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Cardiovascular disease (CVD) is a global health problem and there has been an epidemiological transition of CVD from high income countries to low-middle income countries. In the case of Thailand, the prevalence of heart disease and stroke is increasing. In order to reduce the risk of CVD, the Ministry of Public Health in Thailand has implemented a number of primary CVD prevention strategies within the last decade. These strategies are being specifically implemented to address the future potential economic burden of increasing CVD. However, the economic impact of reducing multiple risk factors, at a population level in Thailand, in terms of health care costs is unclear. In order to plan for investment in public health interventions within finite resources, it is imperative that decision makers have sufficient information to identify the target populations and risk reduction strategies, and to assess the impact of these strategies on the population. This study aims to estimate the future prevalence of CVD in Thailand over the next 5-10 years and the potential economic and health benefits of strategies to reduce the population risk factors during this period. The mathematic CVD cost-offset model has been developed in this study in 7 stages. 1) Descriptive analysis of the CVD risk profile data from the 4th National Health Examination Survey (NHESIV) 2008-2009 data set in order to explore the association of CVD risk factors in Thailand. 2) Calculate the probability of future CVD event which applies the CVD risk prediction equation. 3) Estimate of the number of future CVD events. 4) Validation of the estimated number of annual CVD event with the actual CVD hospitalisation event in Thailand. 5) Calculate the cost of hospital admission due to CVD from the Universal Coverage Health Care Scheme (UC) data in 2009. 6) Estimate the burden of CVD in terms of the DALYs. 7) Estimate the impact of reducingCVD risk factors in different scenarios. The study outcomes being the number of hospitalisation cost savings, number of premature death savings, DALY savings and health care cost savings. The outcomes will also account for the uncertainty analysis. As indicated above, no studies currently exist that focus specifically on the mathematic model for estimating the future situation of CVD in Thailand. Therefore, this study represents an original contribution to that knowledge. The findings of this study will contribute to health policy by providing specific new knowledge and information regarding Thai CVD risk factors and the impact of the risk reduction which will assist health policy makers in the planning and future investment in prevention programs for CVD in Thailand. Moreover, it is expected that the finding from this research will establish a CVD prediction model for Thailand, and one which may be applicable and compatible to the Asia and Pacific regions.