ELSEVIER
Drug and Alcohol Dependence 45 (1997) 185- 196
Sequence of drug use among serious drug users: typical vs atypical
progression
Mary Ellen Mackesy-Amiti a,*, Michael Fendrich a, Paul J. Goldstein b
” Institute
for
Juvenile
b School
Research,
of‘ Public
Department
Health,
of Psychiatry,
The University
The University
of Illinois at Chicago,
907, South
IL 60612, USA
of Illinois
at Chicago,
2121 West Taylor Street, Chicago,
Wolcott
IL
Avenue,
60612.
Chicago.
USA
Received 13 August 1996; accepted 7 February 1997
Abstract
Sequence of drug use was examined in a secondary analysis of two samples of serious drug users: one of 152 men and one of
133 women. The proportions of drug users following specified patterns of drug use onset were compared to proportions obtained
in previous research in samples of high school youth, and serious drug users. The serious drug users were substantially different
from high school samples in their progression of drug use. The serious drug users were less likely to follow the typical sequence
identified in previous studies (alcohol, then marijuana, followed by other illicit drugs). They were more likely to have used
marijuana before using alcohol, and more likely to have used other illicit drugs before using marijuana. We also found that
atypical sequencing was associated with earlier initiation of the use of illicit drugs other than marijuana and greater lifetime drug
involvement. These findings suggest that for a large number of serious drug users, marijuana does not play the role of a ‘gateway
drug’. We conclude that prevention efforts which focus on alcohol and marijuana may be of limited effectiveness for youth who
are at risk for serious drug abuse. 0 1997 Elsevier Science Ireland Ltd.
Keywords:
Secondary analysis; Marijuana; Alcohol; Gateway drug; Prevention efforts
1. Introduction
A growing body of research has described developmental stages of drug use behaviour from adolescence
through young adulthood. In general, researchers have
found that adolescents tend to use alcohol and/or
cigarettes before using marijuana and alcohol and marijuana prior to using other illicit drugs (e.g. Andrews et
al., 1991; Brook et al., 1982; Donovan and Jessor, 1983;
Kandel, 1980; Kandel and Faust, 1975; Kandel and
Yamaguchi, 1993; Welte and Barnes, 1985; Yamaguchi
and Kandel, 1984a,b). The same progression has been
found in samples of youth in other countries (Adler and
Kandel, 1981; Blaze-Temple and Lo, 1992). Guttman
scale models have generally been found to fit about
85-90% of the cases.
* Corresponding
author
Although there is variation across studies, particularly as to the importance and place of cigarettes in the
sequence, there are two consistent findings. The majority of adolescents who use marijuana have also used
alcohol and nearly all adolescents who use illicit drugs
other than marijuana have also used marijuana. The
proportions
of subjects who used other illicit drugs
prior to (or without any) marijuana use are for the
most part less than 5%.
Most of the drug users in a typical sample of high
school students could be described as ‘occasional’ or
‘experimental’ drug users. The majority of those who
report using illicit drugs, especially illicit drugs other
than marijuana, say that they used the drugs only a few
times in their lifetimes. For example, while 6.1% of 12th
grade students in the 1992 Monitoring the Future study
said that they used cocaine, 2.8% said they used only
once or twice, while only 1.7O/o said they used 10 or
0376-8716/97/$17.00 0 1997 Elsevier Science Ireland Ltd. All rights reserved.
PII SO376-8716(97)00032-X
IX6
M.E.
Mackay-Amiii
rt al. /Drug
and Alcoiml
more times (Johnston et al.. 1993). While the typical
sequence of use onset (i.e. alcohol, followed by marijuana, followed by other illicit drugs, hereafter abbreviated as A-M-O) may be characteristic of samples
consisting mainly of occasional or experimental drug
users. there is no clear evidence that it is characteristic
of samples of serious drug users. By serious drug use,
we mean that the drug use occupies a substantial
portion of a person’s daily behaviour-including
activities to raise money to buy the drugs and efforts to
procure the drug, as well as drug consumption-over
an extended period of time (see Goldstein et al., 1992;
Johnson et al., 1985). Youths who are on their way to
becoming serious drug users are more likely to drop out
of school, or to be truant, and therefore are less likely
to be captured in a survey of high school students
(Oetting and Beauvais, 1990).
Studies that report mean ages of first use of various
substances suggest that narcotics users also follow the
A-M-O sequence (Inciardi and Pottieger, 1986; 1991).
That is. the mean age of first alcohol use was lower
than that of first marijuana use and the mean age of
first marijuana use was lower than that of any other
illicit drug. However, these numbers do not tell the
whole story; these studies did not look at the actual
sequence of use onset, or the prevalence of different
sequential patterns. Other studies provide some evidence that serious drug users are less likely to follow
the typical sequence of use compared to others. Golub
and Johnson (1994) found that in a sample of serious
drug users, alcohol was not a prerequisite to marijuana
use, especially for younger respondents. In their sample,
a large proportion of drug users began their drug use
with marijuana, and 15”/0 used cocaine or IV drugs
without first using marijuana. In a study of Australian
youths in which the sample was drawn from households
rather than schools, Blaze-Temple and Lo (1992) reported that a substantial proportion (29%) of those
who used ‘hard’ drugs (speed, LSD, cocaine, heroin)
had never used marijuana.
These findings suggest that those youths who are
most at risk for becoming serious drug users may be
least likely to follow the typical sequence of drug use
initiation. Consequently, prevention campaigns which
focus on preventing alcohol and marijuana use may not
be effective for a large proportion of these high risk
youth. Furthermore, those youths who bypass the gateway stage of ‘soft’ drug use (i.e. alcohol and marijuana), proceeding immediately to the use of ‘harder’
substances, may be at greater risk for developing a
regular drug habit or addiction, or engaging in other
types of problem behaviour.
This study evaluated sequence of drug use in a
sample of serious drug users from New York City
(project DRIVE and FEMDRIVE;
Goldstein et al.,
1987: 1988). We compared the progression of drug use
Deppendencr
45 (1997)
18%
196
in this sample with that in other samples from previous
research. We hypothesized that this sample of drug
users would not conform to the typical sequence of
progression of drug use seen in other samples, that a
substantial proportion would use marijuana prior to or
in the absence of alcohol use, and that a substantial
proportion would use other illicit drugs prior to or in
the absence of marijuana use. As we had information
on a greater number of illicit drugs than did Golub and
Johnson (1994), we expected that the proportion using
illicit drugs without prior use of marijuana would exceed their proportion of 15% who used cocaine or IV
drugs without prior use of marijuana.
If our samples do show more atypical sequencing
than others, it may be due to some confounding factor
that is associated with serious drug use. We examined
the associations between atypical sequencing and several variables which have been associated with increased or decreased rates of drug use, including sex,
ethnicity, child abuse victimization, birth cohort, criminality and age of first illicit drug use. Significant associations between sequencing and any of these variables
would suggest a possible confounding effect. We then
looked at the association between atypical sequencing
and involvement in drug use within our samples, controlling for these potential confounding factors.
We hypothesized that subgroups which have shown
elevated rates of drug use would have higher rates of
atypical sequencing. The Monitoring the Future study
(Johnston et al., 1993) and other studies have found
that Blacks report lower rates of use of illicit drugs. We
predicted that Black subjects would have lower rates of
atypical sequencing, compared to non-Blacks. Childhood physical and sexual abuse are associated with
increased levels of drug use (Dembo et al., 1988; 1989;
Bennett and Kemper, 1994). We predicted that subjects
who were abused as children would show higher rates
of atypical sequencing. We would also expect to find
more atypical sequencing in birth cohorts that experienced elevated rates of illicit drug use. According to the
National Household Survey on Drug Abuse (NIDA,
1988) and the Monitoring the Future study (Johnston
et al., 1993) illicit drug use among high school students
peaked around 1980. Since national epidemiologic data
is not available for years prior to 1975, we do not have
enough information
to generate a firm hypothesis.
However, we conducted an exploratory analysis and
interpreted the results in light of available data.
Previous research has shown that there is a significant
association between substance use and other problem
behaviours, such as delinquent, aggressive, or antisocial
behaviour (Jessor and Jessor, 1977; Donovan and Jessor, 1985; Elliot et al., 1989). In their analysis of
National Youth Survey data, Elliot et al. (1989) found
a clear relationship between levels of drug use and delinquent behaviour. The most serious offenders had the
M.E.
Muckesy-Amici
et al. /Drug
and Alcohol
highest rates of drug use, and nonoffenders the lowest.
Conversely, polydrug users had the highest prevalence
and offending rates of delinquent behaviour, followed by
marijuana users, alcohol users and nonusers. Multiple
illicit drug users accounted for highly disproportionate
numbers of offenses. In addition, Elliot et al. reported
evidence that it was early onset of illicit drug use that
was associated with high risk for other forms of problem
behaviour. We hypothesized that atypical sequencing
would be associated with an earlier age of onset to illicit
drug use, higher levels of drug involvement and increased criminal activity.
2. Methods
2.1. Subjects
For this study, we analyzed data that had been
collected from 152 men and 133 women in two ethnographic studies undertaken on the Lower East Side of
New York City between 1984 and 1987. The studies were
conducted to examine the drugs/violence nexus among
adult drug users and distributors; the male sample was
collected between November 1984 and April 1986 (project DRIVE; Goldstein et al., 1987), and the female
sample was collected between April 1986 and May 1987
(project FEMDRIVE;
Goldstein et al., 1988). Subjects
were recruited from field contacts, through snowball
sampling techniques (see Biernacki and Waldorf, 1981)
and from a local methadone maintenance program. The
goals of the research required the recruitment of subjects
who were active in drug use or distribution at the time
of the study. Respondents who were not active in these
behaviours were excluded or dropped from the study.
Interviewing took place in an ethnographic field station
established solely for the purposes of these projects. The
demographic characteristics of these samples are detailed
elsewhere (Fendrich et al., 1992).
2.2. Procedure
Details about the instruments used in these studies can
be found in previously published papers (see Goldstein
et al., 1987; Fendrich et al., 1992). Respondents in both
studies were interviewed using similar semi-structured
interview instruments. Upon recruitment to the study, all
respondents were given a life history interview, which
covered a wide range of issues including drug use history,
participation in treatment programs, involvement in
drug sales and distribution, criminal history and history
of violence (victimization and perpetration). Life history
interviews were completed in an average of three sessions
for DRIVE men (Goldstein et al., 1987) and in an
average of five sessions for FEMDRIVE
women
(Goldstein et al., 1988). Previous reports have discussed
Dependence
45 (1997)
18%
196
187
methods used by the investigators to identify and eliminate dishonest respondents (see Fendrich et al., 1996).
Additional analyses have examined the reliability of
responses across and within interview phases (Fendrich
et al., 1992, in press). The findings suggested that
respondents showed a high level of consistency in terms
of drug use disclosure, while reports of frequency and
amounts of use showed less consistency.
The subjects were asked about their lifetime and
current use of 12 specific substances or types of substances: alcohol, marijuana, heroin, cocaine, street
methadone, other opiates, barbiturates, amphetamines,
tranquilizers, PCP, psychedelics (hallucinogens) and
glue. They were also asked about any other drugs they
used that were not covered in those categories. Thus,
with the exception of tobacco, we have information on
all the drugs the respondents ever used, to the extent that
they were willing to disclose.
Subjects who reported the use of either alcohol or
marijuana, but did not report an age of first use were
excluded from the analyses, leaving 120 men and 113
women. A profile of subject characteristics is presented
in Table 1. Heroin and cocaine use were quite prevalent
in both samples; 86% of the men and 89% of the women
had used heroin and 98% of the men and all of the
women had used cocaine. Most heroin and cocaine users
were using at least three times a week during their most
recent period of use. Drug use was extensive as well as
intensive. DRIVE men reported using an average of 7.5
different substances in their lifetime, and reported current use of 3.5 substances; FEMDRIVE women reported
using an average of seven different substances and
current use of three substances.
2.2.1. Sequence of drug use
All substances were ranked according to age of first
use, allowing ties’. The information from ‘other’ drugs
used was combined with information from the twelve
specific substance types. Each other drug mentioned
was classified by consulting the Physician’s Desk Reference, or based on the investigators’ knowledge of the
drug’s effects*. The proportions of subjects who con-
’ For the purpose of making comparisons between the ranks of the
various substances, those that were never used were coded as ‘98’.
and missing values were coded as ‘97’. Thus, any substance with a
missing age of first use (other than alcohol or marijuana) would be
considered as if it had an onset later than any other substance that
was used, but prior to any substance that had never been used.
* The other drugs mentioned included inhalants, cough medicine,
one hallucinogen (MDMA), one stimulant (Ritalin), three sedatives
(Placidyl, Dalmane, Doriden), two anti-depressants with sedative
effects (Elavil, Desyrel), one tranquilizer (Xanax), and quaaludes. The
stimulants were grouped with amphetamines, the sedatives and antidepressants were grouped with barbiturates, inhalants were grouped
with glue, and quaaludes were grouped with tranquilizers. Cough
medicine, reported by one respondent, was not classified.
188
M.E.
MackesJl-Amiri
et al. 1 Drug
and Alcohol
formed to each of six possible sequences of drug use onset
for alcohol, marijuana, and other illicit drugs were
calculated. Concurrent use (e.g. of alcohol and marijuana) was treated as being in accordance with the
hypothesized typical sequence of alcohol, marijuana,
other illicit drugs (A-M-O). Thus, someone who used
alcohol, marijuana and cocaine in their first year of drug
use would be in the A-M-O category. We compared the
findings from our samples of drug users with those from
previous studies of conventional samples, and of serious
or ‘hard’ drug users, by computing a 95% confidence
interval around the proportion obtained in our combined
samples, and observing whether the proportion observed
in previous studies fell within the confidence limits. We
did this for the proportion of subjects conforming to
the A-M-O sequence, and for the proportion using
marijuana prior to or concurrently with other illicit
drugs.
Table
Profile
Subject
I
of DRIVE
and FEMDRIVE
profile
Mean age at interview
Year at birth (range)
Black (‘!(I)
Hispanic
(‘Xl)
White/other
(‘“;a)
Single/divorced
(‘Xl)
Completed
secondary
school
(“‘4)
Shelter residence
(‘Xl)
Unemployed
(“IL)
Currently
in drug treatment
(‘1 r,)
Currently
in alcohol
treatment (‘Xl)
Average
number
of drugs
ever used
Average
number
of drugs
currently
used
Ever used alcohol
(‘Xl)
Currently
use alcohol
(%I)
Median
frequency
of alcohol
use (days week)
Ev,er used marijuana
(‘!‘o)
Currently
use marijuana
(‘%)
Median
frequency
of marijuana use (days/week)”
Ever used cocaine
(‘!,;,I
Currently
use cocaine
(%I)
Median
frequency
of cocaine
use (days/week)
Ever used heroin (‘%I)
Currently
use heroin (‘!‘(I)
Median
frequency
of heroin
use (daysweek
r’
Evrer deal drugs (‘X,)
“ Typical
frequency
of use during
samples
DRIVE
(N = 120)
FEMDRIVE
(N= 113)
31
1928
44
22
34
92
55
32
193441967
52
20
27
90
51
1966
51
86
38
40
93
40
8
3
7.5
7.0
3.5
2.9
93
74
I to 2
94
61
1 to 2
98
79
5 to 7
97
60
I to 2
98
80
3 to 4
100
76
3 to 4
86
SO
89
48
3 to 4
77
5 to 7
68
most
recent
period
of use.
Dependence
2.2.2.
Correlates
45 (1997)
185-196
of drug
use sequence
We conducted a series of bivariate analyses (x2) to test
the associations between typical versus atypical drug use
sequence and sex, ethnicity, history of physical or sexual
abuse, birth cohort, and measures of criminality, including ever committed a property crime (burglary, shoplifting, forgery or con games), ever committed a person crime
(robbery/mugging,
purse snatching or rape), and ever
been in prison. Analyses were conducted for A-M-O
versus any other sequence, use of alcohol before marijuana and use of marijuana before other illicit drugs. Due
to small numbers in older cohorts, subjects born before
1943 were excluded from the analysis on birth cohort.
Cohorts were defined as the five-year periods around
1945, 1950, 1955, 1960 and 1965.
We conducted t-tests to look at the association between
drug use sequence and measures of drug and criminal
involvement, including age of first illicit drug use, age of
first illicit drug use other than marijuana, number of drugs
ever used regularlyj, number of drugs currently used4 and
types of crimes ever committed. Drug use variables were
coded for the longest period of use described’ by the
subject for each substance reported. ‘Types of crimes ever
committed’ was based on responses to life history interview questions about lifetime involvement in eleven
different crimes5, and an additional category of ‘any other
crime’. Subjects could report a maximum of twelve different crimes. For all bivariate analyses, including x2 and
t-tests, P values were adjusted for multiple comparisons.
Since there were twelve comparisons, the obtained P values were multiplied by twelve. Thus, an adjusted P value
of 0.05 represents an obtained value of 0.05/12 or 0.004.
Finally, we conducted logistic regression analyses,
predicting atypical drug use sequence from background
and criminal involvement variables, and from drug
involvement variables controlling for background and
criminal involvement variables. Analyses were conducted for the dependent variables A-M-O sequence
versus all others (not A-M-O = 1) and marijuana-other
illicit drug sequence (other before marijuana = 1). In
the first step, the model included the background vari-
’ Regular
use was defined as using three days a week or more.
4 Subjects were coded as currently
using a substance
if they explicitly stated that they were currently
using, or if the last reported
use
was in the current year and they did not say that they were not using.
Subjects
who reported
quitting
a month or less ago were considered
current
users: subjects
who ceased using more than a month ago were
coded as not currently
using, unless this period
of non-use
corresponded
to their typical
pattern
of use (e.g. once every six months).
’ The crimes asked about were shoplifting,
burglary,
robbery/mugging, forgery,
purse snatching,
prostitution,
pimping,
drug dealing,
other drug activity,
con games and rape. FEMDRIVE
subjects were
also asked about assault, but this was not included
in our analyses.
M.E.
Table
Order
2
of use of alcohol,
Order
of drug
marijuana
use
A-M-O
A-O-M”
M-A-O”
M-O-Ah-’
O-A-M’.“
O-M-Ab’.C
Mackes.v-Amiti
and other
Males
illicit
et al. /Drug
and Alcohol
Dependence
45 (1997)
185-196
189
drugs”
(n = 120)
Females
(n = 113)
Combined
(n = 233)
‘%I
N
‘%,
N
‘%I
N
36
14
8
16
15
12
43
17
Y
19
18
14
30
19
12
20
11
I
34
22
14
23
12
8
33
17
10
18
13
Y
77
39
23
42
30
22
” Other illicit drugs include heroin. street methadone,
psychedelics
and glue/inhalants.
b Marijuana
use precedes alcohol
use
’ Other illicit drug use precedes
alcohol
use.
’ Other illicit drug use precedes marijuana
use.
cocaine,
other
opiates,
ables: sex, ethnicity, childhood abuse, birth cohort, and
types of crimes ever committed. The ethnicity effect was
set up as deviation contrasts, with Black versus all
others and Hispanic versus all others. All other effects
were set up as indicator variables. For birth cohort, the
1953357 cohort was chosen as the reference group. In
the second step, number of drugs ever used regularly
was added to the model and age of first other illicit
drug use was added in the third step.
3. Results
3.1. Sequence of’ drug use
Substantial proportions of subjects did not conform
to the conditions described in previous studies. The
proportions conforming to each of the six possible
sequences of use of alcohol, marijuana, and other illicit
drugs are shown in Table 2. The distributions were
similar for males and females; females were slightly
more likely to initiate with marijuana, while males were
more likely to initiate with other illicit drugs. Although
first alcohol use usually preceded, or was concurrent
with first use of marijuana, 35”/0 of males and 40% of
females in our samples used marijuana prior to any
alcohol use (add rows 3, 4 and 6 in Table 2). In
addition, about 40% of both males and females used
other illicit drugs prior to any use of marijuana (add
rows 2, 5 and 6). Less than half of the subjects (36% of
males, 30% of females) followed the typical pathway
described in previous studies, where alcohol use precedes marijuana use and marijuana use precedes the use
of other illicit drugs.
Table 3 shows previous studies that reported a proportion that we could use for comparison with our
results. Each study reported either a proportion of
subjects who conformed to a Guttman scale (either
conventional or modified) in which alcohol preceded
amphetamines/stimulants,
barbiturates/sedatives,
tranquilizers,
PCP,
marijuana and marijuana preceded other illicit drugs
(A-M-O), or a proportion of subjects who used marijuana before using other illicit drugs (M < 0).
Combining our two samples, 33% of the subjects conformed to the A-M-O sequence. The 95% confidence
interval for this proportion is 27-39%; the lowest proportion reported in previous studies to follow the typical sequence was 75% (Andrews et al., 1991) and in
most cases it was above 85%. None of the previous
estimates fall within our confidence interval. In the
combined sample, 61% of the subjects used marijuana
prior to or concurrently with other illicit drugs. The
95% confidence interval for this proportion is 55567%.
Blaze-Temple and Lo (1992) reported that 71% of
‘hard’ drug users in their sample used marijuana prior
to other illicit drugs. Golub and Johnson (1994) reported that 85% of their sample used marijuana before
other illicit drugs and the other three studies reported
proportions exceeding 95%. None of these estimates fall
within our confidence interval.
3.2. Correlates of drug use sequence
The bivariate analyses of sex, ethnicity, .,childhood
abuse and birth cohort are shown in Table 4. All P
values were multiplied by 12 to adjust for multiple
comparisons. There were no effects due to sex. The
ethnicity effect was significant for the typical (A-M-O)
versus atypical comparison (x f = r = 12.01, Bonferroni
adjusted P < 0.05), and the marijuana before other
illicit drugs comparison (x$= , = 15.21, Bonferroni adjusted P < 0.01). This was due mainly to Blacks being
less likely to use other illicit drugs before marijuana
compared to non-Blacks. Subjects who were abused as
children were slightly more likely to use marijuana
before alcohol, however, this was not a significant
effect. There was a significant cohort effect only for
marijuana before other illicit drugs (x$rZ4 = 18.52,
Bonferroni adjusted P < 0.05). There is a small increase
I90
Table 3
Percentage
M.E.
of subjects
conforming
Muckesy-Amiti
to expected
Study
Guitman
SC& (A-M-O)
Andrews
et al. (1991)
Andrews
et al. (1991)
Blaze-Temple
and Lo (1992)
Brook
et al. (1982)
Donovan
and Jessor (1983)
Kandcl
and Faust (1975)
Kandel and Yamaguchi
(1993)
Yamaguchi
and Kandel (198kd)
Mackesy-Amiti,
Fendrich
and Goldstein
95% confidence
interval
Mar+xma
h</ore other illicit drugs
Blaze-Temple
and Lo (1992)
Donovan
and Jessor (1983)
Golub and Johnson
(1994)
Kandel (I 980)
Y amaguchi
and Kandel (1984b)
Mackesy-Amiti.
Fendrich
and Goldstein
95% confidence
interval
-.--___~-__
“Smallest
cigarettes
reported
or pills.
percentage
is given
et al. /Drug
sequence
and Alcohol
Dependence
45 (1997)
185-196
use
Sample
Observed
756
634
1093
663
9658
4847
1108
1325
90
75
85
90
92
90
93
86
33
(27-39)
lo- 16 year olds recruited
by advertisements
lo-16
year olds: 1 year follow-up
13- 17 year olds in Perth households
9th and 10th grade students
in public schools
adolescents,
grades 7-12 (national
study)
public high school students:
5/6 month follow-up
12th grade students
in public and private
schools
former
10th and 11 th grade students
at age 24-25
49 current
illicit drug users age 13.--I7 in Perth households
3958 adolescents,
grades lo- 12 (national
study)
1003 drug abusers, inner city Manhattan
(mean age 28)
5468 high school students:
5/6 month
follow-up
1325 former
10th and 1 Ith grade students
at age 24-25
where
more
than
one result
was reported.
in using other illicit drugs before marijuana from the
1948 - 52 cohort to the 1953-57 cohort, followed by a
sharp decline in this atypical sequence in the succeeding
cohorts. x’-Tests on the dichotomous crime and prison
variables (not shown) were not statistically significant.
Stratified analyses revealed ethnicity effects that
varied by sex (not shown). Hispanic women were more
likely than either Black or White/Other
women to
follow an atypical sequence, and to use other illicit
drugs before marijuana. Hispanic men were similar to
White/Other
men in the proportions of typical and
atypical sequence. There were no other interactions by
sex.
The results of the t-tests on drug and criminal involvement variables are presented in Table 5. Two of
the variables--age
of first other illicit drug use (illicit
drugs other than marijuana), and number of drugs ever
used regularly---showed
significant effects. Subjects
who used other illicit drugs prior to marijuana use
tended to begin their use of these drugs at an earlier age
(14.0 years) than did those who used marijuana before
using other illicit drugs (16.4 years; t,,, = 4.76, Bonferroni adjusted P < 0.01; adjusted for unequal variance)6.
h This may appear tautological,
however,
it is not. While it seems
most likely that persons who used other illicit drugs before marijuana
began other illicit drug use at an earlier age than those who used
marijuana
first. this is not necessarily
the case. They might
have
foregone
marijuana
use at the age when others were initiating
marijuana use and taken up drug use at a later age, beginning
with harder
drugs about the same time others were ‘graduating’
from marijuana
to harder drugs. If this were the case, the groups would differ on age
of first illicit drug USC. but not on age of first other illicit drug use.
In some
cases
the Guttman
percentage”
71
99
85
96
97
61
(55 -67)
scale included
additional
steps e.g.
Subjects who followed an atypical progression of drug
use onset (i.e. not A-M-O) tended to have used a
greater number of drugs on a regular basis (t,:, = 3.26,
Bonferroni
adjusted P = 0.01; adjusted for unequal
variance). There were no significant effects for number
of drugs currently used, or number of crimes ever
committed.
The results of the logistic regression analyses are
presented in Table 6 (A-M-O versus not A-M-O) and
Table 7 (marijuana before other illicit drugs). The first
model included the background variables: sex, ethnicity, birth cohort, childhood abuse, and types of crimes
committed. The second model included the background
variables, plus number of drugs ever used regularly.
Age of first illicit drug use other than marijuana was
added to these variables in the third model.
For the dependent variable A-M-O sequence versus
not A-M-O, ethnicity was the only background variable
to reach significance in the first model (Wald = 6.9,
P < 0.05); Blacks were less likely than non-Blacks to
follow an atypical sequence (OR = 0.6; 95% CT 0.38
0.88). In the second model, number of drugs used
regularly shows no significant association with drug use
sequence, as shown by the Wald test and by the improvement in the model 1’. Age of first other illicit
drug use (Model 3) was significantly associated with
drug use sequence, as shown by the Wald test and the
improvement in the model x2. Those who began other
illicit drug use at an earlier age were more likely to
follow an atypical sequence of drug use onset. For
every one year increase in the age of first other illicit
drug use, the odds of being in the atypical sequence
category decrease by 0.9.
M.E.
Table 4
Subject background
Subject
background
Sex
Male
Female
Ethnicity
White/other
Hispanic
Black
Childhood
Yes
No
by sequence
et al. /Drug
of drug
onset:
A-M-O”
(‘X,)
and Alcohol
proportions
Dependence
and chi-square
A<Mb
185- 196
191
tests
N
Not
120
113
64.2
69.9
0.87
12
49
73.6
81.6
12.01
*
112
56.3
117
116
73.5
60.3
4.56
44.4
30.2
5.07
41.0
37.1
0.38
26
67
65.4
70. I
5.10
23.1
31.3
6.45
46.2
46.3
18.52
*
45
54
28
77.8
64.8
53.6
x2
Not
45 (1997)
(o/o)
x2
Not
35.0
39.8
0.58
40.8
37.2
0.33
41.7
34.7
0.85
45.8
57.1
15.21
**
35.7
M <O’
(X)
x2
26.8
abuse
Cohort
1943m 47
1948-m52
1953 57
1958862
1963367
___~
*
b
’
*
variables
Mackesy-Amiti
Alcohol,
marijuana.
other illicit drugs.
Alcohol
before marijuana.
Marijuana
befort other illicit drugs.
PcO.05,
** P~0.01
(corrected
for multiple
48.9
40.7
42.9
55.6
29.6
10.7
comparisons).
For the marijuana-other illicit drugs sequence, ethnicity (Wald = 7.9, P < 0.05) and cohort (Wald = 14.1,
P < 0.01) were significant predictors in the first model.
Again, Blacks were less likely than non-Blacks to follow the atypical sequence (other illicit drugs before
marijuana); in addition, Hispanics were more likely
than non-Hispanics to follow the atypical sequence.
The significant contrasts for birth cohort were 1953-57
versus 1958-62 (OR =0.3; 95% CI 0.12-0.81) and
1953-57 versus 1963-67 (OR=O.l;
95% CI 0.040.33). The marijuana before other illicit drugs sequence
was more prevalent in the later cohorts. In the second
model, number of drugs used regularly again shows no
significant association with drug use sequence when
adjusting for the background variables. In Model 3, age
of first other illicit drug use was again significantly
associated with drug use sequence (OR = 0.8, 95% CI
0.70-0.86). For every one year increase in the age of
first other illicit drug use, the odds of being in the
atypical sequence category decrease by 0.8.
4. Discussion
We examined the prevalence of drug sequencing patterns in two samples of serious drug users. The prevalence of sequencing in these samples diverged markedly
from the prevalence observed in other studies. In particular, there was major divergence with respect to the
prevalence of the alcohol-marijuana-other
drug (A-MO) progression pattern. Whereas in previous studies
examining sequencing, the vast majority of subjects
followed the A-M-O pattern, in our samples, only
about one third followed this pattern. In previous studies at least three quarters of all subjects initiated marijuana before initiating other substances, while in our
study, only about three fifths of all subjects followed
this pattern.
In attempting to generalize results from our samples
to other studies of similar issues, specific limitations
need to be underscored. Our analyses were based on
two ethnographic samples interviewed in distinct time
periods (1986 and 1987) in a single location (New York
City’s Lower East Side). The unique characteristics of
this sample limit our capacity to make generalizations
to other samples of drug users. Nevertheless, the pattern of findings raises a number of questions which may
be useful in evaluating the generalizability of drug use
sequencing hypotheses.
What accounts for the discrepancies and divergence
observed between findings from our study and other
studies investigating drug use sequencing? Our samples
and those employed in other studies differed in several
ways. Our samples were characterized by extensive drug
involvement; our subjects were generally older than
subjects in other studies; our samples included relatively
large proportions of Black and Hispanic persons; and
socioeconomic indicators, including high school completion, shelter residence, and unemployment, suggest a
high degree of poverty among our subjects.
With respect to general levels of drug involvement,
only two other studies selected a sample paralleling
192
M.E.
Mackesy-Amiti
et al. /Drug
and Alcohol
Dependence
45 (1997)
185-196
Table 5
Drug and criminal involvement by sequence of drug use onset
T-tests
Age of first illicit drug use”
Age of first illicit drug use other
than marijuana
Number of drugs ever used regularly
Number of drugs currently used
Types of crimes ever committedb
A-M-O
Alcohol before marijuana
Marijuana
drugs
before other illicit
Typical n = 77
mean (S.D.)
Atypical n = 156 Typical n = 146 Atypical n = 87 Typical n = 142
mean (SD.)
mean (S.D.)
mean (S.D.)
mean (S.D)
Atypical n = 91
mean (S.D.)
14.6 (2.6)
16.7 (3.1)
13.9 (3.7)
14.8 (3x)**
14.6 (3.6)
15.7 (4.0)
13.4 (2.9)
15.1 (3.3)
14.3 (2.7)
16.4 (3.0)
14.0 (4.2)
14.0 (4.2)**
3.5 (1.9)
4.5 (2.2)*
4.0 (2.2)
4.4 (2.1)
3.9 (2.1)
4.6 (2.3)
3.4 (1.8)
3.7 (2.1)
3.1 (1.8)
4.1 (2.2)
3.2 (1.8)
4.0 (2.3)
3.2 (1.7)
3.8 (2.1)
3.3 (1.7)
3.7 (2.1)
3.1 (1.8)
4.3 (2.3)
“Illicit drugs are marijuana, cocaine, heroin, other opiates, street methadone, stimulants, sedatives, tranquilizers, PCP, inhalants and psychedelics.
‘Crimes are shoplifting, burglary, robbery/mugging, forgery, purse snatching, prostitution, pimping, drug dealing, other drug activity, con games,
rape and any other crime.
* PiO.05. ** P<O.Ol (corrected for multiple comparisons).
those that we employed (Blaze-Temple and Lo, 1992;
Golub and Johnson, 1994). However, Golub and Johnson included only cocaine and IV drugs as other illicit
drugs in their analyses. It is interesting to note that the
sequencing conformity rates for marijuana and other
illicit drugs in the Blaze-Temple and Lo study paralleled the low rates that we observed in our samples.
These parallels support the validity of our findings and
suggest that there might be clear behavioral differences
between occasional or experimental drug users and
those who are more drug involved. Even within the
context of our own serious drug using samples, higher
rates of aberrant sequencing were associated with earlier initiation of illicit drugs other than marijuana, and
greater lifetime drug involvement.
We also found that ethnicity and birth cohort were
significantly associated with drug use sequence. However, Blacks were the most prevalent ethnic group in
our samples, and were the least likely to follow an
atypical drug use sequence. Thus, the high prevalence
of atypical sequencing cannot be explained by the large
proportions of ethnic minorities in our samples. In
addition, the association between sequencing and ethnicity may be a spurious effect due to the association
between ethnicity and drug involvement (number of
drugs used regularly). Blacks reported using fewer
drugs than either Whites or Hispanics, and were least
likely to follow an atypical sequence. As a result, when
number of drugs used regularly was added to the
logistic regression model, the ethnicity effect became
non-significant.
The cohort effects we found, on the other hand, may
partially account for the discrepancies between the findings of this study and previous studies. Most of the
previous studies used samples of high school students
and we found that the youngest cohorts in our samples
were most likely to exhibit the typical pattern of drug
use sequence. These findings suggest that the sequencing of drugs is influenced by drug use norms which may
be in effect during years of drug use initiation (see also,
Golub and Johnson, 1994).
We also note the low socioeconomic status of our
subjects, as inferred by the high prevalence of homelessness and unemployment. The low rate of high school
completion further suggests that the majority of the
subjects probably were poor before they began using
drugs. Our findings suggest the possibility that youths
who grow up in poverty are less likely to follow the
typical sequence of use onset found in general high
school samples, and that this is related to early initiation of ‘hard’ drug use.
Unfortunately, no data were collected from our samples regarding tobacco use. This may explain in part the
high rate of initiation of substance use with marijuana.
Many of those subjects who used marijuana prior to
alcohol may have used tobacco before marijuana, in
which case they would be consistent with a model in
which marijuana use is preceded by the use of legal
drugs (either alcohol or tobacco). However, this does
not affect our findings with regard to the sequencing of
marijuana and other illicit drugs.
It should be underscored that one important variable
that may affect empirical findings with respect to sequencing concerns the number of substances assessed in
the interview. To our knowledge, our assessment was
somewhat more comprehensive in its scope of illicit
drug assessment than most others assessing drug use
sequencing. In particular, we know of no other study
which actually included inhalant use in the assessment
of ‘other drug’ initiation. Clearly our inclusion of inhalants, substances characterized by relatively early age
of onset (Smart, 1986), increased our likelihood of
finding nonconformity
to conventional
sequencing.
Nevertheless, we note that even if inhalants were ex-
M.E.
Mackesy-Amiti
et al. /Drug
and Alcohol
Dependence
45 (1997)
185-
193
196
Table 6
Correlates of drug use sequence: logistic regression analysis predicting not A-M-O
Subject background
Sex
Female”
Male
Ethnicity
Other”
Black
Hispanic
Childhood abuse
No“
Yes
Birth cohort
1943 -47
1948852
1953 -57”
195862
1963-67
Types of crimes ever done
Model 1
Model 2
Model 3
Odds ratio (95% Cl)
Odds ratio (95% CI)
Odds ratio (95% CI)
1.0
1.0
0.8 (0.42L
1.57)
1.0
0.8 (0.41-
0.7 (0.34-1.33)
1.0
1.0
1.0
0.6 (0.38-0.88)*
0.6 (0.41LO.97)
0.7 (0.43-1.06)
1.7 (1.00-3.03)
1.8 (1.02-3.12)
1.8 (1.02. 3.26)
1.0
1.4 (0.75-2.60)
1.0
1.3 (0.72-2.51)
1.0
1.2 (0.6442.29)
0.5 (0.181.62)
0.5 (0.18-1.65)
0.8 (0.34L2.08)
0.8 (0.34-2.12)
1.1 (0.31-3.67)
1.1 (0.4332.88)
1.0
1.0
1.0
0.6 (0.25Sl.67)
0.7 (0.27-1.88)
0.5 (0.17T1.44)
0.6
1.1
1.1 (0.89-1.24)
1.o (0.86- 1.20)
1.1 (0.96-1.34)
1.1 (0.90-1.28)
Number of drugs used regularly
(0.19-1.71)
Age of first ‘hard’ drug useb
Model x2 improvement (df)
1.56)
0.8 (0.3lL2.28)
0.6 (0.20-
1.87)
0.9 (0.77TO.96)**
15.28
(9)
2.2
(1)
8.3
(l)**
* Reference group. The ethnicity contrast was conducted as a deviation contrast. Odds ratios are for Black vs. all other and Hispanic vs. all other.
All other contrasts are indicator variables.
‘Any illicit drug use other than marijuana.
* P<O.O5, ** P<O.Ol
eluded from the list of ‘other’ drugs, our sequencing patterns would have still diverged markedly
from those found in previous studies7. Thus, our
inclusion of inhalants as other drugs does not account for the contrast between the findings presented
here and the findings presented in previous research,
especially with respect to the A-M-O sequence divergence.
Our findings, along with those presented by BlazeTemple and Lo (1992) suggest that conventional sequencing is not as prevalent in samples or subgroups
characterized by high rates of serious drug use as it is
in samples characterized largely by occasional or experimental use (see Andrews et al., 1991; Brook et al.,
‘With inhalants excluded, 40% of the sample conformed to the
A-M-O sequence (95% confidence interval 34 to 46%) and 69% used
marijuana prior to or concurrently with other illicit drugs (95%
confidence interval 63 to 75%).
1982; Kandel and Faust, 1975; Donovan and Jessor,
1983; Kandel and Yamaguchi, 1993). When examined
in the context of other recently published data (see
Miller, 1994), these findings underscore the limited
generalizability of previous conclusions with respect
to drug use sequences. Sequencing patterns may vary
markedly, depending on the overall drug involvement
of the sample under study.
This hypothesized association between serious drug
use and the lack of conventional sequencing has considerable prevention implications. The bulk of illicit drug
consumption in the US is due to ‘chronic hardcore’
users. For example, chronic users comprise about 20%
of the drug-using population, but consume about twothirds of the total amount of cocaine consumed in the
country (ONDCP, 1995). Chronic, hardcore users experience more severe health consequences and are more
likely to engage in criminal activity. For these reasons,
the national drug control strategy identifies the reduction of chronic hardcore drug use through treatment as
194
M.E.
Mackes.v-Amiti
et al. /Drug
and Alcohol
Dependence
45 (1997)
185-196
Table I
Correlates of drug use sequence: Logistic regression analysis predicting not marijuana before other illicit drug
Subject backgound
Model 1
Model 2
Model 3
Odds ratio (95% CI)
Odds ratio (95% CI)
Odds ratio (95% Cl)
Sex
Female”
Male
Ethnicity
Other”
Black
Hispanic
1.0
1.0
I.0
1.4 (0.71-2.65)
1.4 (0.71-2.67)
1.1 (0.5442.15)
1.0
1.0
1.0
0.6 (0.39-0.89)*
1.8 (1.0333.14)*
0.6 (0.3880.91)*
1.8 (1.0333.15)*
0.7 (0.422 1.02)
1.9 (1.05.-3.34)*
1.0
1.0 (05~1.77)
1.0
1.0 (0.51-1.79)
0.7 (0.39.- 1.40)
Birth cohort
1943-47
1948- 52
1953--57”
1958 -62
1963367
0.6
0.8
1.0
0.3
0.1
0.6 (0.20- 1.87)
0.8 (0.33-2.03)
1.0
0.3 (0.12-0.82)*
0.1 (0.04-0.34)**
1.9 (0.5446.44)
1.3 (0.50-3.35)
1.0
0.4 (0.14-0.99)*
0. I (0.04-0.37)**
Types of crimes ever done
1.1
1.1 (0.90-1.24)
1.0 (0.85-1.18)
1.0 (0.84-1.17)
0.9 (0.76- 1.07)
Childhood
No”
Yes
abuse
(0.20- 1.87)
(0.3332.02)
(0.12-0.81)*
(0.0440.33)**
Number of drugs used regularly
Age of first ‘hard’ drug useb
Model x2 improvement (df)
1.0
0.8 (0.70-0.86)***
30.96 (9)***
0.0 (I)
23.4 (l)***
” Reference group. The ethnicity contrast was conducted as a deviation contrast. Odds ratios are for Black vs. all other and Hispanic vs. all other.
All contrasts are indicator variables.
’ Any illicit drug use other than marijuana.
* P<O.O5, ** P <O.Ol, *** P <o.oot.
an important goal. By the same token, it is vital that
prevention efforts reduce hardcore use as well as casual use. Prevention programs must reach high-risk
adolescents as well as low-risk adolescents.
Prevention strategies that focus on tobacco, alcohol
and marijuana as gateway drugs, and presume that
preventing or delaying their use will break the chain
of events that leads to chronic, hardcore use, may be
of limited value. If those who are more likely to be
involved in serious drug abuse report minimal and
later involvement
with softer substances, messages
targeting these gateway substances will not be effective as prevention. Drug abuse prevention messages
based on the gateway model may be least effective
for a segment of the population that potentially will
abuse drugs the most. Evidence that mainstream approaches to drug use prevention
may not reach
youths at risk for serious drug involvement comes
from a study of drug use among American Indian
youth. Beauvais (1996) found that while there was a
decline among American Indian youth in moderateinvolvement drug use from 1980 to 1992, there was
no change in the rate of high-involvement
drug use.
The author concluded that prevention messages were
reaching the more mainstream youth but were not
reaching those who are more prone to serious drug
use.
Indeed, the economically deprived youth, living on
the fringes of society, dropping out of school, often
homeless, and at great risk for developing serious
drug use problems, have not often been considered in
the formulation
of prevention strategies. Our work
exemplifies the need for rethinking
the impact of
strategies formulated
from research based on more
M.E.
Mackesy-Amiti
et al. ,’ Drug
and Alcohol
conventional samples and underscores the need for
further research on the developmental sequencing of
drug use among serious drug users.
Our findings, when viewed in the context of the
unique characteristics of our samples, suggest a future
direction for research. It would be useful to examine
whether our findings hold up in epidemiological samples characterized largely by experimental use; that is,
do extensively drug-involved individuals assessed in
these samples show less conformity to conventional
sequencing than other types of drug users? Secondary
analyses of a range of data sets may provide evidence
with respect to the generalizability of our findings.
The findings also underscore the necessity for evaluating the relative effectiveness of prevention programs
with low and high-risk adolescents (such as Johnson
et al., 1990) and the utility of evaluating substance
use beyond alcohol and marijuana, even among
young adolescents.
While prevention of alcohol and marijuana use, especially early use, is an important goal in its own
right, as a strategy for preventing use of other illicit
drugs it may be of limited utility. Those youth who
are at greatest risk for illicit drug use may be least
likely to follow the typical sequential pattern of progression from alcohol to marijuana to other illicit
drugs. The variation in the prevalence of the typical
pattern over time also suggests that the predominance
of the A-M-O sequence may be a temporary phenomenon. Drug abuse prevention efforts need to focus more attention on the precursors of drug abuse,
such as family dysfunction (Brook et al., 1986; Newcomb and Bentler, 1988), school failure (Elliot et al.,
1989) behavior disorders (Zucker and Noll, 1982),
economic and social deprivation etc. Early identification of, and intervention with youth who are affected
by such problems is vital to drug abuse prevention
efforts.
Acknowledgements
This research was supported in part by grants from
the National Institute on Drug Abuse (R29DA07995
and F32DA05668) and by a grant from the University of Illinois at Chicago. An earlier draft of this
paper was presented at the Sixth Annual Convention
of the American Psychological Society, 1994, Washington. D.C., USA.
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