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Mutual pathways between peer and own e-cigarette use among youth in the United States: a cross-lagged model

BMC Public Health volume 23, Article number: 1609 (2023) Cite this article

Abstract

Background

Electronic cigarettes (e-cigarettes) have become the most common tobacco product used among adolescents in the United States (US). Prior research has shown that peer e-cigarette use was associated with increased risk of own e-cigarette use. Nonetheless, there is little empirical evidence on the directionality of these associations—if peer use predicts own use (peer influence) or if own use predicts peer use (peer selection).

Methods

We estimated the association between peer and own e-cigarette use among US adolescents 12–17 years of age. We used the cross-lagged model to investigate the mutual relationship between peer and own e-cigarette use over time using data from a population-based longitudinal study, Population Assessment of Tobacco and Health. Stratified analyses were conducted by sex and age subgroups.

Results

Results from a cross-lagged model showed a statistically significant predicting path leading from peer use at the prior time point to own use at the following time point, but not vice versa.

Conclusions

We found strong relationships between peer e-cigarette use and own e-cigarette use at within-individual levels. Peer influence paths were more robust than peer selection paths for e-cigarette use. Incorporating peers into prevention and intervention programs may help enhance these strategies.

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Background

Electronic cigarettes (e-cigarettes) have become the most commonly used tobacco product among adolescents and young adults in the United States (US). According to the National Youth Tobacco Survey (NYTS), a nationally representative school survey of middle and high school students, 14.1% of high school students and 3.3% of middle school students reported e-cigarette use during the 30 days prior to the survey in 2022 [1].

Peer use has been identified as one of the most prominent variables associated with tobacco use among adolescents [2,3,4,5,6,7,8,9], and friends’ use was commonly cited as a top reason to use tobacco, including e-cigarettes [10]. As e-cigarettes gained popularity, a few studies documented that friends’ approval and use of e-cigarettes were strongly associated with own e-cigarette use among a range of variables studied [3, 4, 7]. A more recent study among a nationally representative sample of 12th graders found that social competence and positive peer influence, including the perceived prevalence of substance use among peers, were protective factors against both nicotine and marijuana vaping [11]. Other work has found that adolescents may emulate their peers’ behaviors and may initiate the use of specific e-cigarette brands or flavors based on peer use [7, 12]. Taken together, the results of these studies suggested that peers may play an important role in youth use of e-cigarettes.

Some studies have found sex- and age-variations in peer and own tobacco use relationships. For example, Liao and colleagues found stronger peer-and-own-cigarette-smoking relationships among late-adolescent girls compared to boys [13]. In another study, Duan and colleagues found that 12–15-year-old girls with support from classmates smoked cigarettes on more days whereas no such relationship was found in boys [6]. With respect to e-cigarette use, a recent study among 15–20-year-olds found that, although having friends who used e-cigarettes was associated with current e-cigarette use among those who were 16 years of age and older for both boys and girls, the strength of association tended to decrease among females and increase among males with age [14]. These studies suggest potential variations by sex and age. In addition, given that e-cigarette use and access vary by age groups and between boys and girls [15], it is of interest to explore potential variations.

While previous studies have consistently found evidence for social homophily in youth tobacco use and have highlighted the significance of peers in youth e-cigarette use [11, 14], there are still several areas that require further investigation to enhance our understanding of the role of peer e-cigarette use and inform prevention and intervention strategies.

In a cross-sectional setting, the directionality between peer and own e-cigarette use is less clear. Two underlying pathways can contribute to the observed homophily in youth tobacco use: peer influence (e.g., youth using tobacco to be more like their tobacco-using friends) and peer selection (e.g., tobacco-using youth seeking out friends who also use tobacco). Previous studies on youth cigarette smoking have found evidence for both peer influence and peer selection [16,17,18,19]. As youth cigarette smoking continues to decline and e-cigarettes have become the most common tobacco product used [15], it is necessary to systematically investigate the role of peer use over time, as peer use remains one of the most important factors in youth tobacco use. By understanding how peer influence and peer selection operate in the context of e-cigarette use, we can better inform prevention and intervention strategies.

Against this background, we aimed to assess the mutual relationships between peer and own e-cigarette use among adolescents. Using data from a nationally representative longitudinal study of 12- to 17-year-olds in the US we sought to assess the mutual relationships of peer e-cigarette use and own e-cigarette use among youth. That is, whether peer influence or peer selection is more prominent in youth e-cigarette use. We further stratify our analysis by sex and age to explore potential subgroup variations.

Methods

Study population and sample

In this study, the population of interest was US non-institutionalized civilian adolescents 12–17 years of age. Data were from the longitudinal Population Assessment of Tobacco and Health (PATH) study and include data from 5 timepoints: wave 2 (Oct. 2014–Oct. 2015), wave 3 (Oct. 2015–Oct. 2016), wave 4 (Dec. 2016–Jan. 2018), wave 4.5 (Dec. 2017-Dec. 2018), and wave 5 (Dec. 2018-Nov. 2019) surveys. Because peer e-cigarette use was not assessed in the first PATH wave, we did not include data from wave 1. PATH employed a multi-stage sampling method to draw nationally representative samples after Institutional-Review-Board-approved parent consent and youth assent [20]. In contrast to school surveys of adolescents, the PATH sample includes young people irrespective of school attendance, and its sampling frame includes college dormitories and children of active-duty military living in the US. More details about the PATH methodology are provided elsewhere [20]. The current study involved analysis of de-identified PATH Public Use data files only, and therefore is not considered human subject research. Data files were downloaded from https://www.icpsr.umich.edu/icpsrweb/NAHDAP/studies/36498 on Dec. 13, 2019 (waves 2, 3, and 4), Sep. 18, 2020 (wave 4.5), and October 13, 2021 (wave 5).

At wave 1, PATH recruited a “shadow sample” of youth 9 to 11 years of age to be interviewed in later waves once the youth became 12 years of age. Therefore, some individuals in the shadow sample may have been interviewed in wave 2, when they had turned 12 while others would have been interviewed in later waves of the study. In addition, a replenishment sample was drawn at wave 4 to compensate for attrition over time. Therefore, the PATH study can be considered a dynamic cohort. In order to fully utilize all available information across all waves, we structured the data such that the first data point corresponds to the first assessment of the individual (“Time 1”), the second data point corresponds to the second assessment (“Time 2”), and so on. As a result, a youth from the wave 2 cohort can contribute up to five data points across all waves with up to four person-intervals between assessments. Similarly, a youth in the ‘shadow sample’ who became eligible in wave 3 can have up to four data points, with up to three person-intervals). All youth who responded to at least two consecutive assessments were included. This approach allowed us to capture the dynamic nature of the cohort and maximize the statistical power of our analyses.

Assessment

Audio computer assisted self-interviews (ACASI) with standardized multi-item modules were used to assess tobacco use history and a range of related variables. Current e-cigarette use was defined as using an e-cigarette product in the 30 days prior to the assessment. Peer tobacco use was assessed via the question “how many of your best friends use e-cigarettes?” Response options were “none,” “a few,” “some,” “most,” and “all.” Peer e-cigarette use was defined as having at least a few friends who used the tobacco product of interest. We coded “don’t know” and “refused” responses to missing. Once participants turned 18, they were administered the adult version of the questionnaire, which did not collect information about peer e-cigarette use. Therefore, our analytic sample included adolescents who were 12–17 during the study period.

Information about sex (male or female) and age categories (12–14 or 15–17 years of age at baseline, as provided in the PATH public use file) were extracted from survey items in the Demographics module. When these items were missing, information from the household screening roster was drawn.

Analysis

First, we provided descriptive statistics (i.e., unweighted frequencies and weighted proportions) for peer e-cigarette use and e-cigarette use at each assessment.

To answer our research question, we used a cross-lagged model (see Fig. 1 for a depiction) to deconstruct the peer-own-tobacco-use relationships into (a) same variable predicting itself (autoregressive relationships), (b) peer or own tobacco use predicting each other in the future (cross-lagged relationships), and (c) correlations at the same assessment.

Using the aforementioned analytical approach, we gain insights about the nature of the relationship by stipulating specific elements of these relationships over time and provide insights toward the relative strengths of “peer-influence” pathways and “peer-selection” pathways while accounting for the liability of e-cigarette use via autoregressive paths in cross-lagged models. With this approach, we sought to provide insights towards a better understanding of the relationship between peer and own e-cigarette use.

We conducted all analyses for 12–17-year-olds as a whole as well as for subgroups stratified by age groups (i.e., 12–14 years and 15–17 years, as provided in the PATH public use file) and sex (i.e., boys and girls) to explore potential sex- and age-related variations. Potential subgroup variations were statistically assessed using product terms in GLM and testing restrictions on model parameters in multigroup analysis of cross-lagged models (using Model Test command in Mplus).

PATH-derived sample weights for the most recent observation were used to account for selection probabilities, possible deficiencies in the sampling frame, and missingness due to nonresponse patterns and attrition [20]. Variances of estimates were produced using balance repeat replication methods (Fay’s method with fay = 0.3). A robust weighted least square mean and variance (WLSMV) adjusted estimator, which uses a full weight matrix, was used to accommodate categorical variables and complex survey design in structural equation models. Analyses were conducted using Stata 16.0 (StataCorp, College Station, Texas, USA) and Mplus 8.1 (Muthén & Muthén, Los Angeles, CA, USA). Multiple fit indices were used to evaluate the goodness of fit of the cross-lagged model, including root mean square of approximation (RMSEA) [21], comparative fit index (CFI) [22], and Tucker-Lewis index (TLI). A RMSEA < 0.08 and CFI/TLI > 0.90 were considered as indications of reasonably good model fit [23, 24].

Results

The analysis included 50,647 person-time data points aggregated from 13,074 adolescents. At baseline (time 1), the sample consisted of 48.6% (95% CI = 48.2% to 48.9%) of girls and 62.9% (95% CI = 62.3% to 63.5%) 12–14-year-olds; 53.2% (95% CI = 52.7% to 53.7%) were non-Hispanic White, 23.8% (95% CI = 23.4% to 24.2%) were Hispanics, 12.9% (95% CI = 12.6% to 13.2%) were non-Hispanic Black, and 10.1% (95% CI = 9.8% to 10.5%) were non-Hispanic Others. The prevalence of e-cigarette use increased from 1.9% at time one to 3.7%, 4.3%, 8.0%, and 12.1% at subsequent time points. As shown in Table 1, at each time point, 74.6% to 86.5% of adolescents who used e-cigarettes in the past 30 days had at least a few best friends who also used e-cigarettes. In contrast, a lower proportion (12.9% to 40.4%) of adolescents who did not use e-cigarettes in the past 30 days had at least a few best friends who used e-cigarettes.

Table 1 Prevalence of current peer e-cigarette use among adolescents 12–17 years of age. Data from PATH youth surveys wave 2 to wave 5 (2014–2019)
Full size table

The cross-lagged model as shown in Fig. 1 fit the data reasonably well based on fit indices (CFI = 0.994; TLI = 0.986; RMSEA = 0.014, 90% CI = 0.010 to 0.019). Table 2 shows standardized regression coefficients of the cross-lagged model of the overall sample as well as sex- and age-subsamples, and Fig. 1 visualized results of the overall sample. All autoregressive paths were highly significant, signaling strong prediction of the same behavior (i.e., own tobacco use and peer tobacco use) from one time point to the next. With respect to cross-lagged paths, peer e-cigarette use consistently predicted own e-cigarette use at the next time point, whereas own e-cigarette use did not predict peer e-cigarette use at the next time point except for from time 1 to time 2. The covariance term at time 1 and correlations of error terms at other time points were highly significant (p < 0.001). Consistent with the observation that estimates were largely similar for girls and boys, results from the Wald test of parameter constraints indicated that all prediction paths were equal between boys and girls (p = 0.067). For age subgroups, we were only able to test the equality of paths from time 1 to time 3 because 15–17-year-olds had become adults by time 4. The prediction paths were equal between 12–14-year-olds vs. 15–17-year-olds (p = 0.064). To better control for potential cohort effect (e.g., in relation to changes in the availability, marketing practices, and perceptions about e-cigarette products), we conducted analysis using only the wave 2 cohort. Results and statistical inferences were similar and are available upon request.

Fig. 1
figure 1

Results from cross-lagged model. Data from PATH youth surveys wave 2 to wave 5 (2014 to 2019). Solid lines represent paths that were statistically significant at 0.05 level. Dashed lines represent paths that were not statistically significant at 0.05 level. Observed variables are shown in rectangles and unobserved variables (error terms) are shown in circles. Similar variables are presented in rows, and variables from the same assessment wave are shown in columns. Curved lines represent covariances. Straight lines represent regression relationships. Note that we structured the data from multiple PATH waves such that the first data point corresponds to the first assessment of the individual (“Time 1”), the second data point corresponds to the second assessment of the individual (“Time 2”) and so on. As a result, a youth from the wave 2 cohort can contribute up to five data points across all waves, with up to four person-intervals between assessments. Similarly, a youth in the replenishment ('shadow sample') who became eligible in wave 3 can have up to four data points, with up to three person-intervals

Full size image
Table 2 Path estimates (standardized) from cross-lagged models for own and peer e-cigarette use. Data from PATH youth surveys wave 2 to wave 5 (2014 to 2019)
Full size table

Discussion

This study examines the association between peer e-cigarette use and own e-cigarette use among U.S. adolescents using data from waves 2–5 of the Population Assessment of Tobacco and Health (PATH) study. Despite the growing concern about adolescent e-cigarette use and the potential influence of peers, few studies have explored the longitudinal mutual relationships between peer and own e-cigarette use. This study fills this gap by examining the extent to which peer e-cigarette use predicts own use over time.

Our study expands on previous evidence of strong associations between peer and own tobacco use among adolescents by examining the mutual relationships over time in the context of e-cigarette use. Our results support the role of peer influence rather than peer selection in e-cigarette use among youth, as demonstrated by our use of a cross-lagged model. (The statistically significant path from own to peer e-cigarette use between time 1 and 2 is likely a result of a greater statistical power from larger sample sizes at earlier time points. At later time points, sample sizes became smaller due to participants aging out (i.e., becoming adults) and attrition (as shown in Table 1). Nonetheless, the strength of prediction was much weaker compared to the peer-to-own-use paths (i.e., 0.06 vs. 0.29).)

Our study reveals a key difference between the peer influence process for e-cigarette use among adolescents and the peer selection process observed in cigarette smoking among youth (i.e., seeking out friends who also smoke) [16,17,18,19]. Although both behaviors are influenced by social factors such as peer use, e-cigarette use may be more susceptible to peer influence compared to cigarette smoking, potentially due to differences in social norms and perceptions. For example, during the study period (i.e., 2014–2019), e-cigarette use became the most common tobacco product used while cigarette smoking continued to decline [25, 26]. It is possible that e-cigarettes are more socially accepted and perceived as fashionable compared to cigarettes among adolescents [27]. As such, use of e-cigarettes among one’s peers can have a greater influence on one’s own decision to use the products. As a relatively new type of product, e-cigarettes may appeal to adolescents’ novelty seeking tendency [28]. Further, e-cigarettes may be perceived as less harmful or less socially stigmatized compared to cigarette smoking, which could influence the degree to which peers influence each other's behavior. These differences in perception about e-cigarette use and cigarette smoking may contribute to the difference in peer influence vs. peer selection processes to the extent that smoking may be less socially accepted, and individuals who smoke may be more motivated to seek others who smoke. Supportive evidence includes findings that some adolescents perceived e-cigarettes as “cool” or used as a vehicle to socialize with peers [29, 30]. Further research is needed to fully understand the nuances of how peer influence operates in the context of e-cigarette use and how it may differ from traditional cigarette smoking. It is an important area of study given that the prevalence of underage e-cigarette use remains a significant public health concern. Future research comparing reasons to use between e-cigarettes and cigarettes will further be able to shed new light on mechanisms underlying these observed differences.

Findings from this study have important public health implications – peers may be key players in preventing youth e-cigarette use. For example, prevention programs may benefit from incorporating peer-oriented strategies such as fostering resistance to peer pressure among nonusers or using peer-lead approaches to reduce e-cigarette use among users. In a recent publication, Chu and colleagues showed the feasibility of such a program: peer-lead approaches were well accepted among teachers and students [31, 32]. Compared to an expert-led approach, peer-led approaches were more engaging; despite being statistically non-significant partially due to the limited sample size, point estimates suggested that peer-led approaches may be more effective than an expert-lead approach [31]. Previous studies on adolescent alcohol use further suggest that peer influence can be a malleable socio-environmental factor for prevention and intervention [33]. Expanding such research to e-cigarette use initiation and behaviors may shed new light on whether peers can be potential venues for youth tobacco prevention and intervention.

In contrast to previous findings about age- and sex-related variations in the relationship between peer and own e-cigarette use [14] and cigarette smoking [6, 13], we found limited evidence for differences by age and sex. A few methodological differences may contribute to the divergence, including the study design (longitudinal vs. cross-sectional), study population (national vs. regional samples), study period, or statistical models used. It is noteworthy that we were not able to fully investigate the relationship among older adolescents using the cross-lagged model as they became adults in later time points. Nonetheless, our findings provide insights for future studies and are in line with previous studies showing that peer use remains an important factor for e-cigarette use throughout adolescence.

Findings from this study should be interpreted with the following limitations in mind. First, the study is observational in nature. Although the cross-lagged models took into account previous behaviors, potential effects of time-varying variables could not be ruled out. Evidence from experimental studies will complement findings from this study to gauge the size of the potential effect of peer influence on e-cigarette use. Mechanistic research that incorporates potential mediators, such as perceived benefits and self-efficacy, can bear important implications for intervention [33]. Second, we focused on current use status in this study and provided overall estimates. Future studies that separate the onset and persistence of use processes will be able to assess any potential variation by stages of e-cigarette use. In a previous study, we found that best friends offering predicted the onset of e-cigarette use among youth [34]. Another study using PATH data found that having friends who used e-cigarettes predicted both initiation and persistence of use; stronger association was found at the initiation stage [7]. A study on different stages of e-cigarette use will provide further insights about the nature of the relationship. Third, all information was based on self-reporting, which is of reasonable validity [35]. Nonetheless, studies that incorporate bio-verification are less prone to misclassification. In addition, information about peer e-cigarette use was not available once participants turned 18, which, along with attrition, resulted in lower statistical powers at later time points. Fourth, the prevalence of e-cigarette was relatively low at time 1 in this sample as a result of participants being younger and e-cigarettes being a relatively new product at earlier waves of the study. The associations between peer and own e-cigarette use might change as e-cigarette become more common in the marketplace, especially for older youth who had become adults in later waves. Results based on more recent data will shed important light on this issue. Finally, we explored subgroup variations by sex and age, arguably two of the most important covariates in youth e-cigarette use, in this study. Future studies on potential subgroup variations by other characteristics may provide useful information for tailored prevention and intervention strategies. Counterbalancing strengths of the study include (a) the use of a nationally representative sample facilitated the generalization of results to household dwelling youths in the US; and (b) the longitudinal design with five waves of data enabled the use of the cross-lagged model to further interrogate mutual relationships with clear temporal sequences.

Conclusions

We found that peer influence paths were more robust than peer selection paths for youth e-cigarette use.

Availability of data and materials

The datasets generated and/or analyzed during the current study are available at https://www.icpsr.umich.edu/icpsrweb/NAHDAP/studies/36498.

Abbreviations

US:

United States

NYTS:

National Youth Tobacco Survey

GLM:

Generalized linear regression

PATH:

Population Assessment of Tobacco and Health

ACASI:

Audio computer assisted self-interviews

WLSMV:

Weighted least square mean and variance

RMSEA:

Root mean square of approximation

CFI:

Comparative fit index

TLI:

Tucker-Lewis index

References

  1. Cooper M, Park-Lee E, Ren C, Cornelius M, Jamal A, Cullen KA. Notes from the field: e-cigarette use among middle and high school students - United States, 2022. MMWR Morb Mortal Wkly Rep. 2022;71(40):1283–5.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Mertens AEJ, Kunst AE, Lorant V, Alves J, Rimpela A, Clancy L, et al. Smoking cessation among adolescents in Europe: the role of school policy and programmes. Drug Alcohol Depend. 2021;227:108945.

    Article  PubMed  Google Scholar 

  3. Barrington-Trimis JL, Berhane K, Unger JB, Cruz TB, Huh J, Leventhal AM, et al. Psychosocial factors associated with adolescent electronic cigarette and cigarette use. Pediatrics. 2015;136(2):308–17.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Vogel EA, Ramo DE, Rubinstein ML. Prevalence and correlates of adolescents’ e-cigarette use frequency and dependence. Drug Alcohol Depend. 2018;188:109–12.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Pedersen ER, Miles JN, Ewing BA, Shih RA, Tucker JS, D’Amico EJ. A longitudinal examination of alcohol, marijuana, and cigarette perceived norms among middle school adolescents. Drug Alcohol Depend. 2013;133(2):647–53.

    Article  PubMed  Google Scholar 

  6. Duan L, Chou CP, Andreeva VA, Pentz MA. Trajectories of peer social influences as long-term predictors of drug use from early through late adolescence. J Youth Adolesc. 2009;38(3):454–65.

    Article  PubMed  Google Scholar 

  7. Cavazos-Rehg P, Li X, Kasson E, Kaiser N, Borodovsky J, Grucza RA. Investigating the role of familial and peer-related factors on electronic nicotine delivery systems (ENDS) use among U.S. adolescents. J Adolesc. 2021;87:98–105.

    Article  PubMed  Google Scholar 

  8. Alexander C, Piazza M, Mekos D, Valente T. Peers, schools, and adolescent cigarette smoking. J Adolesc Health. 2001;29(1):22–30.

    Article  PubMed  CAS  Google Scholar 

  9. Coleman M, Donaldson CD, Crano WD, Pike JR, Stacy AW. Associations between family and peer e-cigarette use with adolescent tobacco and marijuana usage: a longitudinal path analytic approach. Nicotine Tob Res. 2021;23(5):849–55.

    Article  PubMed  Google Scholar 

  10. Tsai J, Walton K, Coleman BN, Sharapova SR, Johnson SE, Kennedy SM, et al. Reasons for electronic cigarette use among middle and high school students—National Youth Tobacco Survey, United States, 2016. Morb Mortal Wkly Rep. 2018;67(6):196.

    Article  Google Scholar 

  11. Parks MJ, Patrick ME. Protective Factors for Nicotine and Marijuana Vaping Among U.S. Adolescents. Am J Prev Med. 2022;62(3):414–21.

    Article  PubMed  Google Scholar 

  12. Audrain-McGovern J, Rodriguez D, Pianin S, Alexander E. Initial e-cigarette flavoring and nicotine exposure and e-cigarette uptake among adolescents. Drug Alcohol Depend. 2019;202:149–55.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Liao Y, Huang Z, Huh J, Pentz MA, Chou CP. Changes in friends’ and parental influences on cigarette smoking from early through late adolescence. J Adolesc Health. 2013;53(1):132–8.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Etim N, Pike J, Xie B. Age-varying associations between e-cigarette use and peer use, household use, and exposure to e-cigarette commercials among alternative high school students in Southern California. Tob Induc Dis. 2020;18:7.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Gentzke AS, Wang TW, Cornelius M, Park-Lee E, Ren C, Sawdey MD, et al. Tobacco Product Use and Associated Factors Among Middle and High School Students - National Youth Tobacco Survey, United States, 2021. MMWR Surveill Summ. 2022;71(5):1–29.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Ennett ST, Bauman KE. The contribution of influence and selection to adolescent peer group homogeneity: the case of adolescent cigarette smoking. J Pers Soc Psychol. 1994;67(4):653–63.

    Article  PubMed  CAS  Google Scholar 

  17. Engels RC, Knibbe RA, Drop MJ, de Haan YT. Homogeneity of cigarette smoking within peer groups: influence or selection? Health Educ Behav. 1997;24(6):801–11.

    Article  PubMed  CAS  Google Scholar 

  18. Hoffman BR, Monge PR, Chou CP, Valente TW. Perceived peer influence and peer selection on adolescent smoking. Addict Behav. 2007;32(8):1546–54.

    Article  PubMed  Google Scholar 

  19. Loan CM, Khurana A, Wright J, Romer D. Selection versus socialization effects of peer norms on adolescent cigarette use. Tob Use Insights. 2021;14:1179173X211066005.

    Article  PubMed  PubMed Central  Google Scholar 

  20. United States Department of Health and Human Services. Population Assessment of Tobacco and Health (PATH) Study [United States] Public-Use Files User Guide. In: Services DoHaH, editor. Ann Arbor, Michigan 48106: Inter-university Consortium for Political and Social Research; 2017.

  21. Steiger JH. Structural model evaluation and modification: an interval estimation approach. Multivariate Behav Res. 1990;25(2):173–80.

    Article  PubMed  CAS  Google Scholar 

  22. Bentler PM. Comparative fit indexes in structural models. Psychol Bull. 1990;107(2):238–46.

    Article  PubMed  CAS  Google Scholar 

  23. Hu Lt, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Model. 1999;6(1):1–55.

    Article  Google Scholar 

  24. Bentler PM, Bonett DG. Significance tests and goodness of fit in the analysis of covariance structures. Psychol Bull. 1980;88(3):9.

    Article  Google Scholar 

  25. Wang TW, Neff LJ, Park-Lee E, Ren C, Cullen KA, King BA. E-cigarette use among middle and high school students—United States, 2020. Morb Mortal Wkly Rep. 2020;69(37):1310.

    Article  Google Scholar 

  26. Singh T, Arrazola RA, Corey CG, Husten CG, Neff LJ, Homa DM, et al. Tobacco use among middle and high school students-United States, 2011–2015. MMWR Morb Mortal Wkly Rep. 2016;65(14):361–7.

    Article  PubMed  Google Scholar 

  27. Sapru S, Vardhan M, Li Q, Guo Y, Li X, Saxena D. E-cigarettes use in the United States: reasons for use, perceptions, and effects on health. BMC Public Health. 2020;20(1):1518.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Wills TA, Vaccaro D, McNamara G. Novelty seeking, risk taking, and related constructs as predictors of adolescent substance use: an application of Cloninger’s theory. J Subst Abuse. 1994;6(1):1–20.

    Article  PubMed  CAS  Google Scholar 

  29. Kong G, Morean ME, Cavallo DA, Camenga DR, Krishnan-Sarin S. Reasons for electronic cigarette experimentation and discontinuation among adolescents and young adults. Nicotine Tob Res. 2015;17(7):847–54.

    Article  PubMed  Google Scholar 

  30. Bernat D, Gasquet N, Wilson KO, Porter L, Choi K. Electronic cigarette harm and benefit perceptions and use among youth. Am J Prev Med. 2018;55(3):361–7.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Chu KH, Matheny S, Furek A, Sidani J, Radio S, Miller E, et al. Identifying student opinion leaders to lead e-cigarette interventions: protocol for a randomized controlled pragmatic trial. Trials. 2021;22(1):31.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Chu KH, Sidani J, Matheny S, Rothenberger SD, Miller E, Valente T, et al. Implementation of a cluster randomized controlled trial: Identifying student peer leaders to lead E-cigarette interventions. Addict Behav. 2021;114: 106726.

    Article  PubMed  Google Scholar 

  33. Durkin K, Williford DN, Turiano NA, Blank MD, Enlow PT, Murray PJ, et al. Associations between peer use, costs and benefits, self-efficacy, and adolescent e-cigarette use. J Pediatr Psychol. 2021;46(1):112–22.

    Article  PubMed  Google Scholar 

  34. Cheng HG, Lizhnyak PN, Knight NA, Vansickel AR, Largo EG. Youth susceptibility to tobacco use: is it general or specific? BMC Public Health. 2021;21(1):1913.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Wong SL, Shields M, Leatherdale S, Malaison E, Hammond D. Assessment of validity of self-reported smoking status. Health Rep. 2012;23(1):47–53.

    PubMed  Google Scholar 

Download references

Acknowledgements

The authors wish to thank Dr. Edward Largo and Dr. Andrea Vansickel for their constructive comments and PATH staff for making the data publicly available.

Funding

None.

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Authors and Affiliations

  1. Altria Client Services LLC, 601 E. Jackson, Richmond, VA, 23219, USA

    Hui G. Cheng, Pavel N. Lizhnyak & Nadja Richter

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  1. Hui G. Cheng
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Contributions

HGC conceptualized and designed the study. HGC and PNL acquired and analyzed data. HGC and NR wrote the first draft. All authors critically revised the manuscript for important intellectual content.

Corresponding author

Correspondence to Hui G. Cheng.

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Ethics approval and consent to participate

Not applicable. The study used de-identified publicly available data only.

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Competing interests

All authors are employees of Altria Client Services, LLC. The authors have no other competing interests to declare.

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Cheng, H.G., Lizhnyak, P.N. & Richter, N. Mutual pathways between peer and own e-cigarette use among youth in the United States: a cross-lagged model. BMC Public Health 23, 1609 (2023). https://doi.org/10.1186/s12889-023-16470-5

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BMC Public Health

ISSN: 1471-2458

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