BACKGROUND AND OBJECTIVES:

Secondhand smoke (SHS) directly increases exposure to airborne nicotine, tobacco’s main psychoactive substance. When exposed to SHS, nonsmokers inhale 60% to 80% of airborne nicotine, absorb concentrations similar to those absorbed by smokers, and display high levels of nicotine biomarkers. Social modeling, or observing other smokers, is a well-established predictor of smoking during adolescence. Observing smokers also leads to increased pharmacological exposure to airborne nicotine via SHS. The objective of this study is to investigate whether greater exposure to airborne nicotine via SHS increases the risk for smoking initiation precursors among never-smoking adolescents.

METHODS:

Secondary students (N = 406; never-smokers: n = 338, 53% girls, mean age = 12.9, SD = 0.4) participated in the AdoQuest II longitudinal cohort. They answered questionnaires about social exposure to smoking (parents, siblings, peers) and known smoking precursors (eg, expected benefits and/or costs, SHS aversion, smoking susceptibility, and nicotine dependence symptoms). Saliva and hair samples were collected to derive biomarkers of cotinine and nicotine. Adolescents wore a passive monitor for 1 week to measure airborne nicotine.

RESULTS:

Higher airborne nicotine was significantly associated with greater expected benefits (R2 = 0.024) and lower expected costs (R2 = 0.014). Higher social exposure was significantly associated with more temptation to try smoking (R2 = 0.025), lower aversion to SHS (R2 = 0.038), and greater smoking susceptibility (R2 = 0.071). Greater social exposure was significantly associated with more nicotine dependence symptoms; this relation worsened with higher nicotine exposure (cotinine R2 = 0.096; airborne nicotine R2 = 0.088).

CONCLUSIONS:

Airborne nicotine exposure via SHS is a plausible risk factor for smoking initiation during adolescence. Public health implications include limiting airborne nicotine through smoking bans in homes and cars, in addition to stringent restrictions for e-cigarettes.

Despite widespread antitobacco campaigns, roughly half (54%) of children from low-income, middle-income, and North American countries are exposed to secondhand smoke (SHS).1,2 SHS is linked to serious childhood health consequences, including ear infections, respiratory symptoms and infections (asthma, cough, shortness of breath, bronchitis, pneumonia), sudden infant death syndrome, and neurobehavioral disorders.3,4 Among adult nonsmokers, SHS is linked to cardiovascular disease, lung cancer, and stroke.5 Over a decade ago, the US Surgeon General reported that there is “no risk-free level of exposure” to SHS.3 

SHS directly increases exposure to airborne nicotine,6 the main psychoactive substance attributed to tobacco use disorders and nicotine dependence.7 Adult nonsmokers exposed to SHS inhale 60% to 80% of airborne nicotine,8 absorb quantities of nicotine similar to those of smokers, and display relatively high levels of nicotine. For example, the concentrations of nicotine in hair samples of bar and restaurant workers were indistinguishable among nonsmokers and smokers exposed to SHS at work.9,10 Even low exposure confers risk. Hair nicotine concentrations of nonsmokers whose spouse smoked outside the home were higher than in those living with a nonsmoker.11 Moreover, nonsmokers exposed to SHS have blood nicotine concentrations that produce psychoactive effects in smokers at the same levels.12,13 

Childhood and adolescence are particularly sensitive developmental periods for SHS exposure because nicotine interferes with normal brain development.14,15 Neuroimaging findings indicate nonsmoking young adults exposed to SHS have occupancy of α4β2 nicotinic acetylcholine receptors similar to that of smokers.16 Experimental studies with nicotine-naive rats reveal that SHS exposure increases the density of α7 and non-α7 nicotinic acetylcholine receptors17 and alters the synaptic plasticity of the medial prefrontal cortex,18 providing convincing evidence that SHS exposure has neuronal effects. In addition, youth are more physiologically vulnerable to SHS than adults because of their smaller lung volume. Consider that after exposing nonsmoking children and adults to the same amounts of SHS, children had higher urinary cotinine, the predominant metabolite of nicotine, than adults.19 

SHS exposure is a known risk factor for adolescent smoking initiation and other smoking behavior milestones, including ever smoking, smoking in the past month, and established smoking.20,21 Social exposure to smoking, via social modeling or observing smokers across different contextual situations (eg, parents at home, peers at school), predicts adolescent smoking.22,24 Social modeling has been the longstanding and predominant explanation for smoking initiation among youth; yet, observing others smoking is not benign. Observing smokers leads to greater social exposure to smoking and greater pharmacological exposure to airborne nicotine through SHS. SHS has been found to explain the relation between parental smoking and smoking initiation, with evidence for partial25 and full mediation.20 Problematically, most studies only examine self-reported SHS, not objective pharmacological exposure to airborne nicotine.26 

Pharmacological exposure to airborne nicotine can be measured by using biomarkers. Nicotine is predominantly metabolized into cotinine, which can be found in blood, saliva, and urine.27 Salivary cotinine provides an estimate of short-term nicotine exposure over the last 2 to 4 days in children.28 Hair nicotine provides estimates over longer time periods; each centimeter of hair represents an estimate over the last 30 days.29 Becklake et al30 found that salivary cotinine in nonsmoking children predicted smoking initiation 4 years later, even after adjusting for the number of smokers at home. Okoli et al31 showed that adult nonsmokers with higher hair nicotine values were 2.2 times more likely to endorse 4 or more Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition nicotine withdrawal symptoms. These studies provide preliminary, compelling evidence that pharmacological exposure to nicotine contributes to smoking behavior.

Exposure to SHS is also linked to precursors to smoking behavior, such as smoking susceptibility, which precede and influence smoking initiation.32 SHS exposure at home and in automobiles has been shown to contribute to greater smoking susceptibility among adolescent never-smokers.33 Experiencing SHS as an aversive sensation emerged as a protective factor against smoking susceptibility among preadolescent never-smokers, whereas enjoying the smell of cigarette smoke was a risk factor.34,35 Bélanger et al36 observed that SHS exposure predicted endorsement of nicotine dependence symptoms among never-smoking fifth-graders, even after adjusting for sibling and peer smoking. In addition, nicotine-naive rats exposed to SHS exhibited nicotine withdrawal signs.17,37 Recent evidence indicates that individuals who are genetically predisposed to metabolize nicotine slowly (reduced CYP2A6 genotypes) experience more pleasurable sensations during the first cigarette, have increased smoking risk, and progress to nicotine dependence more rapidly. In contrast, those with normal CYP2A6 diplotypes have a lower risk for smoking, fewer withdrawal symptoms, and more successful cessation. Emerging research suggests that epigenetic influences link SHS and airborne nicotine to youth smoking susceptibility and initiation.24,38 Taken together, these findings support a comprehensive tenet that greater social exposure to others smoking, in conjunction with greater pharmacological exposure to airborne nicotine via SHS, underlies smoking initiation in youth.

The aim of the current study was to examine whether greater exposure to airborne nicotine via SHS increases risk of initiation precursors among never-smoking adolescents. Precursors were selected intentionally on the basis of their established relations with smoking behavior and included the following: (1) smoking expectancies, which develop before smoking initiation39 and contribute to smoking40; (2) aversion to SHS exposure, which is associated with home smoking bans,41 and in turn, a lower likelihood of smoking initiation42; (3) nicotine dependence symptoms among adolescent never-smokers,43 which predict the first cigarette puff24; and (4) smoking susceptibility, which is a well-established predictor of smoking initiation38 and is associated with sensitivity to SHS exposure.34 This study investigated the differential effects of social exposure to smoking and pharmacological exposure to airborne nicotine on smoking precursors. Two hypotheses were tested. First, it was hypothesized that pharmacological exposure would significantly predict smoking precursors, after controlling for social exposure. Second, an interaction was hypothesized, such that social exposure to smoking would predict a higher risk of precursors, but only in the context of greater pharmacological exposure.

Secondary students (N = 406; 53% girls; mean age = 12.9 years) from schools in the greater Montreal area participated in AdoQuest II, an ongoing longitudinal study that examines the development of smoking behavior during adolescence. Adolescents were recruited in seventh grade because there was a higher probability that they had never smoked cigarettes, compared with students in higher grades (never-smokers: 91.0% of seventh-graders versus 80.8%–52.3% of eighth- to 12th-graders44). The Concordia University Ethics Review Committee approved the AdoQuest study (#1000116).

After receiving school board approval, school principals and teachers were contacted to obtain authorization to collect data during class time. Informed consent forms were sent home to parents with the students. Data collection consisted of 2 visits per classroom. At the first visit, adolescents completed self-report questionnaires, and trained research assistants collected samples of saliva (cotinine) and hair (nicotine). Passive nicotine monitors were distributed for participants to wear for the next 7 consecutive days. At the second visit, 1 week later, research assistants collected the passive nicotine monitors.

Social Smoking Situations (S3) Scale

Social exposure to smoking was assessed with the Social Smoking Situations (S3) Scale, which measures contextual situations of exposure to smoking.22 Adolescents rated their current level of social exposure in each situation (eg, “my friends smoke after school”; 0 = not true, 1 = somewhat true, 2 = very true). Items were averaged to compute an S3 total score, which represents a mean ranging from 0 to 2. Higher S3 scores are indicative of greater contextual exposure to smoking. Similarly, we computed a separate score for each version of the scale (parents, siblings, peers) and each of its 7 subscales. This scale has excellent internal consistency (Cronbach α = 0.92–0.93).22 

Salivary Cotinine

Salivary cotinine samples were assayed in duplicate by Salimetrics, LLC (Carlsbad, CA). Testing was performed by using a highly sensitive enzyme immunoassay that requires a volume of 20 µL of saliva for each determination and has a 0.15 ng/mL limit of sensitivity with an intra-assay coefficient of variation of 4.5%.45 The mean of the 2 duplicates was used as the salivary cotinine value.

Hair Nicotine

Approximately 10 to 15 strands of hair were collected from each student’s scalp; the centimeter closest to the root was used for analysis. Hair samples were assayed for nicotine by using reversed-phase high performance liquid chromatography with electrochemical detection and a limit of quantification of 0.04 ng/mg.46 

Passive Nicotine Monitors

Passive nicotine monitors measured the concentration of airborne nicotine in ambient air.47,48 The monitor consisted of a windscreen, a filter treated with sodium bisulfate, and a 3.7-cm polystyrene cassette. Adolescents were instructed to wear the monitor continuously for 7 days, except during bathing or showering, physical activity (eg, swimming, martial arts), and sleeping. When not wearing the monitor, they were asked to leave it nearby in their proximal environment (eg, bedside table while sleeping at night). Nicotine collected on the sodium bisulfate filters was assayed by gas chromatography with nitrogen selective detection. The monitors had a limit of detection of 0.01 µg. Nicotine concentration was calculated by dividing the quantity of nicotine found on the sodium bisulfate filters by the estimated total volume of air sampled over 7 days.47 

Smoking Expectancies

Participants completed the Smoking Expectancy Scale for Adolescents,49 French-language version,50 which measures 2 principal factors: expected costs (eg, “get lung cancer”) and expected benefits (eg, “feel less stressed”). By using a 10-point scale (0 = completely unlikely to 9 = completely likely), participants rated the probability that each cost or benefit would occur if they were smokers. An average score was calculated for each factor (0–9). This scale has excellent internal consistency (Cronbach α: expected costs = 0.94, expected benefits = 0.92).50 

Temptations to Try Smoking

Participants rated the extent to which they were tempted to try smoking in 15 situations (eg, “With friends at a party”), by using a 5-point scale (“not at all tempted” to “extremely tempted”). An average score was calculated (0–4). This scale has excellent internal consistency among adolescents (Cronbach α = 0.94).51 

Aversion to SHS Exposure

By using a 3-point scale (“strongly agree” to “do not agree at all”), participants rated the extent to which they dislike SHS exposure, prefer smoke-free places, and support laws banning smoking inside specific public places.41 Scores represent an average, ranging from 0 to 2, in which higher scores represent greater aversion to SHS exposure. This scale has good internal consistency (Cronbach α = 0.83).41 

Smoking Susceptibility

Smoking susceptibility was measured by using 2 items from the Youth Smoking Survey52 (eg, “Have you ever been curious about smoking a cigarette?), and 3 items from Pierce et al38 (eg, “If one of your best friends were to offer you a cigarette, would you smoke it?). A composite score was created by summing the items (0–11) and computing the mean, in which higher scores represent greater susceptibility to smoking. This coding system has been used previously.32 

Nicotine Dependence Symptoms

Participants answered 7 items measuring nicotine dependence symptoms among adolescents. Specifically, 6 items were derived from a “nicotine dependence/craving indicator” (eg, “How often do you have cravings to smoke cigarettes?”),53 and 1 item was derived from the Nicotine Dependence Scale for Adolescents (eg, “I sometimes have strong cravings where it feels like I’m in the grip of a force that I cannot control”).54 Items were summed and divided by the number of items to compute average scores, which were log-transformed to correct for positive skewness. Nicotine dependence symptoms previously assessed with this scale have been shown to be significantly associated with SHS exposure in a car (odds ratio [OR] = 1.2, 95% confidence interval [CI] = 1.0–1.4), sibling smoking (OR = 1.8, 95% CI = 1.1–2.9), and peer smoking (OR = 2.2, 95% CI = 1.2–4.1).36 

Linear regression was used to test social exposure to others smoking (S3 total score) and pharmacological exposure to nicotine (ie, salivary cotinine, hair nicotine, airborne nicotine monitors) as predictors of smoking precursors (ie, expected benefits, expected costs, temptations to try smoking, aversion to SHS, smoking susceptibility, nicotine dependence symptoms). Missing data were imputed with expectation-maximization in SPSS (IBM SPSS Statistics, IBM Corporation). Three models were tested to compare best fit. First, univariate models included each predictor, age, and sex. Second, paired multivariable models included the S3 total score, each pharmacological exposure, and covariates (eg, the paired model includes S3, cotinine, age, and sex). Third, full interaction models included S3 total score, each pharmacological exposure, their interaction, and covariates (eg, the interaction model includes S3, cotinine, S3×cotinine, age, and sex). For each precursor, the best-fitting model (univariate versus paired versus full) was selected on the basis of the statistical significance of the predictors in the model, and by examining significant changes in R2.

The analytic sample included 338 participants (from the original 406 participants) who endorsed that they “never smoked a cigarette, not even a few puffs” (53% girls, mean age = 12.9 years). On average, participants reported that the benefits of smoking were “very unlikely” to happen to them, whereas the costs of smoking were “somewhat likely” to happen to them (see Table 1). Overall, participants reported they were “not at all” tempted to smoke and they endorsed high levels of aversion to SHS exposure. A subset of never-smoking adolescents (19.8%) endorsed at least 1 nicotine dependence symptom. This percentage is higher than that reported in another study of never-smoking youth using identical items (5%),36 which is likely attributable to the younger age of their sample (fifth grade). Nearly half (41%) of adolescents endorsed at least 1 susceptibility item affirmatively, and would thus be classified as susceptible to smoking.38 

TABLE 1

Descriptive Statistics

Measure (Range or Unit)Mean (SD)
Expected benefits (0–9) 2.46 (1.66) 
Expected costs (0–9) 5.83 (2.17) 
Temptations to try smoking (0–4) 0.26 (0.43) 
Aversion to SHS exposure (0–2) 1.75 (0.41) 
Nicotine dependence symptoms (0–3) 0.08 (0.20) 
Smoking susceptibility (0–2) 0.20 (0.31) 
S3 total score (0–2) 0.07 (0.15) 
 Version 
  Parents (0–2) 0.17 (0.35) 
  Siblings (0–2) 0.02 (0.13) 
  Peers (0–2) 0.03 (0.16) 
 Subscale 
  Social activities (0–2) 0.11 (0.21) 
  Moods (0–2) 0.07 (0.18) 
  Meals (0–2) 0.07 (0.19) 
  Belongingness (0–2) 0.02 (0.12) 
  Quiet activities (0–2) 0.01 (0.07) 
  Unpleasant activities (0–2) 0.12 (0.34) 
  At school (0–2) 0.04 (0.20) 
Salivary cotinine (ng/mL) 0.48 (1.21) 
Hair nicotine (ng/mg) 0.38 (1.40) 
Airborne nicotine monitor (µg/m30.59 (2.05) 
Measure (Range or Unit)Mean (SD)
Expected benefits (0–9) 2.46 (1.66) 
Expected costs (0–9) 5.83 (2.17) 
Temptations to try smoking (0–4) 0.26 (0.43) 
Aversion to SHS exposure (0–2) 1.75 (0.41) 
Nicotine dependence symptoms (0–3) 0.08 (0.20) 
Smoking susceptibility (0–2) 0.20 (0.31) 
S3 total score (0–2) 0.07 (0.15) 
 Version 
  Parents (0–2) 0.17 (0.35) 
  Siblings (0–2) 0.02 (0.13) 
  Peers (0–2) 0.03 (0.16) 
 Subscale 
  Social activities (0–2) 0.11 (0.21) 
  Moods (0–2) 0.07 (0.18) 
  Meals (0–2) 0.07 (0.19) 
  Belongingness (0–2) 0.02 (0.12) 
  Quiet activities (0–2) 0.01 (0.07) 
  Unpleasant activities (0–2) 0.12 (0.34) 
  At school (0–2) 0.04 (0.20) 
Salivary cotinine (ng/mL) 0.48 (1.21) 
Hair nicotine (ng/mg) 0.38 (1.40) 
Airborne nicotine monitor (µg/m30.59 (2.05) 

S3 scores revealed that 40% of adolescents endorsed social exposure to smoking. Specifically, 34% endorsed social exposure to parental smoking, 14% endorsed social exposure to sibling smoking, and 15% endorsed social exposure to peer smoking. Similarly, participants reported experiencing social exposure during the following contextual situations: social activities (34%), mood states (25.7%), meals (28.8%), belongingness (13.3%), quiet activities (11.2%), unpleasant activities (21.6%), and at school (13.9%). With respect to pharmacological exposure, the values for the biomarkers were below the cutoff used to distinguish smokers from nonsmokers, suggesting that adolescents accurately reported that they were never-smokers. Specifically, mean salivary cotinine (mean = 0.48 ng/mL, SD = 1.21) was substantially lower than the established cut-off value for categorizing adolescents as smokers (11.4 ng/mL).55 Mean hair nicotine value (mean = 0.38 ng/mg, SD = 1.40) and mean passive airborne nicotine monitor value (mean = 0.59 µg, SD = 2.05) were also consistent with values expected in nonsmokers. In addition, mean values for all 3 measures of pharmacological exposure were above the lower limit of sensitivity (salivary cotinine45 = 0.15 ng/mL; hair nicotine46 = 0.04 ng/mg; passive nicotine monitor47 = 0.01 µg), suggesting most adolescents were exposed to airborne nicotine from SHS. Correlations among social and pharmacological exposures with the smoking precursors are presented in Table 2.

TABLE 2

Correlations of Smoking Precursors With Social and Pharmacological Exposures

ExpectanciesMilestonesNicotine
BenefitsCostsTemptationAversionSusceptibilityDependence
rPrPrPrPrPrP
Social exposure 
 S3 total score 0.078 .154 −0.020 .708 0.153* .005* −0.187* .001* 0.242* .000* 0.255* .000* 
  Version             
   Parent 0.033 .541 −0.015 .787 0.065 .231 −0.110* .044* 0.148* .006* 0.159* .003* 
   Sibling 0.064 .241 0.006 .912 0.064 .238 −0.268* .000* 0.156* .004* 0.162* .003* 
   Peer 0.087 .111 −0.029 .596 0.224* .000* −0.050 .368 0.210* .000* 0.219* .000* 
  Subscales 
   Social activities 0.078 .152 −0.016 .776 0.168* .002* −0.200* .000* 0.263* .000* 0.250* .000* 
   Moods 0.064 .243 −0.021 .698 0.115* .034* −0.130* .018* 0.214* .000* 0.183* .001* 
   Meals 0.051 .349 −0.023 .679 0.037 .500 −0.118* .031* 0.040 .463 0.163* .003* 
   Belonging 0.084 .125 0.018 .746 0.164* .002* −0.169* .002* 0.200* .000* 0.191* .000* 
   Quiet activities 0.071 .193 −0.009 .876 0.108* .047* −0.217* .000* 0.167* .002* 0.221* .000* 
   Unpleasant 0.047 .385 0.008 .890 0.058 .287 −0.096 .080 0.154* .005* 0.170* .002* 
   School 0.084 .125 −0.055 .311 0.226* .000* −0.086 .116 0.231* .000* 0.226* .000* 
Pharmacological exposure 
 Salivary cotinine 0.086 .115 −0.041 .448 −0.036 .514 −0.069 .211 0.013 .813 0.089 .102 
 Hair nicotine 0.052 .340 −0.060 .274 0.004 .935 −0.152* .005* 0.101 .064 0.085 .121 
 Airborne nicotine monitor 0.129* .018* −0.121* .026* 0.003 .962 −0.051 .351 0.020 .715 0.101 .065 
ExpectanciesMilestonesNicotine
BenefitsCostsTemptationAversionSusceptibilityDependence
rPrPrPrPrPrP
Social exposure 
 S3 total score 0.078 .154 −0.020 .708 0.153* .005* −0.187* .001* 0.242* .000* 0.255* .000* 
  Version             
   Parent 0.033 .541 −0.015 .787 0.065 .231 −0.110* .044* 0.148* .006* 0.159* .003* 
   Sibling 0.064 .241 0.006 .912 0.064 .238 −0.268* .000* 0.156* .004* 0.162* .003* 
   Peer 0.087 .111 −0.029 .596 0.224* .000* −0.050 .368 0.210* .000* 0.219* .000* 
  Subscales 
   Social activities 0.078 .152 −0.016 .776 0.168* .002* −0.200* .000* 0.263* .000* 0.250* .000* 
   Moods 0.064 .243 −0.021 .698 0.115* .034* −0.130* .018* 0.214* .000* 0.183* .001* 
   Meals 0.051 .349 −0.023 .679 0.037 .500 −0.118* .031* 0.040 .463 0.163* .003* 
   Belonging 0.084 .125 0.018 .746 0.164* .002* −0.169* .002* 0.200* .000* 0.191* .000* 
   Quiet activities 0.071 .193 −0.009 .876 0.108* .047* −0.217* .000* 0.167* .002* 0.221* .000* 
   Unpleasant 0.047 .385 0.008 .890 0.058 .287 −0.096 .080 0.154* .005* 0.170* .002* 
   School 0.084 .125 −0.055 .311 0.226* .000* −0.086 .116 0.231* .000* 0.226* .000* 
Pharmacological exposure 
 Salivary cotinine 0.086 .115 −0.041 .448 −0.036 .514 −0.069 .211 0.013 .813 0.089 .102 
 Hair nicotine 0.052 .340 −0.060 .274 0.004 .935 −0.152* .005* 0.101 .064 0.085 .121 
 Airborne nicotine monitor 0.129* .018* −0.121* .026* 0.003 .962 −0.051 .351 0.020 .715 0.101 .065 

r = Zero-order Pearson product moment correlations. P = Two-tailed significance value.

*

P < .05.

Comparisons of the univariate, paired, and full interaction models indicated that pharmacological exposure best predicted smoking expectancies in univariate modeling (ie, univariate models were best-fitting). Namely, higher airborne nicotine exposure measured with the passive monitor was significantly associated with a greater likelihood of expected benefits and a lower likelihood of expected costs (see Table 3). Neither social exposure nor its interaction with pharmacological exposure was associated with smoking expectancies. Second, social exposure best predicted smoking behavior precursors in univariate modeling. Specifically, higher S3 total scores were significantly associated with more temptations to try smoking, lower aversion to SHS exposure, and greater smoking susceptibility (see Table 3). Although hair nicotine was significantly correlated with aversion (see Table 2), it was no longer significant when included in the paired and full interaction models with social exposure. Neither pharmacological exposure nor its interaction with social exposure was associated with the remaining smoking precursors. Finally, the interaction between pharmacological exposure and social exposure best predicted nicotine dependence symptoms. The full models based on both salivary cotinine and the airborne nicotine monitor, as well as S3 total scores and their interactions, were significantly associated with nicotine dependence symptoms (see Table 3). Interpretation of these interaction models revealed that greater social exposure was significantly associated with more nicotine dependence symptoms, and the relation was increasingly pronounced in the presence of greater pharmacological exposure (see Fig 1).

TABLE 3

Best-Fitting Linear Regression Models Predicting Smoking Precursors

ExpectanciesPrecursorsNicotine
BenefitsCostsTemptationAversionSusceptibilityDependence
SlopePSlopePSlopePSlopePSlopePSlopeP
Pharmacological (univariate) Model R2 = 0.024 Model R2 = 0.014 — — — — — — — — 
 Airborne nicotine monitora 0.104* .019* −0.125* .033*         
 Age 0.246 .233 0.000 .999         
 Sex −0.158 .390 −0.053 .826         
Social (univariate) — — — — Model R2 = 0.025 Model R2 = 0.038 Model R2 = 0.071   
 S3 total score — — — — 0.463* .004* −0.524* .001* 0.539* .000* — — 
 Age — — — — −0.014 .790 −0.011 .830 −0.029 .443 — — 
 Sex — — — — −0.015 .746 −0.042 .355 −0.053 .113 — — 
Pharmacological × social (full interaction) — — — — — — — — — — Model R2 = 0.096 
 S3 × cotinine — — — — — — — — — — 0.035* .006* 
 S3 total score — — — — — — — — — — 0.082 .002 
 Salivary cotinine — — — — — — — — — — −0.010 .030 
 Age — — — — — — — — — — 0.012 .129 
 Sex — — — — — — — — — — 0.001 .919 
 — — — — — — — — — — Model R2 = 0.088 
 S3 × airborne nicotine — — — — — — — — — — 0.014* .028* 
 S3 total score — — — — — — — — — — 0.090 .001 
 Airborne nicotine monitor — — — — — — — — — — −0.006 .061 
 Age — — — — — — — — — — 0.011 .148 
 Sex — — — — — — — — — — 0.000 .958 
ExpectanciesPrecursorsNicotine
BenefitsCostsTemptationAversionSusceptibilityDependence
SlopePSlopePSlopePSlopePSlopePSlopeP
Pharmacological (univariate) Model R2 = 0.024 Model R2 = 0.014 — — — — — — — — 
 Airborne nicotine monitora 0.104* .019* −0.125* .033*         
 Age 0.246 .233 0.000 .999         
 Sex −0.158 .390 −0.053 .826         
Social (univariate) — — — — Model R2 = 0.025 Model R2 = 0.038 Model R2 = 0.071   
 S3 total score — — — — 0.463* .004* −0.524* .001* 0.539* .000* — — 
 Age — — — — −0.014 .790 −0.011 .830 −0.029 .443 — — 
 Sex — — — — −0.015 .746 −0.042 .355 −0.053 .113 — — 
Pharmacological × social (full interaction) — — — — — — — — — — Model R2 = 0.096 
 S3 × cotinine — — — — — — — — — — 0.035* .006* 
 S3 total score — — — — — — — — — — 0.082 .002 
 Salivary cotinine — — — — — — — — — — −0.010 .030 
 Age — — — — — — — — — — 0.012 .129 
 Sex — — — — — — — — — — 0.001 .919 
 — — — — — — — — — — Model R2 = 0.088 
 S3 × airborne nicotine — — — — — — — — — — 0.014* .028* 
 S3 total score — — — — — — — — — — 0.090 .001 
 Airborne nicotine monitor — — — — — — — — — — −0.006 .061 
 Age — — — — — — — — — — 0.011 .148 
 Sex — — — — — — — — — — 0.000 .958 

Slope represents the unstandardized β coefficient; it indicates the amount and direction of change in the outcome variable, for every unit change in the predictor variable (interpreted similarly to a correlation coefficient). Model R2 indicates the total amount of variance in the outcome variable explained by all predictor variables in the model; it can be considered an index of model fit. See footnote a for example interpretation. Empty cells are not applicable; only best-fitting models for smoking precursors included.

a

For every increase in airborne nicotine detected by the monitor, expectancies of the benefits of smoking increased significantly by 0.104, after controlling for age and sex; the model accounts for 2.4% of the variance in benefits (expectancies).

*

P < .05

FIGURE 1

Interaction between social exposure (S3 total scores) and salivary cotinine predicts nicotine dependence symptoms. (Cotinine median split to facilitate interpretation of interaction; continuous variable retained in statistical modeling reported in Table 3).

FIGURE 1

Interaction between social exposure (S3 total scores) and salivary cotinine predicts nicotine dependence symptoms. (Cotinine median split to facilitate interpretation of interaction; continuous variable retained in statistical modeling reported in Table 3).

Close modal

Emerging research provides compelling evidence that exposure to airborne nicotine from SHS is a plausible risk factor for smoking initiation during adolescence.6 The present findings reveal that pharmacological exposure to airborne nicotine was associated with smoking expectancies that are known to precipitate smoking initiation. Observing others smoke was also associated with precursors to smoking. The passive nicotine monitor was associated with expected benefits (affect control, social benefits, boredom reduction, weight control) and expected costs (addiction, appearance costs, social costs, health costs). Unlike salivary cotinine and hair nicotine, the passive monitor represents the total amount of airborne nicotine exposure, and is not affected by nicotine metabolism. Studies investigating genetic differences in rates of nicotine metabolism suggest that individuals who metabolize nicotine rapidly could have lower biomarker values than similarly exposed individuals who metabolize nicotine slowly.56,57 Greater social exposure to smoking was associated with greater temptations to smoke, lower aversion to SHS exposure, and greater smoking susceptibility. Social exposure’s association with nicotine dependence symptoms was significantly intensified in the context of greater pharmacological exposure. Our results highlight differential relations between smoke exposure (social and pharmacological) and precursors to smoking.

Consistent with the position put forth by Anthonisen and Murray,58 our results support the plausibility of a physiologic pathway between airborne nicotine exposure via SHS and smoking behavior, irrespective of social modeling. According to the Sensitization-Homeostasis Model, neuroadaptations can be observed soon after administration of low nicotine doses.59 Given that nonsmokers exposed to SHS can absorb concentrations of nicotine that produce psychoactive effects in smokers,12,13 the present study lends support to the possibility of neuroadaptations induced by nicotine exposure through SHS. Relatedly, Okoli et al6 have raised the hypothesis that repeated nicotine absorption from SHS may contribute to greater tolerance of its aversive sensations, which could possibly make initial experiences with active smoking less aversive and, consequently, more rewarding. Positive experiences during SHS exposure have been associated with greater smoking susceptibility.34 Overall, airborne nicotine intake from SHS is a probable, unique risk factor for smoking.

The current study indicated that adolescents with greater social exposure to smoking reported more nicotine dependence symptoms, which was further exacerbated in the presence of greater pharmacological exposure. Nicotine dependence is a multifaceted phenomenon involving physiologic processes (eg, being physically addicted to cigarettes), social modeling, and cue exposure (eg, wanting to smoke in the presence of cigarettes or when observing peers who smoke in forbidden places). The present findings support the idea of 3 interconnected mechanisms linking smoke exposure and smoking behavior: social modeling, conditioning (ie, exposure to environmental cues), and pharmacological exposure to airborne nicotine via SHS.60 Further, this finding provides crude, preliminary support to the animal literature in which complex interactions between nicotine and nonpharmacological cues have been reported.61 

Three methodological limitations require consideration. First, the cross-sectional nature of the data precludes establishment of temporal relations between potential predictors and smoking precursors. Future researchers should test the longitudinal relations between pharmacological exposure and smoking milestones, including smoking initiation. However, investigating the risk factors that set never-smokers at risk for initiating smoking from those not at risk is important for better prevention of smoking. Second, we did not examine whether adolescents were compliant with passive monitor instructions. Nevertheless, we relied on 3 distinct indicators of pharmacological exposure for triangulation of nicotine exposure. We used both short-term (cotinine, 2–4 days)28 and long-term (hair nicotine, 30–31 days)29 estimates of nicotine exposure. The moderately high overlap across the 3 pharmacological indicators (average r = 0.52) strongly suggests that adolescents wore the monitor as instructed. Third, it is challenging to tease apart pharmacological exposure from social exposure in an observational study of human participants. Although the biomarkers unequivocally measure nicotine exposure, the question remains whether they can also be used to capture elements of social exposure to smoking. Pharmacological and social exposures are intricately related, given that humans are generally exposed to nicotine, smokers, and smoking cues simultaneously. Animal studies can potentially be used to experimentally isolate the effects of nicotine exposure from SHS. Cohen and George62 developed a model in which rodents are exposed to nicotine vapors noncontingently, which simulates situations in which nonsmoking humans are intermittently exposed to nicotine from SHS. However, evaluating the effects of pharmacological exposure by using biomarkers and statistically adjusting for social exposure in the current study was an optimal methodological approach.

This study is the first in which it has been reported that airborne nicotine exposure via SHS poses a risk for smoking initiation precursors. The psychoactive effects of nicotine have been hypothesized as a plausible mechanism underpinning the association between pharmacological exposure and smoking expectancies. Moreover, this study revealed that social exposure is related to nicotine dependence symptoms, especially within the presence of pharmacological exposure. This suggests that social exposure is necessary, but not sufficient to explain nicotine dependence symptoms among never-smokers. Public health implications include that smoking bans should be implemented in homes and cars where youth spend time, given that it is not merely watching smokers that matters, but also being exposed to nicotine. The findings of this study could be used to inform current debates pertaining to the safety of nicotine delivered via electronic cigarettes, given that airborne nicotine emissions from electronic cigarettes could represent a risk for adolescents who spend time around those who use e-cigarettes.

     
  • CI

    confidence interval

  •  
  • OR

    odds ratio

  •  
  • S3

    Social Smoking Situations Scale

  •  
  • SHS

    secondhand smoke

Drs. McGrath, Okoli, Hammond, & O'Loughlin conceptualized and designed the AdoQuest II study. Drs. McGrath & O'Loughlin secured external funding. Dr. McGrath designed data collection protocol and methods, supervised data collection, oversaw data analyses and their interpretation, and drafted the final manuscript version. Dr. Racicot designed a data collection instrument, coordinated data collection, conceptualized the manuscript, conducted initial analyses and interpretation of data, and drafted the initial manuscript as part of his doctoral dissertation. Dr. Okoli advanced interpretation of data. All authors reviewed and revised the manuscript critically for important intellectual content, and approved the final manuscript as submitted.

FUNDING: AdoQuest II was funded by the Canadian Institutes of Health Research (operating grant MOP97879).

The authors thank all participants, their families, and the Montreal school teachers, principals, and school boards who agreed to participate. Special thanks to the Pediatric Public Health Psychology Laboratory staff and volunteers, especially Sabrina Giovanniello, Leanne Langer, Kathleen Kennedy-Turner, and Natasha Hunt for their continued excellence and dedication. The authors acknowledge the technical contributions of Graeme Mahoney, New Zealand (hair nicotine), and the University of California Berkeley Environmental Health Sciences Division (passive nicotine monitors) for conducting the laboratory analyses. An earlier draft of this article was included in Simon Racicot’s PhD dissertation at Concordia University, Montreal, Quebec. During this work, Jennifer J. McGrath held a Canadian Institutes of Health Research New Investigator Award; Simon Racicot held a Frederick Banting and Charles Best Canada Graduate Scholarship Doctoral Award; and Jennifer O’Loughlin held a Canada Research Chair in the Early Determinants of Adult Chronic Disease.

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Competing Interests

POTENTIAL CONFLICT OF INTEREST: The 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.