Cost-effectiveness analysis (CEA) is widely used as a tool for prioritizing health interventions, particularly in low- and middle-income settings. However, a major drawback of CEA is the omission of healthcare seeking behavior of the underlying population. Secondly, CEA focuses on health interventions, ignoring the heterogeneous mechanisms across populations or regions that determine the delivery of the interventions, and its quality. It identifies the efficient interventions, but is largely silent on how the policymakers can scale up the selected interventions. Finally, CEA is limited to analyzing health outcomes, and does not consider additional economic benefits of health interventions. On the other hand, benefit-cost analysis (BCA), although more comprehensive, are often unattractive to policymakers due to the computational complexities involved. In this paper, we use a new methodology that aims to strike a balance between CEA and BCA. We develop an agent-based simulation model, called the Disease Control Priorities Simulation (DCPSim) model, using India as a case study. We examine various interventions for the secondary prevention of heart attack (acute myocardial infarction), e.g. aspirin, polypill, and a reduction in the response time from symptom to treatment. We use data from the District Level Household Survey (2007-08) and National Sample Survey (2004) of India, to create the underlying population which is used in the simulation process. The model incorporates demographic and socioeconomic characteristics of the agents, along with several interdependencies related to the disease, the interventions, and the quality of healthcare system which determines effective delivery as well as the demand for care. We estimate the effect of the interventions on health outcomes, and financial risk protection outcomes such as out of pocket expenditure averted, value of insurance provided, and the distribution of these benefits across income groups.