Research indicates that patients with longer stays in alcohol and other drug (AOD) treatment have improved health-related outcomes. Since time in treatment and treatment completion are generally associated with more successful outcomes, identification of factors related to treatment retention is important. The growing list of studies confirm the link between time in treatment and improved outcomes (Basso & Bornstein, 2000; Hubbard, et al., 1996; Moos, 2003; Simpson, 1981; Stark, 1992; Zweben & Zuckoff, 2002). Having established this connection, studies have been conducted to determine or predict what clinical interventions or other variables might impact retention (Patterson, 2008).

With this in mind, a retention study was conducted in an intensive outpatient program funded by the Substance Abuse and Mental Health Services Administration (SAMHSA). While the retention intervention being tested did not provide significant findings, there was a significant difference between clients who were entered into SAMHSA’s web-based client follow-up system compared to those not entered. These findings raise an issue of program resource allocation. The question to consider for clinicians is whether they expend similar amounts of energy with clients who they feel will not be available for six-month follow-up.

For instance, a patient’s motivation for treatment as a predictor of treatment compliance has been thoroughly studied (De Weert-Van Oene, et al., 2000; Miller, 2000; Miller & Rollnick, 1991 & 2002; Ryan, et al., 1995; Walizter, et al., 1999). Motivational interviewing (MI) (Miller & Rollnick, 2002) has demonstrated promise in improving treatment adherence, including retention (Zweben & Zuckoff, 2002). Miller and Rollnick (1991) defined motivation as “the probability that a person will enter into, continue and adhere to a specific change strategy,” and prescribed MI principles to increase and sustain motivation to change. Interventions such as MI, or adaptations using these principles, have demonstrated promise in increasing treatment completion and have been shown to be an effective approach for engaging and retaining individuals.

Client Retention During Treatment

While these MI studies focus on the client’s internal motivation, there are also during treatment (e.g., micro) aspects of treatment retention being investigated. For example, the therapeutic relationship during the treatment process could be seen as influencing a patient decision to remain in treatment. According to Simpson and colleagues (1997), what happens during treatment is more important than patient characteristics, such as demographics, when it comes to time in outpatient methadone maintenance treatment. Horvath and Symonds (1991) report a moderate but consistent positive relationship between a patient’s selfreported therapeutic alliance and during-treatment drug use as well as length of stay in treatment. Connors, et al. (2000) evaluated the relationship between therapeutic alliance and treatment participation for those who participated in Project MATCH (Matching Alcoholism Treatments to Client Heterogeneity). According to Connors, et al., Project MATCH outpatient clients with a higher rating of therapeutic alliance were associated with longer stays in treatment.

Whereas there seems to be methodological efforts to understand micro variables predicting a client’s decisions to remain in treatment, this study raises concerns that funding mandates (e.g., a macro variable) may influence patients’ treatment retention rates. Unfortunately, grant-funded programs are sometimes met with the choice between using their limited resources to comply with funding mandates to survive or more fully invest their resources toward clinical services.

For instance, the stress placed on SAMHSA-funded programs to monitor and locate 80 percent of clients after they leave the program or potentially lose their funding, positions these programs to prioritize their resources. Because program resources are continuing to be limited, clients who enter the program with stable living arrangements, local family connections and/ or are employed, are more likely to be located after treatment, thereby increasing the program’s chances of obtaining the 80 percent follow-up rate. It requires a great amount of resources to monitor and locate unstable, transient clients after treatment completion. In order to lower the program’s investment of their limited resources and the risks of losing program funds, SAMHSA-funded programs must be smart when entering clients into the SAMHSA system, thereby requiring an 80 percent follow-up rate.

Follow-Up Requirements

 Programs funded by SAMHSA are designed and expected to comply with all reporting mechanisms. As part of the grant application and at the time of award notice, SAMHSA and the program agree on a number of clients that will be served each year. Once this number is established, the funded program is required to enter data about these clients into SAMHSA’s online system— Government Performance and Results Act (GPRA). For instance, if a program is funded to serve 100 clients per year, the program is contracted to obtain intake data on 100 clients, and locate these same clients at 6 and 12 months after intake. The data from these three contacts (baseline, 6 months and 12 months) have to be entered into the online GPRA database system. Every SAMHSA-funded program is required to receive training on how to interview clients when obtaining these data from clients; how to enter the data into the on-line system; and when the program is able to enter the data.

According to SAMSHA standards, if programs are unable to obtain an 80 percent follow-up rate for the 6-and 12-month follow-up points in time, the program could have funds decreased or lose its program funds altogether. It was recommended by SAMSHA that in order to obtain the 80 percent follow-up rate, programs must be careful when deciding which types of clients it enters into the GPRA system. If a client presents as difficult to follow-up (e.g., unstable living arrangements or homeless, pending legal charges and/or recent move from out of state) the program may admit and provide all treatment services, but can decide not to enter this “hard to follow-up” client into the GPRA system. Funded programs can decide who to enter in the GPRA system as long as it meets the contracted number (e.g., 100 clients per year). For instance, programs can serve 120 clients per year, but are only required to enter the agreed upon 100 clients. However, once client data (e.g., baseline data) go into the GPRA system, an 80 percent follow-up rate on these clients is expected at both 6 and 12 months.

The Intensive Outpatient (IOP) program followed SAMHSA’s recommendations to determine who should be entered into the GPRA system. The program was designed to provide clients with a full bio-psychosocial assessment using the Addiction Severity Index (ASI) (Fureman, et al., 1990) to be eligible for services. The client then completed a SAMHSA-recommended “locator form” with family and friend contact information, past and current living situations, employment-related contacts, along with any other information that enabled an IOP worker to locate the client at both 6 and 12 month follow-up times. Clients who were unable to adequately complete the locator form should be considered hard to follow-up at 6 and 12 months after intake. This places funded programs in a better position to choose which clients to enter into the GPRA, thereby increasing the chances of high follow-up rates.

Social workers must not make program sustainability the top priority over treating the most vulnerable clients.

Those who were admitted for services and evaluated as good candidates for follow-up were entered into the GPRA system. If the client’s information on the GPRA locator form was evaluated as “hard to follow-up,” they were not entered into the GPRA, regardless of whether they had been admitted into the program and offered treatment services. Having been relieved of locating those clients who were evaluated as hard to follow-up, the program could devote its limited resources to clients who were easy to find and ensure the 80 percent follow-up rate.

Participant Selection and Outcomes

The current study focused on whether there were differences in outcomes for the individuals who were evaluated as appropriate for AOD services and entered into the GPRA and those who were not.

The study was conducted within an IOP clinic that has existing research infrastructure that is funded by a SAMHSA Center for Substance Abuse Treatment (CSAT) grant. The grant enabled the IOP clinic specifically to treat individuals with AOD dependency. The IOP clinic accepted referrals from various community-based health organizations. The typical process for admissions to the clinic consisted of completing a bio-psychosocial assessment using the ASI, (Fureman, et al., 1990) along with other paperwork related to client care and rights. Once admitted, patients would then complete the locator form described above to justify being entered into GPRA. The IOP program lasted approximately 24 weeks, five days a week, and consisted of individual and group sessions, AOD educational requirements and selfhelp meetings.

The study sample included 106 patients seeking clinical services at an IOP treatment program. Participants’ mean age was 35 years with a range of 19 to 63 years. The sample was approximately evenly split in terms of gender with 51.9 percent women. In terms of ethnicity 47 percent of the sample described themselves as African American; 49 percent as white; and 4 percent as Hispanic or Native American. Twenty-six percent of the sample reported that they were HIV-positive.

Requirements Can Bias Client Selection

Of the 106 clients, 31 (29.2 percent) completed the IOP treatment program. Being enrolled in GPRA was significantly related to program completion. A total of 61 patients were enrolled in the GPRA database and a higher percentage of these clients completed the program (39.3 percent), than did clients not enrolled in GPRA (15.6 percent). This difference was significant (2(N = 106, df = 1) = 5.98, p = .01). Although clients completing treatment were more likely to be female, not HIV-positive and white rather than non-white, the differences were not significant. Enrollment into GPRA was unrelated to ethnicity, age, gender and HIV status.

Individuals who were entered into the GPRA remained in IOP treatment significantly longer (t = -5.180, p < .001) than those not entered into the GPRA (In GPRA: M = 47, SD = 31, median = 47 versus Not in GPRA: M = 20, SD = 19, median = 10). The results found that in this sample, client demographic characteristics were unrelated to GPRA enrollment and were unrelated to treatment completion. However, GPRA enrollment was significantly related to treatment completion.

The high rates of treatment noncompletion for those not being entered into the GPRA, might suggest that they did not receive the same levels of attention that the individuals who were entered. Given the fact that the group that was not entered into GPRA completed the IOP program warrants serious consideration.

Clinicians may perceive that they have a greater investment in those clients who are entered into GPRA because of the funding requirement to obtain an 80 percent follow-up rate. As a result, SAMSHA’s guidelines may inadvertently result in negative outcomes for those who are not entered into the GPRA. While this study did not measure if staff intentionally or unintentionally invested in those clients who were entered into the GRPA, the pressures associated with meeting SAMSHA follow- up rates could alter a program’s limited investments. Remaining sustainable by meeting the funder’s goals should be an on-going agency concern. When faced with program and clinical investment strategies, the wise clinical endeavor would be on those products (e.g., 80 percent follow-up rate), which best ensure program sustainability.

It seems IOPs are fast becoming the new “safety nets” for anyone needing alcohol and other drug addiction treatment services without regard to matching addicted people with the best level of care (Patterson, 2008). This would increase the likelihood that clinicians in IOPs prioritize limited service investment options, possibly focusing on those clients who are high functioning with some type of social stability. As with any service faced with being overburdened, survival requires a focused attempt at prioritizing.

David Patterson, Silver Wolf (Adelv unegv Waya), PhD, is currently an Assistant Professor at the University at Buffalo’s Schools of Social Work and Director of the Native American Center for Wellness Research.

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