Daniel Boateng1, Charles Agyemang2, Erik Beune2, Karlijn Meeks2, Liam Smeeth3, Matthias B Schulze4, Juliet Addo3, Ama de-Graft Aikins5, Cecilia Galbete4, Silver Bahendeka6, Ina Danquah7, Peter Agyei-Baffour8, Ellis Owusu-Dabo9, Frank P Mockenhaupt10, Joachim Spranger11, Andre P Kengne12, Diederick E Grobbee13, Kerstin Klipstein-Grobusch14. 1. Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, The Netherlands; School of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. Electronic address: d.boateng@umcutrecht.nl. 2. Department of Public Health, Academic Medical Center, University of Amsterdam, Amsterdam Public Health Research Institute, The Netherlands. 3. Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom. 4. Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany. 5. Regional Institute for Population Studies, University of Ghana, Legon, Ghana. 6. Mother Kevin Postgraduate Medical School - Uganda Martyrs University, Kampala, Uganda. 7. Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; Institute for Social Medicine, Epidemiology and Health Economics, Charité - Universitaetsmedizin Berlin, corporate member of Freie Universitaet Berlin & Humboldt-Universitaet zu Berlin, and Berlin Institute of Health, Berlin, Germany. 8. School of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. 9. School of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana; Kumasi Centre for Collaborative Research, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. 10. Institute of Tropical Medicine and International Health, Charité - Universitaetsmedizin Berlin, corporate member of Freie Universitaet Berlin & Humboldt-Universitaet zu Berlin, and Berlin Institute of Health, Berlin, Germany. 11. Charité Center for Cardiovascular Research (CCR), Charité - Universitaetsmedizin Berlin, corporate member of Freie Universitaet Berlin & Humboldt-Universitaet zu Berlin, and Berlin Institute of Health, Berlin, Germany. 12. Non-communicable Disease Research Unit, South African Medical Research Council, Cape Town, South Africa. 13. Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, The Netherlands. 14. Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, The Netherlands; Division of Epidemiology & Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Abstract
BACKGROUND: Validated absolute risk equations are currently recommended as the basis of cardiovascular disease (CVD) risk stratification in prevention and control strategies. However, there is no consensus on appropriate equations for sub-Saharan African populations. We assessed agreement between different cardiovascular risk equations among Ghanaian migrant and home populations with no overt CVD. METHODS: The 10-year CVD risks were calculated for 3586 participants aged 40-70years in the multi-centre RODAM study among Ghanaians residing in Ghana and Europe using the Framingham laboratory and non-laboratory and Pooled Cohort Equations (PCE) algorithms. Participants were classified as low, moderate or high risk, corresponding to <10%, 10-20% and >20% respectively. Agreement between the risk algorithms was assessed using kappa and correlation coefficients. RESULTS: 19.4%, 12.3% and 5.8% were ranked as high 10-year CVD risk by Framingham non-laboratory, Framingham laboratory and PCE, respectively. The median (25th-75th percentiles) estimated 10-year CVD risk was 9.5% (5.4-15.7), 7.3% (3.9-13.2) and 5.0% (2.3-9.7) for Framingham non-laboratory, Framingham laboratory and PCE, respectively. The concordance between PCE and Framingham non-laboratory was better in the home Ghanaian population (kappa=0.42, r=0.738) than the migrant population (kappa=0.24, r=0.732) whereas concordance between PCE and Framingham laboratory was better in migrant Ghanaians (kappa=0.54, r=0.769) than the home population (kappa=0.51, r=0.758). CONCLUSION: CVD prediction with the same algorithm differs for the migrant and home populations and the interchangeability of Framingham laboratory and non-laboratory algorithms is limited. Validation against CVD outcomes is needed to inform appropriate selection of risk algorithms for use in African ancestry populations.
BACKGROUND: Validated absolute risk equations are currently recommended as the basis of cardiovascular disease (CVD) risk stratification in prevention and control strategies. However, there is no consensus on appropriate equations for sub-Saharan African populations. We assessed agreement between different cardiovascular risk equations among Ghanaian migrant and home populations with no overt CVD. METHODS: The 10-year CVD risks were calculated for 3586 participants aged 40-70years in the multi-centre RODAM study among Ghanaians residing in Ghana and Europe using the Framingham laboratory and non-laboratory and Pooled Cohort Equations (PCE) algorithms. Participants were classified as low, moderate or high risk, corresponding to <10%, 10-20% and >20% respectively. Agreement between the risk algorithms was assessed using kappa and correlation coefficients. RESULTS: 19.4%, 12.3% and 5.8% were ranked as high 10-year CVD risk by Framingham non-laboratory, Framingham laboratory and PCE, respectively. The median (25th-75th percentiles) estimated 10-year CVD risk was 9.5% (5.4-15.7), 7.3% (3.9-13.2) and 5.0% (2.3-9.7) for Framingham non-laboratory, Framingham laboratory and PCE, respectively. The concordance between PCE and Framingham non-laboratory was better in the home Ghanaian population (kappa=0.42, r=0.738) than the migrant population (kappa=0.24, r=0.732) whereas concordance between PCE and Framingham laboratory was better in migrant Ghanaians (kappa=0.54, r=0.769) than the home population (kappa=0.51, r=0.758). CONCLUSION: CVD prediction with the same algorithm differs for the migrant and home populations and the interchangeability of Framingham laboratory and non-laboratory algorithms is limited. Validation against CVD outcomes is needed to inform appropriate selection of risk algorithms for use in African ancestry populations.
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