Keywords: Monte Carlo simulation, reduced risk tobacco products, population health
Draft guidance for Modified Risk Tobacco Product (MRTP) applications to the US FDA suggests computational modeling to help assess whether the new product will fulfill the requirement to benefit the health of the population as a whole.
A Monte Carlo simulation model was developed with public data to project the effect on tobacco use and mortality of a reduced-risk tobacco product. Though designed to simulate a full population, it can follow a single birth cohort as well. The model generates random individual product use histories, including cigarettes per day (CPD), to project deaths through year 2100 in a population with versus without the new product. Smokers and new-product users may transition to and from dual use, which affects CPD and cigarette quit rates. The Excess Relative Risk (ERR) experienced by users of the product relative to cigarette smokers is assumed to decay exponentially after users quit or reduce CPD, and to increase in a similar way after they initiate or add a tobacco product. Probabilistic analysis uses random draws of input parameters from specified distributions to simulate distributions around output measures, such as avoided premature deaths (APD).
Hypothetical scenarios show long-run mortality is sensitive to ERR and to the extent of reduction of conventional cigarette smoking by dual users. APD and life-years gained provide more information than the difference in total deaths with versus without the new product, which slowly approaches zero in many scenarios.
The individual simulation approach provides flexibility in capturing the relation of individual product use history to mortality. A long time horizon is important to capture slowly developing effects on death rates. Since population health effects are uncertain and sensitive to patterns of new product uptake, especially dual use, more real-world data is needed to clarify these patterns.