Defense Date

2015

Document Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Pharmaceutical Sciences

First Advisor

David A. Holdford

Abstract

Introduction: Cigarette smoking is associated with lung cancer, cardiovascular disease, and chronic respiratory conditions. It is responsible for high mortality and morbidity risk in the US population. Smokers find sudden quitting difficult and it is reported that a large number of unassisted quitting attempts are eventually unsuccessful. Electronic cigarette or e-cig is a novel battery-driven, nicotine delivery product, currently being used as a smoking cessation tool by current and former smokers. Since its resemblance to a conventional cigarette, and its non-combustible nature, e-cig use has risen exponentially in the last few years. To address such public health issues, the US FDA is working on formulating regulations to manufacture, market, and distribute e-cigs has called for research evidence on the long term use of e-cig use. Objective: The objective of this study was to develop and validate a Discrete Event Simulation model to simulate the electronic cigarette (e-cig) use behavior, and to estimate the long term e-cig use prevalence, in different groups of the US population. Methods: The model population was generated from analyzing the National Health Interview Survey data from 2011-2013. The population was categorized into current, recent former, late former and never smokers. Population birth rates and death rates were applied using the 2012 US Census Bureau data. Model parametrization, transition probabilities and e-cig related risks were obtained and applied using cross sectional survey and longitudinal e-cig studies done on US population. The model was run for the period of 15 years and e-cig use prevalence at the end of the simulation period was estimated. Each simulation was replicated 100 times using Monte Carlo simulation approach. Model validation was performed by the use of null and extreme input values (internal validation), examining programing codes (debugging), verification by tobacco science and system analysis experts (structural and technical validation), comparison of model’s first year results with CDC reports (external validation). Conclusion: Total projected e-cig prevalence in the US population at the end of simulation of period was found to be around 19%. The results showed a gradual reduction in the number of conventional cigarette smokers and an increase in the e-cig users over the simulation period. Highest e-cig users were old, male, white and had less than high school level education. Sensitivity analyses of various model parameters showed that the e-cig prevalence was most sensitive to the impact and timing of policy implementation. As a novel nicotine delivery system, e-cigs are rapidly gaining acceptance in the US and recent reports have shown an exponential rise in the popularity of e-cig among minors and young adults. Our research provides empirical evidence that can be used by the scientific community and regulatory bodies to formulate regulations for marketing and sales of e-cigs in various sections of the population, where the prevalence is expected to rise in future. Our study can also guide the policy makers to introduce relevant policies at specific time points when the e-cig use is expected to rise.

Rights

© The Author

Is Part Of

VCU University Archives

Is Part Of

VCU Theses and Dissertations

Date of Submission

6-24-2015

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