buprenorphine, community-based treatment program, drug addiction, heroin



  1. Murphy, Lyn Stankiewicz PhD, MBA, MS, RN
  2. Oros, Marla T. MS, RN
  3. Dorsey, Susan G. PhD, RN, FAAN


Abstract: Adequate drug treatment for substance users continues to be a challenge for most U.S. cities. To address heroin addiction in Baltimore, the Baltimore Buprenorphine Initiative was implemented as a joint project to promote individualized, patient-centered buprenorphine therapy in conjunction with behavioral treatment to accelerate recovery from opioid addiction. The purpose of this analysis was to explore differences in recovery trajectories predicting length of stay and use this information to predict characteristics that influence an individual's ability to remain in the Baltimore Buprenorphine Initiative program. The sample consisted of 1,039 subjects enrolled in the program between January 2008 and June 2009. The regression modeling determined that age, income, employment, and higher level of treatment were significant predictors of length of stay in the recovery program. The findings of this study have practical implications for the design and implementation of heroin addiction programs. The research indicates that focusing on these specific predictive variables early in the program design phase could increase recovery success rates as measured by length of stay.


Article Content


Opioid drug dependence is a major health and societal issue throughout the United States (U.S.) and the world, affecting close to 15 million individuals (Bell, 2012; Colson, Helm, & Silverman, 2012; Manchikanti et al., 2012; United Nations Office on Drugs and Crime, 2010). In the U.S., it is estimated that there are at least 1.7 million opioid-dependent persons experiencing heroin addiction and dependence, costing approximately $21 billion per year with drug treatment expenses and accounting for 5.7% of the total cost (Hersh, Little, & Gleghorn, 2011; Manchikanti, et al., 2012; Stancliff, Joseph, Fong, Furst, & Comer, 2012). Furthermore, annual costs for prescription opioid medication abuse in the U.S. is estimated at $4.6 billion in the workplace, $2.6 billion in health care, and $1.4 billion to the criminal justice system (Jones et al., 2009). Yet, despite this, the U.S. Department of Health and Human Services Substance Abuse and Mental Health Services Administration (SAMHSA, 2010) report that only about 17% of drug users who request drug treatment actually receive drug treatment. Thus, creating and implementing effective recovery treatment programs for heroin dependence is an important investment for society and to provide substance users the treatment that they desperately need.


To address this concern, traditional treatment for heroin dependency has been the suppression of withdrawal symptoms with methadone and gradual reduction of the methadone dose (Bell, 2012; Bonhomme, Shim, Gooden, Tyus, & Rust, 2012; Hedrich et al., 2011; Mitchell et al., 2009). However, a strict regulatory environment along with long waiting lists deters potential patients from enrolling in drug treatment (Hersh et al., 2011; Peterson et al., 2010). Research has shown that interventions that include some form of pharmacological treatment are pivotal to the reduction of the use of opioid drugs. The World Drug Report presents drug addiction as a treatable health condition that should be part of mainstream health care (United Nations Office on Drugs and Crime, 2010). However, managed withdrawal or detoxification by itself is not an effective treatment for dependency (Bart, 2012; Bell, Trinh, Butler, Randall, & Rubin, 2009; Colson et al., 2012). Although methadone has been relatively effective in treating heroin addiction, there are substantial challenges associated with its use. Pinto et al. (2010) described methadone as a "long-acting full agonist at the mu opiate receptor that consequently can offer a degree of persisting intoxication and sedation" (p. 341). Furthermore, withdrawal from methadone has been documented to be "worse than heroin withdrawal" (Pinto et al., 2010, p. 341). Thus, for many drug users, methadone may not be the most effective treatment.


Second, the use of methadone for maintenance treatment is limited to federally licensed, specialized clinics and to office-based physicians with special dispensation (Hersh et al., 2011; Mattick, Breen, Kimber, & Davoli, 2009). These clinics are regulated by the Food and Drug Administration (FDA) and tend to be "unevenly distributed geographically" (O'Conner, 2011, p. 542), which restricts access and availability to those who are in need. Furthermore, whereas the number of opioid dependence has steadily increased (SAMHSA, 2006), the number of methadone treatment slots has remained relatively constant (O'Conner, 2011; Sohler et al., 2010).


Because of these limitations of methadone as an effective treatment for heroin addiction, it has been shown in recent studies that buprenorphine is a safe, effective alternative to methadone. Moreover, the use of buprenorphine has been associated with increased treatment retention and survival as well as fewer adverse reactions (Bart, 2012; Bonhomme et al., 2012; Oliva, Harris, Trafton, & Gordon, 2012; Walley et al., 2008). Unlike methadone, buprenorphine can be rapidly adjusted with minimal potential for inducing severe complications. It is a partial agonist with a "high affinity at the opiate receptors that should predict superiority over methadone in terms of safety, suppression of illicit opiate use, improvements in social functioning, and progression to detoxification" (Pinto et al., 2010, p. 341). Buprenorphine has a ceiling effect that enhances patient safety by reducing the risk of overdose (SAMHSA, 2006). In addition, it has poor oral bioavailability and is less likely to cause respiratory depression as compared with other opioids (SAMHSA, 2010). Thus, buprenorphine is well suited for use in clinical settings, with patients requiring less stringent medical oversight than is required for methadone maintenance (Fiellin et al., 2008; Fornili & Burda, 2009; Stein et al., 2012).


To promote the use of buprenorphine in the clinical setting, the Drug Addiction Treatment Action of 2000 (DATA 2000) permitted qualifying physicians to address opioid addiction with Schedules III, IV, and V narcotic medications that have been specifically approved by the FDA for that indication. In other words, DATA 2000 enabled physicians to treat opioid addiction in a setting that did not require federal and state licensure (Blum et al., 2011; Bonhomme et al., 2012; Fornili & Burda, 2009). Specifically, the two medications currently FDA approved for the outpatient treatment of opioid dependence include a buprenorphine-naloxone combination (Suboxone) and a buprenorphine monotherapy (Subutext) for use of pregnant women. Buprenorphine may be prescribed as part of an outpatient office visit and, unlike methadone, does not require daily visits to a specialized clinic, "reducing the potential stigma associated with treatment" (O'Conner, 2011, p. 543). In addition, according to current legislation, a certified physician may have a maximum of 30 patients on opioid therapy at any one time for the first year.


Despite attempts to enhance access to drug treatment by moving into an office-based setting, physicians have been slow to respond (Fornili & Burda, 2009; Sohler et al., 2010). Nearly 10,000 physicians were certified to prescribe buprenorphine by December 2006, and that number was expected to rise in 2007. Sohler et al. (2010) posited that this type of model emphasizes the "central role of the patients and their relationship with their health care provider team to achieve optional health outcomes" (p. 153), yet only about half of the certified physicians are actually seeing drug addicted patients and prescribing buprenorphine (Colson et al., 2012). Some of the barriers that certified physicians report include insufficient office and/or institutional support, lack of time in their existing practice, lack of experience in providing this type of care, and the unavailability of backup providers who prescribe buprenorphine. After 1 year, the physician may submit a second notification of need and increase the number of patients to 100 (SAMHSA, 2006).


The Baltimore Buprenorphine Initiative

The city of Baltimore, Maryland, has one of the most severe heroin addiction problems in the U.S. It is estimated that approximately 12% of the city's population is engaged in substance use, and more than 10,000 city residents are admitted for heroin treatment each year (Baltimore Substance Abuse Systems, Inc. [BSAS], 2011). Despite increasing the availability of substance abuse treatment over the last 10 years, Baltimore continues to experience the traumatic negative externalities of substance abuse in terms of high rates of crime, HIV/AIDS, and other comorbid medical and mental health conditions. In summary, the availability of treatment "remains inadequate to meet the need for substance abuse treatment in the city" (BSAS, 2011, p. 9).


To address the persistent heroin problem in Baltimore city, the Baltimore Buprenorphine Initiative (BBI) was created to promote individualized, patient-centered buprenorphine therapy in conjunction with behavioral treatment with a goal of recovery from opioid addiction (BSAS, 2011). BBI is a collaborative effort of the Baltimore City Health Department, BSAS, and Baltimore Health Care Access, Inc., and aims to significantly increase access to substance abuse treatment for people addicted to opioids in Baltimore by bringing together substance abuse treatment centers, community health centers, primary care physicians, and mental health providers. The program aims to significantly increase access to high-quality treatment for opioid addiction using buprenorphine through a system of care that serves patients with buprenorphine in outpatient treatment programs. The goal of the program is to stabilize patients through medication and counseling and then transition the patients for buprenorphine maintenance with individualized behavioral treatment with the hope of complete recovery from opioid addiction. The model promotes a continuum of care that includes an outpatient treatment regimen and medication induction as well as maintenance and stabilization within the frame of the healthcare delivery system (see Figure 1)

Figure 1 - Click to enlarge in new windowFigure 1. The Baltimore Buprenorphine Initiative.

The specific outcome of the BBI is to transfer stable patients from the outpatient treatment setting to a continuing care provider within approximately 90-120 days of treatment. It is important to understand that the BBI employs buprenorphine for long-term maintenance of treatment, not for detoxification. Therefore, although 120 days is a target for patient transfer to a primary care provider based on the guidelines of the program, length of stay may exceed 6 months as patients may remain in medical management and counseling. Treatment center medical and counseling staff working with Baltimore Health Care Access treatment advocates address the patient's identified needs so that a transfer to a continuing care provider can be achieved within this time frame. This provides the necessary intensity of counseling and referrals to services that may be required to assist patients in successfully participating in treatment and transferring to continuing care. Patients who continue to use heroin consistently after being in treatment for 90 days are referred to an alternate treatment setting. If patients are stable on buprenorphine and using other substances, they will be considered for transfer on a case-by-case basis. All patients who do not transfer to continuing care within 120 days receive a case review by the BSAS Clinical Oversight Team.


BBI began serving clients in Fall 2006 with six providers offering 112 treatment slots. By Spring 2009, the initiative had expanded substantially. Three of the original providers had added 30 outpatient slots each, and new treatment providers were added, for a projected increase in capacity to 451 treatment slots. Two sites had instituted a new low-threshold model of care to provide more intensive case management and individualized counseling services. To date, approximately 3,000 clients experiencing heroin addiction were admitted to one of nine treatment programs. Strobbe, Mathias, Gibbons, Humenay, and Brower (2011) concluded that "opportunities exist to improve the quality of services to our patients receiving buprenorphine for opioid therapy" (p. 8). Given the support of the literature and the success of the BBI program, the purpose of this secondary data analysis was to present a descriptive analysis of the BBI sample, explore differences of the BBI sample based on length of stay, and predict characteristics that may influence an individual to remain in the BBI program.



Design, Setting, and Sample

The study was a secondary analysis of data of 1,039 subjects treated with buprenorphine and counseling as part of the BBI program in Baltimore, Maryland, between January 1, 2008, and June 1, 2009. Before enrollment in the program, all potential clients were evaluated to ensure that appropriate levels of care were received and individualized patient-centered plans of care were created. Utilizing the patient placement criteria developed by the American Society of Addiction Medicine (ASAM), patients were evaluated along six dimensions: (1) acute intoxication and/or withdrawal potential; (2) biomedical conditions and complications; (3) emotional, behavioral, or cognitive conditions and complications; (4) readiness to change; (5) relapse/continued problem potential; and (6) recovery environment. In addition, the Addiction Severity Index (ASI) was administered to obtain lifetime information about seven domains commonly affected by alcohol and/or drug dependency.


All information gleaned from the evaluation as well as patient updates were entered into the State of Maryland Automated Record Tracking database, an administrative data system designed to track admissions into substance treatment facilities. Data from the State of Maryland Automated Record Tracking database for this period was retrieved for the specific purpose of building a model to understand the simultaneous impact of a set of variables on the value of a single outcome, length of stay. Ethical approval for this analysis was obtained from the University of Maryland Institutional Review Board, Baltimore, Maryland.



Length of Stay

The primary outcome measure used in this analysis was length of stay, which was chosen as proxy for success in the treatment recovery trajectory. For the purposes of this study, length of stay was calculated as the date of admission to the program to the date of discharge from the program. Mulder, Frampton, Peka, Hampton, and Marsters (2009) posited the effectiveness of substance abuse treatment programs closely related to the length of stay in the program. Thus, as an indicator of treatment success, length of stay was selected as the outcome variable. Studies have consistently found that reduced drug use and improved social functioning are predicted outcomes when subjects are in treatment longer (Bell et al., 2009; Pinto et al., 2010; Walker, 2009). Moreover, researchers have shown an association between positive outcomes and longer treatment or longer length of stay (Brucker, 2010; Greenfield, Burgdorf, Chen, Porowski, & Roberts, 2004; Moos & Moos, 2003; Walker, 2009).


Whereas strong associations have been consistently reported that reductions in drug use were greater in clients treated for 90 days or more (Hubbard, Craddock, Flynn, Anderson, & Etheridge, 1997), Nuttbrock, Rahav, Rivera, Ng-Mak, and Ling (1998) showed positive treatment outcomes at 60 days or more. Completion was operationally defined as the point at which a subject achieves all goals as defined in the treatment plan. Noncompleters are those who drop out or are discharged for noncompliance with treatment (BSAS, 2011). Thus, given the unique characteristics of the BBI program, length of stay was operationalized as a categorical variable in terms of "60 day" groups for comparison as well as operationalized as a continuous outcome variable to examine the effect of specific characteristics on length of stay among the BBI population.


Predictor Variables


The ASI is a valid and reliable instrument that assesses patient functioning over seven key domains: drug use, alcohol use, legal, medical, psychiatric, employment, and family functioning/social relationships. Using a 0- to 1-point scale, scores are self-reported and based on current information and thus are indicators of the present status of the patient and can be used to summarize change in patient status (Kosten, Rounsaville, & Kleber, 1983; McLellan, Cacciola, Alterman, Rikoon, & Carise, 2006; McLellan et al., 1985).


ASAM Patient Placement Criteria

The ASAM Patient Placement Criteria were utilized to determine the most appropriate level of care to address the clients' specific problems and needs. Clinicians used these assessments as well as patient data to recommend patient placement in one of four levels of care, each of which can be further described through subcategories of care: level I (outpatient treatment), level II (intensive outpatient and partial hospitalization), level III (residential/inpatient treatment), and level IV (medically managed intensive inpatient treatment).


Buprenorphine on Admission

According to the Maryland Department of Health and Mental Hygiene, Alcohol and Drug Abuse Administration, Baltimore admitted over 15,000 city residents to publicly funded drug treatment programs in 2009 (BSAS, 2011). It is likely that many of the clients may have been in other treatment programs before entering BBI. Thus, clients were asked to self-report on admission if they had taken or were currently taking buprenorphine as part of their drug treatment regime. This dichotomous variable was coded as binary data in the analysis (yes/no).


Number of Addictions

Upon admission to the program, clients were asked to report the number of addictions that they engaged in during the past 30 days. Carnes, Murray, and Charpentier (2004) found that less than 13% of drug users experience only one addiction, 58% report two to four co-occurring addictions, and 31% report five or more co-occurring addictions. Given this, number of addictions was added as a predictor variable.


Frequency of Use

Clients were asked about the frequency of substance use in the past 30 days before admission to the BBI program. This dichotomized variable was self-reported as daily use or occasional use. Although the exact value of this variable may have been lost in an artificially dichotomized variable, Ulrich and Wirtz (2004) posited that the loss of information is accepted when the actual underlying quantitative data may be questionable or unknown by the subject. In these cases, rather than asking the subject to make a specific estimate, the dichotomized variable allows "the subject to choose between two alternatives" (p. 236). Hence, creating dichotomized variable is acceptable as it enhances the analysis and presentation of the data.


Demographic Variables

Age, gender, race, marital status, income (less than $10,000 per year or at least $10,000 per year), and employment (employed or unemployed) were used as covariates in the analysis. These variables were selected as research has shown that completion of treatment has been linked to specific client characteristics. For example, studies have suggested that clients who are older, men, married with dependents, and wealthier are more likely to successfully complete treatment (Brucker, 2010; Walker, 2009). Given this, these variables were included in the analysis. Additional demographics variables (education, living situation, and venue of admission) were also selected to describe the sample but were not part of the analysis.


Data Analysis

Descriptive statistics were used to summarize the baseline demographics of the BBI sample for all categorical and continuous variables. In some cases, variables were dichotomized based on the distribution of the data. To determine differences among the "60 day" groups, operationally defined by length of stay, analysis of variance was used to identify specific differences among the groups. For the continuous measure of the dependent variable, ordinary least squares regression was used to estimate the relationships between the measures and length of stay, partialling out the effects of the covariates of age, gender, race, marital status, income, and employment. Analyses were considered statistically significant at the .05 level. The Statistical Package for the Social Sciences 19.0 (SPSS, Chicago, IL) was used for all analyses.



Demographics of the BBI Sample

The BBI sample consisted of 1,039 subjects treated in the BBI between January 1, 2008, and June 1, 2009. Ninety-three percent (93%) of the clients were "first time" admissions to drug treatment. The sample was 65.2% men and 34.8% women, with the average age being 44.2 years (SD = 8.0 years). Eighty-seven percent of the sample was African American with 11.5% of the sample reported being Caucasian (see Table 1). In addition, clients were evaluated to determine the most appropriate level of care to address their specific problems and needs. Utilizing ASAM as well as patient history and the ASI interviews, clients were recommended to one of four levels of care (see Tables 2 and 3).

Table 1 - Click to enlarge in new windowTABLE 1 Demographics of BBI Sample, 2008-2009 (
Table 2 - Click to enlarge in new windowTABLE 2 Patient Placement Criteria (American Society of Addiction Medicine;
Table 3 - Click to enlarge in new windowTABLE 3 Addition Severity Index Scores of BBI Subjects (

Length of Stay

The average length of stay for the BBI subjects was 144.08 (SD = 114.1) days with a range of 0-603 days. Forty (40.9) percent of the subjects in the BBI program completed treatment, with 22% of the subjects completing treatment and being transferred to another service and 2.5% completing treatment and receiving a referral to another service. Of those not completing the program, 22.8% willingly left the program, 9.5% were noncompliant with the program, and 1.8% was incarcerated during the program. While participating in the BBI, on average, the subjects attended 13.5 individual counseling sessions, 29 group counseling sessions, and less than one family counseling session.


Using 60 days as a proxy, the sample was divided into five groups based on length of stay (see Table 4). Estimating the difference between groups using analysis of variance, it was determined that a difference exists between the BBI length-of-stay groups based on age (F = 3.71, p < .05), ASI drug scores (F = 3.32, p < .05), ASI family scores (F = 4.00, p < .05), and ASI psych scores (F = 6.22, p < .001). Specifically, BBI subjects who were in treatment for greater than 240 days were older than those who were in treatment for 0-60 days (p < .05). Thus, older patients tended to stay in treatment longer than younger patients. It is concluded that age is a factor that may contribute to a patient's length of stay in the treatment program.

Table 4 - Click to enlarge in new windowTABLE 4 Average Length of Stay By Group (

Moreover, BBI subjects who were in treatment for greater than 240 days had a statistically lower ASI drug score as compared with those who were in treatment for 0-60 days (p < .05). Thus, subjects with a greater severity in terms of their drug addiction tended to have a shorter length of stay. In addition, BBI subjects who were in treatment for 61-120 days had a statistically higher ASI family score as compared with those in treatment for 121-180 days (p < .05) and those in treatment for 181-240 days (p < .05). Thus, those subjects with higher self-reported family issues tended to stay in treatment for 61-120 days as compared with subjects who stayed for 121-240 days. Finally, BBI subjects who were in treatment for 0-60 days had a statistically higher ASI psych score as compared with those in treatment for 121-180 days (p < .05), those in treatment for 181-240 days (p < .001), and those in treatment for greater than 240 days (p < .05). Thus, subjects with a self-reported higher ASI psych score tended to stay in treatment for a shorter amount of time as compared with those staying for greater than 120 days (see Table 5).

Table 5 - Click to enlarge in new windowTABLE 5 Differences Among BBI Subjects by Length of Stay (

Using length of stay as the desired outcome, the following independent variables were used to predict length of stay: age, gender, race, marital status, income, employment, ASI scores, ASAM treatment level of care, receiving buprenorphine on admission, number of addictions, and frequency of use. The valid number of subjects included in the analysis with no missing data was n = 283. Overall, the model was statistically significant (F = 10.603, df = 17, 265, p < .001). The model explained 40% of the variance surrounding the dependent variable length of stay (R2 = .405).


Regression analysis revealed that age, income, employment, and higher level of treatment were significant predictors of length of stay in the BBI sample. Specifically, for every year increase in age, length of stay increases by 1.5 days. Thus, older individuals are more likely to remain in treatment longer. For those individuals making greater than $10,000, their length of stay decreased by 38 days. Thus, those who have higher incomes are likely to have lower length of stay in treatment. For those BBI subjects who were employed, they have a longer length of stay of 65.6 days. Thus, those who are employed are likely to have a longer length of stay. Finally, a higher level of treatment reduces length of stay by 39.5 days. Thus, those who were receiving a higher level of treatment on admission had a lower length of stay (see Table 6).

Table 6 - Click to enlarge in new windowTABLE 6 Predictors of Length of Stay in BBI Clients (



Overall, the findings of this study have practical implications for a broader implementation of the BBI. By appropriately understanding the results of this study, focusing on specific patient variables may impact a patient's length of stay and therefore enhance the client's outcome. As presented in the findings, age was a significant predictor in longer lengths of stay. This is consistent with previous studies that have shown that older adults tend to have better treatment outcomes as compared with younger adults (Butzin, Saum, & Scarpitti, 2002; Oslin, Pettinatti, & Volpicelli, 2002; Smith, Cleeland, & Dennis, 2010; Weis & Petry, 2011). Although Siegal, Falck, Wang, and Carlson (2002) concluded that younger adults are more likely to enter treatment, older and middle-aged substance users are more likely to remain engaged and successfully complete treatment (Maglione, Chao, & Anglin, 2003). Specifically, Satre, Mertens, Area'n, and Weisner (2004) found that older adults had more positive outcomes from drugs and alcohol in the past 30 days as well as over time as compared with younger adults.


Moreover, Siegal et al. (2002) found that, because older patients may have longer histories of drug use and higher number of prior treatment admissions, they may be more engaged in treatment success. As a result, these clients may be more highly motivated because of having endured a longer time through the difficult lifestyle of using and seeking drugs in a tough urban environment, such as Baltimore. Also, age may contribute to a level of maturity that also supports higher levels of motivation for change and engagement in treatment. This phenomenon is commonly known as the "maturing out" of addiction (Labouvie, 1996; Winick, 1962). Winick posited that most users engaged in treatment and abstinence between the ages of 23 and 37 years. This occurs as the result of the process where the problems that originally encouraged the drug addiction had, over time, become less salient and less urgent; thus, the negative side of life as a drug addict had become too much of a burden for the individual. As a result, the individual engages in drug treatment with projected positive outcomes. Thus, the finding of age and its relationship to length of stay is consistent with most of the literature.



An interesting finding of the analysis showed that those individuals in the BBI program who generated an income, as operationally defined as "less than $10,000 per year or at least $10,000 per year," tended to have a shortened length of stay. Kroutil, Trunzo, and Delany (2012) found that clients in drug treatment, especially those who report no use of a primary or secondary drug in 30 days before admission (abstinent admission), tended to report income from wages as well as be employed. It is further posited that generating an income is often associated with "potential social capital," which includes employment and stable living arrangements, which have been indicated by the literature to be predictors of positive outcomes for drug treatment completion and success. Thus, those individuals generating an income may have had a shortened length of stay (Kroutil et al., 2012).



The findings of the study support the hypothesis that employment has a direct correlation with longer length of stay in a drug treatment program (Parran et al., 2010). Specifically, those individuals who were employed had increased length of stay in the BBI sample. This may be explained by the fact that those individuals who were employed were more stable, had more resources and, as a result, did better in treatment. Bartu, Freeman, Gawthrone, Allsop, and Quigley, (2002) found similar results in their study as clients who were "engaged in either full-time or part-time employment were more likely to remain in treatment for a longer period of time" (p. 339). Similarly, Merline, O'Malley, Schulenberg, Bachman, and Johnston (2004) posited that those individuals with a recent history of unemployment and heroin use are more likely not to engage in treatment as compared with those who are employed and engaged in heroin use. Moreover, Bray, Zarkin, Dennis, and French (2000) found that substance use was frequently associated with unemployment and represented a barrier to leaving welfare.


In conclusion, BBI clients who are engaged in full-time or part-time employment and are generating an income tended to have longer lengths of stay as compared with those who were unemployed. Being employed and generating an income may provide the client with an alternative support system and thus encourage a longer length of stay that is necessary to successfully complete treatment. This finding supports the need for drug treatment programs to, first, embrace efforts geared toward assisting clients in maintaining their employment and, second, provide employment-related services that ultimately may contribute to the success of the drug treatment program (Leukefeld, McDonald, Staton, & Mateyoke-Scrivner, 2004).


Higher Level of Treatment

Interesting, having a higher level of treatment was a predictor for a decreased length of stay. One plausible interpretation of this result may be that a higher level of treatment could be considered overwhelming for the individual and thus results in shorter commitment to the program as indicated by a decreased length of stay. Hubbard et al. (1989) posited that over half of all clients leave treatment within 90 days. Dropout rates continue to be a widespread issue among drug treatment programs (King & Canada, 2004). Although there is no consensus on what specific features of a treatment program improve client retention (Simpson, Joe, Rowan-Szal, & Greener, 1995), there are several variables that have been linked to lack of treatment engagement. Early dropout rates have been shown in clients with histories of longer drug use, multiple drug use, and greater previous treatments (Claus, Kindleberger, & Dugan, 2002; King & Canada, 2004). Thus, one may conclude that BBI clients with higher levels of treatment may be linked to a higher level of severity of drug use, including the factors of longer drug use, multiple drug use, and exposure of previous drug treatments, which places them at risk for decreased length of stay. An evaluation of risk factors may be beneficial to identify those individuals who are more likely to "drop out" of a treatment or are unlikely to benefit from program services to enhance client and program outcomes.


Limitations of the Analysis

The conclusions that can be drawn from this study must be considered in relation to the study limitations. The limitations of this study include the research design of the study, as these data were retrospectively analyzed and also are specific to Baltimore City and, as such, are not necessarily generalizable to other community-based drug treatment programs. The analysis was conducted using retrospective information that may be representative of only of one perspective. Most of the variables were categorical in nature and were recoded into binary variables or interval/ratio variable levels so that a multivariate analysis and model fit could be completed. This process may have limited the richness of the reported data, which were mostly continuous in nature. The outcome variable length of stay was a proxy for treatment success, and a better outcome variable might be urinalysis results for individual subject success in recovery during the active phase of treatment.



Despite the limitations of this study, this analysis provides a unique look at novel, community-based initiative to treat opioid addiction in a major metropolitan area where rates of heroin use are high. In addition, it attempts to identify those key factors that influence successful client and treatment outcomes. Mulder et al. (2009) concluded that studies on therapeutic community-based programs report "mixed evidence of useful predictors" (p. 369). In this study, several key predictors were shown to be consistent with current literature surrounding length of stay in the BBI program. As shown in the literature, length of stay is closely related to the effectiveness of community-based programs; thus identifying key predictors of length of stay and using those predictors to develop strategies that minimize dropouts and maximize retention are critical for client success.


Given that there are approximately 2 million opioid-dependent persons in the U.S. and only a small portion have access to effective treatment, being aware of dynamic client characteristics within a specific program and population of clients allow service providers to have the opportunity to influence treatment retention and possibly contribute to greater positive client outcomes. It is likely that not all opioid-dependent individuals can be treated with the same model given their heterogeneity. Future studies should continue to identify and understand specific patient characteristics and innovation service models that impact treatments among opioid-dependent individuals.



The authors would like to thank the Baltimore City Health Department, Baltimore Substance Abuse Systems, and Baltimore Health Care Access, Inc., for their invaluable assistance.




Baltimore Substance Abuse Systems, Inc. (2011). Clinical guidelines for treatment of opioid dependence in the Baltimore Buprenorphine Initiative. Baltimore, MD: Oros, M., & Welsh, C. [Context Link]


Bart G. (2012). Maintenance medication for opiate addiction: The foundation of recovery. Journal of Addictive Diseases, 31 (3), 207-225. [Context Link]


Bartu A., Freeman N. C., Gawthorne G. S., Allsop S. J., Quigley A. J. (2002). Characteristics, retention, and readmission of opioid-dependent clients treated with oral naltrexone. Drug and Alcohol Review, 21, 335-340. [Context Link]


Bell J. (2012). Buprenorphine in the treatment of heroin addiction. The Journal of Psychiatry and Neurological Sciences, 25 (2), 93-100. [Context Link]


Bell J., Trinh L., Butler B., Randall D., Rubin G. (2009). Comparing retention in treatment and mortality in people after initial entry to methadone and buprenorphine treatment. Addiction, 104, 1193-1200.


Blum K., Chen T. J., Bailey J., Bowirrat A., Femino J., Chen A.L., Oscar-Berman M. (2011). Can the chronic administration of the combination of buprenorphine and naloxone block dopaminergic activity causing anti-reward and relapse potential? Molecular Neurobiology, 44, 250-268. [Context Link]


Bonhomme J., Shim R. S., Gooden R., Tyus D., Rust G. (2012). Opioid addiction and abuse in primary care practice: A comparison of methadone and buprenorphine as treatment options. Journal of the National Medical Association, 104 (7-8), 342-350. [Context Link]


Bray J. W., Zarkin G. A., Dennis M. L., French M. T. (2000). Symptoms of dependence, multiple substance use, and labor market outcomes. American Journal of Drug and Alcohol Abuse, 26 (1), 77-95. [Context Link]


Brucker D. (2010). Exploring the relationship between access and retention among substance abuse treatment admissions. Journal of Drug Issues, 40 (3), 553-576. [Context Link]


Butzin C. A., Saum C. A., Scarpitti F. R. (2002). Factors associated with completion of a drug treatment court diversion program. Substance Use & Misuse, 37 (12-13), 1615-1633. [Context Link]


Carnes P. J., Murray R. E., Charpentier L. (2004). Addiction interaction disorder. In Coombs R. H. (Ed.), Handbook of addictive disorders: A practical guide to diagnosis and treatment (pp. 31-62). New York, NY: Wiley. [Context Link]


Claus R. E., Kindleberger L. R., Dugan M. C. (2002). Predictors of attrition in a longitudinal study of substance abusers. Journal of Psychoactive Drugs, 34 (1), 69-74. [Context Link]


Colson J., Helm S., Silverman S. (2012). Office-based opioid dependence treatment. Pain Physician, 15, ES231-ES236. [Context Link]


Fiellin D. A., Moore B. A., Sullivan L. E., Becker W. C., Pantalon M. V., Chawarski M. C., Schottenfeld R. S. (2008). Long-term treatment with buprenorphine-naloxone in primary care: Results at 2-5 years. American Journal of Additions, 17 (2), 116-120. [Context Link]


Fornili K., Burda C. (2009). Buprenorphine prescribing: Why physicians aren't and nurse prescribers can't. Journal of Addictions Nursing, 20 (4), 218-226. [Context Link]


Greenfield L., Burgdorf K., Chen X., Porowski A., Roberts T. (2004). Effectiveness of long-term residential substance abuse treatment for women: Findings from three national studies. American Journal of Drug and Alcohol Abuse, 30, 537-550. [Context Link]


Hedrich D., Alves P., Farrell M., Stover H., Moller L., Mayet S. (2011). The effectiveness of opioid maintenance treatment in prison settings: A systemic review. Addiction, 107, 501-517.


Hersh D., Little S. L., Gleghorn A. (2011). Integrating buprenorphine treatment into a public healthcare system: The San Francisco Department of Public Health's office-based buprenorphine pilot program. Journal of Psychoactive Drugs, 43 (2), 136-145. [Context Link]


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 (4), 261-278. [Context Link]


Hubbard R. L., Marsden M. E., Rachal J. V., Harwood H. J., Cavanaugh E. R., Genzburg H. M. (1989). Drug abuse treatment: A national study of effectiveness. Chapel Hill, NC: University of North Carolina Press.


Jones E. S., Moore B. A., Sindelar J. L., O'Connor P. G., Schottenfeld R. S., Fiellin D. A. (2009). Cost analysis of clinic and office-based treatment of opioid dependence: Results with methadone and buprenorphine in clinically stable patients. Drug and Alcohol Dependence, 99 (1-3), 132-140. [Context Link]


King A. C., Canada S. A. (2004). Client-related predictors of early treatment drop-out in a substance abuse clinic exclusively employing individual therapy. Journal of Substance Abuse Treatment, 26, 189-195. [Context Link]


Kosten T. R., Rounsaville B. J., Kleber H. D. (1983). Concurrent validity of the Addiction Severity Index. Journal of Nervous and Mental Disease, 171, 606-610. [Context Link]


Kroutil L., Trunzo D., Delany P. (2012). Abstinent admissions to substance abuse treatment: Who are they? Center for Behavioral Health Statistics and Quality, June 2012. Washington, DC: Substance Abuse and Mental Health Services Administration. [Context Link]


Labouvie E. (1996). Maturing out of substance use: Selection and self-correction. Journal of Drug Issues, 26 (2), 457-476. [Context Link]


Leukefeld C., McDonald H. S., Staton M., Mateyoke-Scrivner A. (2004). Employment, employment-related problems, and drug use at the drug court entry. Substance Use & Misuse, 39 (13-14), 2559-2579. [Context Link]


Maglione M., Chao B., Anglin M. D. (2003). Correlates of outpatient drug treatment drop-out among methamphetamine users. Journal of Psychoactive Drugs, 32, 221-228. [Context Link]


Manchikanti L., Helm S., Fellows B., Janata J. W., Pampati V., Grider J. S., Boswell M. V. (2012). Opioid epidemic in the United States. Pain Physician, 15, ES9-ES38. [Context Link]


Mattick R. P., Breen C., Kimber J., Davoli M. (2009). Methadone maintenance therapy versus no opioid replacement therapy for opioid dependence. Cochrane Database of Systematic Reviews, (3), CD002209. [Context Link]


McLellan A. T., Cacciola J. C., Alterman A. I., Rikoon S. H., Carise D. (2006). The Addiction Severity Index at 25: Origins, contributions and transitions. The American Journal on Addictions, 1 (2), 113-124. [Context Link]


McLellan A. T., Luborsky L., Cacciola J., Griffith J., Evans F., Barr H. L., O'Brien C. P. (1985). New data from the Addiction Severity Index: Reliability and validity in three centers. Journal of Nervous and Mental Disease, 173, 412-423.


Merline A. C., O'Malley P. M., Schulenberg J. E., Bachman J. G., Johnston L. D. (2004). Substance use among adults 35 years of age: Prevalence, adulthood, predictors, and impact of adolescent substance use. American Journal of Public Health, 94, (1), 96-102. [Context Link]


Mitchell S. G., Kelly S. M., Brown B. S., Reisinger H. S., Peterson J. A., Ruhf A., Schwartz R. P. (2009). Uses of diverted methadone and burprenorphine by opioid-addicted individuals in Baltimore, Maryland. The American Journal on Addictions, 18, 346-355. [Context Link]


Moos R. H., Moos B. S. (2003). Long-term influence of durance and intensity of treatment on previously untreated individuals with alcohol use disorders. Addictions, 98 (3), 325-355. [Context Link]


Mulder R. T., Frampton C. M. A., Peka H., Hampton G., Marsters T. (2009). Predictors of 3-month retention in a drug treatment therapeutic community. Drug and Alcohol Review, 28, 366-371. [Context Link]


Nuttbrock L. A., Rahav M., Rivera J. J., Ng-Mak D. S., Link G. B. (1998). Outcomes of homeless mentally ill chemical abusers in community residences and a therapeutic community. Psychiatric Services, 49, 68-76. [Context Link]


O'Conner A. B. (2011). Nurse practitioners' inability to prescribe buprenorphine: Limitations of the Drug Addiction Treatment Act of 2000. Journal of the American Academy of Nursing Practitioners, 23, 542-545. [Context Link]


Oliva E. M., Harris A. H., Trafton J. A., Gordon A. J. (2012). Receipt of opioid agonist treatment in the Veterans Health Administration: Facility and patient factors. Drug and Alcohol Dependence, 122 (3), 241-246. [Context Link]


Oslin D. W., Pettinati H., Volpicelli J. R. (2002). Alcoholism treatment adherence: Older age predicts better adherence and drinking outcomes. American Journal of Geriatric Psychiatry, 10, 740-747. [Context Link]


Parran T. V., Adelman C. A., Merkin B., Pagano M. E., DeFranco R., Ionescu R. A., Mace A. G. (2010). Long-term outcomes of office-based buprenorphine/naloxone maintenance therapy. Drug and Alcohol Dependence, 106, 56-60. [Context Link]


Peterson J. A, Schwartz R. P., Mitchell S. G., Reisinger H. S., Kelly S. M., O'Grady K. E., Agar M. H. (2010). Why don't out-of-treatment individuals enter methadone treatment programs? International Journal of Drug Policy, 21 (1), 36-42. [Context Link]


Pinto H., Maskrey V., Swift L., Rumbal D., Wagle A., Holland R. (2010). The SUMMIT trial: A field comparison of buprenorphine versus methadone maintenance treatment. Journal of Substance Abuse Treatment, 39 (4), 340-352. [Context Link]


Satre D. D., Mertens J. R., Area'n P. A., Weiser C. (2004). Five-year alcohol and drug treatment outcomes of older adults versus middle-aged and younger adults in a managed care program. Addiction, 99, 1286-1297. [Context Link]


Siegal H. A., Falck R. S., Wang J., Carlson R. G. (2002). Predictors of drug abuse treatment entry among crack-cocaine smokers. Drug and Alcohol Dependence, 68, 159-166. [Context Link]


Simpson D. D., Joe G. W., Rowan-Szal G., Greener J. (1995). Client engagement and change during drug abuse treatment. Journal of Substance Abuse Treatment, 7, 117-134. [Context Link]


Smith D. C., Cleeland L., Dennis M. L. (2010). Reasons for quitting among emerging adults and adolescents in substance-use-disorder treatment. Journal of Studies on Alcohol and Drugs, 10, 400-410. [Context Link]


Sohler N. L., Xuan L., Kunins H. V., Sacajiu G., Giovanniello A., Whitley S., Cunningham C. O. (2010). Home- versus office-based buprenorphine inductions for opioid-dependent patients. Journal of Substance Abuse Treatments, 38, 153-159. [Context Link]


Stancliff S., Joseph H., Fong C., Furst T., Comer S. D. (2012). Opioid maintenance treatment as a harm reduction tool for opioid-dependent individuals in New York City: The need to expand access to buprenorphine/naloxone in marginalized populations. Journal of Addictive Diseases, 31 (3), 278-287. [Context Link]


Stein B. D., Gordon A. J., Sorbero M., Dick A. W., Schuster J., Farmer C. (2012). The impact of buprenorphine of treatment of opioid dependence in a Medicaid population: Recent service utilization trends in the use of buprenorphine and methadone. Drug and Alcohol Dependence, 123, 72-78. [Context Link]


Strobbe S., Mathias L., Gibbons P. W., Humenay E., Brower K. J. (2011). Buprenorphine clinic for opioid maintenance therapy: Program description, process measures, and patient satisfaction. Journal of Addictions Nursing, 22 (1-2), 8-12. [Context Link]


Substance Abuse and Mental Health Services Administration. (2006). A report on the physician waiver program established by the Drug Addiction Treatment Act of 2000 (DATA 2000). Retrieved from[Context Link]


Substance Abuse and Mental Health Services Administration. (2010). Results from the 2009 National Survey on Drug Use and Health: Volume I. Summary of national findings (Office of Applied Studies, NSDUH Series H-38A, HHS Publication No. SMA 10-4586 Findings). Retrieved from[Context Link]


Ulrich R., Wirtz M. (2004). On the correlation of a naturally and an artificially dichotomized variable. The British Journal of Mathematical and Statistical Psychology, 57, 235-251. [Context Link]


United Nations Office on Drugs and Crime. (2010). 2010 world drug report. (Reference Information Number E.10.XI.13). New York, NY. [Context Link]


Walker M. A. (2009). Program characteristics and the length of time clients are in substance abuse treatment. The Journal of Behavioral Health Services & Research, 36 (3), 330-343. [Context Link]


Walley A. Y., Alperen J. K., Cheng D. M., Botticelli M., Castro-Donlan C., Samet J. H., Alford D. P. (2008). Office-based management of opioid dependence with buprenorphine: Clinical practices and barriers. Journal of General Internal Medicine, 23 (9), 1393-1398. [Context Link]


Weis L. M., Petry N. M. (2011). Interaction effects of age an contingency management treatments in cocaine-dependent outpatients. Experimental and Clinical Psychopharmacology, 19 (2), 173-181. [Context Link]


Winick C. (1962). Maturing out of narcotic addiction. Bulletin on Narcotics, 14, 1-7. [Context Link]