OBJECTIVE: To undertake a cost-effectiveness analysis of a Community-based Hypertension Improvement Project (ComHIP) compared with standard hypertension care in Ghana. DESIGN: Cost-effectiveness analysis using a Markov model. SETTING: Lower Manya Krobo, Eastern Region, Ghana. INTERVENTION: We evaluated ComHIP, an intervention with multiple components, including: community-based education on cardiovascular disease (CVD) risk factors and healthy lifestyles; community-based screening and monitoring of blood pressure by licensed chemical sellers and CVD nurses; community-based diagnosis, treatment, counselling, follow-up and referral of hypertension patients by CVD nurses; telemedicine consultation by CVD nurses and referral of patients with severe hypertension and/or organ damage to a physician; information and communication technologies messages for healthy lifestyles, treatment adherence support and treatment refill reminders for hypertension patients; Commcare, a cloud-based health records system linked to short-message service (SMS)/voice messaging for treatment adherence, reminders and health messaging. ComHIP was evaluated under two scale-up scenarios: (1) ComHIP as currently implemented with support from international partners and (2) ComHIP under full local implementation. MAIN OUTCOME MEASURES: Incremental cost per disability-adjusted life-year (DALY) averted from a societal perspective over a time horizon of 10 years. RESULTS: ComHIP is unlikely to be a cost-effective intervention, with current ComHIP implementation and ComHIP under full local implementation costing on average US$12 189 and US$6530 per DALY averted, respectively. Results were robust to uncertainty analyses around model parameters. CONCLUSIONS: High overhead costs and high patient costs in ComHIP suggest that the societal costs of ensuring appropriate hypertension care are high and may not produce sufficient impact to achieve cost-effective implementation. However, these results are limited by the evidence quality of the effectiveness estimates, which comes from observational data rather than from randomised controlled study design.