Video Abstract

Video Abstract

BACKGROUND:

Surveys have been instrumental in describing adolescent use of tobacco, electronic cigarettes (e-cigarettes), and marijuana. However, objective biomarker data are lacking. We compared adolescent self-reported use to urinary biomarkers.

METHODS:

From April 2017 to April 2018, adolescents 12 to 21 years old completed an anonymous questionnaire regarding tobacco, e-cigarette, and marijuana use and provided a urine sample. Urine was analyzed for biomarkers cotinine, total 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol, and tetrahydrocannabinolic acid (THCA).

RESULTS:

Of 517 participants, 2.9% reported using tobacco, 14.3% e-cigarettes, and 11.4% marijuana in the past week. Only 2% reporting no smoking had total 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol levels above cutoff (14.5 pg/mL); 2% of non–e-cigarette users had cotinine above cutoff (10 ng/mL); 2% of those denying marijuana use had THCA above cutoff (10 ng/mL). Daily e-cigarette users showed significantly higher median cotinine than nondaily users (315.4 [interquartile range (IQR) 1375.9] vs 1.69 ng/mL [IQR 28.2]; P < .003). Overall, 40% who reported using nicotine-free products had cotinine >10 ng/mL. Pod users' median cotinine was significantly higher than in nonpod users (259.03 [IQR 1267.69] vs 1.61 ng/mL [IQR 16.3]; P < .003). Median THCA among daily marijuana users was higher than in nondaily users (560.1 [IQR 1248.3] vs 7.2 ng/mL [IQR 254.9]; P = .04). Sixty-one percent of those with cotinine >10 ng/mL vs 39% of those with cotinine<10 ng/mL had THCA >10 ng/mL (P < .001).

CONCLUSIONS:

Adolescents’ self-report correlated with measured urinary biomarkers, but subjects were unaware of their nicotine exposure. More frequent e-cigarette and pod use correlated with elevated biomarkers. Co-use of tobacco, e-cigarettes, and marijuana was corroborated by higher THCA in those with higher cotinine.

What’s Known on This Subject:

Current knowledge about patterns of e-cigarette use among adolescents and e-cigarette co-use with marijuana is based on survey data. In no previous studies have adolescents’ self-reported tobacco, e-cigarette, and marijuana use been compared to urinary biomarkers.

What This Study Adds:

Higher levels of cotinine among adolescents were correlated with frequent e-cigarette use and use of products with high nicotine content (eg, pods). Self-reported co-use of e-cigarettes with marijuana was reflected in high levels of tetrahydrocannabinolic acid in samples with high cotinine levels.

For years, nicotine and marijuana have been among the most popular drugs used by adolescents.1,2 Recently, electronic cigarettes (e-cigarettes) (all generations of electronic nicotine delivery system devices) have surpassed combusted tobacco in popularity. In 2018, 20.8% of high school students reported use of e-cigarettes in the past 30 days (National Youth Tobacco Survey), an increase of 78% from 11.7% reported in 2017.3,4 Marijuana use among teenagers has remained steady: 19.8% of high school students report using marijuana in the past month.5 However, the use of marijuana in vaporizers has increased.6,9 Recent studies reveal that e-cigarette use may be associated with subsequent and even concurrent marijuana use.10,12 

The potential for nicotine addiction is a significant concern with adolescent use of e-cigarettes. The increased risk of subsequent combustible smoking among adolescent e-cigarette users may be indicative of nicotine dependence or addiction.13 Indeed, 2 recent studies have revealed symptoms of nicotine dependence specific to use of e-cigarettes in adolescents.14,15 Such observations are even more alarming in light of the recent popularity of “pods” (the latest generation of e-cigarettes), which have the highest nicotine concentrations to date (59 mg/mL) and have become the most widely used products among adolescents.16,19 

A large and growing body of survey data has been instrumental in describing adolescent use of these products. However, objective biomarker data corroborating this use are lacking. Our primary objective was to compare self-reported use of combusted tobacco, e-cigarettes, and marijuana in adolescents to urinary biomarkers. Secondary objectives were to describe biomarker trends related to self-described use patterns of each product.

From April 2017 to April 2018, a convenience sample of 517 English- or Spanish-speaking adolescents, ages 12 to 21, were recruited from 3 Stony Brook Children’s outpatient offices. Participants were recruited during scheduled clinic visits with a general pediatrician or subspecialist, including adolescent medicine, rheumatology, nephrology, infectious disease, and developmental and/or behavioral providers. Participants were eligible if they were developmentally able to provide assent and complete the survey themselves. Parental consent and adolescent assent were required for participants <18 years old; individuals >18 years old provided their own consent. The study was approved by the Stony Brook University Institutional Review Board.

All participants completed a 60-item anonymous questionnaire regarding their use of combusted tobacco, e-cigarettes, and marijuana. Questions regarding product usage and frequency were adapted from the Population Assessment of Tobacco and Health study.20 Respondents who reported never trying smoking or using e-cigarettes or marijuana were identified as “never users.” Respondents who said they had ever tried smoking, used e-cigarettes, or used marijuana (in any form) were categorized as “ever users” and were then asked about the last time and frequency of use of each product.

All subjects provided a spot urine sample as part of routine clinic intake. Urine samples from all self-reported e-cigarette users (subjects) and a random sample of non–e-cigarette users (as a control group) were stored in a freezer at −20°C, and then sent to Roswell Park Comprehensive Cancer Center for biomarker analysis (see below). Subjects and controls were not chosen on the basis of tobacco-smoking status; analysis for tobacco smoking was conducted on all available samples. On completion of recruitment, an amendment was filed to assess urinary Δ9-tetrahydrocannabinolic acid levels (see below). This analysis was only run on samples for which permission was given by participants for future, unspecified, anonymous testing and included samples from both subject and control groups. Questionnaires and urine samples were identified by and matched with randomly generated study numbers only; no protected health information was collected. All participants received a $5.00 Starbucks gift card for participation.

Before biomarker analysis, we screened all samples for creatinine levels to avoid confounding results from overly dilute or overly concentrated urine specimens. Samples with creatinine concentrations <10 mg/dL or >370 mg/dL were excluded from analysis.21 Samples with creatinine concentrations within acceptable range (10–370 mg/dL) were analyzed for 3 biomarkers: (1) cotinine, a metabolite of nicotine; (2) total 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL), a metabolite of tobacco-specific nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone; and (3) Δ9-tetrahydrocannabinolic acid, the precursor of Δ9-tetrahydrocannabinol in hemp plants.22 The concentration of cotinine in urine samples was measured by liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) by using the method developed by Liang.23 The urine NNAL analysis was conducted by liquid chromatography-tandem mass spectrometry as described by Jacob et al.24 The urinary tetrahydrocannabinolic acid (THCA) level was measured by using an enzyme-linked immunosorbent assay kit (THC Metabolite ELISA Kit; Neogen, Lexington, KY), following manufacturer protocol. The lowest limits of quantitation were 0.1 ng/mL for cotinine, 0.50 pg/mL for NNAL, and 2.0 ng/mL for THCA. For statistical analysis (see below) for those subjects who had undetectable levels of biomarkers, we used inputted biomarker concentrations calculated as the lowest limits of quantitation divided by √2.

For the purposes of comparing self-reported data to urinary biomarkers, specifically cotinine, which has a short elimination half-life, we restricted our analysis to respondents who identified using products within the past week. For cotinine, a level of ≥10 ng/mL (cutoff) was considered to be indicative of either combusted tobacco or e-cigarette use within the past week.25 Because NNAL is a tobacco-specific biomarker, we used a urinary NNAL cutoff of 14.5 pg/mL to distinguish between tobacco smokers (>14.5 pg/mL) and exclusive e-cigarette users (≤14.5 pg/mL).21,26 A urinary THCA concentration of ≥10 ng/mL was used to identify past-week marijuana use.27 

Descriptive statistics (ie, frequency distributions, means, and SDs) were used to describe self-reported use patterns for tobacco smoking, e-cigarette use, and marijuana use. Means, medians, and interquartile ranges (IQRs) were used to describe biomarker levels in each group of self-identified users. Correlations between self-reported use of the different products and urinary metabolites were determined by using the χ2 test of independence or analysis of variance. Parametric tests were followed-up with nonparametric alternatives to confirm P values; the nonparametric P value was considered the accurate value.

All tests of significance were 2 tailed and evaluated at the level of P < .05 and were not adjusted for multiple tests. Analyses were conducted by using SPSS software (version 24; IBM SPSS Statistics, IBM Corporation, Armonk, NY).

A total of 655 eligible patients were approached in the clinics and invited to participate. Of our convenience sample, 79% of those approached (n = 517) were consented for the study. All participants completed the survey and provided a urine sample (Fig 1). After excluding 7% (n = 19) of samples for overly concentrated or dilute creatinine, 51.2% of urine samples (n = 265) were analyzed for cotinine and NNAL. Forty three percent of urine samples (n = 221) was analyzed for THCA. There were 33.8% male respondents, 64.0% female respondents, and 1.9% respondents who were transgender. Approximately half of participants (49.2%) were ages 15 to 17; 22.0% were ages 12 to 14 years, and 28.9% were ages 18 to 21 years old. One respondent did not identify age.

FIGURE 1

Study enrollment and procedures.

FIGURE 1

Study enrollment and procedures.

Of our consented participants (n = 517), 13.9% reported ever use of tobacco, 36.0% reported that they had ever tried e-cigarettes, and 31.3% report that they had ever tried marijuana. Only 2.9% reported smoking in the past week; 14.3% reported past-week e-cigarette use, and 11.4% reported past-week marijuana use (Table 1). Among self-reported past-week smokers or e-cigarette users (n = 81), 11% only smoked, 78% only used e-cigarettes, and 14% both smoked and used e-cigarettes (“dual users”). Among all surveyed (n = 517), 2.1% reported daily smoking, 3.9% reported daily e-cigarette use, and 5.4% reported daily marijuana use. More than half of those surveyed (53%) reported never trying tobacco, e-cigarettes, or marijuana.

TABLE 1

Demographics of Study Participants (N = 517), Ages 12–21, Seen in Stony Brook Children’s Clinics Between April 2017 and April 2018 Who Self-Reported Tobacco Cigarette, E-cigarette, and Marijuana Use

All Surveyed (N = 517), n (%)Tobacco Cigarette UseE-cigarette UseMarijuana Use
Ever (n = 72), n (%)Past 30 d (n = 23), n (%)Past 7 d (n =18), n (%)Ever (n = 187), n (%)Past 30 d (n = 95), n (%)Past 7 d (n = 74), n (%)Ever (n = 162), n (%)Past 30 d (n = 75), n (%)Past 7 d (n = 59), n (%)
Sexa           
 Male 175 (34) 23 (32) 8 (35) 7 (39) 56 (30) 29 (31) 27 (36) 44 (27) 25 (33) 19 (32) 
 Female 331 (64) 46(64) 15 (65) 11 (61) 128 (68) 65 (68) 46 (62) 116 (72) 48 (64) 38 (64) 
 Transgender 10 (2) 3 (4) 3 (2) 1 (1) 1 (1) 2 (1) 2 (3) 2 (3) 
Race and/or ethnicityb           
 White non-Hispanic 343 (66) 49 (68) 16 (70) 13 (72) 129 66 (69) 54 (73) 112 (69) 54 (72) 42 (71) 
 White Hispanic 54 (9) 9 (13) 3 (13) 3 (17) 26 14 (15) 11 (15) 19 (6) 13 (17) 12 (20) 
 African American non-Hispanic 41 (8) 4 (6) 1 (1) 1 (1) 11 5 (5) 4 (5) 12 (7) 7 (9) 6 (10) 
 African American Hispanic 11 (2) 2 (3) 1 (1) 1 (1) 1(1) 1 (1) 2 (1) 18 (24) 14 (24) 
 Other non-Hispanic 1(1) 2 (3) 1 (1) 1 (1) 1 (1) 1 (1) 3 (2) 
 Other Hispanic 58 (11) 15 (21) 1 (1) 1 (1) 19 8 (8) 3 (4) 18 (11) 7 (9) 4 (7) 
Age, ya           
 12–14 113 (22) 6 (8) 16 (9) 8 (8) 6 (8) 7 (4) 2 (3) 2 (3) 
 15–17 254 (49) 26 (36) 13 (57) 9 (50) 95 52 (55) 39 (53) 81 (50) 41 (55) 31 (53) 
 18–21 149 (29) 40 (56) 10 (43) 9 (50) 75 35 (37) 29 (39) 74 (46) 32 (43) 26 (44) 
Grade           
 4–8 71 (14) 2 (3) 7 (4) 3 (3) 3 (4) 1 (1) 1 (1) 1 (1) 
 9–12 295 (57) 30 (42) 11(48) 7 (39) 103 (71) 53 (56) 40 (54) 87 (54) 41 (55) 31 (53) 
 Finished HS, no college 27 (10) 11 (15) 4 (17) 3 (17) 16 (9) 8 (8) 16 (10) 7 (9) 5 (8) 
 College 117 (21) 26 (36) 7 (30) 7 (39) 56 (30) 28 (29) 22 54 (33.3) 25 (33) 21 (36) 
 College graduate 3 (1) 1 (1) 1 (1) 1 (1) 2 (1) 2 (1.2) 
 Otherc 4 (1) 3 (4) 3 (2) 3 (3) 2 (1.2) 1 (1) 1 (1) 
All Surveyed (N = 517), n (%)Tobacco Cigarette UseE-cigarette UseMarijuana Use
Ever (n = 72), n (%)Past 30 d (n = 23), n (%)Past 7 d (n =18), n (%)Ever (n = 187), n (%)Past 30 d (n = 95), n (%)Past 7 d (n = 74), n (%)Ever (n = 162), n (%)Past 30 d (n = 75), n (%)Past 7 d (n = 59), n (%)
Sexa           
 Male 175 (34) 23 (32) 8 (35) 7 (39) 56 (30) 29 (31) 27 (36) 44 (27) 25 (33) 19 (32) 
 Female 331 (64) 46(64) 15 (65) 11 (61) 128 (68) 65 (68) 46 (62) 116 (72) 48 (64) 38 (64) 
 Transgender 10 (2) 3 (4) 3 (2) 1 (1) 1 (1) 2 (1) 2 (3) 2 (3) 
Race and/or ethnicityb           
 White non-Hispanic 343 (66) 49 (68) 16 (70) 13 (72) 129 66 (69) 54 (73) 112 (69) 54 (72) 42 (71) 
 White Hispanic 54 (9) 9 (13) 3 (13) 3 (17) 26 14 (15) 11 (15) 19 (6) 13 (17) 12 (20) 
 African American non-Hispanic 41 (8) 4 (6) 1 (1) 1 (1) 11 5 (5) 4 (5) 12 (7) 7 (9) 6 (10) 
 African American Hispanic 11 (2) 2 (3) 1 (1) 1 (1) 1(1) 1 (1) 2 (1) 18 (24) 14 (24) 
 Other non-Hispanic 1(1) 2 (3) 1 (1) 1 (1) 1 (1) 1 (1) 3 (2) 
 Other Hispanic 58 (11) 15 (21) 1 (1) 1 (1) 19 8 (8) 3 (4) 18 (11) 7 (9) 4 (7) 
Age, ya           
 12–14 113 (22) 6 (8) 16 (9) 8 (8) 6 (8) 7 (4) 2 (3) 2 (3) 
 15–17 254 (49) 26 (36) 13 (57) 9 (50) 95 52 (55) 39 (53) 81 (50) 41 (55) 31 (53) 
 18–21 149 (29) 40 (56) 10 (43) 9 (50) 75 35 (37) 29 (39) 74 (46) 32 (43) 26 (44) 
Grade           
 4–8 71 (14) 2 (3) 7 (4) 3 (3) 3 (4) 1 (1) 1 (1) 1 (1) 
 9–12 295 (57) 30 (42) 11(48) 7 (39) 103 (71) 53 (56) 40 (54) 87 (54) 41 (55) 31 (53) 
 Finished HS, no college 27 (10) 11 (15) 4 (17) 3 (17) 16 (9) 8 (8) 16 (10) 7 (9) 5 (8) 
 College 117 (21) 26 (36) 7 (30) 7 (39) 56 (30) 28 (29) 22 54 (33.3) 25 (33) 21 (36) 
 College graduate 3 (1) 1 (1) 1 (1) 1 (1) 2 (1) 2 (1.2) 
 Otherc 4 (1) 3 (4) 3 (2) 3 (3) 2 (1.2) 1 (1) 1 (1) 

HS, high school.

a

One respondent did not answer this question.

b

Multiple answers allowed.

c

Home schooled, no answer.

A significant proportion of study participants reported concurrent smoking or e-cigarette use and marijuana use: 67% of past-week smokers were past-week marijuana users, 55% of past-week e-cigarette users were also past-week marijuana users, and 67% of past-week dual users were past-week marijuana users. By comparison, 2% of never users of tobacco smoke were current marijuana users, and 11% of never users of e-cigarettes had ever tried marijuana.

Overall, of 265 urine samples analyzed for cotinine and NNAL, 54.7% revealed cotinine concentrations above the cutoff of 10 ng/mL, and 12.8% had levels of NNAL above the cutoff value of 14.5 pg/mL. Among 221 samples analyzed for the marijuana biomarker, 29.2% had THCA concentrations >10 ng/mL.

Nonusers

Among those who reported no past-month or ever smoking, only 2% (n = 4 of 233) had NNAL levels above the cutoff, suggesting misreported smoking status. Among those who reported never smoking tobacco and no past-week or ever using e-cigarettes, only 2% (3 of 165) had cotinine levels above the cutoff. None of these had NNAL levels above the cutoff. Only 2% (2 of 109, for whom we had urine samples) of respondents who reported never trying marijuana had THCA levels above the cutoff. Of the 274 who reported never using any tobacco, e-cigarettes, or marijuana, 1% (1 of 87) had cotinine levels above the cutoff, and 2% (1 of 66) had THCA levels above the cutoff. None had NNAL levels above the cutoff.

Exclusive Tobacco Smokers

Among those past-week users who reported only smoking (n = 6), 67% had cotinine levels above the cutoff, and 33% had NNAL levels above the cutoff. Among past-week tobacco smokers, there were significant differences in median cotinine levels between those who smoked once to several times daily (“daily users”) compared with those who smoked every other day to once per week (“nondaily users”; 140.77 [IQR 856.04] vs 2.04 ng/mL [IQR not applicable]; P < .001). There were no statistically significant differences between the proportion of daily smokers with cotinine levels above the cutoff and nondaily smokers (80% vs 20%; P = .12) or between the proportion of daily smokers with NNAL levels above the cutoff and nondaily smokers (40% vs 60%; P = .44).

E-cigarette Users

Among past-week users who reported only e-cigarette use (n = 55), 37% had cotinine levels above the cutoff. Among past-week e-cigarette users, the median cotinine level of daily users was significantly higher than that of nondaily users (315.4 [IQR 1375.9] vs 1.69 ng/mL [IQR 28.2]; P < .003). Daily e-cigarette users were more likely to report using pods: 78% of past-week daily users, (n = 14) compared with 22% of past-week nondaily users (n = 4), used pods in the past week (P < .001). More than half (57%) of respondents with cotinine levels above the cutoff reported using pods compared with 19% of those with cotinine levels below the cutoff (P < .005). E-cigarette users who had cotinine levels above the cutoff were more likely to report using >1 type of electronic vape product compared with those with cotinine levels below the cutoff (50% vs 15%; P = .03).

Dual Users

Among those both smoking tobacco and using e-cigarettes in the past week (n = 9), 89% had cotinine levels above the cutoff, and 33% had NNAL levels above the cutoff. Cotinine levels of different user groups are shown in Table 2. Biomarker levels above the threshold for different current-user groups are shown in Table 3.

TABLE 2

Comparison of Urinary Cotinine (ng/mL) Concentrations Among Adolescents and Young Adults (n = 517), Ages 12–21, Who Were Seen in Stony Brook Children’s Clinics Between April 2017 and April 2018 Who Reported Past-Week Use of Nicotine-Containing Products

MeanSDMedianIQR
Only tobacco (n = 6) 330.30 517.84 99.97 661.63 
Only e-cigarette (n = 51) 189.72 472.49 3.56 40.2 
Dual users (tobacco and e-cigarette) (n = 9) 524.77 708.45 267.55 940.91 
Pod users (n = 19) 598.68 739.58 259.03 1267.69 
Tobacco alone and marijuana (n = 3) 638.77 619.59 442.92 1191.85 
E-cigarette alone and marijuana (n = 24) 329.67 651.69 7.6 286.27 
Dual users and marijuana (n = 6) 448.73 592.75 297.61 1614.559 
MeanSDMedianIQR
Only tobacco (n = 6) 330.30 517.84 99.97 661.63 
Only e-cigarette (n = 51) 189.72 472.49 3.56 40.2 
Dual users (tobacco and e-cigarette) (n = 9) 524.77 708.45 267.55 940.91 
Pod users (n = 19) 598.68 739.58 259.03 1267.69 
Tobacco alone and marijuana (n = 3) 638.77 619.59 442.92 1191.85 
E-cigarette alone and marijuana (n = 24) 329.67 651.69 7.6 286.27 
Dual users and marijuana (n = 6) 448.73 592.75 297.61 1614.559 
TABLE 3

Proportion of Participants With Biomarker Levels Above Thresholds (Cotinine Level >10 ng/mL, NNAL Level >14.5 pg/mL, and THCA Level >10 ng/mL) for Different Current-User Groups

Cotinine Level >10 ng/mL, %NNAL Level >14.5 pg/mL, %THCA Level >10 ng/mL, %
Any use of tobacco or e-cigarettes (n = 69) 47 11 n/a 
Only tobacco (n = 9) 78 56 n/a 
Only e-cigarettes (n = 51) 42 10 n/a 
Dual use (tobacco and e-cigarettes without marijuana) (n = 2) 100 50 
Any use of marijuana 43 18 68 
Only marijuana (no tobacco or e-cigarettes) (n = 4) 25 25 100 
Marijuana and tobacco alone (n = 3) 100 67 100 
Marijuana and e-cigarettes alone (n = 11) 55 18 46 
Dual use (tobacco and e-cigarettes and marijuana) (n = 6) 83 50 67 
Cotinine Level >10 ng/mL, %NNAL Level >14.5 pg/mL, %THCA Level >10 ng/mL, %
Any use of tobacco or e-cigarettes (n = 69) 47 11 n/a 
Only tobacco (n = 9) 78 56 n/a 
Only e-cigarettes (n = 51) 42 10 n/a 
Dual use (tobacco and e-cigarettes without marijuana) (n = 2) 100 50 
Any use of marijuana 43 18 68 
Only marijuana (no tobacco or e-cigarettes) (n = 4) 25 25 100 
Marijuana and tobacco alone (n = 3) 100 67 100 
Marijuana and e-cigarettes alone (n = 11) 55 18 46 
Dual use (tobacco and e-cigarettes and marijuana) (n = 6) 83 50 67 

n/a, not applicable.

Marijuana Users

The median urinary THCA concentration among self-reported current marijuana users was 31.1 ng/mL (IQR 831.8). Of those who reported past-week marijuana use (n = 36), 67% had THCA levels above the cutoff. Daily marijuana users’ median THCA level was higher than nondaily marijuana users’ median THCA level (560.1 [IQR 1248.3] vs 7.2 ng/mL [IQR 254.9]; P = .04). Significantly higher THCA levels were seen in those with higher cotinine levels: 61% of those with cotinine levels >10 ng/mL vs 39% of those with cotinine levels <10 ng/mL had THCA levels above the cutoff (P < .001).

Of all e-cigarette users with cotinine levels above the cutoff, 27% (n = 10) said that their e-cigarette had no nicotine or that they did not know if it contained nicotine. Of the 19 past-week e-cigarette users with cotinine levels above the cutoff, 16% (n = 3) stated that their e-cigarettes had no nicotine, and 11% (n = 2) stated that they did not know whether there was nicotine. The median cotinine level in those reporting no nicotine in their e-cigarettes was 1.45 ng/mL (IQR 16). The 2 respondents who stated that they did not know whether there was nicotine in their e-cigarette products had cotinine levels of 21 and 187 ng/mL, respectively. One in 10 respondents who used pods (n = 19) claimed use of nicotine-free products. However, pod users’ median cotinine level was significantly higher than that of nonpod users (259.03 [IQR 1267.69] vs 1.61 ng/mL [IQR 16.3]; P < .003).

In our sample, adolescents’ biomarker levels of all products highly correlated with their self-reported use. However, in all product categories, a significant proportion of past-week users had urinary biomarker levels below the cutoff. In the case of e-cigarettes, for example, one might question whether 10 ng/mL is the appropriate cutoff to assess accurate self-report. We chose this cutoff on the basis of previous work25; however, normative data are lacking for e-cigarette use among adolescents. Studies of cotinine levels in adult e-cigarette users have revealed varied results: In 1 study of adult e-cigarette users, salivary cotinine levels were comparable to those of smokers. In that cohort, however, all subjects were former smokers and were noted to increase the nicotine in their e-cigarette liquid to maintain constant cotinine levels.28 Similarly, Göney et al29 and Shahab et al30 found similar urinary cotinine levels between adult e-cigarette users and smokers, whereas in contrast, Hecht et al31 found significantly lower biomarker levels in 28 e-cigarette users. For our sample, those subjects reporting e-cigarette use with subthreshold urinary cotinine levels could be considered overreporters; however, it is more likely that these levels are reflective of the intermittent and irregular patterns of use that are characteristic of many adolescents.

As in other studies, many of our participants were unaware of the nicotine content of the e-cigarette products they were using.19,32,33 This is particularly concerning in light of the significantly higher cotinine levels we saw with more frequent use and with use of pods, which have continued to increase in popularity since we ended data collection. In fact, we suspect that had we extended enrollment, we would have seen a higher percentage of past-week users with high cotinine levels.

Similar to e-cigarette users, not all self-reported tobacco smokers had cotinine or NNAL levels above the cutoff. This could be attributed to the small numbers of smokers in our sample but may also be explained by irregular patterns of adolescent smoking. Fourteen years ago, when teenaged smoking was more common, Caraballo et al34 reported that >20% of those who reported smoking had cotinine levels below the cutoff.

In our analysis of past-week e-cigarette users, significantly higher cotinine levels were seen with increased frequency of use. Although it is standard in many surveys, such as the Population Assessment of Tobacco Health, to identify past-month use as current use, we feel that identifying past-week use, and, particularly, daily use, is crucial in identifying those at highest risk for nicotine exposure and dependence. In addition, surveys should include questions specifically about pod-product use.

Several recent survey studies have revealed that e-cigarette use is associated with co-use or subsequent use of marijuana.9,10,12,35 Our survey data are consistent with these reports, and, importantly, co-use was reflected in the higher urinary THCA levels seen in those with higher cotinine levels. One-third of self-reported past-week marijuana users had THCA levels below the cutoff. As with tobacco smoking and e-cigarette use, one might consider this to be intermittent use, or overreporting, but another plausible explanation is the instability of urinary THCA when frozen. Mean losses of 8% have been reported when samples were frozen for 40 days and of 15.9% when samples were frozen for 1 year.36 Because we did not run the THCA analysis until all samples were collected, at least half of them would have been frozen for >6 months. We did not expect this to be a significant limitation because we used a sensitive method for THCA determination: only those samples that, at the time of collection, had THCA concentrations between 10 and 11 ng/mL would fall under the detection limit because of some degree (∼10%) of degradation over the study period.

In summary, adolescents’ self-reported tobacco smoking and e-cigarette and marijuana use reported by participants in our study was similar to self-reported behaviors in other larger and nationally representative studies. The presented novel data on biomarker confirmation of self-report in our study underscores the importance of continuing to ask these questions to assess emerging and changing use patterns. Pediatricians should ask about smoking and e-cigarette and marijuana use but may not because of lack of knowledge or data regarding these new products.37,38 Given our objective findings of nicotine exposure and the high prevalence of marijuana co-use, correlating with adolescents’ self-report, it is imperative that pediatricians address e-cigarette and marijuana use with counseling and guidance.

Participants in our study were recruited from 3 different clinic sites to survey a representative convenience sample within our patient pool. However, at least half of the participants were scheduled for an appointment with an adolescent medicine specialist and, as such, may represent a higher-risk population than those patients recruited from general practice or other subspecialty practice appointments. Additionally, our study was conducted during the time when pods were becoming increasingly popular. Indeed, we identified higher cotinine levels and more frequent usage patterns among pod users. Had we continued recruiting past April 2018, we suspect that the number of pod users would increase.39,40 

Our conclusions regarding marijuana use may be limited by the small sample size. It is possible that a higher percentage of those participants who did not consent for future testing were marijuana users. However, in many cases, samples were excluded if parents did not agree but subjects did.

Finally, multiple statistical tests were conducted; therefore, some significant findings may potentially be due to chance. Nonetheless, the findings of this small-sample observational study are exploratory and require replication and confirmation by other studies in which different samples are used.41,43 

Adolescents’ self-report of tobacco smoking and e-cigarette and marijuana use correlated with measured urinary biomarkers. Whereas some adolescent e-cigarette users had nicotine biomarker levels below cutoff values, those who used e-cigarettes frequently and used pod systems with high nicotine content showed significantly elevated biomarker levels. Further studies should be used to better characterize cotinine levels regarding nondaily use patterns among adolescents.

Many participants were unaware of the nicotine content in their products.

Reported co-use of tobacco, e-cigarettes, and marijuana was corroborated by higher urinary THCA levels in those with higher cotinine levels.

Adolescents must understand the implications of nicotine exposure in e-cigarettes. Pediatricians should ask about e-cigarette and marijuana use and, specifically, about daily use of these products because this may be associated with significant nicotine exposure and risk for addiction.

Drs Boykan and Goniewicz conceptualized and designed the study, drafted the initial manuscript, and reviewed and revised the manuscript; Dr Messina and Ms Chateau conducted the initial analyses and reviewed and revised the manuscript; Drs Eliscu and Tolentino collected data and reviewed and revised the manuscript; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

FUNDING: Funded by an Intramural Research grant award to Dr Boykan from the Department of Pediatrics, School of Medicine, Stony Brook University.

We thank Mary Palumbo, Taylor Vanderbush, and Noel Leigh from Roswell Park Comprehensive Cancer Center for assistance with biomarker testing.

     
  • e-cigarette

    electronic cigarette

  •  
  • IQR

    interquartile range

  •  
  • NNAL

    total 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol

  •  
  • THCA

    tetrahydrocannabinolic acid

1
US Department of Health and Human Services
.
The Health Consequences of Smoking: 50 Years of Progress. A Report of the Surgeon General
.
Atlanta, GA
:
US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health
;
2014
2
US Department of Health and Human Services
.
E-Cigarette Use Among Youth and Young Adults: A Report of the Surgeon General—Executive Summary
.
Atlanta, GA
:
US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health
;
2016
3
Cullen
KA
,
Ambrose
BK
,
Gentzke
AS
,
Apelberg
BJ
,
Jamal
A
,
King
BA
.
Notes from the field: use of electronic cigarettes and any tobacco product among middle and high school students - United States, 2011-2018.
MMWR Morb Mortal Wkly Rep
.
2018
;
67
(
45
):
1276
1277
[PubMed]
4
Jamal
A
,
Gentzke
A
,
Hu
SS
, et al
.
Tobacco use among middle and high school students - United States, 2011-2016.
MMWR Morb Mortal Wkly Rep
.
2017
;
66
(
23
):
597
603
[PubMed]
5
Kann
L
,
McManus
T
,
Harris
WA
, et al
.
Youth risk behavior surveillance - United States, 2017.
MMWR Surveill Summ
.
2018
;
67
(
8
):
1
114
[PubMed]
6
Lee
DC
,
Crosier
BS
,
Borodovsky
JT
,
Sargent
JD
,
Budney
AJ
.
Online survey characterizing vaporizer use among cannabis users.
Drug Alcohol Depend
.
2016
;
159
:
227
233
[PubMed]
7
Morean
ME
,
Kong
G
,
Camenga
DR
,
Cavallo
DA
,
Krishnan-Sarin
S
.
High school students’ use of electronic cigarettes to vaporize cannabis.
Pediatrics
.
2015
;
136
(
4
):
611
616
[PubMed]
8
Giroud
C
,
de Cesare
M
,
Berthet
A
,
Varlet
V
,
Concha-Lozano
N
,
Favrat
B
.
E-cigarettes: a review of new trends in cannabis use.
Int J Environ Res Public Health
.
2015
;
12
(
8
):
9988
10008
[PubMed]
9
Trivers
KF
,
Phillips
E
,
Gentzke
AS
,
Tynan
MA
,
Neff
LJ
.
Prevalence of cannabis use in electronic cigarettes among US youth.
JAMA Pediatr
.
2018
;
172
(
11
):
1097
1099
[PubMed]
10
Dai
H
,
Catley
D
,
Richter
KP
,
Goggin
K
,
Ellerbeck
EF
.
Electronic cigarettes and future marijuana use: a longitudinal study.
Pediatrics
.
2018
;
141
(
5
):
e20173787
[PubMed]
11
Cassidy
RN
,
Meisel
MK
,
DiGuiseppi
G
,
Balestrieri
S
,
Barnett
NP
.
Initiation of vaporizing cannabis: individual and social network predictors in a longitudinal study of young adults.
Drug Alcohol Depend
.
2018
;
188
:
334
340
[PubMed]
12
Audrain-McGovern
J
,
Stone
MD
,
Barrington-Trimis
J
,
Unger
JB
,
Leventhal
AM
.
Adolescent e-cigarette, hookah, and conventional cigarette use and subsequent marijuana use.
Pediatrics
.
2018
;
142
(
3
):
e20173616
[PubMed]
13
Eaton
DL
,
Kwan
LY
,
Stratton
K
, eds;
National Academies of Sciences, Engineering, and Medicine
;
Health and Medicine Division
;
Board on Population Health and Public Health Practice
;
Committee on the Review of the Health Effects of Electronic Nicotine Delivery Systems
.
Public Health Consequences of E-Cigarettes
.
Washington, DC
:
National Academies Press (US)
;
2018
14
Morean
ME
,
Krishnan-Sarin
S
,
O’Malley
SS
.
Assessing nicotine dependence in adolescent e-cigarette users: the 4-item Patient-Reported Outcomes Measurement Information System (PROMIS) Nicotine Dependence Item Bank for electronic cigarettes.
Drug Alcohol Depend
.
2018
;
188
:
60
63
[PubMed]
15
Case
KR
,
Mantey
DS
,
Creamer
MR
,
Harrell
MB
,
Kelder
SH
,
Perry
CL
.
E-cigarette- specific symptoms of nicotine dependence among Texas adolescents.
Addict Behav
.
2018
;
84
:
57
61
[PubMed]
16
Kavuluru
R
,
Han
S
,
Hahn
EJ
.
On the popularity of the USB flash drive-shaped electronic cigarette Juul.
Tob Control
.
2019
;
28
(
1
):
110
112
[PubMed]
17
McKelvey
K
,
Baiocchi
M
,
Halpern-Felsher
B
.
Adolescents’ and young adults’ use and perceptions of pod-based electronic cigarettes.
JAMA Netw Open
.
2018
;
1
(
6
):
e183535
[PubMed]
18
Goniewicz
ML
,
Boykan
R
,
Messina
CR
,
Eliscu
A
,
Tolentino
J
.
High exposure to nicotine among adolescents who use Juul and other vape pod systems (‘pods’) [published online ahead of print September 7, 2018].
Tob Control
.
[PubMed]
19
Willett
JG
,
Bennett
M
,
Hair
EC
, et al
.
Recognition, use and perceptions of JUUL among youth and young adults.
Tob Control
.
2019
;
28
(
1
):
115
116
[PubMed]
20
National Addiction & HIV Data Archive Program
. Population Assessment of Tobacco and Health (PATH) study series. Available at: https://www.icpsr.umich.edu/icpsrweb/NAHDAP/series/606. Accessed February 8, 2019
21
Goniewicz
ML
,
Smith
DM
.
Are some e-cigarette users “blowing smoke?”: assessing the accuracy of self-reported smoking abstinence in exclusive e-cigarette users [published online ahead of print May 2, 2018].
Nicotine Tob Res
.
22
Moreno-Sanz
G
.
Can you pass the acid test? Critical review and novel therapeutic perspectives of Δ9-tetrahydrocannabinolic acid A.
Cannabis Cannabinoid Res
.
2016
;
1
(
1
):
124
130
[PubMed]
23
Liang
S
. Rapid and accurate LC-MS/MS analysis of nicotine and related compounds in urine using raptor biphenyl LC columns and MS-friendly mobile phases. Available at: www.restek.com/pdfs/CFAN2216-UNV.pdf. Accessed October 26, 2018
24
Jacob
P
 III
,
Havel
C
,
Lee
DH
,
Yu
L
,
Eisner
MD
,
Benowitz
NL
.
Subpicogram per milliliter determination of the tobacco-specific carcinogen metabolite 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol in human urine using liquid chromatography-tandem mass spectrometry.
Anal Chem
.
2008
;
80
(
21
):
8115
8121
[PubMed]
25
Benowitz
NL
,
Nardone
N
,
Jain
S
, et al
.
Comparison of urine 4-(methylnitrosamino)-1-(3)pyridyl-1-butanol and cotinine for assessment of active and passive smoke exposure in urban adolescents.
Cancer Epidemiol Biomarkers Prev
.
2018
;
27
(
3
):
254
261
[PubMed]
26
Goniewicz
ML
,
Lee
L
.
Electronic cigarettes are a source of thirdhand exposure to nicotine.
Nicotine Tob Res
.
2015
;
17
(
2
):
256
258
27
Cary
PL
. The marijuana detection window: determining the length of time cannabinoids will remain detectable in urine following smoking. A critical review of relevant research and cannabinoid detection guidance for drug courts. Available at: https://www.ndci.org/sites/default/files/ndci/THC_Detection_Window_0.pdf. Accessed September 29, 2018
28
Etter
JF
.
A longitudinal study of cotinine in long-term daily users of e-cigarettes.
Drug Alcohol Depend
.
2016
;
160
:
218
221
29
Göney
G
,
Çok
İ
,
Tamer
U
,
Burgaz
S
,
Şengezer
T
.
Urinary cotinine levels of electronic cigarette (e-cigarette) users.
Toxicol Mech Methods
.
2016
;
26
(
6
):
414
418
[PubMed]
30
Shahab
L
,
Goniewicz
ML
,
Blount
BC
, et al
.
Nicotine, carcinogen, and toxin exposure in long-term e-cigarette and nicotine replacement therapy users: a cross-sectional study.
Ann Intern Med
.
2017
;
166
(
6
):
390
400
[PubMed]
31
Hecht
SS
,
Carmella
SG
,
Kotandeniya
D
, et al
.
Evaluation of toxicant and carcinogen metabolites in the urine of e-cigarette users versus cigarette smokers.
Nicotine Tob Res
.
2015
;
17
(
6
):
704
709
32
Gorukanti
A
,
Delucchi
K
,
Ling
P
,
Fisher-Travis
R
,
Halpern-Felsher
B
.
Adolescents’ attitudes towards e-cigarette ingredients, safety, addictive properties, social norms, and regulation.
Prev Med
.
2017
;
94
:
65
71
[PubMed]
33
Pepper
JK
,
Farrelly
MC
,
Watson
KA
.
Adolescents’ understanding and use of nicotine in e-cigarettes.
Addict Behav
.
2018
;
82
:
109
113
[PubMed]
34
Caraballo
RS
,
Giovino
GA
,
Pechacek
TF
.
Self-reported cigarette smoking vs. serum cotinine among U.S. adolescents.
Nicotine Tob Res
.
2004
;
6
(
1
):
19
25
35
Azagba
S
.
E-cigarette use, dual use of e-cigarettes and tobacco cigarettes, and frequency of cannabis use among high school students.
Addict Behav
.
2018
;
79
:
166
170
[PubMed]
36
Musshoff
F
,
Madea
B
.
Review of biologic matrices (urine, blood, hair) as indicators of recent or ongoing cannabis use.
Ther Drug Monit
.
2006
;
28
(
2
):
155
163
[PubMed]
37
Pepper
JK
,
Gilkey
MB
,
Brewer
NT
.
Physicians’ counseling of adolescents regarding e-cigarette use.
J Adolesc Health
.
2015
;
57
(
6
):
580
586
[PubMed]
38
Walley
SC
,
Jenssen
BP
;
Section on Tobacco Control
.
Electronic nicotine delivery systems.
Pediatrics
.
2015
;
136
(
5
):
1018
1026
[PubMed]
39
Chu
KH
,
Colditz
JB
,
Primack
BA
, et al
.
JUUL: spreading online and offline.
J Adolesc Health
.
2018
;
63
(
5
):
582
586
[PubMed]
40
Huang
J
,
Duan
Z
,
Kwok
J
, et al
.
Vaping versus JUULing: how the extraordinary growth and marketing of JUUL transformed the US retail e-cigarette market [published online ahead of print May 31, 2018].
Tob Control
.
[PubMed]
41
Streiner
DL
,
Norman
GR
.
Correction for multiple testing: is there a resolution?
Chest
.
2011
;
140
(
1
):
16
18
[PubMed]
42
Feise
RJ
.
Do multiple outcome measures require p-value adjustment?
BMC Med Res Methodol
.
2002
;
2
:
8
[PubMed]
43
Perneger
TV
.
What’s wrong with Bonferroni adjustments.
BMJ
.
1998
;
316
(
7139
):
1236
1238
[PubMed]

Competing Interests

POTENTIAL CONFLICT OF INTEREST: Dr Goniewicz received a research grant from Pfizer and served as an advisory board member to Johnson & Johnson, pharmaceutical companies that manufacture smoking cessation medications. Additionally, Dr Goniewicz was a member of the National Academies of Sciences, Engineering, and Medicine Committee on the Review of the Health Effects of Electronic Nicotine Delivery Systems, which wrote the report; the report was funded by the US Food and Drug Administration, but the US Food and Drug Administration was not involved in the drafting or review of the National Academies of Sciences, Engineering, and Medicine report or this manuscript. The policy implications written in this article are the views of the authors and do not necessarily represent the views of the other members of the committee; the National Academies of Sciences, Engineering, and Medicine; or the US Food and Drug Administration; the other authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.