The effect of baseline cocaine use on treatment outcomes for heroin dependence over 24 months:...

Journal of Substance Abuse Tre

Regular article

The effect of baseline cocaine use on treatment outcomes

for heroin dependence over 24 months:

Findings from the Australian Treatment Outcome Study

Anna Williamson, (B.Psych. (Hons), Ph.D.)4, Shane Darke, (B.A. (Hons), Ph.D.),

Joanne Ross, (R.N., B.Sc. (Hons), Ph.D.), Maree Teesson, (B.A. (Hons), Ph.D.)

National Drug and Alcohol Research Centre, University of New South Wales, New South Wales 2052, Australia

Received 22 August 2006; received in revised form 19 December 2006; accepted 25 December 2006

Abstract

Aims: The aim of this study was to determine the effects of baseline cocaine use on treatment outcomes for heroin dependence over a

24-month period. Design: A longitudinal cohort (24 months) study was carried out. Interviews were conducted at baseline, 3, 12, and

24 months. Setting: The study setting was Sydney, Australia. Participants: Six hundred fifteen heroin users were recruited for the Australian

Treatment Outcome Study. Findings: Cocaine use was common at baseline (40%) but decreased significantly over the study period. Even

after taking into account age, sex, treatment variables, current heroin use, and baseline polydrug use, baseline cocaine use remained a

significant predictor of poorer outcomes across a range of areas. Baseline cocaine users were more likely to report heroin use, unemployment,

needle sharing, criminal activity, and incarceration over the 24-month study period. Conclusions: Cocaine consumption among heroin users

has repercussions across a range of areas that persist far beyond the actual period of use. Consequently, treatment providers should

regard cocaine use among clients as an important marker for individuals who are at risk of poorer treatment outcome. D 2007 Elsevier Inc.

All rights reserved.

Keywords: Heroin; Cocaine; Treatment; Cohort

1. Introduction

Cocaine use among heroin dependent individuals has

long been a major problem in the United States (Chambers,

Taylor, & Moffett, 1972; Platt, 1997) and, more recently, the

United Kingdom (Beswick et al., 2001). In Australia,

cocaine use among heroin-dependent individuals has tradi-

tionally occurred at low levels due to its prohibitive cost and

poor availability (Hando, Flaherty, & Rutter, 1997). In 2001,

however, heroin became substantially more expensive and

harder to obtain than usual, whereas the opposite was true of

cocaine (Darke, Kaye, & Topp, 2002). This led to a large-

0740-5472/07/$ – see front matter D 2007 Elsevier Inc. All rights reserved.

doi:10.1016/j.jsat.2006.12.009

4 Corresponding author. Dr. Anna Williamson, The Sax Institute, PO

Box 123 Broadway, New South Wales 2007, Australia. Tel.: +61 2 9514

5971; fax:+ 61 2 9514 5951.

E-mail address: [email protected] (A. Williamson).

scale increase in heroin and cocaine co-use, with unknown

implications for the efficacy of existing treatments for

heroin dependence.

The majority of research examining the efficacy of

treatment for concurrent heroin and cocaine use has centered

on methadone maintenance. Although cocaine use at treat-

ment entry has been associated with poorer outcomes

(Downey, Helmus, & Schuster, 2000), some studies have

found methadone maintenance leads to reductions in

cocaine use (Kosten, Rousanville, & Kleber, 1988; Magura,

Siddiqi, Freeman, & Lipton, 1991; Shaffer & La Salvia,

1992), whereas others indicate that significant proportions

of people maintain their pretreatment levels of use (Hartel

et al., 1995) or even commence or increase cocaine use

while on methadone (Kosten et al., 1988; Magura et al.,

1991; Van Beek, Dwyer, & Malcom, 2001). A small number

of studies have also examined the effects of concurrent

atment 33 (2007) 287–293

A. Williamson et al. / Journal of Substance Abuse Treatment 33 (2007) 287–293288

cocaine and heroin use on detoxification and residential

rehabilitation outcomes (Hubbard, Craddock, & Anderson,

2003), but there is a clear dearth of research in this area.

Different patterns of cocaine use have been noted in

different areas. In Australia, where cocaine powder domi-

nates, injection is the primary route of administration (Darke

et al., 2002), whereas in America and the United Kingdom,

where crack cocaine accounts for much of the cocaine-

related harm, smoking cocaine is common (Gossop,

Marsden, Stewart, & Kidd, 2002; Platt, 1997). To date, no

research into the effect of cocaine use on treatment outcome

for heroin dependence has been conducted in Australia.

The Australian Treatment Outcome Study (ATOS), the

first large-scale longitudinal study of treatment entrants for

heroin dependence in Australia, provided an opportunity

to examine the effects of concurrent cocaine use on

treatment outcomes for heroin dependence. Baseline

analyses showed that cocaine was being used by a large

proportion of treatment entrants and that these individuals

displayed particularly poor clinical profiles (Williamson,

Darke, Ross, & Teesson, 2006). Indeed, despite not

differing in drug use history or psychopathology, heroin

users who also used cocaine exhibited a greater degree of

drug-related harm, being more likely to report home-

lessness, criminality, needle sharing, and injection-related

health problems.

The current study examined the effects of baseline

cocaine use on treatment outcomes for heroin dependence

in all the major treatment modalities over a 24-month period.

In order to utilize all data collected and control for possible

confounders, generalized estimating equation (GEE) model-

ing was employed (Liang & Zeger, 1986). One of the major

advantages of GEE modeling is that it allows the inclusion

of subjects with incomplete data (Twisk, 2003).

The current study thus aimed to examine the effects of

baseline cocaine use on treatment outcomes for heroin

dependence over a 24-month period. The specific aim of the

study was to examine the relationship between baseline

cocaine use and outcome while taking into account the

effects of age, sex, baseline polydrug use and heroin use,

and treatment exposure at each time point.

2. Materials and methods

2.1. Procedure

The data were collected from the New South Wales

(NSW) component of ATOS. Data from the Victorian and

South Australian arms of ATOS do not form part of this

article, as the cohorts from these states were not followed

up at 24 months. Baseline interviews were conducted in

NSW between February 2001 and August 2002. ATOS is a

24-month longitudinal study of entrants to treatment for

heroin dependence and a comparison group of nontreat-

ment heroin users. Subjects were recruited from 19

agencies treating heroin dependence in the greater Sydney

region, randomly selected from within treatment modality

and stratified by regional health area. The agencies

comprised 10 outpatient methadone/buprenorphine main-

tenance agencies (MT), four drug-free residential rehabil-

itation agencies (RR), and nine detoxification facilities

(DTX). In addition, a comparison group of heroin users

not currently in treatment were recruited from needle and

syringe exchange programs.

MT programs in Sydney are outpatient and run for an

indefinite period provided that rules are adhered to.

Expulsion as a consequence of drug use during treatment

is rare. The DTX programs examined in this study were

primarily inpatient programs lasting 5–7 days. These

programs aim to address all drug use. A reducing schedule

of buprenorphine was sometimes used in order to facilitate

opiate detoxification. The RR programs in this study target

all drug use. Participants were recruited from two short

(28-day) and two long (3–12 months) inpatient programs.

Drug use while enrolled in an inpatient DTX or RR is

grounds for expulsion.

Participants were interviewed at baseline, 3, 12, and

24 months. Eligibility criteria were the following: (i) no

treatment for heroin dependence in the preceding month, (ii)

no imprisonment in the preceding month, (iii) agreed to give

contact details for follow-up interviews, (iv) had a good

understanding of English, and (v) were 18 years or older.

The total baseline sample consisted of 615 heroin users: MT

(n = 201), DTX (n = 201), RR (n = 133), NT (n = 80). All

subjects were reimbursed A$20 for travel expenses on

completion of each interview.

2.2. Structured interview

At baseline, participants were administered a struc-

tured interview that addressed demographics, treatment

history, drug use history, heroin overdose history, suicide

history, prison history, and a range of psychopathology.

Drug use, needle risk taking, injection-related health

problems, and criminal behaviors over the month

preceding interview were measured with the Opiate

Treatment Index (Darke, Hall, Wodak, Heather, & Ward,

1992). General physical and mental health were measured

with the Short-Form 12 (Gandek et al., 1998; Ware,

Kolinski, & Keller, 1996). A Diagnostic and Statistical

Manual of Mental Disorders, Fourth Edition diagnosis of

past month Major Depression was obtained by using the

Composite International Diagnostic Interview (Andrews,

Hall, Teesson, & Henderson, 1999).

Follow-up interviews incorporated all of the elements

of the baseline interview outlined above. In addition,

participants were asked how many times they had

commenced treatment, in any modality, for heroin depend-

ence since the most recent interview and the time spent in

each treatment episode. At 12 and 24 months, participants

were also asked if they had used heroin, overdosed on

A. Williamson et al. / Journal of Substance Abuse Treatment 33 (2007) 287–293 289

heroin, attempted suicide, or been incarcerated in the

preceding 12 months.

2.3. Statistical analyses

McNemar chi-square tests were used to measure

changes in prevalence across time. A logistic regression

was conducted to determine treatment status at baseline or

cocaine use status at 24 months. In order to examine the

effects of baseline cocaine use over the 24-month study

period taking into account all data collected, GEE

modeling (Liang & Zeger, 1986) was employed. The

GEE method estimates regression coefficients and their

standard errors taking into account the correlation between

subjects’ scores on the outcome measure in question across

the study period (Twisk, 2003). This method yields valid

and robust estimates of variance, even when there is a

known positive correlation between multiple outcome

measures within subjects (Liang & Zeger, 1986). GEE

modeling analyzes the relationships between the variables

of a model at different time points simultaneously (Twisk,

2003). This is an iterative process, using quasi-likelihood

to estimate regression coefficients (Liang & Zeger, 1986).

One of the major advantages of GEE is that it allows the

inclusion of subjects with incomplete data (Twisk, 2003).

GEE offers important advantages over other regression

approaches used to measure change across time (Twisk,

2004). It permits simultaneous modeling of the relation of

specific factors (e.g., baseline cocaine use) with perform-

ance on outcome measures at baseline, 3, 12, and

24 months.

Odds ratios (ORs) and 95% confidence intervals (CIs)

are reported for all GEE analyses. Each model consisted of

the fixed covariates age, sex, baseline cocaine use (yes/no),

and the time-varying covariates past month heroin use

(yes/no), proportion of time spent in treatment since last

Table 1

Prevalence of past-month and 12-month outcomes at baseline, 3, 12, and 24 mon

Variable Baseline (n = 615)

Heroin use (past month), n (%) 584 (95)

(OR, 95% CI)a

Needle sharing (past month), n (%) 208 (34)

(OR, 95% CI)

Unemployment (past month), n (%) 108 (18)

(OR, 95% CI)

Criminal activity (past month), n (%) 336 (55)

(OR, 95% CI)

Prison (12 months), n (%) 102 (17)

(OR, 95% CI)

Severe physical disability (past month), n (%) 53 (9)

(OR, 95% CI)

Heroin overdose (12 months), n (%) 155 (25)

(OR, 95% CI)

Severe psychological disability (past month), n (%) 303 (49)

(OR, 95% CI)

ns = not significant.a Odds of use at previous interview point compared to current time point (G

interview, and number of treatment episodes commenced

since last interview. Treatment is not analyzed in terms of

modality entered at baseline because of the substantial

movement of the cohort both within and between treatment

modalities over the 2-year period. The unstructured corre-

lation option was used, as it has been demonstrated to be the

most accurate working correlation for estimating GEE

models (Twisk, 2004). The results presented here are based

on all the available data, including cases in which

information was not obtained at all follow-up points (n =

615). All analyses were conducted using SAS version 8.02

(SAS Institute Inc., 1999).

3. Results

3.1. Cohort characteristics

The mean age of subjects was 29.3 years (SD = 7.8,

range = 18–56), and 66% were men. Men were significantly

older than women (30.0 vs. 27.8 years), t(613) = �3.34,p b .01. The sample had completed a mean number of 10.0

years (SD = 1.7, range = 2–12) school education. Twenty-

nine percent had completed a trade/technical course, 6% a

university degree, and 65% had no tertiary qualifications.

The three most commonly reported primary sources of

income for the preceding month were a government

allowance (46%), criminal activity (24%), and employment

(18%). A prison history was reported by 41% of the sample,

with 17% having been incarcerated in the 12 months

preceding interview. The mean age at first heroin use was

19.7 years (SD = 5.3, range = 9–43) and the average length

of heroin use career at baseline was 9.6 years (SD = 7.4,

range = 0–35). Follow-up rates at the three time points were

3 months (89%, n = 549), 12 months (80%, n = 495), and

24 months (76%, n = 469).

ths

3 Months (n = 549) 12 Months (n = 495) 24 Months (n = 469)

198 (36) 139 (28) 108 (23)

(4.43, 3.72–5.15) (1.55, 1.25–1.90) ns

59 (11) 43 (9) 37 (8)

(2.36, 1.72–3.22) ns ns

91 (17) 114 (23) 159 (34)

ns (1.58, 1.22–2.05) (1.60, 1.28–1.99)

147 (27) 120 (24) 79 (17)

(2.51, 1.93–3.25) ns ns

58 (12) 71 (15)

Not applicable ns ns

31 (6) 29 (6) 24 (5)

(1.67, 1.07–2.59) ns ns

61 (12) 41 (9)

Not applicable (2.92, 2.20–3.82) ns

134 (24) 110 (22) 88 (19)

(2.10, 1.63–2.69) ns ns

EE).

Table 2

Prevalence of past month and 12-month outcomes at baseline, 3, 12, and 24 months among CUs and NCUs

Variable

Baseline 3 Months 12 Months 24 MonthsGEE analyses:

CU vs. NCU,

ORa (95% CI)

CUb

(n = 246)

NCUc

(n = 369)

CU

(n = 211)

NCU

(n = 337)

CU

(n = 196)

NCU

(n = 299)

CU

(n = 183)

NCU

(n = 286)

Heroin use 99 99 80 43 49 37 39 32 1.51 (1.17–1.95)

Needle sharing 40 30 17 7 14 5 13 5 1.67 (1.13–2.23)

Employment 12 21 12 20 22 23 31 36 0.68 (0.50–0.92)

Criminal activity 65 48 35 22 23 17 21 14 1.63 (1.26–2.10)

Prison (12 months) 22 13 N/A N/A 17 8 18 13 1.73 (1.31–2.29)

Severe physical disability 7 10 6 5 6 6 6 5 ns

Heroin overdose (12 months) 28 24 N/A N/A 14 8 8 8 ns

Severe psychological disability 48 50 27 23 24 22 20 18 ns

N/A = not applicable; ns = not significant.a Odds of use at previous interview point compared to current time point (GEE).b Cocaine user at baseline.c Non-cocaine user at baseline.


3.2. Treatment exposure over the 24-month follow-up period

At 24-month interview, 99% of participants reported

having received treatment for heroin dependence at some

time over the study period. The cohort had spent a median

of 41% of the time since baseline enrolled in treatment for

their heroin dependence and had commenced a median of

three treatment episodes. Approximately half (54%) of those

followed up at 24 months were enrolled in treatment at the

time of interview.

3.3. Prevalence of cocaine use

At baseline, 39% of the cohort reported cocaine use in

the month prior to interview. For analytic purposes, these

subjects (n = 246) were classified as cocaine users (CUs),

whereas those who had not used cocaine in the month prior

to baseline (n = 369) were classified as non-cocaine users

(NCUs). The prevalence of past month cocaine use had

decreased significantly among the cohort at 3 months, v2(1) =

53.57, p b .001, and declined again to 10% at 12 months,

v2(1) = 70.88, p b .001. At 24 months, approximately 7% of

the cohort reported cocaine use in the month prior to

interview, a proportion not significantly different from that

noted at 12 months, v2(1) = 85.32, p = .07.

In order to determine the effect of treatment status at

baseline on cocaine use status at 24 months a logistic

regression was conducted. Factors entered into the equation

were age, sex, cocaine use status at baseline, and treatment

status at baseline (treatment/nontreatment). The final model

was significant, v2(4) = 29.11, p b .001, and a good fit:

Hosmer–Lemeshow v2(8) = 4.80, p = .78. Baseline cocaine

use was the strongest predictor of cocaine use at 24 months

(14% vs. 3%, OR = 4.92, 95% CI = 2.14–11.33). Those who

were not enrolled in or seeking treatment at baseline were

also more likely to have used cocaine at 24 months (19% vs.

6%, OR = 0.40, 95% CI = 0.17–0.93). There were no gender

differences in the prevalence of last month cocaine use at

any follow-up point.

3.4. Cohort changes over time

GEE analyses were used to examine changes in the

prevalence of key outcome measures among the cohort over

24 months. At 3 months, significant declines were noted in

the prevalence of past month heroin use, needle sharing,

criminal activity, severe psychological disability and severe

physical disability (Table 1). At 12 months, the prevalence

of past month heroin declined still further, whereas the

prevalence of heroin overdose was significantly less than

that noted at baseline. The proportion of the sample that

reported being currently employed at 12 months was signif-

icantly greater than that noted at 3 months. No significant

changes in the prevalence of major outcome variables

occurred from 12 to 24 months. The prevalence of past year

incarceration did not change during the study period.

3.5. Effects of baseline cocaine use on outcomes

over 24 months

GEE analyses were used to examine the effects of

baseline cocaine use on major outcome variables across the

study period. After controlling for other factors, baseline

cocaine use was found to be a significant, independent

predictor of higher levels of heroin use (OR = 1.51, 95% CI =

1.17–1.95), needle sharing (OR = 1.67, 95% CI = 1.125–

2.23), unemployment (OR = 0.68, 95% CI = 0.50–0.92),

crime (OR = 1.63, 95% C.I. = 1.26–2.10), and incarcer-

ation (OR = 1.73, 95% CI = 1.31–2.29) over the 24-month

study period (Table 2). Baseline cocaine use did not

significantly predict heroin overdose or severe physical or

psychological disability.

4. Discussion

The substantial increase in cocaine use among heroin-

dependent individuals in NSW in 2001(Darke et al., 2002)

appears to have had long-lasting implications for treatment


outcome. Although the prevalence of cocaine use among

heroin users in NSW had returned to previous levels by

2003 (Roxburgh, Degenhardt, & Breen, 2004) as reflected

by the reduced prevalence of cocaine use in the cohort from

3 months onward, baseline cocaine use was associated with

poorer treatment outcomes for the 2 years after study entry.

Indeed, although the cohort showed significant improve-

ments across most domains during the study period,

baseline cocaine use was a significant, independent pre-

dictor of poorer outcome across a range of important

measures including heroin use, employment, needle sharing,

criminal activity, and incarceration. More importantly, this

effect was present even after controlling for baseline

polydrug use.

Cocaine was unusually affordable and easy to obtain in

2001 (Roxburgh, Degenhardt, Breen, & Barker, 2003), a fact

reflected by the high prevalence of cocaine use in the cohort

at study entry (39%). As a result of changes in the

availability of cocaine in Sydney coupled with the effects

of treatment, a significant decrease in cocaine use was noted

from baseline to 3 months and from 3 to 12 months. At

24 months, the proportion of the cohort using cocaine was

not significantly different from that at 12 months, partly

reflecting the stabilization of Sydney’s cocaine market

(Black, Degenhardt, & Stafford, 2005). Baseline cocaine

use continued to be the strongest predictor of cocaine use at

24 months, whereas those in the nontreatment group at index

were also more likely to have used cocaine at this time.

Across the study period, baseline cocaine use predicted

continuing heroin use, even with treatment, demographic,

and general polydrug use factors taken into account. This

suggests that cocaine use at treatment entry may indicate

poorer treatment outcome in the medium term, even after

cocaine use has ceased for the majority of clients.

Those who reported cocaine use at baseline were

significantly more likely to have shared needles over

24 months. Although numerous studies have linked current

cocaine use to needle sharing (Bux, Lamb, & Iguchi, 1995;

Joe & Simpson, 1995), in the current study baseline

cocaine use was an independent predictor of needle sharing

even 2 years later. This is especially important given that

the majority of baseline cocaine users had ceased use by

3-month follow-up. It appears that treatment providers

should be alert to cocaine use among their clients as its

potential implications in terms of exposure to and trans-

mission of blood borne viruses may far exceed the actual

cocaine use period.

CUs were significantly less likely than NCUs to report

employment across the study period. This is an important

finding, as unemployment has been linked to premature

mortality (Harlow, 1990) and relapse (Llorente del Pozo,

Gomez, Fraile, & Perez, 1998) among heroin users. In

addition, this finding further illustrates the psychosocial

dysfunction that has previously been noted among cocaine

users (Bux et al., 1995; Condelli, Fairbank, Dennis, &

Rachal, 1991; Kosten et al., 1988). It is also indicative of the

fact that baseline cocaine users were less likely than others

to cease heroin use, and hence perhaps to be capable of

commencing full-time work.

Consistent with heavier levels of heroin use and higher

unemployment, baseline cocaine use significantly predicted

criminal activity throughout the study. Both heroin and

cocaine use (Gossop, Marsden, Stewart, & Rolfe, 2000;

Grella, Anglin, & Wugalter, 1995; Hando et al., 1997) alone

and in combination (Best, Sidwell, Gossop, Harris, &

Strang, 2001; Gossop et al., 2000; Kosten et al., 1988)

have been strongly linked to criminal activity, primarily

acquisitive crime conducted in order to raise the necessary

capital to purchase these drugs. It is noteworthy, however,

that the effect of cocaine use on crime persisted for the

duration of the study despite the low levels of cocaine use

noted at 12 and 24 months. In contrast to the decreasing

prevalence of criminal activity reported, the rates of past

year incarceration did not change over 24 months. In

keeping with the greater prevalence of criminal activity

noted among baseline cocaine users throughout the study,

CUs were significantly more likely than NCUs to report past

year incarceration at all interview points.

In contrast to drug use and crime, baseline cocaine use

was not found to be significantly related to general physical

health over the study period. Past year heroin overdoses

were significantly less common at 12 months than at

baseline and this reduction was maintained at 24 months.

Although there has been some suggestion in the literature

that the combination of heroin and cocaine may be

particularly dangerous in relation to heroin overdose (Coffin

et al., 2003), baseline cocaine use at least, was not

associated with a greater risk of overdose in the cohort.

In contrast to the findings of some other studies (Garlow,

Purselle, & D’Orio, 2003; Van Beek et al., 2001), no

relationship was demonstrated between baseline cocaine use

and mental health in this cohort. Several possible explan-

ations for this difference exist. For example, unlike the

cocaine users in much of the literature surrounding the

effects of cocaine use on mental health, the cocaine users in

the ATOS cohort were, in fact, primary heroin users. This

may have implications in relation to potential differences in

the duration and extent of cocaine use careers between study

groups. Moreover, the elevated levels of mental health

problems known to exist among heroin users (Ward,

Mattick, & Hall, 1999) may create a ceiling effect that

obscures any additional harms attributable to cocaine use.

In considering the long-term association between base-

line cocaine use and poorer treatment outcome evident in

this cohort, a question of causality arises. Is this poorer

treatment outcome a consequence of cocaine use itself? Or

rather, does cocaine use serve as a marker for a particularly

briskyQ group of heroin users? Although it is likely that both

factors contribute to some degree, it should be noted that

within-group comparisons of CUs and NCUs on the basis of

3-month cocaine use status revealed that decreased perform-

ance on outcomes including heroin use, needle sharing, and


injection-related health problems was associated with the

commencement or continuation of cocaine use, whereas

cessation of cocaine use resulted in significant improve-

ments on these measures (Williamson et al., 2006). Thus, in

the short term at least, cocaine use appeared to exacerbate

dysfunction, rather than serving as a marker for a more

dysfunctional group of individuals.

In summary, despite the small proportion of CUs who

continued cocaine use throughout the study, baseline

cocaine use was a significant predictor of poorer outcome

across a range of areas including heroin use, employment,

needle sharing, criminal activity, and incarceration. It is

especially striking that this effect was present even after

taking into account age, sex, treatment variables, current

heroin use, and baseline polydrug use. These findings

suggest that any increase in cocaine consumption among

heroin users may have repercussions far beyond the actual

period of use. Consequently, treatment providers should

regard cocaine use among clients as a marker for individuals

who are at greater risk of poor treatment outcome.

Acknowledgments

This research was funded by the National Health and

Medical Research Council and the Commonwealth Depart-

ment of Health and Ageing. The authors thank all

participating agencies.

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