How can decision analytic models improve cardiovascular disease prevention in Sub-Saharan Africa? A recent article on the application of decision analytic modelling to cardiovascular disease prevention in Sub-Saharan Africa explored the use of decision analytic models (DAMs) for evaluating interventions aimed at cardiovascular disease (CVD) prevention in Sub-Saharan Africa (SSA). It highlights that CVDs represent a significant burden in SSA, with an upward trend observed over the last three decades.

The review encompasses 27 studies from eight SSA countries, concentrating on model-based economic evaluations of CVD prevention models. Most studies focused on primary CVD prevention, with pharmacological interventions, particularly antihypertensives and statins, being most common. Markov models were frequently utilized, and the Framingham risk equations were applied to estimate 10-year CVD risk.

Notes from the Study

Most studies centered on primary CVD prevention, with pharmacological interventions being the predominant focus. Markov models and microsimulation models were the most utilized, with Markov models appearing in 13 studies and microsimulation models in seven. Only seven studies integrated equity dimensions, primarily through subgroup analysis, focusing on gender, socioeconomic, and regional inequalities.

Significant data gaps emerged, especially concerning intervention effectiveness and CVD risk equations, highlighting a lack of local data for generating 10-year CVD risk assessments. The mean quality score of the papers was 68.9%, although inconsistencies and data limitations were apparent, with only three studies undertaking model validation.

Health Economic Implications

The findings emphasize the necessity for enhanced health economic evaluations that factor in equity dimensions to guide policy decisions on CVD prevention in SSA. Longitudinal studies are essential for refining CVD risk prediction and local health outcome valuation studies. The review indicated that policymakers must prioritize cost-effective and equitable interventions to strive for universal health coverage (UHC). Strengthening primary healthcare systems is vital for scaling up effective CVD prevention models.

Future Studies

Future studies should aim to address primordial prevention and lifestyle interventions, which are currently undervalued. There is also an urgent need for improved model validation, calibration, and stakeholder engagement in the modeling process.

The review highlighted the importance of adhering to best practices in developing DAMs, such as utilizing local data, ensuring model validation, and maintaining transparency in the modeling process. The absence of local data for generating 10-year CVD risk equations and utility values for QALYs reveals significant research gaps. Incorporating equity considerations in economic evaluations is crucial, particularly through newer methods like extended and distributional cost-effectiveness analyses. There is a call for increased emphasis on longitudinal studies and local health valuation studies to enhance the accuracy and relevance of CVD prevention models in SSA.