Predicting in-treatment performance and post-treatment outcomes in methamphetamine users

Predicting in-treatment performance andpost-treatment outcomes in methamphetamine users

Maureen P. Hillhouse, Patricia Marinelli-Casey, Rachel Gonzales, Alfonso Ang,Richard A. Rawson & the Methamphetamine Treatment Project Corporate Authors*University of California, Los Angeles, Semel Neuropsychiatric Institute, CA, USA

ABSTRACT

Aims This study examines the utility of individual drug use and treatment characteristics for predicting in-treatmentperformance and post-treatment outcomes over a 1-year period. Design, setting and participants Data were collectedfrom 420 adults who participated in the Methamphetamine Treatment Project (MTP), a multi-site study of randomlyassigned treatment for methamphetamine dependence. Interviews were conducted at baseline, during treatment andduring three follow-up time-points: treatment discharge and at 6 and 12 months following admission. MeasurementsThe Addiction Severity Index (ASI); the Craving, Frequency, Intensity and Duration Estimate (CFIDE); and laboratoryurinalysis results were used in the current study. Findings Analyses addressed both in-treatment performance andpost-treatment outcomes. The most consistent finding is that pre-treatment methamphetamine use predictsin-treatment performance and post-treatment outcomes. No one variable predicted all in-treatment performancemeasures; however, gender, route of administration and pre-treatment methamphetamine use were significant pre-dictors. Similarly, post-treatment outcomes were predicted by a range of variables, although pre-treatment metham-phetamine use was significantly associated with each post-treatment outcome. Conclusions These findings provideuseful empirical information about treatment outcomes for methamphetamine abusers, and highlight the utility ofassessing individual and in-treatment characteristics in the development of appropriate treatment plans.

Keywords Methamphetamine, predictors, treatment outcome, treatment response.

Correspondence to: Maureen P. Hillhouse, 11075 Santa Monica Blvd, Suite 200, Los Angeles, CA 90025, USA. E-mail: [email protected]

INTRODUCTION

Substantial research provides evidence that treatmentcan be successful [1], but treatment is not always followedby positive outcomes. Many people continue to use drugsand experience associated problems even after multipletreatment episodes. One ongoing issue is whether specificindividual and treatment characteristics can be identifiedthat predict treatment outcome. Of course, the ability tolink specific characteristics with treatment success and,conversely, specific characteristics with treatment failure,will add a new dimension to our ability to provide effectivetreatment by directing efforts to develop new treatmenttools for those resistant to programs, and by enhancingexisting procedures currently utilized.

Previous attempts to identify predictors of treatmentoutcome have resulted in mixed findings. For instance,

although variables such as demographic characteristicshave been shown to differentiate drug-related behaviors,including age of problem onset, pattern of use and treat-ment participation [2], these do not differentiate treat-ment outcome consistently [3,4]. Conversely, a studyexamining treatment deterioration indicates that demo-graphic characteristics assessed at baseline do have pre-dictive value [5]. Other drug use and treatment variables,such as amount of recent drug use at treatment entry [6],treatment retention and completion [7–9] and frequent12-Step participation before and throughout treatment[10–12], have been found to also predict treatmentoutcome successfully. Research continues in attempts todifferentiate predictors of treatment performance andoutcomes.

The recent explosion in methamphetamine use andassociated problems across all areas of the United Statesilluminates the need to investigate factors associated with

*Corporate authors are listed at the end of the paper and those who contributed to this paper declare no conflict of interests.

All authors declare no conflict of interests.

REPORT

© 2007 The Author(s) Journal compilation © 2007 Society for the Study of Addiction Addiction, 102 (Suppl. 1), 84–95

mailto:[email protected]

successful methamphetamine treatment outcomes. Overthe past two decades treatment providers have witnessedsubstantial changes in stimulant abuse in the westernUnited States, as admission rates for methamphetaminehave greatly increased while the incidence of cocaineabuse treatment admissions have declined [13]. Increas-ingly, more methamphetamine-dependent individualsare presenting for drug abuse treatment. Treatmenttrends reported by the Substance Abuse and MentalHealth Services Administration (SAMHSA) [14] between1992 and 2002 demonstrate this shift, showing decreas-ing admission rates for cocaine abuse by 24% and esca-lating rates for methamphetamine by 52% per 100 000individuals aged 12 years or older. The growing numberof methamphetamine abusers in treatment suggests thattreatment providers need to be aware of the factors asso-ciated with in-treatment performance—engagement,retention, abstinence and completion—as well as druguse outcomes.

More recently, a body of literature on methamphet-amine abuse treatment evaluations has developed. Themajority of studies have focused on identifyingindividual-level factors that characterize methamphet-amine users into profiles, with relatively few studiesspecifically examining factors that predict treatment pat-terns of retention and completion [15,16] and treatmentoutcomes [1,17]. Findings from Brecht et al. [15] indicatethat daily methamphetamine use, injection methamphet-amine use, having less than a high school education,young age at treatment admission and having a disabilityare risk factors for poor retention and low treatmentcompletion in both residential and out-patient programs.Findings have also shown that women and men tend tohave similar patterns of treatment retention and comple-tion [16], with women having slightly better treatmentoutcomes, including improved relationships with familyand fewer medical problems compared to men.

Whereas treatment for methamphetamine abuse hasbeen shown to be effective in terms of psychosocial anddrug use improvements [1,13], the predictors of success-ful treatment performance and outcomes remain largelyunderstudied. While recent studies highlight importantindividual-level characteristics associated with treatmentretention and outcomes, there is still a large degree ofuncertainty surrounding factors associated with success-ful and unsuccessful treatment engagement, and otherin-treatment performance behaviors such as abstinenceand how these performance factors can predict long-termbehaviors after treatment. Moreover, there is a lack ofresearch investigating factors associated with drug-usefollow-up trajectories in terms of predicting patterns ofdrug use such as ‘methamphetamine abstinence’, ‘returnto methamphetamine use’ and alternating periods ofmethamphetamine use and abstinence after treatment.

These types of data are important for understanding thefactors associated with treatment success and failure,especially given the increasing number of treatmentadmissions for methamphetamine abuse across theUnited States. Hence, there is an urgent need to under-stand and identify factors associated with treatment out-comes of methamphetamine-dependent populations inorder to tailor and improve treatment planning.

The Methamphetamine Treatment Project (MTP)provides data to examine potential predictors ofin-treatment performance and post-treatment outcome.From 1998 to 2001, the MTP was funded through SAM-HSA’s Center for Substance Abuse Treatment (CSAT) toinvestigate methods of treatment for those dependent onmethamphetamine. To date, this project is the largest ran-domized clinical trial of psychosocial treatments formethamphetamine dependence [1]. The current analysesutilize follow-up data from the MTP to investigate base-line predictors of in-treatment performance (treatmentengagement, retention, abstinence and completion), aswell as post-treatment outcomes.

METHODS

Participants and treatment sites

Participants were 420 methamphetamine-dependentindividuals who received standardized out-patient treat-ment using the Matrix Model as part of the CSAT-fundedMTP between 1999 and 2001. Treatment centers werelocated in Northern and Southern California and Hawaii.Participants were eligible for this study if they met Diag-nostic and Statistical Manual version IV (DSM-IV) criteriafor methamphetamine dependence; were current meth-amphetamine users (used in the month before treatmententry); had English language proficiency; were aged 18years or older; and resided in the same geographical loca-tion as the treatment facility. Exclusion criteria includedhaving a serious medical or psychiatric health conditionthat required imminent hospitalization or pregnancy.Upon intake, participants who were diagnosed with amedical/psychiatric illness or were found to be pregnantwere provided with referrals to other types of treatmentservices that would meet these more specialized needs, asthese individuals were more likely to need treatmentoptions that were not available to the study sample.

Procedures

The Matrix Model

Participants received intensive out-patient treatment formethamphetamine dependence using the Matrix Model.The multi-component treatment approach has beenorganized into a manualized treatment protocol that

Treatment outcomes for methamphetamine users 85


includes 16 weeks of thrice-weekly sessions. The MatrixModel includes cognitive behavioral therapy groups (36sessions), family education groups (12 sessions), socialsupport groups (four sessions) and individual counseling(four sessions) combined with weekly breath alcoholtesting and urine testing. Weekly or more frequent atten-dance at 12-Step meetings is also encouraged. All treat-ment sessions are delivered using a non-judgmental,non-confrontational style, and employ extensive positivereinforcement for behavior change by therapists andpeers.

Data collection procedures

Data used for analyses include baseline (1 week beforetreatment entry), in-treatment performance and post-treatment follow-up interviews collected at discharge, 6months and 12 months following treatment admission.All participants completed a standardized battery of psy-chosocial assessments at each time-point (see Huber et al.[18] for a detailed description of study measures). Thestudy was conducted under the review and approval ofthe Institutional Review Board (IRB) of the University ofCalifornia, Los Angeles, and the IRBs of each treatmentand research site.

Data used in this study were organized into threecategories: baseline characteristics, in-treatment perfor-mance, and post-treatment outcomes. Two major sets ofanalyses were conducted to clarify the complex relation-ships between these categories: (1) baseline characteris-tics predicting in-treatment performance and (2) baselinecharacteristics and in-treatment performance predictingpost-treatment outcomes.

Measures

Measures used for this investigation include the Addic-tion Severity Index (ASI) 5th edition [19], the CravingFrequency, Intensity and Duration Estimate (CFIDE) [20]and urinalysis results collected during treatment and atpost-treatment follow-up visits.

The ASI is a psychometrically validated instrument fordetermining drug-associated problems among drug-abusing populations. The assessment includes informa-tion on the nature, number and severity of seven differentlife domains: drug, alcohol, employment, family/social,legal, medical and psychiatric [21]. Individual items fromthe ASI that measured current methamphetamine use,life-time methamphetamine use, polydrug use, route ofadministration, previous drug treatment, depression, sui-cidality and criminality were used. Self-reported meth-amphetamine use collected at post-treatment follow-upinterviews from the ASI was also used.

The CFIDE, developed at the Haight Ashbury FreeClinics, is a five-item measure designed to measure the

intensity of methamphetamine cravings, as well as howcravings for methamphetamine change over time afterpeople quit using and whether the type of treatmentthey receive affects craving. Craving intensity during thepast 24 h is measured using a 100-mm visual analogscale and is relative to each participant’s own previousexperience. The scale is anchored on the left with ‘nocraving’ and on the right with ‘most craving everexperienced’.

Objective assessment of methamphetamine use wasobtained by urine samples collected once-weekly fromparticipants during treatment and at each post-treatmentfollow-up contact point (discharge, 6-month and12-month follow-up). Samples were sent to an off-sitecontract laboratory and were analyzed for the presence ofmethamphetamine metabolites.

Baseline characteristics

Baseline demographic characteristics used as predictorsin analyses include gender, ethnicity and educationalattainment. Other variables include depression, suicidal-ity and criminal status (charged with a criminal activity).Drug use variables were also examined. These includemethamphetamine use of � 15 days in the month beforeintake; polydrug use of � 15 days in the month beforeintake; life-time methamphetamine use of 2 + years;cravings for methamphetamine at intake; number of pre-vious drug treatment episodes; and route of drug admin-istration including intranasal (snort), inhalation (smoke)and intravenous (inject). Many of these variables are usedroutinely in drug abuse studies that have been shown topredict in-treatment performance and post-treatmentoutcomes [6,16].

In-treatment performance characteristics

The in-treatment performance variables used in analysesinclude engagement, retention, abstinence and comple-tion. Each of these variables, with the exception ofcompletion, was examined using multiple measures.

Engagement. In order to differentiate between immediatedrop out and early dropout, treatment engagement wasmeasured by two variables: (1) dropout within the first 2weeks of treatment and (2) dropout within the first 30days of treatment. While the first 2 weeks of treatmenthas been cited as being a critical window for engagingdrug users in treatment [22], anecdotal clinical impres-sions suggest that the initial month (30 days) of enteringtreatment may be important. Hence, we sought toinvestigate if there were any differences with respect totreatment engagement as defined by these two differenttime-periods (2 weeks versus 1 month).

86 Maureen P. Hillhouse et al.


Retention. Treatment retention was measured by two dif-ferent variables: (1) total number of weeks in treatment(16 weeks possible) and (2) a dichotomous classificationof staying in treatment less than 90 days and 90 days orlonger. It is important to assess treatment retention usingthese two different measures because longer duration oftreatment has been one of the most consistent and impor-tant predictors of favorable treatment outcomes [23],and retention in treatment longer than 90 days has beensuggested as a benchmark for receiving an adequate‘dose’ of a treatment episode [24].

Abstinence. Methamphetamine use during treatmentwas measured by two different variables to assess theextent to which participants’ attained levels of abstinenceduring the 16-week treatment regimen. Variablesinclude: (1) the mean number of methamphetamine-freeurinalysis tests collected during treatment (1 per week,16 possible) and (2) the occurrence of three consecu-tive methamphetamine-free urinalysis results duringtreatment.

Completion. Treatment completion was measured as adichotomous variable defined as those who completed the16-week Matrix Model with no more than two consecu-tive missed weeks of treatment versus those who did notcomplete treatment.

Post-treatment outcomes

Post-treatment outcome measures include: (1) absti-nence as documented by self-reported methamphet-amine use at discharge, 6-month, and 12-monthfollow-up interviews and (2) methamphetamine-freeurine samples also collected at the three follow-up time-points. The self-reported measure was obtained with theASI: ‘In the past month, how many days did you usemethamphetamine?’, which could range from 0 to 30days. Urinalysis data were dichotomized into positive andnegative results at each time-point. In addition, wecreated another post-treatment outcome variable (usingself-reported methamphetamine use) to distinguishbetween three categories of methamphetamine use pat-terns: (1) ‘methamphetamine free’ at each follow-uptime-point (discharge, 6-month, 12-month), (2) ‘meth-amphetamine use’ at each time-point (discharge,6-month, 12-month) and (3) ‘mixed methamphetamineresults’ among time-points (discharge, 6-month,12-month). These categories serve as proxy measures forparticipants who remained methamphetamine free,returned to methamphetamine use or struggled with thechronic nature of addiction alternating between absti-nence and relapse in the year following treatment.

RESULTS

Baseline characteristics

The clinical assessment of substance use disorders usingDSM-IV was performed at baseline on all participants and100% met the criteria for methamphetamine depen-dence. Approximately half the 420 participants werefemale. Most of the participants were Caucasian, with aminority of Hispanic, Asian, African American and othergroups. The median age of the sample was mid-30s,ranging from 18 to 57 years. The majority of the partici-pants worked full-time or part-time and had a high schooleducation. In terms of marital status, many of the par-ticipants were either single/not married or divorced/separated, and most lived with family and/or friends.There were many participants who indicated experienc-ing depression and suicidal ideation in the last month.Many of the participants also reported having beencharged with engaging in an illegal activity in their life-time and having a current parole or probation legalstatus. For the entire sample, frequency of methamphet-amine use at baseline averaged approximately 12 daysand history of life-time methamphetamine use averagedabout 8 years. The most common route of methamphet-amine administration was smoking, followed by injectingand intranasal use. Roughly half the participants had aprevious history of substance abuse treatment. Table 1presents demographic, drug use and in-treatment char-acteristics. Sample characteristics from the larger MTPhave been reported previously [1].

In-treatment performance characteristics

Most of the participants were deemed immediate treat-ment engagers, with 67.1% of the sample remaining intreatment longer than 2 weeks. Furthermore, 56%remained in treatment longer than 1 month (4 weeks).The total expected treatment length for the sample was16 weeks. The average length of treatment stay averaged7.87 � 6.6 weeks. The percentage of participants thathad good retention as indicated by a treatment stay of 90days or longer was 35%. Urine samples were requiredfrom every participant each week during the course ofthe 16-week treatment. The mean number ofmethamphetamine-free urine samples collected from par-ticipants was 4.79 � 5.86. The percentage of partici-pants who provided three consecutive (in weeks) drug-free urine samples during the course of treatment was45%. The proportion of participants who completed thefull 16-week treatment regimen (i.e. treatment compl-eters) was 33.3%.

Predictors of in-treatment performance

Logistic regression for binary outcomes and linear regres-sion for continuous outcomes were performed to test



for predictors of in-treatment performance. Table 2describes regression analyses for predictors of engage-ment, retention, abstinence and completion.

Immediate treatment engagement (2-week)

Controlling for all baseline factors, analyses showed thatgender, frequency of baseline methamphetamine use,life-time methamphetamine use and route of administra-tion were significant predictors of treatment engagementduring the initial 2 weeks of treatment entry, accountingfor 8.5% of the variance. Specifically, being female(B = -0.638, df = 1, P = 0.013), using methamphet-amine for 15 days or greater at baseline (B = -0.745,df = 1, P = 0.004), having a life-time history ofmethamphetamine use for less than 2 years (B = 0.616,df = 1, P = 0.043) and smoking methamphetamine(B = -0.999, df = 1, P = 0.029) were significant predic-tors of poor treatment engagement.

Treatment engagement (30-day)

We further explored predictors of treatment engagementusing a 30-day window. Similar to findings that predictedimmediate treatment dropout, results showed that femalegender and greater frequency of baseline methamphet-amine use were significant predictors of treatmentengagement during the first month of treatment. Depres-sion also became a significant predictor of poor engage-ment during the initial month of treatment(r2 = 0.108%).

Treatment retention (weeks)

We further explored predictors of treatment retention asmeasured by weeks in treatment using linear regression.The model (F = 50.38, df = 19, P < 0.001) explained73.2% of the variance. Results show that greater fre-quency of methamphetamine use at baseline and meth-amphetamine use during treatment were significantpredictors of shorter length of treatment stay.

Treatment retention (90-day)

Controlling for all baseline factors, logistic regressionanalyses showed that route of administration andin-treatment methamphetamine use (methamphetaminepositive for three consecutive time-points) were signifi-cant predictors of treatment retention using the 90-daybenchmark. Explaining 39.6% of the variance, injectionmethamphetamine use (B = -1.136, df = 1, P = 0.049)and not having three consecutive drug-free urine testsduring treatment (B = 3.571, df = 1, P < 0.001) were sig-nificant predictors of poor treatment retention defined by90 days. Two predictors of poor treatment retention that

Table 1 Sample characteristics (n = 420).

% (Mean � sd)

Baseline characteristicsFemale 53.5EthnicityCaucasian 55.7Hispanic 22.1Asian 17.9African American 2.1Other 2.1Age 33.4Employed 69.2High school education 47.1Marital statusSingle 49.3Divorced/separated 31.9Living with family/friends 45.8Charged with criminal activity 79.8On parole/probation 39.4Depressed in last 30 days 30.0Suicidal in last 30 days 6.4Life-time methamphetamine

use (years)7.65 � 6.0(range 1–32)

Methamphetamine use(days in month before intake)

11.8 � 9.6

Polydrug use (month beforeintake)

4.29 � 6.7(days)

Methamphetamine use (�15days in past month)

39

Polydrug use (�15 days inpast month)

10.5

Life-time methamphetamineuse (2 + years)

79.3

Craving for methamphetamine(baseline)

20

Previous drug abusetreatment

52

Methamphetamine route of administrationSmoking 69.5Intravenous 16.8Snorting 13.7

In-treatment performance characteristicsTreatment stay length

(16 week program)Retention of 90 + days 35EngagementStayed in tx longer than

2 week67.1

Stayed in tx longer than4 week

56

Completed 16 weektreatment

33.3

Mean methamphetamine freeUA samples

4.79 � 5.86

Three consecutivemethamphetamine freeUAs during treatment

45



Tabl

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Reg

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ion

mod

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ent

perf

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ance

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plet

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men

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k)B

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agem

ent

(30

-day

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eeks

)B

Ret

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on(9

0-d

ay)

B

Abs

tine

nce:

(mea

nm

etha

mph

etam

ine

free

urin

ete

sts

duri

ngtr

eatm

ent)

B

Abs

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nce:

thre

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nsec

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etha

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free

urin

ete

sts

duri

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ent)

B

Com

plet

ion

(16

wee

ks)

B

Gen

der:

fem

ale

-0.6

38

**-0

.65

3**

-0.4

45

-0.0

13

-1.0

90

-0.5

99

**-0

.10

6R

ace1

Cau

casi

an1

.05

10

.86

6-0

.28

5-0

.93

12

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61

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21

8.6

03

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.26

21

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.23

9-1

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22

.95

51

.91

01

8.7

75

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pan

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.92

40

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72

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8.5

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-0.3

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approached statistical significance (P < 0.10) includednot having a high school degree and smoking route ofadministration.

Abstinence (mean number of methamphetamine-free urinetests)

Controlling for all baseline factors, linear regressionanalyses found that baseline methamphetamine use androute of administration were significant predictors ofin-treatment abstinence as measured by the mean

number of methamphetamine-free urine tests providedduring the 16-week treatment stay using linear regres-sion. Explaining 14.3% of the variance (F = 3.443,df = 17, P < 0.001), methamphetamine use � 15 days(B = -3.68, P < 0.000), methamphetamine smoking(B = -1.919, P = 0.041) and injection use (B = -2.286,P = 0.049) significantly predicted methamphetamine useduring treatment. Female gender approached statisticalsignificance (P = 0.075).

Abstinence (three consecutive methamphetamine-free urinetests)

We further explored predictors of in-treatment metham-phetamine use as measured by providing three consecu-tive methamphetamine-free urine tests using logisticregression. Explaining 15.3% of the variance, femalegender, methamphetamine use of 15 days or greater atbaseline, as well as smoking and injection routes ofmethamphetamine use significantly predicted not beingable to provide three consecutive methamphetamine-freeurines during treatment.

Treatment completion

Controlling for all baseline factors and in-treatment per-formance factors, regression analyses found that treat-ment completion was associated with having a highschool diploma (B = -0.816, df = 1, P = 0.049), having alife-time history of methamphetamine use for 2 + years(B = 1.517, df = 1, P = 0.005) and providing three con-secutive methamphetamine-free urine samples duringtreatment (indicative of abstinence). Treatment non-completion was associated significantly with smoking(B = -0.858, df = 1, P = 0.05) and injection drug use(B = -1.421, df = 1, P = 0.033). Not having previoustreatment episodes approached statistical significance(P < 0.10) in terms of predicting treatment completion.

Predictors of post-treatment outcomes

Repeated-measures analysis

Predictors of post-treatment methamphetamine use out-comes over time, as measured by self-report and urinaly-sis, was modeled by multivariate repeated-measuresmixed-effects models (RMMEMs) [25] (see Table 3). Thesame baseline and in-treatment variables used in regres-sion analyses were selected for predicting methamphet-amine use outcomes at post-treatment follow-up pointsin the final multivariate repeated-measures models.

Overall, methamphetamine use significantlydecreased over time from treatment discharge to the12-month follow-up period (rate of change = -0.015,P < 0.001). Results indicate that the most consistent

Table 3 Longitudinal mixed models of post-treatment metham-phetamine use.

Estimate SE P-value

Gender: female -0.34 0.57 0.55Race1

Caucasian 2.20 1.79 0.21Hispanic 0.98 2.75 0.72Asian 4.42 1.86 0.02*

Education2

< High school -0.053 0.79 0.95High school graduate -0.63 0.63 0.32

Craving formethamphetamine

0.73 0.70 0.29

Polydrug use (�15 days inpast month)

0.17 0.93 0.85

Methamphetamine use(�15 days in past month)

5.53 1.49 0.001*

Life-time methamphetamineuse (2 + years)

2.27 0.72 0.002*

Previous drug abusetreatment

0.86 0.57 0.13

Route3

Smoke 0.44 0.87 0.61Inject 2.57 1.07 0.02*

Depressed in last 30 days 1.59 0.66 0.02*Charged with criminal

activity-1.10 0.68 0.11

Suicidal in last 30 days 1.03 1.14 0.37Engagement

Stayed in tx > 2 weeks -1.11 1.36 0.42Stayed in tx > 4 weeks 2.16 1.46 0.14

Retention (in weeks) -0.019 0.23 0.93Retention (90 + days) -0.11 1.92 0.95Completed treatment -1.93 0.87 0.03*Three consecutive

methamphetamine freeUAs during tx

-3.32 0.82 0.001*

Rate of change ofmethamphetamine use(15 + days)

-1.25 0.63 0.05*

Rate of change inmethamphetamine useafter discharge

-0.015 0.004 0.001*

*Significant at P < 0.05; 1reference: African American; 2Reference: � 2years post-high school; 3reference: intranasal (snort).



predictor of methamphetamine use after treatment (atdischarge, 6 months and 12 months) is methamphet-amine use � 15 days prior to treatment entry (rate ofchange = -1.25, P = 0.046). Other significant baselinepredictors of post-treatment methamphetamine use out-comes include: injection drug use (B = 2.57, P = 0.017),having previous drug abuse treatment (B = 0.86,P = 0.002), life-time methamphetamine use of 2 + years(B = 5.53, P < 0.001), depression (B = 1.59, P = 0.016)and Asian ethnicity (B = 0.98, P = 0.018). Furthermore,results show that in-treatment performance significantlyinfluenced post-treatment methamphetamine outcomes.Specifically, patients who had in-treatment abstinence, asmeasured by three consecutive methamphetamine-freeurine tests during treatment (B = -3.32, P < 0.001), andcompleted treatment (B = -1.93, P = 0.028) were lesslikely to use methamphetamine after discharge.

Predictors of post-treatment methamphetamine usepatterns

Multinomial logit model analysis

Multinomial logit regression was used to determine theextent to which baseline and in-treatment performancevariables were associated with three different metham-phetamine use patterns: (1) methamphetamine free atdischarge, 6-month and 12-month follow-up, (2) meth-amphetamine use at discharge, 6-month and 12-monthfollow-up and (3) mixed methamphetamine results at dis-charge, 6-month and 12-month follow-up (see Table 4).The validity of independence of irrelevant alternativesassumption was confirmed using Hausman [26] andSmall–Hsiao tests [27]. Relative risks (rr) were calculatedand the 95% empirical confidence intervals for the rela-tive risks were derived with standard bootstrapping

Table 4 Multinomial logit regression of patterns of methamphetamine use after discharge (discharge, 6 and 12 months).

Methamphetamine use patternrelative to methamphetaminefree

Mixed methamphetamineresults pattern relative tomethamphetamine free

Methamphetamine freepattern relative tomethamphetamineuse

Female 1.32 (0.55, 3.21) 0.93 (0.47, 1.81) 0.75 (0.31, 1.81)Race1

Caucasian 0.44 (0.47, 4.07) 0.98 (0.18, 5.41) 2.28 (.24, 21.23)Hispanic 1.12 (0.11, 11.80) 0.89 (0.14, 5.84) 0.89 (0.84, 9.39)Asian 0.87 (0.08, 9.43) 0.84 (0.13, 5.35) 1.14 (0.11, 12.32)

Education2

< High school 0.83 (0.25, 2.71) 0.75 (0.29, 1.93) 1.21 (0.37, 3.95)High school graduate 0.76 (0.29, 1.94) 1.27 (0.63, 2.60) 1.32 (.42, 3.39)

Craving for methamphetamine 1.08 (0.40, 2.90) 1.28 (0.56, 2.89) 0.93 (0.34, 2.49)Polydrug use (�15 days in past month) 1.49 (0.39, 5.71) 2.36 (0.75, 7.48) 0.87 (0.17, 2.57)Methamphetamine use (�15 days in

past month)4.09 (1.61, 10.38)* 0.83 (0.39, 1.77) 0.24 (0.09, 0.62)*

Life-time methamphetamine use(2 + years)

4.33 (1.16, 16.27)* 1.65 (0.75, 3.64) 0.23 (0.06, 0.87)*

Previous drug abuse treatment 2.33 (1.03, 5.46)* 2.15 (1.11, 4.16)* 0.43 (0.18, .99)*Route3

Smoke 2.85 (0.52, 15.89) 0.81 (0.32, 2.06) 0.35 (0.06, 1.95)Inject 3.09 (0.47, 20.05) 0.56 (0.17, 1.83) 0.32 (0.49, 2.10)

Depressed in last 30 days 1.34 (0.50, 3.59) 1.14 (0.52, 2.49) 0.74 (0.27, 1.99)Charged with criminal activity 0.70 (0.26, 1.91) 1.37 (0.62, 3.02) 1.42 (0.52, 3.86)Suicidal in last 30 days 1.77 (0.34, 9.28) 1.41 (0.36, 5.58) 0.56 (0.11, 2.94)Engagement

Stayed in tx > 2 weeks 4.78 (0.26, 8.87) 0.62 (0.09, 4.28) 0.21 (0.01, 3.897)Stayed in tx > 4 weeks 1.22 (0.17, 8.77) 0.86 (0.15, 4.94) 0.82 (0.11, 5.85)

Retention (in weeks) 1.21 (0.88, 1.67) 1.13 (0.84, 1.51) 0.82 (0.59, 1.14)Retention (90 + days) 0.14 (0.01, 2.14) 0.59 (0.49, 7.07) 7.06 (0.47, 16.9)Completed treatment 0.39 (0.09, 1.57) 0.38 (0.15, 0.99)* 2.58 (0.63, 10.44)Three consecutive methamphetamine

free UAs0.10 (0.03, 0.33)* 0.17 (0.06, 0.48)** 9.77 (3.04, 31.44)*

*Significant at P < 0.05. Relative risks and (in parentheses) 95% bias-corrected bootstrapped confidence intervals shown. 1Reference: African American;2reference: � 2 years post-high school; 3reference: intranasal (snort).



methods with replacement, using 1000 replicate samples[28].

Predictors of a methamphetamine-free pattern at dis-charge, 6-month, and 12-month follow-up included notusing methamphetamine at baseline for � 15 days (rela-tive risk (rr) = 0.24, 95% CI 0.09, 0.62); not having pre-vious drug abuse treatment (rr = 0.43, 95% CI 0.18,0.99); life-time methamphetamine use of less than 2years (rr = 0.23, 95% CI 0.06, 0.87) and providing threeconsecutive methamphetamine-free UAs during treat-ment (rr = 9.77, 95% CI 3.04, 31.44), relative to thosewho had a methamphetamine use pattern.

Results show that participants who reported metham-phetamine use at every follow-up interview (discharge,6-month and 12-month) had greater methamphetamineuse severity. This was indicated by methamphetamine useat baseline � 15 days (rr = 4.09, 95% CI 1.61, 10.38);methamphetamine use of 2 + years before treatment(rr = 4.33, 95% CI 1.16, 16.27); previous drug abusetreatment (rr = 2.33, 95% CI 1.03, 5.46); and less likelyto provide three consecutive methamphetamine-free UAsduring treatment (rr = 0.10, 95% CI 0.03, 0.33) relativeto those who were methamphetamine free at each time-point. Participants who had mixed methamphetamineresults (i.e. methamphetamine use and methamphet-amine free results at discharge, 6-month and 12-monthfollow-up) were more likely to have had previous drugabuse treatment (rr = 2.15, 95% CI 1.11, 4.16); lesslikely to have had three consecutive methamphetamine-free UAs during treatment (rr = 0.17, 95% CI 0.06,0.48); and less likely to have completed the 16 weektreatment regimen (rr = 0.38, 95% CI 0.15, 0.99), com-pared to those who had a methamphetamine free pattern.

DISCUSSION

Because treatment outcome may be successful, unsuc-cessful or somewhere between these two extremes, theability to predict who will do well or poorly can be animportant tool in developing and implementing client-specific treatment programs. The current study bothinvestigated pre-treatment factors as predictors ofin-treatment performance and pre-treatment andin-treatment performance factors as predictors oftreatment outcome in methamphetamine-dependentadults—a rapidly growing group of drug users.

In examining predictors of in-treatment performance,the results of these multivariate analyses indicate thatsome variables played a consistent role in many of thein-treatment performance measures, whereas otherfactors had differential effects. Poor treatment engage-ment was associated with being female; using metham-phetamine for � 15 days in the 30 days before intake;reporting life-time methamphetamine use of less than 2

years; smoking route of methamphetamine use; andreporting baseline depression. Shorter length of treat-ment stay (retention) was associated with methamphet-amine use of � 15 days in the 30 days before baseline;injection drug use; and methamphetamine use duringtreatment. Predictors of non-abstinence during treat-ment include being female; methamphetamine of � 15days in the 30 days before intake; and smoking or inject-ing methamphetamine. Not completing treatment wasassociated with life-time methamphetamine use of lessthan 2 years; smoking or injecting methamphetamine;and methamphetamine use during treatment.

In-treatment performance factors were significantpredictors of post-treatment outcomes. Poor post-treatment outcome was associated with methamphet-amine use of � 15 days in the 30 days before intake;life-time methamphetamine of 2 + years; injecting meth-amphetamine; baseline depression; methamphetamineuse during treatment; not completing treatment; andbeing of Asian ethnicity. Those who maintained absti-nence and successfully completed treatment were lesslikely to use methamphetamine after discharge.

These analyses also document predictors of diversemethamphetamine use patterns across the follow-uptime-points. Three patterns were examined: metham-phetamine free described no methamphetamine use overthe follow-up; methamphetamine use describes use at allthree time-points; and mixed methamphetamine resultsdescribes a pattern of use and non-use over the follow-uptime-points. Predictors of a methamphetamine free usepattern include less pre-treatment methamphetamineuse; less life-time methamphetamine use; abstinenceduring treatment; and fewer previous drug treatment epi-sodes. The methamphetamine use pattern was associatedwith more pre-treatment methamphetamine use; moremethamphetamine use during treatment; and more pre-vious drug treatment episodes.

The findings from the present study have importantclinical applications and can provide insight for treat-ment providers, alerting them to several specialconsiderations when working with methamphetamine-dependent clients. Many of the significant predictors ofpoor in-treatment performance and post-treatment out-comes include socio-demographic factors and drug useseverity indicators that participants bring with themwhen entering treatment. These factors cannot bechanged, although they may make individuals more vul-nerable to poor treatment response. The current findings,however, indicate that there are a number of factors thatcan be altered during treatment that are associated withsuccessful post-treatment outcomes. These include meth-amphetamine abstinence during treatment and complet-ing the treatment program. Efforts at developingstrategies that help individuals attain and maintain



abstinence during treatment and meet completion goalsshould be the focus of clinical interventions to improvetreatment outcome. Successful treatment engagementand retention need to be addressed as necessary steps toaccomplish improvements in in-treatment abstinenceand treatment completion.

Some factors related to poor treatment engagementand retention can be addressed by the treatment providerand include being female and experiencing depression.Incorporating gender-specific elements into a treatmentprogram has been shown to improve its effectiveness [2].Similarly, addressing the mental health needs of indi-viduals may improve treatment outcomes [5].

Particularly interesting is the role played by genderin predicting in-treatment performance in thismethamphetamine-dependent sample in light of otherresearch findings showing that gender does not predictperformance in alcohol and cocaine using samples [29].These findings suggest that there may be important dif-ferences in specific predictor variables between metham-phetamine and other drugs of abuse. While we know thata greater percentage of females use methamphetaminecompared to other drugs [30], it appears that theremay also be important treatment differences betweenmethamphetamine-using and other drug-using females.

Route of methamphetamine use administrationdeserves special attention when individuals enter treat-ment, as it is often used as a proxy for severity of use andhas been associated with health status, especially infec-tious disease status [31,32]. Route of administration canbe used to determine if referrals for additional medicalservices are necessary or what level of care is appropriate.Our findings indicate that individuals who smoke meth-amphetamine have more difficulty engaging and stayingin treatment; however, methamphetamine smoking didnot appear to be a predictor of poor post-treatment out-comes. Similar to other findings from other studies [33],these results document that injection drug users are atrisk for poor post-treatment outcomes. Residential treat-ment for injection methamphetamine users may there-fore improve health status and treatment outcome, asmedical services are more likely to be incorporated intothe residential treatment setting. Out-patient substanceabuse treatment may be best limited to methamphet-amine users who snort or smoke the drug.

These analyses also offer supporting evidence for find-ings of previous studies. For example, use of metham-phetamine in both in-treatment performance andpost-treatment outcomes is predicted in this study by pre-treatment methamphetamine use. That previous druguse predicts future drug use is often found in studies uti-lizing a pre- and post-treatment design [6]. In anotherexample, craving was included as a predictor variable, butwas not found to predict any of the in-treatment

performance or post-treatment outcome variables. Otherinvestigations that have tested craving as a predictorvariable also report that the variable washes out in themodel [29].

To date, few studies have examined methamphet-amine treatment effectiveness. Much of the existingresearch on methamphetamine abuse has examinedfactors related to achieving successful treatment perfor-mance such as retention and completion [15], as well astreatment outcomes [1,13,16]. Few studies have investi-gated the extent to which in-treatment performancefactors, such as engagement, retention, abstinence andcompletion, predict post-treatment outcomes over andbeyond basic socio-demographic characteristics. Whileprevious studies highlight important findings for under-standing treatment prognoses, the current study wasundertaken to expand upon these studies by filling insome of the existing gaps in the literature. In addition toexamining in-treatment performance factors, this studyalso investigated predictors of methamphetamine usepatterns after treatment by categorizing methamphet-amine users into different post-treatment use patterns.Distinguishing factors associated with these patterns isuseful for targeting specific high-risk subpopulations ofmethamphetamine users and understanding factorsassociated with treatment success and failure, which canbe used to inform assessment decisions and treatmentplanning.

Overall, this study highlights the fact that clinicalefforts should be targeted at engaging and retainingclients in treatment and, importantly, also helping clientsattain total abstinence and meet completion goals.

In presenting these findings, it is important to recog-nize some of the unique characteristics of this data aspossible study limitations. The first is that, aside fromobjective UA results, many of these data are obtainedfrom self-report. Some investigators have concluded thatunder-reporting of undesirable behaviors, particularly indrug use samples, is commonplace and may compromisethe validity of these results. More recent research,however, has shown that self-reports of drug use areaccurate, and that accuracy of self-report is improvedwhen confidentiality is guaranteed, as was the case in thisstudy [34–36]. Furthermore, UA specimens were col-lected at each follow-up time-point which also increasesthe accuracy of the corresponding self-report [37].

The second potential limitation to this study isthat the sample was not selected randomly, callinginto question the generalizability of these findings.The methamphetamine-dependent study sample wastreatment-seeking, which may indicate a sampleselection issue. Individuals who are ‘at the end of theirrope’, that is, have more severe drug use, depression andpoorer social functioning, may be more likely to seek



treatment. Although severe drug use may precipitatetreatment entry, Rounsaville & Kleber [38] found that itwas not heavy drug use per se that motivated drug usersto seek treatment; rather, it was associated social, legaland psychological problems.

In another generalizability issue, the study includedseven treatment sites located in distinct geographic loca-tions that were selected due to high rates of methamphet-amine use and related problems. As such, observedparticipant characteristics and methamphetamine usepatterns may be unique to these geographical areas. Forinstance, two treatment programs were initially women-only programs, which may potentially inflate the numberof women participants compared to recruitment intypical community treatment programs. Some treatmentprograms also attracted members of minority racial/ethnic groups because of their geographic location andsupporting community. Importantly, the route of meth-amphetamine use was correlated highly with treatmentsite, suggesting that co-linearity exists between the twovariables (site and route of administration) and that thesetwo variables serve as proxy measures for one another.Because route of methamphetamine use was tied inher-ently to geographic differences, we opted to control forroute in the analyses to address some of the potentialnesting effect issues.

Despite these limitations, this study has severalstrengths. Foremost, the study identified useful informa-tion associated with both successful in-treatment perfor-mance and post-treatment outcomes. These findingsprovide important information about the need to maxi-mize positive treatment experiences, in order to maximizepositive treatment outcome. This research also docu-ments the fact that predictors of in-treatment perfor-mance and treatment outcome may be different frompredictors of other drugs of abuse, and that these predic-tors may not be the same across different performanceand outcome variables. The clinical ramifications of thisresearch suggest methods for improving treatment plansfor methamphetamine-dependent individuals. Finally,this study offers useful findings based on a large multi-sitesample of methamphetamine-dependent individuals whoparticipated in standardized treatment and were followedregularly for 12 months.

Corporate authors

M. Douglas Anglin PhD, Richard A. Rawson PhD, PatriciaMarinelli-Casey PhD, Joseph Balabis BA, RichardBradway, Alison Hamilton Brown PhD, Cynthia BurkePhD, Darrell Christian PhD, Judith Cohen PhD, MPH,Florentina Cosmineanu MS, Alice Dickow BA, MelissaDonaldson, Yvonne Frazier, Thomas E. Freese PhD,Cheryl Gallagher MA, Gantt P. Galloway PharmD, VikasGulati BS, James Herrell PhD, MPH, Kathryn Horner BA,

Alice Huber PhD, Martin Y. Iguchi PhD, Russell H. LordEdD, Michael J. McCann MA, Sam Minsky MFT, Pat Mor-risey MA MFT, Jeanne Obert MFT MSM, Susan PennellMA, Chris Reiber PhD, MPH, Norman Rodrigues Jr, JaniceStalcup MSN, DrPH, S. Alex Stalcup MD, Ewa S. StamperPhD, Janice Stimson PsyD, Sarah Turcotte Manser MA,Denna Vandersloot Med, Ahndrea Weiner MS, MFT,Kathryn Woodward BA, Joan Zweben PhD.

Acknowledgements

The authors would like to thank the treatment andresearch staff at the participating community-basedcenter sites, as well as acknowledge the support of thestudy investigators in each region. The research pre-sented in this paper was supported by grants numbers TI11440–01, TI 11427–01, TI 11425–01, TI 11443–01,TI 11484–01, TI 11441–01, TI 11410–01 and TI11411–01, provided by the Center for Substance AbuseTreatment (CSAT), Substance Abuse and Mental HealthServices Administration (SAMHSA), US Department ofHeath and Human Services.

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