SAMJ: South African Medical Journal
versão On-line ISSN 2078-5135
versão impressa ISSN 0256-9574
PEER, N et al. Comparability of total cardiovascular disease risk estimates using laboratory and non-laboratory based assessments in urban-dwelling South Africans: The CRIBSA study. SAMJ, S. Afr. med. j. [online]. 2014, vol.104, n.10, pp.621-696. ISSN 2078-5135.
OBJECTIVES: To establish the prevalence and determinants of the 10-year risk of a cardiovascular disease (CVD) event in 25 - 74-year-old black Africans in Cape Town, South Africa, using Framingham laboratory- and non-laboratory-based and National Health and Nutrition Examination Survey (NHANES) I non-laboratory-based equations. METHODS: CVD risk factors were determined by questionnaires, clinical measurements and biochemical analyses. Survey logistic regression analyses assessed the sociodemographic determinants of CVD risk ≥20%. RESULTS: There were 1 025 participants, 369 men and 656 women. Mean 10-year risk for a CVD event by Framingham laboratory- and non-laboratory-based and NHANES I non-laboratory-based equations for men was 9.0% (95% confidence interval 7.7 - 10.3), 11.1% (9.6 - 12.6) and 9.0% (7.6 - 10.3), and for women 5.4% (4.7 - 6.1), 6.8% (5.9 - 7.7) and 8.7% (7.6 - 9.8). Correlations between laboratory- and non-laboratory-based scores were high (0.915 - 0.963). The prevalence of laboratory-based CVD risk >20% was 13.0% in men and 6.1% in women. In the logistic model for men,≥7 years of education (odds ratio 3.09; 95% CI 1.67 - 5.71) and being unemployed (3.44; 1.21 - 9.81) compared with employed were associated with laboratory-based high risk. In women, high risk was associated with ≥7 years of education (4.20; 1.96 - 9.01), living in formal v. informal housing (2.74; 1.24 - 6.06) and being poor (middle v. lowest tertile 0.29; 0.13 - 0.66). In the Framingham non-laboratory-based logistic models there were no changes in the direction or significance of the variables except for housing, which was no longer significant in women. CONCLUSIONS: Comparability of laboratory- and non-laboratory-based CVD risk estimates illustrates the utility of the latter in resource-constrained settings.