Combining econometric analysis and simulation modeling to evaluate Population-Based health policies for chronic disease prevention and control

OBJECTIVES: Population-based health policies play an important role in preventing and controlling chronic disease. Policymakers need to understand both the short- and long-term impacts of different policies to optimize resource allocation. The objective of this study is to develop a framework that combines econometric analysis and simulation modeling for a comprehensive evaluation of population-based health policies. STUDY DESIGN: Both econometric analysis and simulation modeling were used to evaluate the impact of a population-based health policy. METHODS: We identified a cohort of hypertensive patients from the 2011–2013 China Health and Retirement Longitudinal Study and fitted the data into our framework to evaluate the effectiveness of a community-based hypertension-screening program under the Essential Public Health Services (EPHS) policy on the future burden of cardiovascular disease in China. RESULTS: Using an econometric approach, we identified that the community-based hypertension screening program would lead to a 7.9% improvement in the rate of hypertension control. Using a validated simulation model, we further estimated that if the policy was fully implemented nationwide, it could avert 97,100 cases of myocardial infarction and 215,600 cases of stroke. The policy would cost $2131 on average to save 1 quality-adjusted life year over 10 years. CONCLUSIONS: This study proposed a framework integrating two different methods and assessing both short- and long-term impact of a population-based health policy. Through a case study, we demonstrated that combining econometric analysis and simulation modeling could provide policymakers with a more powerful tool to evaluate health policies for controlling chronic disease.