1 Journal of Substance Abuse Treatment 27 (2004) Regular article A conceptual framework for drug treatment process and outcomes D. Dwayne Simpson, (Ph.D.)* Institute of Behavioral Research, Texas Christian University, TCU Box , Fort Worth, TX 76129, USA Received 18 February 2004; received in revised form 2 May 2004; accepted 15 June 2004 Abstract Evidence from specialized treatment evaluations and large-scale natural studies of treatment effectiveness is organized conceptually into a btreatment modelq for summarizing how drug treatment works. Sequential relationships between patient and treatment program attributes, early patient engagement, recovery stages, retention, and favorable outcomes are discussed along with behavioral, cognitive, and skills training interventions that have been shown to be effective for enhancing specific stages of the patient recovery process. Applications of the treatment model for incorporating science-based innovations into clinical practice for improving early engagement and retention, performance measurements of patient progress, program monitoring and management using aggregated patient records, and organizational functioning and systems change also are addressed. D 2004 Elsevier Inc. All rights reserved. Keywords: Treatment model; Process; Performance; Outcomes; Recovery; Interventions; Program monitoring 1. Introduction A series of visionary research papers were published in 1979 for what was then a bnewq field involving communitybased treatment for illegal drug use. Early evaluations and issues from outpatient drug free (Kleber & Slobetz, 1979), therapeutic communities (De Leon & Rosenthal, 1979), and national multimodality treatment settings (Sells, 1979) pointed to the importance of motivation, during treatment process, retention, and evaluation designs. Citing this work, Jaffe (1979, p. 9) concluded, bthe evidence is overwhelming that while in treatment in a variety of programs, and for varying periods thereafter, a significant proportion of drug users exhibit substantial improvement in a number of areas.q He added, bwhat is still at issue is not that change occurs, but rather the degree of change which can be attributed to the treatment process.q Equally important papers addressed the roles of information management and organizational issues (Deitch, 1979; Sells & Simpson, 1979), transitional aftercare treatment systems (B. S. Brown * Tel.: ; fax: address: (D.D. Simpson). & Ashery, 1979), and mandated correctional treatment systems (McGlothlin, 1979). Over 20 years later, Prendergast, Podus, Chang, and Urada (2002) concluded from their meta-analysis of comparison group studies that drug treatment was effective. More importantly, they recommended that less future attention be paid to outcome evaluations and more to questions of process how treatment works and how it can be improved. Indeed, the need for systematic process studies of drug treatment has continued to be widely recognized (Lamb, Greenlick, & McCarty, 1998; McLellan, Woody et al., 1997; Moos, 2003). Attention has been given to the concepts of drug treatment engagement and recovery progress (Allison & Hubbard, 1985; Joe, Simpson, & Sells, 1994; Melnick, De Leon, Thomas, Kressel, & Wexler, 2001; Sells, Demaree, Simpson, Joe, & Gorsuch, 1977), but development of empirical measurement systems and integrative approaches focused on relationships of patient and program factors with outcomes has been more challenging. Much of our evidence about treatment outcomes in typical community-based settings comes from large-scale national evaluations funded by the National Institute on Drug Abuse (NIDA). Beginning in the early 1970s with the Drug Abuse Reporting Program (DARP), followed by the /04/$ see front matter D 2004 Elsevier Inc. All rights reserved. doi: /j.jsat
2 100 D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) Treatment Outcome Prospective Study (TOPS) a decade later, and continuing through the 1990s with the Drug Abuse Treatment Outcome Studies (DATOS), national evaluations of effectiveness have examined over 65,000 admissions to 272 treatment programs using multi-modality and multi-site followup sampling plans that allow the study of treatment in natural settings (Hubbard et al., 1989; Simpson & Brown, 1999; Simpson & Curry, 1997; Simpson & Sells, 1982). Group-level improvements in drug use and social functioning in the first year following treatment were generally sustained in long-term followup evaluations, ranging up to 12 years after treatment (Hubbard, Craddock, & Anderson, 2003; Simpson, Joe, & Bracy, 1982; Simpson, Joe, & Broome, 2002; Simpson & Sells, 1990). These national projects comprise only part of the large body of evidence from natural and experimental studies accumulated over the past 30 years that supports the general effectiveness of drug treatment (Gerstein & Harwood, 1990; Institute of Medicine, 1996; Lamb et al., 1998; National Institute on Drug Abuse, 1999). Similar results from the National Treatment Outcome Research Studies (NTORS) in England add further support to this evidence base (Gossop, Marsden, Stewart, & Kidd, 2003; Gossop, Marsden, Stewart, & Rolfe, 1999). Length of stay in drug treatment has been one of the most consistent predictors of followup outcomes, with the general relationship between treatment retention and outcomes being replicated across major types of residential and outpatient programs in all four of the previously mentioned national evaluation studies DARP, TOPS, DATOS, and NTORS. Early studies of retention effects documented the high prevalence of treatment dropouts in the first 90 days following admission, which was also the point at which beneficial therapeutic effects begin to materialize (De Leon, Holland, & Rosenthal, 1972; De Leon, Jainchill, & Wexler, 1982; Simpson, 1979, 1981). Although treatment outcomes tend to improve in a generally linear fashion as retention increases from 3 months up to months or more, which is targeted as the goal for many treatment programs (Etheridge, Hubbard, Anderson, Craddock, & Flynn, 1997), a rigid bmore is betterq criterion faces practical limitations from managed care and other cost-containment pressures. Studies from DATOS (Simpson, Joe, Broome et al., 1997; Simpson, Joe, & Brown, 1997) replicated these retention findings but began shifting attention to the concept of achieving bminimum retention thresholdsq for effective treatment that is, approximately 90 days for residential and outpatient care, and a year for methadone (agonist maintenance) treatment programs (National Institute on Drug Abuse, 1999). Note that these thresholds are defined bstatistically,q meaning that patients with treatment retention below the threshold had low probability of showing improved outcomes (comparable to very early dropout comparison groups). As time in treatment increases beyond these thresholds, therapeutic benefits begin to accrue although patients with more serious problem severity at intake require longer and more intensive treatment. However, retention represents a cumulative index for a mixture of patient, therapeutic, and environmental factors that contribute to treatment progress and effectiveness. The influences on a person to remain in treatment include interactions among individual needs, motivation factors, and social pressures with treatment attributes, such as policy and practices, accessibility, services offered, counselor assignment, therapeutic relations, and patient satisfaction. In general, these represent aspects of the bblack boxq of treatment and how they impact stages of patient recovery Background for treatment process research Studies of drug treatment process have extensive background and foundations, especially from psychotherapy and counseling psychology. The similarities in findings across these areas reflect on the generalizability of therapeutic process. Chapters on treatment process and outcomes in the Handbook of Psychotherapy and Behavior Change (Orlinsky & Howard, 1978, 1986; Orlinsky, Rbnnestad, & Willutzki, 2004) have been major resources for promoting better understanding of constructs involved in therapeutic interventions. In the latest iteration of these reviews on process-to-outcome research findings, Orlinsky et al. (2004) stress the importance of considering the broader context of social institutions and cultural patterns as influences on the outcomes of patient and therapist interactions. Namely, treatment outcomes are impacted by social institutions (including organizational attributes of the treatment agency), role-related interactions with family and friends, and normative pressures from society and culture. This type of systems perspective helps emphasize that therapeutic process represents more than just a bclinical intervention.q It directs attention to (1) the importance of bpatient suitabilityq in relation to early therapeutic engagement, which corresponds to the notion of motivation and readiness at treatment intake, (2) the overwhelming support based on over 1,000 studies for the critical role of therapeutic bonding between therapist and patient, (3) cognitive and behavioral change processes during treatment, (4) the duration of treatment as a major predictor of outcomes, (5) influences of organizational and contextual factors on treatment, and (6) a need for further development of treatment monitoring systems to address clinical feedback and performance evaluation needs. These are the same areas given priority in drug treatment process and outcome research. As summarized by Whiston and Sexton (1993), over 50 years of psychotherapy research have illuminated the roles of therapeutic relationships, session factors, patient attributes, and how they interact in conjunction with special interventions and approaches. Using a systems and developmental perspective for focusing on counseling psychology, Hill and Corbett (1993) also provide a useful historical overview with recommendations for the future. They discuss psychotherapy, skills training, behavioral and
3 D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) cognitive strategies, and social influence models interwoven with advances that have occurred in research methodologies. Steps they see as still needed for improvements include practical and analytic issues, especially using research designs that balance rigor with relevance as well as identifying important therapeutic events embedded within a longitudinal context. McLellan (2002) argues that posttreatment outcome evaluation designs for drug treatment have been overvalued and often misapplied. For instance, treatment benefits for other chronic health conditions (like asthma, diabetes, and hypertension) are judged primarily on the basis of interim, in-treatment performance criteria. Hill and Corbett (1993, p. 16) emphasize that bthe overall goals of process and outcome studies should be to develop new theories of therapy, to provide information for practitioners about how to intervene with patients at different points in therapy, and to develop training programs based on empirical results of what works in therapy.q They go on to suggest this includes the need to test an entire (longitudinal) model that incorporates patient pretreatment characteristics, process factors, interim outcomes, external influences, and long-term outcomes. These suggestions resonate with methodological cautions about efforts to impose controlled clinical trials as the sole legitimate design for establishing efficacy of interventions and causality (De Leon, Inciardi, & Martin, 1995). Krause and Howard (2003; p. 754) state that ball clinical trials are quasiexperiments for the foreseeable future, so long as our causal models are not fully specified and all the causal variables are not precisely controlled or accurately measured.q They go on to demonstrate additional limitations of randomized designs in controlling interactions between treatment and patient variables. Ablon and Jones (2002) compared manualized treatment regimens and found overlap between therapeutic process and technique that likewise questions basic assumptions about using controlled experimental designs for establishing the cause of patient improvements. They conclude that the clinical trials model, though appropriate for the medical science field to study medications, fails when applied to psychological treatments because therapeutic process and patient-counselor engagement dynamics cannot be fully controlled. In particular, this approach focuses more on outcomes and less on the linkage of process with outcomes. bpsychotherapy research would profit from the study of change processes as they occur naturalistically, rather than focusing on the empirical validation of brand names of therapyq (p. 782). Others agree with Ablon and Jones about the need for a shift in treatment evaluation research towards more emphasis on change processes (Goldfried & Wolfe, 1996; Howard, Moras, Brill, Martinovich, & Lutz, 1996). It is longitudinal effectiveness studies, as opposed to highly restricted efficacy designs, that emphasize external validity and the interactions of clinical protocol with patient dynamics in natural settings. Furthermore, providers of behavioral health services and policymakers need evidence based on realworld applications of treatment in field studies (Messer, 2002; Moyer & Finney, 2002; Sturm, 2002) Practical applications of treatment models Connors, Donovan, and DiClemente (2001, p. 223) state bresearch to date appears to support a process of change for substance abusers that has a series of steps or phases that require different strategies and address different issues.q They stress the role of cognitive functioning (decisional balance, self-efficacy, and discrete stage perspectives) of patients. This follows work by Rogers (1959) long ago that focused on the relationship between counselor and therapist as a way to improve patient changes, and the notions of Erikson (1963) about stage-based personality changes. These represent phases of the recovery process in treatment settings. A treatment model needs to be more than a description of patient change, however, in order for interventions and other influences to be integrated into it as exemplified by stepped or staged care treatment approaches (Brooner & Kidorf, 2002; Sobell & Sobell, 2000; Weissberg & Greenberg, 1998). Although the NIDA publication Principles of Drug Addiction Treatment: A Research Based Guide (1999) provides an introduction and listing of prominent interventions found to be effective, it is lacking in practical clinical guidelines for when and why each one should be used. By becoming more organized in assembling these components conceptually, we could become more strategic in making applications of evidence-based techniques as well as more strategic in filling the voids. Indeed, there is great heuristic potential for an evidence-based treatment model that summarizes bwhen and whereq to use interventions for maximum effect. Can we therefore assemble a treatment model to serve as a clinical guide for how to determine when various interventions are needed and if they are working? Towards these lofty goals, general features of the stage-based TCU Treatment Model are summarized below, along with a review of related drug treatment, psychological counseling, and psychotherapy literature. The purpose of treatment process and outcome research captured in the model is four-fold. First, it should promote the use of patient performance and monitoring indicators that serve as interim criteria related to treatment planning and effectiveness. Second, it should demonstrate the stages of patient change in treatment and how specific interventions can be used to address particular needs throughout the recovery process. Third, it should clarify the rationale for using individuallevel and aggregated patient records of engagement and performance as indicators for feedback to counselors and patients, program performance monitoring, and management of services. Finally, it should be a foundation and guide for studying treatment gaps and improving organizational functioning and change (i.e., technology transfer, or moving science to services). These are the criteria
4 102 D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) recommended for judging the value of a treatment process and outcome model Developing a research program on drug treatment process Research conducted at Texas Christian University (TCU), especially during the past 15 years, has focused on developing a conceptual framework for drug treatment process and outcome research (see Simpson, 2001). Psychotherapy, counseling psychology, and drug treatment research has identified important therapeutic issues and domains, but these findings have not been integrated efficiently into a conceptual scheme to guide clinical applications and improvements. This step is crucial for communicating convincingly the notion that treatment is a complex process rather than a singular beventq and to capture dynamic aspects of its sequential nature. Our research was therefore designed to be programmatic in its conceptual and methodological approach to treatment process (Chatham & Simpson, 1994; Simpson, Chatham, & Joe, 1993; Simpson, Dansereau, & Joe, 1997), while assimilating the contributions of many others in the psychological and addiction treatment research fields. Studies at TCU have spanned diverse settings and populations, but they share common assessment methodologies and integrated strategies for obtaining longitudinal data in natural and experimental research designs (more details on scientific publications and related treatment intervention manuals and assessment resources are available at www. ibr.tcu.edu). This approach has allowed us to develop sequentially a body of findings that could be assembled into a general treatment model (Simpson, 2001; Simpson, Joe, Dansereau, & Chatham, 1997). A few landmark studies determined in large part the path taken in pursuing this goal, leading up to the conceptual model presented below. After selecting outpatient methadone programs as our initial focus due to its stability and slower pace of therapeutic change (in contrast to short-term and highly diverse outpatient drug free treatment) we launched a comprehensive, prospective assessment system for patient and program functioning as well as development of intervention tools designed to improve services while we studied the process involved (Simpson, Joe, Dansereau, et al., 1997). We relied heavily on experience from numerous descriptive, process, methodological, and outcome studies, including those conducted as part of our first national evaluation of treatment effectiveness in the U.S. (Sells, 1974; Sells & Simpson, 1976; Simpson & Sells, 1982, 1990). As a DATOS Research Center, our conceptual models and measures were re-examined using the diversity of treatment settings represented in DATOS (including longterm residential, outpatient drug free, outpatient methadone, and short-term residential programs), the multi-site representation for each treatment (including over 10,000 patients from 96 agencies), and its distinctly different data system (Flynn, Craddock, Hubbard, Anderson, & Etheridge, 1997). Psychometric calibrations of patient and program measures and incorporation of new methodological techniques (e.g., hierarchical linear modeling) provided the basis for replicating and expanding the evidence for motivational influences on treatment process and retention (Joe, Simpson, & Broome, 1998; K. Knight, Hiller, Broome, & Simpson, 2000). It added broad multi-modality support for the TCU Treatment Model (Joe, Simpson, & Broome, 1999), and more evidence for these treatment process relationships have come from a similar national treatment effectiveness study in England (Gossop et al., 1999; Gossop, Marsden, et al., 2003) as well as treatment evaluations in correctional populations (Broome, Knight, Hiller, & Simpson, 1996). Having a large-scale data system from 96 treatment providers in DATOS also made it possible to examine treatment process at both the patient and program level. When patient-level records within agencies were aggregated to represent program-level functioning, for instance, they showed that programs with higher average patient involvement successfully accessed more social and public health services, maintained more consistent treatment counseling patterns, and appeared to be more focused on the particular needs of patients they served (Broome, Simpson, & Joe, 1999). Thus, treatment process dynamics operate at multiple levels (for patients and programs). 2. Overview of the TCU Treatment Model Followup studies show drug treatment with adequate intensity and duration can improve addiction recovery rates. There are performance variations between programs and patients within programs, however, which raise questions about how to achieve improvements in treatment effectiveness and efficiency. Therefore, growing attention has been given in recent years to dynamic stages of addiction treatment and recovery, along with support for using a bchronic careq approach to evaluating treatment (McLellan, Lewis, O Brien, & Kleber, 2000). At issue are the goals of quality improvement and how treatment systems might adopt bevidence-basedq practices as well as document their effectiveness based on patient performance measures. Toward this end, the TCU Treatment Model identifies key ingredients associated with effective process and outcomes of specific treatment episodes. In particular, it focuses attention on sequential phases of the recovery process and how therapeutic interventions link together over time to help sustain engagement and retention, thereby improving patient functioning during treatment and after discharge. Research findings will be summarized showing the relationships between motivation, engagement, early change, retention, family and social support networks, and followup outcomes. Each sequential facet of the TCU Treatment Model, illustrated in Fig. 1, is described in more detail in the
5 D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) Readiness Interventions Behavioral Interventions Social Skills & Support Social Support Systems Patient Attributes Readiness Severity Program Attributes Resources Staff Climate Mgmt Info Early Engagement Program Participation Therapeutic Relationship Early Recovery Behavioral Change Psycho-Social Change Stabilized Recovery Sufficient Retention Post-treatment Outcomes Recovery Support Networks Drug Use Criminal Activity Social Relations Systems Interventions Cognitive Interventions Recovery Skills Training Personal Health Services Fig. 1. Overview of TCU Treatment Model, representing sequential influences of patient and program attributes, stages of treatment, and evidence-based interventions on post-treatment outcomes. following sections. Although portrayed parsimoniously as an integrated treatment episode, there could be different service providers in a continuum-of-care model, or multiple episodes of treatment (e.g., detoxification, residential, and outpatient) might in practice be chained together. The significance of patient and program attributes for treatment process and outcomes is discussed first, along with examples of evidence-based interventions for increasing patient motivation. Subsequent sections examine components inside the box and interventions that amplify those facets of treatment: early engagement, early recovery, and retention-transition. The review concludes with an examination of bwrap-aroundq services needed, but often difficult to obtain, for social support and personal health care of patients. 3. Patient attributes at intake The left margin of Fig. 1 identifies contextual influences on treatment outcomes involving patient background and organizational functioning. Major patient attributes include motivation for change, readiness for treatment, and problem severity at intake the types of measures believed to be important for deciding treatment program placement and planning the appropriate course of clinical care (Gerstein & Harwood, 1990; Mee-Lee, 2001). In addition to the setting and intensity levels that distinguish between major drug treatment options (e.g., residential vs. outpatient drug free programs, therapeutic communities, outpatient agonist substitution programs), there are also program attributes resources, staff skills, climate, and information systems for clinical and program management relevant to therapeutic effectiveness. The positive relationships between treatment retention and patient outcomes have been repeatedly affirmed across different types of therapeutic settings, but closer study of patient and program factors that mediate and influence recovery stages is needed for bdecomposingq the active ingredients involved (Bell, Richard, & Feltz, 1996; De Leon, 2000; Toumbourou, Hamilton, & Fallon, 1998). Patient sociodemographic and other pretreatment characteristics traditionally have not been strong predictors of posttreatment outcomes. However, improved assessments of patient functioning and better analytic techniques that distill sequential relationships have modified this view. Addiction severity (particularly involving multiple drug use), criminal history, social resources, and psychological dysfunction at treatment intake influence engagement and retention. Of particular importance are patient motivation for treatment and readiness to change (Baekeland & Lundwall, 1975; De Leon & Jainchill, 1986; Simpson & Joe, 1993; Stark, 1992). Compared to drug users entering outpatient methadone treatment and probationers voluntarily entering residential treatment, for instance, treatment readiness scores are much lower among injection drug users in HIV/AIDS outreach programs as well as probationers mandated to drug education programs. Among the most significant patient attributes is motivation for change, which gained much of its contemporary prominence from work by Prochaska and DiClemente (Connors et al., 2001; DiClemente & Prochaska, 1998; Prochaska & DiClemente, 1986) on cognitive and behavioral bstages of changeq as well as by Miller (1985, 1989, 1996) on strategies to increase motivation. De Leon and Jainchill (1986) have emphasized the role of intrinsic vs. extrinsic motivation and readiness for treatment in their assessments for therapeutic community settings, and discrete stages of
6 104 D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) motivation also have been examined (Simpson & Joe, 1993). Especially important is the growing evidence from path analyses of longitudinal records showing programmatic linkages of motivational stages with subsequent indicators of therapeutic engagement and recovery of patients (Broome et al., 1999; Gossop, Stewart, & Marsden, 2003; Joe et al., 1998, 1999; Joe, Simpson, Greener, & Rowan-Szal, 1999; K. Knight et al., 2000; Pantalon, Nich, Frankforter, & Carroll, 2002; Ryan, Plant, & O Malley, 1995). De Leon and associates (De Leon, Melnick, & Tims, 2001; Melnick et al., 2001) point out the additional roles of circumstances and resources in the recovery process. Several of the most widely used assessment instruments for motivational readiness for change including eight patient self-administered questionnaires and three measures based on clinical ratings were examined in terms of reliability and validity by Carey, Purnine, Maisto, and Carey (1999). One of the self-administered assessments was the TCU scales (Simpson & Joe, 1993), which measure problem recognition, desire for help, and treatment readiness as discrete sequential stages. They have good reliabilities in studies of African Americans (Longshore, Grills, Anglin, & Annon, 1997), the homeless (Nwakeze, Magura, & Rosenblum, 2002), and in cross-cultural settings using a Dutch translation (De Weert-Van Oene, Schippers, De Jong, & Schrijvers, 2002). The TCU scales of patient motivation and readiness also have been used in correctional settings (Farabee, Nelson, & Spence, 1993; Hiller, Knight, Leukefeld, & Simpson, 2002; Hiller et al., 2003; K. Knight, Simpson, Chatham, & Camacho, 1997) and included in longitudinal process studies with results consistent with those from community treatment programs (Broome, Knight, Knight, et al., 1997; Broome, Knight, Hiller, & Simpson, 1996; Broome, Knight, Joe, Simpson, & Cross, 1997; Hiller, Knight, Rao, & Simpson, 2002). While significant advances have been made in the theoretical and empirical role of btreatment motivation,q they are only a start. As discussed by De Leon (2000), motivation and treatment readiness are often viewed as global, undifferentiated constructs that can oversimplify their dynamic and complicated role in treatment. Dansereau, Evans, Czuchry, and Sia (2003) have therefore conceptualized readiness in a two-dimensional framework. One dimension represents three interdependent stages of readiness, including readiness for personal change, for the treatment program, and for specific intervention activities. The second dimension represents important patient attributes, including motivation, skills/resources, and confidence/ self efficacy. Because they can fluctuate, repeated measures of these readiness dynamics are needed to examine interactions with interventions and help maximize therapeutic engagement over time. Indicators of problem severity at intake also predict levels of early engagement and retention. An oft-cited study by Woody, McLellan, Luborsky, et al. (1984) demonstrated the importance of psychiatric severity in relation to progress of patients randomly assigned to treatment conditions involving psychotherapy and drug counseling. Increasing levels of severity generally required more intensive psychotherapy. Similar findings were reported by Fals- Stewart and Lucente (1994), based on comparisons of outcomes related to patient retention in residential substance abuse treatment for different antisocial personality and cognitive impairment levels. And Simpson, Joe, Fletcher, Hubbard, and Anglin (1999) found longer retention (over 90 days) in residential treatment for cocaine use was associated with better post-treatment outcomes among highseverity patients, whereas patients with lower problem severity at intake were able to benefit from less intense, outpatient care. bproblem severityq was broadly defined, based on seven indicators of psychological and social functioning, legal status, and drug use history. Like severity of psychiatric symptoms, however, higher pretreatment drug use especially cocaine and crack is often a barrier to favorable engagement and outcomes (Grella, Joshi, & Hser, 2003; Patkar et al., 2002; Rowan-Szal, Joe, & Simpson, 2000). In some instances, of course, heavy use and dependence levels may require medical detoxification to be part of the treatment readiness phase. Because treatment motivation and problem severity appear to interact as predictors, Carey, Maisto, Carey, and Purnine (2001) have argued for assessing treatment motivation even among high severity patients with mental illness. Evidence suggests measures of motivation and problem severity are positively correlated (Boyle, Polinsky, & Hser, 2000), but their linkages to outcomes can be complicated. For instance, using structural equation analysis in a national multi-modality study of treatment effectiveness, Joe, Simpson, and Broome (1999) identified motivation as the best predictor of engagement and retention (with positive contributions from higher pretreatment depression, alcohol problems, and legal pressures); on the other hand, higher severity of cocaine use and hostility at intake predicted early dropout. Low motivation, in turn, is linked to client recollection of history of family dysfunction, deviance of peer groups, and poor psychosocial adjustment before treatment (Griffith, Knight, Joe, & Simpson, 1998) Treatment settings and program attributes Almost 14,000 specialized drug treatment facilities in the U.S. currently provide services in a variety of settings (Substance Abuse and Mental Health Services Administration, 2003), mainly in residential, outpatient drug free, and methadone (agonist maintenance) programs such as those represented in DATOS (Etheridge et al., 1997). Diagnosing drug dependence and abuse is a critical but imperfect step to determining treatment needs and optimal setting (Gerstein & Harwood, 1990). Assessment strategies, treatment resources, and decision rules for program admissions across state and local systems are highly diverse, particularly for correctional populations (Farabee
7 D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) et al., 1999; Hiller, Knight, Rao, et al., 2002). Even though drug use histories and related problems of patients are legitimate and appropriate considerations for selecting treatment approaches and settings, there is growing sentiment that virtually all programs share some common treatment process components (Connors et al., 2001; Norcross & Goldfried, 1992). This does not mean that all programs are alike or equally effective. Indeed, those within a particular therapeutic orientation long-term residential, outpatient drug free, and outpatient agonist substitution treatment vary tremendously in their ability to retain patients in treatment, and the traits of their patients differ widely (Simpson, Joe, Broome, et al., 1997). Since higher levels of addiction severity (including drug injection frequency and alcohol use), criminal history, and psychosocial dysfunction at treatment intake are typically associated with poorer outcomes, programs that draw more high-severity caseloads face more difficult treatment challenges than others. Even after adjusting for patient differences, however, programs within the same type of treatment orientation show differential effectiveness, demonstrating that both patient attributes and program features have distinctive but complex influences on outcomes (Broome et al., 1999). Moos, King, Burnett, and Andrassy (1997) found that in Veterans Administration programs, high expectations for patients, clear policies, structured programming, high proportion of staff in recovery, and more emphasis on psychosocial treatment were related to better participation in treatment (and which independently predicted better outcomes at discharge). Comparable findings from TOPS were reported by Joe, Simpson, and Hubbard (1991). A long-standing call for bmatching patients to treatmentq sometimes mistakenly assumes that centralized and comprehensive assessments are routinely conducted for large numbers of treatment seekers, who then can be appropriately matriculated into a rich diversity and clearly articulated array of specialized treatment programs. More practical, however, is the modest expectation that interventions and services within each program should be tailored to acute patient needs and stage of therapeutic progress (McLellan, Grissom, et al., 1997). But even this limited application of patient-to-treatment matching calls for a level of sophistication in assessments and availability of comprehensive (or bwrap-aroundq) services that are uncommon in the real world. Programs often lack proficiency in customizing services to progressively address distinct stages of patient recovery, but evidence is growing in support of the effectiveness and efficiency of reserving more intensive services for patients with more severe problems (Gottheil, Thornton, & Weinstein, 2002; Hser, Polinsky, Maglione, & Anglin, 1999; Thornton, Gottheil, Weinstein, & Kerachsky, 1998). Similar support for matching patient problem severity to treatment intensity comes from a national study of cocaine users showing low-problem patients do about equally well in virtually any type of program, but outcomes plummet for high-problem cases treated in outpatient and short-term programs. These higher severity patients do much better in long-term, intensive residential services (Simpson et al., 1999). Regardless of problem severity, treatment setting, and post-treatment outcomes, however, there are similarities in the therapeutic processes involved. The weakest evidence represented in the TCU Treatment Model involves these interactions between program effectiveness and organizational dynamics (Simpson, 2002). In particular, better assessments and conceptual models for resources, staff functioning, organizational climate, and how to use information for patient and program management are crucial (Heinrich & Lynn, 2002; McCaughrin & Howard, 1996; Schneider, Salvaggio, & Subirats, 2002). However, the need for this research is gaining attention in the growing national agenda for translational studies on getting evidence-based practices into broader field applications. Metaanalytic results suggest organizational training can be effective, depending on training methods used, the skill or task being trained, and goals for the employee training (Arthur, Bennett, Edens, & Bell, 2003), but organizational readiness for change, climate for acceptance, and systems infrastructure must also be considered in planning intervention strategies for altering institutional functioning Evidence-based interventions for improving patient readiness for treatment Not everyone enters treatment with the same level of motivation or problem severity, so it is not surprising that some patients can benefit from special binductionq efforts (Katz, Brown, Schwartz, Weintraub, Barksdale, & Robinson, 2004; Simpson & Joe, 1993). The use of systematic efforts to improve treatment readiness and engagement of patients reflects a fairly recent change in drug treatment practice. Historically, patient motivation was not assessed comprehensively at intake, but induction strategies now are increasingly viewed as part of the programts public health responsibility. Programs also recognize that high costs are associated with early treatment dropouts. Gottheil, Sterling, and Weinstein (1997) recommend the use of personal (telephone) contacts to increase follow through on treatment admissions, one of several social strategies to improve engagement and retention. Motivational interviewing (Miller, 1996; Miller & Rollnick, 1991, 2002) is among the better-known approaches for raising patient commitment, and it can be adapted to target special applications such as for HIV/AIDS outreach efforts to increase the effectiveness of treatment referrals (Booth, Crowley, & Zhang, 1996). To reduce early dropout from therapeutic communities, De Leon and colleagues (2000) employed bsenior professor induction seminarsq as a motivational strategy, while Foote, DeLuca, Magura, et al. (1999) mounted a Group Motivational Intervention approach to enhance and internalize the need for treatment. Other social strategies include using bsignificant othersq (family
8 106 D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) or friends) as part of the induction plan for support of treatment engagement (De Civita, Dobkin, & Robertson, 2000; Garrett, Landau-Stanton, Stanton, Stellato-Kabat, & Stellato-Kabat, 1997; Landau et al., 2000). Another important approach focuses on reducing organizational barriers to treatment, as illustrated by recent initiatives for bpaths to RecoveryQ being funded collaboratively by Robert Woods Johnson Foundation and Center for Substance Abuse Treatment. Motivational induction is particularly beneficial in settings such as correctional programs where low motivation is a common problem (Farabee, Simpson, Dansereau, & Knight, 1995), among adolescents (Battjes, Gordon, O Grady, Kinlock, & Carswell, 2003), and in outpatient treatment for the mentally ill (Carey, Carey, Maisto, & Purnine, 2002). Adaptations of cognitive-based enhancement tools by Dansereau and associates (Blankenship, Dansereau, & Simpson, 1999; Czuchry & Dansereau, 2000; Sia, Dansereau, & Czuchry, 2000) are effective as the basis for treatment readiness training in small group settings. These tools include a popular pedagogical board game called bdownward SpiralQ as a vicarious approach to personalizing the multidimensional consequences of drug abuse (Czuchry, Sia, Dansereau, & Dees, 1997), along with cognitive exercises and associated homework applications for exploring personal needs and strengths (Sia, Czuchry, & Dansereau, 1999). Results from this series of experimental studies show readiness training raises motivation and program participation, as well as patient ratings of sessions, peers, and counselors. Thus, motivation is viewed as a dynamic bstateq that must be sustained throughout treatment. 4. Early engagement The first major step towards recovery in treatment settings shown in Fig. 1 is early engagement, which refers to the extent to which new admissions show up and actively engage in their role as bpatient.q It is measured primarily by program participation and the formation of therapeutic relationships in the initial weeks of treatment. Evidence supports a sequential view of these components (Simpson & Joe, in press), wherein more highly motivated patients at intake are twice as likely to bparticipateq in treatment (e.g., attend sessions) in the first few months of treatment; furthermore, patients achieving higher participation are then twice as likely to develop a favorable therapeutic relationship with their counselor. Although session attendance logically precedes establishment of clinical relationships, this is not a strictly linear process since interactive influences accrue between participation and therapeutic relationships that mutually strengthen these engagement components. bparticipationq can include session attendance (a more appropriate behavioral indicator in outpatient than inpatient settings) as well as assessments of psychological engagement in these sessions (especially useful for group counseling and residential settings where attendance is mandatory). Session attendance has been examined as a corollary of btreatment retention,q leading to studies of dose-response relationships in several types of treatment settings. In general, higher session attendance predicts better outcomes (Fiorentine & Anglin, 1997; Morral, Belding, & Iguchi, 1999; Rosenblum et al., 1995; Rowan-Szal, Chatham, et al., 2002; Toumbourou, Hamilton, U Ren, Stevens-Jones, & Storey, 2002). Rowan-Szal, Chatham, et al. (2002) show higher total group and individual session exposure in methadone treatment likewise is related to stronger rapport or bonding with counselors; furthermore, they found spending more time in sessions was related to being female and having more alcohol use, childhood problems, higher methadone dose, and more bstructuredq counseling sessions. The literature in counseling psychology also focuses on counseling session attendance, especially in the context of a dose-response interpretation and the threshold required for achieving clinically significant improvement. Lambert and colleagues (Anderson & Lambert, 2001; Lambert, Hansen, & Finch, 2001; Snell, Mallinckrodt, Hill, & Lambert, 2001) find sessions are usually required for at least 50% of patients to show improvement, with further benefits accruing with additional sessions. Depending on their time distributions and scheduling of sessions, therefore, these findings suggest counseling of approximately 3 months may be needed before reliable changes become detectable. Refinements in this line of research focus on session-level impact (Stiles, 1980; Stiles & Snow, 1984) and indicate that session evaluations are positively associated with indices of cognitive understanding, problem solving, and relationship formation (Stiles et al., 1994). Kolden (1996) similarly shows therapeutic openness and bonding are related to insession progress. This implies efforts to increase cognitive engagement in each individual session may have promise as micro-motivational strategies, paralleling motivational interviewing and related techniques commonly used for treatment induction (Czuchry & Dansereau, in press; Miller, 1985; Miller & Rollnick, 1991, 2002; Sia et al., 2000). The other major component of early engagement is the btherapeutic relationship,q commonly considered to be at the very core of effective treatment. Its origin and assessment philosophy come from the concept of bworking allianceq in psychotherapy (Horvath & Greenberg, 1989; Luborsky, McLellan, Woody, O Brien, & Auerbach, 1985; Tracey & Kokotovic, 1989), and earlier work by Rogers (1959) using a patient-centered focus calling for therapist empathy, warmth, and genuineness. The success of counseling is consistently related to the quality of this relationship, which is associated with participation in sessions that patients consider to be effective, and there is general similarity, or congruence, between patient and therapist perceptions of its development (Al-Darmaki & Kivlighan, 1993; Horvath & Symonds, 1991; Mallinckrodt, 1993). While both patient and counselor perceptions of their
9 D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) working alliance are predictive of outcomes, those of patients tend to be most discriminating. Although patient satisfaction with services (reflecting access, confidence in effectiveness, and commitment) is related to drug treatment outcomes (Carlson & Gabriel, 2001), it appears to be secondary to the counseling relationship which is variously referred to as rapport, personal bonding, or therapeutic alliance (Joe, Simpson, Dansereau, & Rowan-Szal, 2001). The association of therapeutic relationship with outcomes is consistently reported across drug use groups and treatment settings, including alcohol outpatient and aftercare programs (Connors, Carroll, DiClemente, Longabaugh, & Donovan, 1997), methadone treatment (Belding, Iguchi, Morral, & McLellan, 1997), buprenorphine treatment (Petry & Bickel, 1999), family-based treatment for adolescents (Diamond, Diamond, & Liddle, 2000), and drug free as well as residential treatment settings (Kasarabada, Hser, Boles, & Huang, 2002). A meta-analytic review by Martin, Garske, and Davis (2000) further concludes that several formats for assessing therapeutic relationship (that is, obtained from patients, counselors, and observers) have adequate reliability, they are similarly effective in predicting outcomes across diverse settings, and the inclusion of moderator variables does not diminish its predictive power. Not surprisingly, the process involved in forming better rapport with patients appears to depend in part on the session format; group therapy calls for more attention to social climate and interactions, while individual treatment focuses more on gaining personal insight and problem solving (Holmes & Kivlighan, 2000; Kivlighan & Schmitz, 1992). Session topics and counseling strategies also appear to be relevant, with stronger rapport reported when drug use problems are addressed by counselors using a positive approach emphasizing relapse prevention and problem solving, compared to using a punitive emphasis on program rules and compliance requirements (Joe, Simpson, & Rowan-Szal, in press). Better patient assessment systems with counselor feedback for monitoring clinical progress, however, are needed to guide this process Evidence-based interventions for improving program participation Behavioral intervention protocols that offer voucherbased incentives for increasing treatment session attendance and drug abstinence have been effective in various types of drug treatment settings (Griffith, Rowan-Szal, Roark, & Simpson, 2000; Higgins, Alessi, & Dantona, 2002). These contingency management approaches originally were more likely to focus on relapse indicators such as urinalysis results, but over time have been expanded to other engagement criteria. They have been particularly useful in outpatient methadone treatment (Higgins, Budney, Bickel, Foerg, Donham, & Badger, 1994; Petry & Simcic, 2002; Robles, Stitzer, Strain, Bigelow, & Silverman, 2002; Silverman, Higgins, et al., 1996; Silverman, Wong, et al., 1996). Low-cost adaptations that emphasize social recognition, small gifts, or treatment supportive items (e.g., bus tokens or car fare) also have been effective for communitybased programs (Rowan-Szal, Joe, Chatham, & Simpson, 1994; Rowan-Szal, Joe, Hiller, & Simpson, 1997), as has a procedure by Petry and colleagues using a bfish bowlq for drawing prizes contingent on negative urinalysis results (Petry & Martin, 2002; Petry, Martin, Cooney, & Kranzler, 2000; Petry et al., 2001). Improving the quality and structure of treatment counseling has likewise shown benefits in raising participation levels and retention rates (Gottheil et al., 2002; Hoffman et al., 1994; Rowan-Szal, Chatham et al., 2002). Merging contingency management with cognitive-behavioral therapy (usually a form or variant of relapse prevention training) has been another method for effectively improving treatment to achieve better attendance, engagement, and retention (Epstein, Hawkins, Covi, Umbricht, & Preston, 2003; Farabee, Rawson, & McCann, 2002; Rawson, Huber, et al., 2002; Rowan-Szal, Bartholomew, Chatham, & Simpson, 2002) Evidence-based interventions for improving therapeutic relationships Shifting focus from session participation to therapeutic relationship calls for increasing emphasis on cognitive tools, counselor skills, intervention strategies, and context. Treatment effectiveness is not strictly aligned with any particular treatment philosophy, orientation, or setting, thereby prompting an interest in how much counselor skills or strategies may interact with patient attributes to determine outcomes. Indeed, there are between-counselor outcome differences (Luborsky, McLellan, Diguer, Woody, & Seligman, 1997) as well as between-program differences (Broome et al., 1999; Joe et al., 1994) that are not explained or accounted for by patient-level measures alone. So what are the treatment program dynamics that could be involved? Studies of general counselor attitudes or beliefs suggest more flexible, eclectic, and abstinence orientations contribute to better outcomes (Caplehorn, Irwig, & Saunders, 1996; Caplehorn, Lumley, & Irwig, 1998; Humphreys, Noke, & Moos, 1996). In terms of specific skills, training, or experience, results sometimes have been obtuse and inconsistent. An early study by McLellan, Woody, Luborsky, and Goehl (1988) compared four counselors on the basis of outcomes for their patients. Their background and education were not related to patient success, but counseling content and process provided a few clues by suggesting that being well organized, systematic, and comprehensive were favorable traits. This implies having more ready access to clinical records that are user-friendly and relevant to treatment needs, as well as being properly trained in their use, would enhance treatment. Joe, Simpson, and Sells (1994) similarly found that
10 108 D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) methadone programs with better patient retention and outcome rates reported higher professional quality in assessing patient needs and planning treatment. Attempts to quantify effective counselor traits point to interpersonal skills and empathy as being important qualities (Miller, 2000; Valle, 1981). When broken down into more explicit dimensions, factors such as expertness, trustworthiness, and attractiveness emerge (Corrigan & Schmidt, 1983; Heppner, Rosenberg, & Hedgespeth, 1992). However, assessing, training, and retaining effective counselors in the drug treatment field continue to be a significant challenge (B. S. Brown, 1997; Gallon, Gabriel, & Knudsen, 2003; Kasarabada et al., 2001). Comprehensive bfull-courseq manualized treatment interventions like the Matrix Model (Obert, London, & Rawson, 2002; Rawson et al., 1995) include a prescribed sequence of behavioral and cognitive approaches, tailored initially for stimulant users in outpatient programs. More specialized cognitive strategies show evidence of having special benefits for improving therapeutic relationships (Ahmed & Boisvert, 2002; Dansereau, Dees, Greener, & Simpson, 1995; Magura, Rosenblum, Fong, Villano, & Richman, 2002), and similar combinations of cognitive-behavioral social skills and cognitive skills training programs are reportedly the most effective for prison settings and correctional populations (Pearson, Lipton, Cleland, & Yee, 2002). Ideally, use of these focused interventions should be bneedsdriven,q based on appropriate assessments of patient functioning and progress (Graham & Fleming, 1998). Studies of counseling based on a cognitive visual representation and communication technique illustrate how engagement, progress during treatment, and followup outcomes can be improved (Dansereau, Joe, & Simpson, 1993; Joe, Dansereau, Pitre, & Simpson, 1997). Simpson & Joe (in press) found it raised by two-fold the odds that methadone treatment patients would have higher engagement scores. This technique, derived from basic psychological research on problem-solving (e.g., Larkin & Simon, 1987) and in educational psychology (e.g., Dansereau & Newbern, 1997), uses cognitive (node-link) maps that allow counselors and patients to display issues and solution plans in a form similar to that of flow charts and organizational diagrams (see Czuchry & Dansereau, 2003, for an integrative overview of this research). Nodes (drawn as boxes or circles) contain ideas, facts, and feelings while links (usually drawn as labeled lines) express relationships between the nodes. Several types of maps are used to serve different needs and functions of counseling. Unstructured, free form maps can be drawn on newsprint or a chalkboard as a session progresses to maintain focus and record discussions about issues, especially in a group setting. Guide maps are pre-formed, bfill-in-the-nodeq maps that address special topics requiring problem solving or personal insights, such as emotional distress, relapse, and decision making. Nodes or boxes in these maps typically contain questions (e.g., bhow have you tried to deal with this in the past?q) that are to be answered, either as part of a homework assignment or during a counseling session. For didactic or knowledge-based applications, information maps are used to present details on important topics such as relapse, communication, HIV/AIDS, depression, or the physiological impact of certain drugs. Results indicate that this type of conceptual visualization technique reduces reliance on purely verbal communication (Dansereau et al., 1993), increases attentional focus (Czuchry, Dansereau, Dees, & Simpson, 1995), and improves memory for session content (K. Knight, Simpson, & Dansereau, 1994). Further, the use of mapping has been shown to be effective in a variety of settings and with a variety of drug treatment outcome measures (Collier, Czuchry, Dansereau, & Pitre, 2001; Czuchry & Dansereau, 1999; Dansereau et al., 1995; Dansereau, Joe, Dees, & Simpson, 1996; Newbern, Dansereau, & Dees, 1997; Pitre, Dansereau, & Joe, 1996; Pitre, Dansereau, Newbern, & Simpson, 1998), including treatment for gambling (Melville, Davis, Matzenbacher, & Clayborne, 2004) and HIV/AIDS risk reduction in prison populations (S. S. Martin, O Connell, Inciardi, Surratt, & Beard, 2003). Workshop, manual, and Web-based methods for transferring mapping have been developed and disseminated (see Dansereau & Dees, 2002). 5. Early recovery The second major stage of treatment process in Fig. 1 is characterized as early recovery, reflecting a series of psychosocial and behavioral changes. Early stages of patient recovery are signified by changes in thinking and acting, comparable is some ways to the transition from cognitivebased bcontemplationq to decision-based bpreparationq and bactionq stages of the transtheoretical model (Connors et al., 2001). It is this bchange in thinking and actingq that builds on successes from the previous engagement stage and sustains retention in treatment for a long enough time to see evidence of enduring change in drug use and related problem behaviors (Joe, Simpson, & Broome, 1999; Simpson, Joe, Rowan-Szal, & Greener, 1997). Evidence for sequential linkages of components in the TCU Treatment Model (Simpson & Joe, in press) indicates that methadone treatment patients who achieved stronger therapeutic relationships with counselors are 2.3 times more likely to report positive change in psychosocial functioning (based on scales for self-esteem, depression, anxiety, risk-taking, social conformity, and decision-making). More favorable levels of psychosocial functioning, in turn, are related by almost a two-fold increase in the likelihood of favorable behavioral change (defined by urinalysis and self-reported use of opiates and cocaine in Month 3 of treatment). And finally, favorable behavioral measures of drug use in this sample were associated with better chances of staying in treatment beyond the minimum threshold (that is, 1 year for outpatient methadone patients).
11 D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) Evidence-based interventions for improving early recovery Relapse prevention (Marlatt & Gordon, 1985) is a classic technique used in substance abuse treatments to enhance behavioral self-control in preventing relapse to drug use and building cognitive vigilance for high-risk situations that represent btriggers.q The intent is to establish new habit patterns for thinking and acting that can be stabilized and maintained over time. The extent to which a patient has already become engaged in treatment in terms of participation and therapeutic relationship will favorably influence the deployment of relapse prevention and related strategies for strengthening recovery. More systematic use of social support systems and networks also has become a focal concern since families often have been omitted from patient treatment plans. Miller (2003) argues that families can be part of the problem as well as the solution; they may themselves need psychosocial treatment to deal with drug use problems of a loved one, but they also can give effective support to recovery of the patient. Family history, childhood background, parental support, and conflict influence psychosocial adjustment in adulthood as well as engagement and progress in drug treatment (Broome, Knight, Knight, et al., 1997; De Civita et al., 2000; D. K. Knight, Cross, Giles- Sims, & Simpson, 1995; D. K. Knight & Simpson, 1996; Mallinckrodt, 1991). The focus of family-based interventions takes into account the existing social structure and resources because as patient age increases, family contacts and investments can be diminished (Lemke & Moos, 2002). After defining an appropriate network of bsignificant others,q there is a variety of strategies that can help strengthen social adjustment and coping skills. Twelvestep programs are examples (Apodaca & Miller, 2003), but other more structured and proactive interventions also are available. The Community Reinforcement and Family Training approach (Meyers, Miller, Smith, & Tonigan, 2002; Miller, Meyers, & Tonigan, 1999) and A Relational Intervention Sequence for Engagement intervention (Landau et al., 2000) follow manualized guides for recruiting and engaging patients in treatment. Similarly, Brief Strategic Family Therapy (Robbins, Bachrach, & Szapocznik, 2002; Szapocznik & Kurtines, 1993), Multidimensional Family Therapy (Liddle et al., 2000, 2002), and Multisystemic Therapy (Henggeler, Schoenwald, Borduin, Rowland, & Cunningham, 1998) address special developmental needs of adolescents. The core objective of these interventions, of course, is to build social skills that link to support systems. These needs are especially important in drug treatment programs for women who have lost custody of children and have poor economic prospects unless family connections and support can be re-established. Knight, Joe, and Simpson (2003) focused specifically on the intersection of social relationships and treatment process for women in residential treatment and found that level of social support was directly associated with engagement indicators and treatment completion. Specialized group education materials often delivered in female-only or male-only group settings for sexual health and communication skills training, parenting skills training, or transition to aftercare training can improve knowledge and psychosocial functioning (Bartholomew, Hiller, Knight, Nucatola, & Simpson, 2000; Bartholomew, Rowan-Szal, Chatham, & Simpson, 1994; Gainey, Catalano, Haggerty, & Hoppe, 1995; Hiller, Rowan-Szal, Bartholomew, & Simpson, 1996). The secondary effect of these six- to eight-session training modules (for residential and outpatient settings, as well as in correctional populations) has been to increase treatment retention and completion. Findings from a recent review of 38 studies on woments treatment by Ashley, Marsden, and Brady (2003) add support to these conclusions. They found six treatment components to be significantly related to longer treatment retention and completion, reduced drug use and HIV risk behaviors, and physical/mental health; these included (1) child care services for mothers in treatment, (2) prenatal care and parenting skill training, (3) use of women-only treatment groups, (4) educational sessions on health care and social skills, (5) access to mental health care, and (6) use of more comprehensive or multi-service combinations of treatment. 6. Retention and transition The third stage of treatment process, retention and transition, helps stabilize recovery by building on progress in the two previous stages and focuses on the need for retaining patients beyond minimum beffectiveness thresholdsq to allow optimal preparation for transition out of primary treatment. This is comparable to the bmaintenanceq stage of the transtheoretical model, which Connors et al. (2001, p. 117) suggest is meant bto sustain change over time to integrate that change into the lifestyle of the individual so that the new behavior, abstinence from drugs, becomes the preferred habitual behavior.q In recognizing the high rate of relapse and return to treatment (e.g., Grella, Hser, & Hsieh, 2003), Dennis, Scott, and Funk (2003) have shown the effectiveness of a brecovery management checkupq protocol for improving this transitional phase by re-engaging relapsers in treatment sooner, keeping them there longer, and subsequently reducing treatment needs at 24 months followup. Within the context of the TCU Treatment Model, this stage reflects the expectation that patients remain in treatment long enough to stabilize recovery habits and support networks, especially before treatment discharge and social re-entry. One of our studies showed patients who stayed in outpatient methadone treatment for at least a year were five times more likely to have favorable followup outcomes on drug use and criminality measures (Simpson, Joe, &
12 110 D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) Rowan-Szal, 1997). These findings made it clear that gaining a better understanding of the sequential dynamics involving patient attributes, engagement, interim performance changes, and retention was needed. Since then, a variety of multivariate analytic models have proven useful in examining data from diverse community and correctional settings to establish converging relationships between patient motivation and problem severity, treatment process stages (i.e., program participation, therapeutic rapport, psychosocial/cognitive improvements, and behavioral change), retention, and followup outcomes (Broome, Knight, Knight et al., 1997; Joe, Simpson, & Broome, 1999; Simpson, Joe, Greener, & Rowan-Szal, 2000; Simpson, Joe, Rowan-Szal, & Greener, 1997). Interventions for this stage of treatment include several of the ones discussed above for early recovery, but the emphasis shifts to bself-managementq of addiction as a chronic condition (Bodenheimer, Lorig, Holman, & Grumbach, 2002) by teaching problem-solving and social functioning skills. Twelve-step programs are popular for this stage (Fiorentine & Hillhouse, 2000; Hillhouse & Fiorentine, 2001; Weiss et al., 2000), along with expanded efforts to make favorable changes in the family and social support networks of patients (Broome, Simpson, & Joe, 2002; D. K. Knight & Simpson, 1996). A training manual entitled Straight Ahead: Transition Skills for Recovery (Bartholomew, Simpson, & Chatham, 1993) provides a counseling guide to meet some of these specific needs, and a companion series of Time Out manuals address communication and sexuality in gender-specific groups (Bartholomew, Chatham, & Simpson, 1994; Bartholomew & Simpson, 1996). Probably the most popular are relapse prevention strategies (Marlatt & Gordon, 1985) that focus on relapse triggers, dangerous situations, and cognitive restructuring. 7. Community wrap-around and transitional services Successful transitions back into the community and social networks following drug treatment, whether coming from community-based or prison-based settings, require a variety of health and social support services that address persistent mental health and social deficits of patients (Moos, Finney, & Moos, 2000; Moos, Pettit, & Gruber, 1995). There are two important but distinct components involved. The first component has been referred to variously as ancillary, comprehensive, or wrap-around services, which are recognized as part of the extended care system that patients need during treatment as well as afterwards. The second component is commonly referred to as transitional, re-entry, or aftercare services, which may include a stepdown stage of continuum-of-care drug treatment or less formal social support networks. Although conceptually distinct, these services typically are procedurally intertwined in the real world. Several studies by McLellan and associates document the positive role played by accessing a set of comprehensive, wrap-around services for medical, psychiatric, family, and employment problems (McLellan et al., 1994, 1998; McLellan, Arndt, Metzger, Woody, & O Brien, 1993; McLellan, Grissom, et al., 1993), and Friedmann, Alexander, and DTAunno (1999) add to the evidence suggesting that some programs (with differences in resources and staffing patterns) appear to be more focused and proficient than others in obtaining these services. The general availability of health and social services to drug treatment programs tended to diminish between the 1980s and early 1990s (D Aunno & Vaughn, 1995; Etheridge, Craddock, Dunteman, & Hubbard, 1995), but with better stability from 1990 to 1995 (Friedmann, Lemon, Durkin, & D Aunno, 2003). Case management techniques (McLellan et al., 1999; Siegal, Rapp, Li, Saha, & Kirk, 1997) are sometime needed to secure, guide, and link together needs and resources in this complicated environment. Epstein, Nordness, et al. (2003) stress the further importance of engaging family and social support networks in this process. Transitional care following primary treatment is a challenging but crucial element of a comprehensive treatment system. Nowhere is the importance of transitional services treatment more evident than for correctional populations, especially community re-entry programs that follow prison-based treatment (K. Knight, Simpson, & Hiller, 1999; S. S. Martin, Butzin, Saum, & Inciardi, 1999; Wexler, Melnick, Lowe, & Peters, 1999). This process requires careful planning prior to release (Farabee et al., 1999; Wolff, Plemmons, Veysey, & Brandli, 2002) and completion of aftercare services by offenders (Butzin, Martin, & Inciardi, 2002; Wexler, 2003). Because transitional services treatment usually requires coordination of different bsystemsq of authority and responsibility, however, it tends to be overlooked or ignored due to costs, complexity, or lack of understanding. Cost-effectiveness analysis of treatment in correctional settings gives further evidence of its benefits, particularly to the value of completing transitional care phases and for high-risk cases (Griffith, Hiller, Knight, & Simpson, 1999). Criminal Justice Drug Abuse Treatment Studies is a major NIDA-funded project focused on these issues (see 8. Conclusions It was suggested at the outset of this paper that the value of the TCU Treatment Model should be weighed against how well it contributes to the four goals discussed below Defining practical patient performance and treatment process indicators Drug treatment services are delivered primarily in facilities that are specialized in their treatment approaches.
13 D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) Regardless of the source of funding, all programs are under increasing pressures to document effectiveness. Records for delivery of medications and units of services typically are routinized for billing requirements, but information on bquality and process of careq is more elusive. This is the type of information sought by various certification boards and funding sources, and if accumulated on a system-wide scale would be extremely valuable for policy decisions about services and setting for treatment agencies. The Addiction Severity Index has been the mainstay for drug treatment intake assessments since 1980, going through numerous revisions (McLellan et al., 1992), checks for internal consistency and validity (Leonhard, Mulvey, Gastfriend, & Shwartz, 2000), efforts to establish clinical norms (Weisner, McLellan, & Hunkeler, 2000), conversions to computer-assisted applications (Butler et al., 1998, 2001), dissemination within new technologies (Carise, Cornely, & Gurel, 2002), and comparisons with alternative assessments (Joe, Simpson, Greener, & Rowan-Szal, in press). Continuing growth in technology makes Internet-based or on-line assessments a high priority for development and information management (Buchanan, 2002), but more work and resources clearly are needed. Some of the most critical assessment needs for complementing an evidence-based practice paradigm, however, are for during-treatment performance indicators of treatment progress and quality control (Barkham et al., 2001). Patient and provider perspectives on services and progress are not necessarily the same, of course, so these have been the focus of several evaluations (Kressel, De Leon, Palij, & Rubin, 2000; Zanis, McLellan, Belding, & Moyer, 1997). As a result, a multi-disciplinary group of providers, researchers, managed care representatives, and public policy representatives have recommended that exhaustive lists for performance indicators be reduced to focus on three domains: (1) identification of treatment needs, (2) initiation of treatment admission process, and (3) engagement in treatment services (Garnick et al., 2002). The TCU Treatment Model supports the rationale for these assessments in terms of how they link to one another over time, as well as how they can serve as dynamic progress indicators for intervention effectiveness and patient change relevant to treatment stages. The core treatment process measurement instrument that evolved from our work is the TCU Client Evaluation of Self and Treatment (CEST) which yields indicators of patient functioning across 16 scales representing four domains motivation and psychosocial functioning, treatment engagement, social support, and ancillary services (see Joe, Broome, Rowan- Szal, & Simpson, 2002). The CEST is self-administered and includes brief patient self-evaluations of motivation (desire for help, treatment readiness, and external pressures), psychological functioning (self-esteem, depression, anxiety, decision making, and self-efficacy), social functioning (hostility, risk taking, and social conformity), specific services needed and received, treatment satisfaction, level of rapport with their counselor, their participation in treatment, peer support (from other patients), and social support (from family). These scales have provided the basis for clinical tracking of patient functioning and engagement throughout the course of treatment, and when aggregated across representative samples of patients, they depict program profiles for problem severity characteristics of the clientele served, level of therapeutic participation and engagement, service needs, etc. These records also are sensitive to and diagnostic of program differences in retention and post-treatment outcomes, and are being integrated into state-wide networks for patient and program performance monitoring systems (e.g., T. G. Brown, Topp, & Ross, 2003) Using patient performance indicators to guide clinical interventions Recognizing and implementing evidence-based interventions appropriately staged to patient needs at each conceptual phase of treatment can improve effectiveness. This is the goal of treatment planning. However, treatment counselors need a practical navigation system with streamlined patient assessments and easy-to-use clinical interpretations of needs and progress that address diagnostic and treatment planning goals. The TCU Treatment Model offers a graphic framework for communicating how these elements fit together for improving efficiency and effectiveness. It also demonstrates areas in which treatment developers, evaluation scientists, and federal agencies have some important work to do. This includes formulating a structure for recognizing bevidencebasedq interventions and assessments, as well as the promotion of effective dissemination strategies. Intervention manuals and strategies must be well organized, userfriendly, prescriptive in procedures and purpose, easily accessible, and packaged for efficient training and adoption. In addition, they need to be categorized according to type of application and purpose, clinical skills required, appropriate treatment settings, and philosophical assumptions. Assessments to orchestrate this process must be brief, focused, practical in clinical value, readily interpretable, packaged in an efficient and user-friendly format, and available for easy access on demand. It is especially important that assessment guidelines and patient information systems eliminate massive redundancies and irrelevancies that now characterize most states, and that assessment components be linked for logical applications and automated for common report generation. And counselors must be trained to use them efficiently and effectively. Assessment systems and treatment intervention manuals (or selected sessions of interest) have become widely available free of charge via the Internet, and the popular response to these resources points to the need for their further development. However, a wider array and better guides for using Web-based assessment and information
14 112 D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) management tools need to be created, tested, demonstrated, and made available to programs and their staff. As noted by Brown and Flynn (2002) as well as Rawson, Marinelli- Casey, and Ling (2002), federal agencies have unique and still unmet obligations in these applications. One of the solutions may be to use Substance Abuse and Mental Health Services Administration Model Programs for this purpose, which relies on a standardized review process involving the National Registry of Effective Program to identify and disseminate bevidence-basedq treatment interventions Applying patient and program assessments to management needs Improving drug treatment effectiveness requires an understanding of the dynamic components of therapeutic process, including patient strengths and deficits, program participation, therapeutic relationships, psychosocial functioning, and behavioral compliance. As reviewed in this paper, research has identified several measurable domains with direct connections to better treatment retention and outcomes. These findings imply that patient-level reports for summarizing needs and progress throughout treatment as well as program-level reports based on aggregated patient records could improve both clinical care and program management (Westermeyer, 1989). More specifically, each patientts cognitive and behavioral responses to services can be used to evaluate performance and progress through successive stages of engagement and recovery (Beutler, 2001; Leon, Kopta, Howard, & Lutz, 1999). At the agency level, efficient assessment systems that include routine monitoring of aggregated patient retention (or dropout) rates, services delivered, drug use (via bioassays), and therapeutic interactions are feasible for better accountability of program functioning, especially with continuing improvements in information technology in recent years. In the long run, this can facilitate efforts to match patient needs with appropriate interventions and manage clinical care (Simpson, 2002), and it is encouraging to see performance and outcome monitoring systems now beginning to mature into reality (see T. G. Brown et al., 2003; Crèvecoeur, Finnerty, & Rawson, 2002; Kordy, Hannfver, & Richard, 2001; Schippers, Schramade, & Walburg, 2002; Soldz, Panas, & Rodriguez-Howard, 2002; Unqtzer, Choi, Cook, & Oishi, 2002). Organizational-level assessments are perhaps the most challenging because they require data to be taken from individuals within an organization (e.g., leaders, staff, patients) and then aggregated in ways that represent bthe organization.q Selection of appropriate scales, data collection format, reliability and validity of measures, selection or sampling of individuals to properly represent the organization, and methodological alternatives for aggregating data are issues that require more attention (Hermann & Provost, 2003). These needs are illustrated by the growing number of studies addressing the relationship of organizational characteristics with access to health services (Alexander et al., 2003; Timko et al., 2003), and how service delivery and quality are tied to cost effectiveness and efficiency (Hilton et al., 2003; Lemak et al., 2003). Long-range implications involve public accountability and further development of breport cardsq for performance comparisons between health service facilities (Marshall et al., 2003). At TCU, assessments of organizational needs and functioning have been developed with these applications in mind. The TCU Organizational Readiness for Change focuses on organizational traits that predict program change (Lehman, Greener, & Simpson, 2002). It includes 18 scales from four major domains motivation, resources, staff attributes, and climate. Motivational factors include program needs, training needs, and pressures for change, while program resources are evaluated in regard to office facilities, staffing, training, computer equipment, and e-communications. Organizational dynamics include scales on staff attributes (growth, efficacy, influence, adaptability, and clinical orientation) and program climate (mission, cohesion, autonomy, communication, stress, and flexibility for change). The TCU Program Training Needs survey is used for identifying and prioritizing treatment issues that program staff believe need attention. Its items are organized into six domains focused on Facilities and Climate, Satisfaction with Training, Preferences for Training Content, Preferences for Training Strategy, Barriers to Training, and Computer Resources. Collectively, this type of information is intended to help guide overall training efforts as well as predict the types of innovations that participating programs are most likely to seek out and adopt Developing organizational strategies for program improvement The literature identifies numerous factors involved in transferring drug treatment research to practice, but improvement is needed in understanding how to do it effectively. Therefore, incorporating these factors as elements into an integrated framework describing how organizations change could help advance the scientific progress and practical contributions in this field. Having an integrated set of assessments for patient, staff, and organizational functioning dimensions is particularly important for conducting systematic studies of efforts to disseminate feasible and effective treatment innovations. By establishing a general bmodel of program changeq representing major stages of change and factors that promote or inhibit success, the process involved can be more readily communicated, studied, and refined. Although bchangeq routinely occurs at both the personal and organizational levels, making it intentional and positive requires attention. This is especially true at the organizational level, which incorporates the collective attitudes, actions, and relationships of a group of individuals. There is growing consensus that problems in transferring research
15 D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) to practice are more likely to be due to organizational factors (e.g., leadership attitudes, staff resources, organizational stress, regulatory and financial pressures, management style, tolerance for change) than to how materials are disseminated. At the core of this type of heuristic framework are four action steps typically involved in the process of technology transfer (Simpson, 2002). Exposure is the first stage, usually involving training through lecture, self-study, workshops, or expert consultants. The second stage, adoption, represents an explicit intention to try an innovation. While this might be a bformal decisionq made by program leadership, it also includes subtle levels of commitments made by individual staff members at a more personal level about whether an innovation is appropriate and should be tried. Implementation comes next, implying that there is a period of trial usage of the new innovation to allow testing of its feasibility and potential. Finally, the fourth stage moves to practice, reflecting the action of incorporating an innovation into regular use and sustaining it (even if it is in some modified form). Each stage is subject to barriers and stimulants to progressive change. Real-world examples of efforts to transfer innovative treatments into new settings demonstrate the types of challenges that face adoption of new medications (Roman & Johnson, 2002; Thomas, Wallack, Lee, McCarty, & Swift, 2003), comprehensive services for adolescents (Liddle et al., 2002), and cognitive-based counseling tools (Dansereau & Dees, 2002) Concluding comments Considerable progress has been made in cracking open the bblack boxq of substance abuse treatment by partitioning the delivery process into dynamic phases of patient recovery, identifying points of impact for specialized interventions, and refining assessments for measuring patient and program functioning. This information can help operationalize efforts to increase therapeutic engagement and retention, thereby improving patient outcomes. We must now find ways to enhance the delivery of services to patients by putting the next generation of clinical technologies into practice. Since treatment programs are not equally receptive or responsive to new innovations, organizational functioning and related barriers should be examined in terms of the climate for change. Improved training models must be implemented, including a technical infrastructure that makes evidence-based materials easily identified, accessible, user-friendly, and inexpensive (preferably created under the initiative of a federal agency, and eventually using Internet-based data collection technology). Simultaneously, program information and management systems must be improved for better documentation of patient care and performance. Anyone who might think these are novel or unrealistic recommendations could consult new treatment guidelines that state bonce a diagnosis has been established, it is critical to identify the targets of each treatment, to have outcome measures that gauge the effect of treatment, and to have realistic expectations about the degrees of improvement that constitute successful treatment.q Emphasis is placed on developing a treatment plan to reduce or eliminate symptoms, maximize quality of life and functioning, and promote recovery. The focus of these guidelines next moves to the crucial role of establishing a therapeutic relationship required for patients to progress successfully through an acute stage of treatment into phases of stabilization. Other issues addressed involve co-occurring disorders, the possibility of multiple treatment episodes, and various options to consider in regard to treatment strategies and settings. Interestingly, these excerpts come from the Executive Summary of Practice Guidelines from the American Journal of Psychiatry Supplement not for substance abuse treatment but on Treatment Recommendations for Patients with Schizophrenia (American Journal of Psychiatry, 2004, p. 3). Acknowledgments The author thanks his senior colleagues (Lois Chatham, Don Dansereau, and Pat Flynn) at the TCU Institute of Behavioral Research for their contributions to this conceptualization of drug treatment process, and especially George Joe who translated concepts about process into analytic models. Barry Brown, George De Leon, Bennett Fletcher, Dennis McCarty, and Tom McLellan also provided insightful editorial and organizational advice. The National Institute of Drug Abuse (Grant No. R37 DA13093) funded the work, but interpretations and conclusions do not necessarily represent the position of NIDA or the U.S. Department of Health and Human Services. Correspondence concerning this paper should be addressed to Institute of Behavioral Research, Texas Christian University, TCU Box , Fort Worth, TX, U.S.A. More information (including data collection instruments and intervention manuals that can be downloaded) is available on the Internet at and electronic mail can be sent to References Ablon, J. S., & Jones, E. E. (2002). Validity of controlled clinical trials of psychotherapy: Findings from the NIMH Treatment of Depression Collaborative Research Program. American Journal of Psychiatry, 159, Ahmed, M., & Boisvert, C. M. (2002). Cognitive skills group treatment for schizophrenia. Psychiatric Services, 53, Al-Darmaki, F., & Kivlighan Jr., D. M. (1993). Congruence in clientcounselor expectations for relationship and the working alliance. Journal of Counseling Psychology, 40, Alexander, J. A., Nahra, T. A., & Wheeler, J. R. C. (2003). Managed care and access to substance abuse treatment services. 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