Daniel Bu: Using health policy simulation models to forecast the health and economic impact of a sugar-sweetened beverage tax. Microsimulation models (MSMs) for health outcomes simulate individual event histories associated with key components of a disease process; these simulated life histories can be aggregated to estimate population-level effects of treatment on disease outcomes and are increasingly being used to provide information to guide health policy decisions to meaningfully impact the health of society. For his thesis research, Daniel Bu, candidate for MD/MSCR 2021 and current NRSA TL1 Scholar, has used and adapted a previously validated microsimulation Cardiovascular Disease Policy Model for the study of public policy impact within R 3.6.1. He works with the Health Policy Modeling Lab to combine nonlinear behavioral economic insights derived from agent-based modeling to individual-level simulations of policy impact aggregated in the hundreds of thousands in validated models. He has used this approach to evaluate the impact of specific tax policies to reduce sugar consumption. Sugar-sweetened beverages (SSB) are currently the single largest source of added sugar in the US diet, and national consumption remains high. Evidence suggests that a high sugar consumption increases the risk of coronary heart disease, stroke, and diabetes. To date, excise taxes have been proposed by international bodies such as the WHO, and implemented in several US jurisdictions. While reductions in SSB consumption have been reported in several places where taxes have been implemented, it is unclear what the long term health and economic impact an SSB tax could have within the demographically and socioeconomically diverse NYC. Additionally, the impact of varying tax structures remains unknown. The results of his studies to date, have been selected as a podium presentation at the 2020 American Heart Association Epidemiology Annual Meeting, an oral presentation at the 2020 New York City Epidemiology Forum (NYCEF) and is currently under review for publication. Under this NRSA TL1 grant, Daniel has also published and presented microsimulation models in the cost-effective use of innovation in otolaryngology, and the efficient allocation of global surgery resources within developing health systems. These studies have significant potential to improve human health through rationale scientifically grounded health policy development and implementation. Daniel’s mentor is Yan Li, PhD, Associate Professor and the Director of the Health Policy Modeling Lab in the Department of Population Health Science and Policy.