Peter Ueda1, Mark Woodward2, Yuan Lu3, Kaveh Hajifathalian4, Rihab Al-Wotayan5, Carlos A Aguilar-Salinas6, Alireza Ahmadvand7, Fereidoun Azizi8, James Bentham9, Renata Cifkova10, Mariachiara Di Cesare11, Louise Eriksen12, Farshad Farzadfar13, Trevor S Ferguson14, Nayu Ikeda15, Davood Khalili16, Young-Ho Khang17, Vera Lanska18, Luz León-Muñoz19, Dianna J Magliano20, Paula Margozzini21, Kelias P Msyamboza22, Gerald Mutungi23, Kyungwon Oh24, Sophal Oum25, Fernando Rodríguez-Artalejo19, Rosalba Rojas-Martinez26, Gonzalo Valdivia27, Rainford Wilks14, Jonathan E Shaw20, Gretchen A Stevens28, Janne S Tolstrup12, Bin Zhou29, Joshua A Salomon1, Majid Ezzati30, Goodarz Danaei31. 1. Department of Global Health and Population, Harvard School of Public Health, Boston, MA, USA. 2. The George Institute for Global Health, University of Oxford, Oxford, UK; The George Institute for Global Health, University of Sydney, Sydney, NSW, Australia; Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA. 3. Yale/Yale-New Haven Hospital, Center for Outcomes Research and Evaluation, New Haven, CT, USA. 4. Department of Internal Medicine, Cleveland Clinic, Cleveland, OH, USA. 5. Central Department of Primary Health Care, Ministry of Health, Kuwait City, Kuwait. 6. Department of Endocrinology and Metabolism, Instituto Nacional de Ciencias Médicas y Nutrición, "Salvador Zubirán", Mexico City, Mexico. 7. MRC-PHE Centre for Environment and Health, Imperial College London, London, UK; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran. 8. Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 9. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK. 10. Center for Cardiovascular Prevention, Charles University in Prague, First Faculty of Medicine and Thomayer Hospital, Prague, Czech Republic. 11. MRC-PHE Centre for Environment and Health, Imperial College London, London, UK; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; Department of Natural Sciences, School of Science and Technology, Middlsex University, London, UK. 12. National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark. 13. Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran, Iran. 14. Epidemiology Research Unit, Caribbean Institute for Health Research, The University of the West Indies, Kingston, Jamaica. 15. Center for International Collaboration and Partnership, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, Tokyo, Japan. 16. Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 17. Institute of Health Policy and Management, Seoul National University College of Medicine, Seoul, South Korea. 18. Statistical Unit, Institute for Clinical and Experimental Medicine, Prague, Czech Republic. 19. Department of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid/Idipaz, and CIBER of Epidemiology and Public Health, Madrid, Spain. 20. Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia. 21. Department of Public Health, Faculty of Medicine, Pontifical Catholic University of Chile, Santiago, Chile. 22. WHO, Malawi Country Office, Lilongwe, Malawi. 23. Non-communicable Diseases Prevention and Control Program at the Ministry of Health, Kampala, Uganda. 24. Division of Health and Nutrition Survey, Korea Centers for Disease Control and Prevention, Cheongwon-gun, South Korea. 25. University of Health Sciences, Phnom Penh, Cambodia. 26. Centro de Investigación en Salud Poblacional, Instituto Nacional de Salud Publica, Cuernavaca, Mexico. 27. División Salud Pública y Medicina Familiar, Pontificia Universidad Católica de Chile, Santiago, Chile. 28. Department of Information, Evidence and Research, WHO, Geneva, Switzerland. 29. MRC-PHE Centre for Environment and Health, Imperial College London, London, UK; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK. 30. MRC-PHE Centre for Environment and Health, Imperial College London, London, UK; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; WHO Collaborating Centre on NCD Surveillance and Epidemiology, Imperial College London, London, UK; Wellcome Trust Centre for Global Health Research, London, UK. 31. Department of Global Health and Population, Harvard School of Public Health, Boston, MA, USA; Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA. Electronic address: gdanaei@hsph.harvard.edu.
Abstract
BACKGROUND: Worldwide implementation of risk-based cardiovascular disease (CVD) prevention requires risk prediction tools that are contemporarily recalibrated for the target country and can be used where laboratory measurements are unavailable. We present two cardiovascular risk scores, with and without laboratory-based measurements, and the corresponding risk charts for 182 countries to predict 10-year risk of fatal and non-fatal CVD in adults aged 40-74 years. METHODS: Based on our previous laboratory-based prediction model (Globorisk), we used data from eight prospective studies to estimate coefficients of the risk equations using proportional hazard regressions. The laboratory-based risk score included age, sex, smoking, blood pressure, diabetes, and total cholesterol; in the non-laboratory (office-based) risk score, we replaced diabetes and total cholesterol with BMI. We recalibrated risk scores for each sex and age group in each country using country-specific mean risk factor levels and CVD rates. We used recalibrated risk scores and data from national surveys (using data from adults aged 40-64 years) to estimate the proportion of the population at different levels of CVD risk for ten countries from different world regions as examples of the information the risk scores provide; we applied a risk threshold for high risk of at least 10% for high-income countries (HICs) and at least 20% for low-income and middle-income countries (LMICs) on the basis of national and international guidelines for CVD prevention. We estimated the proportion of men and women who were similarly categorised as high risk or low risk by the two risk scores. FINDINGS: Predicted risks for the same risk factor profile were generally lower in HICs than in LMICs, with the highest risks in countries in central and southeast Asia and eastern Europe, including China and Russia. In HICs, the proportion of people aged 40-64 years at high risk of CVD ranged from 1% for South Korean women to 42% for Czech men (using a ≥10% risk threshold), and in low-income countries ranged from 2% in Uganda (men and women) to 13% in Iranian men (using a ≥20% risk threshold). More than 80% of adults were similarly classified as low or high risk by the laboratory-based and office-based risk scores. However, the office-based model substantially underestimated the risk among patients with diabetes. INTERPRETATION: Our risk charts provide risk assessment tools that are recalibrated for each country and make the estimation of CVD risk possible without using laboratory-based measurements. FUNDING: National Institutes of Health.
BACKGROUND: Worldwide implementation of risk-based cardiovascular disease (CVD) prevention requires risk prediction tools that are contemporarily recalibrated for the target country and can be used where laboratory measurements are unavailable. We present two cardiovascular risk scores, with and without laboratory-based measurements, and the corresponding risk charts for 182 countries to predict 10-year risk of fatal and non-fatal CVD in adults aged 40-74 years. METHODS: Based on our previous laboratory-based prediction model (Globorisk), we used data from eight prospective studies to estimate coefficients of the risk equations using proportional hazard regressions. The laboratory-based risk score included age, sex, smoking, blood pressure, diabetes, and total cholesterol; in the non-laboratory (office-based) risk score, we replaced diabetes and total cholesterol with BMI. We recalibrated risk scores for each sex and age group in each country using country-specific mean risk factor levels and CVD rates. We used recalibrated risk scores and data from national surveys (using data from adults aged 40-64 years) to estimate the proportion of the population at different levels of CVD risk for ten countries from different world regions as examples of the information the risk scores provide; we applied a risk threshold for high risk of at least 10% for high-income countries (HICs) and at least 20% for low-income and middle-income countries (LMICs) on the basis of national and international guidelines for CVD prevention. We estimated the proportion of men and women who were similarly categorised as high risk or low risk by the two risk scores. FINDINGS: Predicted risks for the same risk factor profile were generally lower in HICs than in LMICs, with the highest risks in countries in central and southeast Asia and eastern Europe, including China and Russia. In HICs, the proportion of people aged 40-64 years at high risk of CVD ranged from 1% for South Korean women to 42% for Czech men (using a ≥10% risk threshold), and in low-income countries ranged from 2% in Uganda (men and women) to 13% in Iranian men (using a ≥20% risk threshold). More than 80% of adults were similarly classified as low or high risk by the laboratory-based and office-based risk scores. However, the office-based model substantially underestimated the risk among patients with diabetes. INTERPRETATION: Our risk charts provide risk assessment tools that are recalibrated for each country and make the estimation of CVD risk possible without using laboratory-based measurements. FUNDING: National Institutes of Health.
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