Tobaco Use

Modeling the Effects of E-cigarettes on Smoking Behavior

Implications for Future Adult Smoking Prevalence

Cherng, Sarah T.; Tam, Jamie; Christine, Paul J.; Meza, Rafael

Author Information

From the aDepartment of Epidemiology, bCenter for Social Epidemiology and Population Health, and cDepartment of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor, MI.

Submitted 18 September 2015; accepted 12 April 2016.

S.T.C. acknowledges funding support from the Office of Behavioral and Social Sciences Research/National Institutes of Health Contract HHSN276200800013C, and the University of Michigan Department of Epidemiology. This publication was also partially made possible by Grants U01CA152956 and U01CA199284 from the National Cancer Institute as part of the Cancer Intervention and Surveillance Modeling Network (CISNET), which provided the smoking initiation, cessation, and mortality rates that informed the baseline model. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Cancer Institute or CISNET.

The authors report no conflicts of interest.

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com).

Correspondence: Rafael Meza, 1415 Washington Heights SPH-II 5533, Ann Arbor, MI 48109. E-mail: [email protected].

Epidemiology 27(6):p 819-826, November 2016. | DOI: 10.1097/EDE.0000000000000497

Abstract

Background: 

Electronic cigarette (e-cigarette) use has increased rapidly in recent years. Given the unknown effects of e-cigarette use on cigarette smoking behaviors, e-cigarette regulation has become the subject of considerable controversy. In the absence of longitudinal data documenting the long-term effects of e-cigarette use on smoking behavior and population smoking outcomes, computational models can guide future empirical research and provide insights into the possible effects of e-cigarette use on smoking prevalence over time.

Methods: 

Agent-based model examining hypothetical scenarios of e-cigarette use by smoking status and e-cigarette effects on smoking initiation and smoking cessation.

Results: 

If e-cigarettes increase individual-level smoking cessation probabilities by 20%, the model estimates a 6% reduction in smoking prevalence by 2060 compared with baseline model (no effects) outcomes. In contrast, e-cigarette use prevalence among never smokers would have to rise dramatically from current estimates, with e-cigarettes increasing smoking initiation by more than 200% relative to baseline model estimates to achieve a corresponding 6% increase in smoking prevalence by 2060.

Conclusions: 

Based on current knowledge of the patterns of e-cigarette use by smoking status and the heavy concentration of e-cigarette use among current smokers, the simulated effects of e-cigarettes on smoking cessation generate substantially larger changes to smoking prevalence compared with their effects on smoking initiation.

Copyright © 2016 Wolters Kluwer Health, Inc. All rights reserved.

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