1. 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

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

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

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

5. 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

6. 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: ; Hippisley-Cox. BMJ. 2008;336(7659) .

7. 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

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

9. 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 p. 245–51.

10. Comparative analysis - Predicted 10-year CVD risk

11. 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)
(p=0.434)
**Significant at p<0.001

12. 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

13. 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)

14. UNDER THE FRAMEWORK PROGRAMME
Acknowledgement
UNDER THE FRAMEWORK PROGRAMME

15. Thank you for our attention!?
Contact:
Daniel Boateng
Julius Global Health, Utrecht University Medical Centre, The Netherlands
URL: http//