Tobacco use continues to be the greatest cause of preventable death and disease in the US, and socioeconomic disparities persist in many communities and populations. New and innovative tobacco control interventions focused on the retail environment are gaining ground, and aim to reduce the pervasiveness of tobacco products and eliminate disparities, chiefly through one of three mechanisms: place (e.g., lowering tobacco retailer density), price (e.g., curbing discounts), or product availability (e.g., limiting flavored tobacco products). However, the evidence supporting the effectiveness of these strategies is just emerging. Developing a strong evidence base for retail tobacco control is a priority for practitioners. While prospective experimental studies to assess these interventions are difficult, if not impossible, to conduct, computational modeling is poised to help fill the evidence gap. The goal of the proposed study is to develop a computational agent-based model to examine and compare the potential impact of various retail tobacco control strategies across different community contexts. The study has three primary aims. 1) Develop Tobacco Town, an agent-based model that represents community environments with individual current and potential tobacco users. The model will incorporate realistic geographic and commercial spaces, individual tobacco use behaviors (e.g., initiation, cessation, purchase), and retailer practices (e.g., pricing and product availability). 2) Use the model as a simulated laboratory to implement innovative strategies to better understand how each might impact smoking-related behaviors. We will also consider the strategies in terms of vulnerable populations, specifically racial/ethnic and sexual minorities and low-income residents. 3) Tailor Tobacco Town to represent diverse neighborhoods in 10-12 large urban- suburban areas in the US to examine potential context-specific effects of different interventions. While helping to build the evidence base for innovative tobacco control strategies, the Tobacco Town model will also enhance the relevance of this evidence through simulating various interventions in different contexts. Finally, the model and its results will advance the use of computational approaches to tobacco control and reducing tobacco use, and public health more broadly.

Public Health Relevance

/Public Health Relevance Tobacco use continues to be one of the greatest causes of preventable disease in the US, and socioeconomic disparities manifest themselves in higher smoking rates in communities with higher tobacco retailer concentration and cheaper tobacco products. While new tobacco control strategies focused on the retail environment are taking hold, the evidence base for these interventions is lacking, and traditional experimental studies of their impacts are often impossible. Through agent-based modeling, the proposed study will help public health strategy development by examining and comparing various innovative interventions and their potential impacts on reduced tobacco product purchasing and consumption across diverse community contexts.

National Institute of Health (NIH)
National Cancer Institute (NCI)
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Special Emphasis Panel (ZCA1)
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University of North Carolina Chapel Hill
Chapel Hill
United States
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