for the RODAM Consortium Cardiovascular disease risk prediction among sub-Saharan African populations Daniel Boateng, MPH, MSc for the RODAM Consortium Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, The Netherlands School of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

Increased burden of cardiovascular diseases (CVD) in low- and middle-income countries Cardiovascular disease death rates (per 100, 000), 2016 WHO. Cardiovascular diseases. 2018 [Online]; IHME. 2017. Global Burden of Disease Study 2016 (GBD 2016) 

Country characteristics and rate of CVD deaths IDF. Diabetes and Cardiovascular disease. 2016

Risk based strategy for CVD prevention IDF. Diabetes and Cardiovascular disease. 2016

CVD risk algorithms – extensively used in high-income countries Framingham Risk Calculator Laboratory Non laboratory Age Sex Systolic BP Diabetes Smoking Total cholesterol HDL cholesterol BMI

Conflicting evidence on appropriateness of available risk scores Framingham equation criticized for inaccurate estimation of risk among ethnic minority groups QRISK2 performed poorly in identifying high risk African Caribbeans developed and validated among individuals from different ethnic groups in England and Wales Pooled Cohort Equations (PCE) performed well in estimating CVD risk in ethnic minority populations developed and validated among African American and European men and women D’Agostino RB et al. Circulation. 2008; 117:743-53; Goff DC et al. J Am Coll Cardiol. 2014; 63:2935-59; Hippisley-Cox. BMJ. 2008;336(7659) .

Lack of evidence from sub-Saharan Africa No population based study conducted in sub-Saharan African (SSA) countries for development of CVD risk algorithms Little evidence on the comparability of existing risk algorithms in identifying high-risk individuals among SSA populations We compared the risk stratification of Framingham laboratory, Framingham non-laboratory and the Pooled Cohort Equations among Ghanaian populations in Europe and Ghana

Research on Obesity & Diabetes on African Migrants (RODAM) ( RODAM) Amsterdam London Berlin Urban Rural Agyemang et al, BMJ Medicine 2016. 14:166

Estimation and analysis CVD risk classified as low (<10%), moderate (10–20%) or high risk (>20%) Agreement between risk algorithms - Cohen’s kappa coefficient poor-to-fair (<0.40), moderate (0.41–0.60), substantial (0.61–0.80) and excellent (kappa of 0.81–1.0) Correlation between continuous CVD risk - Spearman correlation Correlation coefficients across the various settings - Steiger’s Z test Landis JR & Koch GG. Biometrics. 1977;33(1):159–74; Steiger JH. Psychological Bulletin. 1980. p. 245–51.

Comparative analysis - Predicted 10-year CVD risk

PCE versus Framingham laboratory Comparative analysis – Pooled Cohort Equation versus Framingham non-laboratory PCE versus Framingham non-laboratory PCE versus Framingham laboratory Ghana Europe Kappa, 95% CI 0.42 (0.37, 0.47) 0.24 (020, 0.28) 0.51 (0.47, 0.56) 0.54 (0.50, 0.58) P-value <0.001 Agreement % 73.1 54.9 79.7 77.5 Spearman correlation 0.738** 0.732** 0.758** 0.769** Steiger’s Z (p-value)§   0.39 (p=0.699) -0.783 (p=0.434) **Significant at p<0.001

Comparative analysis – Framingham laboratory versus non-laboratory Ghana Europe Kappa (95% CI) 0.74 (0.70, 0.77) 0.55 (0.52, 0.58) P-value <0.001 % Agreement 86.25 72.26 Spearman correlation 0.866** 0.820** Steiger’s Z (p-vale)§ 4.75 (p<0.0001) **Significant at p<0.001

Key findings/ conclusions Predicted 10- year CVD risk differs for the Ghanaian migrant and home populations Discrepancies higher among the Ghanaian population in Europe than among Ghanaians in Ghana Different levels of agreement between the various CVD risk scores Inter-changeability of BMI and cholesterol algorithms limited Overestimation of CVD risk has implications for prevention / management of (high-risk) groups Validation against hard CVD outcomes needed to inform appropriate selection of risk algorithms moderate between Framingham laboratory and non-laboratory low between PCE and the Framingham algorithms - patients, e.g. either no / or unnecessary medication - costs to community (e.g. care for patients) / society (e.g health care costs)

UNDER THE FRAMEWORK PROGRAMME Acknowledgement UNDER THE FRAMEWORK PROGRAMME

Thank you for our attention! ? Contact: Daniel Boateng Julius Global Health, Utrecht University Medical Centre, The Netherlands Email: dboateng@umcutrecht.nl URL: http//www.globalhealth.eu