Factors associated with transitions in tobacco product use states among young adults aged 18-29 years.

BACKGROUND AND OBJECTIVES: This study examined young adults' tobacco use transitions based on their past 30-day use states, and identified factors associated with their transitions. METHODS: Participants (N = 12377) were young adults aged 18-29 years at Wave 1 of the Population Assessment of Tobacco and Health (PATH) study. Self-reported tobacco use states were categorized by the number of past-month use days (0, 1-4, 5-8, 9-12, 13-30 days) for cigarettes, electronic cigarettes [e-cigarettes], traditional cigars, filtered cigars, cigarillos, smokeless tobacco (SLT), and hookah. Multistate Markov models examined transitions between use states across Waves 1-5 of unweighted PATH data and multinomial logistic regressions examined predictors of transitions. RESULTS: Most young adults remained nonusers across adjacent waves for all products (88%-99%). Collapsed across waves, transitioning from use at any level to nonuse (average 46%-67%) was more common than transitioning from nonuse to use at any level (average 4%-10%). Several factors that predicted riskier patterns of use (i.e., transitioning to use and/or remaining a user across adjacent waves) were similar across most products: male, Black, Hispanic, lower education levels, and lower harm perceptions. In contrast, other factors predicted riskier patterns for only select products (e.g., e-cigarette and SLT use among Whites). DISCUSSION AND CONCLUSIONS: Few sampled young adults escalated their tobacco use over time, and escalations for many products were predicted by similar factors. SCIENTIFIC SIGNIFICANCE: Prevention and regulatory efforts targeted towards adolescents should continue, but also be expanded into young adulthood. These same efforts should consider both shared and unique factors that influence use transitions.

Electronic Cigarette Terminology: Where Does One Generation End and the Next Begin?

IMPLICATIONS: The electronic cigarette (ECIG) research community faces several challenges when it comes to regulatory science; not only is the ECIG market changing at a rapid pace, but the terms used by researchers, health organizations, ECIG users, and ECIG manufacturers/distributors to describe devices are inconsistent. These discrepancies make it difficult to advance science and develop regulations. Although researchers have used "generations" to categorize ECIG device types based on various characteristics, with the constantly evolving ECIG market, it is unclear where one "generation" of devices ends and the next begins.

Predictors of electronic cigarette dependence among non-smoking electronic cigarette users: User behavior and device characteristics.

INTRODUCTION: ECIGs differ in their ability to deliver nicotine to the user and, consequently, they may differ in their ability to produce dependence. This study examined individual device characteristics, device type, and user behaviors as predictors of ECIG dependence in a sample of never-smoking ECIG users. METHODS: Participants (N = 134) completed an online survey that assessed demographics, ECIG use behavior, and ECIG dependence as measured via the Penn State Electronic Nicotine Dependence Index (PSECDI) and E-cigarette Dependence Scale (EDS-4). Participants uploaded a picture of their personal ECIG device/liquid, which was coded by raters to identify product features. Multivariable linear regressions examined device characteristics (e.g., adjustable power, nicotine concentration) and device type (e.g., vape pen, mod, pod, modern disposable) as predictors of dependence controlling for demographics and user behaviors (e.g., ECIG use duration and frequency, other tobacco use). RESULTS: Longer durations of ECIG use and more use days/week were associated significantly with higher PSECDI (ß's = 0.91 and 1.90, respectively; p's < 0.01) and EDS-4 scores (ß's = 0.16 and 0.28, respectively; p's < 0.01). Higher nicotine concentrations were associated with higher PSECDI scores only (ß = 0.07, p =.011). Dependence scores did not differ as a function of ECIG device types after controlling for covariates. CONCLUSIONS: ECIG dependence was observed among the never-smoking ECIG users in this sample, regardless of their ECIG device/liquid features. Findings suggest that regulatory efforts aimed at reducing the dependence potential of ECIGs in never smokers should focus on overall nicotine emissions rather than product features.

Longitudinal transitions in adolescent polytobacco use across waves 1-4 of the Population Assessment of Tobacco and Health study.

AIMS: This study aims to identify adolescent patterns of polytobacco use and measure transitions between patterns over time. DESIGN: Longitudinal analysis using data derived from waves 1-4 (2013-18) of the Population Assessment of Tobacco and Health (PATH) study. Transitions in tobacco use patterns were examined via latent transition analysis, and then, socio-demographic characteristics were used to predict transitions via logistic regression. SETTING: United States. PARTICIPANTS: Participants included 975 adolescents who used at least one tobacco product at any wave (W1 mean age = 13.29, standard deviation = 0.86; W4 54.2% male; 54.5% white, 25.9% Hispanic). MEASUREMENTS: Measurements included past 30-day use of cigarettes, electronic cigarettes (e-cigs), traditional cigars, cigarillos, filtered cigars, snus, smokeless tobacco (SLT) or hookah. FINDINGS: Six latent classes were identified. Cigarette users (43.5-58.8%) and SLT users (50.8-79.6%) tended to persist in their use over time. E-cig users began to probably transition to non-users (80.0%), but became more likely to persist in this use over time (31.1%). Non-users at a given wave were most likely to transition to e-cig users (8.5-43.7%) or cigarette users (6.7-28.6%). Cigarillo/poly-users and hookah/poly-users displayed more variable transition patterns. Adolescents were more likely to transition to non-use (versus become/remain e-cig users) if they were older (cigarette users, SLT users), younger (e-cig users), other race (SLT users), male (SLT users) or had less-educated parents (SLT users) compared with their counterparts. Hispanic (versus white) cigarette users were more likely to transition to non-users than to persist in this use. CONCLUSIONS: Among adolescents in the United States, patterns of tobacco use characterized by the use, mainly, of single, specific products appear to be stable, particularly by late adolescence. In contrast, patterns characterized by polytobacco use appear to be more variable and may represent experimentation without specialization.

Transitions in tobacco use profiles among adolescents: Results from the Population Assessment of Tobacco and Health (PATH) study waves 3 and 4.

INTRODUCTION: Despite increases in adolescents' polytobacco use, little work has utilized recent national data to examine transitions in polytobacco use over time or predictors of such transitions. METHODS: Data derived from the Population Assessment of Tobacco and Health (PATH) study. Participants used at least one tobacco product (cigarettes, electronic cigarettes [ECIGs], traditional cigars, cigarillos, filtered cigars, snus, smokeless tobacco [SLT], hookah) at Wave 3 (W3; 2015-2016) or 4 (W4; 2016-2018) and had Wave 1 (W1) data (N = 1072; Mage= 13.71, SD =1.71, 46.4% female; 56.6% White, 23.2% Hispanic). Latent transition analysis (LTA) examined probabilities of transitioning between classes across waves and sociodemographic correlates of transitions. RESULTS: Five latent classes were identified: Low Users (n = 372, 34.7% W3; n = 249, 23.3% W4), ECIG Users (n = 256, 23.8% W3; n = 286, 26.7% W4), Cigarette Users (n = 215, 20.1% W3; n = 293, 27.3% W4), SLT Users (n = 91, 8.5% W3; n = 92, 8.6% W4), and Polytobacco Users (n = 138, 12.9% W3; n = 152, 14.1% W4). Cigarette Users and SLT Users displayed the highest probabilities for class stability. Low Users were most likely to transition to ECIG Users or Cigarette Users, whereas Polytobacco Users were most likely to transition to Low Users. ECIG Users were likely to transition to Low Users or Cigarette Users. Older and White adolescents were more likely to display riskier transitions. CONCLUSIONS: Although Cigarette Users and SLT Users remained largely stable in membership, findings demonstrate greater movement across polytobacco use classes relative to previous research and suggest that ECIGs may be the most common entry point to tobacco use.

Device features and user behaviors as predictors of dependence among never-smoking electronic cigarette users: PATH Wave 4.

INTRODUCTION: Electronic cigarettes (ECIGs) vary greatly in their ability to deliver nicotine, which suggests they may also vary in their ability to produce dependence. This study examined individual and combined ECIG device features, and also user behaviors, as predictors of dependence in never-smoking ECIG users. Methods Data were collected from 711 current ECIG users who had smoked <100 cigarettes in their lifetime at Wave 4 of the Population Assessment of Tobacco and Health (PATH) Study. Multivariable linear regressions examined individual (e.g., contains nicotine, uses a tank, flavor preference) and combined (e.g., refillable tanks, refillable mods) device features, and user behaviors (e.g., uses/day) as predictors of dependence, withdrawal, and craving after accounting for demographic variables. Results Results for ECIG dependence and craving showed a similar pattern; higher levels were observed for older age, more frequent past 30-day use, using an ECIG containing nicotine (vs no nicotine), and using a non-refillable cartridge or refillable tank style (vs disposables). Higher withdrawal levels were observed for higher education levels and individual device features of tank (vs no tank), cartridge (vs no cartridge), refillable (vs non-refillable), and "other" flavor preference (vs tobacco flavor). Lower withdrawal levels were associated with a preference for sweet/fruit flavor(s) (vs tobacco flavor). Conclusions Few use behaviors and device features, whether examined alone or in combination, predicted dependence-related outcomes. Findings underscore the challenge with regulating ECIG products in the current marketplace, which is inundated with a myriad of device types.