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Published in final edited form as: Prev Med. 2021 Sep 3;153:106781. doi: 10.1016/j.ypmed.2021.106781

E-Cigarette Initiation Predicts Subsequent Academic Performance among Youth: Results from the PATH Study

Craig T Dearfield a, Julia C Chen-Sankey b, Timothy S McNeel c, Debra H Bernat a, Kelvin Choi b
PMCID: PMC8595658  NIHMSID: NIHMS1741986  PMID: 34487749

Abstract

Research shows cigarette smoking is associated with lower academic performance among youth. This study examines how initiating e-cigarette use is associated with subsequent academic performance. Data from Waves 2–4 youth and parent surveys of the Population Assessment of Tobacco and Health (PATH) Study were analyzed. Youth (12–15 years old) who reported never using any tobacco products at Wave 2 were included in the analysis (n=4,960). Initiation of e-cigarettes and cigarettes was assessed at Wave 3. Weighted multivariable linear regression models were tested to assess the association between e-cigarette and cigarette initiation at Wave 3 and academic performance at Wave 4, controlling for covariates at Wave 2. At Wave 3, 4.3% and 1.9% of youth initiated e-cigarette and cigarette use, respectively. Youth who initiated e-cigarette use at Wave 3 had lower academic performance at Wave 4, compared to those who did not initiate e-cigarette use (adjusted regression coefficient [ARC] −0.22, 95% confidence interval [CI] −0.43, −0.02). Initiating cigarettes was also associated with lower academic performance (ARC −0.51, 95% CI −0.84, −0.18). Results indicate that e-cigarette use initiation is associated with lower subsequent academic performance, independent from the association between cigarette use initiation and lower academic performance among U.S. youth. Future research needs to examine whether preventing youth e-cigarette and cigarette use can lead to improvement in academic performance.

Introduction

National cross-sectional studies consistently show a negative relationship between educational attainment and cigarette smoking (Stiby et al., 2015; Townsend et al., 2007; Wang et al., 2018a). According to the 2019 U.S. National Health Interview Survey, the use of any tobacco product was 26.4% among adults with no high school diploma, and 8.7% among those with a graduate degree (Cornelius et al., 2020). In 2019, estimates from the National Survey on Drug Use and Health (NSDUH) indicated that 12.4% of adults (18 years or older) with college degrees or higher were current users of any tobacco product compared with 29.6% of those without a high school degree and 30.4% of those who are high school graduates. Heavier tobacco use and nicotine dependence have been consistently shown to be higher in those with lower academic performance and attainment (Coban et al., 2018; Corona et al., 2009; Cox et al., 2007; Gilman et al., 2008; Moor et al., 2015; Stea and Torstveit, 2014).

Evidence suggests that cigarette smoking is related to later academic performance in youth (Bryant et al., 2000; Ellickson et al., 2001; Georgiades and Boyle, 2007; Latvala et al., 2014; Tucker et al., 2008). Two studies assessed smoking and academic achievements in a U.S. sample from Oregon and California students in 7th grade and 12th grade. The first study found that heavier cigarette smoking early in adolescence was associated with a higher likelihood of later poor academic achievement (Tucker et al., 2008). Another study of this group identified as cigarette smokers in 7th grade were more likely to have poorer academic achievement in 12th grade compared to nonsmokers (Ellickson et al., 2001). Findings from these cohort studies indicate cigarette use in youth is associated with lower educational attainment, which could lead to poorer health outcomes later in life (Ellickson et al., 2001; Georgiades and Boyle, 2007; Latvala et al., 2014).

Prior studies have focused primarily on the role of cigarette smoking on academic achievement, as conventional cigarettes have traditionally been the tobacco product most commonly used by middle and high school students (Bryant et al., 2000; Coban et al., 2018; Corona et al., 2009; Cox et al., 2007; Gentzke et al., 2019; Gilman et al., 2008; Moor et al., 2015; Tucker et al., 2008). However, youth cigarette smoking in the U.S. has declined and e-cigarette use has increased (Mooney-Leber and Gould, 2018; Wang et al., 2019). Between 2011 and 2019, current conventional cigarette use among high school students decreased from 15.8% to 4.3% (Wang et al., 2018b; Wang et al., 2019). During the same period, current e-cigarette use increased from 1.5% to 20.0% (Wang et al., 2018b; Wang et al., 2019). Frequent e-cigarette use is also prevalent, with 38.9% of current high school e-cigarette users indicated using the product on >20 days in the past 30 days, and 22.5% indicated using e-cigarettes daily in 2020 (Wang et al., 2020). Further, 73.4% of high school students have seen e-cigarettes being used on school grounds (Gentzke et al., 2020).

Despite the increased prevalence of youth e-cigarette use, no longitudinal studies have examined the association of e-cigarette use initiation on subsequent academic achievement. Findings from one cross-sectional study of a U.S. nationally representative sample of high school seniors indicated that exclusive e-cigarette users had lower academic achievement and were less likely to plan to go to college compared with nonusers, but had higher academic achievement and were more likely to plan to go to college than those who exclusively use conventional cigarettes (McCabe et al., 2017). A longitudinal study of 7th graders in Helsinki, Finland found that poor academic achievement in 7th grade predicted e-cigarette experimentation by 9th grade, but did not report the association between e-cigarette experimentation in 7th grade on academic achievement at 9th grade (Kinnunen et al., 2018).

Therefore, the present study examines the longitudinal association of e-cigarette initiation on subsequent academic performance. The current study utilizes prospective data from the Population Assessment of Tobacco and Health (PATH) study, which includes a national sample of youth, to examine how e-cigarette use initiation is associated with subsequent academic performance. The current study fills an important gap in the literature by examining the effects of e-cigarettes on subsequent academic performance, which is an important factor for educational attainment, a well-documented social determinant of health.

Methods

Study Sample

This study used data from Waves 2–4 youth survey public-use files (2014–2018) of the PATH Study, which includes a nationally representative, longitudinal cohort of civilian, non-institutionalized youth in the U.S. (Hyland et al., 2017). Wave 2 was administered from October 2014 to October 2015, Wave 3 was administered from October 2015 to October 2016, and Wave 4 was administered from December 2016 to January 2018. The PATH Study’s weighted response rate at Wave 1 was 78.4% for youth, and the weighted retention rates for Waves 2, 3, and 4 among Wave 1 youth respondents were 87.3%, 83.3%, and 79.5%, respectively (U.S. Food and Drug Administration, 2018). The PATH Study youth survey was also accompanied by a questionnaire completed by youth respondents’ parents or legal guardians for all waves. Detailed study methods of the PATH Study have been published (Hyland et al., 2017; U.S. Food and Drug Administration, 2018). For this prospective analysis, the sample was restricted to youth respondents who completed Waves 2–4 surveys and had never used any tobacco product at Wave 2 (ages 12–15 at Wave 2; n=5,675). All respondents were in high school and were aged under 18 years at Wave 4.

Measures

Academic Performance.

At Wave 4, youth respondents’ parents reported how they would describe their child’s performance at school in the past 12 months. Parental report of a child’s grades is adequately valid (Gilger, 1992). Response options ranged mostly A’s (1) mostly F’s (9) and included an option to indicate if their child’s school was ungraded. A new continuous variable (range=1–9) for academic performance was created, with higher numbers indicating better grades and lower numbers indicating poorer grades (i.e., 1=Mostly F’s, 9=Mostly A’s). The scale of “Mostly A’s” to “Mostly F’s” has been used in studies assessing the relationship between cigarette use and academic performance, (Cox et al., 2007; Tucker et al., 2008) and has been used in analyses with PATH data as an outcome evaluating the effect of secondhand smoke on academic performance (Choi et al., 2020; Sawdey et al., 2019).

E-cigarette and cigarette use initiation.

At Wave 3, youth respondents indicated whether they had used a range of tobacco products: cigarettes, e-cigarettes, and other tobacco products (including traditional cigars, filtered cigars, cigarillos, hookah, snus, smokeless tobacco, dissolvable, pipe tobacco, bidis, and kreteks). The respondents who answered “Yes” to the questions regarding the use of a product at Wave 3 were considered to have initiated using that product between Waves 2 and 3. One variable was created for those who indicated they initiated cigarette use and a second variable was created indicated those who initiated e-cigarette use between Waves 2 and 3.

Covariates.

Sociodemographic characteristics measured at Wave 2 were included as covariates: age, sex, race/ethnicity, highest educational attainment of the respondents’ parents, and living with a tobacco user (see Table 1 for variable categories). These variables were included as known risk factors for youth cigarette use (Conway et al., 2017; Sawdey et al., 2019) and poorer academic achievement (Cox et al., 2007; Valdez et al., 2011). A variable was created to indicate youth who initiated use of other tobacco products specified above. Psychosocial covariates at Wave 2 included self-reported past-month internalizing problem symptoms (comprised of symptoms of depression, anxiety, distress, and trouble sleeping), externalizing problem symptoms (comprised of having a hard time paying attention, having a hard time listening to directions, having bullied or threatened others, started a physical fight, felt restless, and answered before the other person finished asking the question), and substance use problems (comprised of dependence, withdrawal, and health and psychiatric symptoms related to any alcohol or drug use disorders) (Dennis et al., 2006; Sawdey et al., 2019). Internalizing, externalizing, and substance use problems were measured by metrics from the Global Appraisal of Individual Needs Short Screener (GAIN-SS) (Conway et al., 2017; Dennis et al., 2006) and have shown moderate to high validity and reliability among youth (Iacono et al., 2008). Scores from PATH based on the GAIN-SS for these covariates were calculated to indicate whether the respondent had none of the symptoms in the scale or one or more symptoms in the last year. Respondents also reported other alcohol or other drug use and spending a lot of time getting alcohol and other drugs. Parent-reported Wave 2 respondents’ academic performance was also included as a covariate.

Table 1.

Sample characteristics of Wave 2 never tobacco use who completed Waves 3 and 4, PATH Study Youth Survey (2014-2017; N=4,960)

Characteristics N Weighted %
Wave 2 age (years)
12-14 3,846 77.1
15 1,114 22.9
Wave 2 sex
Male 2,464 49.9
Female 2,496 50.1
Wave 2 race/ethnicity
Hispanic 1,467 23.2
Non-Hispanic White 2,333 53.0
Non-Hispanic Black 698 13.9
Non-Hispanic other 462 10.0
Wave 2 parent education
High school or less 1,674 29.6
Some college 1,390 27.7
College or more 1,507 35.0
Missing 389 7.7
Wave 2 past-12-month internalized problems
No 1,855 36.8
Yes 3,105 63.2
Wave 2 past-12-month externalized problems
No 1,397 27.4
Yes 3,563 72.6
Wave 2 past-12-month substance use problems
No 4,737 95.5
Yes 223 4.5
Wave 2 living with at least one tobacco user
No 3,611 73.8
Yes 1,349 26.2
Wave 2 past-12-month academic performance (1-9) 7.62 (0.02)
Wave 3 ever used tobacco other than cigarettes / e-cigarettes
No 4,863 98.1
Yes 97 1.9
Wave 3 ever smoked cigarette
No 4,870 98.1
Yes 90 1.9
Wave 3 ever used e-cigarette
No 4,749 95.7
Yes 211 4.3
Wave 4 past-12-month academic performance (1-9) 7.52 (0.02)
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*

Means and standard errors were provided for academic performances at Waves 2 and 4.

Statistical Analysis

First, sociodemographic and psychosocial characteristics of the youth respondents who had never used any types of tobacco products at Wave 2 were examined. Second, a series of linear regression models were used to assess bivariate associations of Wave 3 initiation of cigarette use, e-cigarette use, other tobacco product use, and Wave 2 covariates separately with Wave 4 academic performance. Third, multivariable linear regression model was conducted to assess the association between the Wave 3 initiation of cigarette and e-cigarette use and Wave 4 academic performance, controlled for Wave 2 sociodemographic and psychosocial characteristics, Wave 2 academic performance, and Wave 3 other tobacco product use initiation. Wave 4 weights were used when calculating proportions with 95% confidence intervals, using the balanced repeated replications (BRR) method with Fay’s adjustment of 0.3 Wave 4 weights also accounted for respondents lost to follow-up from across the three waves (U.S. Food and Drug Administration, 2018). Imputed socioeconomic covariates were used when available, including an “undetermined” category for variables with missing values greater than 5%, and observations with missing values were excluded by listwise deletion for regression models (Hamilton, 2012), resulting in a final analytic sample n=4,960). These statistical analyses were performed using SAS® Enterprise version 9.4.

To assess the impact of missing values on the findings, full information maximum likelihood models were used in a sensitivity analysis on the association between cigarette and e-cigarette initiation and subsequent academic performance adjusting for covariates (n=5,675). This analysis was conducted in Mplus version 8. This research only involved the use of de-identified data, which is not considered human subjects research and requires no review or approval by an institutional review board per National Institutes of Health policy and 45 CFR 46.

Results

The characteristics of the study sample are shown in Table 1. Overall, at Wave 2, 50.1% of respondents who reported never using tobacco were female; 77.1% were 12-14 years old, 22.9% were 15 years old; 23.2% were Hispanic, 13.9% were non-Hispanic Black, 53.0% were non-Hispanic White, and 10.0% were non-Hispanic other; 29.6% had parents with a high school education or less; 63.2% reported any internalizing problem symptoms, 72.6% reported any externalizing problem symptoms, and 4.5% reported substance use problems in the past year; and 26.2% were living with at least one tobacco user. The average parent-reported academic performance at Wave 2 was 7.62, which was between “Mostly B’s” and “A’s and B’s.” At Wave 4, the average academic performance was 7.52, which was also between “Mostly B’s” and “A’s and B’s,” but slightly lower. Among never tobacco users at Wave 2, 4.3% initiated using e-cigarettes and 1.9% initiated cigarette use at Wave 3.

Table 2 shows the unadjusted and adjusted associations between Wave 3 cigarette and e-cigarette use initiation and Wave 4 academic performance among youth who had not used any tobacco products at Wave 2. In the unadjusted model, all the tested variables except initiating the use of a tobacco product other than cigarettes and e-cigarettes significantly affected academic performance in Wave 4 (Tables 3; p<0.05).

Table 2.

Association between Wave 3 cigarette and e-cigarette use initiation and Wave 4 academic performance among Wave 2 never tobacco users: PATH Study Youth Survey (2014-2017; N=4,960)

Characteristics Wave 4 academic performance
Crude RC
(95% CI)		 Adjusted RC
(95% CI)
Wave 3 cigarette use initiation (Ref: No) −0.98 (−1.42, −0.54) −0.51 (−0.84, −0.18)
Wave 3 e-cigarette use initiation (Ref: No) −0.56 (−0.82, −0.31) −0.22 (−0.43, −0.02)
Wave 3 other tobacco use initiation (Ref: No) −0.27 (−0.85, 0.31) 0.22 (−0.28, 0.73)
Wave 2 age (years; Ref: 15)
12-14 −0.11 (−0.22, −0.00) −0.11 (−0.20, −0.03)
Wave 2 sex (Ref: Female)
Male −0.54 (−0.65, −0.43) −0.29 (−0.37, −0.21)
Wave 2 race/ethnicity (Ref: Non-Hispanic White)
Hispanic −0.49 (−0.60, −0.38) −0.11 (−0.21, −0.01)
Non-Hispanic Black −0.68 (−0.91, −0.54) −0.21 (−0.31, −0.11)
Non-Hispanic other 0.24 (0.08, 0.39) 0.18 (0.08, 0.28)
Wave 2 parent education (Ref: College or more)
High school or less −1.00 (−1.11, −0.89) −0.33 (−0.43, −0.23)
Some college −0.76 (−0.87, −0.65) −0.32 (−0.41, −0.22)
Missing −0.33 (−0.46, −0.19) −0.07 (−0.19, 0.04)
Wave 2 past-12-month internalized problems (Ref: No) 0.13 (0.03, 0.22) 0.00 (−0.07, 0.07)
Wave 2 past-12-month externalized problems (Ref: No) 0.14 (0.03, 0.26) 0.07 (−0.03, 0.16)
Wave 2 past-12-month substance use problems (Ref: No) −0.34 (−0.60, −0.09) −0.15 (−0.36, 0.05)
Wave 2 living with at least one tobacco user (Ref: No) −0.53 (−0.64, −0.41) −0.15 (−0.23, −0.07)
Wave 2 past-12-month academic performance 0.65 (0.61, 0.68) 0.58 (0.54, 0.61)
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In the adjusted model, initiating e-cigarette use was significantly associated with lower academic performance over the one-year period (adjusted regression coefficient [ARC]=−0.22, 95% confidence interval [CI]=−0.43, −0.02). Additionally, initiating cigarette use was significantly associated with lower academic performance over the one-year period (ARC=−0.51, 95% CI=−0.84, −0.18), and the association was apparently stronger than that between e-cigarette use initiation and academic performance. Nonetheless, the confidence intervals of these adjusted regression coefficients largely overlapped. In a sensitivity analysis using a full information maximum likelihood model, we found similar association of initiating e-cigarette use (ARC=−0.21, 95% CI=−0.41, −0.01) and initiating cigarette use (ARC=−0.50, 95% CI==0.80, −0.20) with subsequent academic performance.

Several covariates were significantly associated with academic performance in the adjusted model(Tables 3; p<0.05). Higher academic performance at Wave 2 was significantly associated with higher academic performance at Wave 4. Respondents who were between the ages of 12 and 14 years at the time of the survey (ref. age 15 years), male (ref. female), Hispanic and non-Hispanic Black (ref. non-Hispanic White), having parents with high school or less and some college (ref. parents with a college degree or more), and living with at least one tobacco user (ref. living with no tobacco users) was significantly associated with lower academic performance at Wave 4. Self-identification as non-Hispanic other (ref. non-Hispanic Whites) was associated with higher academic performance.

Discussion

The purpose of this study was to examine the relationship between e-cigarette use initiation and subsequent academic performance. Using a national sample of youth from the PATH study, we found that initiation of e-cigarette use at Wave 3 was associated with lower academic performance at Wave 4. One potential pathway for this finding is the association between nicotine exposure and attentional and learning problems. Most e-cigarette products deliver nicotine (U.S. Department of Health and Human Services, 2016). Higher nicotine dependence and adverse effects of abstinence are associated with a higher degree of attentional problems and memory issues essential for academic performance (Mooney-Leber and Gould, 2018). Nicotine exposure is also associated with sleep disruption, having insufficient sleep and rest, and feeling tired or not well-rested (Boehm et al., 2016; Sabanayagam and Shankar, 2011), all of which have been shown to negatively affect cognitive function and focus in the classroom (Waisman Campos et al., 2016). These symptoms have been shown to manifest within a year of tobacco use initiation, and often in much less time (DiFranza et al., 2000; Kandel et al., 2007; Ridenour et al., 2006). Additionally, nicotine and tobacco-related illnesses affect absenteeism, which is linked to lower academic performance (García and Weiss, 2018). Further research is needed to test the unique effects of nicotine on hypothesized pathways of cognitive functioning and sleep disruption as it relates to e-cigarette use and academic performance. This research may be especially important as the nicotine concentration in e-cigarettes have been increasing (Mooney-Leber and Gould, 2018; Romberg et al., 2019; Wang et al., 2019).

Furthermore, the identified association can be explained by youth e-cigarette users commonly using the products on the school ground or during school time (Dai, 2021). Students who use e-cigarettes may be more distracted at school when attending classes or completing homework than the youth who do not use e-cigarettes. Additionally, potential punishment, such as suspension or expulsion, for youth who are caught using tobacco at school might reduce time in school for learning (Public Health Law Center, 2019). Effective school-based educational and tobacco-free programs are needed to prevent youth e-cigarette use on school grounds and in other settings. Future studies are needed to examine whether efforts to prevent youth tobacco use, especially e-cigarette and cigarette use, can improve youth academic performance in addition to health.

The results of this study suggest that both e-cigarette and cigarette initiation are associated with academic performance. While the association for conventional cigarettes appeared to be greater than the association for e-cigarettes, the 95% confidence intervals of these two adjusted regression coefficients largely overlap, indicating that these two associations were unlikely to be different from each other. The current regression models included parental education and psychosocial covariates to control for common predictors of e-cigarette and cigarette use and factors that affect risk profiles for youth who use these products (Wills, 2017; Wills et al., 2015). Therefore, future research is needed to further assess the relative effects of cigarette and e-cigarette use on academic performance and factors that drive any potential differences.

This study had several limitations. The analysis models did not examine past-30-day or progression to regular cigarette and e-cigarette use. This is because few youth respondents became past-30-day e-cigarette users and cigarette smokers between Waves 2 and 3. It is possible that youth who initiated e-cigarette use by Wave 3 became current or regular e-cigarette users by the Wave 4 assessment, experiencing greater influence from e-cigarette use and dependency on academic performance. Further research is needed to test these hypothesized pathways between tobacco use behaviors, both experimentation and regular use, and academic performance. This should include assessing the long-term social and health impacts of sustained tobacco use beyond high school academic achievement. Other limitations were due to the observational nature of the study. Academic performance was based on parental reports of the students’ performance in the past year, which could be subject to recall errors. Child self-report and school records are not available in the PATH data. Previous findings show that parent report of grades is adequately valid compared to child self-report (Gilger, 1992). Other studies, both of PATH data and other data sources, have used parental reported 9-point academic achievement scale as a continuous variable (Choi et al., 2020; Cox et al., 2007; Sawdey et al., 2019; Tucker et al., 2008), and a limitation of the current study is that grade point average as a continuous scale was not available in the PATH dataset. Another limitation is that although the current analysis adjusted for previous academic performance, internalizing and externalizing problems, and other known risk factors for tobacco use and academic performance, uncontrolled confounders could attenuate our findings. This would include exploring common liabilities that were not examine in our analysis. As part of this limitation, Wave 2 PATH did not include peer use or tobacco-related social norms, so we could not control for those factors, despite that adolescents are influenced by the tobacco use norms of their peers (Cooper et al., 2016; Eisenberg and Forster, 2003; Eisenberg et al., 2014). Finally, due to the low number of youth initiating both e-cigarette and cigarette use during the study period (n=48), we were unable to examine the joint effect of initiating both products on academic performance.

Conclusion

The current study adds to the literature on the relationship between tobacco-cigarette use and academic performance. Results show that e-cigarette use initiation is associated with later low academic performance independent from cigarette and other tobacco product use initiation. These findings are of particular concern given that e-cigarettes are now the most widely used tobacco product among U.S. youth. Our findings reinforce the importance of reducing all tobacco use among youth, which may improve future educational attainment in addition to protecting their health. Future research is needed to examine whether interventions including tobacco use prevention programs and tobacco-free policies in school settings, age-appropriate tobacco use cessation treatments, and educational activities on the short- and long-term impact of tobacco use may help improve academic performance among youth.

Supplementary Material

1

Highlights:

  • E-cigarette use initiation is associated with lower subsequent academic performance

  • This association is independent from cigarette use initiation

  • Initiation of cigarettes and e-cigarettes independently influence later academic performance

Acknowledgments:

Drs. Choi and Chen-Sankey’s work on this article was funded by the Division of Intramural Research, National Institute on Minority Health and Health Disparities. Opinions and comments expressed in this article belong to the authors and do not necessarily reflect those of the U.S. Government, the Department of Health and Human Services, the National Institutes of Health, the National Institute on Minority Health and Health Disparities, and the U.S. Food and Drug Administration.

Funding Source:

Drs. Choi and Chen-Sankey’s work on this article was funded by the Division of Intramural Research, National Institute on Minority Health and Health Disparities. Dr. Chen-Sankey is also funded by the NIH grant K99CA242589.

Footnotes

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Financial Disclosures: The remaining authors have no financial relationships relevant to this article to disclose.

Conflict of Interest: The other authors have no conflicts of interest to disclose.

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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