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Addiction (1998) 93(4), 493 503

RESEARCH REPORT

Marijuana use and treatment outcome amongopioid-dependent patientsALAN J. BUDNEY, WARREN K. BICKEL & LESLIE AMASSUniversity of Vermont, Departments of Psychiatry and Psychology, 200 Twin Oaks Terrace,

S. Burlington, VT 05403, USA

Abstract

Aims. Information concerning the association between marijuana use and opioid dependence and its

treatment is needed to determine effective clinical guidelines for addressing marijuana use among opioid

abusers. Setting and participants. Marijuana use was assessed in 107 people enrolled in treatment for

opioid dependence. Design and measurement. Univariate comparisons of marijuana users and non-users

and multivariate regression analyses were performed to examine associations between marijuana use and

socio-demographic, psychosocial, medical and substance-use variables. The relationship between marijuanause and treatment outcome was also explored in a subset of this sample who received treatment that included

buprenorphine detoxi cation and behavior therapy (N 79). Findings. Sixty-six per cent of participants

were current marijuana users and almost all (94%) continued to use during treatment. Users were less likely

to be married than non-users, and more likely to report nancial dif culties, be involved in drug dealing and

engage in sharing of needles (p 0.05). A unique effect of marijuana use on drug dealing and sharing needles

was retained after statistically controlling for the in uence of heroin and alcohol use and other socio-demo-

graphic variables. No signi cant adverse relations were observed between marijuana use and treatment

outcome. Conclusion. Pending a more comprehensive understanding of the function and consequences of

marijuana use on psychosocial functioning, it appears that progress in treatment for opioid dependence can

be made without mandating that patients abstain from marijuana use.

Introduction

The majority of opioid-dependent individuals

who seek treatment in the United States arepolydrug abusers. Marijuana use is the most

prevalent type of illicit substance use among this

clinical population with estimates of concurrent

use ranging from 50% to 85% (Ball et al., 1988;

Saxon et al., 1993; Darke & Hall, 1995; Niren-

berg et al., 1996). Such high marijuana-use rates

raise at least two important clinical questions.

First, what types of impairment and adverse

consequences are associated with marijuana use

among opioid abusers? Secondly, does concur-

rent marijuana use present additional treatment

needs or affect outcomes? Scienti c information

addressing these issues is needed to determinehow to effectively approach marijuana use in

opioid-dependent patients.

Adverse effects associated with marijuana use

have been reported in diverse populations. Mari-

juana-related impairments in health, psychoso-

cial and psychiatric functioning in non-clinical

populations have been observed (Halikas et al.,

1983; Kandel, 1984). Substantial numbers of


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Correspondence to: Alan J. Budney PhD, Department of Psychiatry, University of Vermont, 200 Twin Oaks

Terrace, S. Burlington, VT 05403, USA. Tel: 802-865-3333; Fax: 802-865-3396; e-mail: [email protected]

Submitted 11th April 1997; initial review completed 28th July 1997; nal version accepted 10th October 1997.

0965 2140/98/0400493 11 $9.50 Society for the Study of Addiction to Alcohol and Other Drugs

Carfax Publishing Limited


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494 Alan J. Budney et al.

individuals seek treatment for problems related

to marijuana use and the majority of such people

exhibit symptoms of marijuana dependence

(Stephens, Roffman & Simpson, 1993). Mari-

juana-associated impairment has been detected

even among individuals seeking treatment for

cocaine dependence (Budney, Higgins & Wong,

1996). The speci c types of marijuana-related

problems in these diverse samples include

impairment in memory, concentration, motiv-

ation, health, interpersonal relationships,

employment, as well as increased psychiatric

symptoms, lower participation in conventional

roles of adulthood and more participation in

deviant activities (Halikas et al., 1983, Kandel,1984, Stephens et al., 1993; Budney et al., 1996).

In contrast, the few studies that have examined

the impact of marijuana use on health and psy-

chosocial functioning in the opioid-dependent

treatment population have not detected substan-

tial adverse effects associated with its use. One

study reported no detectable impact of marijuana

use on high risk behavior for contracting AIDS

(Saxon & Calsyn, 1992). A second study failed to

observe any marijuana-associated psychosocial

impairment among methadone-maintenance

patients except for the endorsement of more

items on the schizoid, schizotypal and psychoticthinking scales of the Millon Clinical Multiaxial

Inventory and a greater ASI drug severity rating

(Saxon et al., 1993).

Two studies have speci cally assessed the

in uence of concurrent marijuana use on treat-

ment outcome for opioid dependence. Saxon et

al. (1993) compared outcomes of marijuana

users and nonusers enrolled in methadone-

maintenance treatment and found no relation

between marijuana use and the use of opioids or

other drugs (cocaine or benzodiazepines) during

treatment. The authors noted that the aforemen-

tioned personality-style factors associated withmarijuana use may interfere with wider social

rehabilitation goals, although no data relevant to

this issue were available. A second comparative

study also reported no observable impact of

marijuana use on opioid or other drug use among

methadone-maintained patients (Nirenberg et

al., 1996). Consistent with these ndings, two

studies examining the in uence of concurrent


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marijuana use on treatment for cocaine depen-

dence also failed to show any observable effects

of marijuana use on treatment outcome (Budney

et al., 1996, 1991).

Research aimed at increasing our understand-

ing of this type of polydrug use appears war-ranted given the high prevalence rate of

marijuana smoking among this dif cult clinical

population. Current opioid dependence treat-

ment philosophies in the United States range

from a mandated drug-free policy (i.e. all sub-

stances of abuse are equal and patients must

commit to remaining abstinent from all sub-

stances to remain in treatment) to a mainte-

nance-only approach (i.e. other drug use is

ignored and the patient is provided with metha-

done and minimal supportive therapy). The pur-

pose of the present study was to replicate and

extend the previous research on the impact ofmarijuana use on opioid-dependent people and

the effect of such use on treatment outcome. We

examined marijuana-associated effects on a

broader range of socio-demographic, health, psy-

chosocial and psychiatric variables than have

been previously investigated. This study also

included a more diverse sample than previous

studies, which employed primarily male (99%)

Veterans Administration patients from urban

environments (Saxon et al., 1993; Nirenberg et

al., 1996). The current study included 37%

women and participants resided in a semi-rural

area. Many participants traveled 1 2 hours by

car because of the lack of other treatment ser-

vices in their local communities. In addition, we

explored the relation between marijuana use and

treatment outcome variables (i.e. drug use and

psychosocial changes) among patients receiving

buprenorphine detoxi cation and behavior ther-

apy in contrast to the methadone-maintenance

treatment environments examined in previous

studies. Buprenorphine is a partial MU-opioid

agonist currently being investigated as a replace-

ment medication for opioid dependence (Bickel

& Amass, 1995).

Method

Subjects

Subjects were 107 opioid-dependent adults

enrolled in our outpatient treatment research

clinic located in Burlington, Vermont, USA. All

clients met DSM-III-R criteria for opioid depen-

dence and Food and Drug Administration

(FDA) guidelines for methadone treatment (i.e.

a history of opioid dependence and either


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signi cant current opioid use or signs of opioid

withdrawal). Clients were excluded if they were


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Marijuana use and opioid dependence 495

pregnant, actively psychotic, at high risk for sui-

cide or had a medical condition that would con-

traindicate the administration of buprenorphine.

These clients were primarily Caucasian (94%)

males (63%) with a mean age of 34 years. Thir-

teen per cent were currently married and 66%

had the equivalent of a high school education or

less. Eighty-nine per cent reported life-time

heroin use and 67% reported i.v. use as their

current preferred route of administration.

The subsample used to examine the associ-

ation between marijuana use and treatment out-

come included only participants who received

buprenorphine and behavior therapy (N 79)

(Bickel, Amass & Higgins, 1995; Bickel et al.,1998; see Bickel & Amass, 1995 for a review of

buprenorphine). These clients were selected

because all received similar treatment and thus

type of treatment would probably not confound

the interpretation of the outcome data. Also,

participants who dropped out during the rst 2

weeks of treatment (N 5) were excluded

because urine samples were not available to

accurately classify their during treatment mari-

juana-use status. Three of the ve dropouts

reported marijuana use during the 30 days prior

to treatment.

Intake assessmentIntake assessments were 3 4 hours in duration,

were conducted in a single session, and written

informed consent was obtained prior to study

participation. Assessments were conducted by

trained intake workers under the supervision of

a doctorate-level psychologist. All substance-use

diagnoses were reviewed by the psychologist

who re-interviewed the patient if the diagnosis

was unclear. The following instruments were

used in the assessments: (a) Psychoactive Sub-

stance Abuse Disorder sections of the DSM-III-

R Checklist (Hudziak et al., 1993), (b)

Addiction Severity Index (ASI) (McLellan et al.,1985), (c) Michigan Alcoholism Screening Test

(MAST) (Selzer, 1971), (d) Beck Depression

Inventory (BDI) (Beck et al., 1961), (e) opioid-

related consequences checklist (51 items:

adapted from the Cocaine Consequences

Checklist: Washton, Stone & Hendrikson, 1989)

and (g) a socio-demographic and drug-history

questionnaire developed in our clinic. Intake


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workers were trained to administer the ASI and

DSM-III-R checklist via manual review, obser-

vation and supervised practice interviews.

Treatment

Participants were enrolled in one of three ran-

domized, controlled clinical trials that examined

the ef cacy of buprenorphine detoxi cationcombined with behavioral therapies. The details

of these treatments have been described else-

where (Bickel et al., 1995; Bickel et al., 1998).

Brie y, trial 1 was 26 weeks in duration and

compared two treatments: the Community

Reinforcement Approach (CRA) plus contin-

gency management (CRA CM) vs. standard

methadone counseling (Bickel et al., 1998).

Trial 2 was 32 weeks in duration and compared

two treatments: CRA plus enhanced contin-

gency management (CRA ECM) vs.

CRA CM. Trial 3 was also 32 weeks in dur-

ation and compared two treatments:CRA ECM plus contingent pay for naltrexone

compliance vs. CRA ECM.

Counseling. CRA was implemented in 1-hour

individual sessions scheduled 1 3 times weekly

with the goal being to increase the availability of

natural sources of reinforcement for prosocial

behavior, including drug abstinence (Higgins,

Budney & Bickel, 1994). CRA sessions included

functional analysis training, detoxi cation skills

training, drug refusal training, social/recreational

counseling and, if indicated, vocational counsel-

ing, relaxation, assertiveness, problem solving,

time management, relationship counseling andsocial skills training. Therapists also engaged in

extensive outreach efforts and scheduled addi-

tional sessions as needed to assist patients to

attend sessions and meet treatment goals. Stan-

dard methadone counseling (trial 1 only)

involved one 30 45-minute counseling session

per week focused on life-style management (Ball

& Ross, 1991).

Marijuana use during treatment was discour-

aged by providing a clinical rationale and rec-

ommendation for discontinuing marijuana use.

Therapists then assisted patients who were inter-

ested in reducing their marijuana use byemploying the same skills training and related

interventions noted above. Contingency-

management procedures were not affected by

marijuana use.


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Contingency management. CM (trials 1 and 2)

procedures involved patients earning vouchers

for providing opioid-negative urine specimens

during weeks 2 24. Additional vouchers could

be earned for engaging in weekly prosocial activ-

ities that were approved by the therapists as

consistent with current therapeutic goals (see

Bickel et al., 1998 for details). Use of these

vouchers involved therapists and patients jointly

selecting retail items and activities (e.g. gift

certi cates to restaurants and the cinema, con-

tinuing education materials, sport equipment,

YMCA membership) to reinforce opioid absti-

nence and prosocial behavior.

ECM (trials 2 and 3) involved identical proce-dures to those employed in CM plus additional

bonuses for each full week of opioid-negative

urine tests and contingencies on buprenorphine

dose. Opioid-negative urine specimens resulted

in the participants choice of alternate-day dos-

ing (Amass et al., 1994) or an additional non-

drug reinforcer valued at approximately $20.00.

Opioid-positive specimens resulted in a 50%

decrease in dose and forfeiture of the aforemen-

tioned bonus.

Buprenorphine. Participants received between a

1- and 10-week stabilization dose of 2, 4 or

8 mg/70 kg of buprenorphine. The stabilizationdose was determined during the rst week of

treatment based on pretreatment reports of opi-

oid use, observations of opioid withdrawal and

observed reaction to a 4 mg dose of buprenor-

phine. Participants received a 7 22-week

buprenorphine detoxi cation depending on the

trial and the buprenorphine dose received. Doses

were decreased at a rate of 10 15% per week on

average across the trials during the detoxi cation

phase of the treatment.

Urine testing. Urine specimens were collected

under staff supervision three times per week

throughout treatment. Specimens were screenedimmediately via an on-site Enzyme Multiplied

Immunoassay Technique (EMIT, Syva Corp,

San Jose, CA, USA). All specimens were

screened for opiates, methadone and pro-

poxyphene and one randomly selected specimen

per week was also screened for cannabinoids,

benzoylecgonine (cocaine) and benzodiazepines.

Outcome measures. Treatment retention was


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de ned as the number of weeks of treatment

completed. Opioid abstinence was de ned as the

longest period of continuous abstinence achieved

and was determined by the number of consecu-

tive, scheduled opioid-negative urine specimens

provided. Urine specimens not provided asscheduled were counted as opioid positive.

Abstinence from marijuana, cocaine and benzo-

diazepines was determined by the percentage of

urine specimens collected that were drug-negative.

The ASI was re-administered at 12 months after

treatment entry to all participants who could be

located.

Data analyses

Comparative analyses. To examine associations

between marijuana use and socio-demographic,

psychosocial, medical and substance use vari-

ables, univariate comparisons between marijuana

users and non-users were performed on selectedvariables collected at intake using 2 tests for

categorical measures and t-tests for continuous

measures. For these analyses, marijuana users

were de ned as participants who reported mari-

juana use during the 30 days prior to enrolling in

treatment or who provided at least one can-

nabinoid-positive urine specimen during treat-

ment. These liberal criteria were used so that all

participants who reported marijuana use or for

those for whom we had objective evidence of use

were included in the marijuana-use group. Ident-

ical comparative analyses were performed that

included in the marijuana-use group only those

who reported at least weekly marijuana use or

who provided at least 50% cannabinoid-positive

urine specimens during treatment (i.e. regular

users). These comparisons permitted a test of a

regular marijuana-use group to a group of

non-or light users. The two sets of analyses

yielded very similar results. Therefore, only the

former set of ndings are presented in this

report.

Multiple logistic regression was used to deter-

mine whether differences observed in the uni-

variate analyses remained signi cant after

adjusting for between-group differences in other

socio-demographic and drug-use variables. The

potential explanatory variables in the logistic

regression models were selected because they

either differed signi cantly between marijuana-

use status groups on the univariate analyses or

represented important subject characteristics

thought to be associated with the dependent

variables. The purpose of the regression analyses


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Table 1. Comparison of subject characteristics

Marijuana user Non-user

(N 71) (N 36)

Gender (male)

a

69% 50%

Age

c

33.6 (7.8) 34.8 (8.0)

Race (Caucasian) 94% 94%

Education (high school or less) 68% 64%

Employment (full-time) 39% 42%

Income ($ per month) 1124 (1727) 1070 (1413)

Marital status

b(currently married) 08% 22%

a

p 0.10;

b

p 0.05;

c

mean (standard deviation).

was not to develop predictive models or to

account for individual variability in the depen-

dent variables, but rather to examine whether the

signi cant univariate relation between mari-

juana-use status and the dependent variable was

solely or partially the result of confoundingbetween marijuana-use status and other subject

characteristics.

Outcome analyses. To examine the association

during treatment between marijuana use and

treatment outcome, preliminary 2 2 (treat-

ment marijuana-use group) ANOVAs were

performed on the outcome data for each of the

three clinical trials. Marijuana users were de ned

as those who provided at least one cannabinoid-

positive urine specimen during treatment. No

signi cant marijuana-group main effects or treat-

ment by marijuana-use group interaction effects

were observed in any trial for any of the outcomemeasures. Therefore, data from the three trials

were combined for the analyses presented in this

report; t-tests were then performed to test for

differences between these combined groups

(marijuana users vs. non-users) on treatment

retention, documented opioid abstinence and

other drug abstinence. Treatment retention was

measured using percentage of treatment weeks


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completed rather than number of treatment

weeks because of the difference in treatment

duration across studies. For all outcome analy-

ses, an intention-to-treat model was used and

missing urine specimens were treated as opioid

positives. For other substances (marijuana,

cocaine and benzodiazepines), only those urinespecimens collected were included in the com-

parative analyses.

Repeated measures analysis of variance was

used to examine changes from pre-treatment to

follow-up on the seven ASI composite scores for

those participants who completed the two ASI

assessments (N 53). The group time interac-

tion term was used to indicate whether the mari-

juana groups changed differentially over the

course of treatment and follow-up. Statistical

signi cance was determined at the 5% level for

all analyses in this exploratory study.

ResultsParticipant comparisons

Socio-demographic characteristics. Sixty-six per

cent of participants (71/107) met the criteria

speci ed above and were designated as mari-

juana users. The only socio-demographic vari-

able that differed signi cantly between marijuana

users and non-users was current marital status

(Table 1). A lower percentage of marijuana users

(8%) than non-users (22%) were currently mar-

ried, which appeared to be primarily accounted

for by differences in divorce rates between users

(44%) and non-users (28%). We also observed a

non-signi cant trend suggesting a greater per-centage (69%) of males among the marijuana

users than the non-users (50%).

Substance use. Marijuana users reported an

average of 12.2 8.5 years of regular marijuana

use and smoking 10.3 11.6 days/month (Table

2). Twenty-eight per cent of the users were daily

smokers ( 20 days/month) and 17% met cri-

teria for current marijuana dependence. Mari-

juana users were more likely to report use of

heroin (97% vs. 78%) and alcohol (73% vs.


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Table 2. Substance use comparisons


(N 71) (N 36)

Current substance use

(% used during prior 30 days)

Heroin

a

97% 78%

Methadone

b

15% 33%

Other opioids 73% 81%

Alcohol

a

73% 36%Cocaine 48% 44%

Sedatives 63% 50%

(no. of days used during prior 30 days)*

Heroin** 23.2 (8.9) 21.1 (10.3)

Methadone

c

4.4 (2.9) 9.4 (8.2)

Other opioids 15.0 (10.3) 15.1 (11.2)

Alcohol 13.3 (10.6) 8.2 (8.6)

Cocaine 8.2 (9.6) 6.0 (8.4)

Sedatives

c

9.6 (9.2) 14.5 (12.8)Marijuana 10.3 (11.6) 0.0

$ spent on opioids

(prior 30 days) 351 (348) 294 (278)

Years of regular use

Heroin 7.8 (7.6) 6.5 (8.5)

Methadone 3.2 (12.1) 2.4 (5.1)

Other opiates 7.4 (7.2) 7.4 (8.7)

Alcohol

c

13.0 (8.5) 9.5 (8.6)

Cocaine 5.5 (6.7) 4.5 (6.0)

Sedatives 4.2 (5.8) 5.5 (8.5)

Marijuanab

12.2 (8.5) 5.7 (8.1)

Life-time intravenous use (%)

b

94% 78%

Current preferred route

% intravenous 69% 63%

% intranasal 15% 14%


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ASI drug score

b

0.40 (0.10) 0.35 (0.10)

ASI alcohol score

b

0.17 (0.21) 0.09 (0.17)

MAST score 17.6 (15.6) 16.0 (16.2)Alcohol dependence (%) 27% 14%

Cocaine dependence (%) 32% 20%

Sedative dependence (%) 20% 19%

Marijuana dependence (%) 17%

Cigarette smokers (%) 87% 80%

No. of prior treatment attempts 3.0 (4.8) 3.6 (4.8)

*Includes only participants who reported use of each drug during the prior 30 days;

**mean (standard deviation)

a

p 0.01;

b

p 0.05;

cp 0.10.

36%) during the 30 days prior to treatment.

scales. They were also signi cantly more likely to

Non-users were more likely to report illicit

report a history of intravenous use (94% vs.

methadone use (15% vs. 33%). Among users of

78%) than non-users and, among intravenous

each substance, frequency of use did not differ

users, to share needles (77% vs. 57%).

signi cantly between the two groups, although

non-signi cant trends suggested greater fre-

Adverse consequences comparisons. Substantial

quency of sedative and illicit methadone useproportions of all participants reported various

among patients who did not use marijuana.

consequences related to opioid use on the

Marijuana users had signi cantly higher scores

adverse consequences checklist, but signi cant

on the ASI drug and the ASI alcohol composite

group differences emerged on only three


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items. Marijuana users reported more nancial

problems (96% vs. 81%), more selling of drugs

(57% vs. 31%), and less experiences of panic

related to their drug use (21% vs. 40%).

Multivariate analyses. Stepwise logistic

regression was employed to determine if the vari-

ables that differed between marijuana-use groups

in the univariate analyses were the result of con-

founding between marijuana-use status and

other potential explanatory variables. Separate

logistic regression analyses were performed to

identify predictors of eight variables that differed

between the marijuana-use groups (i.e. heroin-

use status, alcohol-use status, history of intra-

venous use, needle sharing, marital status,nancial problems, dealing drugs, experiences of

panic). The control variables examined in these

analyses were gender, age, heroin-use status,

alcohol-use status, methadone-use status and

history of intravenous use. The logistic

regression examining needle-sharing included

only participants with a history of intravenous

use (N 95); the other regression analyses

included all participants (N 107).

Marijuana-use status was selected into four of

the logistic regression equations indicating that it

was signi cantly related to heroin-use status

(coef cient of variation (cv) 2.29 0.82,p 0.01, odds ratio (OR) 9.9), alcohol-use

status (cv 1.58 0.44, p 0.01, OR 4.8),

needle sharing (cv 0.94 0.47, p 0.05,

OR 2.5) and dealing drugs (cv 1.08 0.44,

p 0.02, OR 3.0) after controlling for the

aforementioned other variables. Marijuana-use

status was not selected into the four other

regression analyses, indicating that marijuana-

use status did not have a unique effect on marital

status, history of intravenous use, nancial

dif culties or experiences of panic after con-

trolling for the other variables.

Marijuana use and treatment outcomeSixty- ve per cent (51/79) of patients (i.e.

buprenorphine and behavior therapy partici-

pants) provided at least one marijuana-positive

urine specimen during treatment. Marijuana

users provided, on average, 45% (SD 35%)

marijuana-positive urine specimens during treat-

ment. Only three participants who reported

marijuana use prior to treatment did not show


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any evidence of use during treatment. Frequency

of marijuana use prior to treatment was

signi cantly associated with percentage of mari-

juana-positive urinalysis tests during treatment

(r(79) 0.64, p 0.001).

Comparisons between those who did and did

not use marijuana during treatment revealed nosigni cant differences on any of the treatment

outcome measures (i.e. treatment retention, opi-

oid, cocaine or benzodiazepine abstinence and

pre post changes on the ASI) (Table 3). Separ-

ate analyses by gender also did not reveal any

signi cant relations between marijuana use and

the outcome measures (data not presented).

Moreover, frequency of marijuana use during

treatment (% cannabinoid-positive urine speci-

mens) was not signi cantly correlated with

weeks of opioid abstinence (r(79) 0.07) or

percentage of weeks retained in treatment

(r(79) 0.21).Discussion

The rate of marijuana use (66%) observed

among this sample is consistent with prior

reports documenting a high prevalence of mari-

juana use among individuals seeking treatment

for opioid dependence (Saxon et al., 1993;

Darke & Hall, 1995; Nirenberg et al., 1996).

This report documented concurrent marijuana

use among males and females residing in a rural

environment indicating that such use is common

in diverse samples of opioid-dependent patients.

Almost all marijuana users (94%) continued to

smoke while enrolled in treatment underscoringthe need for the development of empirically

based clinical strategies to address such use.

Marijuana use was associated with only a few

markers of psychosocial impairment in this

study. Users were less likely to be married and

reported more nancial dif culties; however, a

unique effect of marijuana use on these variables

was not retained after statistically controlling for

the in uence of heroin and alcohol use.

Nonetheless, these types of problems are consist-

ent with ndings from general population and

clinical studies of marijuana users that note

lower participation and stability in conventionalroles such as marriage and employment among

marijuana users (Kandel, 1984; Stephens et al.,

1993; Budney et al., 1996). More notable was

the failure to observe differences between mari-

juana users and non-users on the many other

indicators of problem severity at intake. Such


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Table 3. Opioid dependence treatment outcome


(N 54) (N 25)

Retention* 65% (32) 60% (33)

(% of wks completed)

Opiate abstinence

(no. of continuous wks) 8.4 (6.5) 8.5 (7.2)

Other drug use

(% positive urine specimens)

Benzodiazepines 32% 40%

Cocaine 13% 14%

ASI composite change scores*

(intake 12-month follow-up)

Medical 0.07 (0.45) 0.09 (0.50)

Employment 0.05 (0.27) 0.06 (0.35)Legal 0.03 (0.30) 0.15 (0.22)

Alcohol 0.05 (0.29) 0.10 (0.16)

Drug 0.24 (0.16) 0.20 (0.18)

Family social 0.11 (0.27) 0.21 (0.26)

Psychiatric 0.01 (0.30) 0.04 (0.24)

Includes only participants who received buprenorphine and behavioral

treatment. Excludes participants who dropped out during the rst 2 weeks

of treatment.

*mean (standard deviation); **raw change scores are presented to

preserve clarity. Only those who completed both ASI assessments are

included (n 53). ANCOVA analyses revealed no signi cant group time

interaction effects across subscales.

ndings are consistent with those of a similarstudy that reported only a few differences

between marijuana users and nonusers (Saxon et

al., 1993). At least two potential explanations for

these ndings deserve comment. First, the

consequences of opioid dependence and its asso-

ciated life-style may obscure any effects of mari-

juana use. That is, the baseline level of severity

and types of problems experienced by opioid

abusers may be so broad and of such a large

magnitude that we could not detect any addi-

tional consequences of marijuana use. Secondly,

people who do not use marijuana may engage in

alternative substance-use behavior that has simi-lar effects on psychosocial functioning. For

example, we observed a non-signi cant trend

suggesting that, among those who report use of

sedatives and methadone, non-users of mari-

juana reported more frequent use of these drugs.

Thus, non-users may simply have an alternative

choice of drug that results in comparable effects

or consequences.


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Two other substance use-related differences

between marijuana users and non-users were

observed at intake. Users were more likely than

non-users to be involved in drug dealing and to

engage in sharing needles, two deviant behaviors

with signi cant medical and psychosocial risks.

Marijuana use retained its unique effect on thesehigh-risk behaviors after controlling for use of

other drugs (including the greater heroin and

alcohol use observed among marijuana users),

gender and age. The increased probability of

needle-sharing was in contrast to a previous

report that found high risk behavior for HIV

infection, including number of needle sharing

partners, to be linked to alcohol use among

intravenous drug users, but not marijuana use

(Saxon & Calsyn, 1992). Saxon & Calsyn (1992)

found that, among their intravenous drug-using

sample, those who used both alcohol and mari-

juana reported the highest rate of needle-sharingand marijuana-only users reported the lowest

rate. We found equally high rates of needle-

sharing (75%) among those who used both

alcohol and marijuana and those who used mari-

juana only; both these groups reported higher

rates than those who used alcohol only (55%) or

those who did not use either substance (53%). A

number of differences between the samples and

measures used to examine needle-sharing could

account for these disparate ndings. The Saxon


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35/44


36/44


study used a continuous measure of needle-

sharing, i.e. number of needle-sharing partners

in past 30 days, while the present study

employed a dichotomous measure, i.e. any his-

tory of needle-sharing. The marijuana-only sub-

jects in the Saxon sample were older on average

and more likely to be enrolled in methadone

maintenance than the other groups in their sam-

ple; both these factors could be related to less

engagement in high-risk behavior for HIV

(Darke & Hall, 1995). Additional research is

needed to understand better the relationship

between HIV-risk behaviors and polydrug abuse

among opioid-dependent individuals.

The present study found no adverse relationsbetween marijuana use and any of the treatment

outcome measures (i.e. retention, documented

opioid, cocaine or benzodiazepine abstinence or

ASI change scores). These results are consistent

with two previous studies that failed to detect an

in uence of marijuana use on opioid or other

drug use among methadone-maintenance

patients (Saxon et al., 1993; Nirenberg et al.,

1996). Moreover, the inclusion of the ASI as an

outcome measure also showed that marijuana

use may not signi cantly affect treatment-related

changes in psychosocial function. Thus, the lack

of a clear association between marijuana use andtreatment outcome was replicated in and

extended to a rural, mixed-gender sample of

opioid-dependent patients receiving buprenor-

phine detoxi cation and behavior therapy.

The absence of an association between mari-

juana use and outcome has been observed only

in retrospective studies and has not been exam-

ined during post-treatment follow-up periods.

Prospective studies are needed to determine

more clearly how different treatment approaches

may impact marijuana use and treatment out-

come. Notwithstanding these limitations, the

extant ndings suggest that progress in treatmentfor opioid dependence can be made without

mandating that patients abstain from marijuana

use. Treatment approaches to other drug use

among opioid-dependent patients may be best

developed within harm reduction models such as

those adopted in many European countries

(Marlatt & Taper, 1993). Others have suggested

that marijuana use in this population may serve


37/44

a function during the treatment process that

perhaps helps patients remain in treatment

(Shaffer & LaSalvia, 1992; Nirenberg et al.,

1996). For example, many opioid patients state

that marijuana helps them to deal with opioid

withdrawal symptoms. On the other hand, we

know that chronic marijuana use is associatedwith impaired psychosocial functioning,

especially among users with a history of other

drug problems. In our clinic we currently advise

against marijuana use, but spend minimal time

addressing such use during treatment unless the

client expresses interest in setting a reduction

goal or we have information which clearly indi-

cates that marijuana is interfering with the

achievement of other treatment goals. A better

understanding of the function of marijuana use

in this population will provide information that is

important for determining whether or not, when,

or how to address marijuana use.A number of limitations of these ndings

deserve comment. First, the use of the ASI as the

only outcome measure of psychosocial function-

ing and the relatively small sample size (N 53)

employed in those analyses warrants cautious

interpretation of the results that failed to show

an in uence of marijuana use on psychosocial

change. For example, the observed associations

between marijuana use and needle-sharing and

drug-dealing suggest that future medical and

legal consequences may be more probable

among opioid-dependent marijuana users, yet

our global measures of functioning (i.e. ASI) inthese areas did not show any association to mari-

juana use. Future research should include more

comprehensive measures of psychosocial out-

come. Secondly, the likelihood of spurious

ndings in this study was relatively high because

of the large number of statistical tests conducted

between the groups. We chose not to control for

experiment-wise error rates due to the

exploratory nature of the study; thus, the unique

ndings of this study should be interpreted cau-

tiously pending replication.

Finally, the differential impact of the use of

other drugs of abuse on treatment process andoutcome among opioid-dependent patients war-

rants comment. The present study, along with

other previous studies, document the ubiquitous

nature of polydrug use among opioid-dependent

patients. As discussed elsewhere, the functional

relations between different combinations of

drugs of abuse may vary and require unique

clinical approaches (Bickel, DeGrandpre & Hig-


38/44

gins, 1995). This study is the third to document

the relative independence of marijuana and

opioid use in the treatment environment. An


39/44


40/44


41/44


analog study from our laboratory provided fur-

ther support for such functional independence

(Petry & Bickel, 1998). When asked to report on

how they would hypothetically allot their money

when given choices to spend it on different drugs

of abuse, opioid-dependent patients purchases

of marijuana were relatively independent of the

price of heroin, or at best a weak substitute. In

contrast, their decisions to purchase benzodi-

azepines and cocaine were signi cantly affected

by changes in the price of heroin. Valium was a

strong substitute for heroin (i.e. valium pur-

chases increased as the price of heroin

increased). Cocaine was both a complement and

substitute for heroin (i.e. when heroin priceswere low cocaine was purchased concurrently

with heroin; when heroin prices increased,

cocaine purchases increased). These ndings are

consistent with behavioral economic conceptions

of drug use and support clinical observations

that the use of benzodiazepines and cocaine are

related to opioid use during treatment for opioid

dependence (e.g. Des Jarlais et al., 1992; Darke

et al., 1993; Nirenberg et al., 1996). Alcohol

abuse among opioid-dependent patients is also

associated with negative outcomes such as

premature termination from treatment,

increased health risks and greater mortality(Bickel, Marian & Lawinson, 1987; Bickel &

Amass, 1993). Similar ndings regarding the

differential impact of other drug use have been

reported in clinical samples of cocaine-depen-

dent individuals. Two studies have shown the

relative independence of marijuana and cocaine

use, while other studies have shown a positive

association between alcohol and cocaine use

(Budney et al., 1991, 1996; Carroll, Rounsaville

& Bryant, 1993; Higgins et al., 1993). These

ndings underscore the need to examine sepa-

rately the effects and function of various drug

combinations observed in the treatment-seekingpopulation. Effective treatment approaches will

need to acknowledge such differences and

employ clinical strategies based on such infor-

mation.

Acknowledgements

This paper was presented in part at the College

on Problems of Drug Dependence 57th annual

scienti c meeting, June 1996, San Juan, Puerto


42/44

Rico. This research was supported by National

Institute on Drug Abuse research grants R29-

DA08655, R01-DA06969, and T32-07242. We

thank Evan Tzanis for his assistance with data

analysis. Correspondence concerning this article

should be addressed to Alan J. Budney, Ph.D.,Department of Psychiatry, University of Ver-

mont, 200 Twin Oaks Terrace, S. Burlington,

VT 05403. Leslie Amass is now with the Depart-

ment of Psychiatry, University of Colorado

School of Medicine, 4200 East 9th Ave, Box

C253, Denver, CO 80206

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