Estimating the burden of cardiovascular risk in community dwellers over 40 years old in South Africa, Kenya, Burkina Faso and Ghana

• Published in: BMJ global health 2021-01, Vol.6 (1), p.e003499
• Main Authors: Wagner, Ryan G, Crowther, Nigel J, Micklesfield, Lisa K, Boua, Palwende Romauld, Nonterah, Engelbert A, Mashinya, Felistas, Mohamed, Shukri F, Asiki, Gershim, Tollman, Stephen, Ramsay, Michèle, Davies, Justine I
• Format: Article
• Language: English
• Subjects: Algorithms; Blood pressure; Body mass index; cardiovascular disease; Cardiovascular diseases; community-based survey; Data collection; Diabetes; Disease; Epidemiology; Global health; Glucose; Health administration; Health risks; Hypertension; Laboratories; Medical research; Medicin och hälsovetenskap; Original Research; Population; Principal components analysis; Risk factors; Socioeconomic factors
• ISSN: 2059-7908; 2059-7908
• DOI: 10.1136/bmjgh-2020-003499

IntroductionCardiovascular disease (CVD) risk factors are increasing in sub-Saharan Africa. The impact of these risk factors on future CVD outcomes and burden is poorly understood. We examined the magnitude of modifiable risk factors, estimated future CVD risk and compared results between three commonly used 10-year CVD risk factor algorithms and their variants in four African countries.MethodsIn the Africa-Wits-INDEPTH partnership for Genomic studies (the AWI-Gen Study), 10 349 randomly sampled individuals aged 40–60 years from six sites participated in a survey, with blood pressure, blood glucose and lipid levels measured. Using these data, 10-year CVD risk estimates using Framingham, Globorisk and WHO-CVD and their office-based variants were generated. Differences in future CVD risk and results by algorithm are described using kappa and coefficients to examine agreement and correlations, respectively.ResultsThe 10-year CVD risk across all participants in all sites varied from 2.6% (95% CI: 1.6% to 4.1%) using the WHO-CVD lab algorithm to 6.5% (95% CI: 3.7% to 11.4%) using the Framingham office algorithm, with substantial differences in risk between sites. The highest risk was in South African settings (in urban Soweto: 8.9% (IQR: 5.3–15.3)). Agreement between algorithms was low to moderate (kappa from 0.03 to 0.55) and correlations ranged between 0.28 and 0.70. Depending on the algorithm used, those at high risk (defined as risk of 10-year CVD event >20%) who were under treatment for a modifiable risk factor ranged from 19.2% to 33.9%, with substantial variation by both sex and site.ConclusionThe African sites in this study are at different stages of an ongoing epidemiological transition as evidenced by both risk factor levels and estimated 10-year CVD risk. There is low correlation and disparate levels of population risk, predicted by different risk algorithms, within sites. Validating existing risk algorithms or designing context-specific 10-year CVD risk algorithms is essential for accurately defining population risk and targeting national policies and individual CVD treatment on the African continent.