The mediation role of licit drugs in the influence of socializing on cannabis use among adolescents: A quantitative approach

Addictive Behaviors 35 (2010) 890–895

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Addictive Behaviors

The mediation role of licit drugs in the influence of socializing on cannabis use among adolescents: A quantitative approach Aurélie Mayet a,b,c,⁎, Stéphane Legleye a,c,d,e, Nearkasen Chau a,c, Bruno Falissard a,c a

INSERM U 669, Paris Sud Innovation Group in Adolescent Mental Health, Maison de Solenn, Paris, France Département d'épidémiologie et de santé publique, École du Val de Grâce, Paris, France Paris-Sud and Paris Descartes Universities, UMR-S0669, Paris, France d Observatoire français des drogues et des toxicomanies, Saint Denis, France e Institut national des études démographiques, Paris, France b c

a r t i c l e

i n f o

Keywords: Adolescent Cannabis Licit drugs Peer influence Structural equation modelling

a b s t r a c t Licit substance use could be an early stage leading on to cannabis use. The aim of the study was to test a hypothetical sequential process leading from socializing to cannabis use so as to evaluate the mediator role of tobacco and alcohol. Data was derived from a French nationwide survey carried out in 2005 involving 29,393 teenagers aged 17. The analysis used structural equation modelling. The sequence tested was: socializing with friends–tobacco/alcohol use–cannabis use–cannabis use disorders (CUD). Tobacco and alcohol consumptions appeared to be similarly influenced by the time spent with friends. However, tobacco mediation explained 57% of the sequence leading to cannabis use and 61% of the sequence leading to CUD, while the role of alcohol was weaker, at around 13%. Our results underline the effect of peer influence, in the course of night-out socializing, on substance use among adolescents, and the importance of tobacco mediation in the process leading to cannabis use and misuse. This suggests that prevention in places frequented by adolescents should primarily target tobacco consumption, which explains the largest part of cannabis use variance. However, processes linking substance uses seem to be more complex, with the existence of reverse pathways from cannabis to licit drugs. Thus, the gateway effects of tobacco and alcohol require further exploration in relation to simultaneous polysubstance use. © 2010 Published by Elsevier Ltd.

1. Background Cannabis is the most commonly-used illicit drug in many industrialized countries (Compton, Thomas, Conway, & Colliver, 2005; European monitoring centre for drugs and drug addiction, 2008). In France in 2005, nearly half of the 17-year-old adolescents interviewed reported a lifetime cannabis experiment, and 10% reported 10 uses or more in the last month (Beck, Legleye, & Spilka, 2006). These prevalences are among the highest in Europe (European monitoring centre for drugs and drug addiction, 2008). If cannabis use can be explained by many social and individual characteristics (Guxens, Nebot, & Ariza, 2007; Guxens, Nebot, Ariza, & Ochoa, 2007; Hawkins, Catalano, & Miller, 1992; Von Sydow, Lieb, Pfister, Höfler, & Wittchen, 2002), two factors seem particularly important: licit substance consumption (tobacco and alcohol) (Guxens, Nebot, Ariza, & Ochoa, 2007; Kohn, Dramaix, Favresse, Kittel, & Piette, 2005) and peer influence (Agrawal, Lynskey, Bucholz, Madden, & Heath, 2007; Hoffman, Monge, Chou, & Valente, 2007; Kuntsche & Delgrande, 2006; Chabrol et al., 2008). ⁎ Corresponding author. INSERM U 669, PSIGIAM, Maison de Solenn, 97 bd de Port Royal, 75679 Paris cedex 14, France. Tel.: + 33 1 43 98 49 96; fax: + 33 1 43 98 54 33. E-mail address: [email protected] (A. Mayet). 0306-4603/$ – see front matter © 2010 Published by Elsevier Ltd. doi:10.1016/j.addbeh.2010.06.001

Dishion's research on “deviancy training” suggests that peer reinforcement by way of rule-breaking or norm-violating talk and actions increases subsequent deviant behaviour (Dishion, Spracklen, Andrews, & Patterson, 1996). Further to this, the influence of non-structured activities on deviant behaviour has been investigated by Osgood, who underlined the influence of certain unsupervised leisure activities, like going to parties, on deviant behaviours, including substance use (Wills & Vaughan, 1989;Osgood & Anderson, 2004; Osgood, Anderson, & Shaffer, 2004). Thus, psychoactive substances, particularly licit substances, are often initiated in evenings out, in a festive context, among adolescents who gather around unifying themes like music (Mulder et al., 2009). This process appears to be mediated by the presence of peers, the absence of authority figures and a lack of structure. In addition, cannabis use seems to fall into a sequential process evolving in stages. This is the gateway theory, according to which tobacco or alcohol could lead on to cannabis use, which can itself potentially lead to hard drug use (Kandel, 1975; Kandel & Faust, 1975; Fergusson, Boden, & Horwood, 2006). With regard to this hypothesized pathway from licit drugs (alcohol and tobacco) to cannabis, two types of evidence support the view that the links observed between licit drug use and cannabis use may be causal: the use of tobacco or alcohol precedes the use of cannabis in most cases (Guxens, Nebot, & Ariza, 2007; Guxens,

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Nebot, Ariza, & Ochoa, 2007; Willner, 2001), and the association between licit drug use and cannabis use appears to be strong. A metaanalysis conducted on several studies judged to be of high methodological quality found that tobacco or alcohol users were 1.7–2.6 times more likely to use cannabis (Guxens, Nebot, Ariza, & Ochoa, 2007). Another survey found a 2.2 times higher risk for a cannabis user to evolve towards more frequent use if he/she has smoked tobacco regularly (Von Sydow et al., 2002). However, none of these statistical associations, or their combinations in a longitudinal survey, provides sufficient evidence for concluding to a causal relationship, since they could result from the influence of certain confounding factors (Oetting, & Donnermeyer, 1998). Further to this, if nicotine addiction can lead to cannabis use, the inverse relationship is also observed (Patton, Coffey, Carlin, Sawyer, & Lynskey, 2005; Timberlake et al., 2007). This “reverse” gateway hypothesis is strengthened by the fact that, among adolescents, the most common substance associated with cannabis is tobacco (Beck, Legleye, & Spilka, 2008). Given this setting, we would like to propose a description of the early stages of the substance use process among adolescents, combining the idea of the gateway theory and Becker's conception of the career of the drug user evolving by stages: according to this theory of deviant opportunities, evenings or nights out and other peer-oriented activities are strongly correlated with cannabis use, but this relationship depends on the levels of use that are considered (Becker, 1985; Peretti-Watel, Beck, Legleye, & Moatti, 2007). Drug use is first shaped by lifestyle, and, conversely, drug use later reshapes lifestyle: initiation and occasional use are more common among people who participate in different social activities (outdoor activities, sports, etc.), which increase the drug use opportunities, while more regular use is associated with a more selective lifestyle, less dependent on peer presence, and more focused on drug use as a central activity. The aim of our study was thus to verify the mediation roles, from a quantitative point of view, of tobacco and alcohol, in a hypothetical process leading from socializing habits in the course of nights out with friends, to cannabis use and misuse, on a sample of French adolescents. 2. Methods 2.1. Subjects This work used the 2005 ESCAPAD survey database (Enquête sur la Santé et les Consommations lors de l'Appel de Préparation à la Défense). The ESCAPAD studies are repeated cross-sectional studies conducted by the French monitoring centre for drugs and drug addiction (Observatoire français des drogues et des toxicomanies, OFDT) since 2000. The aim of these studies is to describe trends of substance uses over time (Beck, Legleye, & Spilka, 2005). The ESCAPAD survey is administered during the National Defence Preparation Day (NDPD) by OFDT, with cooperation from the National Service Department. For all 17-year-old adolescents, attendance at this one-day session on civil and military information is compulsory, and required for enrolment in any public examination (driving licence, university exams, etc.). Sessions are organized throughout the year in 300 civilian or military centres across the national territory. Data were collected over two weeks of NDPD sessions. When the young people attended the session, they completed a self-administered anonymous questionnaire focusing on their health and drug use. This questionnaire followed the recommendations of the European monitoring centre for drugs and drug addiction. Many methods were employed to preserve participants' anonymity: impossibility for two adolescents who lived in same town to participate on the same day, same questionnaire response time for substance users and non-users, envelope containing the questionnaires sealed in front of the participants just after completion. In 2005, 30,020 adolescents, corresponding to 5% of the 17-year-old population, were surveyed in metropolitan France. The final sample consisted of 29,393 teenagers (153 refusals and exclusion of 474 questionnaires with missing data).

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The response rate exceeded 98% for the socio-demographic and drug use questions. The sample appeared comparable with French population in terms of gender and geographical distribution. The survey obtained appropriate authorisations from the relevant ethics and data protection bodies in France. 2.2. Measures 2.2.1. Substance uses Ages of first tobacco and cannabis use, initiation of daily tobacco and cannabis use, and first drunkenness episode were collected to identify temporal sequences of use. A cannabis consumption scale, ranging from 0 to 5, was constructed on the basis of the observed frequency of cannabis use (lifetime and last 30 days): 0: no lifetime use; 1: no use in the last 30 days; 2: use on 1–9 occasions in the last 30 days; 3: use on 10–19 occasions in the last 30 days; 4: near-daily use, on 20–29 occasions in the last 30 days; 5: daily use. Recent use was defined as at least one occasion in the last 30 days. Intensive use was defined as near-daily or daily use. Frequency scales for use over the last 30 days were also constructed for tobacco and alcohol. Scores for tobacco were: 0: non-smoker; 1: no cigarette in the last month; 2: less than 1 cigarette per day; 3: 1–9 cigarettes per day; and 4: more than 9 cigarettes per day. Scores for alcohol were: 0: no use; 1: no use in the last month; 2: use on 1–9 occasions; 3: use on 10–19 occasions; and 4: use on more than 19 occasions in the last month. 2.2.2. Cannabis use disorders Two scales exploring cannabis use disorders were constructed based on certain DSM IV criteria (Finch & Welch, 2005). An abuse intensity scale, ranging from 0 to 9, was constructed, using 3 questions exploring cannabis abuse during the last 12 months: Have you had problems in school or at work, or bad results, because you used cannabis? Have you had a serious quarrel with your friends, or money problems because you used cannabis? Have you driven a car, a bike or a scooter after having used cannabis? A similar scale, ranging from 0 to 15, was constructed to evaluate the intensity of dependence. It used 5 questions: Have you smoked cannabis before going to school or to work? Have your friends or your family told you to reduce your cannabis consumption? Has it been difficult to get through a day without cannabis? Have you lacked energy or motivation to do usual things because you used cannabis? Have you tried to reduce your cannabis consumption without managing to do so? Each question has 4 response choices: 0: No; 1: Once or twice; 2: From time to time; and 3: Often. Global abuse and dependence intensity scores were constructed by summing responses. 2.2.3. Socializing For socializing, we used the frequency of going out with friends to places where the most frequent peer-induced unsupervised activities occur (Johnsson & Berglund, 2003). Frequencies of nights out in the last 12 months at friend's houses, in pubs or bars and in nightclubs were collected: for each of these three, scores ranging from 0 to 4 were constructed: 0: No night out; 1: once or twice; 2: less than once a month; 3: at least once a month; and 4: at least once a week. The aim of these scales was to measure the social context underpinning the reports of use frequencies. 2.3. Statistical analysis We used structural equation modelling (SEM), which is a useful statistical procedure for researchers who want to test a theory involving a non-straightforward pattern of relationships, and therefore well suited to the management of cross-sectional data for inferential purposes (Loehlin, 1992; Falissard, 2005). SEM enables the simultaneous performance of several multiple linear regressions. In addition, a given variable can be predictive in one equation and predicted in another equation. After designing a path diagram representing the

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investigators' hypotheses concerning links between variables in accordance with data from the literature, SEM was used to verify the statistical plausibility of these links. SEM also enables the use of latent variables, which can be deduced from a group of measured variables correlated to one another. Thus, the structural model integrated three directly measured variables (frequency scales for tobacco, alcohol and cannabis use) and two multi-factorial latent variables (Table 1): – Socializing concerned time spent out at night with friends, by way of frequency scales for parties among friends, nights out in pubs or bars and night clubs; – Cannabis use disorders involved scales for abuse and dependence, which are variables known to be correlated one with the other (Agrawal & Lynskey, 2007). The model tested was based on the following hypothetical sequential process: socializing habit → tobacco/alcohol use → cannabis use → cannabis use disorders (Fig. 1): – Socializing habits, inferred from time spent with friends, were chosen as the process starting-point because peers are known to be a risk factor for psychoactive drug use (Becker, 1985), although not directly leading to problematic use (Von Sydow et al., 2002). – The second stage of the hypothetical process was the consumption of tobacco or alcohol, which are licit drugs, and consequently easy to buy and use in a group in usual teenage meeting places like pubs or nightclubs (Johnsson & Berglund, 2003); – The hypothesis of a link from licit substance use to cannabis use rested on numerous results mentioning tobacco and alcohol as risk factors for cannabis use in the literature (Guxens, Nebot, & Ariza, 2007; Guxens, Nebot, Ariza, & Ochoa, 2007). Some of these studies were longitudinal and able to demonstrate a temporal link between different types of substance use. – The hypothesis of a link from cannabis use to cannabis use disorders was suggested by the observation that intensive cannabis smokers have a different psychological profile and an earlier age of first cannabis experiment than occasional smokers (Von Sydow et al., 2002). Structural equation models were estimated on Mplus software with a covariance matrix as the input, using maximum likelihood estimation (Muthen & Muthen, 2007). Model fit was measured using 3 indexes: – RMSEA (root mean square error of approximation), a measure of model discrepancy per degree of freedom, where a value smaller than 0.1 is considered as a good fit, and as a very good fit when it is smaller than 0.05; – RMSEA confidence interval (CI); – CFI (comparative fit index), where a value over 0.95 is considered as a good fit. CIs of the standardized coefficients were estimated using 500 bootstrap iterations, to take into account non-normal distributions of the variables. The sample was finally stratified to estimate models for either gender.

3. Results 3.1. Preliminary analysis Boys represented 50.8% of the sample. Mean age (17.6 years) was similar for girls and boys (p = 0.2). The sample included 14,229 cannabis users (48.4%). More boys (51.8%) than girls (44.8%) reported using cannabis (p b 10− 4). While alcohol-experimenting prevalence was also higher in boys (93.2% versus 91.7% in girls, p b 10− 4), girls more frequently reported tobacco experimenting (72.3% versus 70.4%, p b 10− 4). Recent cannabis users (at least one cannabis use during the last month) represented 42.6% of cannabis users (6060/14,229). Intensive users (use on 20 or more occasions during the last month) represented 7.4% of the total sample and 15.2% of cannabis users. Among cannabis users, the earlier the age of the first cannabis experiment, the more likely were the subjects to be intensive users, with a significant trend test (p b 10− 4). Concerning cannabis use disorders, 30.4% of cannabis users reported at least one abuse, and 39.8% reported at least one dependence criterion. The proportion of abusers and dependent subjects increased with cannabis use frequency in the last month (p b 10− 4). The proportion of cannabis use disorders also increased with earlier the age of the first experiment (p b 10− 4). The mean age at the first episode of drunkenness (alcohol initiation) was 15.1 years (15.0 among boys and 15.3 among girls, p b 0.0001). The mean age for tobacco experiment was 13.4 years (13.3 among boys and 13.5 among girls, p b 0.001), while the mean age for cannabis experiment was 15.1 years (15.0 among boys and 15.2 among girls, p b 0.001). Tobacco experiment occurred before cannabis initiation for 98% of cannabis users, and first drunkenness preceded cannabis experiment for 76% of cannabis users, which is in support of the use-sequence chosen in our model.

3.2. Structural equation modelling 3.2.1. Model likelihood assessment Correlation matrices were calculated for all variables included in the model (Table 1). Correlations of cannabis use with tobacco use, cannabis abuse scores and cannabis dependence scores were high (respectively 0.58, 0.61 and 0.70). The relationship between cannabis abuse and cannabis dependence scores was also strong (0.73). As shown at Fig. 1, the fitted model provided a very good fit to the data (CFI = 99% and RMSEA = 0.046 [CI: 0.043–0.048]). The final model was also compared with nested models (without pathway through tobacco, without pathway through alcohol, and without correlation pathway between tobacco and alcohol); it had the best fit (p b 0.001). A possible reverse influence of drug use on time out and leisure activities was then tested, since this might exist among individuals at a later stage in the cannabis-user career (Becker, 1985) while our model was designed to test the early stage: one pathway from Cannabis use disorders to Socializing was added, but it did not prove significant.

Table 1 Pearson's correlation coefficients, means and standard deviations (SD) for all variables included in the model among French 17-year adolescents (N = 29,393). 1 1. 2. 3. 4. 5. 6. 7. 8.

Cannabis use (range 0–5) Night out (0–4) Pub/bar (0–4) Nightclub (0–4) Tobacco (0–4) Alcohol (0–4) Cannabis abuse (0–9) Cannabis dependence (0–15)

1.00 0.24 0.35 0.22 0.58 0.34 0.61 0.70

All correlation coefficients are significant at p b 0.0001.

2 1.00 0.42 0.43 0.34 0.32 0.14 0.14

3

1.00 0.37 0.35 0.28 0.24 0.25

4

1.00 0.33 0.27 0.17 0.15

5

1.00 0.33 0.37 0.41

6

1.00 0.22 0.22

7

1.00 0.73

8

Mean

SD

1.00

0.99 2.82 3.17 2.44 1.57 1.87 0.37 0.75

1.35 1.26 1.11 1.39 1.38 0.80 1.11 2.05

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Fig. 1. Structural equation model showing standardized path coefficients for all statistically significant associations (hypothesis of a sequential evolution from tobacco and alcohol uses to cannabis use and misuse according to night socializing habit) among French 17-year adolescents (N = 29,393).

3.2.2. Coefficient estimation A large majority of estimated standardized coefficients were significant at p b 0.001, but this should be considered with caution since the size of the sample studied is large. The magnitude of the coefficient confidence intervals is a better reflection of the strength of a given relationship. The latent variables provided a good explanation for the corresponding measured variables (coefficients were over 0.60 for the socializing latent variable, and over 0.80 for the cannabis use disorders latent variable). The first result was that correlation coefficients between tobacco and alcohol uses frequencies were weak (0.10), although coefficients associated with socializing influences on tobacco use frequency (0.51 [0.49–0.53]) and on alcohol use frequency (0.45 [0.43–0.46]) were similar: these two uses appeared relatively independent despite a common influential variable. If the sequence socializing → tobacco/ alcohol use → cannabis use is considered, the direct effect of socializing (0.12 [0.09–0.14]) explained only 29% of the variance of cannabis use frequency (Table 2). Indeed, 57% of the cannabis use frequency variance was explained by the tobacco-mediated indirect effect (0.51 × 0.47 = 0.24 [0.23–0.25]). In contrast, although significant, the role of alcohol mediation (0.45 × 0.12 = 0.06 [0.05–0.07]) was weaker than the direct effect of socializing, explaining 14% of the variance of cannabis use frequency. If the entire sequence socializing → tobacco and alcohol use → cannabis use → cannabis use disorders is considered, results were similar pinpointing the predominance of tobacco mediation, which explained 61% of the variance of cannabis use disorders intensity while the role of alcohol mediation was weaker than the direct effect. Cannabis use

frequency was strongly associated with cannabis use disorder intensity (0.76 [0.75–0.77]). Stratification of the sample and fitting the model for each gender gave similar results among boys and girls. Tobacco mediation showed the same effect for the sequence leading from socializing to cannabis use (58% of the explained variance for girls versus 57% for boys), and for the sequence leading to cannabis use disorders (60% of the explained variance for girls versus 62% for boys). 4. Discussion Our results highlight peer influence, in the course of night-out socializing, on substance use among adolescents, and they are compatible with the existence of tobacco mediation in the process leading to cannabis use and misuse, while the role of alcohol seems to be less significant. In addition, tobacco and alcohol use seems to be relatively independent despite the common influence of socializing. This study has an advantage in that it focuses on the French adolescents aged 17 years. Although the consequence is that cannabis use and cannabis use disorders at older ages were not considered, it provides knowledge of risk patterns that may be useful for early preventive measures to limit later cannabis use in the French population. Our sample can be considered as representative of the French adolescent population, as a result of the sampling design of the study, the large size of the sample and the very high response rate. An information bias could arise from the data collection by self-administered questionnaires: illicit substance consumption could in fact be underestimated for fear of breach of trust. However, it can be thought

Table 2 Description of different effects contributing to the processes studied — structural equation modelling among French 17-year-old adolescents (N = 29,393). Process Socializing → tobacco/alcohol uses → cannabis use Direct effect Indirect effect (tobacco use) Indirect effect (alcohol use) Total effect Socializing → tobacco/alcohol uses → cannabis use → CUD Direct effect (cannabis use) Indirect effect (tobacco use → cannabis use) Indirect effect (alcohol use → cannabis use) Total effect CI: confidence interval. CUD: cannabis use disorders. Proportion of effect = (coefficient of studied effect ⁎ 100) / coefficient of total effect.

Standardized coefficient (95% bootstrap CI)

Proportion of effect (%)

0.12 0.24 0.06 0.42

[0.09–0.14] [0.23–0.25] [0.05–0.07] [0.40–0.44]

29 57 14 100

0.09 0.20 0.04 0.33

[0.07–0.11] [0.18–0.21] [0.03–0.05] [0.30–0.35]

27 61 12 100

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that the data can be treated as reliable because numerous means were implemented to preserve participants' anonymity. The possibility of recall bias would be limited because the time interval from the onset of tobacco or cannabis use up to the age of 17 was small in our sample. Moreover, cannabis or tobacco experiments appear as important events in an adolescent's life, as they often occur in a novelty-seeking context or as a form of intentional transgression (Fergusson, Boden, & Horwood, 2008). Further to this, the collection of retrospective data can give rise to some over-interpretation in our description of a sequential process, but there are arguments that enable this effect to be minimized. First, the aim of SEM in this study was more to determine the strength of links between uses from a statistical point of view than to assess causal links. Hence, while path analysis was originally designed for causal modelling (Wright, 1921), Pearl notes that the prevailing interpretation of SEM differs from that made by its originators (Pearl, 2000). Structural equations are often interpreted as simple carriers of probabilistic information but the causal interpretation is not generally feasible. We based our modelling on a convincing hypothesis of a plausible social mechanism for the development of cannabis use during adolescence. While no causal inference can be formally advanced, our model should be interpreted from an associational point of view as proposed by Russo (2008). Indeed, the sequence of uses tested in the model was temporally verified in most of our sample by collecting ages of use onsets. Model likelihood was statistically assessed, showing that the influence of uses on socializing, which would concern later stages of use processes, is not observed in our model where the aim was to describe the early stages. Another limitation could be the presence of unobserved decreases in use, but we can consider that the life period studied appears relatively short: cigarette initiation occurred at age 13.4 and cannabis initiation occurred at age 15.1, while data were collected at age 17.6. Moreover, considering median age differences, equal to 1 year, between tobacco experiment and daily use, and between cannabis experiment and daily use, it can be supposed that substance consumption cannot increase to become intensive and then secondarily decrease over such a short period. Concerning sequences between socializing and substance uses, data about time out concerned the last 12 months while data about substance uses concerned the last month. Thus, measurement of time spent with friends was likely to reflect a habit that existed before the time scale used for recent substance use frequencies. Finally, our model was statistically the most compatible with the predominance of the path socializing → tobacco use → cannabis use → cannabis use disorders in comparison with alternative paths. The variances of cannabis use frequency and cannabis use disorder intensity in the model were mainly explained by tobacco mediation. An Israeli study that used three cross-sectional surveys conducted between 1989 and 1995, in which the protocol was similar to that of the ESCAPAD study, demonstrated a causal sequential process leading from tobacco use to cannabis use. This study tested the causal gateway theory of drug use dynamics by way of a natural experiment, which randomized cigarette smoking according to age group and cigarette prices (Beenstock & Rahav, 2002). A further point is that, according to Becker, deviant behaviours induce deviant motives, which appear as a consequence of an experimentation process (Becker, 1985). Thus, different stages in cannabis consumption are described: training in techniques required for consuming a drug, learning to perceive drug effects, and finally experience of pleasure induced by these effects. According this theory, tobacco, initially more accessible than cannabis, could have an influence on cannabis use by early training in the smoking route. Hence, while our results are compatible with cannabis use initiation being mediated by tobacco use, which is confirmed from a temporal point of view (98% of cannabis users experimented tobacco before cannabis), links between these two substances seem to be more complex, with the existence of a reverse sequence leading from cannabis to tobacco. In all events, a survival study which compared the tobacco → cannabis sequence with the cannabis → tobacco sequence did not highlight any significant

difference concerning development into problematic cannabis use (Tarter, Vanyukov, Kirisci, Reynolds, & Clark, 2006). Another study demonstrated that cannabis use during adolescence was associated with a slightly higher risk of daily tobacco use in young adulthood (Timberlake et al., 2007). These data suggest a reciprocal relationship between tobacco and cannabis rather than a formal transition from one substance to the other. This is not surprising, because adolescents frequently mix cannabis with tobacco when smoking. Indeed, the smoked route of administration may play an important role in this observed association between tobacco and cannabis, as it could reflect a physiological adaptation of the aerorespiratory system, or cultural influences associated with the smoked forms (Agrawal & Lynskey, 2009; Agrawal et al., 2008). These substances are also known to have antagonist pharmacological effects, particularly appetite and cognition, and a possible explanation of substance consumption could be compensation of these antagonist effects (Viveros, , Marco, & File, 2006). Another explanation of this simultaneous cannabis and tobacco use could be a shared genetic liability (Neale, Harvey, Maes, Sullivan, & Kendler, 2006). Finally, a Canadian study, which evaluated selfdefined tobacco and cannabis use status, hypothesizes that cannabis use increases adolescents' perceived tobacco addiction and may play an important role in the transition to regular tobacco use, indicating a common syndrome of vulnerability to substance use and to addiction (Okoli, Richardson, Ratner, & Johnson, 2009). The role of alcohol appeared to be less important, but significant. Indeed, some recent studies have explored a possible pathway from alcohol to cannabis (Bretteville-Jensen, Melberg, & Jones, 2008; Willner, 2001). One explanation could be that alcohol de-inhibition seemed to be particularly important in facilitating adolescents' initiation into a new drug. However, cannabis is also known to have a similar de-inhibiting effect (Laqueille, Launay, & Kanit, 2008). Thus, these two substances also appear to be consumed as complements to each other, and the direction of the relationship appears hard to demonstrate (Pape, Rossow, & Storvol, 2009). Finally, if some theories have defined pathways from one substance use to another, it appears that these transitions could be bidirectional. Thus, the gateway effects of licit drugs should be further explored in relation to adolescents' simultaneous polysubstance use. Role of Funding Source INSERM Unit 669, which analysed the data, and OFDT, which conducted the study, are two French national entities, which are financed by public funds. Contributors All authors have participated in this study and concur with the submission and subsequent revisions submitted by the corresponding author. Aurélie Mayet contributed in the analysis and interpretation of data, and redaction of the paper. Stéphane Legleye assisted in the conception and design of study, and data collection. Nearkasen Chau participated in the conception and design of study, and interpretation of data. Bruno Falissard contributed in the conception and design of study, analysis and interpretation of data, and acted as the manager. Conflict of Interest The authors state that they have no conflicts of interest.

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