Factors associated with lapses to heroin use during methadone maintenance

Drug and Alcohol Dependence 52 (1998) 183 – 192

Factors associated with lapses to heroin use during methadone maintenance David A. Wasserman a,b,*, Meryle G. Weinstein a, Barbara E. Havassy a, Sharon M. Hall a b

a Department of Psychiatry, Uni6ersity of California, San Francisco, San Francisco, CA 94143, USA Mental Health Ser6ice (116W), San Francisco Veterans Affairs Medical Center, 4150 Clement Street, San Francisco, CA 94121, USA

Received 12 January 1998; accepted 13 April 1998

Abstract This prospective, observational study investigated factors predicting a lapse to heroin use in 74 heroin-abstinent methadone maintenance patients. After baseline data collection, participants were assessed twice per week for 7 weeks and again at 6 months after baseline. Proportional hazards regression and logistic regression were used to investigate the effects of study predictors on heroin use. A goal of absolute heroin abstinence consistently predicted a lower risk of a lapse, whereas marijuana use was associated with a greater risk. Stress variables were not predictive. The abstinence goal and stress results were consistent with the authors’ previous studies of other drug treatment samples. This line of research suggests that factors influencing lapses are similar across drug treatment populations and the role of stress in precipitating relapse remains unresolved. © 1998 Elsevier Science Ireland Ltd. All rights reserved. Keywords: Methadone maintenance; Heroin; Cocaine; Marijuana; Relapse

1. Introduction The goal of this study was to identify factors that predict a lapse to heroin use in methadone maintenance patients. Heroin use during methadone maintenance treatment (MMT) is common. A 1990 General Accounting Office report sampling 24 MMT programs indicated that from 1 to 47% of patients in treatment for longer than 6 months used heroin, depending on the program (United States General Accounting Office, 1990). Ball et al. (1988) found that 28.9% of 388 male methadone patients who had been in treatment for at least 1 year reported using heroin in the last month. In a study of 222 Australian MMT patients with a mean treatment duration of 27.8 months, 42.3% of participants reported using heroin in the previous month (Darke et al., 1994). * Corresponding author. E-mail: [email protected].

WASSERMAN.DAVID –

Although many studies have examined predictors of continuing heroin use during MMT (Caplehorn et al., 1993; Joe et al., 1991; Lehmann et al., 1993), few have identified predictors of a lapse, or heroin re-use, following a documented period of heroin abstinence. Lapses are crucial to study because they provide an opportunity to intervene early in the relapse process. Lapses may lead to prolonged episodes of heroin use and thereby increase the risk of problems developing or becoming more severe in many life areas, including health. Serious health risks associated with heroin use by injection include human immunodeficiency virus (HIV) infection, hepatitis B and C infection, endocarditis, and soft tissue infections such as abscesses and cellulitis.

1.1. Factors associated with abstinence and lapses In our earlier studies with alcoholics, cigarette smokers, and methadone-detoxification patients (Hall et al.,

0376-8716/98/$19.00 © 1998 Elsevier Science Ireland Ltd. All rights reserved. PII S0376-8716(98)00092-1

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1990) and cocaine patients (Hall et al., 1991), we investigated several domains of variables potentially related to maintenance of abstinence or occurrence of a lapse across drug treatment populations. Based on the findings of these studies, we chose to explore the same domains in the current study.

native reinforcers. Further, the availability of alternative reinforcers, and constraints on access to them, may influence drug consumption (Vuchinich and Tucker, 1988). Although we did not assess alternative reinforcers in our previous work, a pleasant event measure was included in the current study.

1.1.1. Abstinence goals In our earlier work (Hall et al., 1990, 1991), a goal of absolute abstinence, compared to less stringent goals, predicted a decreased risk of a first lapse in the first 12 weeks after treatment completion. In the current study, we sought to determine whether commitment to abstinence functioned differently in MMT patients. Because MMT patients take an opioid medication to substitute for their major drug of abuse, they may be less likely than patients in abstinence-oriented treatments to hold rigorous abstinence goals, and abstinence goals may be less predictive of abstinence.

1.1.4. Other drug use Other drug use was not included in formal models in our earlier studies. MMT patients, however, use multiple other drugs besides heroin, including cocaine, marijuana, benzodiazepines, alcohol, and nicotine. Cocaine use is especially prevalent in heroin-abusing MMT patients and has received much attention in the treatment literature (Silverman et al., 1996; Brooner et al., 1997). Marijuana use, although discussed less frequently, is also common in MMT. In an Australian study, 64.2% of methadone patients reported using marijuana in the preceding month (Darke et al., 1993). Relatively few MMT programs screen for marijuana (Saxon et al., 1991), although most programs expect patients to discontinue using it. Recently, marijuana has been shown to increase dopamine transmission in the nucleus accumbens in rats, as does heroin (Tanda et al., 1997). This new finding suggests that marijuana use by MMT patients may create a heightened vulnerability to heroin relapse.

1.1.2. Stress Psychosocial stress, particularly negative affect, occupies a central role in formal theories of relapse (Marlatt and Gordon, 1985). Most of the data supporting the importance of stress and negative affect in relapse have been collected retrospectively and are subject to the failures and distortions of recall (O’Doherty and Davies, 1987; Niaura et al., 1988). In our work, stress has been operationalized as negative moods, hassles, major life events, and physical symptoms (Hall et al., 1990, 1991). To date, we have found no evidence that higher levels of stress predict lapses when stress is assessed prospectively, that is, in the days preceding a lapse. On the other hand, when stress preceding a lapse is measured retrospectively, that is, after the lapse occurs, large effects are often detected. These results suggest that the resumption of drug use may alter perceptions of stress preceding the lapse. We included several stress variables in the current study to test the robustness of our previous findings. 1.1.3. Positi6e moods, dispositions, and e6ents In our cocaine study (Hall et al., 1991), positive moods, an aspect of subjective well-being, were a significant negative predictor of a lapse. Because positive moods may be related to temperament and traits, we added, in the current study, measures of two dispositional variables related to subjective well-being: mastery (Pearlin et al., 1981) and optimism (Scheier and Carver, 1987). In a study of alcoholics, Strack et al. (1987) found that greater optimism predicted completion of an aftercare program. Level of nondrug-related positive reinforcement is another potential predictor of heroin use in MMT. A social learning model would suggest that positive moods reflect, at least in part, the availability of alter-

1.2. Current study This prospective study of predictors of illicit drug use in MMT patients is the most recent in a line of research, conducted since 1982, on relapse predictors across different drug treatment populations. The goal of the study was to determine whether selected variables in the domains just described would predict a first lapse to heroin use after a period of abstinence. Participants were patients in four MMT programs. Participants were selected according to stringent eligibility criteria (see Section 2) and were not intended to be representative of the MMT population. In our earlier studies, participants were recruited near the end of time-limited treatment episodes, then were followed after treatment discharge. Because methadone maintenance may continue indefinitely, participants were evaluated during treatment, rather than after. Intensive in-treatment follow-up, consisting of twiceweekly assessments for 7 weeks after baseline data collection, was followed by an in-treatment 6-month post-baseline assessment. We tested these hypotheses: (a) A goal of permanent abstinence from heroin will be negatively associated with lapses to heroin use. (b) Measures of stress, including negative moods, perceived stress, major life events, opioid withdrawal symptoms, and psychological symptoms, will be positively related to heroin lapses, but

D.A. Wasserman et al. / Drug and Alcohol Dependence 52 (1998) 183–192

only retrospectively, that is, using stress data collected after the lapse. They will not predict prospectively. (c) Positive moods, dispositional optimism, mastery, and positive reinforcement will also be negatively related to heroin lapses. (d) Use of other drugs (cocaine, marijuana, and benzodiazepines) and alcohol will predict a lapse to heroin. 2. Methods

2.1. Participants From June 1992 through March 1995, 528 patients at four MMT programs in the San Francisco Bay area were approached to participate. At recruitment, patients were required to have been in MMT treatment between 3 and 18 months. This was intended to exclude patients still in the stabilization phase of treatment, during which time they are usually unable to achieve prolonged heroin abstinence, and also those in treatment for such an extended time that relapse would be unlikely. Because the study concerned lapses to heroin use in demonstrably heroin-abstinent patients, all participants had to have reported at least 2 weeks of abstinence from heroin at the eligibility assessment and then maintain biochemically confirmed heroin abstinence during a 2-week prestudy period and also during the week of study entry (baseline). These criteria ensured that all participants had at least 3 weeks of verified heroin abstinence. Of the patients approached for the study, 74 (14.0% of 528) became participants. There were 344 (65.1%) excluded for not meeting study entrance criteria and 110 (20.8%) who declined. The most frequent reason for exclusion was heroin use, either in the 2 weeks prior to the eligibility assessment (n =83, 24.1% of exclusions) or in the prestudy weeks or baseline period (n = 101, 29.4%). Other reasons were: failure to complete the 2-week prestudy phase (n =41, 11.9%), a diagnosis of AIDS (n =31, 9.0%), being in treatment longer than 18 months by the time the eligibility interview occurred (n=24; 7.0%), no history of injection heroin use (n= 17, 4.9%), inability to read or write English (n= 11, 3.2%), current psychiatric symptoms severe enough to preclude study participation (n = 10, 2.9%), failure to attend the first study assessment after the prestudy phase (n =5, 1,5%), and other (e.g. partner already in study; n =21, 6.1%). Demographic characteristics of the sample of 74 abstinent patients are shown in Table 1. Participants were predominantly male and Caucasian. At study intake, the mean methadone dose was 65.3 mg (S.D.= 20.2); the median was 65 mg (range 15 – 150). The median length of current MMT episode was 25 weeks (range 16 – 81). A total of two-thirds of participants (67.5%) had at least one prior episode of MMT.

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2.2. Settings Patients were recruited from four MMT programs located in three counties in the San Francisco Bay area. The programs were diverse in clientele, census, and treatment cost. One program was fee-for-service, two had some treatment subsidized by public funding, and one was supported by the Department of Veterans Affairs.

2.3. Design o6er6iew This was an 8-week prospective study with a followup assessment 6 months after study start. Participants were followed for 7 weeks after the baseline week, and assessments were conducted twice per week to maximize detection of drug metabolites in urine and to Table 1 Descriptive characteristics Variable

n

Percent

Gender Male Female

44 30

59.5 40.5

Race/ethnicity African-American Caucasian Hispanic/other Other

15 45 9 5

20.3 60.8 12.1 6.8

Full- or part-time employment Yes No

19 55

25.7 74.3

Legal status Clear Probation, parole, other

55 19

74.3 25.7

Arrested past year Yes No

17 57

23.0 77.0

Monthly income: Mdn Age: M (SD, range) Education: M (SD, range)

$501 – 750 42.8 years (9.3, 19 – 67) 12.2 years (2.2, 7 – 20)

DSM-III-R disorders

n

Percent

Major depressive episode Lifetime Past month

32 9

43.2 12.2

Dysthymia Lifetime Past month

17 4

23.0 5.4

Posttraumatic stress disorder Lifetime Past month

36 16

48.6 21.6

Antisocial personality disorder Lifetime Past month

32 15

43.2 20.3

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assess predictors close in time to participants’ lapses. The design was similar but not identical to those of our previous relapse studies (Hall et al., 1990, 1991; Havassy et al., 1991, 1995), in which we followed participants for 12 weeks and assessed them once per week. Patients first completed a study eligibility interview. Eligible, consenting patients entered a 2-week prestudy phase to verify abstinence from heroin. The prestudy phase consisted of four face-to-face assessments 3–4 days apart. At each assessment, patients gave a urine specimen and completed a brief questionnaire about current drug use. Patients who missed one prestudy assessment were required to restart the prestudy phase. If a prestudy assessment was missed during the second attempt, the patient was dropped but allowed to reapply to the study after 90 days. Patients who completed all four prestudy assessments were asked to participate in the 8-week study, which immediately followed the prestudy. Again, participants were assessed twice per week. Week 1 assessments (assessments 1–2) consisted of an intake interview, sections from the Diagnostic Interview Schedule (DISIII-R; Robins et al., 1989), and self-administered questionnaires. In the follow-up phase, Weeks 2–8 (assessments 3–16), we gathered self-report data on a variety of psychosocial variables and current drug and alcohol use. Urine samples were collected at all study assessments. Participants were discontinued if they did not complete the first two assessments (study baseline), missed more than two assessments in a row, or left MMT. A final follow-up assessment was held 6 months after the baseline assessment. We attempted to assess all 74 participants at 6 months regardless of their study status at the end of week 8. All assessments were conducted face-to-face at the clinics, usually just after patients received their methadone dose. Participants were reimbursed $5 for each prestudy assessment, $50 for the first week (assessments 1 and 2), $7.50 for each of the 14 subsequent study visits (assessments 3 – 16), and $20 for the 6-month assessment. The study was approved by the institutional review boards at the University of California, San Francisco and the San Francisco Veterans Affairs Medical Center.

Table 2 Administration schedule of study instruments, assessments 1–16

2.4. Instruments

2.4.3. Abstinence goals Thoughts about Abstinence (revised; Hall et al., 1990, 1991). Participants indicated their ‘real expectations’ regarding their heroin use by choosing one of five options: (a) quit using heroin once and for all and never use again; (b) not use heroin for a period of time, then decide later about using again; (c) use less heroin than before MMT; (d) use as much or more heroin than before MMT; (e) no clear goal. Using the same response options, participants also chose an ‘ideal’ goal.

Each instrument administered in the study is described below. The administration schedule for study instruments during study assessments 1 – 16 (weeks 1–8) may be found in Table 2.

2.4.1. Background information An intake interview (adapted from Simpson, 1990) collected data on demographic characteristics, drug and

Instrument

Assessments administered

Intake interview Urine toxicology Self-reported alcohol, drug use DIS-III-R Thoughts about Abstinence Mood Questionnaire Life Orientation Test Mastery Scale Pleasant Events Schedule Perceived Stress Scale Stressful Life Events Scale Subjective Opioid Withdrawal Scale SCL-90-R

1 1 – 16 1 – 16 2 1, 3, 5, 7, 9, 11, 13, 15 1, 3 – 16 1 1 1, 3, 5, 7, 9, 11, 13, 15 1, 3 – 16 1, 5, 9, 13 1, 3 – 16 2

Assessments occurred twice per week for 8 weeks. Assessments 1 and 2 were study baseline assessments.

alcohol use history, and drug and alcohol treatment history, including the current MMT episode.

2.4.2. Current drug use Urine toxicology. Specimens were analyzed by an offsite commercial laboratory for opioids (morphine and codeine), cocaine metabolite (benzoylecgonine), marijuana metabolite (THC), benzodiazepines, amphetamines, barbiturates, propoxyphene, phencyclidine, and methadone. Initial testing was by enzyme immunoassay (EMIT), and positive findings were confirmed with thin-layer chromatography (TLC). Urine collection was not observed because we anticipated that participants would not have an incentive to manipulate collection. Urine results were not released to the MMT programs and carried no treatment consequences. Adhesive temperature strips were placed on collection bottles as a validity check. Alcohol and drug use (adapted from Hall et al., 1991; Havassy et al., 1995). This measure assessed the frequency and amount of use of alcohol and other drugs. At each assessment, the participant indicated which substances were used since the previous assessment; amounts used for heroin, cocaine, and alcohol; route of cocaine administration; and cocaine binge behaviour.

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Finally, they rated on a 10-point scale how hard it would be not to use heroin in the coming months. Ratings reflected how the participants felt at the time of the assessment.

2.4.4. Stress The Mood Questionnaire (Ryman et al., 1974). This questionnaire contains four negative-mood subscales (depression, anger, fatigue, and fear) and two positivemood subscales (activity and happiness). Negative moods were interpreted as indicating stress. Participants used a 5-point scale (not at all to extremely) to rate each mood for the period since their last study assessment. Alphas for total negative and positive mood scales at baseline were 0.95 and 0.91, respectively. The Perceived Stress Scale (Cohen et al., 1983) is a measure of the degree to which respondents find their lives unpredictable, uncontrollable, and overloading. We used the 4-item version described in Cohen and Williamson (1988). Items were rated on a 5-point scale (from never to very often) with reference to the past 7 days. Alpha at baseline was 0.62. The Stressful Life Events scale (revised, Hall et al., 1990) was adapted from Billings and Moos (1982). It asked participants to indicate if they had experienced any of 26 major discrete events (e.g. assaulted, robbed, birth of a child) in the last 12 months (assessment 1) or the last 2 weeks (subsequent follow-up administrations). The Subjective Opiate Withdrawal Scale (Handelsman et al., 1987), consists of 16 items reflecting a wide range of common signs and symptoms of the opiate withdrawal syndrome. Participants rated the intensity of each symptom on a scale of 0 – 4 based upon how they were feeling at the time of the assessment. Total scores range from 0 to 64. Alpha at baseline was 0.90. The SCL-90-R (Derogatis, 1994) is a 90-item self-report measure that assesses severity of current psychological symptoms during the past 7 days. Participants received scores on nine symptom dimensions (e.g. depression, hostility) and three global indices, including the global severity index (GSI), a measure of mean severity of distress; positive symptom distress index (PSDI), a measure of symptom intensity; and positive symptom total (PST), a measure of symptom breadth. Alphas for the subscales ranged from 0.77 to 0.92. 2.4.5. Positi6e moods, dispositions, and e6ents The Mood Questionnaire (Ryman et al., 1974), as described above, was used to measure positive moods occurring since the previous study assessment. The Life Orientation Test (Scheier and Carver, 1985; assessment 1), an 8-item scale, assessed enduring positive outcome expectancies, or ‘dispositional optimism’ (e.g. ‘I always look on the bright side of things’).

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Participants rated items on a 4-point scale (strongly disagree to strongly agree). Alpha was 0.66. The Mastery Scale (Pearlin et al., 1981), a 7-item scale, was used to measure the extent to which people see themselves as being in control of their lives. Participants rated on a 4-point scale (from strongly agree to strongly disagree) the extent to which they agreed or disagreed with each of the seven items. Alpha was 0.61. Pleasant Events Schedule (PES; MacPhillamy and Lewinsohn, 1982). The mood-related subscale from this instrument was used to measure positive reinforcement. Participants were asked about the occurrence of each event in the past 7 days. Responses were: (1) this has not happened in the past 7 days; (2) this has happened a few times (1–3) in the past 7 days and; (3) this has happened often (4 or more times) in the past 7 days. Frequency ratings were summed for a total score.

2.4.6. Psychiatric diagnoses We administered selected sections of the Diagnostic Interview Schedule (DIS-III-R; Robins et al., 1989) in order to determine the prevalence of the following psychiatric disorders: major depressive episode, dysthymia, post traumatic stress disorder, and antisocial personality disorder. Prevalence rates of lifetime disorders and current (in the past month) disorders are given in Table 1. 2.4.7. 6 -Month follow-up assessment A 6-month follow-up interview identified changes in demographic information (employment status, living situation, etc.) and asked about drug use since the last research assessment attended. A final urine sample was obtained. 2.5. Data analysis strategy The analytic model for predicting a lapse to heroin use during Weeks 2–8 of the study was Cox’s proportional hazards regression, which permits testing of variables that change over time, a useful feature when such changes are believed to alter the risk of the outcome of interest. Abstinence goals, positive moods, pleasant events, negative moods, life events, perceived stress, opioid withdrawal symptoms, and use of other drugs were measured repeatedly in the study, beginning at baseline, and were treated as time-dependent (or timevarying) factors in the analyses. Primary analyses of time-dependent covariates, such as moods, were prospective (lagged). For covariates measured at each assessment, a lapse at time t was predicted from the covariate score at time t-1 (before the lapse). For covariates measured less often, the most recent covariate score was used. When covariates were measured at each follow-up assessment, we conducted retrospective analyses as well, in which lapses were

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predicted from covariate scores obtained in the days after the lapse occurred. The results of prospective analyses, compared to retrospective, are less vulnerable to the rival hypothesis of reverse causation, for example, a lapse caused an increase in negative moods, not the reverse. For purposes of statistical analysis, lost participants were assumed to have used heroin after their last assessment attended. We chose this alternative over classifying lost participants as censored (i.e. not experiencing the event during data collection) because survival analysis assumes that censoring and event occurrence (in this case, relapse) are independent, and, in substance abuse research, this assumption usually is untenable. In sensitivity analyses, we reclassified lost participants as censored to determine whether the findings were robust against varying assumptions about missing data. A series of logistic regressions was used to predict 6-month outcomes from baseline values of the predictor variables. Participants were classified as having used heroin (self-report or urine screen) during the period from Week 2 through the 6-month follow-up assessment) or not. Participants who were heroin-abstinent during Weeks 2–8 but did not complete the 6-month assessment were omitted from the primary analyses. In sensitivity analyses, lost participants were first treated as abstinent in the analyses and then as having lapsed. All chi-squares reported are likelihood-ratio chisquares. Alpha for each of the four major hypotheses was set at 0.05. For hypotheses with multiple parts (hypotheses b, c, and d), we conservatively set alpha at 0.01 for each part to control for the number of comparisons.

according to self-report and urine toxicology results for morphine. A total of 30 other participants (40.5%) either reported using heroin or tested positive for morphine on at least one occasion, and seven (9.5%) were dropped because of missed assessments or were lost to follow-up before the 7 weeks had ended. These seven participants were classified as lapsed in subsequent analyses, bringing the total number of participants classified as lapsed to 37 (50.0%). The survival curve for heroin abstinence is presented in Fig. 1. Most participants who lapsed did so during the first 2 weeks of follow-up, between assessments 2 and 6 (n=21, or 56.8%). Evidence of continued heroin use after the first lapse was examined to determine whether initial lapses were isolated events or the beginnings of extended periods of heroin use. Among the 30 participants known to have lapsed, 21 had subsequent positive assessments. The median percentage of heroin-positive assessments after the first positive assessment was 14% (range 0–100%).

3.2.1. Abstinence goals At study baseline (assessment 1), participants whose realistic goal was to ‘quit using heroin once and for all and never use again for the rest of my life’ (n=41, or 55.4%) were significantly more likely to stay abstinent than participants with less stringent goals, x 2 (1, N= 74)= 4.88, PB 0.03. When this variable was treated as time-dependent, the effect was again significant, x 2 (1, N= 74)= 3.88, PB0.05. Abstinence goal and difficulty ratings were not highly intercorrelated across assessments (median r= − 0.23; range= −0.04, P B0.73, to − 0.44, PB 0.002). Ideal goals, as opposed to realistic goals, were not predictive. Almost all participants (n=

3. Results

3.1. Background 6ariables Several treatment-related variables that have been associated with MMT outcomes in other studies were examined as potential background variables. These were methadone dose at study intake, duration of current MMT episode, number of methadone takehome doses earned by adhering to program requirements, and time since last injection of heroin (by self-report). Treatment setting also was considered. None of the variables had a significant univariate relationship with a lapse to heroin use (all P \ = 0.10), and, consequently, we did not adjust for them in subsequent data analyses.

3.2. First lapses to heroin, weeks 2 – 8 During weeks 2– 8 of follow-up, 37 participants (50.0%) remained abstinent from heroin for all 7 weeks

Fig. 1. Survival curve for heroin abstinence.

D.A. Wasserman et al. / Drug and Alcohol Dependence 52 (1998) 183–192

66, or 89.2%) endorsed the most stringent ideal goal at baseline.

3.2.2. Stress There were no significant prospective relationships between baseline negative moods, perceived stress, major life events, or withdrawal symptoms and heroin use; nor did we find any effects when these variables were analyzed as time-dependent factors. Participants with higher baseline PSDI scores (greater symptom intensity) on the SCL-90-R lapsed to heroin use with greater frequency, but this finding was not significant using our conservative alpha of 0.01, x 2 (1, N =74) = 5.47, PB 0.02; no other SCL-90-R subscale or global scores were predictive. In retrospective analyses, higher levels of negative moods were associated with heroin use, but again not significantly, x 2 (1, N= 74) =5.30, P B0.03. 3.2.3. Positi6e moods, dispositions, and e6ents Positive moods, dispositional optimism, mastery, and pleasant events had no significant effects on lapses to heroin use, either prospective or retrospective. 3.2.4. Psychiatric disorders None of the psychiatric disorders we assessed, whether lifetime or in the past month, was related to heroin use (P\0.15). 3.2.5. Other drug use According to self-report and urine toxicology during the eight study weeks, the following percentages of participants used other drugs besides heroin: 44.6% used cocaine, 47.3% used marijuana; and 39.2% used benzodiazepines. Additionally, 43.2% used alcohol (self-report only). Use of these substances during the baseline week of the study (assessments 1 and 2) did not predict subsequent heroin use, with one exception: baseline marijuana use significantly increased the risk of a lapse to heroin, x 2 (1, N =74) =8.39, P B0.004. In prospective analyses where drug use was treated as a time-dependent variable, again only marijuana use was a significant predictor, x 2 (1, N= 74) =7.62, P B0.006. Cocaine, x 2 (1, N = 74) = 4.74, P B0.03, and benzodiazepines, x 2 (1, N =74) = 5.19, P B 0.03 had nonsignificant effects in the expected direction. Alcohol was unrelated to heroin use. In retrospective analyses, where heroin use was predicted from other drug use detected at the same assessment, only cocaine was significantly associated with heroin use, x 2 (1, N =67) = 11.44, (PB 0.001), probably indicating injection of the two drugs simultaneously in ‘speedballs’. 3.2.6. Sensiti6ity analyses We reanalyzed the 7-week results with lost or dropped participants classified as censored instead of lapsed. The results did not change substantially, but the

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following differences were noted. The effect for realistic abstinence goal at baseline became significant at the 0.01 level (PB 0.002) rather than 0.05, as did the effect for the same variable when treated as time-dependent (PB 0.005). The effect for the SCL-90-R PSDI score changed from PB0.02 to P B 0.18. The effect for marijuana as a time-dependent factor became nonsignificant under our conservative alpha criterion, with the P value B 0.05 rather than B 0.01. 3.3. 6 -Month heroin abstinence Six-month assessments were conducted with 64 of the 74 participants. Of the remaining ten participants, five had lapsed during weeks 2–8 and therefore could be categorized as lapsed in the 6-month analyses. The other five participants were negative for heroin during weeks 2–8, and their use between week 8 and month 6 was unknown. These participants were omitted from the analyses. Thus, the sample size for the 6-month analyses was 69. Of these 69, 52 (75.4%) were categorized as lapsed during the 6 months and 17 (24.6%) as abstinent. A total of 11 (21.2%) of the 52 lapsed participants experienced their first lapse between week 8 and the 6-month assessment. Six-month results were similar but not identical to the results for weeks 2–8. A realistic goal of absolute abstinence at baseline, negatively related to heroin use in weeks 2–8, predicted also at 6 months, x 2 (1, N= 69)= 7.32, PB 0.007. The hostility subscale from the SCL-90-R, not predictive earlier, became predictive at 6 months, x 2 (1, N= 69)= 9.17, PB 0.003. Optimism was negatively but not significantly related to a lapse, x 2 (1, N= 69)=4.00, PB0.05. Baseline marijuana use predicted a heroin lapse, x 2 (1, N=69)=7.90, PB 0.005, but cocaine and benzodiazepine use were not predictive. Alcohol use was negatively but not significantly related to a lapse, x 2 (1, N=69)=4.05, PB 0.05. In sensitivity analyses where participants with unknown 6-month status classified as having lapsed, effects remained significant for abstinence goal (PB 0.009), hostility (PB0.002), and marijuana (PB 0.005). When the same five participants were categorized as abstinent, however, abstinence goal, hostility, and marijuana use were no longer significant at the 0.01 level (P values increased to PB 0.02, P B0.07, and PB 0.03, respectively). Considering together the results for weeks 2–8 and the 6-month follow-up assessment, the most robust predictors of a heroin lapse were abstinence goal (negative predictor) and marijuana use (positive predictor). Results for these variables were significant for both time periods. No other variables predicted significantly in weeks 2–8. Baseline hostility was a significant predictor at 6 months. As in our earlier work, stress variables were not prospectively related to a lapse.

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4. Discussion The methadone maintenance patients in this study who endorsed ‘never use again’ as their realistic heroin abstinence goal were more likely to stay abstinent from heroin (as assessed by self-report confirmed with morphine-negative urine assay) than those who endorsed less stringent goals. This result replicates our earlier findings with other drug-patient samples (Hall et al., 1990, 1991) and demonstrates further that a commitment to life-long abstinence is a robust predictor of outcomes across drug treatment modalities. Participants’ ratings of how hard it would be not to use heroin were not predictive; nor were abstinence goals and difficulty ratings highly intercorrelated. This suggests that the abstinence goal variable, despite referring to participants’ ‘real expectations’ of future heroin use, was not a proxy for self-efficacy, another cognitive variable previously found to predict drug abstinence (Reilly et al., 1995). In this and our earlier studies, we encountered a notable lack of prospective associations between stress variables, such as negative moods, and a lapse. Based on results from numerous retrospective studies, stress, particularly negative affect, is widely presumed to be a precipitant of lapses across drugs of abuse, including nicotine. In view of the consistent differences our group has found in the results of prospective and retrospective analyses, we suggest that relapse may lead to increased stress more often than stress leads to relapse. Alternatively, relapse may cause patients to report more stress, even if they have not experienced it, as a way of making sense of or justifying their use. These arguments notwithstanding, we may not have assessed stress variables close enough in time to a lapse for prospective effects to be detected. Assessing negative moods even 1 day before a lapse may be too distant in time to demonstrate an effect. Shiffman et al. (1996) have shown that when negative affect is assessed in ‘real time’ within minutes of a lapse to cigarette smoking, negative affect and lapses are related. As Shiffman et al. (1996) have suggested, research in this area should be conducted using frequent, randomly timed assessments, some of which would occur just hours or minutes before a lapse. Use of other substances, particularly marijuana, preceded lapses to opiate use. These results suggest that MMT patients who continue abusing another drug may have difficulty quitting heroin; nevertheless, the findings should not be interpreted as demonstrating that use of other drugs causes heroin relapse. Factors not measured in this study may influence certain patients to abuse any or all of these drugs. The significant findings regarding marijuana use are at odds with an earlier report by Saxon et al. (1993), which concluded that MMT patients who tested positive for marijuana were

no more likely than marijuana-negative patients to test positive for other illicit drugs, including opiates. That study was cross-sectional rather than prospective, however, and the chronological relationship of marijuana to opiate use was not reported. Budney et al. (1996), in a study of cocaine patients, showed that neither pre-treatment nor in-treatment frequency of marijuana use predicted poorer cocaine treatment outcomes. We do not have a ready explanation for the discrepancy between these findings and our own; however, as Budney et al. (1996) point out, the functional relations of different pairs of drugs are likely to differ. The lack of relationship between alcohol use and lapses to heroin use in weeks 2–8, and the nonsignificant negative relationship at 6 months, are curious. One explanation is that alcohol, unlike marijuana or cocaine, is a legal substance. Therefore, patients can consume it while avoiding environments that are laden with heroin-related cues. Alternatively, patients who report alcohol use may be different from those who do not. Patients who report alcohol use may be more concerned about their substance use overall and may try harder to abstain from heroin. Because we did not biochemically confirm self-reports of abstinence from alcohol, the alcohol data may be less valid than the drug data. Two treatment-related variables, methadone dose and time in treatment, included in this study because of their association with outcomes in previous studies, were not predictive of heroin use. Another recent study also failed to find a relationship between methadone dose and heroin use (Wolff et al., 1996), and an Australian study (Darke et al., 1994) found no effect for either of these variables. Positive associations between methadone dose and heroin abstinence may be more likely to be found in controlled studies where methadone dose levels are experimentally manipulated. As for treatment duration, our eligibility criteria restricted the range of this variable. Had we recruited longer-term patients into the study, a relationship between time in treatment and abstinence might have been evident.

4.1. Strengths and limitations There were four major strengths associated with this study. The first was the ability to analyze data both prospectively and retrospectively. Most published studies on relapse factors are retrospective, that is, factors are measured only after a lapse, not before. A second strength was the use of twice-weekly urine toxicology screens to verify self-reports of abstinence. Third, we administered many of our instruments multiple times during follow-up. As a result, we were able to detect significant associations that would not have been significant using only baseline predictors. Fourth, data were collected at multiple treatment sites.

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There are also several limiting factors associated with this study. The first is the population to whom the results may be generalized. We did not randomly sample patients from the four participating MMT programs. New patients and long-term patients were excluded from the study, as were patients who reported using heroin in the recent past. These constraints were necessary in order to limit the sample to participants who were heroin-abstinent yet still at moderate risk for a lapse. Because patients with AIDS were excluded, the findings may not suggest anything about this subpopulation. Another limitation was the sample size. Lack of power may have affected the robustness of certain findings against alternative assumptions about missing data. The sample size was unanticipated and resulted from a scarcity of abstinent MMT patients. In addition to the 74 patients reported on here, another 101 patients who reported abstaining from heroin were accepted into the study; however, upon subsequent assessment, they were found to be current opiate users. Had we used different inclusion criteria for duration of treatment episode (say, between 1 – 5 years), perhaps we would have needed to exclude a smaller proportion of patients for recent heroin use. On the other hand, the twice-weekly toxicology screens in this study may have enabled us to identify opiate-using patients who, under less stringent testing protocols, would have gone undetected. The number of analyses conducted for this study was large, reflecting the number of variables and assessment points examined. Although we used a conservative alpha of 0.01 for multi-part hypotheses, legitimate concerns might still be raised that familywise error rates were not sufficiently protected against. The similarity of the results to our previous findings, while not obviating these concerns, increases our confidence that the results are reliable. The 7-week length of the intensive follow-up and the lack of intervening assessments between week 8 and the 6-month assessment are other possible limitations. In pilot work for this study, we followed participants for 11 weeks after baseline. Because virtually all patients who stayed abstinent for 7 weeks stayed abstinent for 11 weeks, we decided that we could not justify the cost of the longer follow-up. Ultimately, most patients who lapsed during Weeks 2 – 8 did so in the first 2 weeks. The lack of research assessments between week 8 and the 6-month assessment lessens our ability to assert that participants who were opiatenegative at all assessments were continuously abstinent for the 6 months.

4.2. Clinical implications In light of the robust predictiveness of stringent

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abstinence goals, now demonstrated in three studies conducted with five different drug treatment populations, we are led to speculate whether interventions to enhance life-long commitment to abstinence might be beneficial to patients. Motivational interviewing strategies (Miller, 1996; Miller and Rollnick, 1991), which reinforce the patient’s own concerns and reasons for change, may provide an avenue for increasing commitment to abstinence, particularly in new MMT patients. Saunders et al. (1995) reported on the efficacy of a brief motivational intervention in this population. Participants who received the motivational intervention, compared to those assigned to a control procedure, were more likely to express an intent to stay abstinent. This finding did not hold over time, however. Experimental participants also had a longer time to first re-use of heroin, but this effect was significant only when lost participants were classified as relapsed. More powerful interventions for enhancing commitment to abstinence are likely to be valuable adjuncts in MMT. Participants in the current study who used other drugs, particularly marijuana, were more likely to resume opiate use. Although a causal relationship cannot be inferred, this suggests that once initial opiate abstinence is achieved in MMT patients (via optimal methadone doses, counselling, and behavioral interventions), use of other illicit drugs might need to be targeted if opiate abstinence is to be sustained. Structured treatments for marijuana dependence, such as that described by Stephens et al. (1994), should be studied in this population. In light of recent animal research suggesting that marijuana use increases dopamine transmission (Tanda et al., 1997), and that cessation of use can produce a withdrawal syndrome (Rodriguez de Fonseca et al., 1997), marijuana treatment might prove to be important for the marijuanausing MMT population, both for cessation of marijuana use itself and for indirectly assisting patients in their efforts to maintain abstinence from heroin.

Acknowledgements This work was supported by grants cR01 DA03082, c P50 DA09253, and c R01 DA09124 from the National Institute on Drug Abuse, and also a Research Career Scientist Award and Merit Review grant from the Department of Veterans Affairs to Sharon M. Hall. We gratefully acknowledge the work of Rachael Korcha, the study co-ordinator; Dia Hartz, who conducted the data analysis; and Christa A. Curtin, Lise Flores, Lawrence S. Miller, and Michael D. Ray, the study interviewers. We also would like to thank the clients and staff at the four participating treatment programs.

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References Ball, J.C., Lange, W.R., Meyers, C.P., Friedman, S.R., 1988. Reducing the risk of AIDS through methadone maintenance treatment. J. Health Soc. Behav. 29, 214–226. Billings, A.C., Moos, R.H., 1982. Stressful life events and symptoms: a longitudinal model. Health Psychol. 2, 17–99. Brooner, R., King, V.L., Kidorf, M., Schmidt, C.W., Bigelow, G.E., 1997. Psychiatric and substance use comorbidity among treatment-seeking opioid abusers. Arch. Gen. Psychiatry 54, 71 – 80. Budney, A., Higgins, S.T., Wong, C.J., 1996. Marijuana use and treatment outcome in cocaine-dependent patients. Exp. Clin. Psychopharmacol. 4, 396–403. Caplehorn, J.R.M., Reilly, D.K., Wodak, A., 1993. Detected heroin use in an Australian methadone maintenance program. J. Subst. Abuse Treat. 10, 553–559. Cohen, S., Kamarck, T.A., Mermelstein, R.J., 1983. A global measure of perceived stress. J. Health Soc. Behav. 24, 385–396. Cohen, S., Williamson, G., 1988. Perceived stress in a probability sample of the United States. In: Spacapan, S., Oskamp, S. (Eds.), The Social Psychology of Health. Sage, Newbury Park, CA, pp. 31– 68. Darke, S., Swift, W., Hall, W., Ross, M., 1993. Drug use, HIV risk-taking and psychosocial correlates of benzodiazepine use among methadone maintenance clients. Drug Alcohol Depend. 34, 67 – 70. Darke, S., Swift, W., Hall, W., Ross, M., 1994. Predictors of injecting and injecting risk-taking behaviour among methadone maintenance clients. Addiction 89, 311–316. Derogatis, L.R., 1994. SCL-90-R: Administration, Scoring, and Procedures Manual-third edition. National Computer Systems, Inc., Minneapolis. Hall, S.M., Havassy, B.E., Wasserman, D.A., 1990. Commitment to abstinence and acute stress in relapse to alcohol, opiates, and nicotine. J. Consult. Clin. Psychol. 58, 175–181. Hall, S.M., Havassy, B.E., Wasserman, D.A., 1991. Effects of commitment to abstinence, positive moods, stress, and coping on lapses to cocaine use. J. Consult. Clin. Psychol. 59, 526–532. Handelsman, L., Cochrane, K.J., Aronson, M.J., Ness, R., Rubinstein, L.J., Kanof, P.D., 1987. Two new rating scales for opiate withdrawal. Am. J. Drug Alcohol Abuse 13, 293–308. Havassy, B.E., Hall, S.M., Wasserman, D.A., 1991. Social support and relapse: commonalities among alcoholics, opiate users, and cigarette smokers. Addict. Behav. 16, 235–246. Havassy, B.E., Wasserman, D.A., Hall, S.M., 1995. Social relationships and abstinence from cocaine in an American treatment sample. Addiction 50, 699–710. Joe, G.W., Simpson, D.D., Hubbard, R.L., 1991. Treatment predictors of tenure in methadone maintenance. J. Subst. Abuse 3, 73– 84. Lehmann, F., Lauzon, P., Amsel, R., 1993. Methadone maintenance: predictors of outcome in a Canadian milieu. J. Subst. Abuse Treat. 10, 85 – 89. MacPhillamy, D.J., Lewinsohn, P.M., 1982. The pleasant events schedule: studies in reliability, validity, and scale intercorrelations. J. Consult. Clin. Psychol. 54, 447–453. Marlett, G.A., Gordon, J.R. (Eds), 1985. Relapse Prevention: Maintenance Strategies in the Treatment of Addictive Behaviors. Guilford Press, New York. Miller, W.R., 1996. Motivational interviewing: research, practice, and puzzles. Addict. Behav. 21, 835–842. Miller, W.R., Rollnick, S., 1991. Motivational Interviewing: Preparing People to Change Addictive Behavior. Guilford Press, New York. .

Niaura, R.S., Rohsenow, D.J., Binkoff, J.A., Monti, P.M., Pedrazza, M., Abrams, D.B., 1988. Relevance of cue reactivity to understanding alcohol and smoking relapse. J. Abnorm. Psychol. 97, 133 – 152. O’Doherty, F., Davies, J.B., 1987. Is drug use a response to stress? Drug Alcohol Depend. 29, 97 – 106. Pearlin, L.I., Menaghan, E.G., Lieberman, M.A., Mullan, J.T., 1981. The stress process. J. Health Soc. Behav. 22, 337 – 356. Reilly, P.M., Sees, K.L., Shopshire, M.S., et al., 1995. Self-efficacy and illicit opioid use in a 180-day methadone detoxification treatment. J. Consult. Clin. Psychol. 63, 158 – 162. Robins, L., Heltzer, J., Cotter., Goldring, G., 1989. NIMH diagnostic interview schedule, version III revised (DIS-III-R). Unpublished manuscript, Washington University. Rodriguez de Fonseca, F., Carrera, M.R.A., Navarro, M., Koob, G.F., Weiss, F., 1997. Activation of corticotropin-releasing factor in the limbic system during cannabinoid withdrawal. Science 276, 2050 – 2054. Ryman, D.H., Biersner, R.J., La Rocco, J.M., 1974. Reliabilities and validities of the mood questionnaire. Psychol. Rep. 35, 479–484. Saunders, B., Wilkinson, C., Phillips, M., 1995. The impact of a brief motivational intervention with opiate users attending a methadone programme. Addiction 90, 415 – 424. Saxon, A.J., Calsyn, D.A., Blaes, P.A., Haver, V.M., Greenberg, D.M., 1991. Marijuana use by methadone maintenance patients. In: Harris, L. (Ed.), Problems of Drug Dependence 1990 (NIDA Research Monograph 105). United States Government Printing Office, Washington, DC, pp. 306 – 307. Saxon, A.J., Calsyn, D.A., Greenberg, D., Blaes, P., Haver, V.M., Stanton, V., 1993. Urine screening for marijuana among methadone-maintained patients. Am. J. Addict. 2, 207 – 211. Scheier, M.F., Carver, C.S., 1985. Optimism, coping and health: assessment and implications of generalized outcome expectancies. Health Psychol. 4, 219 – 247. Scheier, M.F., Carver, C.S., 1987. Dispositional optimism and physical well-being. The influence of generalized outcome expectancies on health. J. Pers. 55, 169 – 210. Shiffman, S., Paty, J.A., Gnys, M., Kassel, J.D., Hickcox, M., 1996. First lapses to smoking: within-subjects analyses of real time reports. J. Consult. Clin. Psychol. 64, 366 – 379. Silverman, K., Higgins, S.T., Brooner, R.K., et al., 1996. Sustained cocaine abstinence in methadone maintenance patients through voucher-based reinforcement therapy. Arch. Gen. Psychiatry 53, 409 – 415. Simpson, D.D., 1990. Drug abuse treatment for AIDS risk reduction (DATAR): forms manual. Unpublished manual, Texas Christian University Institute of Behavioral Research. Stephens, R.S., Roffman, R.A., Simpson, E.E., 1994. Treating adult marijuana dependence: a test of the relapse prevention model. J. Consult. Clin. Psychol. 62, 92 – 99. Strack, S., Carver, C.S., Blaney, P.H., 1987. Predicting successful completion of an aftercare program following treatment for alcoholism: the role of dispositional optimism. J. Pers. Soc. Psychol. 53, 579 – 584. Tanda, G., Pontieri, F.E., Di Chiara, G., 1997. Cannabinoid and heroin activation of mesolimbic dopamine transmission by a common m1 opioid receptor mechanism. Science 276, 2048–2050. United States General Accounting Office, 1990. Methadone Maintenance: Some Treatment Programs are not Effective; Greater Federal Oversight Needed. Author, Washington, DC. Vuchinich, R.E., Tucker, J.A., 1988. Contributions from behavioral theories of choice to an analysis of alcohol abuse. J. Abnorm. Psychol. 97, 181 – 195. Wolff, K., Hay, A.W., Vail, A., Harrison, K., Raistrick, D., 1996. Non-prescribed drug use during methadone treatment by clinicand community-based patients. Addiction 91, 1699 – 1704.