Grant Number:	1R01CA266966-01A1 Interpret this number
Primary Investigator:	Bickel, Warren
Organization:	Virginia Polytechnic Inst And St Univ
Project Title:	Experimental Tobacco Marketplace: Forecasting the Health Equity of Novel Tax Proposals
Fiscal Year:	2022

PROJECT SUMMARY/ABSTRACT The objective of this project is to forecast the impact of novel tobacco tax proposals in an evolving and complex tobacco marketplace, including how these proposals may interact with tobacco-related socioeconomic disparities and the introduction of low nicotine cigarettes (LNCs). Toward this end, we have developed the Experimental Tobacco Marketplace (ETM) to estimate, before implementation, the effects of potential policies on patterns of tobacco purchasing, including between-product substitution and poly-tobacco purchasing. The ETM places the mix of products, prices, and specific regulations under experimental control to provide estimates of policy impact under conditions that simulate “real-world” circumstances. Here we extend this method to examine the impact of four novel tax proposals that levy taxes across products based on either: (1) parity, (2) nicotine content, (3) potential for harm, or (4) the Food and Drug Administration designation as a modified-risk tobacco product. To accomplish these objectives, we will leverage our extensive experience in the behavioral economics of tobacco purchasing, in general, and with our unique and innovative ETM, in particular, to forecast the impact and health equity of these tax proposals. Specifically, Aim 1 will examine the effects of these four tax proposals on tobacco product purchasing in the ETM, including between-product substitution and poly-tobacco purchasing, in a laboratory-based sample of cigarette smokers. Aim 2 will examine the equity of these tax proposals by testing their effects in a nationally representative prospective sample of lower, medium and higher socioeconomic status smokers. Finally, Aim 3 will examine the impact of these tax proposals when LNCs are introduced to the market and when modeling a regulation that bans conventional cigarettes with higher nicotine content in a laboratory-based sample of cigarette smokers. Overall, this project is highly innovative and impactful because it prioritizes a clear translational path into public health policy and practice.




Predictors of smoking cessation outcomes identified by machine learning: A systematic review.
Authors: Bickel W.K. , Tomlinson D.C. , Craft W.H. , Ma M. , Dwyer C.L. , Yeh Y.H. , Tegge A.N. , Freitas-Lemos R. , Athamneh L.N. .
Source: Addiction neuroscience, 2023 Jun; 6, .
EPub date: 2023-01-31.
PMID: 37214256
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