Abstract
Objective:
We aimed to examine the individual and combined associations of e-cigarette policies with e-cigarette use among U.S. adolescents and young adults aged 12-20 years.
Methods:
We used data from Waves 7 (January 2022-April 2023) and 7.5 (April 2023-December 2023, conducted approximately one year after completion of the Wave 7 survey) of the Population Assessment of Tobacco and Health (PATH) study (N=11,418 U.S. adolescents and adults aged 12-20 years) and state-level e-cigarette policy data on flavor bans, e-cigarette-inclusive smoke-free policies, excise tax, product packaging, and retail licensing. We conducted multivariable logistic regression analyses to examine the individual and combined associations between e-cigarette policies and lifetime, past 30-day, and frequent e-cigarette use at Wave 7, and initiation of e-cigarette use at Wave 7.5 among those who reported no lifetime e-cigarette use at Wave 7.
Results:
Flavor bans were associated with lower odds of lifetime e-cigarette use (AOR=0.69, 95% CI=0.55, 0.85). E-cigarette-inclusive smoke-free policies had significant associations with lifetime and frequent e-cigarette use (AOR range=0.56-0.78, ps<.05). The individual associations between e-cigarette excise taxes, product packaging, and retail licensing policies and underage e-cigarette use were not statistically significant. The combined e-cigarette policies scale was linked to reduced odds of lifetime e-cigarette use (AOR=0.91, 95% CI=0.87, 0.96).
Conclusions:
Comprehensive state-level e-cigarette policies, including but not limited to flavor bans and e-cigarette-inclusive smoke-free policies, may be crucial in mitigating e-cigarette use among adolescents and young adults, thereby providing critical evidence to inform future policy initiatives in the regulatory landscape of e-cigarettes.
Keywords: policy, adolescents, young adults, electronic cigarettes
Introduction
Electronic cigarettes (hereafter, e-cigarettes) are the most commonly used nicotine/tobacco product among adolescents and young adults (AYAs) in the United States (U.S.) and remain a pressing public health concern (Arrazola et al., 2025; Jamal et al., 2024). Recent data from the 2024 National Youth Tobacco Survey indicate that approximately 1.21 million high school students (7.8%) and 410,000 middle school students (3.5%) currently use e-cigarettes (Jamal et al., 2024). The nicotine present in e-cigarettes poses significant risks, particularly during adolescence, due to its addictive nature and potential long-term adverse effects on brain development (U.S. Department of Health and Human Services [DHHS], 2016). Furthermore, while at much lower levels than combustible cigarette use, e-cigarette use exposes young individuals to a myriad of harmful chemicals, including carcinogens and ultrafine particles that may cause lung damage (National Academies of Sciences, Engineering, and Medicine [NASEM], 2018). Evidence suggests that e-cigarette use is associated with an increased risk of anxiety, depression, suicidality, sleep disturbances, and various respiratory, circulatory, and nervous system problems (Burt & Li, 2020; King et al., 2020; Lee & Lee, 2019; Tobore, 2019). The long-term health consequences of e-cigarette use remain uncertain due to the limited availability of long-term clinical data (NASEM, 2018; Sgai et al., 2025).
Youth experience widespread exposure to e-cigarette advertisements and marketing campaigns that directly target them (Gentzke, 2022; Jeong et al., 2024). This exposure, alongside the availability of flavored products, may contribute to the initiation and perpetuation of e-cigarette use among youth (Villanti et al., 2019). The packaging of e-cigarettes often features bright colors and imagery reminiscent of popular food items or candy, making them particularly attractive to AYAs (Gomes et al., 2024). The majority of AYAs who initiate e-cigarette use do so with flavored varieties, which is associated with subsequent progression to regular use (Villanti et al., 2019). An estimated 87.6% of U.S. adolescents who currently use e-cigarettes prefer flavored e-cigarettes (Park-Lee et al., 2024).
International jurisdictions have implemented diverse policies to regulate e-cigarettes, including bans and categorizing them as consumer, pharmaceutical, or tobacco products, with some remaining unregulated. In countries where e-cigarettes are allowed as consumer products, a dual regulatory framework is often used to balance the potential of e-cigarettes as a smoking cessation or harm reduction tool against the risk of initiation among non-smokers, especially youth (Klein et al., 2020). In response to the growing concern of underage e-cigarette use, the U.S. Food and Drug Administration (FDA) asserted regulatory authority over e-cigarettes as tobacco products through the deeming rule in 2016. Beginning August 2018, the FDA mandated that all e-cigarette packages and advertisements must display a specific health warning statement stating: "WARNING: This product contains nicotine. Nicotine is an addictive chemical". Additionally, the Tobacco 21 (T21) legislation was enacted in December 2019, raising the federal minimum legal sales age for tobacco products, including e-cigarettes, from 18 to 21 years. Although the prevalence of e-cigarette use among high school and middle school students has declined in recent years (from 27.5% and 10.5%, respectively, in 2019 [Cullen et al., 2019] to 10.0% and 4.6%, respectively, in 2023 [Birdsey et al., 2023]), the prevalence among young adults aged 18 to 20 years has risen (from 8.1% in 2019 to 10.3% in 2023 [Vahratian et al., 2025]), thus highlighting the need for continued policy efforts.
Despite the strides made by the FDA, there remains an absence of comprehensive federal legislation regulating the sale, use, and taxation of e-cigarettes. State, local, tribal, and territorial governments possess the authority to implement more stringent regulations governing the sale, distribution, possession, exposure, and access to e-cigarettes, with the aim of reducing use among AYAs (U.S. DHHS, 2016). States have adopted varying approaches to regulate e-cigarettes, resulting in a diverse legal landscape across the U.S. Some states have enacted comprehensive laws that encompass e-cigarettes, while others have laws that exclusively regulate traditional combustible smoking, and several have minimal or no regulatory measures in place (Centers for Disease Control and Prevention [CDC], 2024b; U.S. DHHS, 2016). As of September 2024, 36 states have enacted e-cigarette retail licensing laws, which mandate that businesses obtain a license or permit to sell e-cigarettes and related products in face-to-face transactions (CDC, 2024c). As of September 2024, 33 states have enacted legislation imposing an excise tax on e-cigarettes (CDC, 2024c). E-cigarette product packaging laws focus on child-resistant packaging and labeling requirements, including warnings about nicotine content and its addictive nature, notice of the illegality of sales to minors, and prohibition of advertising e-cigarettes as a modified risk product without prior FDA approval. As of June 2024, 33 states have enacted e-cigarette product packaging laws with varying scopes and dimensions (Public Health Law Center, 2024a). Currently, only 6 states have implemented comprehensive bans on the sale of flavored e-cigarettes to address concerns about youth e-cigarette use (Bach, 2025). Examining state-level policies that may mitigate underage e-cigarette use can inform future policy initiatives and help address this pressing public health concern.
Research on the influence of state-level e-cigarette policies on e-cigarette use among U.S. adolescents and adults using cross-sectional data has yielded mixed results. Restrictions on flavored e-cigarette sales have been linked to reduced frequent and daily e-cigarette use among AYAs (Friedman et al., 2024). E-cigarette licensing policies have also been associated with decreased e-cigarette use among adolescents (Azagba et al., 2020) and adults (Du et al., 2020). However, the impact of e-cigarette excise taxes on AYA e-cigarette use is inconsistent, with some studies finding a significant reduction (Han et al., 2023; Pastrana et al., 2023) and others finding no association (Choi et al., 2022).
The relationship between state-level e-cigarette-inclusive smoke-free policies and e-cigarette use is multifaceted, with studies yielding varied results. Among adolescents, the implementation of such policies has been associated with decreased prevalence of lifetime and past 30-day e-cigarette use, compared to states lacking these policies (Choi et al., 2022). Another study found a slight uptick in adolescent e-cigarette use initially following the implementation of e-cigarette-inclusive smoke-free legislation; however, this trend reversed over time, with a decrease observed at least one year post-policy implementation (Pastrana et al., 2023). Research on adults revealed age-varying associations, with e-cigarette-inclusive smoke-free policies linked to reduced odds of e-cigarette use among adults aged 25-59 years, but not among younger adults aged 18-24 years (Lee et al., 2019). A longitudinal study found no significant differences in e-cigarette use behaviors among adults residing in states with and without these policies (Yang et al., 2022). Overall, the existing evidence underscores the complexity of the issue and highlights the need for further investigation into the influence of e-cigarette-inclusive smoke-free policies on underage e-cigarette use.
Most existing studies have focused on lifetime, current, and frequent e-cigarette use, with relatively less attention paid to the critical issue of underage e-cigarette initiation. Moreover, the influence of state-level e-cigarette product packaging legislation on underage e-cigarette use in the U.S. remains unexamined. Using recent longitudinal data from a larger cohort study, we examined the individual and combined associations of state-level e-cigarette policies, including flavor bans, e-cigarette-inclusive smoke-free policies, excise tax, product packaging, and retail licensing, with lifetime, past 30-day, and frequent e-cigarette use among AYAs aged 12-20 years, as well as the initiation of underage e-cigarette use among those with no prior lifetime use. We hypothesized that individuals residing in states with e-cigarette regulations or restrictions will have lower odds of e-cigarette use compared to those living in states without such policies.
Methods
Data Source
We used data from Waves 7 (Jan 2022-April 2023) and 7.5 (April 2023-December 2023) of the Population Assessment of Tobacco and Health (PATH) study, a nationally representative, longitudinal cohort study that collects data on U.S. adolescents and adults aged 12 years and older (Hyland et al., 2017; N=14,326 respondents who participated in both Waves 7 and 7.5). Wave 7.5 was a special collection specifically focused on adolescents (ages 12-17 years) and young adults (ages 18-22 years) at the time of the Wave 7.5 interview. The surveys were administered at a mean of 12.18 months apart (range 1-23), utilizing a mixed-mode data collection approach that combined in-person and telephone interviews. The Wave 7.5 weighted response rates for the Wave 7 cohort were 84.3% for adults and 78.8% for adolescents (Westat, 2025). The analytic sample was restricted to AYAs aged ≤20 years at Wave 7.5 (n= 11,418). This study was deemed exempt from Institutional Review Board (IRB) review by the University of Michigan's IRB, as it involved secondary analysis of existing de-identified data from the PATH study, which was previously approved by the Westat IRB.
Measures
E-cigarette outcome measures
Lifetime use at Wave 7 was determined by an affirmative response to the item, “Have you ever used an electronic nicotine product, even one or two times? (Yes/No).” Past 30-day use at Wave 7 was defined as any e-cigarette use, even one or two times, in the past 30 days. Frequent use at Wave 7 was defined as use on ≥20 days in the past 30 days among respondents who reported past 30-day use, consistent with prior work (Choi et al., 2022; Pastrana et al., 2023). Initiation was defined as any e-cigarette use in the past 12 months at Wave 7.5 among respondents who reported no lifetime e-cigarette use at Wave 7. Increase in past 30-day use frequency was based on the number of days used within the past 30 days from Wave 7 to 7.5. This binary variable represents an increase in e-cigarette use versus no increase.
E-cigarette policies
State-level e-cigarette policy statuses as of 2023 were obtained from the Campaign for Tobacco-free Kids (https://www.tobaccofreekids.org/) for data on flavor bans; the Centers for Disease Control and Prevention’s (CDC) State Tobacco Activities Tracking and Evaluation (STATE) System (https://www.cdc.gov/statesystem/index.html) for e-cigarette-inclusive smoke-free indoor air laws and excise tax; and the Public Health Law Center (https://www.publichealthlawcenter.org/) for e-cigarette packaging and retail licensing policies. These policy datasets have been used in prior research (Azagba et al., 2023; Choi et al., 2022; Du et al., 2020; Lee et al., 2019) and were selected because they contained information on enactment dates, which enabled us to align the policy data with the PATH’s Waves 7 and 7.5 data. States were classified as having the following statewide policies in effect: flavor bans on e-cigarette sales (Bach, 2025); e-cigarette-inclusive smoke-free indoor air laws in private worksites, restaurants, and bars (CDC, 2024b); e-cigarette excise tax (CDC, 2024a); e-cigarette packaging policies (Public Health Law Center, 2024a); and e-cigarette retail licensing policy (Public Health Law Center, 2024b) versus the absence of each of these policies (see Supplemental Table A). The scope of e-cigarette packaging policies by state is available in Supplemental Table B (Public Health Law Center, 2025).
We created an e-cigarette policies scale ranging from 0 to 5 by summing the number of policies effective within each state (see Supplemental Figure 1), similar to other work (Alciati et al., 1998). The 2023 state-level e-cigarette policy data was linked to the restricted-use PATH datasets using the state identifiers for each participant (U.S. DHHS. National Institutes of Health. National Institute on Drug Abuse & United States Department of Health and Human Services. Food and Drug Administration. Center for Tobacco Products, 2025).
Control Variables
We included the following additional covariates: sex (male/female), age (years), race (White, Black, Asian, American Indian Alaskan Native/Native Hawaiian and other Pacific Islander/Multiracial), Hispanic ethnicity (yes/no), past 30-day any combustible tobacco use (yes/no), past 30-day any alcohol use (yes/no), and past 30-day any other drug use (yes/no; nonmedical use of Ritalin/Adderall, painkillers, or sedatives/tranquilizers, or use of cocaine/crack, methamphetamine/speed, heroin, inhalants/solvents, or hallucinogens).
Statistical Analyses
To describe the analytic sample, we calculated univariate frequency distributions, presenting unweighted sample sizes alongside weighted percentages. We used the svyglm function in R to fit survey-weighted generalized linear models with a logit link for binary outcomes, accounting for the survey design. We conducted multivariable logistic regression models to estimate the odds of ever, current, and frequent e-cigarette use at Wave 7, and the initiation of and increase in e-cigarette use frequency at Wave 7.5 as a function of each state-level e-cigarette policy individually. We then estimated the odds of each of the outcomes as a function of the combined e-cigarette policies scale. The combined scale score allowed us to assess the potential cumulative effect of multiple e-cigarette policies on e-cigarette use, as individual policies may have small or moderate effects that are difficult to detect in isolation. This approach allowed us to assess the overall impact of e-cigarette policies on e-cigarette use outcomes. Prior research has used a comparable methodology, where a continuous scale served as a measure of the intensity of tobacco control policy implementation (Alciati et al., 1998). All regression models adjusted for sociodemographic variables, past 30-day combustible tobacco, alcohol, and other drug use. We conducted complete-case analysis, restricting to participants with complete data on predictors, outcomes, and covariates for each model. Models were estimated at the respondent level and included all covariates simultaneously. Supplemental Figure 2 presents a flowchart illustrating the pattern of missing data in our study. To address overdispersion in binary outcomes, we utilized the quasibinomial family distribution. The resulting coefficients and 95% confidence intervals (CIs) were exponentiated to obtain adjusted odds ratios (AORs). We used the Bonferroni correction in our analyses to adjust for multiple comparisons.
Those who responded at the Wave 7.5 survey were significantly younger than non-respondents (mean age ≈ 15.5 versus 17.1 years); respondents and non-respondents were not significantly different on sex, race, or Hispanic ethnicity. This analysis utilized PATH Wave 7.5 adult/youth single-wave replicate weights for the PATH Wave 7 cohort. The Wave 7.5 single- wave weights for the Wave 7 cohort used in these analyses account for the sample design of the Wave 7 cohort and adjusted for nonresponse at Wave 7.5 (Westat, 2025). All statistical analyses were performed using R (version 4.5.1). We used the svrepdesign function from the survey package (version 4.0) to account for the complex survey design, incorporating the main weight and 100 replicate weights with balanced repeated replication (Fay's adjustment factor=0.3). This approach is equivalent to using Stata’s svyset command to define the survey design. To account for potential extra-binomial variation, we initially used a quasibinomial family but ultimately employed a binomial family as the results were unchanged and design-based standard errors captured uncertainty through the survey design. We verified the robustness of our findings by demonstrating equivalent results under both binomial and quasibinomial specifications. We used the STROBE reporting guideline (Von Elm et al., 2007) to draft this manuscript.
Results
Table 1 provides the weighted univariate distributions of all the study variables. Half of our analytic sample were male (50.91%). The mean age was 15.48 years. Two-thirds of respondents identified as White (66.11%), and one-quarter reported Hispanic ethnicity (25.58%). An estimated 3.47% reported past 30-day any combustible tobacco use, 29.11% reported past 30-day any alcohol use, and 4.41% reported past 30-day any other drug use. One in five respondents (20.62%) lived in states with enacted flavor bans on e-cigarette sales. One-third of respondents (33.72%) lived in states with e-cigarette-inclusive smoke-free policies. Over half of respondents (57.24%) lived in states with an excise tax imposed on e-cigarettes. Most respondents lived in states with enacted e-cigarette product packaging policies (77.36%) and retail licensing policies (79.67%). Almost one-quarter of respondents (22.36%) reported lifetime e-cigarette use. One in ten respondents reported past 30-day e-cigarette use (10.40%), and 22.11% of those respondents reported frequent use. Among respondents who reported no lifetime e-cigarette use at Wave 7, an estimated 6.92% initiated e-cigarette use by Wave 7.5. Among respondents who reported past 30-day e-cigarette use at Waves 7 and 7.5, an estimated 7.71% reported an increase in past 30-day use frequency at Wave 7.5.
Table 1.
Descriptive characteristics of adolescents and young adults ages 12-20 years in PATH, Waves 7 to 7.5 (n = 11,418)
| Variables | N (weighted %) | Mean (SE) Median (IQR [Q3-Q1]) |
|---|---|---|
| Sex | ||
| Male | 5882 (50.91) | |
| Female | 5536 (49.09) | |
| Age | 15.48 (0.01) | |
| 15 (4) | ||
| Race | ||
| White | 7718 (66.11) | |
| Black | 1675 (15.13) | |
| Asian | 513 (5.67) | |
| AIAN/NHPI/Multiracial | 1512 (13.09) | |
| Hispanic ethnicity | 3504 (25.58) | |
| Past 30-day any combustible tobacco use | 391 (3.47) | |
| Past 30-day any alcohol usea | 3246 (29.11) | |
| Past 30-day any other drug useb | 509 (4.41) | |
| Flavor Bansc | ||
| Absent | 7532 (79.38) | |
| In effect | 1960 (20.62) | |
| E-cigarette-inclusive smoke-free policiesc | ||
| Absent | 6253(66.28) | |
| In effect | 3239 (33.72) | |
| E-cigarette excise taxc | ||
| Absent | 4071 (42.76) | |
| In effect | 5421 (57.24) | |
| E-cigarette product packaging policiesc | ||
| Absent | 2231 (22.64) | |
| In effect | 7261 (77.36) | |
| E-cigarette retail licensing policiesc | ||
| Absent | 1922 (20.33) | |
| In effect | 7570 (79.67) | |
| E-cigarette policies scale (range 0-5) c | 2.69 (0.05) | |
| 2 (2) | ||
| Lifetime e-cigarette used | 2106 (22.36) | |
| Past 30-day e-cigarette used | 972 (10.40) | |
| Frequent e-cigarette used,e | 178 (22.11) | |
| Initiation of e-cigarette usef | 520 (6.92) | |
| Increase in past 30-day e-cigarette use frequencyg | 735 (7.71) |
Notes. AIAN, American Indian Alaskan Native; NHPI, Native Hawaiian or Other Pacific Islander. Measures are based on responses at Wave 7, except for initiation of and increase in past 30-day e-cigarette use frequency, which include information from both Wave 7 and Wave 7.5. a Missing data = 38. b Missing data = 25. c Missing data = 1,926 respondents missing on Wave 7 covariates or state identifiers (N = 9,492); d Missing data = 65 (N = 9,427). Based on non-missing wave 7 e-cigarette use (lifetime or past 30-day). e ≥20 days within the past 30 days among respondents who reported any past 30-day e-cigarette use at Wave 7. f Past 12-month use of e-cigarettes at Wave 7.5 among respondents who reported no lifetime e-cigarette use at Wave 7; (N = 7,307). g Based on the number of days used within the past 30 days between Wave 7 and Wave 7.5 among respondents who reported a valid response on past 30-day e-cigarette use at Wave 7 and Wave 7.5; (N = 9,146).
State-level Policies and Lifetime Use
Table 2 displays the individual and combined associations of state-level e-cigarette policies on lifetime e-cigarette use among AYAs at Wave 7. Living in a state with flavor bans and living in a state with e-cigarette-inclusive smoke-free policies were each associated with a reduced odds of lifetime use (AOR=0.69, 95% CI=0.55, 0.85 and AOR=0.78, 95% CI=0.66, 0.92, respectively) compared to living in a state without those policies. In examining the e-cigarette policies scale, we found that living in a state with more e-cigarette policies was associated with a lower odds of lifetime e-cigarette use (AOR=0.91, 95% CI=0.87, 0.96).
Table 2.
Multivariable logistic regression models of e-cigarette policies on lifetime e-cigarette use among adolescents and young adults at Wave 7, individual and combined associations
| Models | E-cigarette Policy Variables | Lifetime E-cigarette Use AOR (95% CI) |
|---|---|---|
| Model 1 (n = 9,427) | Flavor Bans | |
| Absent | REF | |
| In effect | 0.69 (0.55, 0.85)** | |
| Model 2 (n = 9,427) | E-cigarette-inclusive smoke-free policies | |
| Absent | REF | |
| In effect | 0.78 (0.66, 0.92)* | |
| Model 3 (n = 9,427) | E-cigarette excise tax | |
| Absent | REF | |
| In effect | 0.88 (0.76, 1.01) | |
| Model 4 (n = 9,427) | E-cigarette product packaging policies | |
| Absent | REF | |
| In effect | 0.81 (0.66, 1.00) | |
| Model 5 (n = 9,427) | E-cigarette retail licensing policies | |
| Absent | REF | |
| In effect | 0.82 (0.67, 1.01) | |
| Model 6 (n = 9,427) | E-cigarette policies scale | 0.91 (0.87, 0.96)** |
Notes. All models controlled for sex, age, race, Hispanic ethnicity, past 30-day any combustible tobacco use, past 30-day any alcohol use, and past 30-day any other drug use. Asterisks denote statistical significance (***p < .001, **p < .01, *p < .05).
State-level Policies and Past 30-day Use
We found no significant associations of living in a state with e-cigarette policies and past 30-day e-cigarette use (Table 3).
Table 3.
Multivariable logistic regression models of e-cigarette policies on past 30-day e-cigarette use among adolescents and young adults at Wave 7, individual and combined associations
| Models | E-cigarette Policy Variables | Past 30-day E-cigarette Use AOR (95% CI) |
|---|---|---|
| Model 1 (n = 9,427) | Flavor Bans | |
| Absent | REF | |
| In effect | 0.76 (0.58, 1.00) | |
| Model 2 (n = 9,427) | E-cigarette-inclusive smoke-free policies | |
| Absent | REF | |
| In effect | 0.85 (0.69, 1.05) | |
| Model 3 (n = 9,427) | E-cigarette excise tax | |
| Absent | REF | |
| In effect | 0.93 (0.78, 1.12) | |
| Model 4 (n = 9,427) | E-cigarette product packaging policies | |
| Absent | REF | |
| In effect | 0.94 (0.75, 1.19) | |
| Model 5 (n = 9,427) | E-cigarette retail licensing policies | |
| Absent | REF | |
| In effect | 0.82 (0.63, 1.06) | |
| Model 6 (n = 9,427) | E-cigarette policies scale | 0.94 (0.89, 1.00) |
Notes. All models controlled for sex, age, race, Hispanic ethnicity, past-30-day any combustible tobacco use, past 30-day any alcohol use, and past 30-day any other drug use. Asterisks denote statistical significance (***p < .001, **p < .01, *p < .05).
State-level Policies and Frequent Use
Among respondents who engaged in past 30-day e-cigarette use, we found that respondents living in states with e-cigarette-inclusive smoke-free policies had lower odds of frequent use (AOR=0.56, 95% CI=0.37, 0.85). (Table 4).
Table 4.
Multivariable logistic regression models of e-cigarette policies on frequent e-cigarette use among adolescents and young adults who reported past 30-day e-cigarette use at Wave 7, individual and combined associations
| Models | E-cigarette Policy Variables | Frequent E-cigarette Usea AOR (95% CI) |
|---|---|---|
| Model 1 (n = 771) | Flavor Bans | |
| Absent | REF | |
| In effect | 0.63 (0.39, 1.03) | |
| Model 2 (n = 771) | E-cigarette-inclusive smoke-free policies | |
| Absent | REF | |
| In effect | 0.56 (0.37, 0.85)* | |
| Model 3 (n = 771) | E-cigarette excise tax | |
| Absent | REF | |
| In effect | 0.93 (0.63, 1.36) | |
| Model 4 (n = 771) | E-cigarette product packaging policies | |
| Absent | REF | |
| In effect | 0.64 (0.38, 1.08) | |
| Model 5 (n = 771) | E-cigarette retail licensing policies | |
| Absent | REF | |
| In effect | 0.71 (0.42, 1.21) | |
| Model 6 (n = 771) | E-cigarette policies scale | 0.86 (0.75, 0.97)b |
Notes. All models controlled for sex, age, race, Hispanic ethnicity, past-30-day any combustible tobacco use, past 30-day any alcohol use, and past 30-day any other drug use. Asterisks denote statistical significance (***p < .001, **p < .01, *p < .05). a≥20 days within the past 30 days among respondents with valid data on the number of days used within the past 30 days at Wave 7. bResult is not statistically significant after applying the Bonferroni correction.
State-level Policies and Initiation
We found no significant associations of living in a state with e-cigarette policies and the initiation of e-cigarette use at Wave 7.5 among AYAs who reported no lifetime use at Wave 7 (Table 5).
Table 5.
Multivariable logistic regression models of e-cigarette policies on the initiation of e-cigarette use among adolescents and young adults at Wave 7.5, individual and combined associations
| Models | E-cigarette Policy Variables | Initiation of E-cigarette Usea AOR (95% CI) |
|---|---|---|
| Model 1 (n = 7,307) | Flavor Bans | |
| Absent | REF | |
| In effect | 0.85 (0.61, 1.18) | |
| Model 2 (n = 7,307) | E-cigarette-inclusive smoke-free policies | |
| Absent | REF | |
| In effect | 0.96 (0.76, 1.20) | |
| Model 3 (n = 7,307) | E-cigarette excise tax | |
| Absent | REF | |
| In effect | 1.01 (0.83, 1.23) | |
| Model 4 (n = 7,307) | E-cigarette product packaging policies | |
| Absent | REF | |
| In effect | 0.87 (0.69, 1.09) | |
| Model 5 (n = 7,307) | E-cigarette retail licensing policies | |
| Absent | REF | |
| In effect | 0.94 (0.74, 1.19) | |
| Model 6 (n = 7,307) | E-cigarette policies scale | 0.97 (0.90, 1.04) |
Notes. All models controlled for sex, age, race, Hispanic ethnicity, past-30-day any combustible tobacco use, past 30-day any alcohol use, and past 30-day any other drug use. Asterisks denote statistical significance (***p < .001, **p < .01, *p < .05). aAmong respondents who reported no lifetime e-cigarette use at Wave 7.
Coefficient estimates for all covariates included are displayed in Supplemental Tables C through F.
Supplementary Analyses
We conducted supplementary analyses to examine the individual and combined associations between state policies and increase in past 30-day e-cigarette use frequency at Wave 7.5. The results were not statistically significant after applying the Bonferroni correction (Supplemental Table G).
We also conducted sensitivity analyses excluding the past 30-day any combustible tobacco, alcohol, and other drug use variables from the models. Our sensitivity analyses largely replicated the primary findings, but with some exceptions. The associations between e-cigarette inclusive smoke-free policies and lifetime e-cigarette use, as well as frequent e-cigarette use, were no longer statistically significant. Overall, our results suggest a degree of robustness across different model specifications.
Discussion
Our study yields important insights that can inform the development, implementation, and enforcement of state-level e-cigarette policies aimed at safeguarding AYAs from the potential harms associated with e-cigarette use. Our results showed that statewide flavor bans on the sale of e-cigarettes—despite being enacted in only six states (i.e., California, Massachusetts, New Jersey, New York, Rhode Island, and Utah)—were linked to lower odds of lifetime e-cigarette use among AYAs. This finding is particularly salient given the widespread appeal of flavored e-cigarettes among this demographic (Park-Lee et al., 2024). As the prevalence of flavored e-cigarettes continues to be a driving factor in the youth e-cigarette epidemic, broader adoption of flavor bans across the U.S. could play a crucial role in mitigating underage e-cigarette use and reducing the associated public health risks. By restricting access to flavored e-cigarettes, policymakers may be able to decrease the attractiveness and accessibility of these products to youth and young adults, ultimately helping to prevent the development of nicotine dependence and other adverse health outcomes.
State-level policies that prohibit e-cigarette use in private worksites, restaurants, and bars were associated with reduced e-cigarette use. Specifically, these policies were linked to lower odds of lifetime and frequent e-cigarette use. These policies bolster existing protections against secondhand smoke exposure by limiting opportunities for e-cigarette use (U.S. DHHS, 2016). By prohibiting e-cigarette use in indoor settings, these policies may help prevent the normalization of e-cigarette use, reinforce a tobacco- and nicotine-free culture, reduce e-cigarette use by making it less convenient, and support cessation efforts among underage e-cigarette consumers (U.S. DHHS, 2016).
We investigated the relationship between state-level product packaging legislation and underage e-cigarette consumption and found that such legislation was not significantly associated with e-cigarette use among AYAs. Packaging plays a crucial role in conveying product information and health-related messages to consumers, as well as preventing accidental poisoning. U.S. policymakers have attempted to regulate e-cigarette packaging to reduce its appeal to AYAs. Our findings differ from those of international studies, which have shown that standardized packaging and prominent health warnings can deter e-cigarette uptake among young people (Gomes et al., 2024; Jones et al., 2025; Taylor et al., 2023). This discrepancy may be attributed to differences between the U.S. and other countries in terms of regulatory environments, product availability, or marketing practices. Notably, e-cigarette product packaging laws vary widely across states, encompassing a range of requirements and restrictions. To better understand the impact of packaging regulations, future research should examine the effect of specific packaging design elements, such as nicotine warning labels, on underage e-cigarette consumption.
Our findings revealed no significant associations between retail licensing laws and underage e-cigarette consumption, diverging from earlier studies that demonstrated significant negative relationships between the enactment of retail licensing laws and e-cigarette use among adolescents (Azagba et al., 2020) and adults (Du et al., 2020). Previous research has also highlighted the considerable variability in e-cigarette retail licensing laws across U.S. states, as well as the challenges associated with enforcing these laws (Patel et al., 2020). Future studies should investigate the effectiveness of enforcement mechanisms and compliance checks in reducing e-cigarette use among this population.
While the individual relationships between e-cigarette excise taxes, product packaging, retail licensing policies, and underage e-cigarette use did not reach statistical significance, our analysis revealed that the cumulative effect of these state-level e-cigarette policies, as measured by the e-cigarette policies scale, was significantly associated with reduced odds of lifetime e-cigarette use among AYAs (Table 2, Model 6). This finding suggests that a comprehensive approach to e-cigarette regulation, encompassing multiple policy components, may be more effective in mitigating e-cigarette use among young people than discrete, isolated policies. The state-level e-cigarette policies scale may capture the cumulative effect of different regulatory strategies, thereby providing a more nuanced understanding of the relationship between e-cigarette policies and underage e-cigarette use. It is worth noting that this scale assumes equal weighting of its components, and the effects of individual policies may vary depending on their type, enforcement, or context. The significant association between the e-cigarette policies scale and reduced odds of lifetime e-cigarette use highlights the importance of adopting a multifaceted approach to e-cigarette regulation, one that incorporates various policy levers to address the complex issue of e-cigarette use among AYAs.
Careful examination of the intended and potential unintended consequences of regulatory approaches is necessary to inform future programming and legislation. Some studies have suggested that e-cigarette restrictions, including sales restrictions on flavored e-cigarettes, may result in substitution effects of other tobacco products for a minority of AYAs using e-cigarettes (Abouk et al., 2021; Romm et al., 2022). As the e-cigarette landscape continues to evolve, it is crucial that policymakers remain vigilant in assessing the impact of their regulatory decisions on nicotine use and public health.
The strengths of our study included the use of cross-sectional and longitudinal data from a nationally representative, probability-based sample. We integrated multiple state-level e-cigarette policies into our analyses and examined their individual and combined associations on pertinent e-cigarette use outcomes. We note some study limitations. The e-cigarette use measures were based on self-report and thus, are susceptible to social desirability bias and recall bias. There is considerable heterogeneity in the enactment of e-cigarette policies within U.S. states that we were unable to account for in our analysis. For example, an individual may reside in a city with local excise taxes on e-cigarettes but within a state without a statewide e-cigarette excise tax. It is possible that other unmeasured state-level factors, beyond the e-cigarette policies examined, may be driving the observed associations, highlighting the need for future research to consider additional state-level variables and more nuanced measures of policy exposure. Additionally, the assumption of equal weighting and additive effects for the summed e-cigarette policy scale is a limitation of this study, as it does not account for differences in policy strength and enforcement. Future research may benefit from exploring alternative approaches that account for these potential differences. Lastly, the null findings for certain outcomes may be due to limited statistical power, particularly for small subgroups, which may have resulted in type II errors. The relatively brief exposure to some state-level e-cigarette policies and potential measurement imprecision may have also contributed to the lack of significant associations observed in our study. In several instances, the effective dates of some state-level e-cigarette policies coincided with the data collection period for Waves 7 and 7.5, which may have limited our ability to fully capture the impact of these policies. Policy approaches and regulatory contexts vary widely across countries, differing from those in the U.S. The effectiveness of these policies and their associations with e-cigarette use may also vary in non-U.S. contexts. Lastly, our results showed variations in e-cigarette use across different demographic and socioeconomic subgroups (Supplemental Tables C-F), suggesting that the relationship between e-cigarette policies and use may differ across these subgroups. Future research should investigate whether the effects of e-cigarette policies vary across demographic subgroups, such as age, sex, or socioeconomic status, to inform targeted interventions and policy development. Our analysis also revealed differences in e-cigarette use across sociodemographic subgroups (Supplemental Tables C-G), highlighting the need for further research to examine whether the relationship between e-cigarette policies and use differs across these subgroups.
In summary, our findings underscore the importance of implementing comprehensive e-cigarette policies at the state level to curb e-cigarette use among AYAs. To achieve this goal, policymakers should consider a multifaceted approach that includes enacting flavor bans and incorporating e-cigarettes into existing smoke-free policies. Future research endeavors should continue to investigate the efficacy of e-cigarette policies in mitigating e-cigarette use, while also vigilantly monitoring potential unintended consequences, thereby ensuring that policy interventions are optimized to protect public health.
Supplementary Material
Public Health Significance Statement:
Findings from this study suggest that comprehensive e-cigarette policies, including flavor bans and e-cigarette-inclusive smoke-free policies, are associated with lower odds of e-cigarette use among adolescents and young adults. Policymakers can use these findings to inform evidence-based decisions about e-cigarette regulations. The study also highlights the need for continued research into the impact of e-cigarette policies on adolescent and young adult e-cigarette use, including the potential for combined policies to have a greater impact than individual policies.
Funding Support:
National Cancer Institute (R01CA270546)
Data Availability Statement:
The Population Assessment of Tobacco and Health (PATH) Study data is made available to researchers through the National Addiction & HIV Data Archive Program (NAHDAP) at the Inter-university Consortium for Political and Social Research (ICPSR). We used the Restricted Use Files (RUFs) for this study. These files contain indirect identifiers and require researchers to apply for access to a secure server, the ICPSR Virtual Data Enclave (VDE). Access to these files requires an application and approval.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The Population Assessment of Tobacco and Health (PATH) Study data is made available to researchers through the National Addiction & HIV Data Archive Program (NAHDAP) at the Inter-university Consortium for Political and Social Research (ICPSR). We used the Restricted Use Files (RUFs) for this study. These files contain indirect identifiers and require researchers to apply for access to a secure server, the ICPSR Virtual Data Enclave (VDE). Access to these files requires an application and approval.