Studying the Impacts of Higher Taxes and Bans on Electronic Cigarettes to Improve Public Health
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.
Studieoversikt
Status
Forhold
Intervensjon / Behandling
Detaljert beskrivelse
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).
Studietype
Registrering (Antatt)
Fase
- Ikke aktuelt
Kontakter og plasseringer
Studiekontakt
- Navn: The Ohio State University Comprehensive Cancer Center
- Telefonnummer: 800-293-5066
- E-post: OSUCCCClinicaltrials@osumc.edu
Studiesteder
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Ohio
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Columbus, Ohio, Forente stater, 43210
- Ohio State University Comprehensive Cancer Center
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Ta kontakt med:
- Ce Shang, PhD
- Telefonnummer: 614-685-3741
- E-post: ce.shang@osumc.edu
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Hovedetterforsker:
- Ce Shang, PhD
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Deltakelseskriterier
Kvalifikasjonskriterier
Alder som er kvalifisert for studier
Tar imot friske frivillige
Kjønn som er kvalifisert for studier
Beskrivelse
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
Studieplan
Hvordan er studiet utformet?
Designdetaljer
- Primært formål: Forebygging
- Tildeling: Randomisert
- Intervensjonsmodell: Sekvensiell tildeling
- Masking: Ingen (Open Label)
Våpen og intervensjoner
Deltakergruppe / Arm |
Intervensjon / Behandling |
|---|---|
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Eksperimentell: 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). |
Ancillary studies Complete EC tax base, rate, nicotine level, and flavor VCEs
Andre navn:
Complete tiered tax condition VCEs
Andre navn:
Complete banned condition VCEs
Andre navn:
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Eksperimentell: 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). |
Ancillary studies Complete EC tax base, rate, nicotine level, and flavor VCEs
Andre navn:
Complete tiered tax condition VCEs
Andre navn:
Complete banned condition VCEs
Andre navn:
|
|
Eksperimentell: 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). |
Ancillary studies Complete EC tax base, rate, nicotine level, and flavor VCEs
Andre navn:
Complete tiered tax condition VCEs
Andre navn:
Complete banned condition VCEs
Andre navn:
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Hva måler studien?
Primære resultatmål
Resultatmål |
Tiltaksbeskrivelse |
Tidsramme |
|---|---|---|
|
Electronic cigarette (EC) use among adolescent and young adult (AYA) current/susceptible users (Aim 1)
Tidsramme: 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)
Tidsramme: 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)
Tidsramme: 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)
Tidsramme: 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)
Tidsramme: 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)
Tidsramme: 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)
Tidsramme: 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)
Tidsramme: 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)
Tidsramme: 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)
Tidsramme: 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
|
Samarbeidspartnere og etterforskere
Samarbeidspartnere
Etterforskere
- Hovedetterforsker: Ce Shang, PhD, Ohio State University Comprehensive Cancer Center
Publikasjoner og nyttige lenker
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Datoer for studieregistrering
Først innsendt
Først innsendt som oppfylte QC-kriteriene
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Siste oppdatering sendt inn som oppfylte QC-kriteriene
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Mer informasjon
Begreper knyttet til denne studien
Andre studie-ID-numre
- OSU-25157
- NCI-2026-02421 (Registeridentifikator: CTRP (Clinical Trial Reporting Program))
- R01CA303567 (U.S. NIH-stipend/kontrakt)
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