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.