OBJECTIVES: To examine the application of decision analytic modelling to cardiovascular disease (CVD) prevention in sub-Saharan Africa (SSA).

METHODS: The systematic review was conducted and reported using PRISMA 2020 guidelines, with review protocol preregistered on PROSPERO (CRD42023457106). Model-based economic evaluations of interventions targeting CVD prevention among adult populations in SSA countries were included. An iterative process was used to develop the search strategy, which was applied on seven databases. The final search was performed on September 12, 2023. All articles were screened by two reviewers, data extracted using Microsoft Excel and narrative synthesis conducted. Quality assessment was performed using Philips checklist.

RESULTS: From an initial 2033 search results, we included 27 articles from eight SSA countries. South Africa had the highest number of studies (seven) while Uganda had only one. Majority of the studies focused on primary prevention (mostly pharmacological interventions), while six studies each focused on lifestyle risk factors and health systems strengthening interventions. Thirteen studies were Markov models, seven were microsimulation models, while three studies each used multistate life table and WHO CHOICE modelling methods. Ischemic heart disease and stroke were the commonest CVD outcomes. The Framingham risk equation was the most used to model the 10-year CVD risk among patient cohorts. Seven studies incorporated equity dimensions in the modelling, which were assessed mainly through subgroup analysis. The mean quality score of the papers based on the Philips checklist was 70.6% and ranged from 48.2% to 86.8% (median= 74.1%). Most studies reported data challenges while only three studies conducted model validation.

CONCLUSIONS: The review finds paucity of studies modelling the impact of interventions targeting primordial prevention and those aimed at improving access to CVD prevention. There is a need to conduct equity-informative economic evaluation of CVD prevention interventions to inform the design of universal health coverage interventions in SSA.