Tobacco Town: Computational Modeling of Policy Options to Reduce Tobacco Retailer Density
Tobacco Town: Computational Modeling of Policy Options to Reduce Tobacco Retailer Density
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DOI：
10.2105/ajph.2017.303685
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发表时间：
2017-05-01
影响因子：
12.7
通讯作者：
Henriksen, Lisa
中科院分区：
文献类型：
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作者：
Luke, Douglas A.;Hammond, Ross A.;Henriksen, Lisa
目标.研究旨在降低烟草零售商密度的烟草控制政策的行为机制和效果。我们开发了烟草城基于代理的仿真模型来研究4种类型的零售商减少策略：（1）随机零售商减少，（2）限制零售商类型，（3）限制零售商到学校的距离，（4）限制零售商之间的距离。该模型考察了这些政策单独和组合在4种不同类型城镇中的影响，这些城镇由2个人口密度水平（城市与郊区）和2个收入水平（较高与较低）定义。模型结果表明，零售商密度的降低有可能通过推高搜索和购买成本来降低烟草产品的可及性。政策效应因城镇类型而异：邻近政策在密集的城市城镇效果更好，而零售商类型和随机零售商减少在不太密集的郊区环境中效果更好。全面的零售商密度降低政策具有很好的潜力，以减少社区烟草使用的公共卫生负担。
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.