Part 1. Review of
published data and metaanalysis
Methods
Method
of selecting studies for inclusion in the review
Articles appropriate for the review were selected by several
methods. First, literature searches were conducted in PsycINFO, Medline,
and Web of Science using combinations of the keywords "adolescent,"
"adolescence," "drug or substance," "use or
abuse," and "psychiatric comorbidity." The search was
limited to articles in the English language. Literature that focused
on parental drug abuse as a predictor of adolescent drug abuse were
excluded, as were manuscripts from Dissertation Abstracts International.
Second, the bibliographies of these articles were examined for the purpose
of yielding additional articles. Third, the inhouse reference library
of the Center for Developmental Epidemiology was searched. Finally,
advisors to the Center were asked for references that might be appropriate
for our purposes. One hundred fortyone papers were found that contained
all the keywords or were found through the other methods described.
The list can be found in appendix A.
Selection
criteria for metaanalysis
Two criteria had to be met before papers were included
in the list for metaanalysis: communitybased sampling and formal psychiatric
diagnostic procedures. We had hoped to include only studies that contained
information on the temporal ordering or risk and protective factors,
but too few met this criterion, so it was dropped.
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1. Communitybased
samples
Information about psychiatric disorders that cooccur
with substance use or abuse comes from two main sources: clinical studies
and studies using representative populationbased samples. This metaanalysis
is based on the latter. While clinicbased studies of comorbidity can
be valuable for suggesting developmental pathways, causal patterns,
or likely interventions, they cannot be used as sources of information
about the extent of comorbidity, its distribution in different groups,
or attributable risk. The reasons are that:
 People with two illnesses are more likely to seek treatment than
people with either one of those illnesses separately (Berkson, 1946).
This means that a clinicbased sample is likely to have a higher proportion
of comorbid people in it than does the general population, and so
one cannot use them to estimate the size of the problem.
 Some combinations of disorders may bring people into treatment settings
more often than others. For example, youth with substance abuse disorder
and conduct disorder might be referred to clinics in higher proportions
than youth with substance abuse disorder and an anxiety disorder.
This would give clinicians the impression that comorbidity with conduct
disorder is very common and comorbidity with anxiety very rare. This
may or may not be true, but the point cannot be established from clinic
samples.
 Clinic cases may or may not be more severely affected with either
of the comorbid conditions than community cases. Alternatively, comorbid
cases may seek clinical treatment even though they have less severe
symptoms of one or other disease than most community cases.
 The temporal ordering of comorbidity may or may not be the same
in clinical as in community cases.
 Comorbidity seen in clinic cases may or may not be precipitated
by the same risk factors as those that precipitate cases in the general
population. These potential differences can be checked empirically
for diseases where almost all cases get into treatment. However, in
the case of drug abuse there is very strong evidence that this is
not so. We also know that many people with psychiatric disorders never
receive treatment. For these reasons we cannot assume that the patterns
of association and risk seen in clinic samples mirror those seen in
the population.
 What appear to be risk factors for one or other disorder may in
fact be predictors of treatment referral. For example, it might appear
from a clinic study that poverty was associated with one or more disorders,
where in fact stiff managed care regulations restricted access to
treatment for children from privately insured families, while Medicaid
regulations were more generous. This could mean that only children
on Medicaid had much chance of getting treatment, and so poor children
were overrepresented in the clinic sample.
For all these reasons, the scientific study of prevalence,
incidence, development, and risk for comorbidity cannot rely solely
on clinicbased samples.
Among communitybased samples, one would of course like
to use only those that employed appropriate (random or stratified random)
sampling of representative population samples. These are hard to find.
Many studies of adolescent drug use and abuse, for example, have relied
on school samples, which tend to miss children who are absent or have
dropped out — often a highrisk group. However,
we have included studies using schoolbased samples to increase the
number suitable for analysis.
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2.
Formal diagnostic rules and procedures
This metaanalysis includes studies that use structured
interviewerbased or respondentbased interviews and formal scoring
algorithms or bestestimate diagnostic procedures to generate psychiatric
diagnoses using one of the recent taxonomies: ICD9 or ICD10, DSMIII,
DSMIIIR, or DSMIV. Three exceptions to this criterion that were included
because authors made an attempt to generate diagnosticlike symptom
clusters from survey questionnaires were the Ontario study (Offord et
al., 1987), the National Household Survey of Drugs and Alcohol (SAMHSA,
1993), and the Middle Adolescent Vulnerability Study (Windle & Davies,
1999). Five diagnostic groupings were available for analysis of comorbidity
in a large enough number of studies: conduct disorder (CD), oppositional
defiant disorder (ODD), attention deficit hyperactivity disorder (ADHD),
depressive disorders, and anxiety disorders.
When it came to drug abuse and dependence the requirement
of a formal diagnosis was somewhat relaxed. The DSMIV rules for dependence
rely heavily on physical or workrelated incapacities that most adolescents
have not had time or been in a position to experience. In this metaanalysis
we have taken researchers' definitions of abuse and dependence at face
value, even if they do not demonstrate that they are using strict adult
diagnostic criteria.
We have where possible distinguished between any use and
abuse/dependence. However, it is important to note that the studies
that report "any use" often do not report abuse/dependence
separately; thus, the rates for any use usually include those
for the nested category of abuse/dependence. Too few studies were available
with the necessary information for us to model psychiatric comorbidity
with specific drugs.
Comorbidity is used differently in different studies
(Angold, Costello, & Erkanli, 1999), to refer to everything from
concurrent cooccurrence of two disorders at the time of interview to
their both having been diagnosed at some time during the lifetime of
the individual. Because there were so few papers that met even basic
criteria for inclusion in the analysis, we chose to include papers across
the entire spectrum, from lifetime to concurrent comorbidity.
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3. Risk
factors and correlates
We had hoped to include only studies that could advance
our understanding of risk factors for comorbidity, but there were not
enough studies to make this possible. The tables and figures present
the data for both sexes combined and separately, but the available data
did not permit analysis at any more finegrained level. There are not
enough studies for separate analyses by race/ethnicity, or even by age.
Analytic method
In combining information from multiple studies we used
randomeffects hierarchical Bayesian regression models to account for
studytostudy variability. Specifically, we assumed that the estimated
logodds ratios obtained from available studies were distributed normally
and independently with each study having its own unknown mean, and a
variance equal to the (estimated) variance of the logodds ratios in
each sample. This constituted the first level of the hierarchy. At the
second level, the unknown means of study effects (i.e., the "true"
logodds ratios) were assumed to be independently and identically distributed
with a normal distribution having a common (unknown) population mean
and (unknown) variance. At the third and final level these common population
parameters were given independent noninformative priors. Posterior computations
were then performed using Gibbs sampling.
Among the available studies, that is, the studies that
provided estimates of odds ratios and their standard errors, two only
published the estimated odds ratios. So before applying the hierarchical
model described above we imputed the unknown standard errors by imposing
a statistical model on them: they are independently distributed as Gamma
with unknown scale and shape parameters a and ß, respectively. Once
again assuming a Bayesian model, we treated these parameters as random
having independent uniform prior distributions between 0 and 100. These
values were uninformative so the prior distributions had no influence
on the imputed values of unknown standard errors.
Assuming that any missing data were missing at random,
these unknown standard errors were simulated from their posterior predictive
distributions, and the arithmetic means of the simulated values were
treated as imputed values, which in turn replaced the missing values
in the data set needed to run the hierarchical model as discussed above.
All the necessary computations for imputing the unknown
standard errors and estimating the combined (population) estimates of
the odds ratios for each substance use group, each malefemale group,
and the overall population estimates were performed using the latest
version of the Bayesian software WinBUGS1.3, which implements a Markov
chain Monte Carlo approach based on the Gibbs sampler. Documentation
and more information on the technical background to Gibbs sampling and
other related issues are available at http://www.mrcbsu.cam.ac.uk/bugs.
During these computations, we used 1,000 simulations to initialize the
posterior distributions (burnin time), and 10,000 iterations for convergence.
More details including the WinBUGS codes used for these analyses can
be obtained from Dr. Erkanli at al@psych.mc.duke.edu.
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Conditional
comorbidity estimates
The analyses described above were applied to each
comorbidity pair, e,g., substance use/depression, independent of other
comorbidity pairs. In other words, the estimates of oddsratio for one
pair was not adjusted for the other pairs. We know that in reality the
estimated odds ratios obtained from each study were correlated across
comorbidity pairs (Angold et al., 1999). However, most published papers
did not provide crosstabulations between substance use and all the
other diagnoses considered simultaneously, so there was no way of knowing
the true correlation between any comorbidity pair and another. In the
absence of this information, the best we could do was to use a statistical
model to try to recover these correlations, adjusting the population
odds ratios for other pairs of comorbid diagnoses. We used an extension
of the model we described above. At the first level, estimated logodds
ratios from each data set and for each comorbidity pair were assumed
to be statistically independent conditional on unknown mean and variance
equal to the (estimated) variance of the logodds ratios in each study
and comorbidity pair. At the second level, the unknown logodds ratios
were assumed to be independently distributed with a normal distribution
having a common (unknown) population mean and (unknown) variance, for
each comorbidity pair. At the third and final level these common population
parameters were given dependent priors, using a slightly modified version
of the conditional autoregressive (CAR) model of Besag (1974). These
priors, in effect, induced correlations between the population estimates
of the logodds ratios for each comorbidity pair. Computations were
again performed using Gibbs sampling in WinBUGS1.3.
Results
Articles
included in the metaanalysis
Twentyone of the 141 articles evaluated met the basic
criteria for inclusion in the metaanalysis. Articles were excluded
if they did not meet the criteria for populationbased, community studies
(n=34), were reviews or conceptual papers (n=28), provided insufficient
comorbidity data (n=24), included adults ages 20 or older in the sample
in such a way that they could not be excluded for analytic purposes
(n=19), did not provide lifetime or current prevalence rates (n=10),
or provided data only on comorbid use, abuse, or dependence regarding
one specific substance (n=5). Appendix B provides a summary of the studies
used in the analyses. Appendix C summarizes the results from each individual
study in both tabular and descriptive form. Table 1 lists the eight
studies that provided only information on comorbidity with any substance
use, including abuse or dependence. Table 2 lists studies that provided
information on abuse or dependence.
The tables of comorbidity findings presented in this
paper show that (1) not many studies have published the data needed
to assess comorbidity with substance abuse, and (2) the estimates generated
from these studies show wide variability. It is easy to see why this
happens. Apart from all the issues of different diagnostic methods and
scoring algorithms, estimates of comorbidity are likely to be small
and therefore unstable when derived from small samples. As an example,
if we were to assume that the population base rate of drug abuse in
adolescents was 2 percent and that of major depressive disorder 2 percent
(Costello et al., 1996), then the two would cooccur by chance four
times in 1,000 observations. If they cooccured twice as often as expected
by chance there would still only be eight cases in 1,000 observations.
Given the uncertainties of measurement, a single study with 1,000 subjects
could easily overestimate or underestimate the "real" association.
Combining estimates from different studies using the methods described
above enabled us to provide much more stable estimates than most individual
studies can offer.
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Results of metaanalysis
Figure 1 provides a general picture, showing the extent
of comorbidity with the five diagnostic groupings, for any use, abuse,
or dependence, for both boys and girls together. Results are presented
in the form of odds ratios with their standard errors. An odds ratio
of one indicates that the comorbid condition Y is equally likely to
occur in persons with the index condition X and in those without X.
An odds ratio of 2 indicates that Y is twice as likely in those with
X as it is in those without X. The rule of thumb is that an odds ratio
whose 95percent confidence interval (CI) does not include 1 is "statistically
significant." In general, an odds ratio of two or more is statistically
significant. There are no standard procedures for statistically comparing
two odds ratios derived from the modeling procedures described above.
However, it is reasonable to assume that in the figures in this report
two odds ratios are "significantly" different if one odds
ratio is outside the 95percent CI of the other (i.e., in the figures
the CI bar on the odds ratio bar for one type of comorbidity does not
overlap the top of the odds ratio bar for the other type of comorbidity).
Figure 1 shows that the extent of comorbidity varied widely
from one type of psychiatric disorder to another. The extent of comorbidity
with anxiety disorders was low (OR 1.9, 95% CI 1.42.2), while that
for disruptive behavior disorders (DBDs: conduct disorder, oppositional
defiant disorder, attention deficit hyperactivity disorder) was high
(ORs 5.66.9), and not significantly different from one DBD to the other.
The odds ratio for comorbidity with depression (OR 4.2, 95% CI 2.96.1)
fell between those for anxiety (OR 1.9 95% CI 1.42.2) and ADHD (5.6,
95% CI 3.28.6). It was significantly higher than that for anxiety,
and lower than those for CD and ODD, but not significantly lower than
that for ADHD.
Comorbidity
with any substance use and with substance abuse/dependence
Some of the studies available for analysis presented rates
of any substance use, a category that included abuse and dependence.
Others provided data for abuse/dependence only, or for the two categories
separately. We wanted to use the data to test the hypothesis that comorbidity
was more likely in cases of abuse/dependence than in cases of use. Our
ability to do so was limited by the fact that many studies reporting
any use include in that category abuse/dependence. From the data sets
available it was not possible generate odds ratios for use alone.
We divided the data sets into 13 that provided information on comorbidity
with substance abuse and/or dependence, and 8 that provided information
on any use, abuse, or dependence.
Figure 2 shows the odds ratios associated with abuse/dependence
(grey bars) and any use (white bars). Comparing figure 2 with figure
1, we see that the rank ordering of odds ratios for comorbidity with
the various diagnostic categories remained the same for both substance
abuse/dependence and any use, although the smaller number of studies
in each group led to wider confidence intervals. In the studies of abuse/dependence,
the odds ratios for comorbidity with the DBDs were not significantly
different from one another, but were significantly higher than the odds
ratios for comorbidity with depression or anxiety; the latter two were
not significantly different. In the case of any use, the odds ratios
for comorbidity with CD and ODD were not significantly different, but
they were higher than that for ADHD, which was higher than that for
depression, while depression was in turn higher than anxiety. The odds
ratio for comorbidity between any use and anxiety was not itself much
above the level that might be seen by chance alone (OR 1.6, 95% CI 1.32.1).
For every diagnosis except CD, the odds of a comorbid
psychiatric condition was significantly higher for adolescents with
abuse/dependence than for those with any use. The difference was most
extreme for ADHD, where abuse/dependence involved a 2.5fold increase
of risk compared with any use, followed by anxiety and depression (twofold
increase of risk). The increase was lowest for conduct disorder, where
the increase in risk was only around 20 percent, and ODD, where it was
45 percent.
In summary, the metaanalysis showed a stable pattern
of high comorbidity with DBDs, low comorbidity with anxiety disorders,
and intermediate comorbidity with depression. It is worth noting that
even at the lowest level, substance use/abuse/dependence was associated
with a doubling of the likelihood of an anxiety disorder, while the
risk of a DBD increased six to eightfold. However, these analyses do
not permit us to draw any conclusions about the temporal ordering or
causal relationship between one disorder and the other, only that the
association between them greatly exceeds what would be expected by chance
alone.
Effects
of comorbidity among other diagnoses
If the comorbid conditions were themselves comorbid, this
could affect the estimates shown in figures 1 and 2.
Figure 3 presents the results after attempting to model
other forms of comorbidity in the data. It is immediately clear that
(1) the rates of comorbidity maintained the same ordering as before,
with higher comorbidity with DBDs and lower comorbidity with emotional
disorders, but (2) the odds ratios were very much lower than they were
for the simple twoway comorbidity. Only two were greater than 2: comorbidity
with CD and ODD. In the case of depression and anxiety comorbidity was
around 1; that is, substance abuse was no more likely in the presence
of the other diagnosis than in its absence. The 95percent confidence
intervals were very wide, reflecting the extreme conservatism of the
model and the small number of data sets available. In no case did the
confidence interval exclude 1, an indication that the odds ratios were
not significantly different from 1.
Given our limited ability to model the conditional comorbidities
in the available data, the rest of these analyses are based on the twoway
analyses, uncorrected for comorbidity among other diagnoses. It is,
however, important to bear in mind that the relationship between drug
abuse and any of the psychiatric disorders is likely to be influenced
by comorbidity among the latter.
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Correlates
of and risk factors for comorbidity
We might get closer to understanding the causes of comorbidity
if we could establish clear associations with putative risk factors.
The published literature provides almost no data to make a metaanalytic
review possible at this level. Sex was the only variable on which information
was available in all studies.
Comorbidity
in boys and girls
Figures 4 and 5 show the odds ratios associated with abuse/dependence
(figure 4) and any use (figure 5) by sex. Figure 4 shows that for abuse/dependence
the odds ratios were higher for girls than for boys, for every diagnosis,
although in the case of depression the difference was very slight. The
higher odds ratios for girls' comorbidity were statistically significant
for CD and anxiety.
Figure 5 shows a similar pattern for any use. Girls who
used alcohol or drugs were at greater risk of comorbidity than boys,
for every type of disorder except depression; the difference was statistically
significant for CD and anxiety. Thus the pattern of sex differences
in comorbidity was similar for both types of substance use.
Figures 6 and 7 look at the question another way, comparing
the odds ratios associated with any use versus abuse/dependence within
sex. These figures show that although, as seen in figures 4 and 5, the
odds ratios were lower for boys than for girls, the increase in risk
associated with abuse/dependence relative to any use was similar across
the sexes. Thus, the increase in risk associated with abuse/dependence
compared with any use was highly significant for every diagnostic group
except for anxiety, where comorbidity was actually slightly higher (though
not significantly so) with any use than with abuse. A comparison of
the size of the difference in odds ratios between users and abusers
shows that for both boys and girls it was highest for ADHD (a 2.5fold
increase in risk for with abuse/dependence compared with any use) and
depression (2.4fold increase in risk). CD showed the smallest difference
in risk between users and abusers: a 40percent excess risk for girls,
and a 60percent excess risk for boys.
In summary, patterns of comorbidity were similar for boys
and girls, but more extreme in girls. Substanceusing girls were at
higher risk than boys for every type of comorbidity except for that
with depression. Although DBDs are less common in girls than in boys,
the higher odds ratios for girls imply that when they do occur, they
are more likely to be associated with other harmful behaviors, such
as drug abuse. In both sexes, for every diagnosis except anxiety comorbidity
was higher in association with abuse/dependence than with any use, an
effect that was strongest for ADHD and depression and weakest for conduct
disorder.
Other risk factors
Although individual studies discuss other risk factors
(see appendix C), too few studies provide data on any one factor for
metaanalysis to be possible. This is not to say that the data were
not collected, only that they have not yet been published in a form
that makes aggregation feasible. Thus we could draw no general conclusions
from the published literature on the association between different diagnostic
comorbidities and, for instance, age, race/ethnicity, poverty, family
history, early development, or (most importantly for this meeting) the
effects of treatment. Nor could metaanalysis help with teasing out
the temporal ordering of different disorders because of a lack of published
data to address this question.
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Discussion
This analysis of published studies makes it absolutely
clear that adolescents who use or abuse substances are significantly
more likely than other youth to have one or more of the five major groups
of psychiatric disorder. The risk ranges from twofold for anxiety disorders
to sevenfold for CD. The risk of comorbidity is higher for girls than
for boys for everything except depression. Controlling for comorbidity
among psychiatric diagnosis the pattern is similar, although the odds
ratios are lower. However, with so few studies the confidence intervals
are very wide.
It is important to emphasize that these findings are based
on crosssectional (correlational) analyses. Few published studies provide
the data needed to examine temporal relationships, and no attempt has
been made to do so here. Without this dimension, we cannot talk about
cause or prediction; even the word risk carries
etiologic implications that the data cannot support. These analyses
merely confirm that when substance use or abuse is present, other psychiatric
diagnoses are present to a degree not expected by chance alone.
The data contain two hints of causal explanations, although
these are very weak. First, the higher comorbidities associated with
abuse/dependence than with any use, for every psychiatric diagnosis
except anxiety, suggest a doseresponse relationship, which is one step
on the path to establishing causality (Robins & Guze, 1970). Second,
the fact that girls, who are less likely to have DBDs (Loeber &
Keenan, 1994), are more likely to use or abuse substances if they do
have a DBD raises the possibility of a causal arrow from DBDs to substance
use/abuse rather than vice versa. However, we have also to account for
the fact that girls are both more likely to have anxiety disorders (Costello
& Angold, 1995) and more likely to show comorbidity.
It is important to bear in mind the limitations of these
analyses. Apart from those already discussed, there are many reasons
why comorbidity itself could be the result of methodologic artifacts
rather than a real phenomenon (Angold et al., 1999). Also, a metaanalysis
can only be as good as the data that go into it, and even stretching
our criteria to the limits there were many problems with the data; in
particular, the construction of DSM diagnoses from instruments not designed
for that purpose. We could stratify by sex but by no other variable.
The number of psychiatric diagnoses was limited, and these had to be
grouped into what may be misleading categories. For example, analysis
of the Great Smoky Mountains data set showed no association between
substance use or abuse/dependence and anxiety disorders as a group,
controlling for other psychiatric comorbidities (Costello, Erkanli,
Federman, & Angold, 1999). However, further analysis showed that
separation anxiety reduced the likelihood of alcohol use, and increased
the age at first use, whereas generalized anxiety disorder increased
the risk of alcohol use and lowered the age at first use (Kaplow, Curran,
& Costello, 2000). This suggests the importance of being able to
look at the association of substance abuse with different psychiatric
disorders separately. One would also want to examine the association
of different psychiatric disorders with specific substances (alcohol,
cannabis, etc.). Further, investigation of the association between substance
abuse, psychiatric comorbidity, and mental health service use is desirable
but not possible given the limits of the studies included in the metaanalysis.
Finally, it would be fascinating to be able to use the power of Markov
chain Monte Carlo modeling to examine the temporal relations among disorders.
None of these is possible using published papers. Comorbidity
with psychiatric disorder has seldom been the focus of publications
from the data sets we used for the metaanalysis. But for longitudinal
psychiatric epidemiologic surveys a great deal of information has to
be collected that is usually not published in scientific papers. For
example, a study has to collect detailed information on a wide range
of drugs that adolescents might use, even to publish a simple analysis
of "drug abuse." The detailed information that lies behind
the single published figure may never appear in print. The literature
on child psychopathology contains reports from several large studies
of adolescent substance abuse with representative samples, careful,
consistent methods of data collection and data aggregation, and at least
some information about cooccurring psychiatric disorders. Several are
panel studies with repeated assessments of the same participants over
years or even decades. This raised for us the question of whether these
data sets could potentially yield more than they have so far on psychiatric
comorbidity with drug abuse. Recent advances in data transfer, computing
speed, and statistical methods raise the possibility of exploiting this
wealth of information. In the next section we report on a preliminary
exploration of what is "out there" to address the question
of psychiatric comorbidity as a risk for later drug abuse.
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