DYNAMIC POPULATION MODELING FOR TOBACCO HARM REDUCTION - A TIME-SERIES APPROACH
Author(s)
Xavier Cahours, PhD1, Thomas Verron, PhD1, Mengran Guo, MSc1, Libby Clarke, MSc2, Thomas Nahde, PhD2;
1Imperial Brands - SEITA, Paris, France, 2Imperial Brands, Bristol, United Kingdom
1Imperial Brands - SEITA, Paris, France, 2Imperial Brands, Bristol, United Kingdom
OBJECTIVES: Dynamic Population Models (DPM) are widely used to assess the potential public health benefit of tobacco harm-reduction strategies. Traditional DPM relies on longitudinal transition data, which is often unavailable in many markets. To address this limitation, we developed an alternative approach that uses time-series prevalence trends to forecast future smoking and vaping patterns. The objective of this study is to validate a time-series-based DPM using U.S. data, where longitudinal transition information is available, and then apply the model to assess the health impact of Electronic Vapor Products (EVPs) in a market lacking such data, specifically the UK.
METHODS: We compared two scenarios: (1) a cigarette-only market and (2) coexistence of cigarettes and EVPs. Historical prevalence data were modeled using the Prophet forecasting model to project future smoking and vaping trends. These projections were combined with relative risk estimates for smoking and vaping to estimate mortality. Validation was performed using U.S. NHIS data and mortality estimates from a validated U.S. model accounting for age, sex, and smoking status. Outputs from the time-series model were compared with those from a reference transition-based model to confirm accuracy and computational efficiency. After validation, the model was applied to UK prevalence trends.
RESULTS: Our new time-series model accurately replicated U.S. prevalence trends and mortality outcomes. Applied to the UK, the model predicts that EVP availability could prevent 0.36-0.85 million premature deaths by 2040, with up to 1.53 million deaths averted under higher switching assumptions. The greatest benefit was observed for lung cancer, followed by heart disease and stroke.
CONCLUSIONS: EVPs, as a potentially less harmful alternative to cigarettes, could significantly reduce smoking-related mortality. This time-series-based DPM offers a practical, scalable solution for harm-reduction assessment in data-limited settings and supports rapid policy evaluation.
METHODS: We compared two scenarios: (1) a cigarette-only market and (2) coexistence of cigarettes and EVPs. Historical prevalence data were modeled using the Prophet forecasting model to project future smoking and vaping trends. These projections were combined with relative risk estimates for smoking and vaping to estimate mortality. Validation was performed using U.S. NHIS data and mortality estimates from a validated U.S. model accounting for age, sex, and smoking status. Outputs from the time-series model were compared with those from a reference transition-based model to confirm accuracy and computational efficiency. After validation, the model was applied to UK prevalence trends.
RESULTS: Our new time-series model accurately replicated U.S. prevalence trends and mortality outcomes. Applied to the UK, the model predicts that EVP availability could prevent 0.36-0.85 million premature deaths by 2040, with up to 1.53 million deaths averted under higher switching assumptions. The greatest benefit was observed for lung cancer, followed by heart disease and stroke.
CONCLUSIONS: EVPs, as a potentially less harmful alternative to cigarettes, could significantly reduce smoking-related mortality. This time-series-based DPM offers a practical, scalable solution for harm-reduction assessment in data-limited settings and supports rapid policy evaluation.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
EPH227
Topic
Epidemiology & Public Health
Topic Subcategory
Public Health
Disease
No Additional Disease & Conditions/Specialized Treatment Areas