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Cocaine Abstinence Symptomatology and Treatment Attrition
Journal of Substance Abuse Treatment, Vol. 16, No. 2, pp. 129–135, 1999 Copyright © 1999 Elsevier Science Inc. All rights reserved. 0740-5472/99 $–see front matter
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ARTICLE
Cocaine Abstinence Symptomatology and Treatment Attrition Frank D. Mulvaney, bs,* Arthur I. Alterman, phd,* Chris R. Boardman, ms,† and Kyle Kampman, md† *University of Pennsylvania School of Medicine, Department of Psychiatry, Veterans Affairs Medical Center, Philadelphia PA †The Children’s Hospital of Philadelphia, Division of Biostatistics and Epidemiology, Philadelphia, PA
Abstract – Premature termination from outpatient cocaine treatment predicts a number of poor outcomes, including higher rates of relapse and unemployment. This study attempted to predict dropouts from outpatient cocaine treatment, as well as those unable to achieve initial abstinence from cocaine, using two baseline variables that had previously been shown to predict treatment dropout: a measure of the severity of cocaine abstinence symptomatology using the Cocaine Selective Severity Assessment (CSSA) and the initial urine toxicology. Results of logistic regression analyses indicated that those with more intense abstinence symptoms, as measured by the CSSA, were five times more likely to terminate treatment prematurely. When combined with the CSSA, the initial urine did not significantly predict dropouts. The CSSA and the baseline urine were equal in their ability to predict those who would fail in their initial attempts to achieve abstinence. Implications for treatment are discussed. © 1999 Elsevier Science Inc. All rights reserved. Keywords – cocaine; treatment attrition, CSSA; logistic regression.
INTRODUCTION High dropout rates continue to present a major problem in the outpatient treatment of cocaine dependence (Agosti, Nunes, Stewart, & Quitkin, 1991; Alterman, McKay, Mulvaney, & McLellan, 1996; Kleinman et al., 1992). Premature termination often predicts a return to cocaine use and an increase in the associated problems of unemployment and incarceration (Agosti et al., 1991; Holland, 1978; Stark, 1992). Since attrition is so clearly associated with poorer outcomes, a number of studies have attempted to identify those patient characteristics
This work was supported by a National Institute on Drug Abuse Center Grant DA05186 and by the Department of Veterans Affairs. Requests for reprints should be addressed to Frank D. Mulvaney, BS, VA Medical Center, Substance Abuse Unit, Building 7, University and Woodland Avenues, Philadelphia, PA 19104.
predictive of early withdrawal from outpatient cocaine treatment (Alterman et al., 1996; Collins & Allison, 1983; Higgins et al., 1994; Kleinman et al., 1992). For example, Alterman and colleagues (1996) found that the initial urine toxicology result was a powerful predictor of dropout from a 27 hour per week, 4-week outpatient treatment program. Patients from the sample of male veterans with a cocaine positive urine at intake were twice as likely as those with a negative urine to drop out of treatment. This is consistent with the work of Higgins and colleagues (1994), who found that the presence of cocaine in the initial urine predicted treatment attrition. A second study by our center (Kampman et al., in press) also found that a high score on a measure of severity of cocaine abstinence symptomatology, the Cocaine Selective Severity Assessment (CSSA), predicted early termination from outpatient cocaine treatment. In this study, both cocaine-dependent and cocaine- and alcohol-depen-
Received September 2, 1997; Revised January 5, 1998; Accepted February 17, 1998.
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dent patients with elevated scores on a baseline CSSA were more likely to drop out of treatment within the first week. To summarize, our findings, based on two separate studies, indicate that both the initial urine toxicology and the CSSA predict treatment attrition. Few studies, however, have examined the combined predictive power of these two variables. The current study examines the combined predictive validity of the baseline urine toxicology and the CSSA for two outcomes—treatment dropout and the attainment of initial abstinence.
Despite the emphasis on patient abstinence, the program’s philosophy was fairly tolerant of episodic cocaine use, and a patient was not automatically discharged for this behavior. A relapse was not seen as a treatment failure, but as an opportunity to learn more about the conditioned triggers that can initiate a relapse. Relapses are also viewed as opportunities to develop coping strategies to help manage craving and other uncomfortable affective states that occur during the treatment period, particularly in the early stages of abstinence. Retention rates, then, should be considered in light of this relatively lenient discharge policy.
METHODS Predictor Variables Subjects Subjects for this study were 87 cocaine-dependent men and women from the University of Pennsylvania’s Treatment Research Center (TRC), an outpatient addictions treatment research facility for cocaine, alcohol, and opioid dependence. The subjects were recruited at TRC from March 1992 to April 1993, and only those who completed the CSSA, gave an initial urine, and whose treatment status and urine toxicology were known at the 1-month follow-up were included in the study. All subjects met Diagnostic and Statistical Manual of Mental Disorders, third edition, revised (DSM-III-R; American Psychiatric Association, 1987) criteria for cocaine dependence, but were excluded if they had another current Axis I disorder other than substance use disorders. Alcohol abuse and dependence were the other substance use disorders most commonly present, with 37% (n 5 32) of the participants meeting DSM-III-R criteria for alcohol dependence. Most of the subjects were African American (90%), single (85%), and male (70%), with slightly more than half of the sample working (52%). They averaged almost 5 years (M 5 4.70; SD 5 3.5) of regular cocaine use and almost 11 days (SD 5 9.6) of cocaine use in the month prior to entering treatment. A majority of the participants smoked cocaine (77%), with the remaining subjects equally divided in their preference for snorting and injecting. Pharmacotherapy was available to subjects if needed, with the most common prescriptions written for oxazepam (n 5 14, 16%). Treatment Program The subjects participated in a treatment program that offered a combination of twice weekly individual relapseprevention counseling sessions with a master’s level therapist and twice weekly group therapy, again led by a master’s level therapist. The program was similar to most substance abuse treatment programs in that abstinence was the primary goal. Participation in 12-step groups was encouraged, although there were no meetings on-site.
Two measures were used to predict treatment attrition: the initial urine toxicology for cocaine and the CSSA scale. The urine specimens were analyzed by our Center’s toxicology laboratory using the semiquantitative method of fluorescence polarization immunoassays (FPIA-Abbott ADx machine), with positive samples confirmed by gas chromatography/mass spectrometry. For both methodologies a reading of more than 300 ng/ ml was taken as an indication of recent cocaine use. The second predictor, the CSSA, is an 18-item, interviewer-administered measure of the severity cocaine abstinence symptomatology (Kampman et al., in press). Some of the symptoms associated with initial cocaine abstinence and measured by the CSSA include the intensity and frequency of cocaine craving, dysphoric mood, fatigue, increases or decreases in appetite, and irritability (American Psychiatric Association, 1994; Cottler et al., 1993). Ratings on all 18 items of the CSSA range from scores of 0 to 7, with 0 indicating that the symptom is absent all the time and 7 indicating that the symptom is present most of the time. Two sets of items are paired, such that a high score on one of the items requires a low score on its pair, or vice versa. For example, a score of 7 on an item indicating hypophagia, a decrease in appetite, necessitates a score of 0 on another item indicating hyperphagia, an increased appetite. Therefore, the total CSSA score ranges from 0 to 112, which is slightly lower than the sum of all the items because of the pairs. Higher scores indicate more intense cocaine abstinence symptoms. Patients were administered the CSSA upon admission to the clinic, which was an average of 13 days after they had last used cocaine (SD 5 71 days). The median number of days between the administration of the CSSA and patients’ last cocaine use was 3. In a recent study of 138 admissions to an outpatient substance abuse clinic, the CSSA demonstrated excellent interrater reliability (Pearson’s r 5 .92, p , .001) and high internal consistency in cocaine-dependent subjects (Cronbach’s alpha 5 .80) (Kampman et al., in press). Moreover, the CSSA was able to discriminate abstinence symptoms between cocaine-dependent subjects and alcohol-dependent subjects, with cocaine-dependent sub-
Abstinence Symptomatology and Attrition
jects producing higher scores on the CSSA than the alcohol-dependent subjects. Since it measures cocaine abstinence symptomatology on a continuous scale and because of its robust psychometric properties, the CSSA was chosen to provide a subjective, self-report measure of cocaine abstinence symptomatology. The initial urine toxicology result was used as a measure of recency of cocaine use. Both measures have independently demonstrated a relationship to treatment attrition (Alterman et al., 1996; Kampman et al., in press; Stark, 1992), but what has not been examined is their relationship to each other and any possible increase in the ability to reliably predict attrition by combining the two measures. Outcome Measures Two dichotomous measures of unfavorable treatment response were used, (a) dropped from treatment within the first 30 days and (b) failed to attain initial abstinence within the first 30 days. Three successive clean cocaine urines within the first month of treatment, independent of the baseline urine, defined the second measure of unfavorable treatment response. Patients normally gave three urines a week, and all missed urines were considered cocaine positive. Both outcome measures were coded dichotomously, with 1 indicating treatment dropouts or a failure to remain abstinent, and 0 indicating treatment completion or clean urines for the first month of treatment. This may seem counterintuitive, that treatment completion was coded a 0 and treatment dropout was coded a 1, but this direction was chosen because of the desire to predict dropouts. Analyses Logistic regression analyses were performed for the two outcome measures with the statistical software STATA® (Stata Corporation, 1995). Regression models generated for the predictor variables first considered independently, then with both predictors entered simultaneous. For these regression models sensitivity indicates the percentage of dropouts correctly predicted and specificity indicates the percentage of retained subjects correctly predicted. Along with the model chi-square, sensitivity and specificity are the usual means of interpreting the predictive power of individual models. Additionally, as part of the logistic regression procedure, STATA® produces a receiver operating characteristics (ROC) curve. For logistic regression models, the ROC curve is equivalent to a plot of the sensitivity by 1-specificity values, and the area subsumed by this plot serves as a single, quantitative value indicating the probability of correctly predicting categorical outcomes (Hanley & McNeil, 1982). In essence, the area under the ROC curve represents the probability that the regression model will correctly identify completors and dropouts or
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abstainers and users; a combined sensitivity and specificity probability. Probabilities range from 0.5, indicating no discrimination between the two possible outcomes, and 1.0, indicating perfect discrimination. The area under the ROC curves for the different models are reported. Along with the sensitivity, specificity, and model chisquare, the area under the curve provides a basis for evaluating different models and for identifying that score from the CSSA that has the highest probability of correctly identifying the outcomes. In separate analyses, dichotomous cutpoints for the entire range of CSSA scores were established and used to obtain a functional threshold for the application of the CSSA in predicting the outcomes. These dichotomous cutpoints were then entered with the initial urine into the logistic regression equations to establish the CSSA cutoff score that would result in the best predictive model, as measured by the area under the ROC curve. A cutpoint was also sought for the CSSA to assist clinicians in determining the score from the CSSA that above which would predict a high risk of premature treatment termination. Finally, as a check of the robustness of the resultant models, 10% of the subjects were randomly removed and the analyses repeated to determine if a different sample would change the conclusions or change the predictors’ significance.
RESULTS Correlations Between Predictor Variables and Outcome Variables Before undertaking logistic regression analyses we first examined the bivariate correlations between the two predictor variables and the two outcome measures. Pointbiserial correlations revealed that the total CSSA score was correlated significantly with dropping out of treatment (r 5 .39, p , .001) and with a failure to attain initial abstinence (r 5 .24, p 5 .03). The initial urine, however, correlated significantly with the achievement of initial abstinence (r 5 .29, p 5 .01), but not with treatment dropout (r 5 .20, p 5 .07). Intercorrelations between the two predictors were then examined. While the recency of cocaine use, as measured by the initial urine toxicology, and the subjective severity of cocaine abstinence symptoms, as measured by the CSSA, are conceptually related, they appear to be measuring different aspects of cocaine use and withdrawal. The point-biserial correlations between the two predictors were low (r 5 .15, p 5 .16). This indicates that, in this sample at least, the initial urine and the CSSA are largely independent of each other. Their independence legitimates the use of regression analyses to determine the separate and combined effects of the two on the outcome measures.
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Predicting Dropouts Within the First Month of Treatment A total of 37 (43%) patients dropped out of treatment within the first 30 days. The CSSA score was considered first as a continuous measure to predict dropouts. When the CSSA was entered alone into a logistic regression model the chi-square was 14.18 (df 5 1, p 5 .001), with a sensitivity of 49% (18/37), a specificity of 84% (42/50), and an overall probability of correct prediction at 0.74. When the baseline urine was entered alone the model chisquare was 3.35 (df 5 1, p 5 .07), with a sensitivity of 68% (25/37), a specificity of 52% (26/50), and an overall probability of correct prediction at 0.60. Entering both independent variables simultaneously produced a model that was better than chance at predicting dropouts (x2 5 16.11, df 5 2, p , .001). With a sensitivity of 54% (20/37), a specificity of 84% (42/50), and an overall probability of correct prediction at 0.73, the combined model was not appreciably better than the model with the CSSA alone. Next, the CSSA was dichotomized along the entire range of scores and each dichotomous variable was entered into a separate logistic regression model. Based on maximum values of sensitivity, specificity, and area under the ROC curve, CSSA scores greater than or equal to 24 proved to be the best level of the instrument for predicting dropouts. For the model with the dichotomized version of the CSSA, the model chi-square was 14.49 (df 5 2, p , .001), and subjects with a CSSA score of 24 or more were five times (confidence interval [CI] 5 2.08–13.25, p , .01) more likely than those with scores less than 24 to leave treatment within the first month of the program. The sensitivity for this model was 65% (24/ 37), with a specificity of 74% (37/50) and an overall probability of .69. When the baseline urine and the dichotomous cut of the CSSA were entered simultaneously into a regression equation, indicated in Table 1, the model chi-square was 14.49 (df 5 2, p , .001), with a sensitivity of 65% (24/37), a specificity of 74% (37/50), and an overall probability of correct prediction at 0.72. This model, with both predictors, was better than the urine
alone model (ROC area 5 .72 vs. .60) but not appreciably different than the CSSA only model (ROC area 5 .72 vs. .69).
Predicting a Failure to Achieve Initial Abstinence Within the First Month of Treatment With the CSSA score again entered as a continuous measure and entered alone into a model predicting a failure to attain abstinence, the model chi-square was 5.6 (df 5 1, p , .05), the sensitivity was 100% (63/63), the specificity was 0% (0/24), and the overall probability of correct prediction was .66. Entering only the baseline urine produced a logistic regression model with a chi-square of 7.14 (p , .01), a sensitivity of 100% (63/63), a specificity of 0% (0/24), and an overall predictive probability of .66. Finally, entering both predictors simultaneously produced a model chi-square of 11.04 (p , .01), a sensitivity of 92% (58/63), a specificity of 25% (6/18), and an overall predictive probability at .73. The combined model, therefore, was slightly better than the individual models at predicting those would not attain initial abstinence (area under ROC 5 .73 vs. .66 and .66), while there was no difference in predictive strength between the individual models (area under the ROC was .66 for both). In a series of steps similar to those conducted when attempting to best predict dropouts, the CSSA was dichotomized along the entire range of scores and each dichotomous variable was entered into a separate logistic regression model. Based on an evaluation of the area under the ROC curve, CSSA scores above 31 proved the best level of the instrument for predicting a failure to achieve initial abstinence. When a subject’s CSSA score was 31 or more they were five times more likely than those with scores less than 31 to fail in attempts to attain abstinence in the first month of treatment (95% CI 5 1.33–18.22, p 5 .02). For this model, the chi-square was 7.26 (p , .01), with a sensitivity of 100% (63/63), a specificity of 0% (0/24), and an overall probability of correct prediction at .64.
TABLE 1 Prediction of Dropouts and Those Who Failed to Attain Initial Abstinence
Model Variables
Odds Ratio
Significance
95% CI
Dropped out of treatment within the first 30 days Model x2 5 14.49 Sensitivity 5 65%
p , .001 Baseline urine 1.67 0.29 Specificity 5 74% CSSA cut at 24 4.75 ,0.01 Probability of correctly identifying dropouts and completors 5 0.72
0.64–4.35 1.85–12.18
Unable to attain initial abstinence within the first 30 days Model x2 5 12.06 p , .01 Baseline urine 3.08 0.03 Sensitivity 5 100% Specificity 5 0% CSSA cut at 31 3.99 0.04 Probability of correctly identifying those who failed to attain initial abstinence 5 0.72 CI 5 confidence interval; CSSA 5 Cocaine Selective Severity Assessment.
1.10–8.59 1.04–15.25
Abstinence Symptomatology and Attrition
The initial urine was also a powerful predictor of the second outcome measure, with an initial dirty urine associated with a four-fold increase in the likelihood of failing to achieve initial abstinence (95% CI 5 1.38–10.07, p 5 .01). With the baseline urine entered alone the model chi-square was 7.14 (df 5 1, p , .01), with a sensitivity of 100% (63/63), a specificity of 0% (0/24), and an overall probability of correct prediction at .66. The model combining the two predictors did offer a slight improvement over the individual models (area under the ROC 5 .72 vs. .64 and .66). The combined model yielded a chisquare of 12.06 (p , .01), with a sensitivity of 100% (63/ 63), a specificity of 0% (0/24), and an overall probability of correct prediction at .72 (see Table 1). Since the models predicting failure to attain initial abstinence resulted in extreme values for sensitivity and specificity, 100% and 0%, respectively, we explored several possible influences on the parameter estimates and ways to investigate the impact of this imbalance on the model’s sensitivity and specificity. First, it seems reasonable that the relatively small sample size of 87 subjects could bias the parameter estimates (Schaeffer, 1983). One way to correct for this small sample size would be to repeat these analyses with a larger, similar sample. Unfortunately, it is unrealistic for this clinic to conduct a larger study at this time. Another concern was the unbalanced nature of the two outcome categories influencing sensitivity and specificity percentages, with only 28% (n 5 24) of the subjects attaining initial abstinence within the first month and the other 72% (n 5 63) giving a cocainepositive urine in that time period. To evaluate this question, the analyses were repeated with 50 subjects; 24 subjects who attained initial abstinence and a randomly selected group of 24 subjects from the 63 who did not attain abstinence. This analysis resulted in a sensitivity of 83% and a specificity of 58%. Therefore, it appears that the unbalanced nature of the outcome measure was dramatically influenced the sensitivity and specificity of the models predicting a failure to attain abstinence. Robustness of the Models For the model predicting dropouts within the first month, reanalysis of the data with 10% of the sample randomly removed produced a model almost identical to the regression model generated with the entire sample. The CSSA total score cut at 24 remained stronger than the initial urine as a predictor of dropouts within the first month, with an odds ratio of 4.65 (CI 5 1.1826.12). This model, using a random 90% of the larger sample, had a sensitivity of 62% (21/34) and a specificity of 76% (34/45), similar to the 65% (24/37) sensitivity and the 74% (37/50) specificity seen in the entire sample. This indicates that the first model was robust. For the second model predicting achievement of initial abstinence the model was less robust. When 10% of the sample were randomly removed the model chi-square
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remained significant (x2 5 8.10, df 5 2, p 5 .02), but neither the initial urine (Wald coefficient 5 2.96, p 5 .09) nor the dichotomous categorization of CSSA into scores above and below 31 (Wald coefficient 5 2.99, p 5 .08) predicted the second outcome measure. This lack of robustness, along with the extreme values of sensitivity and specificity, indicate the instability of this second model. A final set of analyses were conducted to examine whether gender predicted treatment attrition or the failure to attain initial abstinence. Gender and the interaction between gender and the two predictors—the CSSA cut scores and the initial urine—were entered into logistic regression models. Gender alone did not discriminate between those who dropped out of treatment and those who completed treatment (Wald 5 .21, p 5 .65), nor was the interaction between gender and the CSSA cut at a score of 24 a significant predictor of dropouts (Wald 5 .01, p 5 .94). Gender did not interact with the baseline urine to influence the prediction of dropouts (Wald 5 .001, p 5 .97). The pattern of men and women having a similar outcome on the dependent measure continued in the prediction of those who failed to achieve initial abstinence. Gender distinctions proved unimportant both alone (Wald 5 .02, p 5 .90) and in combination with the CSSA cut at 31 (Wald 5 .99, p 5 .32) when predicting those who failed to achieve abstinence in the first month of treatment. Gender alone (Wald 5 .97, p 5 .33) and as an interaction term with the initial urine (Wald 5 .83, p 5 .36) indicated that men and women failed to achieve initial abstinence in roughly equal proportions. To summarize the findings related to gender, all the analyses conducted that used gender as an independent variable indicated that the CSSA and the initial urine toxicology predicted the two outcomes equally well for both men and women. DISCUSSION Successfully completing a specified length of time in an outpatient treatment program, usually a minimum of 28 to 30 days to a more optimal time of 6 months, is a reliable predictor of reduced substance misuse and better social, psychological, and occupational outcomes in cocaine-dependent patients (Kleinman et al., 1992; Stark, 1992). Researchers have also demonstrated that recent cocaine use at the beginning of treatment, indicative of a more serious substance use disorder, predicts premature treatment termination and worse outcomes (Alterman et al., 1996; Higgins et al., 1994). Those individuals with more severe problems at treatment entry of those at greater risk of terminating treatment prematurely are not necessarily lost causes. Once identified, patients with more severe substance use problems initially can achieve treatment gains similar to those who entered treatment with less severe problems when more intensive, individual services are available (Carroll et al., 1994; Hoffman
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et al., 1994; McKay et al., 1997; Woody, McLellan, Luborsky, & O’Brien, 1995). Given the aforementioned negative correlates of dropping out of treatment, the primary objective of this study was to increase the reliable identification of those with an increased chance of dropping out or responding poorly to treatment by combining two predictors found independently correlated with these outcomes, that is, the intake urine and the intake CSSA score (Alterman et al., 1996; Higgins et al., 1994; Kampman et al., in press). Logistic regression analyses indicated that the CSSA was better than the initial urine at predicting treatment attrition. Patients who scored greater than 24 on the CSSA were five times more likely than those with a score of less than 24 to terminate treatment prematurely. A second logistic regression demonstrated that the CSSA and the initial urine toxicology were equal in their ability to predict those who abstained from cocaine during their first month in treatment. Those with either a score of 31 or more on the CSSA or an initial cocainepositive urine were three to four times more likely to fail to achieve abstinence within the first month of treatment. Combining both the initial urine and the CSSA did not increase the logistic regression model’s ability to correctly identify those who would continue to use cocaine. Choosing a questionnaire over a lab result may also represent a reasonable, practical choice. First, hospital labs in most clinics and hospitals require a few days to return urine toxicology results. For free-standing drug clinics or clinics in more remote areas, the time from when an intake urine is given and when the lab results are returned is usually longer. Patients with intense cocaine craving may be gone, using again, before these results ever return. The CSSA takes approximately 15 minutes to administer and score. If the CSSA can predict the likelihood of treatment dropout as well as an initial urine toxicology, which this study demonstrates, clinicians may consider using the questionnaire as a way to identify at intake those who are at risk of not completing treatment. There is more pressure on clinicians today to make rapid judgments about the types of treatments that patients need, particularly the intensity level of treatment necessary. Sometimes staff are given an incomplete or distorted presentation by a client on intake, and the clinician may feel that he or she does not have adequate information to make sound decisions. Although there is some preliminary evidence that propranolol increases treatment retention in the outpatient treatment of cocaine dependence (Kampman et al., 1997), currently there are no approved medications to assist patients during detoxification protocols in a way that benzodiazepines are available for alcohol detoxification and methadone for opioid detoxification. The CSSA can give clinicians additional information about the intensity of cocaine craving and cocaine abstinence symptoms. This information can be used to decide whether a patient can manage in less structured, outpatient programs, or whether the con-
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straints and control of an inpatient hospital are necessary. Brief inpatient or residential stays, possibly of 2 to 3 days to reduce the instability produced by recent cessation of cocaine, may give patients a better chance of making it in an outpatient program in the absence of approved pharmacology. Limitations Several limitations of the research should be noted with these findings. The somewhat different treatment philosophy of the program these subjects enrolled in limits the generalizability of the findings. As stated previously, this program was lenient with regard to cocaine use during treatment. Since treatment attrition and cocaine use, the primary outcome measures in this study, were influenced by the clinic’s administrative policy, the predictive validity of the CSSA and the initial urine results may differ in treatment programs with more standard treatment philosophies, where patients are often discharged from treatment for dirty urines. Optimal cut scores from the CSSA that predict a poor response to treatment may vary in a separate sample. Participants in this study also participated in one of several other psychosocial studies on addiction as a condition of their receiving free treatment. Subjects who volunteer for treatment and for a research protocol suggests a desire for treatment and the motivation necessary to begin a recovery process. Patients forced into treatment by family members or by a court order may differ in their level of motivation to complete treatment and remain abstinent. This study was also conducted with urban, primarily African American cocaine users. It would be important to extend the findings to programs where treatment is independent of research, and with a more ethnically diverse group of patients. Finally, limitations in the sample size (n 5 87) could have had an important impact on the lack of predictive power of the derived models. A larger sample size would strengthen the parameter estimates of the combined models, which would presumably increase their predictive power. REFERENCES Agosti, V., Nunes, E., Stewart, J.W., & Quitkin, F.M. (1991). Patient factors leading to early attrition from an outpatient cocaine research clinic: A preliminary report. International Journal of Addictions, 26, 327–334. Alterman, A.I., McKay, J.R., Mulvaney, F.D., & McLellan, A.T. (1996). Prediction of attrition from day hospital treatment in lower socioeconomic cocaine dependent men. Drug and Alcohol Dependence, 40, 227–233. American Psychiatric Association. (1987). Diagnostic and statistical manual of mental disorders (3rd ed., Rev.). Washington, D.C.: Author. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author. Carroll, K.M., Rounsaville, B.J., Gordon, L.T., Nich, C., Jatlow, P., Bi-
Abstinence Symptomatology and Attrition sighini, R.M., & Gawin, F.H. (1994). Psychotherapy and pharmacotherapy for ambulatory cocaine abusers. Archives of General Psychiatry, 51, 177–187. Collins, J.J., & Allison, M. (1983). Legal coercion and retention in drug abuse treatment. Hospital and Community Psychiatry, 34, 1145– 1149. Cottler, L.B., Shillington, A.M., Compton, W.M., Mager, D., & Spitznagel, E.L. (1993). Subjective reports of withdrawal among cocaine users: Recommendations for DSM-IV. Drug and Alcohol Dependence, 33, 97–104. Hanley, J.A., & McNeil, B.J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29–36. Higgins, S.T., Budney, A.J., Bickel, W.K., Foerg, F.E., Donham, R., & Badger, G.J. (1994). Incentives improve outcome in outpatient behavioral treatment of cocaine dependence. Archives of General Psychiatry 51, 568–576. Holland, S. (1978). Gateway Houses: Effectiveness of treatment of criminal behavior. The International Journal of the Addictions, 13, 369–381. Hoffman, J.A., Caudill, B.D., Koman, J.J., Luckey, J.W., Flynn, P.M., & Hubbard, R.L. (1994). Comparative cocaine abuse treatment strategies: Enhancing client retention and treatment exposure. Journal of Addictive Diseases, 13, 115–128.
135 Kampman, K., McGinnis, D., Alterman, A., Weinrieb, R., D’Angelo, L., & Volpicelli, J. (1997). A case series of newly abstinent cocaine dependent outpatients treated with propanolol. NIDA Research Monograph, 174(Serial No. 184). Kampman, K., Volpicelli, J., McGinnis, D., Alterman, A., Weinrieb, R., D’Angelo, L., & Epperson L. (in press). Reliability an validity of the Cocaine Selective Severity Assessment (CSSA). Addictive Behaviors. Kleinman, P.H., Kang, S.Y., Lipton, D.S., Woody, G.E., Kemp, J., & Millman, R.B. (1992). Retention of cocaine abusers in outpatient psychotherapy. American Journal of Drug and Alcohol Abuse, 18, 29–43. McKay, J.R., Alterman, A.I., Cacciola, J.S., Rutherford, M.J., O’Brien, C.P., & Koppenhaver, J. (1997). Journal of Consulting and Clinical Psychology 65, 778–788. Schaeffer, R.L. (1983). Bias correction in maximum likelihood logistic regression. Statistics in Medicine, 2, 71–78. Stark, M.J. (1992). Dropping out of substance abuse treatment: A clinically oriented review. Clinical Psychology Review, 12, 93–116. Stata Corporation. (1995). Stata statistical software: Release 4.0. College Station, TX: Stata Corporation. Woody, G.E., McLellan, A.T., Luborsky, L., & O’Brien, C.P. (1995). Psychotherapy in community methadone programs: A validation study. American Journal of Psychiatry 152, 1302–1308.
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ARTICLE
Cocaine Abstinence Symptomatology and Treatment Attrition Frank D. Mulvaney, bs,* Arthur I. Alterman, phd,* Chris R. Boardman, ms,† and Kyle Kampman, md† *University of Pennsylvania School of Medicine, Department of Psychiatry, Veterans Affairs Medical Center, Philadelphia PA †The Children’s Hospital of Philadelphia, Division of Biostatistics and Epidemiology, Philadelphia, PA
Abstract – Premature termination from outpatient cocaine treatment predicts a number of poor outcomes, including higher rates of relapse and unemployment. This study attempted to predict dropouts from outpatient cocaine treatment, as well as those unable to achieve initial abstinence from cocaine, using two baseline variables that had previously been shown to predict treatment dropout: a measure of the severity of cocaine abstinence symptomatology using the Cocaine Selective Severity Assessment (CSSA) and the initial urine toxicology. Results of logistic regression analyses indicated that those with more intense abstinence symptoms, as measured by the CSSA, were five times more likely to terminate treatment prematurely. When combined with the CSSA, the initial urine did not significantly predict dropouts. The CSSA and the baseline urine were equal in their ability to predict those who would fail in their initial attempts to achieve abstinence. Implications for treatment are discussed. © 1999 Elsevier Science Inc. All rights reserved. Keywords – cocaine; treatment attrition, CSSA; logistic regression.
INTRODUCTION High dropout rates continue to present a major problem in the outpatient treatment of cocaine dependence (Agosti, Nunes, Stewart, & Quitkin, 1991; Alterman, McKay, Mulvaney, & McLellan, 1996; Kleinman et al., 1992). Premature termination often predicts a return to cocaine use and an increase in the associated problems of unemployment and incarceration (Agosti et al., 1991; Holland, 1978; Stark, 1992). Since attrition is so clearly associated with poorer outcomes, a number of studies have attempted to identify those patient characteristics
This work was supported by a National Institute on Drug Abuse Center Grant DA05186 and by the Department of Veterans Affairs. Requests for reprints should be addressed to Frank D. Mulvaney, BS, VA Medical Center, Substance Abuse Unit, Building 7, University and Woodland Avenues, Philadelphia, PA 19104.
predictive of early withdrawal from outpatient cocaine treatment (Alterman et al., 1996; Collins & Allison, 1983; Higgins et al., 1994; Kleinman et al., 1992). For example, Alterman and colleagues (1996) found that the initial urine toxicology result was a powerful predictor of dropout from a 27 hour per week, 4-week outpatient treatment program. Patients from the sample of male veterans with a cocaine positive urine at intake were twice as likely as those with a negative urine to drop out of treatment. This is consistent with the work of Higgins and colleagues (1994), who found that the presence of cocaine in the initial urine predicted treatment attrition. A second study by our center (Kampman et al., in press) also found that a high score on a measure of severity of cocaine abstinence symptomatology, the Cocaine Selective Severity Assessment (CSSA), predicted early termination from outpatient cocaine treatment. In this study, both cocaine-dependent and cocaine- and alcohol-depen-
Received September 2, 1997; Revised January 5, 1998; Accepted February 17, 1998.
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dent patients with elevated scores on a baseline CSSA were more likely to drop out of treatment within the first week. To summarize, our findings, based on two separate studies, indicate that both the initial urine toxicology and the CSSA predict treatment attrition. Few studies, however, have examined the combined predictive power of these two variables. The current study examines the combined predictive validity of the baseline urine toxicology and the CSSA for two outcomes—treatment dropout and the attainment of initial abstinence.
Despite the emphasis on patient abstinence, the program’s philosophy was fairly tolerant of episodic cocaine use, and a patient was not automatically discharged for this behavior. A relapse was not seen as a treatment failure, but as an opportunity to learn more about the conditioned triggers that can initiate a relapse. Relapses are also viewed as opportunities to develop coping strategies to help manage craving and other uncomfortable affective states that occur during the treatment period, particularly in the early stages of abstinence. Retention rates, then, should be considered in light of this relatively lenient discharge policy.
METHODS Predictor Variables Subjects Subjects for this study were 87 cocaine-dependent men and women from the University of Pennsylvania’s Treatment Research Center (TRC), an outpatient addictions treatment research facility for cocaine, alcohol, and opioid dependence. The subjects were recruited at TRC from March 1992 to April 1993, and only those who completed the CSSA, gave an initial urine, and whose treatment status and urine toxicology were known at the 1-month follow-up were included in the study. All subjects met Diagnostic and Statistical Manual of Mental Disorders, third edition, revised (DSM-III-R; American Psychiatric Association, 1987) criteria for cocaine dependence, but were excluded if they had another current Axis I disorder other than substance use disorders. Alcohol abuse and dependence were the other substance use disorders most commonly present, with 37% (n 5 32) of the participants meeting DSM-III-R criteria for alcohol dependence. Most of the subjects were African American (90%), single (85%), and male (70%), with slightly more than half of the sample working (52%). They averaged almost 5 years (M 5 4.70; SD 5 3.5) of regular cocaine use and almost 11 days (SD 5 9.6) of cocaine use in the month prior to entering treatment. A majority of the participants smoked cocaine (77%), with the remaining subjects equally divided in their preference for snorting and injecting. Pharmacotherapy was available to subjects if needed, with the most common prescriptions written for oxazepam (n 5 14, 16%). Treatment Program The subjects participated in a treatment program that offered a combination of twice weekly individual relapseprevention counseling sessions with a master’s level therapist and twice weekly group therapy, again led by a master’s level therapist. The program was similar to most substance abuse treatment programs in that abstinence was the primary goal. Participation in 12-step groups was encouraged, although there were no meetings on-site.
Two measures were used to predict treatment attrition: the initial urine toxicology for cocaine and the CSSA scale. The urine specimens were analyzed by our Center’s toxicology laboratory using the semiquantitative method of fluorescence polarization immunoassays (FPIA-Abbott ADx machine), with positive samples confirmed by gas chromatography/mass spectrometry. For both methodologies a reading of more than 300 ng/ ml was taken as an indication of recent cocaine use. The second predictor, the CSSA, is an 18-item, interviewer-administered measure of the severity cocaine abstinence symptomatology (Kampman et al., in press). Some of the symptoms associated with initial cocaine abstinence and measured by the CSSA include the intensity and frequency of cocaine craving, dysphoric mood, fatigue, increases or decreases in appetite, and irritability (American Psychiatric Association, 1994; Cottler et al., 1993). Ratings on all 18 items of the CSSA range from scores of 0 to 7, with 0 indicating that the symptom is absent all the time and 7 indicating that the symptom is present most of the time. Two sets of items are paired, such that a high score on one of the items requires a low score on its pair, or vice versa. For example, a score of 7 on an item indicating hypophagia, a decrease in appetite, necessitates a score of 0 on another item indicating hyperphagia, an increased appetite. Therefore, the total CSSA score ranges from 0 to 112, which is slightly lower than the sum of all the items because of the pairs. Higher scores indicate more intense cocaine abstinence symptoms. Patients were administered the CSSA upon admission to the clinic, which was an average of 13 days after they had last used cocaine (SD 5 71 days). The median number of days between the administration of the CSSA and patients’ last cocaine use was 3. In a recent study of 138 admissions to an outpatient substance abuse clinic, the CSSA demonstrated excellent interrater reliability (Pearson’s r 5 .92, p , .001) and high internal consistency in cocaine-dependent subjects (Cronbach’s alpha 5 .80) (Kampman et al., in press). Moreover, the CSSA was able to discriminate abstinence symptoms between cocaine-dependent subjects and alcohol-dependent subjects, with cocaine-dependent sub-
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jects producing higher scores on the CSSA than the alcohol-dependent subjects. Since it measures cocaine abstinence symptomatology on a continuous scale and because of its robust psychometric properties, the CSSA was chosen to provide a subjective, self-report measure of cocaine abstinence symptomatology. The initial urine toxicology result was used as a measure of recency of cocaine use. Both measures have independently demonstrated a relationship to treatment attrition (Alterman et al., 1996; Kampman et al., in press; Stark, 1992), but what has not been examined is their relationship to each other and any possible increase in the ability to reliably predict attrition by combining the two measures. Outcome Measures Two dichotomous measures of unfavorable treatment response were used, (a) dropped from treatment within the first 30 days and (b) failed to attain initial abstinence within the first 30 days. Three successive clean cocaine urines within the first month of treatment, independent of the baseline urine, defined the second measure of unfavorable treatment response. Patients normally gave three urines a week, and all missed urines were considered cocaine positive. Both outcome measures were coded dichotomously, with 1 indicating treatment dropouts or a failure to remain abstinent, and 0 indicating treatment completion or clean urines for the first month of treatment. This may seem counterintuitive, that treatment completion was coded a 0 and treatment dropout was coded a 1, but this direction was chosen because of the desire to predict dropouts. Analyses Logistic regression analyses were performed for the two outcome measures with the statistical software STATA® (Stata Corporation, 1995). Regression models generated for the predictor variables first considered independently, then with both predictors entered simultaneous. For these regression models sensitivity indicates the percentage of dropouts correctly predicted and specificity indicates the percentage of retained subjects correctly predicted. Along with the model chi-square, sensitivity and specificity are the usual means of interpreting the predictive power of individual models. Additionally, as part of the logistic regression procedure, STATA® produces a receiver operating characteristics (ROC) curve. For logistic regression models, the ROC curve is equivalent to a plot of the sensitivity by 1-specificity values, and the area subsumed by this plot serves as a single, quantitative value indicating the probability of correctly predicting categorical outcomes (Hanley & McNeil, 1982). In essence, the area under the ROC curve represents the probability that the regression model will correctly identify completors and dropouts or
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abstainers and users; a combined sensitivity and specificity probability. Probabilities range from 0.5, indicating no discrimination between the two possible outcomes, and 1.0, indicating perfect discrimination. The area under the ROC curves for the different models are reported. Along with the sensitivity, specificity, and model chisquare, the area under the curve provides a basis for evaluating different models and for identifying that score from the CSSA that has the highest probability of correctly identifying the outcomes. In separate analyses, dichotomous cutpoints for the entire range of CSSA scores were established and used to obtain a functional threshold for the application of the CSSA in predicting the outcomes. These dichotomous cutpoints were then entered with the initial urine into the logistic regression equations to establish the CSSA cutoff score that would result in the best predictive model, as measured by the area under the ROC curve. A cutpoint was also sought for the CSSA to assist clinicians in determining the score from the CSSA that above which would predict a high risk of premature treatment termination. Finally, as a check of the robustness of the resultant models, 10% of the subjects were randomly removed and the analyses repeated to determine if a different sample would change the conclusions or change the predictors’ significance.
RESULTS Correlations Between Predictor Variables and Outcome Variables Before undertaking logistic regression analyses we first examined the bivariate correlations between the two predictor variables and the two outcome measures. Pointbiserial correlations revealed that the total CSSA score was correlated significantly with dropping out of treatment (r 5 .39, p , .001) and with a failure to attain initial abstinence (r 5 .24, p 5 .03). The initial urine, however, correlated significantly with the achievement of initial abstinence (r 5 .29, p 5 .01), but not with treatment dropout (r 5 .20, p 5 .07). Intercorrelations between the two predictors were then examined. While the recency of cocaine use, as measured by the initial urine toxicology, and the subjective severity of cocaine abstinence symptoms, as measured by the CSSA, are conceptually related, they appear to be measuring different aspects of cocaine use and withdrawal. The point-biserial correlations between the two predictors were low (r 5 .15, p 5 .16). This indicates that, in this sample at least, the initial urine and the CSSA are largely independent of each other. Their independence legitimates the use of regression analyses to determine the separate and combined effects of the two on the outcome measures.
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Predicting Dropouts Within the First Month of Treatment A total of 37 (43%) patients dropped out of treatment within the first 30 days. The CSSA score was considered first as a continuous measure to predict dropouts. When the CSSA was entered alone into a logistic regression model the chi-square was 14.18 (df 5 1, p 5 .001), with a sensitivity of 49% (18/37), a specificity of 84% (42/50), and an overall probability of correct prediction at 0.74. When the baseline urine was entered alone the model chisquare was 3.35 (df 5 1, p 5 .07), with a sensitivity of 68% (25/37), a specificity of 52% (26/50), and an overall probability of correct prediction at 0.60. Entering both independent variables simultaneously produced a model that was better than chance at predicting dropouts (x2 5 16.11, df 5 2, p , .001). With a sensitivity of 54% (20/37), a specificity of 84% (42/50), and an overall probability of correct prediction at 0.73, the combined model was not appreciably better than the model with the CSSA alone. Next, the CSSA was dichotomized along the entire range of scores and each dichotomous variable was entered into a separate logistic regression model. Based on maximum values of sensitivity, specificity, and area under the ROC curve, CSSA scores greater than or equal to 24 proved to be the best level of the instrument for predicting dropouts. For the model with the dichotomized version of the CSSA, the model chi-square was 14.49 (df 5 2, p , .001), and subjects with a CSSA score of 24 or more were five times (confidence interval [CI] 5 2.08–13.25, p , .01) more likely than those with scores less than 24 to leave treatment within the first month of the program. The sensitivity for this model was 65% (24/ 37), with a specificity of 74% (37/50) and an overall probability of .69. When the baseline urine and the dichotomous cut of the CSSA were entered simultaneously into a regression equation, indicated in Table 1, the model chi-square was 14.49 (df 5 2, p , .001), with a sensitivity of 65% (24/37), a specificity of 74% (37/50), and an overall probability of correct prediction at 0.72. This model, with both predictors, was better than the urine
alone model (ROC area 5 .72 vs. .60) but not appreciably different than the CSSA only model (ROC area 5 .72 vs. .69).
Predicting a Failure to Achieve Initial Abstinence Within the First Month of Treatment With the CSSA score again entered as a continuous measure and entered alone into a model predicting a failure to attain abstinence, the model chi-square was 5.6 (df 5 1, p , .05), the sensitivity was 100% (63/63), the specificity was 0% (0/24), and the overall probability of correct prediction was .66. Entering only the baseline urine produced a logistic regression model with a chi-square of 7.14 (p , .01), a sensitivity of 100% (63/63), a specificity of 0% (0/24), and an overall predictive probability of .66. Finally, entering both predictors simultaneously produced a model chi-square of 11.04 (p , .01), a sensitivity of 92% (58/63), a specificity of 25% (6/18), and an overall predictive probability at .73. The combined model, therefore, was slightly better than the individual models at predicting those would not attain initial abstinence (area under ROC 5 .73 vs. .66 and .66), while there was no difference in predictive strength between the individual models (area under the ROC was .66 for both). In a series of steps similar to those conducted when attempting to best predict dropouts, the CSSA was dichotomized along the entire range of scores and each dichotomous variable was entered into a separate logistic regression model. Based on an evaluation of the area under the ROC curve, CSSA scores above 31 proved the best level of the instrument for predicting a failure to achieve initial abstinence. When a subject’s CSSA score was 31 or more they were five times more likely than those with scores less than 31 to fail in attempts to attain abstinence in the first month of treatment (95% CI 5 1.33–18.22, p 5 .02). For this model, the chi-square was 7.26 (p , .01), with a sensitivity of 100% (63/63), a specificity of 0% (0/24), and an overall probability of correct prediction at .64.
TABLE 1 Prediction of Dropouts and Those Who Failed to Attain Initial Abstinence
Model Variables
Odds Ratio
Significance
95% CI
Dropped out of treatment within the first 30 days Model x2 5 14.49 Sensitivity 5 65%
p , .001 Baseline urine 1.67 0.29 Specificity 5 74% CSSA cut at 24 4.75 ,0.01 Probability of correctly identifying dropouts and completors 5 0.72
0.64–4.35 1.85–12.18
Unable to attain initial abstinence within the first 30 days Model x2 5 12.06 p , .01 Baseline urine 3.08 0.03 Sensitivity 5 100% Specificity 5 0% CSSA cut at 31 3.99 0.04 Probability of correctly identifying those who failed to attain initial abstinence 5 0.72 CI 5 confidence interval; CSSA 5 Cocaine Selective Severity Assessment.
1.10–8.59 1.04–15.25
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The initial urine was also a powerful predictor of the second outcome measure, with an initial dirty urine associated with a four-fold increase in the likelihood of failing to achieve initial abstinence (95% CI 5 1.38–10.07, p 5 .01). With the baseline urine entered alone the model chi-square was 7.14 (df 5 1, p , .01), with a sensitivity of 100% (63/63), a specificity of 0% (0/24), and an overall probability of correct prediction at .66. The model combining the two predictors did offer a slight improvement over the individual models (area under the ROC 5 .72 vs. .64 and .66). The combined model yielded a chisquare of 12.06 (p , .01), with a sensitivity of 100% (63/ 63), a specificity of 0% (0/24), and an overall probability of correct prediction at .72 (see Table 1). Since the models predicting failure to attain initial abstinence resulted in extreme values for sensitivity and specificity, 100% and 0%, respectively, we explored several possible influences on the parameter estimates and ways to investigate the impact of this imbalance on the model’s sensitivity and specificity. First, it seems reasonable that the relatively small sample size of 87 subjects could bias the parameter estimates (Schaeffer, 1983). One way to correct for this small sample size would be to repeat these analyses with a larger, similar sample. Unfortunately, it is unrealistic for this clinic to conduct a larger study at this time. Another concern was the unbalanced nature of the two outcome categories influencing sensitivity and specificity percentages, with only 28% (n 5 24) of the subjects attaining initial abstinence within the first month and the other 72% (n 5 63) giving a cocainepositive urine in that time period. To evaluate this question, the analyses were repeated with 50 subjects; 24 subjects who attained initial abstinence and a randomly selected group of 24 subjects from the 63 who did not attain abstinence. This analysis resulted in a sensitivity of 83% and a specificity of 58%. Therefore, it appears that the unbalanced nature of the outcome measure was dramatically influenced the sensitivity and specificity of the models predicting a failure to attain abstinence. Robustness of the Models For the model predicting dropouts within the first month, reanalysis of the data with 10% of the sample randomly removed produced a model almost identical to the regression model generated with the entire sample. The CSSA total score cut at 24 remained stronger than the initial urine as a predictor of dropouts within the first month, with an odds ratio of 4.65 (CI 5 1.1826.12). This model, using a random 90% of the larger sample, had a sensitivity of 62% (21/34) and a specificity of 76% (34/45), similar to the 65% (24/37) sensitivity and the 74% (37/50) specificity seen in the entire sample. This indicates that the first model was robust. For the second model predicting achievement of initial abstinence the model was less robust. When 10% of the sample were randomly removed the model chi-square
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remained significant (x2 5 8.10, df 5 2, p 5 .02), but neither the initial urine (Wald coefficient 5 2.96, p 5 .09) nor the dichotomous categorization of CSSA into scores above and below 31 (Wald coefficient 5 2.99, p 5 .08) predicted the second outcome measure. This lack of robustness, along with the extreme values of sensitivity and specificity, indicate the instability of this second model. A final set of analyses were conducted to examine whether gender predicted treatment attrition or the failure to attain initial abstinence. Gender and the interaction between gender and the two predictors—the CSSA cut scores and the initial urine—were entered into logistic regression models. Gender alone did not discriminate between those who dropped out of treatment and those who completed treatment (Wald 5 .21, p 5 .65), nor was the interaction between gender and the CSSA cut at a score of 24 a significant predictor of dropouts (Wald 5 .01, p 5 .94). Gender did not interact with the baseline urine to influence the prediction of dropouts (Wald 5 .001, p 5 .97). The pattern of men and women having a similar outcome on the dependent measure continued in the prediction of those who failed to achieve initial abstinence. Gender distinctions proved unimportant both alone (Wald 5 .02, p 5 .90) and in combination with the CSSA cut at 31 (Wald 5 .99, p 5 .32) when predicting those who failed to achieve abstinence in the first month of treatment. Gender alone (Wald 5 .97, p 5 .33) and as an interaction term with the initial urine (Wald 5 .83, p 5 .36) indicated that men and women failed to achieve initial abstinence in roughly equal proportions. To summarize the findings related to gender, all the analyses conducted that used gender as an independent variable indicated that the CSSA and the initial urine toxicology predicted the two outcomes equally well for both men and women. DISCUSSION Successfully completing a specified length of time in an outpatient treatment program, usually a minimum of 28 to 30 days to a more optimal time of 6 months, is a reliable predictor of reduced substance misuse and better social, psychological, and occupational outcomes in cocaine-dependent patients (Kleinman et al., 1992; Stark, 1992). Researchers have also demonstrated that recent cocaine use at the beginning of treatment, indicative of a more serious substance use disorder, predicts premature treatment termination and worse outcomes (Alterman et al., 1996; Higgins et al., 1994). Those individuals with more severe problems at treatment entry of those at greater risk of terminating treatment prematurely are not necessarily lost causes. Once identified, patients with more severe substance use problems initially can achieve treatment gains similar to those who entered treatment with less severe problems when more intensive, individual services are available (Carroll et al., 1994; Hoffman
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et al., 1994; McKay et al., 1997; Woody, McLellan, Luborsky, & O’Brien, 1995). Given the aforementioned negative correlates of dropping out of treatment, the primary objective of this study was to increase the reliable identification of those with an increased chance of dropping out or responding poorly to treatment by combining two predictors found independently correlated with these outcomes, that is, the intake urine and the intake CSSA score (Alterman et al., 1996; Higgins et al., 1994; Kampman et al., in press). Logistic regression analyses indicated that the CSSA was better than the initial urine at predicting treatment attrition. Patients who scored greater than 24 on the CSSA were five times more likely than those with a score of less than 24 to terminate treatment prematurely. A second logistic regression demonstrated that the CSSA and the initial urine toxicology were equal in their ability to predict those who abstained from cocaine during their first month in treatment. Those with either a score of 31 or more on the CSSA or an initial cocainepositive urine were three to four times more likely to fail to achieve abstinence within the first month of treatment. Combining both the initial urine and the CSSA did not increase the logistic regression model’s ability to correctly identify those who would continue to use cocaine. Choosing a questionnaire over a lab result may also represent a reasonable, practical choice. First, hospital labs in most clinics and hospitals require a few days to return urine toxicology results. For free-standing drug clinics or clinics in more remote areas, the time from when an intake urine is given and when the lab results are returned is usually longer. Patients with intense cocaine craving may be gone, using again, before these results ever return. The CSSA takes approximately 15 minutes to administer and score. If the CSSA can predict the likelihood of treatment dropout as well as an initial urine toxicology, which this study demonstrates, clinicians may consider using the questionnaire as a way to identify at intake those who are at risk of not completing treatment. There is more pressure on clinicians today to make rapid judgments about the types of treatments that patients need, particularly the intensity level of treatment necessary. Sometimes staff are given an incomplete or distorted presentation by a client on intake, and the clinician may feel that he or she does not have adequate information to make sound decisions. Although there is some preliminary evidence that propranolol increases treatment retention in the outpatient treatment of cocaine dependence (Kampman et al., 1997), currently there are no approved medications to assist patients during detoxification protocols in a way that benzodiazepines are available for alcohol detoxification and methadone for opioid detoxification. The CSSA can give clinicians additional information about the intensity of cocaine craving and cocaine abstinence symptoms. This information can be used to decide whether a patient can manage in less structured, outpatient programs, or whether the con-
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straints and control of an inpatient hospital are necessary. Brief inpatient or residential stays, possibly of 2 to 3 days to reduce the instability produced by recent cessation of cocaine, may give patients a better chance of making it in an outpatient program in the absence of approved pharmacology. Limitations Several limitations of the research should be noted with these findings. The somewhat different treatment philosophy of the program these subjects enrolled in limits the generalizability of the findings. As stated previously, this program was lenient with regard to cocaine use during treatment. Since treatment attrition and cocaine use, the primary outcome measures in this study, were influenced by the clinic’s administrative policy, the predictive validity of the CSSA and the initial urine results may differ in treatment programs with more standard treatment philosophies, where patients are often discharged from treatment for dirty urines. Optimal cut scores from the CSSA that predict a poor response to treatment may vary in a separate sample. Participants in this study also participated in one of several other psychosocial studies on addiction as a condition of their receiving free treatment. Subjects who volunteer for treatment and for a research protocol suggests a desire for treatment and the motivation necessary to begin a recovery process. Patients forced into treatment by family members or by a court order may differ in their level of motivation to complete treatment and remain abstinent. This study was also conducted with urban, primarily African American cocaine users. It would be important to extend the findings to programs where treatment is independent of research, and with a more ethnically diverse group of patients. Finally, limitations in the sample size (n 5 87) could have had an important impact on the lack of predictive power of the derived models. A larger sample size would strengthen the parameter estimates of the combined models, which would presumably increase their predictive power. REFERENCES Agosti, V., Nunes, E., Stewart, J.W., & Quitkin, F.M. (1991). Patient factors leading to early attrition from an outpatient cocaine research clinic: A preliminary report. International Journal of Addictions, 26, 327–334. Alterman, A.I., McKay, J.R., Mulvaney, F.D., & McLellan, A.T. (1996). Prediction of attrition from day hospital treatment in lower socioeconomic cocaine dependent men. Drug and Alcohol Dependence, 40, 227–233. American Psychiatric Association. (1987). Diagnostic and statistical manual of mental disorders (3rd ed., Rev.). Washington, D.C.: Author. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author. Carroll, K.M., Rounsaville, B.J., Gordon, L.T., Nich, C., Jatlow, P., Bi-
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