE 1

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Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 flow diagram.

Meta-analysis and certainty of evidence

Among the eighteen factors eligible for meta-analysis, ten were found to have a statistically significant link to hypertension. The most notable associations were with dyslipidaemia (OR: 3.79, CI 1.84–7.80), age over 50 years (OR: 3.49, CI 1.82–6.70), diabetes (OR: 2.35, CI 1.89–2.91) and obesity (OR: 2.57, CI 1.86–3.53). After evaluating the evidence, dyslipidaemia (OR 3.79, low) and age over 50 (OR 3.49, low) were categorised as having a strong association, albeit with low certainty. Diabetes (OR 2.57, moderate) and BMI (obesity, OR 2.35, moderate), along with exposure to gold-mine dust (OR 2.35, moderate), were also strongly linked to hypertension, supported by moderate evidence. Secondary education (OR 0.64, very low) and tertiary education (OR 0.56, very low) appeared to have protective effects, though the certainty was very low. Low intake of fruits and vegetables (OR 1.16, low) and living in urban areas (OR 1.20, very low) were close to the baseline, indicating minimal association strength and low certainty. Family history (OR 1.45, low) and residing in regions other than Limpopo (OR 2.12, low) showed modest associations with low certainty (Table 2 and Table 3).

TABLE 2.

Meta-analysis of factors associated with hypertension in South Africa.

Factors Ref Exposure Odds ratio 95% CI I2 (%) Egger’s test p k Ri GRADE Rw Rw-adjusted cRW cRW-adjusted GTT
Age (years) < 50 > 50 3.49 1.82–6.70 97.8 0.263 7 0.86 0.50 3.00U 1.50 3.00 1.50 0.93
Education None Tertiary 0.56 0.39–0.80 95.0 0.810 12 0.75 0.25 1.34L 0.34 4.34 1.84 0.88
BMI Normal Obesity 2.35 1.89–2.91 88.3 0.630 17 0.88 0.50 2.07M 1.04 6.41 2.87 0.94
Family History of HTN No Yes 1.45 1.22–1.71 48.8 0.580 4 0.75 0.50 1.09M 0.55 7.50 3.42 0.71
Diabetes No Yes 2.57 1.86–3.53 89.1 0.180 15 0.80 0.75 2.06U 1.55 9.56 4.96 0.90
Dyslipidemia No Yes 3.79 1.84–7.80 90.5 0.420 4 0.75 0.50 2.84M 1.42 12.40 6.38 0.71
Fruits and vegetables Sufficient Low intake 1.16 1.02–1.33 51.7 0.830 5 0.40 0.50 0.46L 0.23 12.86 6.61 0.70
Gold-mine dust Unexposed (> 5 km away) Exposed (< 5 km away) 2.35 1.70–3.25 78.0 0.600 3 1.00 0.75 2.35U 1.76 15.21 8.37 0.57
Settlement Rural Urban 1.20 1.00–1.43 87.4 0.770 10 0.40 0.25 0.48L 0.12 15.69 8.49 0.70
Region/province Limpopo Others 2.02 1.70–2.39 0.00 0.683 5 1.00 0.50 2.02M 1.01 17.71 9.50 1.00
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BMI, body mass index; HTN, hypertension; I2, heterogeneity; k, number of studies involved in the synthesis of a given risk factor; Ri, risk responsiveness; RW, risk weight; GTT, geotemporal trend; U, upper class high (> 1.48); M, middle class: 0.39–1.48; L, lower class: ≤ 0.39.

TABLE 3.

Grading of evidence for risk factors of hypertension in South Africa.

Risk factor (population: adults in South Africa) Effect OR, 95% CI Certainty of evidence (GRADE) Reasons for downgrade or upgrade Interpretation
Age > 50 3.49 1.82–6.70 ⨁⨁ ◯ ◯ Low Imprecision −1; Large effect +1 Older age strongly associated with hypertension; evidence limited by imprecision.
Education (secondary) 0.64 0.48–0.86 ⨁ ◯ ◯ ◯ Very low Inconsistency −1 Secondary education may be protective; inconsistent findings.
Education (tertiary) 0.56 0.39–0.80 ⨁ ◯ ◯ ◯ Very low Imprecision −1 Tertiary education likely protective but imprecise estimates.
BMI (overweight) 1.58 1.42–1.76 ⨁⨁ ◯ ◯ Low None Overweight modestly increases hypertension risk.
BMI (obesity) 2.35 1.89–2.91 ⨁⨁⨁ ◯ Moderate Large effect +1 Obesity strongly increases hypertension risk; upgraded for large effect.
Family history of HTN 1.45 1.22–1.71 ⨁⨁ ◯ ◯ Low None Family history minimally increases risk; no large effect upgrade.
Diabetes 2.57 1.86–3.53 ⨁⨁⨁ ◯ Moderate Large effect +1 Diabetes strongly associated with hypertension; upgraded for large effect.
Dyslipidaemia 3.79 1.84–7.80 ⨁⨁ ◯ ◯ Low Imprecision −1; Large effect +1 High risk but downgraded for imprecision.
Fruits and vegetables (low) 1.16 1.02–1.33 ⨁⨁ ◯ ◯ Low None Minimal effect; evidence remains low certainty.
Gold-mine dust 2.35 1.70–3.25 ⨁⨁⨁ ◯ Moderate Large effect +1 Occupational dust exposure increases hypertension risk; upgraded for large effect.
Settlement (urban) 1.20 1.00–1.43 ⨁ ◯ ◯ ◯ Very low Indirectness −1 Urban residence shows a small effect; downgraded for indirectness.
Region (others vs Limpopo) 2.12 1.78–2.51 ⨁⨁ ◯ ◯ Low Indirectness −1; Large effect +1 Regional differences suggest higher risk outside Limpopo; upgrade for large effect offsets indirectness.
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BMI, body mass index; HTN, Hypertension.

Model parameters

Among the ten identified risk factors for hypertension, age over 50 years (Rw: 3.00, GTT: 0.93), dyslipidaemia (Rw: 2.84, GTT: 0.71) and exposure to gold-mine dust (Rw: 2.35, GTT: 0.57) were classified as high risk. In contrast, obesity (Rw: 2.07, GTT: 0.94), geographic location (Rw: 2.02, GTT: 1.0), along with family history of hypertension, educational background and low intake of fruits and vegetables, were categorised as lower risk. Most risk factors demonstrated substantial geotemporal coverage (GTT: 0.7–1.0). Environmental factors showed the lowest interaction density (Figure 2). The critical risk threshold at the 75th percentile was determined to be 7.93 (Table 2).

FIGURE 2.

FIGURE 2

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Directed acyclic diagram showing the interaction of hypertension risk factors.

The results show that there were thirty-seven possible ways in which the ten risk factors could combine to reach the critical risk threshold (CRT) (Online Appendix 1, Table 1-A1 and Table 2-A2). Upon examination of the hypothetical interaction among the ten risk factors of hypertension in South Africa (Figure 2), result shows that model 37 was robust and the most parsimonic model with minimal interaction density (Supplementary File 1). Therefore, the predictive algorithm was set forth in Table 4.

TABLE 4.

Clinical prediction algorithm for hypertension in South Africa.

Factors Reference Exposure or protective factor Point
Age (years) < 50 > 50 +1.50
Education None/primary Tertiary −0.84
None/primary Secondary −0.29
BMI Normal Obesity +1.04
Normal Overweight +0.42
Family history of HTN No Yes +0.55
Diabetes No Yes +1.55
Dyslipidaemia No Yes +1.42
Fruits and vegetables Sufficient Low intake +0.23
Gold-mine dust Unexposed (> 5 km away) Exposed (< 5 km away) +1.76
Settlement Rural Urban +0.12
Region/province Limpopo Others +1.01
Open in a new tab

BMI, body mass index; HTN, Hypertension.

Low risk → values ≤ 3.01 (at or below the 25th percentile).

Moderate risk → values > 3.01 up to 5.67 (between the 25th and 50th percentiles).

Elevated risk → values > 5.67 up to 7.93 (between the 50th and 75th percentiles).

High risk → values > 7.93 (above the 75th percentile).

Discussion

This study first synthesised pooled evidence on risk factors associated with hypertension in South Africa and then used these findings to develop a context-specific clinical prediction model. The meta-analysis identified older age, obesity, diabetes, dyslipidaemia, gold-mine dust exposure and certain geographic factors as significant predictors of hypertension, while education appeared protective. These pooled estimates were subsequently translated into the algorithmic structure of the prediction model, enabling the meta-analytic findings to inform a practical risk stratification tool. Overall, the results provide a more robust summary of the South African evidence base than any single primary study and highlight the importance of both conventional metabolic predictors and context-specific structural exposures.

The pooled findings are broadly consistent with previous epidemiological evidence showing that hypertension risk increases with age, obesity, diabetes and dyslipidaemia (Forouzanfar et al. 2017:165–174; Mills et al. 2020:223–237; Roth et al. 2020:2982–3021). The strong association observed for age over 50 years reflects the well-established relationship between ageing, arterial stiffness and cumulative cardiovascular risk exposure (Franklin et al. 1997:308–315; Lakatta & Levy 2003:139–146; North & Sinclair 2012:1097–1108). Similarly, the associations for obesity and diabetes affirm the central role of metabolic dysfunction in hypertension (Mills et al. 2020:223–237; Stringhini et al. 2017:1229–1237). The positive association with gold-mine dust exposure is especially important in the South African context, where occupational and environmental risk factors may be under-recognised in conventional risk models (Stanaway et al. 2018:1923–1944). The observed protective association for education is also plausible, as higher educational attainment may be linked to improved health literacy, healthier behaviours and better access to healthcare (Cutler & Lleras-Muney 2010:1–28; Kaplan & Keil 1993:1973–1998). Nevertheless, some predictors were supported by low or very low certainty evidence, which warrants caution in interpretation (Guyatt et al. 2008:924–926; Page et al. 2021:71).

Our proposed hypertension risk algorithm, developed using South African data, includes factors such as age, BMI, family history of hypertension, diabetes, dyslipidaemia, low consumption of fruits and vegetables, exposure to occupational dust, type of settlement and region or province, with education considered a protective element.

The weighting system gives the highest values to gold-mine dust (Rw = 1·76), diabetes (1·55), age over 50 (1·50), and dyslipidaemia (1·42), while BMI, family history, and regional factors have smaller contributions, and secondary or tertiary education receives negative points. This setup captures both well-known metabolic influences and specific exposures relevant to South Africa (Mayosi et al. 2009:934–947; Shisana et al. 2013).

When compared to international prediction models, the differences in factor selection are significant. The Framingham hypertension risk score, originally developed in the United States, prioritises baseline systolic and diastolic blood pressure, followed by age, parental history, BMI and smoking (D’Agostino et al. 2008:743–753). In contrast, our model omits baseline blood pressure and smoking, thus reducing the data requirements in primary care settings. Global cardiovascular risk models, like the World Health Organization (WHO) Cardiovascular disease (CVD) risk charts and the QRISK series, aim to predict 10-year CVD events rather than new cases of hypertension, typically including age, sex, smoking, blood pressure, lipids and diabetes (Hippisley-Cox, Coupland & Brindle 2017:2099; Kaptoge et al. 2019:e1332–e1345).

These tools are well validated across various populations but do not account for environmental and occupational exposures that are particularly relevant in South Africa.

Locally, the only similar tool is the six-item hypertension risk score recently created for South Africa, which focuses on older age and adiposity but does not include environmental or geographic factors (Wand et al. 2022:451–461). National practice otherwise depends on the South African hypertension guideline and WHO HEARTS protocols, which categorise care by blood pressure level and absolute CVD risk rather than predicting new hypertension cases (Unger et al. 2020:1334–1357; WHO 2025). Our model’s inclusion of education, occupational exposure, settlement and provincial differences offers a level of contextual specificity not found in existing global or national tools.

The exclusion of sex and smoking from our final model sets it apart from most international algorithms, where these factors, though having a modest effect, are standard (D’Agostino et al. 2008:743–753; Hippisley-Cox et al. 2017:j2099). The choice to leave out baseline blood pressure also differentiates our approach from Framingham and QRISK, but it was necessary to create a simplified, office-friendly screening tool. The protective weighting of education aligns with the social gradient in hypertension observed in South African epidemiological studies (Shisana et al. 2013), while the positive weighting of gold-mine dust and urban settlement highlights the structural determinants of hypertension risk in the country. The resulting score provides discrimination using factors that are both measurable in routine care and directly relevant to national health priorities.

The weighting scheme is consistent with epidemiological findings that metabolic risk factors are the primary indicators of hypertension while also emphasising the additional explanatory power of context-specific exposures (Mayosi et al. 2009:934–947). Unlike the Framingham score, which prioritises blood pressure and age (D’Agostino et al. 2008:743–753), our model gives equal importance to diabetes, dyslipidaemia and exposure to occupational dust. These distinctions underscore the necessity for models tailored to specific contexts and suggest the importance of formal calibration against absolute risk estimates for comparison with global tools. To ensure generalisability, external validation across different provinces and among populations with and without mining exposure will be crucial. Despite this, the model’s feasibility and contextual relevance indicate a strong potential for its integration into South African primary care as a tool for screening and early prevention.

This study is not without limitations. Firstly, the evidence base was largely dominated by cross-sectional studies with relatively few cohort studies, which limits inferences about temporality and causation and increases susceptibility to residual confounding. Secondly, the certainty of evidence was low or very low for many predictors included in the model. Thirdly, although we applied a global certainty-based shrinkage to reduce overfitting, the algorithm has not yet undergone internal–external or external validation; its discrimination, calibration and clinical utility outside the derivation evidence base remain untested.

Implications and recommendations

The findings of this review have both clinical and public health implications. Clinically, they suggest that hypertension risk assessment in South Africa should extend beyond conventional metabolic markers to include occupational, social and geographic determinants where relevant. From a public health perspective, the results support the need for more context-specific approaches to screening and prevention. There is also a need for internal validation using independent South African cohorts and external validation in other African populations.

For implementation research, pilot studies in primary care and occupational health settings are recommended to evaluate feasibility, acceptability and utility relative to current practice.

Conclusion

This study synthesised pooled South African evidence on risk factors associated with hypertension and used these findings to inform development of a clinical prediction model. The pooled evidence showed that older age, obesity, diabetes, dyslipidaemia, gold-mine dust exposure and selected geographic factors were among the most important predictors, while education appeared protective. Based on these findings, a context-specific prediction algorithm was developed that incorporates metabolic, social, environmental and geographic factors relevant to South Africa. The model offers a potentially useful screening approach for primary care and prevention but requires internal and external validation before routine application.

Acknowledgements

This article is based on data from a larger study. A related article focusing on the preliminary synthesis of factors associated with hypertension in South Africa has been published in Frontiers in Cardiovascular Medicine, Volume 13-2026, Article ID:1710798, doi.org/10.3389/fcvm.2026.1710798. The present article addresses risk factors of hypertension in South Africa.

Competing interests

The author reported that they received funding from Heart and Stroke Foundation South Africa (HSFSA), which may be affected by the research reported in the enclosed publication. The author has disclosed those interests fully and has implemented an approved plan for managing any potential conflicts arising from their involvement. The terms of these funding arrangements have been reviewed and approved by the affiliated university in accordance with its policy on objectivity in research.

CRediT authorship contribution

Martins Nweke: Conceptualisation, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Validation, Visualisation, Writing – original draft, Writing – review & editing. Julian Pillay: Conceptualisation, Funding acquisition, Methodology, Supervision, Validation, Writing – review & editing. All authors reviewed the article, contributed to the discussion of results, approved the final version for submission and publication and take responsibility for the integrity of its findings.

Data availability

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary material.

Disclaimer

The views and opinions expressed in this article are those of the authors and are the product of professional research. They do not necessarily reflect the official policy or position of any affiliated institution, funder, agency or that of the publisher. The authors are responsible for this article’s results, findings, and content.

Funding Statement

Funding information This work was supported by the Heart and Stroke Foundation South Africa (HSFSA) (grant number: HSFSAJDPGRANT2024).

Footnotes

How to cite this article: Nweke, M. & Pillay, J., 2026, ‘Risk factors of hypertension in South Africa: A systematic review and meta-analysis to inform the development of a clinical prediction model’, Health SA Gesondheid 31(0), a3385. https://doi.org/10.4102/hsag.v31i0.3385

Note: Additional supporting information may be found in the online version of this article as Online Appendix 1.

References

  1. Addo, J., Smeeth, L. & Leon, D.A., 2007, ‘Hypertension in sub-Saharan Africa: A systematic review’, Hypertension 50(6), 1012–1018. 10.1161/HYPERTENSIONAHA.107.093336 [DOI] [PubMed] [Google Scholar]
  2. Akpa, O.M., Okekunle, A.P., Asowata, O.J., Chikowore, T., Mohamed, S.F., Sarfo, F. et al., 2022, ‘Frequent vegetable consumption is inversely associated with hypertension among indigenous Africans’, European Journal of Preventive Cardiology 29(18), 2359–2371. 10.1093/eurjpc/zwac208 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Ataklte, F., Erqou, S., Kaptoge, S., Taye, B., Echouffo-Tcheugui, J.B. & Kengne, A.P., 2015, ‘Burden of undiagnosed hypertension in sub-Saharan Africa: A systematic review and meta-analysis’, Hypertension 65(2), 291–298. 10.1161/HYPERTENSIONAHA.114.04394 [DOI] [PubMed] [Google Scholar]
  4. Ayo-Yusuf, O.A. & Ayo-Yusuf, I.J., 2008, ‘Association between tooth loss and hypertension in South African adults’, South African Medical Journal 98(5), 381–385. [PubMed] [Google Scholar]
  5. Booth, A., Clarke, M., Dooley, G., Ghersi, D., Moher, D., Petticrew, M. et al., 2012, ‘The nuts and bolts of PROSPERO: An international prospective register of systematic reviews’, Systematic Reviews 1, 148. 10.1186/2046-4053-1-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Candy, G., Samani, N., Norton, G., Woodiwiss, A., Radevsky, I., Wheatley, A. et al., 2000, ‘Association analysis of β2 adrenoceptor polymorphisms with hypertension in a Black African population’, Journal of Hypertension 18, 167–172. 10.1097/00004872-200018020-00006 [DOI] [PubMed] [Google Scholar]
  7. Chidumwa, G., Maposa, I., Kowal, P., Micklesfield, L.K. & Ware, L.J., 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’, International Journal of Environmental Research and Public Health 18, 359. 10.3390/ijerph18010359 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Chidumwa, G., Olivier, S., Ngubane, H., Zulu, T., Sithole, M., Gunda, R. et al., 2024, ‘Association between tobacco smoking and prevalence of HIV, tuberculosis, hypertension and diabetes in rural South Africa’, BMC Public Health 24(1), 3306. 10.1186/s12889-024-20791-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Chikafu, R. & Chimbari, M.J., 2021, ‘Hypertension and associated risk factors among adults in KwaZulu-Natal, South Africa’, African Health Sciences 21(3), 1152–1161. [Google Scholar]
  10. Collins, G.S., Reitsma, J.B., Altman, D.G. & Moons, K.G.M., 2015, ‘Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement’, Annals of Internal Medicine 162(1), 55–63. 10.7326/M14-0697 [DOI] [PubMed] [Google Scholar]
  11. Cutler, D.M. & Lleras-Muney, A., 2010, ‘Understanding differences in health behaviors by education’, Journal of Health Economics 29(1), 1–28. 10.1016/j.jhealeco.2009.10.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. D’Agostino, R.B. Sr, Vasan, R.S., Pencina, M.J., Wolf, P.A., Cobain, M., Massaro, J.M. et al., 2008, ‘General cardiovascular risk profile for use in primary care: The Framingham Heart Study’, Circulation 117(6), 743–753. 10.1161/CIRCULATIONAHA.107.699579 [DOI] [PubMed] [Google Scholar]
  13. Du Plessis, J.P., Lammertyn, L., Schutte, A.E. & Nienaber-Rousseau, C., 2022, ‘H-Type hypertension among Black South Africans and the relationship between homocysteine, its genetic determinants and estimates of vascular function’, Journal of Cardiovascular Development and Disease 9(12), 447. 10.3390/jcdd9120447 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Euser, A.M., Zoccali, C., Jager, K.J. & Dekker, F.W., 2009, ‘Cohort studies: Prospective versus retrospective’, Nephron Clinical Practice 113(3), c214–c217. 10.1159/000235241 [DOI] [PubMed] [Google Scholar]
  15. Forouzanfar, M.H., Liu, P., Roth, G.A., Ng, M., Biryukov, S., Marczak, L. et al., 2017, ‘Global burden of hypertension and systolic blood pressure of at least 110 to 115 mm Hg, 1990–2015’, JAMA 317(2), 165–182. 10.1001/jama.2016.19043 [DOI] [PubMed] [Google Scholar]
  16. Franklin, S.S., Gustin, W., IV, Wong, N.D., Larson, M.G., Weber, M.A., Kannel, W.B. et al., 1997, ‘Hemodynamic patterns of age-related changes in blood pressure: The Framingham Heart Study’, Circulation 96(1), 308–315. 10.1161/01.CIR.96.1.308 [DOI] [PubMed] [Google Scholar]
  17. Gass, J.D., Stein, D.J., Williams, D.R. & Seedat, S., 2010, ‘Intimate partner violence, health behaviours, and chronic physical illness among South African women’, South African Medical Journal 100(9), 582–585. 10.7196/SAMJ.4274 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. GBD 2019 Risk Factors Collaborators , 2020, ‘Global burden of 87 risk factors in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019’, The Lancet 396(10258), 1223–1249. 10.1016/S0140-6736(20)30752-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Geldsetzer, P., Manne-Goehler, J., Marcus, M.-E., Ebert, C., Zhumadilov, Z., Wesseh, C.S. et al., 2019, ‘The state of hypertension care in 44 low-income and middle-income countries: A cross-sectional study of nationally representative individual-level data from 1.1 million adults’, The Lancet 394(10199), 652–662. 10.1016/S0140-6736(19)30955-9 [DOI] [PubMed] [Google Scholar]
  20. George, C., Goedecke, J.H., Crowther, N.J., Jaff, N.G., Kengne, A.P., Norris, S.A. et al., 2016, ‘The role of body fat and fat distribution in hypertension risk in urban Black South African women’, PLoS One 11(5), e0154894. 10.1371/journal.pone.0154894 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Govender, K.C. & Naidoo, M., 2025, ‘Prevalence and determinants of apparent treatment-resistant hypertension among patients in South African primary care: A single-centre observational study’, BMC Cardiovascular Disorders 25, 373. 10.1186/s12872-025-04813-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Grimsrud, A., Stein, D.J., Seedat, S., Williams, D. & Myer, L., 2009, ‘The association between hypertension and depression and anxiety disorders: Results from a nationally-representative sample of South African adults’, PLoS One 4(5), e5552. 10.1371/journal.pone.0005552 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Gupta, R.D., Talukder, A., Hossain, M.B., Akonde, M. & Al Kibria, G.M., 2023, ‘Prevalence and associated factors of hypertension among South African adults: Findings from the Demographic and Health Survey 2016’, Journal of Public Health 31(6), 967–977. 10.1007/s10389-021-01607-w [DOI] [Google Scholar]
  24. Guyatt, G.H., Oxman, A.D., Vist, G.E., Kunz, R., Falck-Ytter, Y., Alonso-Coello, P. et al., 2008, ‘GRADE: An emerging consensus on rating quality of evidence and strength of recommendations’, British Medical Journal 336(7650), 924–926. 10.1136/bmj.39489.470347.AD [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Hill, A.B., 1965, ‘The environment and disease: Association or causation?’, Journal of the Royal Society of Medicine 58(5), 295–300. 10.1177/003591576505800503 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Hippisley-Cox, J., Coupland, C. & Brindle, P., 2017, ‘Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease’, British Medical Journal 357, j2099. 10.1136/bmj.j2099 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Ibeneme, S.C., Odoh, E., Martins, N. & Ibeneme, G.C., 2024, ‘Developing an HIV-specific falls risk prediction model with a novel clinical index: A systematic review and meta-analysis’, BMC Infectious Diseases 24(1), 1402. 10.1186/s12879-024-10141-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Ibrahim, M.M. & Damasceno, A., 2012, ‘Hypertension in developing countries’, The Lancet 380(9841), 611–619. 10.1016/S0140-6736(12)60861-7 [DOI] [PubMed] [Google Scholar]
  29. Jacobs, A., Schutte, A.E., Ricci, C. & Pieters, M., 2019, ‘Plasminogen activator inhibitor-1 activity and blood pressure in Africans’, Journal of Hypertension 37(12), 2361–2370. 10.1097/HJH.0000000000002204 [DOI] [PubMed] [Google Scholar]
  30. Jamshidi-Naeini, Y., Khodayari Bavil, A. & Egal, A., 2019, ‘Hemoglobin and ferritin concentrations associated with blood pressure in older adults’, Asia Pacific Journal of Clinical Nutrition 28(3), 533–543. [DOI] [PubMed] [Google Scholar]
  31. Kandala, N.-B., Nnanatu, C.C., Dukhi, N., Sewpaul, R., Davids, A. & Reddy, S.P., 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, 5445. 10.3390/ijerph18105445 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Kaplan, G.A. & Keil, J.E., 1993, ‘Socioeconomic factors and cardiovascular disease: A review of the literature’, Circulation 88(4), 1973–1998. 10.1161/01.CIR.88.4.1973 [DOI] [PubMed] [Google Scholar]
  33. Kaptoge, S., Pennells, L., De Bacquer, D., Cooney, M.T., Kavousi, M., Stevens, G. et al., 2019, ‘World Health Organization cardiovascular disease risk charts’, Lancet Global Health 7(10), e1332–e1345. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Kayima, J., Wanyenze, R.K., Katamba, A., Leontsini, E. & Nuwaha, F., 2013, ‘Hypertension awareness, treatment and control in Africa: A systematic review’, BMC Cardiovascular Disorders 13, 54. 10.1186/1471-2261-13-54 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Lakatta, E.G. & Levy, D., 2003, ‘Arterial and cardiac aging: Major shareholders in cardiovascular disease enterprises: Part I: Aging arteries: A “set up” for vascular disease’, Circulation 107(1), 139–146. 10.1161/01.CIR.0000048892.83521.58 [DOI] [PubMed] [Google Scholar]
  36. Lategan, R., Van den Berg, V.L. & Walsh, C.M., 2014, ‘Body adiposity indices are associated with hypertension in a black, urban Free State community’, African Journal of Primary Health Care & Family Medicine 6(1), a581. 10.4102/phcfm.v6i1.581 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Mabhida, S.E., Sharma, J.R., Apalata, T., Masilela, C., Nomatshila, S., Mabasa, L. et al., 2022, ‘The association of MTHFR (rs1801133) with hypertension in an indigenous South African population’, Frontiers in Genetics 13, 937639. 10.3389/fgene.2022.937639 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Madela, S.L.M., Harriman, N.W., Sewpaul, R., Mbewu, A.D., Williams, D.R., Sifunda, S. et al., 2023, ‘Individual and area-level socioeconomic correlates of hypertension prevalence, awareness, treatment, and control in uMgungundlovu, KwaZulu-Natal, South Africa’, BMC Public Health 23, 417. 10.1186/s12889-023-15247-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Magodoro, I.M., Okello, S., Dungeni, M., Castle, A.C., Mureyani, S. & Danaei, G., 2022, ‘Association between HIV and prevalent hypertension and diabetes mellitus in South Africa: Analysis of a nationally representative cross-sectional survey’, International Journal of Infectious Diseases 121, 217–225. 10.1016/j.ijid.2022.05.035 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Marmot, M., Friel, S., Bell, R., Houweling, T.A.J. & Taylor, S., 2008, ‘Closing the gap in a generation: Health equity through action on the social determinants of health’, The Lancet 372(9650), 1661–1669. 10.1016/S0140-6736(08)61690-6 [DOI] [PubMed] [Google Scholar]
  41. Masilela, C., Pearce, B., Ongole, J.J., Adeniyi, O.V. & Benjeddou, M., 2020, ‘Cross-sectional study of prevalence and determinants of uncontrolled hypertension among South African adult residents of Mkhondo municipality’, BMC Public Health 20, 1069. 10.1186/s12889-020-09174-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Matshazi, D.M., Weale, C.J., Erasmus, R.T., Kengne, A.P., Davids, S.F.G., Raghubeer, S. et al., 2021, ‘MicroRNA profiles in normotensive and hypertensive South African individuals’, Frontiers in Cardiovascular Medicine 8, 645541. 10.3389/fcvm.2021.645541 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Mayosi, B.M., Flisher, A.J., Lalloo, U.G., Sitas, F., Tollman, S.M. & Bradshaw, D., 2009, ‘The burden of non-communicable diseases in South Africa’, Lancet 374(9693), 934–947. 10.1016/S0140-6736(09)61087-4 [DOI] [PubMed] [Google Scholar]
  44. Mazenda, A., Katiyatiya, C.L.F. & Sari, N.P.W.P., 2025, ‘Examining the relationship between consumption of a protein-based diet and hypertension among urban households: A cross-sectional study’, Health Science Reports 8(3), e70540. 10.1002/hsr2.70540 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Mills, K.T., Stefanescu, A. & He, J., 2020, ‘The global epidemiology of hypertension’, Nature Reviews Nephrology 16(4), 223–237. 10.1038/s41581-019-0244-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Modjadji, P., Salane, M.C., Mokwena, K.E., Mudau, T.S., Mphekgwana, P.M., 2022, ‘Utility of obesity indicators for predicting hypertension among older persons in Limpopo Province, South Africa’, Applied Sciences 2022, 12(9), 4697. 10.3390/app12094697 [DOI] [Google Scholar]
  47. Monakali, S., Ter Goon, D., Seekoe, E. & Owolabi, E.O., 2018, ‘Prevalence, awareness, control and determinants of hypertension among primary health care professional nurses in Eastern Cape, South Africa’, African Journal of Primary Health Care & Family Medicine 10(1), a1758. 10.4102/phcfm.v10i1.1758 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Motlhale, M. & Ncayiyana, J.R., 2019, ‘Migration status and hypertension risk in Gauteng Province’, BMJ Open 9, e027427. 10.1136/bmjopen-2018-027427 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Mphekgwana, P.M., Malema, N., Monyeki, K.D., Mothiba, T.M., Makgahlela, M., Kgatla, N. et al., 2020, ‘Hypertension prevalence and determinants among Black South African adults in semi-urban and rural areas’, International Journal of Environmental Research and Public Health 17(20), 7463. 10.3390/ijerph17207463 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Munn, Z., Stern, C., Aromataris, E., Lockwood, C. & Jordan, Z., 2018, ‘What kind of systematic review should I conduct? A proposed typology and guidance for systematic reviewers in the medical and health sciences’, BMC Medical Research Methodology 18, 5. 10.1186/s12874-017-0468-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Naidoo, S., Fabian, J. & Norris, S.A., 2023, ‘Prevalence and associated risk factors for elevated blood pressure in young adults in South Africa’, Cardiovascular Journal of Africa 34(3), 157–163. 10.5830/CVJA-2022-036 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Naidoo, S., Kagura, J., Fabian, J. & Norris, S.A., 2019, ‘Early life factors and longitudinal blood pressure trajectories are associated with elevated blood pressure in early adulthood’, Hypertension 73(2), 301–309. 10.1161/HYPERTENSIONAHA.118.11992 [DOI] [PubMed] [Google Scholar]
  53. NCD Risk Factor Collaboration (NCD-RisC) , 2021, ‘Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: A pooled analysis of 1201 population-representative studies with 104 million participants’, The Lancet 398(10304), 957–980. 10.1016/S0140-6736(21)01330-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Nguyen, K.A., Abrahams, N., Jewkes, R., Mhlongo, S., Seedat, S., Myers, B. et al., 2022, ‘The associations of intimate partner violence and non-partner sexual violence with hypertension in South African women’, International Journal of Environmental Research and Public Health 19, 4026. 10.3390/ijerph19074026 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Nkeh, B., Tiago, A., Candy, G.P., Woodiwiss, A.J., Badenhorst, D., Luker, F. et al., 2002, ‘Association between an atrial natriuretic peptide gene polymorphism and normal blood pressure in subjects of African ancestry’, Cardiovascular Journal of South Africa 13(3), 97–101. [PubMed] [Google Scholar]
  56. Nkosi, V., Shirinde, J., Rathogwa-Takalani, F. & Voyi, K., 2021, ‘High blood pressure and exposure to dust from gold mine dumps among the elderly in South Africa: A cross-sectional study’, Public Health in Practice 2, 100146. 10.1016/j.puhip.2021.100146 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. North, B.J. & Sinclair, D.A., 2012, ‘The intersection between aging and cardiovascular disease’, Circulation Research 110(8), 1097–1108. 10.1161/CIRCRESAHA.111.246876 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Ntenda, P.A.M., El-Meidany, W.M.R., Tiruneh, F.N., Motsa, M.P.S., Nyirongo, J., Chirwa, G.C. et al., 2022, ‘Determinants of self-reported hypertension among women in South Africa: Evidence from the population-based survey’, Clinical Hypertension 28, 39. 10.1186/s40885-022-00222-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Ntlahla, E.E., Mfengu, M.M.O., Engwa, G.A., Nkeh-Chungag, B.N. & Sewani-Rusike, C.R., 2021, ‘Gut permeability is associated with hypertension and measures of obesity but not with endothelial dysfunction in South African youth’, African Health Sciences 21(3), 1172–1184. 10.4314/ahs.v21i3.26 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Ntlebi, V., Made, F., Wilson, K., Kootbodien, T., Tlotleng, N. & Naicker, N., 2020, ‘Hypertension and associated risk factors among informal waste pickers in Johannesburg, South Africa’, Occupational Health Southern Africa 26(6), 282–286. [Google Scholar]
  61. Ntuli, S.T., Maimela, E., Alberts, M., Choma, S. & Dikotope, S., 2015, ‘Prevalence and associated risk factors of hypertension amongst adults in a rural community of Limpopo Province, South Africa’, African Journal of Primary Health Care & Family Medicine 7(1), a847. 10.4102/phcfm.v7i1.847 [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Nweke, M. & Mshunqane, N., 2025, ‘Characterization and stratification of risk factors of stroke in people living with HIV: A theory-informed systematic review’, BMC Cardiovascular Disorders 25(1), 405. 10.1186/s12872-025-04833-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Nweke, M., Ibeneme, S., Pillay, J.D. & Mshunqane, N., 2025a, ‘Characterization and risk stratification of coronary artery disease in people living with HIV: A global systematic review’, Frontiers in Cardiovascular Medicine 12, 1586019. 10.3389/fcvm.2025.1586019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Nweke, M., Oyirinnaya, P., Nwoha, P., Mitha, S.B., Mshunqane, N., Govender, N. et al., 2025b, ‘Development of a high-performing, cost-effective and inclusive Afrocentric predictive model for stroke: A meta-analysis approach’, BMC Neurology 25(1), 282. 10.1186/s12883-025-04229-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Owolabi, E.O., Ter Goon, D., Adeniyi, O.V. & Seekoe, E., 2017, ‘Social epidemiology of hypertension in Buffalo City Metropolitan Municipality (BCMM): Cross-sectional study of determinants of prevalence, awareness, treatment and control among South African adults’, BMJ Open 7(6), e014349. 10.1136/bmjopen-2016-014349 [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D. et al., 2021, ‘The PRISMA 2020 statement’, British Medical Journal 372, n71. 10.1136/bmj.n71 [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Patel, S.A., Ali, M.K., Alamy, D., Yan, L.L., Levitt, N.S., Bernabe-Ortiz, A. et al., 2016, ‘Obesity and its relation with diabetes and hypertension: A cross-sectional study across 4 geographical regions’, Global Heart 11(1), 71–79. 10.1016/j.gheart.2016.01.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Peer, N., Levitt, N., Lombard, C., George, J. & Kengne, A.-P., 2025, ‘Prevalence and associations of hypertension detection, treatment and control in Cape Town’, BMC Public Health 25, 1674. 10.1186/s12889-025-22307-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Peltzer, K. & Phaswana-Mafuya, N., 2013, ‘Hypertension and associated factors among adults in South Africa’, Cardiovascular Journal of Africa 24(3), 67–71. 10.5830/CVJA-2013-002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Pisa, P.T., Micklesfield, L.K., Kagura, J., Ramsay, M., Crowther, N.J. & Norris, S.A., 2018, ‘Different adiposity indices and their association with blood pressure and hypertension in middle-aged urban black South African men and women: Findings from the AWI-GEN South African Soweto Site’, BMC Public Health 18, 524. 10.1186/s12889-018-5443-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Raphadu, T.T., Mphekgwana, P.M., Matshipi, M. & Monyeki, K.D., 2023, ‘A longitudinal investigation on the effects of sodium and potassium intake on the development of hypertension and abdominal obesity from childhood to young adulthood amongst Ellisras rural population, South Africa’, Children 10, 1330. 10.3390/children10081330 [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Renta, V., Walker, R.J., Nagavally, S., Dawson, A.Z., Campbell, J.A. & Egede, L.E., 2022, ‘Differences in the relationship between social capital and hypertension in emerging vs. established economies in Sub-Saharan Africa’, BMC Public Health 22, 1038. 10.1186/s12889-022-13471-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Roth, G.A., Mensah, G.A., Johnson, C.O., Addolorato, G., Ammirati, E., Baddour, L.M. et al., 2020, ‘Global burden of cardiovascular diseases and risk factors, 1990–2019: Update from the GBD 2019 study’, Journal of the American College of Cardiology 76(25), 2982–3021. 10.1016/j.jacc.2020.11.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Rothman, K.J., 1976, ‘Causes’, American Journal of Epidemiology 104(6), 587–592. 10.1093/oxfordjournals.aje.a112335 [DOI] [PubMed] [Google Scholar]
  75. Rudasingwa, G., Cho, S.I. & Ishimwe, N.C., 2024, ‘Spatial distribution and determinants of hypertension in South Africa’, BMC Public Health 24(1), 145. [Google Scholar]
  76. Schlesselman, J.J., 1982, Case-control studies: Design, conduct, analysis, Oxford University Press, New York, NY. [Google Scholar]
  77. Schutte, A.E., Schutte, R., Huisman, H.W., Oosthuizen, W., Fourie, C.M.T., Malan, L. et al., 2012, ‘Behavioural risk factors associated with conversion to hypertension in black South Africans’, International Journal of Epidemiology 41(4), 1114–1123. 10.1093/ije/dys106 [DOI] [PubMed] [Google Scholar]
  78. Seedat, Y.K., Rayner, B.L. & Veriava, Y., 2014, ‘South African hypertension guideline 2014’, South African Medical Journal 104(3), 288–294. 10.5830/CVJA-2014-062 [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Sekgala, M.D., Opperman, M., Mpahleni, B. & Mchiza, Z.J.-R., 2022, ‘Anthropometric indices and cut-off points for screening of metabolic syndrome among South African taxi drivers’, Frontiers in Nutrition 9, 974749. 10.3389/fnut.2022.974749 [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Sharma, J.R., Fokkens, H., Laubscher, R., Apalata, T.R., Nomatshila, S.C., Alomatu, S.Y. et al., 2024, ‘No association between AGT gene polymorphisms with hypertension in a South African population’, Diabetes, Metabolic Syndrome and Obesity 17, 1853–1865. 10.2147/DMSO.S452272 [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Sharma, J.R., Mabhida, S.E., Myers, B., Apalata, T., Nicol, E., Benjeddou, M. et al., 2021, ‘Prevalence of hypertension and its associated risk factors in a rural Black population of Mthatha Town, South Africa’, International Journal of Environmental Research and Public Health 18(3), 1215. 10.3390/ijerph18031215 [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Shisana, O., Labadarios, D., Rehle, T., Simbayi, L., Zuma, K., Dhansay, A. et al., 2013, South African National Health and Nutrition Examination Survey (SANHANES-1), HSRC Press, Cape Town. [Google Scholar]
  83. Solomons, N., Kruger, H.S. & Puoane, T., 2020, ‘Association between dietary adherence, anthropometric measurements and blood pressure in an urban black population, South Africa’, South African Journal of Clinical Nutrition 33(1), 1–9. 10.1080/16070658.2018.1489602 [DOI] [Google Scholar]
  84. Stanaway, J.D., Afshin, A., Gakidou, E., Lim, S.S., Abate, D., Abate, K.H. et al., 2018, ‘Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017’, The Lancet 392(10159), 1923–1994. 10.1016/S0140-6736(18)32225-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Steyerberg, E.W. & Harrell, F.E., 2016, ‘Prediction models need appropriate validation’, Journal of Clinical Epidemiology 69, 245–247. 10.1016/j.jclinepi.2015.04.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Steyn, K., Bradshaw, D., Norman, R. & Laubscher, R., 2008, ‘Determinants and treatment of hypertension in South Africans: The first Demographic and Health Survey’, South African Medical Journal 98(5), 376–380, viewed 01 July 2026, from https://pubmed.ncbi.nlm.nih.gov/18637309/ [PubMed] [Google Scholar]
  87. Stringhini, S., Carmeli, C., Jokela, M., Avendaño, M., Muennig, P., Guida, F. et al., 2017, ‘Socioeconomic status and the 25 × 25 risk factors as defined by WHO: A multicohort study’, The Lancet 389(10077), 1229–1237. 10.1016/S0140-6736(16)32380-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Unger, T., Borghi, C., Charchar, F., Khan, N.A., Poulter, N.R., Prabhakaran, D. et al., 2020, ‘2020 International Society of Hypertension global hypertension practice guidelines’, Hypertension 75(6), 1334–1357. 10.1161/HYPERTENSIONAHA.120.15026 [DOI] [PubMed] [Google Scholar]
  89. Wand, H., Vujovich-Dunn, C., Moodley, J., Reddy, T. & Naidoo, S., 2022, ‘Developing and validating risk algorithm for hypertension in South Africa’, High Blood Pressure & Cardiovascular Prevention 29(5), 451–461. 10.1007/s40292-022-00534-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. Wandai, M.E., Norris, S.A., Aagaard-Hansen, J. & Manda, S.O.M., 2020, ‘Geographical influence on the distribution of the prevalence of hypertension in South Africa: A multilevel analysis’, Cardiovascular Journal of Africa 31(1), 47–54. 10.5830/CVJA-2019-047 [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Ware, L.J., Chidumwa, G., Charlton, K., Schutte, A.E. & Kowal, P., 2019, ‘Predictors of hypertension awareness, treatment and control in South Africa: Results from the WHO-SAGE population survey (Wave 2)’, Journal of Human Hypertension 33(2), 157–166. 10.1038/s41371-018-0125-3 [DOI] [PubMed] [Google Scholar]
  92. Wells, G., Shea, B., O’Connell, D., Peterson, J., Welch, V., Losos, M. et al., 2000, The Newcastle–Ottawa Scale (NOS) for assessing the quality of non-randomised studies in meta-analysis, Ottawa Hospital Research Institute, Ottawa. [Google Scholar]
  93. World Health Organization (WHO) , 2025, HEARTS: Technical package for cardiovascular disease management in primary health care, WHO, Geneva. [Google Scholar]

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Data Availability Statement

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary material.

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