Disparities in the non-laboratory INTERHEART risk score and its components in selected countries of Europe and sub-Saharan Africa: analysis from the SPICES multi-country project

Abstract

Abstract Aims Accurate prediction of a person’s risk of cardiovascular disease (CVD) is vital to initiate appropriate intervention. The non-laboratory INTERHEART risk score (NL-IHRS) is among the tools to estimate future risk of CVD. However, measurement disparities of the tool across contexts are not well documented. Thus, we investigated variation in NL-IHRS and components in selected sub-Saharan African and European countries. Methods and results We used data from a multi-country study involving 9309 participants, i.e. 4941 in Europe, 3371 in South Africa, and 997 in Uganda. Disparities in total NL-IHRS score, specific subcomponents, subcategories, and their contribution to the total score were investigated. The variation in the adjusted total and component scores was compared across contexts using analysis of variance. The adjusted mean NL-IHRS was higher in South Africa (10.2) and Europe (10.0) compared to Uganda (8.2), and the difference was statistically significant (P < 0.001). The prevalence and per cent contribution of diabetes mellitus and high blood pressure were lowest in Uganda. Score contribution of non-modifiable factors was lower in Uganda and South Africa, entailing 11.5% and 8.0% of the total score, respectively. Contribution of behavioural factors to the total score was highest in both sub-Saharan African countries. In particular, adjusted scores related to unhealthy dietary patterns were highest in South Africa (3.21) compared to Uganda (1.66) and Europe (1.09). Whereas, contribution of metabolic factors was highest in Europe (30.6%) compared with Uganda (20.8%) and South Africa (22.6%). Conclusion The total risk score, subcomponents, categories, and their contribution to total score greatly vary across contexts, which could be due to disparities in risk burden and/or self-reporting bias in resource-limited settings. Therefore, primary preventive initiatives should identify risk factor burden across contexts and intervention activities need to be customized accordingly. Furthermore, contextualizing the risk assessment tool and evaluating its usefulness in different settings are recommended.