Journal
BMJ GLOBAL HEALTH
Volume 6, Issue 1, Pages -Publisher
BMJ PUBLISHING GROUP
DOI: 10.1136/bmjgh-2020-003499
Keywords
cardiovascular disease; epidemiology; hypertension; community-based survey
Categories
Funding
- National Human Genome Research Institute (NHGRI) of the National Institutes of Health [U54HG006938]
- Office of the Director (OD) of the National Institutes of Health [U54HG006938]
- Eunice Kennedy Shriver National Institute of Child Health AMP
- Human Development (NICHD) of the National Institutes of Health [U54HG006938]
- National Institute of Environmental Health Sciences (NIEHS) of the National Institutes of Health [U54HG006938]
- Office of AIDS Research (OAR) of the National Institutes of Health [U54HG006938]
- National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institutes of Health [U54HG006938]
- H3Africa Consortium
- Department of Science and Innovation, South Africa [DST/CON 0056/2014]
- National Department of Science and Innovation
- University of the Witwatersrand
- Medical Research Council, South Africa
- Wellcome Trust, United Kingdom [058893/Z/99/A, 069683/Z/02/Z, 085477/Z/08/Z, 085477/B/08/Z]
- Wellcome Trust [085477/B/08/Z, 085477/Z/08/Z] Funding Source: Wellcome Trust
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In sub-Saharan Africa, the prevalence of cardiovascular disease (CVD) risk factors is on the rise, but the impact of these factors on future CVD outcomes remains poorly understood. This study analyzed modifiable risk factors and estimated future CVD risk using various algorithms across four African countries. The results showed significant variations in risk levels between sites and highlighted the need for context-specific risk algorithms to accurately assess population risk and guide CVD treatment in Africa.
Introduction Cardiovascular 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. Methods In 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. Results The 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. Conclusion The 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.
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