Development of a computational modeling laboratory for examining tobacco control policies: Tobacco Town. (January 2020)

• Record Type: Journal Article
• Authors: Hammond, Ross A.; Combs, Todd B.; Mack-Crane, Austen; Kasman, Matt; Sorg, Amy; Snider, Doneisha; Luke, Douglas A.
• Is Part Of: Health & place. Volume 61(2020:Jan.)
• Publication Date: 2020-01
• Subjects: Tobacco control -- Tobacco retailer density -- Agent-based modeling -- Chronic disease prevention -- Systems science; Health -- Social aspects -- Periodicals; Health services accessibility -- Periodicals; Public health -- Periodicals; Political planning -- Periodicals; Social medicine -- Periodicals; Epidemiology -- Periodicals; Health Policy -- Periodicals; Health Services Accessibility -- Periodicals; Public Health -- Periodicals; Public Policy -- Periodicals; Sociology, Medical -- Periodicals; Épidémiologie -- Périodiques; Politique sanitaire -- Périodiques; Santé, Services de -- Accessibilité -- Périodiques; Health services accessibility; Health -- Social aspects; Political planning; Public health; Social medicine; Periodicals; 613
• DOI: 10.1016/j.healthplace.2019.102256
• Languages: English
• ISSNs: 1353-8292

Abstract

Abstract: A key focus of recent policy efforts to curb tobacco product usage has been the role of place—specifically the density of retail and advertising and the resulting spatial pattern of access and exposure for consumers. Policies can alter the environment by reducing density or shifting distribution of tobacco retail and thus limiting access and exposure. Since little empirical evidence exists for the potential impact of these policies across potentially heterogeneous places, we develop and apply an original spatial computational model to simulate place-based retail tobacco control policies. The model is well-grounded in theory and available empirical evidence. We apply the model in four representative settings to demonstrate the utility of this approach as a policy laboratory, to develop general insights on the relationship between retailer density, retail interventions, and tobacco costs incurred by consumers, and to provide a framework to guide future modeling and empirical studies. Our results suggest that the potential impact on costs of reducing tobacco retailer density are highly dependent on context. Projected impacts are also influenced by assumptions made about agent (smoker) purchasing decision-making processes. In the absence of evidence in this area, we tested and compared three alternative decision rules; these interact with environmental properties to produce different results. Agent properties, namely income and cigarettes per day, also shape purchasing patterns before and after policy interventions. We conclude that agent-based modeling in general, and Tobacco Town specifically, hold much potential as a platform for testing and comparing the impact of various retail-based tobacco policies across different communities. Initial modeling efforts uncover important gaps in both data and theory and can provide guidance for new empirical studies in tobacco control. Highlights: ABM holds much potential for testing and comparing the impact of retail-based tobacco policies across different communities. There is a generally nonlinear relationship between tobacco retailer density and total costs for cigarettes. Context matters; tobacco control via retailer density reduction has varying results across different communities.