Murdoch Children's Research Institute, Parkville, Victoria
Murdoch Children's Research Institute and University of Melbourne, Australia
Washington University, St Louis, Missouri, USA
Murdoch Children's Research Institute and University of Melbourne
Murdoch Children's Research Institute, Parkville, Victoria, Australia
Correspondence: Ms Carolyn Coffey, Centre for Adolescent Health, 2 Gatehouse Street, Parkville 3052, Victoria, Australia. Tel: 3 9345 6538; fax: 3 9345 6502; e-mail: carolyn.coffey{at}rch.org.au
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ABSTRACT |
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Aims To examine adolescent precursors of young-adult cannabis dependence.
Method Putative risk factors were measured in a representative sample (n=2032) of secondary students in the State of Victoria, Australia, six times between 1992 and 1995. Cannabis dependence was assessed in 1998, at age 20-21 years.
Results Of 1601 young adults, 115 met criteria for cannabis dependence. Male gender (OR=2.6, P < 0.01), regular cannabis use (weekly: OR=4.9; daily: OR=4.6, P=0.02), persistent antisocial behaviour (linear effect P=0.03) and persistent cigarette smoking (linear effect P=0.02) independently predicted cannabis dependence. Neither smoking severity (P=0.83) nor persistent psychiatric morbidity (linear effect P=0.26) independently predicted dependence. Regular cannabis use increased risk only in the absence of persistent problematic alcohol use.
Conclusions Weekly cannabis use marks a threshold for increased risk of later dependence, with selection of cannabis in preference to alcohol possibly indicating an early addiction process.
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INTRODUCTION |
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METHOD |
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Adolescent phase: waves 1 to 6
Altogether, 1947 adolescents (96% of the intended sample) participated at
least once during waves 1 to 6, with a gender ratio (males 48.6%) similar to
that in Victorian schools at the time of sampling
(Australian Bureau of Statistics,
1993). Surveys were self-administered at school using laptop
computers, thereby allowing the use of branched questions. Participants
unavailable for follow-up at school completed the questionnaire by
telephone.
Young-adult survey (wave 7, 1998)
The young-adult survey was carried out by telephone using computer-assisted
interviews consistent with the adolescent phase. A total of 1601 young adults
(82% of cohort participants; mean age 20.7 (s.d.=0.5) years, 46.0% male) were
interviewed between April and December 1998. All analyses are based on this
subset. Reasons for non-participation at wave 7 were: refusal
(n=152); person traced but non-contactable (n=59); person
not traced (lost) (n=133); and death (n=2). Of the 1601
participants interviewed, 71%, 27% and 3% respectively lived at home, with
others or alone; 82% had completed the final school year; 85% had commenced
post-school study, with 68% still studying at the time of the interview; 82%
were in paid employment; 8% were neither studying nor employed.
Characteristics of non-completers at wave 7 were examined in a multivariate logistic regression model. Males were over-represented (odds ratio (OR)=1.9, 95% CI 1.5-2.4), as were those who had experienced parental divorce or separation (OR=1.8, 95% CI 1.4-2.5) and those reporting daily smoking at study inception (OR=2.1, 95% CI 1.5-2.9).
Outcome measure: DSMIV cannabis dependence
A DSMIV diagnosis of dependence required evidence that, within the
previous 12 months, an individual continued cannabis use despite significant
substance-related problems (American
Psychiatric Association, 1994), supported by endorsement of three
of the following seven criteria: tolerance to the effects of cannabis;
withdrawal symptoms on ceasing or reducing use; cannabis used in larger
amounts or for a longer period than intended; a persistent desire or
unsuccessful efforts to reduce or cease use; a disproportionate amount of time
spent obtaining, using and recovering from use; social, recreational or
occupational activities reduced or given up owing to cannabis use; and use
continued despite knowledge of physical or psychological problems induced by
cannabis (American Psychiatric Association,
1994).
To generate the DSMIV criteria for a diagnosis of cannabis dependence, the Composite International Diagnostic Interview 2.1, 12-month version (CIDI; Hall et al, 1999), was administered. We assessed cannabis dependence only in participants reporting weekly cannabis use in the preceding 12 months, to minimise responder fatigue. We considered that a diagnosis of cannabis dependence was consistent only with regular cannabis use, given the DSMIV description of substance dependence as occurring with a pattern of repeated [substance] self-administration (American Psychiatric Association, 1994).
Population prevalence estimates for cannabis dependence and dependence symptoms in the cohort at wave 7 have been reported earlier (Coffey et al, 2002). We estimated that 7% of the cohort, equivalent to 13% of ever-users, met criteria for DSMIV cannabis dependence within the preceding 12 months. The most prevalent symptoms were persistent desire or unsuccessful abstinence attempts (10%) and unintentional use (8%). Tolerance (2%) and social consequences of use (1%) were the least prevalent symptoms. Eleven wave 7 participants did not report on their cannabis use and were classified as non-users for all analyses.
Measures: waves 1 to 6
Demographic variables
Gender and country of birth were recorded at study entry. Parental
partnership status was assessed throughout the study.
Cannabis use
Cannabis use during the previous 6 months was assessed using the following
rating scale: never used; not used in the past 6 months; a few times; monthly;
weekly; daily. Those reporting the use of cannabis at least a few times in the
past 6 months were classified as any users.
Cigarette smoking
Participants reporting that they had smoked on 6 or 7 days in the previous
week were categorised as daily smokers. Occasional smoking was defined as
reporting smoking in the past month, but on fewer than 6 days in the past
week.
Alcohol consumption
Participants reporting that they had drunk alcohol in the week before the
survey completed a 1-week retrospective alcohol diary (specifying beverage and
quantity), allowing derivation of two measures of problematic alcohol
consumption: frequent drinking on 3 or more days in the previous
week, and high-dose drinking with an average consumption of 5
units or more of ethanol per drinking day (1 unit is equivalent to one
standard drink containing 9 g ethanol).
Antisocial behaviour
Ten items from the Moffitt & Silva
(1988) self-report Early
Delinquency Scale assessed antisocial behaviour relating to property damage,
interpersonal conflict and theft in the previous 6 months. Antisocial
behaviours were categorised according to whether more than one behaviour was
endorsed more than once, in order to distinguish participants
with more-global antisocial behaviours.
Psychiatric morbidity
A computerised form of the Clinical Interview Schedule (CIS) was used to
quantify the severity of psychiatric morbidity
(Lewis et al, 1992).
Scores greater than 11 were taken to indicate psychiatric morbidity,
reflecting the level at which clinical intervention is appropriate.
Explanatory variables: waves 1 to 6
Responses on adolescent risk factors (waves 1 to 6) were summarised as
follows:
Missing waves of data collection: waves 1 to 6
Seventy-five per cent of the cohort completed five of the first six waves
of data collection, but owing to the staged recruitment, 54% of observations
were missing from the first wave (Fig.
1). Missing observations for waves 2, 3, 4, 5 and 6 were 11%, 13%,
16%, 19% and 21% respectively. Overall, 59% of participants missed at least
one wave. Multiple imputation was used to handle this fact, enabling summary
measures to be defined for each participant in each of five
completed data-sets. Imputation was performed using the
multivariate mixed effects model of Schafer & Yucel
(2002).
Data analysis
Logistic regression analyses were performed on the binary outcome of
cannabis dependence. In multivariable models, exposure effects were estimated
as linear trends in the log odds ratio across ordered categories of exposure
on explanatory variables. Two-tailed P values are reported based on
Wald tests.
All analyses were performed using Stata 7.0 for Windows (Stata, 2001). We used the method of Rubin (1987) for creating valid inferences with the multiple imputation model, by combining over standard analyses performed on each of the imputed data-sets. Software for facilitating these analyses was written in Stata (details available from the authors upon request).
Ethical approval
Ethical approval for the study was obtained from the Royal Children's
Hospital Ethics in Human Research Committee. Written parental consent was
obtained at study inception and individuals gave informed verbal consent
before commencing the wave 7 interview.
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RESULTS |
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Overall, 32% (95% CI 30-35) of the 1601 wave 7 participants reported cannabis use in the adolescent waves 1-6. Eighteen per cent (95% CI 14-21) of wave 1-6 users and 32% (95% CI 25-39) of those reporting at least weekly use later met criteria for cannabis dependence. Conversely, of the 115 with cannabis dependence at wave 7: 17% (95% CI 10-25) reported occasional use in waves 1-6; 22% (95% CI 10-34) weekly use; 38% (95% CI 27-49) daily use; and 22% (95% CI 14-30) initiated cannabis use after wave 6.
Univariate associations between young-adult cannabis dependence (wave
7) and adolescent exposures (waves 1-6)
The frequencies of a range of adolescent factors were estimated and crude
associations between these and cannabis dependence were assessed
(Table 1).
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Maximum frequency of cannabis use and cigarette smoking
Maximum frequency of cannabis use in waves 1-6 showed strong association
with cannabis dependence in wave 7, with both weekly and daily maximum use
carrying about a 20-fold increase in odds, indicating evidence of a threshold
at weekly use. There was a strong increase in frequency of dependence with
increase in maximum frequency of cigarette smoking from occasional to
daily.
Persistence of adolescent behaviours
Strong associations, with evidence of linear relationships, were observed
for the number of waves in which cannabis use, cigarette smoking, high-dose
drinking and antisocial behaviour were reported, with a two-fold or greater
average odds increase with each increase in level of reporting frequency. For
all four measures the most persistent levels carried elevated odds of ten-fold
or greater. A weaker association, but still with some evidence of a linear
relationship, was observed with the number of waves in which psychiatric
morbidity was identified, with an average increase in odds of 1.3 with
increasing level of reporting frequency. With frequent drinking the clearest
difference was between none and some, with weak
evidence for a dose-related effect.
Independent associations between young-adult cannabis dependence
(wave 7) and adolescent exposures (waves 1-6)
We used multiple logistic regression to quantify the independent predictive
associations and to adjust for possible confounding. To aid parsimony,
measures of persistence (all of which showed univariate linear relationships)
were entered in the multivariate model as linear effects. After adjustment,
the only adolescent measures (apart from gender) demonstrating an independent
relationship with cannabis dependence were: maximum frequency of cannabis use;
and the number of waves in which each of cigarette smoking and antisocial
behaviour were reported (Table
2). There was no evidence of first-order interaction effects
between gender and any explanatory variable.
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The relationship between cannabis dependence and persistent frequent
drinking in adolescence changed direction, from a risk association in the
univariate model to a protective association in the adjusted model. We
therefore examined the interaction between this factor and maximum cannabis
use, adjusting only for factors influential in the multivariate model reported
in Table 2. We selected
individuals reporting frequent drinking in two or more waves, and identified
evidence of an interaction between this characteristic and maximum weekly or
daily cannabis use (Wald 2 P=0.01). Elevated risk for
later dependence associated with maximum weekly or daily cannabis use was
evident only in participants not reporting frequent drinking in two or more
waves (OR=7.4, 95% CI 3.9-14; P <0.01). There was no evidence that
those reporting both weekly or daily cannabis use and multiple waves of
frequent drinking were at risk of later cannabis dependence (OR=1.2, 95% CI
0.28-5.0; P=0.81).
Confounding by cigarette smoking and antisocial behaviour on the
effect of early-onset cannabis use
The reason for a lack of independent association between cannabis
dependence and early cannabis use was explored in three further models. We
characterised individuals who reported using cannabis in the first three waves
of follow-up, i.e. in year 9 or year 10 (average 359 of a total of 517 users
in waves 1 to 6). We compared the association of early use v. later
onset only in young adult participants reporting any adolescent use,
progressively adjusting for the persistence of smoking and antisocial
behaviour (Table 3). Both
cigarette smoking and antisocial behaviour confounded the effect of early
cannabis use. Persistent cigarette smoking showed the greater confounding
effect, particularly when reported in four or more waves, that is, with early
onset. After adjusting for these factors there was no evidence of an
independent association between early cannabis use and later dependence.
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DISCUSSION |
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Cannabis dependence was assessed at an age of peak cannabis use in a close-to-representative sample with high participation into young adulthood. To circumvent bias from non-response during the adolescent waves, multiple imputation of missing covariate values was performed using a model based on background measures (available for 96% of the sampling frame). This allowed us to define exposure measures of time-varying adolescent behaviours based on all six waves of data collected in the adolescent phase for all 1601 participants who were interviewed in wave 7 aged 20-21 years.
A potential study limitation was the underspecification of cannabis dependence. First, although the response rate in wave 7 was high, differential under-ascertainment of illicit substance users a notoriously difficult group to reach might have occurred. Second, as a third of young adult cannabis users had commenced using only in the preceding 3 years (that is, since wave 6), it is likely that some currently non-dependent participants would develop cannabis dependence in the next few years (Rosenberg & Anthony, 2001). We have assumed that the risk profile for cannabis dependence in our sample would be the same for all members of the cohort, but these possible sources of error could result in attenuation of the observed associations.
In defining adolescent measures of smoking we elected not to distinguish between persistent occasional smoking and daily smoking. This decision was taken to aid parsimony and was supported by the similarity in risk association of occasional and daily smoking in the adjusted model describing cannabis dependence. We assessed persistence only in problematic alcohol use, as any alcohol use was too common to be informative.
Predictors
Gender
Males were marginally more likely than females to use cannabis overall, but
the transition to dependence was considerably more likely in males. We found
no evidence of effect modification by gender, indicating that some underlying
unmeasured factors were responsible. The suggestion that gender differences
might be due to differing opportunity rather than differing transition rates
is not supported by our findings (Van
Etten & Anthony, 2001).
Adolescent cannabis use, antisocial behaviour and cigarette
smoking
Early initiation of cannabis use, often preceded by antisocial behaviour
and cigarette smoking, is generally accepted as an important predictor of
escalation in drug use (Fergusson & Horwood,
1997,
1999). Although we found that
early cannabis uptake predicted later dependence in the crude analysis,
cigarette smoking and antisocial behaviour largely accounted for this effect
in the adjusted model. Furthermore, as no dose effect was evident with
frequency of cigarette smoking, our findings are consistent with the
suggestion of Bierut et al
(1998) that daily smoking is
not a specific marker for an underlying vulnerability to cannabis dependence.
This non-specific association with cigarette smoking probably reflects the
social environment in which both activities occur, rather than individual
biological susceptibility.
Why does early deviant behaviour predict cannabis dependence? It is possible that the prolonged cannabis exposure that often accompanies early deviant behaviour might bring forward the transitions from occasional use to regular use and thence to dependent use evident in our young adult sample. If this is so, the effect could moderate as the cohort ages, because older initiators might make the transition to dependence later.
The threshold of risk that we observed with weekly cannabis use indicates that it is the transition to regular use that provides sufficient drug exposure in the development of early dependent use. The slow metabolism of cannabis results in the persistence of measurable physical and psychological changes well beyond the duration of the subjective effects (Ameri, 1999). The maintenance of a low but stable frequency of intake might be sufficient to produce longlasting neuro-adaptive changes thought to be associated with the drug-wanting, seeking and taking process which occurs with the initiation of addictive behaviour (Hyman & Malenka, 2001). Interestingly, out-of-control use early in the cannabisusing career has been reported to distinguish individuals who make the transition to dependence from non-dependent users, supporting the notion of an early biological response (Rosenberg & Anthony, 2001).
Adolescent alcohol use
An apparently counterintuitive finding was that persistent frequent alcohol
use as a teenager negated the risk of developing cannabis dependence in
regular cannabis users. It is well established that problematic adolescent
alcohol use is one of the constellation of behaviours associated with cannabis
initiation (e.g. Donovan & Jessor,
1985), but our findings indicate that a different picture emerges,
with escalation of use in the transition between adolescence and adulthood.
This reflects the divergence in criminality in the transition to young
adulthood observed in early drug users compared with adolescent alcohol users
identified by Newcomb & Bentler
(1988: pp. 102-119). Our
findings may therefore illustrate a social process whereby individuals select
into either a predominantly alcohol-using or a cannabis-using lifestyle. From
the physiological perspective, preferential cannabis use as an early
indication of dependence is consistent with a substance-specific biological
susceptibility to addiction (Hyman &
Malenka, 2001). Selective regular cannabis use during adolescence
may mark a neurophysiological and psychological precursor of dependence.
Adolescent psychiatric morbidity
Although cannabis use has been linked with increased rates of depression
and anxiety cross-sectionally (Johns,
2001), we did not find that adolescent psychiatric morbidity
independently predicted cannabis dependence. This observation argues against
self-medication as a mechanism for continuing problematic cannabis use beyond
the teenage years and is consistent with earlier findings
(McGee et al, 2000).
Conversely, we have reported separately that regular cannabis use in
adolescence predicts later psychiatric morbidity in young women
(Patton et al,
2002).
Implications
Hall & Babor (2000)
pointed out that we have not yet adequately explored the patho-physiological
consequences of cannabis use a process that took many years with
tobacco and eventually led to broadranging policies aimed at reducing
consumption. The recent reclassification of cannabis from a class B drug to a
class C drug by the Home Office in the UK in part reflects a view that
cannabis use poses a lesser public health problem than use of other illicit
substances. The lethality and withdrawal severity of cannabis may indeed
differ from other drugs, but its use is far more common
(Hall et al, 1999;
Johnston et al, 2002). As well as the increasing prevalence of cannabis use in
young people, the transition rate to dependence would appear to be increasing,
with concomitant personal, social and physical harms resulting from prolonged
heavy use and addictive behaviour (Hall
& Babor, 2000;
Ashton,
2002). In 1990-1992 it was estimated that 9% of ever-users were at
life-time risk of dependence (Anthony
et al, 1994) but more recent estimates report that
between 13% and 16% of users are at risk by their early 20s
(Poulton et al, 1997;
Fergusson & Horwood, 2000; Coffey et al, 2002).
The case for a more concerted public health response seems strong.
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Clinical Implications and Limitations |
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LIMITATIONS
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ACKNOWLEDGMENTS |
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Received for publication June 5, 2002. Revision received August 30, 2002. Accepted for publication October 10, 2002.
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