Studying the Impacts of Higher Taxes and Bans on Electronic Cigarettes to Improve Public Health

May 6, 2026 updated by: Ce Shang, Ohio State University Comprehensive Cancer Center

Design Tiered Tax Rates for Electronic Cigarettes (ECs) Based on Their Appeals to Youth and Young Adults

This clinical trial studies whether imposing higher taxes and bans on electronic cigarettes (EC) with appealing features impacts tobacco use among current and susceptible adolescents and young adults (AYA) EC users and adults who use EC or are open to EC use. ECs are currently the most popular form of nicotine or tobacco product in the United States. Compared to burned cigarette products, ECs generally pose fewer short-term harms, making them a promising tool for lowering users' exposure to toxins and cancer-causing chemicals from smoking, promoting better public health outcomes. However, evidence shows that EC marketing has increased overall initiation into nicotine use among AYAs, and that EC users are at a higher risk of becoming smokers, which could have negative public health outcomes. Therefore, understanding the public health impact of EC use and regulation remains a major goal in tobacco control research. This trial studies different scenarios which impose higher taxes or bans on ECs with appealing features. Researchers hope that by studying participant responses to the different scenarios they may be able to identify which ones best discourage EC use among AYAs while promoting adult EC users to quit smoking, which may improve public health.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

PRIMARY OBJECTIVES:

I. Provide empirical evidence on how tiered EC taxes - imposing higher taxes on ECs with AYA appealing features - impact EC use, combustible tobacco use, and the prevalence of cross-border or illegal purchases.

II. Examine how tiered taxes on AYA-appealing features (flavors, product type, nicotine concentration) impact EC use and combustible tobacco smoking among current and susceptible AYA EC users. (Aim 1) III. Assess how tiered taxes on AYA-appealing features impact EC and combustible tobacco smoking among adult smokers who either use or are open to using ECs. (Aim 2) IV. Compare tiered EC taxes with sales bans on ECs with AYA-appealing features in terms of their impacts on tobacco use, cross-border shopping, and illegal EC purchases. (Aim 3)

OUTLINE: Participants are assigned to 1 of 3 aims.

AIMS 1 & 2: Participants complete volumetric choice experiments (VCEs) over 20 minutes on study with random assignment to: 1) Nicotine levels (low versus [vs.] high); 2) Flavors (fruit/sweet vs. ice vs. menthol/mint vs. tobacco); 3) EC tax bases (by product type vs. by flavor vs. by nicotine concentration), and 4) rate levels (status quo [equal rates] vs. 50% higher vs. 100% higher vs. 200% higher) among six different products (tanks, pods, disposables, cigarettes, cigars, and oral nicotine pouches [ONPs]) and opt-out options (none of the six products or quitting).

AIM 3: Participants are randomized to 1 of 2 groups.

GROUP 1: Participants complete VCEs over 20 minutes on study with random assignment to: 1) nicotine levels (low versus vs. high) and 2) flavors (fruit/sweet vs. ice vs. menthol/mint vs. tobacco) with optimal tiered tax conditions among four different products (preferred EC type, cigarettes, cigars, ONPs) and opt-out options (none of the six products or quitting).

GROUP 2: Participants complete VCEs over 20 minutes on study with random assignment to: 1) nicotine levels (low versus vs. high), 2) flavors (fruit/sweet vs. ice vs. menthol/mint vs. tobacco), and 3) purchasing sources (out-of-state legal vs. local/online illegal, vs. local legal) with banned conditions among four different products (preferred EC type, cigarettes, cigars, ONPs) and opt-out options (none of the six products or quitting).

Study Type

Interventional

Enrollment (Estimated)

3400

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • United States
    • Ohio
      • Columbus, Ohio, United States, 43210
        • Ohio State University Comprehensive Cancer Center
        • Contact:
        • Principal Investigator:
          • Ce Shang, PhD

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

Inclusion Criteria:

  • AYAs aged 15-24 who are daily or nondaily users of ECs and are not smoking in the past 30 days
  • AYA tobacco nonusers aged 15-24 who are susceptible to EC or tobacco use (i.e., curiosity about the product, intention to try it in the near future, and likely response if a best friend were to offer them the product)
  • Adults aged 18+ who are daily or nondaily users of ECs and combustible tobacco in the past 30 days
  • Adults aged 18+ who are daily or nondaily users of combustible tobacco in the past 30 days, not currently using ECs but are open to trying ECs

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Prevention
  • Allocation: Randomized
  • Interventional Model: Sequential Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: Aim 3 group 1 (tiered tax condition VCEs)
Participants complete VCEs over 20 minutes on study with random assignment to: 1) nicotine levels (low versus vs. high) and 2) flavors (fruit/sweet vs. ice vs. menthol/mint vs. tobacco) with optimal tiered tax conditions among four different products (preferred EC type, cigarettes, cigars, ONPs) and opt-out options (none of the six products or quitting).
Other: Survey Administration
Ancillary studies
Other: Discrete Choice Study
Complete EC tax base, rate, nicotine level, and flavor VCEs
Other Names:
  • DCE
  • Discrete Choice Experiment
  • Discrete Choice Task
Other: Discrete Choice Study
Complete tiered tax condition VCEs
Other Names:
  • DCE
  • Discrete Choice Experiment
  • Discrete Choice Task
Other: Discrete Choice Study
Complete banned condition VCEs
Other Names:
  • DCE
  • Discrete Choice Experiment
  • Discrete Choice Task
Experimental: Aim 3 group 2 (banned condition VCEs)
Participants complete VCEs over 20 minutes on study with random assignment to: 1) nicotine levels (low versus vs. high), 2) flavors (fruit/sweet vs. ice vs. menthol/mint vs. tobacco), and 3) purchasing sources (out-of-state legal vs. local/online illegal, vs. local legal) with banned conditions among four different products (preferred EC type, cigarettes, cigars, ONPs) and opt-out options (none of the six products or quitting).
Other: Survey Administration
Ancillary studies
Other: Discrete Choice Study
Complete EC tax base, rate, nicotine level, and flavor VCEs
Other Names:
  • DCE
  • Discrete Choice Experiment
  • Discrete Choice Task
Other: Discrete Choice Study
Complete tiered tax condition VCEs
Other Names:
  • DCE
  • Discrete Choice Experiment
  • Discrete Choice Task
Other: Discrete Choice Study
Complete banned condition VCEs
Other Names:
  • DCE
  • Discrete Choice Experiment
  • Discrete Choice Task
Experimental: Aims 1 & 2 (EC tax base and rate VCEs)
Participants complete VCEs over 20 minutes on study with random assignment to: 1) Nicotine levels (low vs. high); 2) Flavors (fruit/sweet vs. ice vs. menthol/mint vs. tobacco); 3) EC tax bases (by product type vs. by flavor vs. by nicotine concentration), and 4) rate levels (status quo [equal rates] vs. 50% higher vs. 100% higher vs. 200% higher) among six different products (tanks, pods, disposables, cigarettes, cigars, and oral nicotine pouches [ONPs]) and opt-out options (none of the six products or quitting).
Other: Survey Administration
Ancillary studies
Other: Discrete Choice Study
Complete EC tax base, rate, nicotine level, and flavor VCEs
Other Names:
  • DCE
  • Discrete Choice Experiment
  • Discrete Choice Task
Other: Discrete Choice Study
Complete tiered tax condition VCEs
Other Names:
  • DCE
  • Discrete Choice Experiment
  • Discrete Choice Task
Other: Discrete Choice Study
Complete banned condition VCEs
Other Names:
  • DCE
  • Discrete Choice Experiment
  • Discrete Choice Task

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Electronic cigarette (EC) use among adolescent and young adult (AYA) current/susceptible users (Aim 1)
Time Frame: Up to 2 years
Will examine how tiered taxes on AYA-appealing features (flavors, product type, nicotine concentration) impact EC use among current and susceptible AYA EC users. All hypotheses will be tested using regression analyses. Will use count data models, such as the Negative Binomial model, the Poisson model, and Multiple Discrete-Continuous Extreme Value (MDCEV) Model. Will use clustered sandwich estimators and consider mixed effects, random effects, and fixed effects in the modeling to account for the hierarchical structure (participant-choice set-purchase) and correlations among choices made by the same individual (repeated measures). Analyses will also control for sociodemographic characteristics such as age, sex, race/ethnicity, income, education, tobacco use histories/status, and purchase expenditures.
Up to 2 years
Combustible tobacco smoking among AYA current/susceptible users (Aim 1)
Time Frame: Up to 2 years
Will examine how tiered taxes on AYA-appealing features (flavors, product type, nicotine concentration) impact combustible tobacco smoking among current and susceptible AYA EC users. All hypotheses will be tested using regression analyses. Will use count data models, such as the Negative Binomial model, the Poisson model, and MDCEV Model. Will use clustered sandwich estimators and consider mixed effects, random effects, and fixed effects in the modeling to account for the hierarchical structure (participant-choice set-purchase) and correlations among choices made by the same individual (repeated measures). Analyses will also control for sociodemographic characteristics such as age, sex, race/ethnicity, income, education, tobacco use histories/status, and purchase expenditures.
Up to 2 years
EC use among adult smokers who use or are open to ECs (Aim 2)
Time Frame: Up to 2 years
Will assess how tiered taxes on AYA-appealing features (flavors, product type, nicotine concentration) impact EC smoking among adult smokers who either use or are open to using ECs. All hypotheses will be tested using regression analyses. Will use count data models, such as the Negative Binomial model, the Poisson model, and MDCEV Model. Will use clustered sandwich estimators and consider mixed effects, random effects, and fixed effects in the modeling to account for the hierarchical structure (participant-choice set-purchase) and correlations among choices made by the same individual (repeated measures). Analyses will also control for sociodemographic characteristics such as age, sex, race/ethnicity, income, education, tobacco use histories/status, and purchase expenditures.
Up to 2 years
Combustible tobacco smoking among adult smokers who use or are open to ECs (Aim 2)
Time Frame: Up to 2 years
Will assess how tiered taxes on AYA-appealing features (flavors, product type, nicotine concentration) impact combustible tobacco smoking among adult smokers who either use or are open to using ECs. All hypotheses will be tested using regression analyses. Will use count data models, such as the Negative Binomial model, the Poisson model, and MDCEV Model. Will use clustered sandwich estimators and consider mixed effects, random effects, and fixed effects in the modeling to account for the hierarchical structure (participant-choice set-purchase) and correlations among choices made by the same individual (repeated measures). Analyses will also control for sociodemographic characteristics such as age, sex, race/ethnicity, income, education, tobacco use histories/status, and purchase expenditures.
Up to 2 years
Tobacco use among AYA EC current/susceptible users (Aim 3)
Time Frame: Up to 2 years
Will compare tiered EC taxes with sales bans on ECs with AYA-appealing features in terms of their impacts on tobacco use. All hypotheses will be tested using regression analyses. Will use count data models, such as the Negative Binomial model, the Poisson model, and MDCEV Model. Will use clustered sandwich estimators and consider mixed effects, random effects, and fixed effects in the modeling to account for the hierarchical structure (participant-choice set-purchase) and correlations among choices made by the same individual (repeated measures). Analyses will also control for sociodemographic characteristics such as age, sex, race/ethnicity, income, education, tobacco use histories/status, and purchase expenditures.
Up to 2 years
Cross-border shopping among AYA EC current/susceptible users (Aim 3)
Time Frame: Up to 2 years
Will compare tiered EC taxes with sales bans on ECs with AYA-appealing features in terms of their impacts on cross-border shopping. In order to assess differences in the proportion of cross-border EC purchases between the two randomization groups, the occurrence of a cross-border EC purchase will be modeled as a binary variable. All hypotheses will be tested using regression analyses. Will use count data models, such as the Negative Binomial model, the Poisson model, and MDCEV Model. Will use clustered sandwich estimators and consider mixed effects, random effects, and fixed effects in the modeling to account for the hierarchical structure (participant-choice set-purchase) and correlations among choices made by the same individual (repeated measures). Analyses will also control for sociodemographic characteristics such as age, sex, race/ethnicity, income, education, tobacco use histories/status, and purchase expenditures.
Up to 2 years
Illegal EC purchases among AYA EC current/susceptible users (Aim 3)
Time Frame: Up to 2 years
Will compare tiered EC taxes with sales bans on ECs with AYA-appealing features in terms of their impacts on illegal EC purchases. In order to assess differences in the proportion of illegal EC purchases between the two randomization groups, the occurrence of an illegal EC purchase will be modeled as a binary variable. All hypotheses will be tested using regression analyses. Will use count data models, such as the Negative Binomial model, the Poisson model, and MDCEV Model. Will use clustered sandwich estimators and consider mixed effects, random effects, and fixed effects in the modeling to account for the hierarchical structure (participant-choice set-purchase) and correlations among choices made by the same individual (repeated measures). Analyses will also control for sociodemographic characteristics such as age, sex, race/ethnicity, income, education, tobacco use histories/status, and purchase expenditures.
Up to 2 years
Tobacco use among adult smokers who use or are open to ECs (Aim 3)
Time Frame: Up to 3 years
Will compare tiered EC taxes with sales bans on ECs with AYA-appealing features in terms of their impacts on tobacco use. All hypotheses will be tested using regression analyses. Will use count data models, such as the Negative Binomial model, the Poisson model, and MDCEV Model. Will use clustered sandwich estimators and consider mixed effects, random effects, and fixed effects in the modeling to account for the hierarchical structure (participant-choice set-purchase) and correlations among choices made by the same individual (repeated measures). Analyses will also control for sociodemographic characteristics such as age, sex, race/ethnicity, income, education, tobacco use histories/status, and purchase expenditures.
Up to 3 years
Cross-border shopping among adult smokers who use or are open to ECs (Aim 3)
Time Frame: Up to 2 years
Will compare tiered EC taxes with sales bans on ECs with AYA-appealing features in terms of their impacts on cross-border shopping. In order to assess differences in the proportion of cross-border EC purchases between the two randomization groups, the occurrence of a cross-border EC purchase will be modeled as a binary variable. All hypotheses will be tested using regression analyses. Will use count data models, such as the Negative Binomial model, the Poisson model, and MDCEV Model. Will use clustered sandwich estimators and consider mixed effects, random effects, and fixed effects in the modeling to account for the hierarchical structure (participant-choice set-purchase) and correlations among choices made by the same individual (repeated measures). Analyses will also control for sociodemographic characteristics such as age, sex, race/ethnicity, income, education, tobacco use histories/status, and purchase expenditures.
Up to 2 years
Illegal EC purchases among adult smokers who use or are open to ECs (Aim 3)
Time Frame: Up to 3 years
Will compare tiered EC taxes with sales bans on ECs with AYA-appealing features in terms of their impacts on illegal EC purchases. In order to assess differences in the proportion of illegal EC purchases between the two randomization groups, the occurrence of an illegal EC purchase will be modeled as a binary variable. All hypotheses will be tested using regression analyses. Will use count data models, such as the Negative Binomial model, the Poisson model, and MDCEV Model. Will use clustered sandwich estimators and consider mixed effects, random effects, and fixed effects in the modeling to account for the hierarchical structure (participant-choice set-purchase) and correlations among choices made by the same individual (repeated measures). Analyses will also control for sociodemographic characteristics such as age, sex, race/ethnicity, income, education, tobacco use histories/status, and purchase expenditures.
Up to 3 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Ohio State University Comprehensive Cancer Center

Investigators

  • Principal Investigator: Ce Shang, PhD, Ohio State University Comprehensive Cancer Center

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

Helpful Links

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

August 1, 2026

Primary Completion (Estimated)

December 31, 2027

Study Completion (Estimated)

December 31, 2027

Study Registration Dates

First Submitted

May 6, 2026

First Submitted That Met QC Criteria

May 6, 2026

First Posted (Actual)

May 12, 2026

Study Record Updates

Last Update Posted (Actual)

May 12, 2026

Last Update Submitted That Met QC Criteria

May 6, 2026

Last Verified

April 1, 2026

More Information

Terms related to this study

Other Study ID Numbers

  • OSU-25157
  • NCI-2026-02421 (Registry Identifier: CTRP (Clinical Trial Reporting Program))

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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