||1R01CA266966-01A1 Interpret this number
||Virginia Polytechnic Inst And St Univ
||Experimental Tobacco Marketplace: Forecasting the Health Equity of Novel Tax Proposals
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.
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, Craft W.H.
, Ma M.
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, Yeh Y.H.
, Tegge A.N.
, Freitas-Lemos R.
, Athamneh L.N.
Addiction neuroscience, 2023 Jun; 6, .