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A longitudinal evaluation of treatment engagement and recovery stages
Journal of Substance Abuse Treatment 27 (2004) 89 – 97
Regular article
A longitudinal evaluation of treatment engagement and recovery stages D. Dwayne Simpson, (Ph.D.) *, George W. Joe, (Ed.D.) Institute of Behavioral Research, Texas Christian University, Fort Worth, TX, USA Received 28 March 2003; received in revised form 18 February 2004; accepted 15 March 2004
Abstract Recent methodological advancements for structural equation modeling were used to test a comprehensive version of the TCU Treatment Model, especially for addressing the hypothesized sequential relationships of early engagement components (participation and therapeutic relationship) and early recovery (psychosocial and behavioral changes) that contribute to retention and posttreatment recovery. Relationships among pretreatment patient motivation, treatment process elements, a cognitive-based treatment strategy, retention, and drug use outcomes were estimated using intake, during treatment, and 1-year followup data for 711 patients in outpatient methadone treatment. Hypothesized sequential elements representing treatment process and patient functioning were supported, and relationships between these components were estimated also as odds ratios as an aid for translating the findings and increasing their clinical usefulness to treatment settings. D 2004 Elsevier Inc. All rights reserved. Keywords: Treatment process; Patient functioning; Engagement; Recovery stages; Motivation; Cognitive-based treatment
1. Introduction Posttreatment studies of patients treated in publiclyfunded, community-based drug treatment programs have shown consistently throughout the past 30 years that drug use and related problems decrease significantly from intake levels (e.g., Finney, Ouimette, Humphreys, & Moos, 2001; Gerstein & Harwood, 1990; Gossop, Marsden, Stewart, & Rolfe, 1999; Hubbard, Craddock, Flynn, Anderson, & Etheridge, 1997; Hubbard et al., 1989; Simpson & Curry, 1997; Simpson & Sells, 1982). These evaluations have included different types of outpatient methadone treatment, residential treatments such as therapeutic communities, and various outpatient drug free programs using natural designs for documenting treatment impact in real-world settings. They also have focused on thresholds of ‘‘adequate treatment,’’ defined as the length of stay in treatment needed to achieve statistically significant changes in posttreatment outcomes, building on early outcome studies generally showing retention to be the strongest and most reliable predictor of drug use and criminality improvements (e.g., De Leon, Jainchill, & Wexler, 1982; Joe, Simpson, & Hubbard, 1991; Simpson,
* Corresponding author. Tel.: +1-817-257-7226; fax: +1-817-257-7290. E-mail address: [email protected] (D.D. Simpson). 0740-5472/04/$ – see front matter D 2004 Elsevier Inc. All rights reserved. doi:10.1016/j.jsat.2004.03.001
1979, 1981; Simpson, Joe, Fletcher, Hubbard, & Anglin, 1999). Minimum retention thresholds for different treatment modalities were identified, averaging about 3 months for residential and outpatient drug-free treatments and a year for outpatient methadone treatment. Depending on patient problem severity at intake and therapeutic setting, treatment improvements typically begin when retention extends beyond these ‘‘thresholds.’’ Treatment retention also has been recognized, however, as a convenient index that can mediate the impact of a diverse set of patient background, therapeutic, and environmental factors on treatment effectiveness. Some research has been refocused to address patient and treatment indicators that predict retention (e.g., Buhringer, Gossop, Turk, Wanigaratne, & Kaplan, 2001; Festinger, Rubenstein, Marlowe, & Platt, 2001; Hser, Grella, Chou, & Anglin, 1998; Simpson, Joe, Rowan-Szal, & Greener, 1995; Siqueland et al., 2002; Tims, Leukefeld, & Platt, 2001), but the interrelationships among patient, process, and environmental domains still need attention to advance our understanding of treatment effectiveness (Lamb, Greenlick, & McCarty, 1998; Melnick, De Leon, Thomas, Kressel, & Wexler, 2001; Simpson, 2001). Particularly important are the broader cognitive, social, and environmental factors that contribute to the recovery process (De Leon, 2000; Prochaska & DiClemente, 1986) as well as the motivation needed to sustain the process (Miller, 1989). Similarities
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are found in examples of stepped or staged care treatment models (Brooner & Kidorf, 2002; Sobell, & Sobell, 2000; Weissberg & Greenberg, 1998). Because recovery from addiction often involves recurrent relapse and treatment readmission episodes, we also need better conceptual frameworks for conducting long-term longitudinal outcome studies using large representative samples in natural research designs (Hser, Anglin, Grella, Longshore, & Prendergast, 1997; Simpson & Sells, 1990). Establishing conceptual foundations representing the way treatment process and outcomes are related has been the primary focus of research summarized by the TCU Treatment Model (Simpson, 2001, 2004; Simpson, Joe, Dansereau, & Chatham, 1997; Simpson & Knight, 2003). This model was created initially as a heuristic to integrate a body of research that accrued as part of several interrelated projects conducted over the past three decades (Simpson & Brown, 1999; Simpson, Joe, Rowan-Szal, & Greener, 1997; Simpson & Sells, 1982, 1990). Hypotheses concerning the relationships among its components have been tested and supported in diverse treatment settings and samples (e.g., Broome, 1996; Broome, Joe, & Simpson, 2001; Broome, Simpson, & Joe, 1999; Hiller, Knight, Leukefeld, & Simpson, 2002; Joe, Simpson, & Broome, 1999), and analytic models have become more comprehensive over time for making integrative predictions about retention and posttreatment outcomes (Simpson, Joe, Greener, & Rowan-Szal, 2000). The consistency of findings across diverse data systems is suggestive of the model’s robustness, but further testing is needed on particular sequencing of the behavioral and cognitive treatment engagement stages involved. Core components of ‘‘early engagement’’ include session participation and the forging of therapeutic relationships in the initial few weeks following treatment entry, but these are impacted by patient motivation, readiness for treatment, and problem severity at intake. These engagement steps are related, in turn, to ‘‘early recovery’’ indicators of psychosocial (including cognitive) and behavioral changes. In this framework, the primary role of treatment is to promote and sustain the progression of changes in patient thinking and acting, so we have focused on examining behavioral and cognitive interventions shown to be effective in selectively improving stage-specific treatment indicators (Joe, Dansereau, Pitre, & Simpson, 1997; Simpson, Joe, Dansereau, et al., 1997). Central elements of this treatment process model are the two early engagement components (program participation and therapeutic relationship) and their impact on early recovery (represented by favorable psychosocial and behavioral functioning). Depending on the particular timing of data collection schedules, engagement components have been modeled as either having a reciprocal (i.e., nonrecursive) or unidirectional (recursive) relationship between program participation and therapeutic relationship. Early engagement is also seen in the model as having effects on
(subsequent) indicators of early recovery. Previous research has focused most heavily on the prediction of behavioral change (e.g., urinalysis results), but psychosocial functioning also deserves careful attention as an important intervening variable based on its relationship to engagement (Joe et al., 1999), drug use during treatment (Broome, 1996; Joe, Knezek, Watson, & Simpson, 1991), and retention (Carroll, Power, Bryant, & Rounsaville, 1993; Joe et al., 1999; Simpson & Joe, 1993; Siqueland et al., 1998). Problem severity and motivation of patients at intake (e.g., Broome, 1996; Hiller et al., 2002) as well as use of cognitive-based interventions (Dansereau, Joe, & Simpson, 1993) also are related to psychosocial functioning during treatment. In previous studies, the relationships between these components have been estimated and found to be statistically significant, but only in ‘‘submodels.’’ That is, all of the relationships were not estimated simultaneously, owing in large part to statistical software limitations that required complete data when those studies were carried out. The present study takes advantage of recent methodological advancements for handling missing data in performing simultaneous evaluations of the treatment process and patient functioning (including pretreatment motivation which has been shown to influence treatment progress). A secondary objective is to provide drug treatment programs with user-friendly measures of ‘‘relative risk’’ representing these relationships. The reason for this objective is to translate complex statistical parameters into indicators more useful to drug treatment programs and thereby interpret research results for practical applications (see Joe, Broome, Rowan-Szal, & Simpson, 2002; Simpson, 2002). Although there are several alternatives possible, a commonly used procedure involving odds-ratios was selected as a way to represent sequential relationships in terms of the ‘‘statistical odds’’ of an event occurring. Using multivariate findings from the structural equation model as a guide, therefore, measures were dichotomized and used in contingency table analyses to estimate odds-ratios for simplifying clinical interpretations.
2. Method 2.1. Sample A total of 711 patients were admitted to three not-forprofit, community-based outpatient methadone treatment programs (located in two large urban areas and one medium sized city) during 1990 to 1993 (Simpson, Joe, Dansereau, et al., 1997). A comprehensive series of intake, during treatment, and followup assessments were completed which fully complied with federal and Institutional Review Board requirements to protect rights of human subjects. Of these 711 admissions during this period, 643 were successfully located in a 1-year followup (90.5%). Face-to-face interviews were completed with 435 (61%),
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but 14 (2%) refused to be interviewed, 21 (3%) were deceased, 3 (0.5%) were medically incapacitated, 36 (5%) had moved out of the state, and 134 (19%) were not interviewed due to imprisonment. We found no evidence that patient participation and performance for this incarcerated sample differed significantly from other patients during treatment (Hiller, Simpson, Broome, & Joe, 2000). The large number of patients in prison at followup was associated primarily with expansion of statewide prison facilities and an increased rate of overall parole revocations in Texas during our study period (e.g., 48% of all 711 admissions entered treatment with a legal status, including 35% on parole). Over half (55%) of the 711 admissions terminated treatment within 180 days, and 36% within 90 days, with only a fourth (25%) staying a year or more. Average age was 37; 71% were male; 36% of the patients were white, 45% Hispanic, and 16% black; 44% were married or living as married; 44% had a full or part time job; 48% had a legal status at admission. Also, in terms of drug use, besides opiates, 29% were daily users of alcohol, 22% weekly users of marijuana, and 45% weekly users of cocaine (27% cocaine alone and 34% in combination with heroin). Because the patients were from multiple treatment programs, several factors were considered in the decision process for defining sample subgroups. One of the two urban area programs ceased operations early in the study, and consequently contributed only 73 patients (10%) to the total sample. Its patients were therefore combined with those from the other treatment program from a large urban area, forming one of the two groups (n = 320) included in the multigroup structural equation analysis. The other group (n = 391) was comprised of patients treated in a mediumsized city. The race-ethnic composition, education, and cocaine use supported this combining of the urban-area patient groups for analysis. For instance, the program in the medium-sized city (Group M) had only 1.5% blacks, compared to 52% and 29%, respectively, for the two urban programs that were combined (Group U). The patients in the urban programs (Group U) were more highly educated (53% and 44% were high school graduates, respectively) and more likely to be using cocaine daily (53% and 35%, respectively); in Group M, 28% were high school graduates and 19% were daily cocaine users. Overall group comparisons showed the 320 patients in Group U had an average age of 39, 25% were Hispanic, 34% were Black, 42% were married or living as married, 42% were employed full or part time, and 45% had a legal status at admission. In terms of drug use, 30% used alcohol daily, 19% used marijuana weekly, and 54% used cocaine weekly. For the 391 patients in Group M, the average age was 35, 62% were Hispanic, 2% were Black, 46% were married or living as married, 47% were employed full or part time, and 51% had a legal status at admission. Regarding drug use, 29% used alcohol daily, 25% used marijuana weekly, and 38% used cocaine weekly.
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2.2. Measures The measures addressed in the TCU Treatment Model include treatment readiness at intake, session attendance in Month 2, counseling rapport in Month 2, psychosocial functioning in Month 3, opiate or cocaine use in Month 3, time in treatment, and opiate or cocaine use at the 1-year followup interview. Treatment readiness is a motivation indicator for the model and is measured using eight items from the TCU motivation scale with each item rated on a five-point Likert scale for assessing commitment levels and expectations about how helpful treatment will be (Joe et al., 2002; Simpson & Joe, 1993). Coefficient alpha reliability was .75, with scores ranging from 0 to 4. Session attendance at Month 2 was defined by the number of counseling sessions attended during the second month in treatment, and ranged from 0 to 16. The measure of counseling rapport was used in previous research as the primary indicator of therapeutic relationship between counselor and patient and was found to be a significant predictor of during treatment (Simpson, Joe, Rowan-Szal, et al., 1997) and posttreatment treatment outcomes (Joe, Simpson, Dansereau, & Rowan-Szal, 2001). It consists of six items (‘‘easy to talk to,’’ ‘‘warm and caring,’’ ‘‘honest and sincere,’’ ‘‘understanding,’’ ‘‘not suspicious,’’ ‘‘not in denial about problems’’), each rated on a five-point Likert scale (Never, Rarely, Sometimes, Often, Almost Always) by counselors of their patients, and for this study the ratings at Month 2 of the index treatment were used. The rapport scale had an alpha reliability of .79, with scores ranging from 0 to 4. The psychosocial functioning index was based on six scales from the TCU Client Evaluation of Self and Treatment (CEST; Joe et al., 2002) that represented the domains of psychological and social functioning. The CEST was completed by patients each month during the first 3 months of treatment, but only scores from the third month were used in this study. The number of items varied between six and nine for these scales, with each based on self-ratings using five-point Likert-type items (where 2 was the midpoint of the response scale). The intent of the index was to capture psychosocial functioning in terms of the number of problems. The index was scored in the ‘‘positive’’ direction represented by high self-esteem ( > 2.6), low depression (V1.5), low anxiety (V1.1), low risk taking (<1.3), high social conformity (>2.0), and high decision making (>2.3). The range of the psychosocial functioning index was 0 to 6. Information on self-reported illegal opioid and cocaine use was collected at Month 3 in treatment and at the 1-year followup interview. Self-reported use of these two drugs was combined into a single measure of ‘‘opioid and cocaine use,’’ where the range of scores was ‘‘0’’ (no use) to ‘‘8’’ (4 or more times a day). To facilitate interpretation with respect to direction of relationships among the variables in the structural modeling, the combined illegal opioid and
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cocaine use measure was reverse-scored to reflect ‘‘low opioid and cocaine use’’ in the analysis; that is, a high score reflected low use while a low score reflected high use. Separate measures were computed for Month 3 during treatment and for the 1-year followup period. The influence of a cognitive treatment strategy, called node-link mapping, was also assessed. Counselors were taught to use a two-dimensional method for representing personal issues that provided a visual focus for on-task attention of both patient and counselor, thereby enhancing counseling (Dansereau et al., 1993; Simpson, Joe, Dansereau, et al., 1997). Node-link mapping depicts thoughts, feelings, and actions as nodes and describes relationships between nodes by named links (Dansereau & Dees, 2002). This graphical display is developed and used by the counselor and patient to explore the antecedents and consequences of problem situations. Data for the present study were collected from an experimental study assessing the effects of mapping vs. non-mapping treatment, with patients having been randomly assigned to counselors who had been randomly assigned either to use mapping enhancement (or not) in their counseling. Time in treatment was the number of days in treatment, from intake to termination. For the analyses, the square root transformation was performed. 2.3. Analysis A two-group structural equations model was estimated using Mplus (Muthe´n & Muthe´n, 1998). The estimates were based on all available data for each variable in the model, using the ‘‘missing data method’’ to obtain maximum likelihood estimates under the assumption of missing data at random. The root mean square error of approximation (RMSEA) was the model fit index used to evaluate the adequacy of the model (Browne & Cudeck, 1993). For this index, a value of .05 or less indicates a close fit, a value of about .08 or less a reasonable error of approximation, and a value greater than .10 a poor fit (Browne & Cudeck, 1993). For the second objective involving computation of measures of relative risk representing the relationships, contingency table analyses adjusted for treatment program membership (Group M and Group U) were performed on contiguous variables in the model, based on all available data. For these contingency tables, the variables were dichotomized and results interpreted as odds ratios to improve clinical applications at drug treatment programs. Treatment readiness was dichotomized, using scores below the 20th percentile to identify patients with the lowest motivation for treatment (i.e., a score of ‘‘0’’ for those falling below and ‘‘1’’ for those at or above the cutpoint). Session attendance was dichotomized at four sessions (this represented the 55th percentile, and was the whole number closest to the median), with a score of ‘‘0’’ below the cutpoint and ‘‘1’’ for those attending four or
more sessions. Counseling at the participating outpatient methadone programs was required on an infrequent basis, and a cutpoint of four sessions meant attendance of two or more per month in the first 2 months of treatment. Counseling rapport was dichotomized at 3, representing the 73rd percentile (3 was the whole number closest to the 75th percentile) and generally representing good rapport. Patients scoring 3 or above were coded ‘‘1’’ and those below this score were coded ‘‘0.’’ Psychosocial functioning was dichotomized at 2 for this study, which represented the 40th percentile. Scores below the cutpoint were coded ‘‘0’’ and those at or above the cutpoint were coded ‘‘1’’; this meant that patients in the ‘‘1’’ category were higher functioning in that they had two or fewer psychosocial functioning scales that indicated a problem. No opioid or cocaine use was used as the drug outcome during treatment and at followup. Because results of urinalyses were also available, the abstinence category represented not only self-reported abstinence but also negative tests for heroin and cocaine metabolites. Abstinence is an important concept for treatment success and is one measure by which drug treatment programs evaluate their effectiveness. Treatment retention was dichotomized at 360 days. Those who stayed at least 360 days were scored ‘‘1,’’ and 25% met this criterion which has been demonstrated to be a critical predictor of posttreatment outcomes (e.g., Hubbard, Craddock, & Anderson, 2003; MacGowan et al., 1996; Moolchan & Hoffman, 1994; Simpson, 1979, 1981).
3. Results 3.1. Homogeneity of covariance matrices The covariance matrices based on unequal ns for the two patient groups were tested for homogeneity (Morrison, 1976) and found to be significantly different; m2(36) = 193.84, p < .0001. This difference implies that a common model for the two groups should not be expected to account for the data and thus a two-group model is appropriate. 3.2. Two-group structural model The core model tested included two exogenous variables (treatment readiness and mapping intervention) and six endogenous variables (session attendance, counseling rapport, psychosocial functioning, drug use during treatment, time in treatment, and drug use at followup). From previous research it was hypothesized that higher treatment readiness would have a positive effect on session attendance, and that cognitive mapping would have a positive effect on counseling rapport. It was further expected that a reciprocal positive relationship would exist between session attendance and counseling rapport (both measured at Month 2), and both were hypothesized as precedents to lower drug use (opioid/ cocaine) during treatment in Month 3. Also, the role of
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Treatment Motivation (Intake)
.62** (.15)
Session Attendance (Month 2)
.07***
.07***
Counseling Rapport (Month 2) Cognitive Mapping Strategy
Low Opiate/ Cocaine Use (Month 3)
.13***
.60***
.97**** .43**
Treatment Retention (1 Year)
.19**
.13***
Psychosocial Functioning (Month 3)
93
.08** .35****
.13*
χ2(49) = 88.78; RMSEA = .048 (.031, .063)
Low Opiate/ Cocaine Use (Follow-up)
Fig. 1. Estimates of weights from structural equations analysis of treatment process model. (*p < .05, **p < .01, ***p < .001, ****p < .0001).
psychosocial functioning in Month 3 was viewed as a possible intervening variable between counseling rapport and future drug use; that is, greater counseling rapport would have: (1) direct effects on lower drug use while in treatment; as well as (2) indirect effects through its effect on psychological and social functioning. Treatment retention, which has been a much studied predictor of followup outcomes, is positioned in the model as an endogenous variable predicted by lower drug use during treatment and by session attendance (all three representing behavioral indicators). That is, both higher session attendance and lower drug use should have positive effects on time in treatment. Finally, increased time in treatment and lower drug use during treatment were hypothesized to affect lower posttreatment drug use in the 1-year followup. In the estimation of the two-groups model, the initial model tested hypothesized that common estimates for each path existed for the two groups. The fit of this model was only marginally satisfactory; m2(50) = 147.98, p < .00001;
Treatment Motivation (Intake)
Session Attendance (Month 2)
1.85*
1.90**
1.90**
Counseling Rapport (Month 2) Cognitive Mapping Strategy
1.81**
RMSEA = .074 (.061, .088). Inspection of the modification indices from the analysis indicated many of the indices that involved session attendance were large (between 50 and 60), so the first path involving treatment readiness to session attendance was allowed to differ between the two patient groups. This revised model was found to ‘‘closely fit’’ the data based on the Browne and Cudeck (1993) criteria [m2(49) = 88.77, p < .0004; RMSEA = .048 (.031, .063)], and it represented a significant improvement over the previous model [m2(1) = 59.21, p < .0001]. Additional evidence that this was a good model lay in the magnitude of the modification indices from the analysis, which were generally small; of the 146 indices, only 11 were above 5 and only two of those exceeded seven (the exceptions were 10.8 and 9.3). The common estimates for this model are presented in Fig. 1. It shows that all of the paths having common estimates were statistically significant ( p < .05). For the effects of treatment readiness at intake on session
1.73**
No Opiate/ Cocaine Use (Month 3)
1.98**
2.32**
4.04**** 1.72**
Treatment Retention (1 Year)
1.68*
Psychosocial Functioning (Month 3)
1.74* 4.69****
No Opiate/ Cocaine Use (Follow-up)
Fig. 2. Odds ratios from contingency table analysis of significant treatment process components. (*p < .05, **p < .01, ***p < .001, ****p < .0001).
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attendance in Month 2, which was allowed to be estimated separately for the two patient groups, it was found that the relationship was significant in Group M [b = .62, t = 3.11, CI (.23, 1.01)] but not in Group U [b = .15, t = .77, CI ( .23, .53)]. For all remaining path coefficients, however, common estimates were satisfactory. Use of the cognitive mapping strategy is shown to have positive direct effects on rapport in Month 2 [b = .13, t = 2.22, CI (.02, .24)] and the reciprocal relationship between session attendance and rapport was positive [b = .07, t = 7.01, CI (.05, .09)]. Rapport had a positive effect on psychosocial functioning in Month 3 [b = .67, t = 4.26, CI (.36, .98)], and psychosocial functioning had a positive effect on lower Month 3 during-treatment drug use [b = .19, t = 3.31, CI (.08, .30)]. Both session attendance [b = .13, t = 3.66, CI (.06, .19)] and counseling rapport [b = .13, t = 3.66, CI (.06, .19)] were estimated to have equal effects on low drug use during treatment because of their reciprocal relationship. Furthermore, both lower drug use during treatment [b = .43, t = 3.34, CI (.18, .68)] and session attendance [b = .97, t = 13.85, CI (.84, 1.11)] had a positive effect on treatment retention for a year or longer. Time in treatment, in turn, had a positive effect on low posttreatment drug use at the 1-year followup [b = .08, t = 3.16, CI (.03, .14)], as did low during-treatment drug use [b = .35, t = 3.57, CI (.16, .54)]. 3.3. Odds ratios from contingency tables A series of contingency table analyses using dichotomized scores of the variables in the model provided another approach to testing statistical significance of these relationships. The odds ratios (OR) for the significant relationships corresponding to those identified in the structured equations model analysis are therefore reported as alternate interpretations of results. These are depicted in Fig. 2 and are summarized as follows: treatment readiness at intake and session attendance in Month 2 [m2(1) = 6.23, p < .012, OR = 1.85, CI (1.15, 2.97)], session attendance and counseling rapport in Month 2 [m2(1) = 6.90, p < .009, OR = 1.90, CI (1.18, 3.06)], session attendance and no illegal opioid or cocaine use during Month 3 [m2 (1) = 6.92, p < .009, OR = 1.73, CI (1.15, 2.60)], session attendance in Month 2 and treatment retention for a year or more [m2(1) = 54.81, p < .0001, OR = 4.04, CI (2.74, 5.95)], counseling rapport in Month 2 and psychosocial functioning in Month 3 [m2(1) = 8.87, p < .003, OR = 2.32, CI (1.33, 4.06)], counseling rapport and no illegal opioid or cocaine use during Month 3 [m2(1) = 8.64, p < .004, OR = 1.98, CI (1.25, 3.14)], psychosocial functioning and no illegal opioid or cocaine use during Month 3 [m2(1) = 4.40, p < .04, OR = 1.68, CI (1.03, 2.73)], no illegal opioid or cocaine use during Month 3 and treatment retention [m2(1) = 7.02, p < .009, OR = 1.72, CI (1.15, 2.57)], no illegal opioid or cocaine use during Month 3 and no illegal opioid or cocaine use at the year-1 followup [m2(1) = 32.91, p < .0001, OR = 4.69, CI (2.70, 8.15)], and treatment retention for over a year and no illegal opioid or
cocaine use at the year-1 followup [m2(1) = 5.12, p < .024, OR = 1.74, CI (1.07, 2.81)]. The effect of the cognitive mapping strategy with counseling rapport was also significant [m2(1) = 6.99, p < .009, OR = 1.81, CI (1.16, 2.82)].
4. Discussion This study provides support for the core elements and their sequential pathways included in the TCU Treatment Model (Simpson, 2001, 2004). Overall, the hypothesized relationships, particularly representing the key stage-based contiguous components of the model, were statistically significant and in the direction expected in the estimation of the structural equation model. The t-tests associated with the b-weights were significant at or beyond the .01 level. The fact that a common model was satisfactory for all but one path adds support to the generalizability of the model for methadone programs. The practical implications for drug treatment are in identifying measures that can be useful for monitoring and improving treatment program effectiveness. That is, drug use following treatment was predicted not only by time in treatment but more importantly by a more detailed picture of dynamic elements that define treatment process. These relationships have been found to be generally consistent across treatment populations, settings, and type of outcomes examined (e.g., Broome et al., 2001; Hiller et al., 2002; Joe et al., 1999). Systematic measurement of these elements therefore offers a way to monitor patient needs and progress in treatment, including responses to interventions and better treatment management. Although the adjacent links between indicators of motivation at intake, engagement in Month 2, early recovery functioning in Month 3, retention, and drug use at Year 1 followup were all statistically significant, these were not exclusively stepwise relations. For instance, the figures illustrate that as hypothesized by the treatment model, attendance was related to rapport in Month 2, but attendance in Month 2 also predicted drug use in Month 3 and long-term retention. Similarly, rapport in Month 2 was the strongest predictor of psychological functioning in Month 3, but it was significantly related to drug use during treatment as well. However, the scheduling of process measurements collected in the present study did not lend itself to rigid testing of a parsimonious sequential model, and indeed it is reasonable that the model and linkages among its elements may vary somewhat across different settings. In outpatient methadone treatment, for example, session attendance and low during-treatment drug use represent ‘‘behavioral measures’’ of process, and both are directly linked to retention and followup outcomes. On the other hand, in residential settings where treatment attendance and duration is sometimes legally mandated (thereby limiting variance) and access to street drugs is restricted, the paths for some of these treatment process linkages may shift. Part of the reason is that
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assessment constraints may be related to treatment circumstances and settings that can alter their relationships in a generic model of treatment process. Refining measures of these process components that can apply across treatment settings (Joe et al., 2002) will help in further testing generalizability of the TCU Treatment Model. Translation of findings into odds ratios illustrate in clinical terms the interrelatedness of these measures and how treatment outcomes subsequently can be affected by therapeutic strategies. Their magnitudes fell into two categories. The first represents very strong relationships; that is, odds ratios of 4 or greater (viz., session attendance in Month 2 with retention, as well as abstinence during treatment and abstinence at the 1-year followup after treatment). The effect sizes of the remaining relationships fall in the area between small and medium, as defined by the magnitudes of the odds ratios (i.e., between 1.7 and 2.3) and the magnitudes of the percentages being compared. The odds ratios also are suggestive of the degree of influence that might be expected between different contiguous components of the model. They serve to indicate how influencing the level of one component might affect the level of another, and thereby treatment process and outcomes. For example, patients who were randomly assigned to counselors taught to use a cognitive strategy, called nodelink mapping, were found to be nearly twice as likely (1.8) to have high scores on positive rapport as those who were assigned to counselors who were not taught to use this counseling enhancement. Using techniques (e.g., Sia, Dansereau, & Czuchry, 2000) for improving patient readiness for outpatient methadone treatment from ‘‘unmotivated’’ to ‘‘motivated’’ can double the odds that counseling sessions (i.e., the median number or more) will be attended in the early months of treatment. For other time-sequenced relationships, such as the effects of rapport on psychosocial functioning, it is seen that positive rapport increased the odds of positive psychosocial functioning by two. The results also show that abstinence from opiate and cocaine use during treatment increased the odds for staying in treatment for a year or longer by 1.7, and that treatment retention increased the odds of abstinence in the 1-year followup by 1.7. The increase in odds noted between counseling session attendance and counseling rapport was nearly 2. With regard to the latter, it is noted that because these two engagement indicators were measured for the same time period, their odds ratios were interpreted to be equivalent (regardless of whether session attendance or rapport is used as the ‘‘independent variable’’ or ‘‘dependent variable’’). Although it is reasonable to expect that session attendance is a necessary precursor for establishing counseling rapport in the early weeks of treatment, continued session attendance and participation are likely influenced (positively or negatively) by the level of counseling rapport that emerges. Only patients in community-based outpatient methadone treatment programs were included in this study. Although
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essentially the same treatment process components have been found in evaluations of residential programs (e.g., Knight, Joe, & Simpson, 2003), the nature of residential programs suggest that additional factors related to relationships with family members, peers in and out of treatment, and social support are important to consider, especially in relation to psychosocial functioning during treatment. Additional research is needed to examine the model in criminal justice residential drug treatment programs, particularly in terms of the effects of aftercare and supportive networks, and with respect to elements of organizational functioning in treatment programs (Simpson & Knight, 2003). The latter is important because treatment process factors of rapport and satisfaction also appear to be related to organizational climate and staff attributes (Lehman, Greener, & Simpson, 2002). Additionally, characteristics of the treatment program have been shown to influence the patient engagement process, particularly counselor attributes and counseling skills (Joe et al., 2001) and aspects of the program organization and climate (Lehman et al., 2002). In future applications, they too need to be estimated in the full treatment model. Other important influences on the process involve support systems, including family, friends, support networks, and social support services (e.g., Broome, Simpson, & Joe, 2002; Griffith, Knight, Joe, & Simpson, 1998; Knight & Simpson, 1996). The current study grew out of a long-term program of research that has as one of its goals a better understanding of treatment process, so efforts were made to collect systematic data at intake, periodically during treatment, and at followup in order to conduct this investigation. The results demonstrate the practical value of assessing during-treatment engagement process and patient functioning as performance indicators. Because they are sensitive to interventions, they provide treatment planning and program management tools (Joe et al., 2002; Simpson, 2002). At the patient level, functioning can be monitored in reference to therapeutic thresholds (clinically defined or statistically determined) and linked to treatment events or interventions. At the program level, aggregated patient data can help identify patterns of needs, intervention effectiveness, and clinical impact of organizational changes. It is also hoped that presenting assessment results in terms of effect-size indicators might encourage and increase the clinical applications of research findings like these.
Acknowledgments This work was funded by the National Institute on Drug Abuse (Grant No. R37 DA13093). Its foundations, however, began in 1989 with NIDA funding of our DATAR-1 project (Improving Drug Abuse Treatment for AIDS-Risks Reduction), followed by DATAR-2 (Improving Drug Abuse Treatment Assessments and Resources) and continuing in our current DATAR-3 phase (Transferring
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Drug Abuse Treatment Assessments and Resources). The interpretations and conclusions, however, do not necessarily represent the position of NIDA or the Department of Health and Human Services.
References Broome, K. M. (1996). Antisocial personality and drug abuse treatment process. Unpublished doctoral dissertation, Texas Christian University, Fort Worth. Broome, K. M., Joe, G. W., & Simpson, D. D. (2001). Engagement models for adolescents in DATOS-A. Journal of Adolescent Research, 16, 608 – 623. Broome, K. M., Simpson, D. D., & Joe, G. W. (1999). Patient and program attributes related to treatment process indicators in DATOS. Drug and Alcohol Dependence, 57, 127 – 135. Broome, K. M., Simpson, D. D., & Joe, G. W. (2002). The role of social support following short-term inpatient treatment. American Journal on Addictions, 11, 57 – 65. Brooner, R. K., & Kidorf, M. (2002). Using behavioral reinforcement to improve methadone treatment participation. Science & Practice Perspectives, 1, 38 – 46. Browne, M. W, & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen, & J. S. Long (Eds.), Testing structural equation models ( pp. 136 – 162). Newbury Park, CA: Sage Publications. Buhringer, G., Gossop, M., Turk, D., Wanigaratne, S., & Kaplan, C. (2001). European perspectives on relapse and relapse prevention. In F. M. Tims, C. G. Leukefeld, & J. J. Platt (Eds.), Relapse and recovery in addictions ( pp. 60 – 108). New Haven, CT: Yale University Press. Carroll, K. M., Power, M. D., Bryant, K., & Rounsaville, B. J. (1993). One year follow-up status of treatment-seeking cocaine abusers: Psychopathology and dependence severity as predictors of outcome. Journal of Nervous and Mental Disease, 181, 71 – 79. Dansereau, D. F., & Dees, S. M. (2002). Mapping training: The transfer of a cognitive technology for improving counseling. Journal of Substance Abuse Treatment, 22, 219 – 230. Dansereau, D. F., Joe, G. W., & Simpson, D. D. (1993). Node-link mapping: A visual representation strategy for enhancing drug abuse counseling. Journal of Counseling Psychology, 40, 385 – 395. De Leon, G. (2000). The therapeutic community: Theory, model and method. New York: Springer Publishing Company. De Leon, G., Jainchill, N., & Wexler, H. K. (1982). Success and improvement rates 5 years after treatment in a therapeutic community. International Journal of the Addictions, 17, 703 – 747. Festinger, D. S., Rubenstein, D. F., Marlowe, D. B., & Platt, J. J. (2001). Relapse, contributing factors, causative models, and empirical considerations. In F. M. Tims, C. G. Leukefeld, & J. J. Platt (Eds.), Relapse and recovery in addictions ( pp. 122 – 142). New Haven, CT: Yale University Press. Finney, J. W., Ouimette, P. C., Humphreys, K., & Moos, R. H. (2001). A comparative, process-effectiveness evaluation of VA substance abuse treatment. In M. Galanter (Ed.), Recent developments in alcoholism, Volume 15: Services Research in the era of managed care ( pp. 373 – 391). New York: Kluwer Academic/Plenum Publishers. Gerstein, D. R., & Harwood, H. J. (Eds.). (1990). Treating drug problems: Vol. 1. A study of evolution, effectiveness, and financing of public and private drug treatment systems. Washington, DC: National Academy Press. Gossop, M., Marsden, J., Stewart, D., & Rolfe, A. (1999). Treatment retention and 1 year outcomes for residential programmes in England. Drug and Alcohol Dependence, 57, 89 – 98. Griffith, J. D., Knight, D. K., Joe, G. W., & Simpson, D. D. (1998). Implications of family and peer relations for treatment engagement
and follow-up outcomes: An integrative model. Psychology of Addictive Behaviors, 12, 113 – 126. Hiller, M. L., Knight, K., Leukefeld, C., & Simpson, D. D. (2002). Motivation as a predictor of therapeutic engagement in mandated residential substance abuse treatment. Criminal Justice and Behavior, 29, 56 – 75. Hiller, M. L., Simpson, D. D., Broome, K. M., & Joe, G. W. (2000). Legal status at intake and posttreatment incarceration: 12-month follow-up of methadone maintenance treatment. Journal of Maintenance in the Addictions, 1, 27 – 43. Hser, Y., Anglin, M. D., Grella, C., Longshore, D., & Prendergast, M. L. (1997). Drug treatment careers: A conceptual framework and existing research findings. Journal of Substance Abuse Treatment, 14, 543 – 558. Hser, Y., Grella, C. E., Chou, C. P., & Anglin, M. D. (1998). Relationships between drug treatment careers and outcomes: Findings from the national Drug Abuse Treatment Outcome Study. Evaluation Review, 22, 496 – 519. Hubbard, R. L., Craddock, S. G., & Anderson, J. (2003). Overview of 5-year follow-up outcomes in the Drug Abuse Treatment Outcome Studies (DATOS). Journal of Substance Abuse Treatment, 25, 125 – 134. Hubbard, R. L., Craddock, S. G., Flynn, P. M., Anderson, J., & Etheridge, R. M. (1997). Overview of 1-year follow-up outcomes in the Drug Abuse Treatment Outcome Study (DATOS). Psychology of Addictive Behaviors, 11, 261 – 278. Hubbard, R. L., Marsden, M. E., Rachal, J. V., Harwood, H. J., Cavanaugh, E. R., & Ginzburg, H. M. (1989). Drug abuse treatment: A natural study of effectiveness. Chapel Hill: University of North Carolina Press. Joe, G. W., Broome, K. M., Rowan-Szal, G. A., & Simpson, D. D. (2002). Measuring patient attributes and engagement in treatment. Journal of Substance Abuse Treatment, 22, 183 – 196. Joe, G. W., Dansereau, D. F., Pitre, U., & Simpson, D. D. (1997). Effectiveness of node-link mapping enhanced counseling for opiate addicts: A 12-month posttreatment follow-up. Journal of Nervous and Mental Disease, 185, 306 – 313. Joe, G. W., Knezek, L., Watson, D., & Simpson, D. D. (1991). Depression and decision-making among intravenous drug users. Psychological Reports, 68, 339 – 347. Joe, G. W., Simpson, D. D., & Broome, K. M. (1999). Retention and patient engagement models for different treatment modalities in DATOS. Drug and Alcohol Dependence, 57, 113 – 125. Joe, G. W., Simpson, D. D., Dansereau, D. F., & Rowan-Szal, G. A. (2001). Relationships between counseling rapport and drug abuse treatment outcomes. Psychiatric Services, 52, 1223 – 1229. Joe, G. W., Simpson, D. D., & Hubbard, R. L. (1991). Treatment predictors of tenure in methadone maintenance. Journal of Substance Abuse, 3, 73 – 84. Knight, D. K., Joe, G. W., & Simpson, D. D. (2003). Social relationships and treatment process among women in residential substance abuse treatment. Manuscript submitted for publication. Knight, D. K., & Simpson, D. D. (1996). Influences of family and friends on client progress during drug abuse treatment. Journal of Substance Abuse, 8, 417 – 429. Lamb, S., Greenlick, M. R., & McCarty, D. (1998). Bridging the gap between practice and research: Forging partnerships with communitybased drug and alcohol treatment. Washington, DC: National Academy Press. Lehman, W. E. K., Greener, J. M., & Simpson, D. D. (2002). Assessing organizational readiness for change. Journal of Substance Abuse Treatment, 22, 197 – 209. MacGowan, R. J., Swanson, N. M., Brackbill, R. M., Rugg, D. L., Barker, T., & Molde, S. (1996). Retention in methadone maintenance treatment programs, Connecticut and Massachusetts, 1990 – 1993. Journal of Psychoactive Drugs, 28, 259 – 265. Melnick, G., De Leon, G., Thomas, G., Kressel, D., & Wexler, H. K. (2001). Treatment process in prison therapeutic communities: Motivation, participation, and outcome. American Journal of Drug and Alcohol Abuse, 27, 633 – 650.
D.D. Simpson, G.W. Joe / Journal of Substance Abuse Treatment 27 (2004) 89–97 Miller, W. R. (1989). Increasing motivation for change. In R. K. Hester, & W. R. Miller (Eds.), Handbook of alcoholism treatment approaches: Effective alternatives ( pp. 67 – 80). New York: Pergamon Press. Moolchan, E. T., & Hoffman, J. A. (1994). Phases of treatment: A practical approach to methadone maintenance treatment. International Journal of the Addictions, 29, 135 – 160. Morrison, D. F. (1976). Multivariate statistical methods. New York: McGraw-Hill. Muthe´n, L. K., & Muthe´n, B. O. (1998). Mplus user’s guide. Los Angeles: Authors. Prochaska, J. O., & DiClemente, C. C. (1986). Toward a comprehensive model of change. In W. R. Miller, & N. Heather (Eds.), Treating addictive behaviors: Processes of change ( pp. 3 – 27). New York: Plenum Press. Sia, T. L., Dansereau, D. F., & Czuchry, M. (2000). Treatment readiness training and probationers’ evaluation of substance abuse treatment in a criminal justice setting. Journal of Substance Abuse Treatment, 19, 459 – 467. Simpson, D. D. (1979). The relation of time spent in drug abuse treatment to posttreatment outcomes. American Journal of Psychiatry, 136, 1449 – 1453. Simpson, D. D. (1981). Treatment for drug abuse: Follow-up outcomes and length of time spent. Archives of General Psychiatry, 38, 875 – 880. Simpson, D. D. (2001). Modeling treatment process and outcomes. Addiction, 96, 207 – 211. Simpson, D. D. (2002). A conceptual framework for transferring research to practice. Journal of Substance Abuse Treatment, 22, 171 – 182. Simpson, D. D. (2004). A conceptual framework for drug treatment process and outcomes: Applications for improving treatment effectiveness. Manuscript submitted for publication. Simpson, D. D., Brown B. S. (Eds.) (1999). Special issue: Treatment process and outcome studies from DATOS. Drug and Alcohol Dependence, 57. Simpson, D. D., Curry S. J. (Eds.) (1997). Special issue: Drug Abuse Treatment Outcome Study (DATOS). Psychology of Addictive Behaviors, 11. Simpson, D. D., & Joe, G. W. (1993). Motivation as a predictor of early dropout from drug abuse treatment. Psychotherapy, 30, 357 – 368. Simpson, D. D., Joe, G. W., Dansereau, D. F., & Chatham, L. R. (1997).
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Strategies for improving methadone treatment process and outcomes. Journal of Drug Issues, 27, 239 – 260. Simpson, D. D., Joe, G. W., Fletcher, B. W., Hubbard, R. L., & Anglin, M. D. (1999). A national evaluation of treatment outcomes for cocaine dependence. Archives of General Psychiatry, 56, 507 – 514. Simpson, D. D., Joe, G. W., Greener, J. M., & Rowan-Szal, G. A. (2000). Modeling year 1 outcomes with treatment process and post-treatment social influences. Substance Use & Misuse, 35, 1911 – 1930. Simpson, D. D., Joe, G. W., Rowan-Szal, G. A., & Greener, J. M. (1995). Client engagement and change during drug abuse treatment. Journal of Substance Abuse, 7, 117 – 134. Simpson, D. D., Joe, G. W., Rowan-Szal, G. A., & Greener, J. M. (1997). Drug abuse treatment process components that improve retention. Journal of Substance Abuse Treatment, 14, 565 – 572. Simpson, D. D., & Knight, K. (2003). TCU model of treatment process and outcomes in correctional settings. In J. A. Gondles (Ed.), The state of corrections ( pp. 211 – 222). Lanham, MD: American Correctional Association. Simpson, D. D., & Sells, S. B. (1982). Effectiveness of treatment for drug abuse: An overview of the DARP research program. Advances in Alcohol and Substance Abuse, 2, 7 – 29. Simpson, D. D., & Sells, S. B. (Eds.). (1990). Opioid addiction and treatment: A 12-year follow-up. Malabar, FL: Krieger. Siqueland, L., Crits-Cristoph, P., Frank, A., Daley, D., Weiss, R. D., Chittams, J., Blaine, J., & Luborsky, L. (1998). Predictors of dropout from psychosocial treatment for cocaine dependence. Drug and Alcohol Dependence, 52, 1 – 13. Siqueland, L., Crits-Cristoph, P., Gallop, R., Barber, J. P., Griffin, M. L., Thase, M. E., Daley, D., Frank, A., Gastfriend, D. R., Blaine, J., Connolly, M. B., & Gladis, M. (2002). Retention in psychosocial treatment of cocaine dependence: Predictors and impact on outcome. American Journal on Addictions, 11, 24 – 40. Sobell, M. B., & Sobell, L. C. (2000). Stepped care as a heuristic approach to the treatment of alcohol problems. Journal of Consulting and Clinical Psychology, 68, 573 – 579. Tims, F., Leukefeld C. G., Platt J. J. (Eds.) (2001). Relapse and recovery in addictions. New Haven, CT: Yale University Press. Weissberg, R. P., & Greenberg, M. T. (1998). Prevention science and collaborative community action research: Combining the best from both perspectives. Journal of Mental Health, 7, 479 – 492.
Regular article
A longitudinal evaluation of treatment engagement and recovery stages D. Dwayne Simpson, (Ph.D.) *, George W. Joe, (Ed.D.) Institute of Behavioral Research, Texas Christian University, Fort Worth, TX, USA Received 28 March 2003; received in revised form 18 February 2004; accepted 15 March 2004
Abstract Recent methodological advancements for structural equation modeling were used to test a comprehensive version of the TCU Treatment Model, especially for addressing the hypothesized sequential relationships of early engagement components (participation and therapeutic relationship) and early recovery (psychosocial and behavioral changes) that contribute to retention and posttreatment recovery. Relationships among pretreatment patient motivation, treatment process elements, a cognitive-based treatment strategy, retention, and drug use outcomes were estimated using intake, during treatment, and 1-year followup data for 711 patients in outpatient methadone treatment. Hypothesized sequential elements representing treatment process and patient functioning were supported, and relationships between these components were estimated also as odds ratios as an aid for translating the findings and increasing their clinical usefulness to treatment settings. D 2004 Elsevier Inc. All rights reserved. Keywords: Treatment process; Patient functioning; Engagement; Recovery stages; Motivation; Cognitive-based treatment
1. Introduction Posttreatment studies of patients treated in publiclyfunded, community-based drug treatment programs have shown consistently throughout the past 30 years that drug use and related problems decrease significantly from intake levels (e.g., Finney, Ouimette, Humphreys, & Moos, 2001; Gerstein & Harwood, 1990; Gossop, Marsden, Stewart, & Rolfe, 1999; Hubbard, Craddock, Flynn, Anderson, & Etheridge, 1997; Hubbard et al., 1989; Simpson & Curry, 1997; Simpson & Sells, 1982). These evaluations have included different types of outpatient methadone treatment, residential treatments such as therapeutic communities, and various outpatient drug free programs using natural designs for documenting treatment impact in real-world settings. They also have focused on thresholds of ‘‘adequate treatment,’’ defined as the length of stay in treatment needed to achieve statistically significant changes in posttreatment outcomes, building on early outcome studies generally showing retention to be the strongest and most reliable predictor of drug use and criminality improvements (e.g., De Leon, Jainchill, & Wexler, 1982; Joe, Simpson, & Hubbard, 1991; Simpson,
* Corresponding author. Tel.: +1-817-257-7226; fax: +1-817-257-7290. E-mail address: [email protected] (D.D. Simpson). 0740-5472/04/$ – see front matter D 2004 Elsevier Inc. All rights reserved. doi:10.1016/j.jsat.2004.03.001
1979, 1981; Simpson, Joe, Fletcher, Hubbard, & Anglin, 1999). Minimum retention thresholds for different treatment modalities were identified, averaging about 3 months for residential and outpatient drug-free treatments and a year for outpatient methadone treatment. Depending on patient problem severity at intake and therapeutic setting, treatment improvements typically begin when retention extends beyond these ‘‘thresholds.’’ Treatment retention also has been recognized, however, as a convenient index that can mediate the impact of a diverse set of patient background, therapeutic, and environmental factors on treatment effectiveness. Some research has been refocused to address patient and treatment indicators that predict retention (e.g., Buhringer, Gossop, Turk, Wanigaratne, & Kaplan, 2001; Festinger, Rubenstein, Marlowe, & Platt, 2001; Hser, Grella, Chou, & Anglin, 1998; Simpson, Joe, Rowan-Szal, & Greener, 1995; Siqueland et al., 2002; Tims, Leukefeld, & Platt, 2001), but the interrelationships among patient, process, and environmental domains still need attention to advance our understanding of treatment effectiveness (Lamb, Greenlick, & McCarty, 1998; Melnick, De Leon, Thomas, Kressel, & Wexler, 2001; Simpson, 2001). Particularly important are the broader cognitive, social, and environmental factors that contribute to the recovery process (De Leon, 2000; Prochaska & DiClemente, 1986) as well as the motivation needed to sustain the process (Miller, 1989). Similarities
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are found in examples of stepped or staged care treatment models (Brooner & Kidorf, 2002; Sobell, & Sobell, 2000; Weissberg & Greenberg, 1998). Because recovery from addiction often involves recurrent relapse and treatment readmission episodes, we also need better conceptual frameworks for conducting long-term longitudinal outcome studies using large representative samples in natural research designs (Hser, Anglin, Grella, Longshore, & Prendergast, 1997; Simpson & Sells, 1990). Establishing conceptual foundations representing the way treatment process and outcomes are related has been the primary focus of research summarized by the TCU Treatment Model (Simpson, 2001, 2004; Simpson, Joe, Dansereau, & Chatham, 1997; Simpson & Knight, 2003). This model was created initially as a heuristic to integrate a body of research that accrued as part of several interrelated projects conducted over the past three decades (Simpson & Brown, 1999; Simpson, Joe, Rowan-Szal, & Greener, 1997; Simpson & Sells, 1982, 1990). Hypotheses concerning the relationships among its components have been tested and supported in diverse treatment settings and samples (e.g., Broome, 1996; Broome, Joe, & Simpson, 2001; Broome, Simpson, & Joe, 1999; Hiller, Knight, Leukefeld, & Simpson, 2002; Joe, Simpson, & Broome, 1999), and analytic models have become more comprehensive over time for making integrative predictions about retention and posttreatment outcomes (Simpson, Joe, Greener, & Rowan-Szal, 2000). The consistency of findings across diverse data systems is suggestive of the model’s robustness, but further testing is needed on particular sequencing of the behavioral and cognitive treatment engagement stages involved. Core components of ‘‘early engagement’’ include session participation and the forging of therapeutic relationships in the initial few weeks following treatment entry, but these are impacted by patient motivation, readiness for treatment, and problem severity at intake. These engagement steps are related, in turn, to ‘‘early recovery’’ indicators of psychosocial (including cognitive) and behavioral changes. In this framework, the primary role of treatment is to promote and sustain the progression of changes in patient thinking and acting, so we have focused on examining behavioral and cognitive interventions shown to be effective in selectively improving stage-specific treatment indicators (Joe, Dansereau, Pitre, & Simpson, 1997; Simpson, Joe, Dansereau, et al., 1997). Central elements of this treatment process model are the two early engagement components (program participation and therapeutic relationship) and their impact on early recovery (represented by favorable psychosocial and behavioral functioning). Depending on the particular timing of data collection schedules, engagement components have been modeled as either having a reciprocal (i.e., nonrecursive) or unidirectional (recursive) relationship between program participation and therapeutic relationship. Early engagement is also seen in the model as having effects on
(subsequent) indicators of early recovery. Previous research has focused most heavily on the prediction of behavioral change (e.g., urinalysis results), but psychosocial functioning also deserves careful attention as an important intervening variable based on its relationship to engagement (Joe et al., 1999), drug use during treatment (Broome, 1996; Joe, Knezek, Watson, & Simpson, 1991), and retention (Carroll, Power, Bryant, & Rounsaville, 1993; Joe et al., 1999; Simpson & Joe, 1993; Siqueland et al., 1998). Problem severity and motivation of patients at intake (e.g., Broome, 1996; Hiller et al., 2002) as well as use of cognitive-based interventions (Dansereau, Joe, & Simpson, 1993) also are related to psychosocial functioning during treatment. In previous studies, the relationships between these components have been estimated and found to be statistically significant, but only in ‘‘submodels.’’ That is, all of the relationships were not estimated simultaneously, owing in large part to statistical software limitations that required complete data when those studies were carried out. The present study takes advantage of recent methodological advancements for handling missing data in performing simultaneous evaluations of the treatment process and patient functioning (including pretreatment motivation which has been shown to influence treatment progress). A secondary objective is to provide drug treatment programs with user-friendly measures of ‘‘relative risk’’ representing these relationships. The reason for this objective is to translate complex statistical parameters into indicators more useful to drug treatment programs and thereby interpret research results for practical applications (see Joe, Broome, Rowan-Szal, & Simpson, 2002; Simpson, 2002). Although there are several alternatives possible, a commonly used procedure involving odds-ratios was selected as a way to represent sequential relationships in terms of the ‘‘statistical odds’’ of an event occurring. Using multivariate findings from the structural equation model as a guide, therefore, measures were dichotomized and used in contingency table analyses to estimate odds-ratios for simplifying clinical interpretations.
2. Method 2.1. Sample A total of 711 patients were admitted to three not-forprofit, community-based outpatient methadone treatment programs (located in two large urban areas and one medium sized city) during 1990 to 1993 (Simpson, Joe, Dansereau, et al., 1997). A comprehensive series of intake, during treatment, and followup assessments were completed which fully complied with federal and Institutional Review Board requirements to protect rights of human subjects. Of these 711 admissions during this period, 643 were successfully located in a 1-year followup (90.5%). Face-to-face interviews were completed with 435 (61%),
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but 14 (2%) refused to be interviewed, 21 (3%) were deceased, 3 (0.5%) were medically incapacitated, 36 (5%) had moved out of the state, and 134 (19%) were not interviewed due to imprisonment. We found no evidence that patient participation and performance for this incarcerated sample differed significantly from other patients during treatment (Hiller, Simpson, Broome, & Joe, 2000). The large number of patients in prison at followup was associated primarily with expansion of statewide prison facilities and an increased rate of overall parole revocations in Texas during our study period (e.g., 48% of all 711 admissions entered treatment with a legal status, including 35% on parole). Over half (55%) of the 711 admissions terminated treatment within 180 days, and 36% within 90 days, with only a fourth (25%) staying a year or more. Average age was 37; 71% were male; 36% of the patients were white, 45% Hispanic, and 16% black; 44% were married or living as married; 44% had a full or part time job; 48% had a legal status at admission. Also, in terms of drug use, besides opiates, 29% were daily users of alcohol, 22% weekly users of marijuana, and 45% weekly users of cocaine (27% cocaine alone and 34% in combination with heroin). Because the patients were from multiple treatment programs, several factors were considered in the decision process for defining sample subgroups. One of the two urban area programs ceased operations early in the study, and consequently contributed only 73 patients (10%) to the total sample. Its patients were therefore combined with those from the other treatment program from a large urban area, forming one of the two groups (n = 320) included in the multigroup structural equation analysis. The other group (n = 391) was comprised of patients treated in a mediumsized city. The race-ethnic composition, education, and cocaine use supported this combining of the urban-area patient groups for analysis. For instance, the program in the medium-sized city (Group M) had only 1.5% blacks, compared to 52% and 29%, respectively, for the two urban programs that were combined (Group U). The patients in the urban programs (Group U) were more highly educated (53% and 44% were high school graduates, respectively) and more likely to be using cocaine daily (53% and 35%, respectively); in Group M, 28% were high school graduates and 19% were daily cocaine users. Overall group comparisons showed the 320 patients in Group U had an average age of 39, 25% were Hispanic, 34% were Black, 42% were married or living as married, 42% were employed full or part time, and 45% had a legal status at admission. In terms of drug use, 30% used alcohol daily, 19% used marijuana weekly, and 54% used cocaine weekly. For the 391 patients in Group M, the average age was 35, 62% were Hispanic, 2% were Black, 46% were married or living as married, 47% were employed full or part time, and 51% had a legal status at admission. Regarding drug use, 29% used alcohol daily, 25% used marijuana weekly, and 38% used cocaine weekly.
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2.2. Measures The measures addressed in the TCU Treatment Model include treatment readiness at intake, session attendance in Month 2, counseling rapport in Month 2, psychosocial functioning in Month 3, opiate or cocaine use in Month 3, time in treatment, and opiate or cocaine use at the 1-year followup interview. Treatment readiness is a motivation indicator for the model and is measured using eight items from the TCU motivation scale with each item rated on a five-point Likert scale for assessing commitment levels and expectations about how helpful treatment will be (Joe et al., 2002; Simpson & Joe, 1993). Coefficient alpha reliability was .75, with scores ranging from 0 to 4. Session attendance at Month 2 was defined by the number of counseling sessions attended during the second month in treatment, and ranged from 0 to 16. The measure of counseling rapport was used in previous research as the primary indicator of therapeutic relationship between counselor and patient and was found to be a significant predictor of during treatment (Simpson, Joe, Rowan-Szal, et al., 1997) and posttreatment treatment outcomes (Joe, Simpson, Dansereau, & Rowan-Szal, 2001). It consists of six items (‘‘easy to talk to,’’ ‘‘warm and caring,’’ ‘‘honest and sincere,’’ ‘‘understanding,’’ ‘‘not suspicious,’’ ‘‘not in denial about problems’’), each rated on a five-point Likert scale (Never, Rarely, Sometimes, Often, Almost Always) by counselors of their patients, and for this study the ratings at Month 2 of the index treatment were used. The rapport scale had an alpha reliability of .79, with scores ranging from 0 to 4. The psychosocial functioning index was based on six scales from the TCU Client Evaluation of Self and Treatment (CEST; Joe et al., 2002) that represented the domains of psychological and social functioning. The CEST was completed by patients each month during the first 3 months of treatment, but only scores from the third month were used in this study. The number of items varied between six and nine for these scales, with each based on self-ratings using five-point Likert-type items (where 2 was the midpoint of the response scale). The intent of the index was to capture psychosocial functioning in terms of the number of problems. The index was scored in the ‘‘positive’’ direction represented by high self-esteem ( > 2.6), low depression (V1.5), low anxiety (V1.1), low risk taking (<1.3), high social conformity (>2.0), and high decision making (>2.3). The range of the psychosocial functioning index was 0 to 6. Information on self-reported illegal opioid and cocaine use was collected at Month 3 in treatment and at the 1-year followup interview. Self-reported use of these two drugs was combined into a single measure of ‘‘opioid and cocaine use,’’ where the range of scores was ‘‘0’’ (no use) to ‘‘8’’ (4 or more times a day). To facilitate interpretation with respect to direction of relationships among the variables in the structural modeling, the combined illegal opioid and
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cocaine use measure was reverse-scored to reflect ‘‘low opioid and cocaine use’’ in the analysis; that is, a high score reflected low use while a low score reflected high use. Separate measures were computed for Month 3 during treatment and for the 1-year followup period. The influence of a cognitive treatment strategy, called node-link mapping, was also assessed. Counselors were taught to use a two-dimensional method for representing personal issues that provided a visual focus for on-task attention of both patient and counselor, thereby enhancing counseling (Dansereau et al., 1993; Simpson, Joe, Dansereau, et al., 1997). Node-link mapping depicts thoughts, feelings, and actions as nodes and describes relationships between nodes by named links (Dansereau & Dees, 2002). This graphical display is developed and used by the counselor and patient to explore the antecedents and consequences of problem situations. Data for the present study were collected from an experimental study assessing the effects of mapping vs. non-mapping treatment, with patients having been randomly assigned to counselors who had been randomly assigned either to use mapping enhancement (or not) in their counseling. Time in treatment was the number of days in treatment, from intake to termination. For the analyses, the square root transformation was performed. 2.3. Analysis A two-group structural equations model was estimated using Mplus (Muthe´n & Muthe´n, 1998). The estimates were based on all available data for each variable in the model, using the ‘‘missing data method’’ to obtain maximum likelihood estimates under the assumption of missing data at random. The root mean square error of approximation (RMSEA) was the model fit index used to evaluate the adequacy of the model (Browne & Cudeck, 1993). For this index, a value of .05 or less indicates a close fit, a value of about .08 or less a reasonable error of approximation, and a value greater than .10 a poor fit (Browne & Cudeck, 1993). For the second objective involving computation of measures of relative risk representing the relationships, contingency table analyses adjusted for treatment program membership (Group M and Group U) were performed on contiguous variables in the model, based on all available data. For these contingency tables, the variables were dichotomized and results interpreted as odds ratios to improve clinical applications at drug treatment programs. Treatment readiness was dichotomized, using scores below the 20th percentile to identify patients with the lowest motivation for treatment (i.e., a score of ‘‘0’’ for those falling below and ‘‘1’’ for those at or above the cutpoint). Session attendance was dichotomized at four sessions (this represented the 55th percentile, and was the whole number closest to the median), with a score of ‘‘0’’ below the cutpoint and ‘‘1’’ for those attending four or
more sessions. Counseling at the participating outpatient methadone programs was required on an infrequent basis, and a cutpoint of four sessions meant attendance of two or more per month in the first 2 months of treatment. Counseling rapport was dichotomized at 3, representing the 73rd percentile (3 was the whole number closest to the 75th percentile) and generally representing good rapport. Patients scoring 3 or above were coded ‘‘1’’ and those below this score were coded ‘‘0.’’ Psychosocial functioning was dichotomized at 2 for this study, which represented the 40th percentile. Scores below the cutpoint were coded ‘‘0’’ and those at or above the cutpoint were coded ‘‘1’’; this meant that patients in the ‘‘1’’ category were higher functioning in that they had two or fewer psychosocial functioning scales that indicated a problem. No opioid or cocaine use was used as the drug outcome during treatment and at followup. Because results of urinalyses were also available, the abstinence category represented not only self-reported abstinence but also negative tests for heroin and cocaine metabolites. Abstinence is an important concept for treatment success and is one measure by which drug treatment programs evaluate their effectiveness. Treatment retention was dichotomized at 360 days. Those who stayed at least 360 days were scored ‘‘1,’’ and 25% met this criterion which has been demonstrated to be a critical predictor of posttreatment outcomes (e.g., Hubbard, Craddock, & Anderson, 2003; MacGowan et al., 1996; Moolchan & Hoffman, 1994; Simpson, 1979, 1981).
3. Results 3.1. Homogeneity of covariance matrices The covariance matrices based on unequal ns for the two patient groups were tested for homogeneity (Morrison, 1976) and found to be significantly different; m2(36) = 193.84, p < .0001. This difference implies that a common model for the two groups should not be expected to account for the data and thus a two-group model is appropriate. 3.2. Two-group structural model The core model tested included two exogenous variables (treatment readiness and mapping intervention) and six endogenous variables (session attendance, counseling rapport, psychosocial functioning, drug use during treatment, time in treatment, and drug use at followup). From previous research it was hypothesized that higher treatment readiness would have a positive effect on session attendance, and that cognitive mapping would have a positive effect on counseling rapport. It was further expected that a reciprocal positive relationship would exist between session attendance and counseling rapport (both measured at Month 2), and both were hypothesized as precedents to lower drug use (opioid/ cocaine) during treatment in Month 3. Also, the role of
D.D. Simpson, G.W. Joe / Journal of Substance Abuse Treatment 27 (2004) 89–97
Treatment Motivation (Intake)
.62** (.15)
Session Attendance (Month 2)
.07***
.07***
Counseling Rapport (Month 2) Cognitive Mapping Strategy
Low Opiate/ Cocaine Use (Month 3)
.13***
.60***
.97**** .43**
Treatment Retention (1 Year)
.19**
.13***
Psychosocial Functioning (Month 3)
93
.08** .35****
.13*
χ2(49) = 88.78; RMSEA = .048 (.031, .063)
Low Opiate/ Cocaine Use (Follow-up)
Fig. 1. Estimates of weights from structural equations analysis of treatment process model. (*p < .05, **p < .01, ***p < .001, ****p < .0001).
psychosocial functioning in Month 3 was viewed as a possible intervening variable between counseling rapport and future drug use; that is, greater counseling rapport would have: (1) direct effects on lower drug use while in treatment; as well as (2) indirect effects through its effect on psychological and social functioning. Treatment retention, which has been a much studied predictor of followup outcomes, is positioned in the model as an endogenous variable predicted by lower drug use during treatment and by session attendance (all three representing behavioral indicators). That is, both higher session attendance and lower drug use should have positive effects on time in treatment. Finally, increased time in treatment and lower drug use during treatment were hypothesized to affect lower posttreatment drug use in the 1-year followup. In the estimation of the two-groups model, the initial model tested hypothesized that common estimates for each path existed for the two groups. The fit of this model was only marginally satisfactory; m2(50) = 147.98, p < .00001;
Treatment Motivation (Intake)
Session Attendance (Month 2)
1.85*
1.90**
1.90**
Counseling Rapport (Month 2) Cognitive Mapping Strategy
1.81**
RMSEA = .074 (.061, .088). Inspection of the modification indices from the analysis indicated many of the indices that involved session attendance were large (between 50 and 60), so the first path involving treatment readiness to session attendance was allowed to differ between the two patient groups. This revised model was found to ‘‘closely fit’’ the data based on the Browne and Cudeck (1993) criteria [m2(49) = 88.77, p < .0004; RMSEA = .048 (.031, .063)], and it represented a significant improvement over the previous model [m2(1) = 59.21, p < .0001]. Additional evidence that this was a good model lay in the magnitude of the modification indices from the analysis, which were generally small; of the 146 indices, only 11 were above 5 and only two of those exceeded seven (the exceptions were 10.8 and 9.3). The common estimates for this model are presented in Fig. 1. It shows that all of the paths having common estimates were statistically significant ( p < .05). For the effects of treatment readiness at intake on session
1.73**
No Opiate/ Cocaine Use (Month 3)
1.98**
2.32**
4.04**** 1.72**
Treatment Retention (1 Year)
1.68*
Psychosocial Functioning (Month 3)
1.74* 4.69****
No Opiate/ Cocaine Use (Follow-up)
Fig. 2. Odds ratios from contingency table analysis of significant treatment process components. (*p < .05, **p < .01, ***p < .001, ****p < .0001).
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attendance in Month 2, which was allowed to be estimated separately for the two patient groups, it was found that the relationship was significant in Group M [b = .62, t = 3.11, CI (.23, 1.01)] but not in Group U [b = .15, t = .77, CI ( .23, .53)]. For all remaining path coefficients, however, common estimates were satisfactory. Use of the cognitive mapping strategy is shown to have positive direct effects on rapport in Month 2 [b = .13, t = 2.22, CI (.02, .24)] and the reciprocal relationship between session attendance and rapport was positive [b = .07, t = 7.01, CI (.05, .09)]. Rapport had a positive effect on psychosocial functioning in Month 3 [b = .67, t = 4.26, CI (.36, .98)], and psychosocial functioning had a positive effect on lower Month 3 during-treatment drug use [b = .19, t = 3.31, CI (.08, .30)]. Both session attendance [b = .13, t = 3.66, CI (.06, .19)] and counseling rapport [b = .13, t = 3.66, CI (.06, .19)] were estimated to have equal effects on low drug use during treatment because of their reciprocal relationship. Furthermore, both lower drug use during treatment [b = .43, t = 3.34, CI (.18, .68)] and session attendance [b = .97, t = 13.85, CI (.84, 1.11)] had a positive effect on treatment retention for a year or longer. Time in treatment, in turn, had a positive effect on low posttreatment drug use at the 1-year followup [b = .08, t = 3.16, CI (.03, .14)], as did low during-treatment drug use [b = .35, t = 3.57, CI (.16, .54)]. 3.3. Odds ratios from contingency tables A series of contingency table analyses using dichotomized scores of the variables in the model provided another approach to testing statistical significance of these relationships. The odds ratios (OR) for the significant relationships corresponding to those identified in the structured equations model analysis are therefore reported as alternate interpretations of results. These are depicted in Fig. 2 and are summarized as follows: treatment readiness at intake and session attendance in Month 2 [m2(1) = 6.23, p < .012, OR = 1.85, CI (1.15, 2.97)], session attendance and counseling rapport in Month 2 [m2(1) = 6.90, p < .009, OR = 1.90, CI (1.18, 3.06)], session attendance and no illegal opioid or cocaine use during Month 3 [m2 (1) = 6.92, p < .009, OR = 1.73, CI (1.15, 2.60)], session attendance in Month 2 and treatment retention for a year or more [m2(1) = 54.81, p < .0001, OR = 4.04, CI (2.74, 5.95)], counseling rapport in Month 2 and psychosocial functioning in Month 3 [m2(1) = 8.87, p < .003, OR = 2.32, CI (1.33, 4.06)], counseling rapport and no illegal opioid or cocaine use during Month 3 [m2(1) = 8.64, p < .004, OR = 1.98, CI (1.25, 3.14)], psychosocial functioning and no illegal opioid or cocaine use during Month 3 [m2(1) = 4.40, p < .04, OR = 1.68, CI (1.03, 2.73)], no illegal opioid or cocaine use during Month 3 and treatment retention [m2(1) = 7.02, p < .009, OR = 1.72, CI (1.15, 2.57)], no illegal opioid or cocaine use during Month 3 and no illegal opioid or cocaine use at the year-1 followup [m2(1) = 32.91, p < .0001, OR = 4.69, CI (2.70, 8.15)], and treatment retention for over a year and no illegal opioid or
cocaine use at the year-1 followup [m2(1) = 5.12, p < .024, OR = 1.74, CI (1.07, 2.81)]. The effect of the cognitive mapping strategy with counseling rapport was also significant [m2(1) = 6.99, p < .009, OR = 1.81, CI (1.16, 2.82)].
4. Discussion This study provides support for the core elements and their sequential pathways included in the TCU Treatment Model (Simpson, 2001, 2004). Overall, the hypothesized relationships, particularly representing the key stage-based contiguous components of the model, were statistically significant and in the direction expected in the estimation of the structural equation model. The t-tests associated with the b-weights were significant at or beyond the .01 level. The fact that a common model was satisfactory for all but one path adds support to the generalizability of the model for methadone programs. The practical implications for drug treatment are in identifying measures that can be useful for monitoring and improving treatment program effectiveness. That is, drug use following treatment was predicted not only by time in treatment but more importantly by a more detailed picture of dynamic elements that define treatment process. These relationships have been found to be generally consistent across treatment populations, settings, and type of outcomes examined (e.g., Broome et al., 2001; Hiller et al., 2002; Joe et al., 1999). Systematic measurement of these elements therefore offers a way to monitor patient needs and progress in treatment, including responses to interventions and better treatment management. Although the adjacent links between indicators of motivation at intake, engagement in Month 2, early recovery functioning in Month 3, retention, and drug use at Year 1 followup were all statistically significant, these were not exclusively stepwise relations. For instance, the figures illustrate that as hypothesized by the treatment model, attendance was related to rapport in Month 2, but attendance in Month 2 also predicted drug use in Month 3 and long-term retention. Similarly, rapport in Month 2 was the strongest predictor of psychological functioning in Month 3, but it was significantly related to drug use during treatment as well. However, the scheduling of process measurements collected in the present study did not lend itself to rigid testing of a parsimonious sequential model, and indeed it is reasonable that the model and linkages among its elements may vary somewhat across different settings. In outpatient methadone treatment, for example, session attendance and low during-treatment drug use represent ‘‘behavioral measures’’ of process, and both are directly linked to retention and followup outcomes. On the other hand, in residential settings where treatment attendance and duration is sometimes legally mandated (thereby limiting variance) and access to street drugs is restricted, the paths for some of these treatment process linkages may shift. Part of the reason is that
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assessment constraints may be related to treatment circumstances and settings that can alter their relationships in a generic model of treatment process. Refining measures of these process components that can apply across treatment settings (Joe et al., 2002) will help in further testing generalizability of the TCU Treatment Model. Translation of findings into odds ratios illustrate in clinical terms the interrelatedness of these measures and how treatment outcomes subsequently can be affected by therapeutic strategies. Their magnitudes fell into two categories. The first represents very strong relationships; that is, odds ratios of 4 or greater (viz., session attendance in Month 2 with retention, as well as abstinence during treatment and abstinence at the 1-year followup after treatment). The effect sizes of the remaining relationships fall in the area between small and medium, as defined by the magnitudes of the odds ratios (i.e., between 1.7 and 2.3) and the magnitudes of the percentages being compared. The odds ratios also are suggestive of the degree of influence that might be expected between different contiguous components of the model. They serve to indicate how influencing the level of one component might affect the level of another, and thereby treatment process and outcomes. For example, patients who were randomly assigned to counselors taught to use a cognitive strategy, called nodelink mapping, were found to be nearly twice as likely (1.8) to have high scores on positive rapport as those who were assigned to counselors who were not taught to use this counseling enhancement. Using techniques (e.g., Sia, Dansereau, & Czuchry, 2000) for improving patient readiness for outpatient methadone treatment from ‘‘unmotivated’’ to ‘‘motivated’’ can double the odds that counseling sessions (i.e., the median number or more) will be attended in the early months of treatment. For other time-sequenced relationships, such as the effects of rapport on psychosocial functioning, it is seen that positive rapport increased the odds of positive psychosocial functioning by two. The results also show that abstinence from opiate and cocaine use during treatment increased the odds for staying in treatment for a year or longer by 1.7, and that treatment retention increased the odds of abstinence in the 1-year followup by 1.7. The increase in odds noted between counseling session attendance and counseling rapport was nearly 2. With regard to the latter, it is noted that because these two engagement indicators were measured for the same time period, their odds ratios were interpreted to be equivalent (regardless of whether session attendance or rapport is used as the ‘‘independent variable’’ or ‘‘dependent variable’’). Although it is reasonable to expect that session attendance is a necessary precursor for establishing counseling rapport in the early weeks of treatment, continued session attendance and participation are likely influenced (positively or negatively) by the level of counseling rapport that emerges. Only patients in community-based outpatient methadone treatment programs were included in this study. Although
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essentially the same treatment process components have been found in evaluations of residential programs (e.g., Knight, Joe, & Simpson, 2003), the nature of residential programs suggest that additional factors related to relationships with family members, peers in and out of treatment, and social support are important to consider, especially in relation to psychosocial functioning during treatment. Additional research is needed to examine the model in criminal justice residential drug treatment programs, particularly in terms of the effects of aftercare and supportive networks, and with respect to elements of organizational functioning in treatment programs (Simpson & Knight, 2003). The latter is important because treatment process factors of rapport and satisfaction also appear to be related to organizational climate and staff attributes (Lehman, Greener, & Simpson, 2002). Additionally, characteristics of the treatment program have been shown to influence the patient engagement process, particularly counselor attributes and counseling skills (Joe et al., 2001) and aspects of the program organization and climate (Lehman et al., 2002). In future applications, they too need to be estimated in the full treatment model. Other important influences on the process involve support systems, including family, friends, support networks, and social support services (e.g., Broome, Simpson, & Joe, 2002; Griffith, Knight, Joe, & Simpson, 1998; Knight & Simpson, 1996). The current study grew out of a long-term program of research that has as one of its goals a better understanding of treatment process, so efforts were made to collect systematic data at intake, periodically during treatment, and at followup in order to conduct this investigation. The results demonstrate the practical value of assessing during-treatment engagement process and patient functioning as performance indicators. Because they are sensitive to interventions, they provide treatment planning and program management tools (Joe et al., 2002; Simpson, 2002). At the patient level, functioning can be monitored in reference to therapeutic thresholds (clinically defined or statistically determined) and linked to treatment events or interventions. At the program level, aggregated patient data can help identify patterns of needs, intervention effectiveness, and clinical impact of organizational changes. It is also hoped that presenting assessment results in terms of effect-size indicators might encourage and increase the clinical applications of research findings like these.
Acknowledgments This work was funded by the National Institute on Drug Abuse (Grant No. R37 DA13093). Its foundations, however, began in 1989 with NIDA funding of our DATAR-1 project (Improving Drug Abuse Treatment for AIDS-Risks Reduction), followed by DATAR-2 (Improving Drug Abuse Treatment Assessments and Resources) and continuing in our current DATAR-3 phase (Transferring
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Drug Abuse Treatment Assessments and Resources). The interpretations and conclusions, however, do not necessarily represent the position of NIDA or the Department of Health and Human Services.
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D.D. Simpson, G.W. Joe / Journal of Substance Abuse Treatment 27 (2004) 89–97 Miller, W. R. (1989). Increasing motivation for change. In R. K. Hester, & W. R. Miller (Eds.), Handbook of alcoholism treatment approaches: Effective alternatives ( pp. 67 – 80). New York: Pergamon Press. Moolchan, E. T., & Hoffman, J. A. (1994). Phases of treatment: A practical approach to methadone maintenance treatment. International Journal of the Addictions, 29, 135 – 160. Morrison, D. F. (1976). Multivariate statistical methods. New York: McGraw-Hill. Muthe´n, L. K., & Muthe´n, B. O. (1998). Mplus user’s guide. Los Angeles: Authors. Prochaska, J. O., & DiClemente, C. C. (1986). Toward a comprehensive model of change. In W. R. Miller, & N. Heather (Eds.), Treating addictive behaviors: Processes of change ( pp. 3 – 27). New York: Plenum Press. Sia, T. L., Dansereau, D. F., & Czuchry, M. (2000). Treatment readiness training and probationers’ evaluation of substance abuse treatment in a criminal justice setting. Journal of Substance Abuse Treatment, 19, 459 – 467. Simpson, D. D. (1979). The relation of time spent in drug abuse treatment to posttreatment outcomes. American Journal of Psychiatry, 136, 1449 – 1453. Simpson, D. D. (1981). Treatment for drug abuse: Follow-up outcomes and length of time spent. Archives of General Psychiatry, 38, 875 – 880. Simpson, D. D. (2001). Modeling treatment process and outcomes. Addiction, 96, 207 – 211. Simpson, D. D. (2002). A conceptual framework for transferring research to practice. Journal of Substance Abuse Treatment, 22, 171 – 182. Simpson, D. D. (2004). A conceptual framework for drug treatment process and outcomes: Applications for improving treatment effectiveness. Manuscript submitted for publication. Simpson, D. D., Brown B. S. (Eds.) (1999). Special issue: Treatment process and outcome studies from DATOS. Drug and Alcohol Dependence, 57. Simpson, D. D., Curry S. J. (Eds.) (1997). Special issue: Drug Abuse Treatment Outcome Study (DATOS). Psychology of Addictive Behaviors, 11. Simpson, D. D., & Joe, G. W. (1993). Motivation as a predictor of early dropout from drug abuse treatment. Psychotherapy, 30, 357 – 368. Simpson, D. D., Joe, G. W., Dansereau, D. F., & Chatham, L. R. (1997).
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Strategies for improving methadone treatment process and outcomes. Journal of Drug Issues, 27, 239 – 260. Simpson, D. D., Joe, G. W., Fletcher, B. W., Hubbard, R. L., & Anglin, M. D. (1999). A national evaluation of treatment outcomes for cocaine dependence. Archives of General Psychiatry, 56, 507 – 514. Simpson, D. D., Joe, G. W., Greener, J. M., & Rowan-Szal, G. A. (2000). Modeling year 1 outcomes with treatment process and post-treatment social influences. Substance Use & Misuse, 35, 1911 – 1930. Simpson, D. D., Joe, G. W., Rowan-Szal, G. A., & Greener, J. M. (1995). Client engagement and change during drug abuse treatment. Journal of Substance Abuse, 7, 117 – 134. Simpson, D. D., Joe, G. W., Rowan-Szal, G. A., & Greener, J. M. (1997). Drug abuse treatment process components that improve retention. Journal of Substance Abuse Treatment, 14, 565 – 572. Simpson, D. D., & Knight, K. (2003). TCU model of treatment process and outcomes in correctional settings. In J. A. Gondles (Ed.), The state of corrections ( pp. 211 – 222). Lanham, MD: American Correctional Association. Simpson, D. D., & Sells, S. B. (1982). Effectiveness of treatment for drug abuse: An overview of the DARP research program. Advances in Alcohol and Substance Abuse, 2, 7 – 29. Simpson, D. D., & Sells, S. B. (Eds.). (1990). Opioid addiction and treatment: A 12-year follow-up. Malabar, FL: Krieger. Siqueland, L., Crits-Cristoph, P., Frank, A., Daley, D., Weiss, R. D., Chittams, J., Blaine, J., & Luborsky, L. (1998). Predictors of dropout from psychosocial treatment for cocaine dependence. Drug and Alcohol Dependence, 52, 1 – 13. Siqueland, L., Crits-Cristoph, P., Gallop, R., Barber, J. P., Griffin, M. L., Thase, M. E., Daley, D., Frank, A., Gastfriend, D. R., Blaine, J., Connolly, M. B., & Gladis, M. (2002). Retention in psychosocial treatment of cocaine dependence: Predictors and impact on outcome. American Journal on Addictions, 11, 24 – 40. Sobell, M. B., & Sobell, L. C. (2000). Stepped care as a heuristic approach to the treatment of alcohol problems. Journal of Consulting and Clinical Psychology, 68, 573 – 579. Tims, F., Leukefeld C. G., Platt J. J. (Eds.) (2001). Relapse and recovery in addictions. New Haven, CT: Yale University Press. Weissberg, R. P., & Greenberg, M. T. (1998). Prevention science and collaborative community action research: Combining the best from both perspectives. Journal of Mental Health, 7, 479 – 492.