See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/12897473 Identifying methadone maintenance clients at risk for poor treatment response: Pretreatment and early progress indicators Article in Drug and Alcohol Dependence July 1999 Impact Factor: 3.42 DOI: 10.1016/S0376-8716(98)00176-8 Source: PubMed CITATIONS 49 READS 27 3 authors, including: Andrew Morral RAND Corporation 84 PUBLICATIONS 2,210 CITATIONS Martin Y Iguchi RAND Corporation 112 PUBLICATIONS 3,098 CITATIONS SEE PROFILE SEE PROFILE Available from: Martin Y Iguchi Retrieved on: 10 May 2016
Drug and Alcohol Dependence 55 (1999) 25 33 Identifying methadone maintenance clients at risk for poor treatment response: pretreatment and early progress indicators Andrew R. Morral a, *, Mark A. Belding b, Martin Y. Iguchi a a Drug Policy Research Center (RAND), PO Box 2138, 1700 Main Street, Santa Monica, CA 90407-2138, USA b Uni ersity of Pennsyl ania, Pennsyl ania, USA Received 31 July 1998; accepted 10 October 1998 Abstract Exhaustive searches have uncovered few demographic or other pretreatment factors that reliably predict performance in substance abuse treatments. In this study we evaluate whether early treatment response offers improved prediction of treatment response 6 and 9 months later. New admissions to methadone maintenance treatment (n=59) were dichotomized into outcome groups based on treatment retention and ongoing drug use as revealed by urinalysis results 6 and 9 months after admission. Regression analyses revealed two early (week 2) performance measures, counseling attendance and opiate abstinence, could be used to correctly classify the outcomes of more than 80% of the sample. Strikingly, of the 20 participants who neither submitted an opiate-negative urine sample in week 2 nor attended at least two scheduled counseling sessions by that time, not one achieved a superior 6-month outcome. The odds of having a superior outcome increased considerably for those who submitted two opiate negative urine samples and attended two counseling sessions by week 2. Thus, 6-month outcomes were well predicted by treatment performance in week 2. Similar results are reported for month 9 outcomes. 1999 Elsevier Science Ireland Ltd. All rights reserved. Keywords: Substance abuse treatment; Treatment outcomes; Prognosis; Methadone maintenance therapy; Urinalyses 1. Introduction The effectiveness of methadone maintenance treatment in reducing opiate use, related crime and HIV risk behaviors is well documented (Ball and Ross, 1991; McLellan et al., 1993). Nonetheless, many patients drop out of treatment or continue using opiates and other illicit drugs (Morral et al., 1997; Nunes et al., 1997; Belding et al., 1998). Early identification of such patients could facilitate the development of intensive treatments targeted to their needs. Attempts to identify poor responders using demographic and drug-use history variables have failed to yield consistent predictors (McLellan, 1983; Hubbard et al., 1989; Morral et al., 1997). However, several studies suggest that patterns of * Corresponding author. drug abuse treatment response might become evident quite early in treatment (Alterman et al., 1997; Morral et al., 1997; Cacciola et al., 1998). Early treatment performance might therefore be useful for identifying those patients likely to respond poorly to further treatment. In this study we use measures of performance 2 weeks after admission to predict treatment response 6 and 9 months later. A recent review of clinic records at a VA methadone clinic revealed that of patients who received methadone treatment for at least 6 months, 22% used illicit opiates regularly as indicated by urinalysis results (Belding et al., 1998). Similarly, an earlier survey of patients at four methadone clinics in the Philadelphia area found that among patients who received treatment for at least 6 months, 23% reported using illicit opiates on 10 or more days in the 30 days prior to the survey (Belding et al., 1995). A total of 12% of these patients reported 10 0376-8716/99/$ - see front matter 1999 Elsevier Science Ireland Ltd. All rights reserved. PII: S0376-8716(98)00176-8
26 A.R. Morral et al. / Drug and Alcohol Dependence 55 (1999) 25 33 or more days of cocaine use. When the definition of a poor or inferior treatment response at 6 months is enlarged to include clients who drop out of treatment, these rates often rise to 50 80% (see e.g. GAO, 1990) Unfortunately, few robust predictors of treatment performance have been found. Among these few, greater severity of drug use and psychiatric problems are among the most consistent predictors of poor outcomes (McLellan et al., 1983, 1994; Ball and Ross, 1991). Typically, however, these factors account for only a small fraction of outcome variance, they are not consistently found to be related to outcomes, and antithetical results are common (Saxon et al., 1994). Moreover, many studies find no strong or consistent relations between pretreatment factors and drug abuse treatment outcomes (Hubbard, et al., 1989; Stitzer et al., 1992; Saxon et al., 1994; Phillips, et al., 1995; Morral, et al., 1997). Weak and equivocal predictors such as these provide little value to treatment providers who wish to identify clients at a high risk for poor treatment response. Although pre-treatment characteristics have little predictive value, several studies suggest it might be possible to predict treatment response based on patients drug use in the days immediately preceding treatment admission (McLellan et al., 1997). For example, Alterman et al. (1997) found that the urine specimens provided by clients at admission could be used to predict treatment response in four samples of clients receiving different treatments for cocaine dependence. Across samples, of the 107 clients who completed treatment, 79% submitted a cocaine-free baseline urine, compared to only 39% of the 132 clients who did not complete their respective treatments. Similarly, among the 123 who succeeded in submitting at least three consecutive cocaine-free urine samples during treatment (not including the baseline sample), 77% had submitted a cocaine-free baseline sample, compared to just 41% of the 133 clients who never achieved initial cocaine abstinence. Whereas the foregoing studies examined client factors that could be known at treatment admission, client performance in the first weeks of treatment offers an especially promising set of predictors of treatment response in subsequent months. Using a sample of 169 methadone maintenance patients, Morral et al. (1997) showed that 41 of 43 treatment dropouts (84%) were found among those clients who failed to achieve abstinence from illicit drugs during the first month in treatment. Similarly, in a study of outpatient treatment for cocaine, Budney et al. (1996) compared patients who did and did not achieve early cocaine abstinence, as shown by submitting three cocaine-negative urine specimens during the second week of treatment. Over the first 24 weeks of treatment, they found significantly lower rates of cocaine use and greater treatment retention among the early abstainers. In this study we examine whether outcomes 6 or 9 months after treatment entry can be accurately predicted by objective measures of treatment performance that are commonly available to clinics within the first 2 weeks of methadone treatment. This time interval was selected to balance the competing interests of early identification (i.e. before large commitments of time and resources are devoted to treatment) with the enhanced predictive value of later treatment performance. Significant treatment dropout typically occurs in the first weeks and months of treatment and it would be clinically useful to identify potential drop-outs before they leave treatment. Furthermore, data from the first week in treatment could be misleading since some patients transfer into methadone treatment from brief periods of inpatient treatment, and evidence of pretreatment drug use might remain in subject s urine samples during this week. 2. Method 2.1. Participants Participants were 59 methadone maintenance patients taking part in a treatment outcome study of the effectiveness of a contingency management intervention (Iguchi et al., 1997a). All participants were admitted to methadone maintenance treatment at the Philadelphia Veterans Affairs Medical Center (PVAMC) between April and November 1995. The sample represents consecutive admissions except for 21 patients not included in the sample. Of those excluded, 11 refused to participate in the treatment outcome study, three left treatment within 1 week of admission, two failed to complete research admission assessments within 1 week of admission; three were excluded because of serious medical problems; two patients initially admitted to the study were subsequently removed because of repeated inpatient medical and psychiatric hospitalizations during the course of treatment. A total of 95% of the 59 study participants were male; 68% were black, and 32% white. Participants ranged in age from 24 to 66 years (M=43.6 years). Of these, 29% of participants reported full-time employment; 39% were unemployed; 2% were homemakers; 20% received disability payments; 8% reported occasional or part-time employment; and 2% were retired. Participants reported a mean of 15.9 years of regular (at least three times per week) heroin use, and 5.8 years of regular cocaine use before admission. 2.2. Treatment On admission, consenting patients were randomly assigned to receive either standard methadone mainte-
A.R. Morral et al. / Drug and Alcohol Dependence 55 (1999) 25 33 27 nance treatment (standard; n=30) or a token economy intervention (token; n=29). Token group participants earned vouchers for providing urine specimens testing negative for unauthorized drugs (drug-free urines), and for providing objective evidence of completing tasks individually tailored to each clients treatment plan. For instance, a client working toward the treatment plan goal of taking on more parental responsibility might be assigned the task of attending a forthcoming parent teacher conference and bringing materials from this meeting to the counselor. Participants in this condition could earn vouchers worth up to $20 that could be redeemed for goods and services approved by their counselors. Except that the participants in the token condition were assigned tasks and had the opportunity to earn vouchers, the standard and token treatment conditions were identical. All patients were scheduled to meet with their counselors a minimum of once a week throughout the intervention. These study procedures and the consent forms used were approved by the institutional review boards of the VA hospital and Allegheny University of the Health Sciences. Analysis of results over the first 6 months of the study indicated that the token intervention had no significant effect on any measured outcome variables including urinalysis results (Iguchi et al., 1997a). 2.3. Measures 2.3.1. Addiction se erity index (ASI) The ASI is a structured interview assessing recent drug and alcohol use (past 30 days) as well as problem severity in five other target areas: medical status, employment status, legal status, family/social relationships and psychiatric symptoms. The measure has proven reliable and valid in a variety of studies (McLellan et al., 1985; Kosten et al., 1983; McLellan et al., 1992). In this study, ASI interviews were conducted at treatment admission and again at months 3, 6 and 9. 2.3.2. Urinalysis results Urine samples were collected twice weekly on a random basis from all participants. Specimens were collected under staff observation to ensure authenticity. Specimens were subjected to on-site enzyme multiplied immunoassay technique analysis (Behring-Syva, Palo Alto, CA) to test for recent use of methadone, illicit opiates, benzodiazepines, cocaine, THC, amphetamines and barbiturates. Analyses focused on urinalysis results in week 2 and in months 6 and 9 of treatment (i.e. weeks 21 24 and weeks 33 36). 2.3.3. Week 2 performance Attendance of scheduled counseling sessions and results of week 2 urinalyses were used as objective indices of treatment performance in week 2. By the end of week 2, all participants were scheduled to attend at least two counseling sessions. Amphetamines and barbiturates were detected in 5% or less of the week 2 urine samples, so were not considered as predictors of treatment outcome for this study. Similarly, every patient was positive for methadone, so this variable was excluded. Although participants could have zero, one or two urine specimens test positive for each assay in week 2, the distribution of positive assays was bimodal; for each assay, all but six or fewer participants were positive in all or none of their week 2 urine samples. Therefore, week 2 measures for use of illicit opiates, cocaine, THC, and benzodiazepines were dichotomized as abstinent or not abstinent. 2.3.4. Outcomes at 6 and 9 months We combined information on treatment dropout and objective data on participants continued use of illicit opiates and cocaine to create two dichotomous measures, 6-month and 9-month outcomes. Participants were described as having superior outcomes at 6 or 9 months if they remained in treatment until the end of month 6 or 9, respectively, and if fewer than four of the eight urines samples submitted during months 6 or 9 revealed use of illicit opiates or cocaine. Poor 6 month outcomes were assigned to clients who dropped out before the end of month 6, or whose urine samples during month 6 were positive for opiates or cocaine on at least four of the eight occasions. Similarly, poor 9 month outcomes were assigned to clients who dropped out before the end of month 9 or submitted four or more urine samples revealing use of cocaine or illicit opiates during month 9. We used urine results from just month 6 or month 9, rather than from the entire 6 or 9 month duration of treatment, so that our measure of outcome would be sensitive to those participants who improved, but only after several months of inferior performance. This composite measure of treatment outcomes is subject to the criticism that some clients who dropped out of methadone treatment might have become drugfree or had other positive outcomes, in which case classifying all dropouts as having inferior outcomes would be misleading. However, counting dropout as an inferior outcome is supported by the frequent finding that treatment dropout in the first year is associated with high rates of relapse to intravenous heroin use (Simpson, 1979; Ball and Ross, 1991; Simpson et al., 1997). Since methadone treatment is primarily designed to treat opiate use disorders, not cocaine use, including cocaine use in our definition of methadone treatment response could also be considered unwarranted. We chose to include cocaine use because only the pharmacotherapy offered in methadone maintenance treatment targets opiate use exclusively. Individual counseling, group and family therapy, psychoeducation and self-
28 A.R. Morral et al. / Drug and Alcohol Dependence 55 (1999) 25 33 help components of the treatment are designed to eliminate all illicit substance use. Ongoing cocaine use compromises the success of these components of methadone maintenance therapy. Furthermore, the strong association routinely found between cocaine use and illicit opiate use among methadone patients makes cocaine use a clear risk factor for relapse to heroin use (Kosten et al., 1988; Dunteman et al., 1992; Hartel et al., 1995). Finally, our decision to dichotomize outcomes resolved several measurement problems. First, there is no good way to estimate opiate and cocaine use among the dropouts, but treatment dropout in the first 6 to 9 months is usually associated with poor outcomes (Simpson, 1979; Ball and Ross, 1991; Simpson et al., 1997). Second, among participants remaining in treatment, opiate and cocaine use was moderately correlated (r=0.56 and 0.53, for months 6 and 9, respectively). And third, neither opiate nor cocaine use was normally distributed. Indeed, in months 6 and 9, these measures were bimodally distributed, with 71 81% of all participants submitting urine samples that were nearly all drug free (zero or one sample out of eight testing positive for cocaine or illicit opiates) or nearly all drug positive (seven or eight of eight testing positive for cocaine or illicit opiates). 2.4. Procedure We used a blocked, step-wise logistic regression procedure to assess whether outcomes could be predicted by pre-treatment patient characteristics or by early treatment performance. Patient pre-treatment characteristics and experimental group assignment were entered in the first block. Week 2 performance measures were entered in the second block to determine whether they improved prediction of performance beyond what could be explained by pre-treatment factors alone. Our 6 and 9 month outcome measures treat all dropouts as inferior outcomes, even though some might have done well after dropping out. Furthermore, they are insensitive to reductions in drug use that fall short of abstinence. Thus, we conducted secondary analyses in which self-reports of the number of days of cocaine and heroin use at follow up were regressed onto the early treatment indicators found useful in the prediction of our outcome classifications. We succeeded in following up and interviewing 86 and 80% of the sample at the 6 and 9 month assessments, respectively. Whereas our follow up of clients who were still in treatment was nearly perfect (98 and 100%), follow up for dropouts was considerably lower (59 and 45%). We approached this missing data problem in two ways, both of which are conservatively designed to reduce the likelihood of finding statistical significance when no true relationship exists between the predictors and outcomes (type I errors). First, we replaced missing values with the value provided by participants at their last completed interview. This interpolation procedure adopts the unrealistic assumption that dropouts do not increase their use of heroin and cocaine. Because this assumption obscures the negative outcomes associated with dropout, it leads to range restrictions that present a greater challenge to our predictive model than might reasonably be expected had we successfully interviewed all dropouts. In the second procedure we evaluate the predictive model using only those subjects we successfully interviewed. This procedure reduces the power of the predictive model to detect genuine associations between week 2 performance and outcomes, because it reduces the sample size and restricts the range of outcomes. For the 6 and 9 month outcome models we report the results of analyses conducted using both missing data procedures. 3. Results 3.1. Outcome groups Table 1 describes the outcome status assigned to study participants at 6 and 9 months. Among participants who remained in treatment, but had inferior outcomes, more than half submitted 100% opiate positive urine specimens in month 6 and 9. Only one participant in month 6 and five in month 9 were classed as inferior outcomes because of their cocaine use alone. Among those in the superior outcome group, the majority (56% in month 6 and 57% in month 9) did not submit a single urine specimen testing positive for cocaine or opiates. Thus, most participants were either consistently drug-free in month 6 and 9 or consistently used opiates or opiates and cocaine. Characterizing outcomes on the basis of treatment retention and urinalysis results was supported by selfreports of drug use made by the 47 participants (80%) Table 1 Outcome classifications at months 6 and 9 Month 6 Month 9 n % n % Superior outcome 15 25 14 24 Inferior outcome 44 75 45 76 Drop out a 17 29 22 37 Substance use b 27 46 23 39 Total 59 100 59 100 a The subsets of clients with inferior outcomes because they dropped out of treatment. b The subset of clients with inferior outcomes because they continued to abuse heroin or cocaine.
A.R. Morral et al. / Drug and Alcohol Dependence 55 (1999) 25 33 29 interviewed at 6 and 9 months. Among the 33 participants interviewed at month 9 from the inferior outcome group, the average frequency of heroin use fell significantly from 21.97 days of use at admission to 7.91 days at month 9, t(32)=7.06, P 0.001. The superior outcomes group, however, showed a 97% reduction over the same period, reducing their use from 22.86 days at admission to just 0.07 days at month 9, P 0.001. Similarly, although the frequency of cocaine use diminished slightly in the inferior group (from 8.73 to 6.70 days of use, t[32] =1.09, n.s.), the superior outcome group reduced their cocaine use frequency by 98% (from 5.93 to 0.07 days of use, t[13]=3.09, P 0.01). An identical pattern of self-reported cocaine and heroin use was found for the 6 month outcomes. Finally, among clients with inferior outcomes, frequencies of cocaine and heroin use at follow-up were higher for participants who dropped out of treatment than for those remaining in treatment, but these differences were not statistically significant. 3.2. Week 2 performance During week 2, urinalysis results indicated that 25 participants (42%) were opiate abstinent, 29 (49%) were cocaine abstinent, 35 (59%) were THC abstinent, and 53 (90%) were benzodiazepine abstinent. Of the 47 participants continuing to use illicit drugs, nearly half (49%) were using both cocaine and illicit opiates. By the end of week 2, participants attended a mean of 1.46 counseling sessions. Seven participants attended no scheduled sessions, 23 attended one session, 25 attended two sessions, three attended three sessions and one participant attended four sessions. To reduce the effect of the four clients who had more than the scheduled number of counseling sessions, this variable was dichotomized between those who did, versus did not attend at least two counseling sessions in the first 2 weeks of treatment. 3.3. Predicting treatment response at 6 and 9 months We evaluated the usefulness of the week 2 performance measures for predicting treatment outcomes by including all five (counseling attendance and abstinence from four drug classes) in the second block of a forward stepwise logistic regression procedure. The first block contained variables that could be known about participants at treatment entry: patient demographic characteristics (age and race), pre-treatment characteristics derived from the admission ASI (days of heroin use, days of cocaine use, days of THC use, days of sedative use, days of alcohol use, days of employment, years of heroin use, years of cocaine use, and composite scores from the six non-drug problems areas: alcohol, family, employment, legal, medical and psychiatric), Table 2 Estimated coefficients, estimated standard errors and odd ratios for the multivariate model predicting outcome classifications Term Estimate S.E. P Odds (95% CI) Parameter estimates for positi e 6-month outcomes Week 2 opiate ab- 2.28 0.80 0.0042 9.80 (1.99 stinence 48.30) Week 2 attendance 2.46 0.89 0.0056 11.69 (1.98 68.85) Parameter estimates for positi e 9-month outcomes Age 0.16 0.06 0.0106 1.17 (1.04 1.33) Week 2 opiate ab- 2.04 0.93 0.0291 7.69 (1.19 stinence 49.81) Week 2 atten- 2.55 0.96 0.0081 12.75 (1.87 dance 87.16) and experimental group assignment (from the parent study from which this sample derives). Unscorable responses led to one missing value on the ASI employment and four missing values for the ASI legal severity composite scores. For the regressions reported below, replacing these five missing values with sample means or excluding the incomplete variables from the analyses led to identical results. After the first block of variables was entered in the month 6 outcomes regression, no variables were retained in the equation. Entry of the second block resulted in a significant two-covariate solution, likelihood-ratio 2 (2, n=59)=22.25, P 0.0001. Week 2 opiate abstinence and week 2 counseling attendance each contributed significantly and independently to the prediction of outcome classification (Table 2). After controlling for counseling attendance, the odds of a positive 6-month outcome were almost ten times greater for participants who were opiate abstinent by week 2 than for those who submitted at least one urine sample testing positive for illicit opiates. Similarly, attending at least two counseling sessions improved clients odds of a good 6 month outcome by a factor of more than 11. This two factor model fit the observed outcomes well, correctly classifying the treatment responses of 83% of the sample. Its specificity (correct identification of participants with inferior outcomes) was 89%, and its sensitivity (correct identification of participants with superior outcomes) was 66%. Of the 20 patients who did not attend at least two counseling sessions and who were not opiate abstinent in week 2, not one was in the superior outcome group at 6 months. On the other hand, of the 15 patients who were opiate abstinent and attended at least two sessions by the end of week 2, two-thirds (ten) had positive outcomes. Finally, we reestimated the model including a term for the interaction of week 2 opiate abstinence and counseling attendance, which did not result in a significant improvement for the model.
30 A.R. Morral et al. / Drug and Alcohol Dependence 55 (1999) 25 33 Table 3 Estimated coefficients for week 2 performance predictors of self-reported drug use at follow up with drop outs omitted and dropouts interpolated Dependent variable Parameter estimates Model Opiate abstinence (S.E.) Counseling attendance (S.E.) R 2 F 7.95 (2.51) 0.30 Outcomes at month 6 Days of heroin use 7.04 (2.55) 10.21*** Dropouts omitted a Days of cocaine use 4.95 (2.78) 5.88 (2.74) 0.16 4.50* Days of heroin use 8.86 (2.50) 7.51 (2.47) 0.32 13.36*** Dropouts interpolated b Days of cocaine use Outcomes at month 9 Dropouts omitted a Days of heroin use 4.61 (2.49) 4.68 (1.89) 4.97 (2.46) 4.74 (1.86) 0.14 0.26 4.62* 7.57** Days of cocaine use 1.09 (2.11) 4.44 (2.08) 0.11 2.67 Days of heroin use 5.02 (2.20) 7.24 (2.18) 0.26 9.86*** Dropouts interpolated b Days of cocaine use 1.97 (2.30) 5.24 (2.28) 0.11 3.50* a For 6 month models, n=51 and df=2,48. For 9 month models, n=47 and df=2,44. b For both 6 and 9 month models, n=59 and df=2,56. * P 0.05. ** P 0.01. *** P 0.0005. When we repeated the foregoing regression procedures on the month 9 outcome data, age survived the first block and week 2 opiate abstinence and counseling attendance again survived the second block (Table 2). This final regression model was highly significant, likelihood ratio 2 (3, n=59)=21.69, P 0.0001. Model fit was again good, correctly classifying 86% of all clients; sensitivity of the model was 50%, and the specificity was 98%. Opiate abstinence in week 2 again demonstrated a significant independent contribution to explaining the likelihood of superior 9-month treatment outcomes. Those who achieved abstinence by the second week of treatment were more than seven times as likely to remain in treatment and be using little or no cocaine or illicit opiates 9 months after treatment entry, when controlling for age and counseling attendance. Similarly, participants who attended two counseling sessions by the end of the second week of treatment were more than 12 times as likely to have superior 9 month outcomes. Indeed, of the 30 participants who attended fewer than two counseling sessions, only two had superior 9-month outcomes. The effect of age on treatment outcomes was comparatively slight. Controlling for the week 2 performance indicators, for every year older our participants were, their chances of having a superior 9 month outcome improved 17%. This means that other factors being equal, the likelihood of a superior outcome would be about five times greater for participants who are 35 than those who are 25. 3.4. Predicting self-reported drug use at 6 and 9 months Table 3 presents the regression coefficients for week 2 opiate abstinence and treatment attendance for each of the self-reported drug use outcomes. Week 2 measures of opiate abstinence and counseling attendance were excellent predictors of both the frequency of heroin use and cocaine use at the 6 month outcome assessments. Moreover, this proved to be true both for analyses excluding subjects with missing data, and for those analyses that interpolated missing data based on subjects last reported drug use frequencies. For each model, opiate abstinence and counseling attendance by week 2 were associated with large reductions in the number of days of cocaine and heroin use reported half a year later by participants. For 9 month outcomes, all associations were significant, except for one model of cocaine use self-reports at 9 months. Similarly, in a series of secondary analyses we found that counseling attendance and opiate abstinence accounted for these drug use outcomes beyond what could be explained by self-reported use at program admission. That is, adding the early treatment indicators to regression models containing just self-reported use at admission resulted in significant increments in explained variance for all cocaine and heroin use outcomes except cocaine use at 9 months (interpolated and noninterpolated). With the exception of the 9-month model, the least significant
A.R. Morral et al. / Drug and Alcohol Dependence 55 (1999) 25 33 31 finding was for interpolated 6-month cocaine use; in this model week-2 predictors increased explained variance by 0.11, F(2,55)=5.96, P 0.005. 4. Discussion Although pretreatment characteristics have not proven to be robust predictors of treatment response, the results of this study suggest that 6 and 9 month treatment outcomes can be predicted using objective measures of treatment response that are available to providers within 2 weeks of program admission. Specifically, opiate abstinence and counseling attendance by the second week of treatment were better predictors of treatment response by months 6 and 9 than were our pretreatment variables, and together they could be used to correctly classify 90% of clients who went on to have inferior treatment responses, with an overall correct classification rate of well over 80%. In secondary analyses, these two variables also accounted for a large portion of variance in self-reported heroin and cocaine use 6 and 9 months after treatment entry. Our finding that counseling attendance in the first weeks of treatment predicts treatment response in the ensuing three quarters of a year can be interpreted in several ways. Early counseling attendance could serve as a marker of those clients with greater treatment motivation or greater treatment compliance, each of which could contribute to superior treatment outcomes. Furthermore, to the extent that the counselor is an active ingredient in the treatment process, greater attendance early in treatment corresponds to an increased dose of treatment, and might thereby result in superior treatment outcomes months later. The predictive value of opiate abstinence in the second week of treatment corresponds to other recent findings indicating that drug use early in treatment predicts poor outcomes (Budney et al., 1996; Alterman et al., 1997; Morral et al., 1997). Our finding that opiate abstinence in the second week of treatment predicts treatment response in the ensuing three quarters of a year can be interpreted similarly. Early abstinence may serve as a marker for greater treatment motivation or treatment compliance. It may also be that individuals demonstrating abstinence early in treatment receive strong social support and reinforcement for their behavior, leading to an increase in that response. Regardless of mechanism, the predictive value of opiate abstinence in the second week of treatment corresponds to other findings indicating that drug use early in treatment predicts poor outcomes (Budney, et al., 1996; Alterman et al., 1997; Morral et al., 1997). As noted earlier, our decision to include dropouts and cocaine users among those in the inferior outcome group could raise theoretical objections. We note, however, that this decision was strongly supported by the present results. First, the association of regular cocaine use with regular heroin use was so great that only one participant received the inferior outcome classification at month 6 on the basis of cocaine use alone. Second, participants who dropped out of treatment reported using heroin more frequently than others in the inferior response group, a group which on average was using heroin more than 25 times as often per month as the superior outcome group. Although we were unable to interview a portion of the dropouts, it is likely that if our follow-up cohort offers a biased sample of dropouts, the bias would lead to underestimates of actual drug use among dropouts, not overestimates. Our inferior outcome designation should be understood in the context of the relative rates of improvement among all clients. Even among the inferior outcome group, rates of heroin use after treatment entry were significantly lower than immediately prior to treatment. A total of 6 months after treatment entry, clients with inferior outcomes reported using heroin on half as many days as in the month preceding treatment entry. Therefore, methadone treatment was associated with a substantial reduction in risk behaviors even among this group. Nevertheless, participants classed as having inferior outcomes, who constitute more than three-quarters of our sample, continued to use heroin and cocaine at unacceptably high rates, even after 9 months of a methadone treatment in which they received peak methadone doses averaging over 79 mg and ranging up to 130 mg. Thus, our results demonstrate that although methadone treatment was partially effective for the majority in our sample, there is a great need for alternative or auxiliary interventions. The present findings suggest a simple technique that might prove useful in community methadone treatment settings for the early identification of clients at greatest risk for dropout or continued use of heroin and/or cocaine. If opiate abstinence and attendance of at least 2 weekly counseling sessions by the second week of treatment are counted as distinct protective factors against inferior treatment response, clients can be considered high risk for dropout or continued drug use if they have neither protective factor, moderately high risk if they have only one, and only moderate risk if they have both. Using these criteria with the present sample, 34% would have been classed as high risk, of whom 100% had inferior 6 month outcomes; another 41% would have been classed as at moderately high risk, of whom 79% had inferior outcomes; and the remaining 25% would have been classed as having only moderate risk, of whom 33% had inferior 6 month outcomes. Since clients might be especially willing to acknowledge continued use of opiates early in their methadone treatment, this proposed procedure might not require expensive urinalyses.
32 A.R. Morral et al. / Drug and Alcohol Dependence 55 (1999) 25 33 The contingency management intervention used in the parent study was not found to significantly increase participants chances of achieving a superior treatment outcome. Nevertheless, other contingency management interventions are among the few special interventions that appear promising for clients who are not substantially improving early in treatment. Morral et al. (1997), for instance, found that for clients whose initial response to treatment was relatively poor, the best predictor of subsequent superior performance was whether or not the client later received a contingency management intervention. Clients provided with clearly defined incentives for reducing their drug use or making progress toward other treatment plan goals were more than ten times as likely to show marked improvement during their methadone treatment. Furthermore, these improvements were rapid and appeared to begin immediately after the contingency management interventions were implemented. There is also substantial evidence that well designed contingency management interventions can reduce drug use and increase counseling attendance (Kidorf et al., 1994; Rowan-Szal et al., 1994; Iguchi et al., 1996; Silverman et al., 1996a,b; Iguchi et al., 1997b), the two factors found in the present study to best predict superior treatment outcomes. Despite the apparent predictive validity of opiate use and counseling attendance in the second week of treatment, limitations of this study suggest the need to further evaluate these factors. First, the sample size in the present study was small and not selected in such a way as to ensure the generalizability of our findings to other populations of methadone patients. Our sample chiefly consisted of male veterans in Philadelphia. Whether the week 2 performance factors we identified will have predictive utility in other samples of methadone clients remains to be studied. Furthermore, the stepwise regression procedure we used to identify outcome predictors can be criticized for increasing the likelihood of finding chance associations in a sample when the true association in the population is actually weaker. The wide variation in methadone treatment outcomes highlights the need for identifying those subgroups of clients for whom standard care will be more or less effective. Identifying these subgroups could improve treatment planning and facilitate development of new treatment interventions better matching the needs of clients who are unlikely to have superior treatment responses. The enormous potential value which such identifiers might have is suggested, in part, by the literally hundreds of studies that have sought them among demographic, drug use history, and other client factors. Unfortunately, these studies have not yielded robust predictors of methadone treatment response. In this study we found that early treatment response, during the first 2 weeks following admission, provides a powerful predictor of later treatment performance. In particular, opiate abstinence in the second week of treatment and the number of counseling sessions attended by the second week were shown to correctly classify the treatment performance of 83% of our sample 6 and 9 months later. These findings suggest that although client characteristics at program admission might provide little guidance in treatment planning, client performance early in treatment might offer an especially powerful indicator that could be used to guide subsequent treatment planning. Acknowledgements This research was supported by Nation Institute on Drug Abuse grants R01 DA06096 (Iguchi), R01 DA10778 (Iguchi) and P50 DA07705 (McLellan). The data reported in this article were collected by our research assistants, Rebecca Fidler-Sheppard and Karen Mosher-Waite, whose dedication and careful work we gratefully acknowledge. Mark Belding is now at Allan Collautt Associates, 1974 Sproul Road, Suite 200, Broomall, PA 19008. References Alterman, A.I., Kampman, K., Boardman, C.R., Cacciola, J.S., Rutherford, M.J., McKay, J.R., Maany, I., 1997. Prediction of attrition from day hospital treatment in lower socioeconomic cocaine-dependent men. Drug Alcohol Depend. 46, 79 85. Ball, J.C., Ross, A., 1991. The Effectiveness of Methadone Maintenance Treatment. Springer, New York. Belding, M.A., Iguchi, M.Y., Lamb, R.J., Lakin, M., Terry, R., 1995. Stages and processes of change among polydrug users in methadone maintenance treatment. Drug Alcohol Depend. 39, 45 53. Belding, M.A., McLellan, A.T., Zanus, D.A., Incmikoski, R., 1998. Characterizing non-responsive methadone patients. J. Subst. Abuse Treat. 15, 1 8. Budney, A.J., Higgins, S.T., Wong, C., Bickel, W.K., 1996. Early abstinence predicts outcome in behavioral treatment for cocaine dependence. In: Harris, L.S. (Ed.), Problems of drug dependence, 1995: Proceedings of the 57th annual scientific meeting, NIDA research monograph. National Institute on Drug Abuse, Rockville, MD. Cacciola, J.S., Alterman, A.I., Rutherford, M.J., McKay, J.R., McLellan, A.T., 1998. The early course of change in methadone maintenance. Addiction 93, 41 49. Dunteman, G.H., Condelli, W.S., Fairbank, J.A., 1992. Predicting cocaine use among methadone patients: analysis of findings from a national study. Hosp. Community Psychiatry 43, 608 611. GAO, 1990. Methadone maintenance: some treatment programs are not effective; greater Federal oversight needed. United States General Accounting Office, Washington, DC, GAO/HRD-90-104. Hartel, D.M., Schoenbaum, E.E., Selwyn, P.A., Kline, J., et al., 1995. Heroin use during methadone maintenance treatment: the importance of methadone dose and cocaine use. Am. J. Public Health 85, 83 88. Hubbard, R.L., Marsden, M.E., Rachal, J.V., Harwood, H.J., Ginzburg, H.M., 1989. Drug Abuse Treatment: A National Study
A.R. Morral et al. / Drug and Alcohol Dependence 55 (1999) 25 33 33 of Effectiveness. University of North Carolina Press, Chapel Hill, NC. Iguchi, M.Y., Lamb, R.J., Belding, M.A., Platt, J.J., Husband, S.D., Morral, A.R., 1996. Contingent reinforcement of group participation versus abstinence in a methadone maintenance program. Exp. Clin. Psychopharmacol. 4, 315 321. Iguchi, M.Y., Belding, M.A., Morral, A.R., Lamb, R.J., Woody, G.E., 1997a. A token economy intervention for methadone maintenance patients. In: Harris, L.S. (Ed.), Problems of drug dependence, 1996: Proceedings of the 58th annual scientific meeting, NIDA research monograph 174. National Institute on Drug Abuse, Rockville, MD, p. 304. Iguchi, M.Y., Belding, M.A., Morral, A.R., Lamb, R.J., Husband, S.D., 1997b. Reinforcing operants other than abstinence: an effective alternative for reducing drug-use. J. Couns. Clin. Psychol. 65, 421 428. Kidorf, M., Stitzer, M.L., Brooner, R.K., Goldberg, J., 1994. Contingent methadone take-home doses reinforce adjunct therapy attendance of methadone maintenance patients. Drug Alcohol Depend. 36, 221 226. Kosten, T.R., Rounsaville, B.J., Kleber, H.D., 1983. Concurrent validity of the Addiction Severity Index. J. Nerv. Ment. Dis. 171, 606 610. Kosten, T.R., Rounsaville, B.J., Kleber, H.D., 1988. Antecedents and consequences of cocaine abuse among opioid addicts: a 2.5-year follow-up. J. Nerv. Ment. Dis. 176, 176 181. McLellan, A.T., 1983. Patient characteristics associated with outcome. In: Cooper, J.R., Altman, F., Brown, B.S., Czechowicz, D. (Eds.), Research on the Treatment of Narcotic Addiction: State of the Art. (NIDA Treatment Research Monograph Series, DHHS publication no. ADM 83-128). US Government Printing Office, Washington, DC, pp. 500 529. McLellan, A.T., Luborsky, L., Woody, G.E., O Brien, C.P., Druley, K.A., 1983. Predicting response to alcohol and drug abuse treatments: role of psychiatric severity. Arch. Gen. Psychiatry 40, 620 625. McLellan, A.T., Luborsky, L., Cacciola, J., Griffith, J.E., 1985. New data from the Addiction Severity Index: reliability and validity in three centers. J. Nerv. Ment. Dis. 173 (7), 412 423. McLellan, A.T., Alterman, A.I., Cacciola, J., Metzger, D., O Brien, C.P., 1992. A new measure of substance abuse treatment: initial studies of the treatment services review. J. Nerv. Ment. Dis. 180 (2), 101 110. McLellan, A.T., Arndt, I.O., Metzger, D.S., Woody, G.E., O Brien, C.P., 1993. The effects of psychosocial services in substance abuse treatment. J. Am. Med. Assoc. 269 (15), 1953 1959. McLellan, A.T., Alterman, A.I., Metzger, D.S., Grissom, G.R., Woody, G.E., Luborsky, L., O Brien, C.P., 1994. Similarity of outcome predictors across opiate, cocaine and alcohol treatments: role of treatment services. J. Consult. Clin. Psychol. 62 (6), 1141 1158. McLellan, A.T., Alterman, A.I., Metzger, D.S., Grissom, G.R., Woody, G.E., Luborsky, L., O Brien, C.P., 1997. Similarity of outcome predictors across opiate cocaine and alcohol treatments: role of treatment services. J. Consult. Clin. Psychol. 62, 1141 1158. Morral, A.R., Iguchi, M.Y., Belding, M.A., Lamb, R.J., 1997. Natural classes of treatment response. J. Consult. Clin. Psychol. 65, 673 685. Nunes, E.V., Coyne, T.H., Young, S., Hoffman, M., Woytek, R., Bertino, L., Kroger, J., Amann, K., 1997. Group psychotherapy for treatment refractory methadone maintenance patients: a clinical experience. J. Maint. Addict. 1, 103 113. Phillips, C.D., Hubbard, R.L., Dunteman, G., Fountain, D.L., Czechowicz, D., Cooper, J.R., 1995. Measuring program performance in methadone treatment using in-treatment outcomes: an illustration. Special section: Outcomes research. J. Ment. Health Adm. 22 (3), 214 225. Rowan-Szal, G., Joe, G.W., Chatham, L.R., Simpson, D.D, 1994. A simple reinforcement system for methadone clients in a community-based treatment program. J. Subst. Abuse Treat. 11, 217 223. Saxon, A., Fleming, C., Jackson, T.R., Wells, E., Calsyn, D.A., 1994. Predictors of Methadone Treatment Outcome. NIDA Research Monograph Series 153, 143. Silverman, K., Higgins, S.T., Brooner, R.K., Montoya, I.D., Cone, E.J., Schuster, C.R., Preston, K.L., 1996a. Sustained cocaine abstinence in methadone maintenance patients through voucherbased reinforcement therapy. Arch. Gen. Psychiatry 53, 409 415. Silverman, K., Wong, C., Higgins, S.T., Brooner, R.K., Montoya, I.D., Contoreggi, C., Umbricht-Schneiter, A., Schuster, C.R., Preston, K.L., 1996b. Increasing opiate abstinence through voucher-based reinforcement therapy. Drug Alcohol Depend. 41, 157 165. Simpson, D.D., 1979. The relation of time spent in drug abuse treatment to posttreatment outcome. Am. J. Psychiatry 136, 1449 1453. Simpson, D.D., Joe, G.W., Rowan-Szal, G.A., 1997. Drug abuse treatment retention and process effects on follow-up outcomes. Drug Alcohol Depend. 47, 227 235. Stitzer, M.L., Iguchi, M.Y., Felch, L.J., 1992. Contingent take-home incentives: effects on drug use of methadone maintenance patients. J. Consult. Clin. Psychol. 60, 927 934..