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Modelling the impact of menthol sales restrictions and retailer density reduction policies: insights from tobacco town Minnesota
  1. Todd B Combs1,
  2. Virginia R McKay1,
  3. Joseph Ornstein2,
  4. Margaret Mahoney3,
  5. Kerry Cork4,
  6. Deena Brosi5,
  7. Matt Kasman6,
  8. Benjamin Heuberger6,
  9. Ross A Hammond2,6,
  10. Douglas Luke2
  1. 1 Center for Public Health Systems Science, Washington University in St Louis, St Louis, Missouri, USA
  2. 2 Brown School, Washington University in St Louis, St Louis, Missouri, USA
  3. 3 Minneapolis, Minnesota, USA
  4. 4 Public Health Law Center, Mitchell Hamline School of Law, Saint Paul, Minnesota, USA
  5. 5 Colorado School of Public Health, University of Colorado at Denver—Anschutz Medical Campus, Aurora, Colorado, USA
  6. 6 Center on Social Dynamics and Policy, Brookings Institution, Washington, DC, USA
  1. Correspondence to Dr Todd B Combs, Center for Public Health Systems Science, Washington University in St. Louis, St. Louis, Missouri, USA; toddcombs{at}wustl.edu

Abstract

Introduction Tobacco control policies focused on the retail environment have the potential to reduce tobacco use and tobacco-related health disparities through increasing direct and indirect costs. Recently, national and subnational governments have begun to restrict the sale of menthol products and reduce tobacco retailer density.

Methods We developed an agent-based model to project the impact of menthol cigarette sales restrictions and retailer density reduction policies for six types of communities and three priority populations. During each simulated day, agents smoke cigarettes, travel in the community and make purchase decisions—whether, where and which product type to purchase—based on a combination of their own properties and the current retail environment.

Results Of the policies tested, restricting all cigarette sales or menthol cigarette sales to tobacco specialty shops may have the largest effect on the total (direct and indirect) costs of purchasing cigarettes. Coupling one of these policies with one that establishes a minimum distance between tobacco retailers may enhance the impact. Combining these policies could also make the costs of acquiring cigarettes more equal across communities and populations.

Discussion Our simulations revealed the importance of context, for example, lower income communities in urban areas begin with higher retailer density and may need stronger policies to show impact, as well as the need to focus on differential effects for priority populations, for example, combinations of policies may equalise the average distance travelled to purchase. Adapting and combining policies could enhance the sustainability of policy effects and reduce tobacco use.

  • public policy
  • disparities
  • prevention

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Footnotes

  • Contributors TBC led data collection for input data, analyses of model results and paper development and writing. VRM worked on analyses of model results and paper development, organisation and writing.JO, MK and BH participated in model development and execution and in paper writing. MM and KC participated in data collection for input data and paper writing. DB aided data collection for input data, analyses of model results and paper writing. RAH led model development and participated in model execution and paper writing. DL oversaw data analyses and participated in paper writing.

  • Funding This study was funded by ClearWay Minnesota (Grant number: RC-2017-0010).

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data may be obtained from a third party and are not publicly available.

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