Tobacco Town: Computational Modeling of Policy Options to Reduce Tobacco Retailer Density

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Objectives. To identify the behavioral mechanisms and effects of tobacco control policies designed to reduce tobacco retailer density.

Methods. We developed the Tobacco Town agent-based simulation model to examine 4 types of retailer reduction policies: (1) random retailer reduction, (2) restriction by type of retailer, (3) limiting proximity of retailers to schools, and (4) limiting proximity of retailers to each other. The model examined the effects of these policies alone and in combination across 4 different types of towns, defined by 2 levels of population density (urban vs suburban) and 2 levels of income (higher vs lower).

Results. Model results indicated that reduction of retailer density has the potential to decrease accessibility of tobacco products by driving up search and purchase costs. Policy effects varied by town type: proximity policies worked better in dense, urban towns whereas retailer type and random retailer reduction worked better in less-dense, suburban settings.

Conclusions. Comprehensive retailer density reduction policies have excellent potential to reduce the public health burden of tobacco use in communities.

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Douglas A. Luke, PhD, Ross A. Hammond, PhD, Todd Combs, PhD, Amy Sorg, MPH, Matt Kasman, PhD, Austen Mack-Crane, BA, Kurt M. Ribisl, PhD, and Lisa Henriksen, PhDDouglas A. Luke, Todd Combs, and Amy Sorg are with the Center for Public Health System Science, George Warren Brown School of Social Work, Washington University in St Louis, St Louis, MO. Ross A. Hammond, Matt Kasman, and Austen Mack-Crane are with Center on Social Dynamics and Policy, Brookings Institution, Washington, DC. Kurt M. Ribisl is with the Gillings School of Global Public Health, University of North Carolina, Chapel Hill. Lisa Henriksen is with Stanford Prevention Research Center, Stanford University, School of Medicine, Stanford, CA. “Tobacco Town: Computational Modeling of Policy Options to Reduce Tobacco Retailer Density”, American Journal of Public Health 107, no. 5 (May 1, 2017): pp. 740-746.

PMID: 28398792