Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Luke DA

PROVIDER: S-EPMC5388950 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Luke Douglas A DA Hammond Ross A RA Combs Todd T Sorg Amy A Kasman Matt M Mack-Crane Austen A Ribisl Kurt M KM Henriksen Lisa L

American journal of public health 20170501 5

<h4>Objectives</h4>To identify the behavioral mechanisms and effects of tobacco control policies designed to reduce tobacco retailer density.<h4>Methods</h4>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 ...[more]

PMID: 28398792

Similar Datasets

Project description:IntroductionStudies find differences in tobacco retailer density according to neighborhood sociodemographic characteristics, raising issues of social justice, but not all research is consistent.Aims and methodsThis study examined associations between tobacco retailer density and neighborhood sociodemographic characteristics in the United States at four timepoints (2000, 2007, 2012, and 2017) and investigated if associations remained stable over time. Data on tobacco retailers came from the National Establishment Time-Series Database. Adjusted log-linear models examined the relationship between retailer density and census tract sociodemographic characteristics (% non-Hispanic Black [Black], % Hispanic, % vacant housing units, median household income), controlling for percentage of youth, urbanicity, and US region. To examine whether the relationship between density and sociodemographic characteristics changed over time, additional models were estimated with interaction terms between each sociodemographic characteristic and year.ResultsTobacco retailer density ranged from 1.22 to 1.44 retailers/1000 persons from 2000 to 2017. There were significant, positive relationships between tobacco retailer density and the percentage of Black (standardized exp(b) = 1.05 [95% CI: 1.04% to 1.07%]) and Hispanic (standardized exp(b) = 1.06 [95% CI: 1.05% to 1.08%]) residents and the percentage of vacant housing units (standardized exp(b) =1.08 [95% CI: 1.07% to 1.10%]) in a census tract. Retailer density was negatively associated with income (standardized exp(b) = 0.84 [95% CI: 0.82% to 0.86%]). From 2000 to 2017, the relationship between retailer density and income and vacant housing units became weaker.ConclusionsDespite the weakening of some associations, there are sociodemographic disparities in tobacco retailer density from 2000 to 2017, which research has shown may contribute to inequities in smoking.ImplicationsThis study examines associations between tobacco retailer density and neighborhood sociodemographic characteristics in the United States at four timepoints from 2000 to 2017. Although some associations weakened, there are sociodemographic disparities in tobacco retailer density over the study period. Research suggests that sociodemographic disparities in retailer density may contribute to inequities in smoking. Findings from this study may help identify which communities should be prioritized for policy intervention and regulation.

Dataset Information

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

Objectives

Methods

Results

Conclusions

Publications

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

Similar Datasets

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