Treatment can be considered one in the panoply of strategies that could reduce demand for drugs. The logic is simple: if drug users can be systematically removed from the drug marketplace through participation in treatment, demand will be reduced. The extent of the impact on demand would depend on the numbers removed, the amount they consume, and the duration of their removal (Meara and Frank, 2005; Reuter and Pollack, 2006). This logic is compelling and supported by the observation that treatment episodes are frequently associated with reduction (in relation to pretreatment levels) or cessation of drug use (see Carroll and Onken, 2005; Higgins et al., 2000; Hubbard et al., 1997). The empirical question is whether the reductions in drug use are sustained enough, given the high relapse and dropout rates that characterize treatment, to make a large difference in total demand.
We emphasize treatment rather than prevention primarily because the evidence on the effectiveness of prevention programs at the population level is discouraging. Caulkins and colleagues (1999) demonstrated that even an optimistic reading of the research literature at that time showed a limited capacity of prevention to reduce total drug consumption; for a more recent review of research on efficacy of prevention see Faggiano et al. (2005). Developing better drug prevention programs is an important issue, but it is one that goes beyond what this committee was able to consider.
This chapter takes a broad perspective to examine the question of whether and how treatment can affect drug demand. We start by explor-
ing what is currently known about the natural history of drug use and the role of treatment in the lives of drug users: what proportion of users enter treatment, when, how, and why they do so. We next briefly explore current treatment data. We then turn to the potential for reducing demand through expansion of treatment, with particular attention to integration of drug treatment with the criminal justice system. Finally, we consider how the impact of policy changes designed to expand, improve, or better integrate drug abuse treatment services can be modeled and researched.1
NATURAL HISTORY OF DRUG USE
Entry into substance abuse treatment is clearly not the only or even the most prevalent pathway to stopping harmful levels of psychoactive substance use. Natural history cohort studies of alcoholics, for example, have revealed that most people modulate or stop heavy use on their own, without formal treatment. One large survey of randomly selected adults in the general population (Sobell et al., 1996) found that 78 percent of individuals who had recovered from an alcohol problem for 1 year or more did so without help or treatment. Dawson and colleagues (2005) reported findings from another large population-based study, the National Epidemiologic Survey of Alcohol and Related Conditions. Overall, independent of current recovery status, only 26 percent of adults with prior alcohol dependence reported having received treatment. However, this percentage was considerably higher (49 percent) among those who were currently abstaining and much lower (12-19 percent) among those who were still drinking at some level. One factor clearly associated with independent change versus treatment entry is severity of alcohol use and associated problems: those who elect treatment have a more serious substance use history than those who change on their own (Carballo et al., 2008).
Long-term studies that contribute to understanding of the natural history of opiate and stimulant use have also been conducted with drug users. In contrast to the research with alcoholics, however, for which large samples of the general population have formed the basis for research, samples of drug users have generally been drawn from people in treatment programs. This difference may reflect, in part, the difficulties of conducting population-based research with the relatively small (in relation to the general population) and “hidden” population represented by drug users.
George Vaillant (1973) pioneered the research with opiate users by
following for 25 years a cohort of 100 heroin abusers who had been incarcerated at the federal facility at Lexington, Kentucky, a program designed specifically to benefit a population of urban heroin users. The theory was that these individuals would desist from drug use after return to their home environment following a lengthy period of enforced abstinence accompanied by “healthful” activities at the Lexington facility. At 20 years after the index incarceration, 35 percent of the original sample were indeed stably abstinent in the community. However, 48 percent were still addicted or had died, with 17 percent having an unknown outcome.
Another group of epidemiological researchers at the University of California, Los Angeles, have expanded on these early natural history studies by conducting a 33-year follow-up of 581 male opiate addicts who were admitted to the California Civil Addict program (a treatment alternative to incarceration) in 1962-1964. Over the course of the follow-up, many individuals stopped opiate use for extended periods, with nearly half (47 percent) reporting abstinence for 5 years or longer at some time. Yet the overall findings at the time of the 33-year follow-up, when the average age of the cohort was 47.6 years, were less favorable: 41 percent did test negative for opiates, indicating at least recent abstinence, but 31 percent tested positive for opiates, and 28 percent had died (Hser et al., 2001).
Hser and colleagues (2007b) identified three groups of opiate abusers with distinctive profiles: (1) stable high-level users (59 percent) who maintained consistent use over time despite intermittent periods of abstinence, (2) decelerating users (32 percent), who decreased use only after extended periods (10 years or more) of regular use; and (3) early quitters (9 percent), who ceased use within 10 years of initiation. Although many opiate users in follow-up studies have reported trying both self-help and formal treatment at various times, a relatively low percentage (< 10 percent) reported being enrolled in methadone treatment at any given time (Hser et al., 2001). This finding suggests that treatment does not play a major role in the lives of the majority of these drug users. We note, however, that these longitudinal studies yield little information about the role of treatment exposure, if any, in the long-term trajectory of drug users who do eventually stop in comparison with those who do not (Hser et al., 2007a). These studies do suggest, however, a potentially useful way of looking at long-term outcome data to determine whether the proportion of drug users in the decelerating trajectories could be increased by treatment intervention.
Since lifetime patterns of use may be influenced by the type of drug being abused, it is useful to conduct separate longitudinal analyses with primary users of nonopiate drug classes. Hser and colleagues (2007a) examined long-term patterns of cocaine, methamphetamine, marijuana,
and heroin use of 566 drug users selected from a sample recruited at jails, hospital emergency rooms, and clinics for sexually transmitted diseases in Los Angeles County in 1992-1994. For marijuana, weekly use was reported by slightly more than 40 percent of the sample at age 20 but by only 15 percent at age 43: this difference suggests a natural decline as these users take on adult responsibilities. Reports of methamphetamine use were low to begin with but declined further: from about 8 percent to 2 percent of the sample between ages 20 and 43. For cocaine, weekly use increased from 17 percent at age 20 to 37 percent in the mid-30s and declined somewhat thereafter. Heroin use increased with age: from about 7 percent who reported weekly (or more) use or more at age 20 to nearly 20 percent at age 43. These data suggest that lifetime trajectories of use differ across specific substances. However, as with the opiate sample studies, they provide little information about the role of treatment in altering these trajectories.
Another study by Hser and colleagues (2006) examined outcomes for a sample of 266 male veterans admitted for treatment of cocaine dependence in 1988-1989. The study found that 52 percent achieved stable recovery by maintaining abstinence from cocaine for 5 years or longer. Although quantitative data on treatment exposure was not reported in this sample, there was a positive relationship between treatment participation and changes in drug use over time. Both treatment and early response to treatment predicted a higher rate of decline in cocaine use over time.
Researchers at Chestnut Health Systems followed a cohort of drug users (N = 1,162) recruited at 22 treatment programs in Chicago between 1996 and 1998 and interviewed annually for 8 years (Dennis et al., 2007; Scott et al., 2003, 2005a). Over half, 54 percent, were opioid users; 82 percent had used stimulants; and 73 percent were marijuana users. At the 8-year follow-up interview, 57 percent were actively using drugs. Long-term abstinence (1 year or more) was documented for 23 percent of the sample, while 20 percent had been abstinent for at least 1 month but less than 1 year (i.e., unstable or short-term abstinence) at the time of the 8-year follow-up. Although the time frame is much shorter in this than in other studies, these data are consistent with those of Hser and colleagues (2001, 2007b) in that about 60 percent of drug users followed after an index treatment episode had poor long-term outcomes as evidenced by continued drug use.
The data reported in longitudinal studies suggests that there may be discernible patterns of drug use over the course of a lifetime and that these patterns may vary across substances. While the role of treatment over the life span is not at all well documented, the hope is that the lifetime pattern of decline in and cessation of drug use observed in subsets of
drug users would occur earlier, at a faster pace, or increased in prevalence if more drug users were involved in effective treatment intervention.2
Currently, drug dependence is usually well established and lengthy before first entry into treatment; the average time between initiation of use of the problem drug and entry into treatment is 10-15 years (Dennis et al., 2007; Hser et al., 2006, 2007b). This gap suggests that treatment is not an especially attractive option for drug users early in their drug use careers. Drug users may fail to enter treatment for a variety of reasons, including limited availability in the health care system and the stigma, cost, and reporting burden of participation, as well as the competing attraction of continued drug use so long as associated problems are not overwhelming. These dynamics and others may explain why drug users tend not to seek treatment for many years after drug use is initiated. However, more research on the determinants of first entry timing would be valuable.
Also valuable would be research on the interplay between natural fluctuations in drug use and participation in episodes of treatment, as well as the impact of treatment entry on the long-term course of drug use. Drug dependence has increasingly been defined as a chronic relapsing brain disorder for which permanent abstinence may not be a realistic goal of any single round of treatment for heavy long-term users (National Research Council, 2001). Dennis and colleagues (2005), for example, document that multiple treatment episodes are the norm for drug users and suggest that prior treatment exposure may be associated with poor outcomes. But other data suggest that long-term prognosis appears to be better for those who re-enter treatment promptly after relapsing (Moos and Moos, 2007; Scott et al., 2003). These findings highlight the current lack of clear understanding about the interplay between treatment entry and drug use trajectories.
Importantly, however, it has been observed that the long-term effects of treatment can be predicted by a person’s short-term response during treatment (e.g. Higgins et al., 2000) and participation in aftercare and self-help programs (Scott et al., 2003; Weisner et al., 2003a). For example, Higgins and colleagues (2000), with data from 190 clients who had participated in a variety of specific treatment research conditions, found a linear relationship between the percent abstinent at the 12-month follow-up and the duration of documented abstinence achieved during treatment, irrespective of the conditions under which this abstinence was attained. Weisner et al. (2003a) showed that 5-year outcomes were strongly predicted by 6-month outcomes in a large sample of patients (N = 784) from a managed care chemical dependency program, and Dennis and colleagues
(2007) have documented the positive life-style changes that are associated with prolonged periods of abstinence. These observations suggest the potential importance of treatments that can effectively promote abstinence and potentially lengthen the perspective on intervention with drug users to include longer-term monitoring and re-treatment (Dennis et al., 2003; Scott and Dennis, 2009; Scott et al., 2005b).
TREATMENT AVAILABILITY, EFFECTIVENESS, AND USE
This section considers the capacity and effectiveness of the current drug abuse treatment system and its adequacy to affect levels and patterns of drug use. In the United States, there are about 14,000 facilities that offer drug abuse treatment, and they serve more than 1.1 million drug and alcohol users, according to the National Survey of Substance Abuse Treatment Services (Substance Abuse and Mental Health Services Administration, 2008). The majority of these (more than 70 percent) are “drug-free” treatment programs that offer outpatient psychosocial counseling, generally of 3-6 months’ duration; the others offer short-term (1 month or less) or long-term residential programming, outpatient methadone maintenance, or brief detoxification services. The large national studies that have been conducted to examine the effectiveness of the treatment in the United States have generally supported the effectiveness of all treatment modalities as intervention for drug users, with the exception of brief detoxification. This research includes several long-term follow-up studies of large samples of treated drug users: the Drug Abuse Report Program, conducted in 1969-1973 (Sells and Simpson, 1980; Simpson et al., 1979); the Treatment Outcome Prospective Study conducted in 1979-1981 (Hubbard et al., 1989); and the Drug Abuse Treatment Outcome Studies (DATOS), conducted in 1991-1993 (Hubbard et al., 1997). Findings are consistent with treatment benefits reported in national survey studies from both Australia (Teesson et al., 2004) and Great Britain (Gossop et al., 2003).
Using a pre-post comparison design, these studies show that the amount of drug use in the years following treatment entry is lower than the amount reported prior to treatment entry. Because these are not controlled studies, however, it is difficult to know how much this reduction is due to the treatment itself and how much to a natural recovery from heavy periods of drug use. Nonetheless, cost–benefit analyses have supported the benefit to society of treatment intervention for drug users. One study conducted in California, for example (Ettner et al., 2006), calculated that $7 is saved for every $1 spent on drug abuse treatment; the main benefits are from reductions in drug-use-associated criminal activity and increases in employment earnings. Given that studies show the effectiveness of treatment, it is underutilized. National treatment databases, compared
with national estimates of drug use prevalence, support the evidence from epidemiological studies that a relatively small proportion of drug users are in treatment at any given time. For example, the Treatment Episode Data Set (TEDS) shows that for 2000 (the year of the most recent estimate of the number of chronic users of cocaine and heroin) there were an estimated 898,000 chronic heroin users in the United States (Office of National Drug Control Policy, 2001) but only about 270,000 treatment admissions for people whose primary drug problem was with heroin. This comparison across databases suggests that as many as 30 percent of users were in treatment. However, this may be an overestimate since TEDS does not differentiate between multiple admissions of the same individual and those entering long- or short-term (including detoxification) treatment. In 2006, the number of admissions for opioid use problems in the United States climbed to about 310,000, due mostly to an increase in admissions of prescription opioid users to the same system that serves heroin users. Although the exact percentage of drug users who are in treatment is debatable, it is clear that the majority of users are not in treatment at any given time. The relatively low treatment participation rate may reflect insufficient availability, low treatment acceptability among users, or low treatment efficacy such that capacity is used to recycle previously treated clients rather than new, previously untreated, users (McCarty et al., 2000). In contrast to the pattern in the United States, a number of Western European nations (including the Netherlands, Switzerland, and the United Kingdom) have treatment participation rates of more than 50 percent for those who are opioid dependent.
If treatment is going to have a larger impact on the demand for drugs, it would be important to increase its reach by attracting the participation of more drug users. Improving voluntary participation may require some new strategies that remove barriers to treatment entry while making treatment itself a more attractive option. For example, there has been some success in promoting treatment entry by using vouchers that are distributed to drug users at the locations such as needle exchange sites (Booth et al., 1998; Strathdee et al., 2006). This strategy could be expanded to contact drug users in other sites, such as medical and mental health facilities, pediatric clinics, drop-in centers, and welfare and child protective services agencies. Research and evaluation would be needed to support the effectiveness of such efforts. The acceptability of treatment could be enhanced by removing some of the barriers to entry (e.g., expanding treatment
hours and locations) and by including more tangible and desired services for clients, such as employment and housing services (Laudet and White, 2010), as well as evidence-based incentive interventions (DeFulio et al., 2009; Silverman et al., 2002, 2007; Stitzer and Petry, 2006). The issue of treatment acceptability and attractiveness to clients is critical in the case of voluntary participation, and more research is needed on the attributes of treatment that drug users would find desirable.
Integrating Treatment with the Criminal Justice System
Although the methods that may be needed to increase voluntary participation are currently speculative, one logical and more certain pathway for enhancing the reach of drug abuse treatment to a large relevant population of drug users would be through coordination with the criminal justice system (Chandler et al., 2009). The interplay between drug use and criminal behavior has been well documented in longitudinal research with opiate users (Nurco et al., 1985). It is also well known that a high percentage of people in prison have been involved with drugs. For example, a 1997 national survey showed that more than half of state and federal inmates reported drug involvement in the month before their offense and 70-80 percent reported some past drug use (Mumola, 1999). Similarly, a 2004 survey indicated that 53 percent of state and 45 percent of federal prisoners met the psychiatric criteria3 for drug dependence or abuse (Mumola and Karberg, 2006).
Behavior Therapy Approaches
In response to the clear overlap between drug use and criminal involvement, both state and federal prisons have begun to provide drug abuse treatment services to inmates. Overall, it is estimated that substance abuse treatment services are offered in about half of correctional system agencies, including jails, prisons, and probation and parole departments (Taxman et al., 2007). As reviewed by Grella and colleagues (2007) and Taxman and colleagues (2007), however, the majority of these services consist of low intensity education and counseling, although some more intensive in-prison therapeutic communities and counseling services have been established and evaluated. Mitchell and colleagues (2007) reviewed the research on efficacy of these intensive incarceration-based treatment models and conducted a meta-analysis of published studies. They found that in-prison therapeutic community
treatment was consistently associated with reductions in both criminal recidivism and drug use of inmates when released in comparison with those who did not receive this type of treatment. Thus, the effectiveness of therapeutic community treatment appears to be supported. In-prison counseling programs were also associated with lower rates of recidivism but not with lower rates of postrelease drug use. The latter finding needs further study because it is inconsistent with the assumption of a functional association between drug use and criminal behavior.
The effectiveness of in-prison treatment may be further enhanced by continuing postrelease interventions. Pelissier and colleagues (2007) provide a thoughtful review of aftercare research that highlights the difficulties of interpreting and drawing conclusions from the existing literature. These difficulties include cross-study inconsistencies in the definition of aftercare (e.g., residential or outpatient services), as well as differences in definition and analysis of outcomes. There are also interactions between aftercare, in-prison treatment, and judicial supervision practices that make clear conclusions about the role of aftercare very difficult to draw. Furthermore, it is important to keep in mind a basic weakness of all the criminal justice treatment literature, which is that participation in special programs has been voluntary so that samples are self-selected rather than randomly assigned to treatment conditions. Thus, while research generally supports the efficacy of in-prison treatment followed by community aftercare (see, e.g., Aos et al., 2006), there is much more information needed to fully elucidate the nature, amount, and timing of effective treatment and the characteristics of drug-involved offenders who can benefit from various treatment configurations.
Although existing data on the benefits of treatment for drug-involved offenders may be less rigorous than is desirable, there are studies that show treatment of drug-involved offenders has a positive cost-benefit ratio when analyses are conducted with a variety of comparison groups and drug-using populations (Daley et al., 2004; Ettner et al., 2006; Godfrey et al., 2004; McCollister et al., 2004). For a more complete discussion on the effects of drug treatment for drug-involved offenders, see National Research Council (2001, Chapter 8).
Drug courts represent a relatively new and innovative variation on pretrial diversion strategies whose goal is to integrate treatment with criminal justice supervision. Drug courts, which have been operating in the United States since 1994 (Belenko, 1998, 2000; U.S. Department of Justice, 1995, 1998, 2006), generally mandate that drug-involved offenders receive treatment in the community in lieu of serving time in jail or
prison. Within the system, judges have the discretion to impose a mixture of sanctions (including incarceration) and rewards based on evidence of active treatment participation and abstinence from drug use. These types of programs are likely a cost-saving alternative to prosecution and incarceration for drug-involved criminals, considering the high cost of prosecution and incarceration. In addition, the sanctions available in the criminal justice system can provide a strong motivation for positive outcomes in drug court participants.
There is a growing body of evidence of the effectiveness of drug courts, particularly with regard to reduced recidivism (Belenko, 2001; U.S. Government Accountability Office, 2005). However, a recent Urban Institute study (Bhati et al., 2008), notes that eligibility for drug court participation is currently highly restrictive and only a trivial proportion of criminally involved drug users participate in such programs. In that study (Bhati et al., 2008), a synthetic dataset was constructed from several sources—the National Survey on Drug Use and Health, the Arrestee Drug Abuse Monitoring Program, and DATOS—to examine the theoretical crime reduction benefits that could be expected if treatment were provided to all offenders in the United States with a history of drug abuse or dependence (estimated at 1.5 million offenders). The study found that the current system saves $2.2 in costs to society for every $1 spent on the diversion program but that only about half of those eligible under current criteria are actually treated. In this model, expansion of treatment to all atrisk arrestees would remain cost-beneficial, with an estimated $3.36 saved for every $1 spent. This is a provocative study that supports in theory the benefits of expanding diversion programs.
A recent addition to the literature on treatment of drug-involved offenders highlights the utility of medication-based interventions for individuals with histories of opioid dependence (Gordon et al., 2008; Kinlock et al., 2008). In this study, prisoners with a history of opioid dependence (N = 211) were randomly assigned to receive methadone maintenance treatment initiated either before or shortly after release from incarceration; the control group who was released received drug abuse counseling without medication. The study demonstrated significantly better outcomes on measures of treatment entry, drug use, and criminal activity both 3 months after (Kinlock et al., 2008) and 6 months after release (Gordon et al., 2008) for those who could access methadone maintenance in comparison with those who were not offered this option. Among those who received counseling only, 65 percent tested positive on a urine test for opiate use at 6 months follow-up in comparison with 48 percent and 28
percent, respectively, for those who initiated methadone treatment shortly after or before release from prison. Those in the methadone maintenance groups reported about half the number of crime days as those in the counseling-only group. Finally, there was a substantial difference in the amount of treatment participation reported by the groups, with means of 65, 32, and 11 days, respectively, reported at 6 months for the prerelease methadone, postrelease methadone and counseling-only groups. This is an important finding from a relatively small but well-designed study that suggests more widespread implementation of methadone treatment for incarcerated opioid abusers would be useful. Whether such an initiative would be acceptable to the criminal justice system—and the conditions under which it could be implemented—remain to be determined.
A second option for medication treatment of opioid-involved offenders is the long-acting formulation of the opioid antagonist, naltrexone. A recent randomized clinical trial (Hulse et al., 2009) demonstrated the efficacy of sustained-release formulations in comparison with short-acting oral medication. Naltrexone, when implanted, sustained higher blood levels across time and significantly reduced rates of opioid relapse at 6 months in comparison with an oral formulation (relapse rates of 8 and 30 percent). The potential utility of sustained-release naltrexone as a treatment alternative for use with opioid-involved criminal justice clients seems apparent, and the strategy was acknowledged (though not funded) in the Second Chance Act signed by President George Bush in 2008. The utility of this intervention has been demonstrated in a study conducted with federal parole and probation clients (Cornish et al., 1997), but research on this model remains sparse (but see Patapis and Nordstrom, 2006), and the Second Chance Act has been largely ignored. Additional research on the feasibility and effectiveness of sustained-release naltrexone for use in criminal justice populations is warranted.
Overall, better coordination of treatment and criminal justice programs could be a very effective component of a demand reduction strategy. Expansion of treatment to accommodate more users involved in crime would almost certainly affect a large number of drug users, including those who would not otherwise go voluntarily to treatment. It is estimated that there are about 2.3 million adults incarcerated in the United States (Bureau of Justice Statistics, 2009), and, as noted above, about half of them used drugs in the month prior to their incarceration (Mumola and Karberg, 2006). An additional 4.8 million adults are on probation or parole in community settings (Glaze and Bonczar, 2006), with a similar percentage of drug-involved individuals. To the extent that prison inmates and releasees under community supervision are an accessible and receptive population, the reach of treatment programs could be substantial. However, treatment of more than 3 million new drug users would require
approximately a doubling of the current 1.8 million annual nationwide drug abuse treatment admissions reported in the TEDS for 1996-2006. Clearly, this strategy would come at some cost, though it appears that diversion of more drug users into treatment or direct delivery of treatment services in the criminal justice system could be cost-beneficial, with the costs of treatment offset by reduction in future criminal justice costs, including arrests, prosecutions, and incarceration of recidivist offenders (Bhati et al., 2008; Daley et al., 2004; Ettner et al., 2006; Godfrey et al., 2004; McCollister et al., 2004).
One caveat to any cost–benefit analysis is that expanded treatment might bring more severe cases into treatment, a factor that could reduce the cost–benefit tradeoff that is based on current treatment clients. But additional support for the potential effectiveness of this approach comes from research on outcomes for coerced participants, which shows that the outcomes are similar to or better than those for voluntary participants (Perron and Bright, 2008). More research would be beneficial to provide actual rather than theoretical data on the cost-benefit tradeoff of therapeutic jurisprudence programs and to broaden the circumstances under which coerced versus voluntary treatment is examined.
Barriers and Issues in Treatment Expansion
Expansion of the current treatment system would require allocation of additional funding that could come from a variety of sources. In addition to the identification of funding sources, several other challenges are also historically associated with the ability to expand treatment services. For example, it may be difficult to identify physical locations for new treatment programs due to the reluctance of neighborhood residents to host drug treatment clinics in their area. This means that innovation may be required for treatment expansion that requires new physical sites. One example is use of mobile treatment vans that can park either in a single location or move to service several different locations during a day. There have been successful mobile methadone maintenance programs in Baltimore (Butler and Swanton, 2006; Greenfield et al., 1996) and other cities (Boston, San Francisco, Seattle), as well as entire states and territories (New Jersey, Vermont, and Puerto Rico). Availability of buprenorphine by prescription at physician offices is yet another innovation that could expand treatment availability, at least for opioid users (Sullivan and Fiellin, 2008). New psychosocial counseling programs may be more easily established than programs that dispense medication since they could be established at novel sites, including primary care and mental health facilities, as well as community service agencies or drop-in centers. However,
research would be needed to determine how such integration could be most effectively accomplished.
The mobile approach in Baltimore was one part of a major expansion of the treatment system that the city undertook in 1996. The expansion involved a tripling of funding and an increase in the number of treatment slots, from about 5,000 to nearly 9,000 in 2003, a 62 percent increase (Baltimore City Health Department, 2006). One interesting observation from this natural experiment is that the treatment slots were taken at high rates, suggesting that accessibility is a limiting factor in treatment use, at lease under some circumstances. Unfortunately, such public health initiatives are rarely conducted with adequate evaluation support to be able to document an impact on broader measures of community drug use and crime. It should also be noted, however, that an increase from 5,000 to 9,000 treatment slots for opioid abusers may not be expected to produce noticeable changes in citywide rates of drug use or crime given a city with an estimated population of opiate drug users that is perhaps five times larger (McAuliffe et al., 2008; Substance Abuse and Mental Health Services Administration, 2007).
Limitations in the quality and quantity of the counseling workforce are other potential barrier to expansion of the treatment system. Counselors may be poorly compensated, especially in not-for-profit clinics (Olmstead et al., 2005), and there is a chronic shortage as well as a high turnover in most counseling staffs (McLellan and Meyers, 2004). Clinic leadership also has a remarkably high turnover rate, a factor that affects both the quality and stability of services delivered: McLellan and colleagues (2003) reported a 53 percent annual turnover rate of clinic directors within 175 nationally representative drug and alcohol treatment programs interviewed in 2001. These issues can be addressed by expansion of training for substance abuse counselors and may also be aided by management training for clinic leaders, many of whom may have risen through the ranks of clinical staff and assumed leadership roles with little expertise or experience in management.
Another innovative solution to treatment expansion is the use of modern technology. Several small studies (Bickel et al., 2008; Carroll et al., 2008; Marsch et al., 2007) have recently been conducted that show efficacy for psychosocial counseling treatment by computer. In the study by Carroll and colleagues (2008), for example, drug use outcomes were improved when a computerized cognitive-behavioral therapy was added to the usual treatment, while in the study by Bickel and colleagues (2008) the outcomes were the same whether the therapy was delivered by computer or human counselors. Adoption of computerized intervention technology could facilitate treatment expansion by reducing the need for human service workers while retaining the benefits of evidence-based
treatment interventions. More research is needed on efficacy and effectiveness of this innovative technology.
Once a drug user enters treatment, there is a window of opportunity to promote behavior and attitude change. As discussed above, drug treatment programs are associated with positive outcomes in terms of drug use reduction and improved social functioning (Hser et al., 2006; Hubbard et al., 1997; Weisner et al., 2003b). In addition, long-term outcomes have been directly related to the duration of abstinence during treatment (Higgins et al., 2000; Weisner et al., 2003a). Thus, if the goal is to reduce demand for drugs through treatment, it would be beneficial to have improved treatments that could more reliably engender sustained periods of abstinence. However, this goal has to be tempered with the findings that relapse to drug use is a consistent and pervasive occurrence following treatment episodes for the majority of those who enter programs, a dynamic similar to that observed for other chronic illnesses, such as diabetes and hypertension (McLellan et al., 2005a).
There are several strategies that could be used for improving the outcomes of existing or expanded treatment programs (see also Sindelar and Fiellin, 2001):
addition of evidence-based medications for treatment of drug and alcohol dependencies (e.g., Strain and Stitzer, 2006),
adoption of evidence-based behavioral and psychosocial counseling strategies (see Carroll and Onken, 2005),
better methods for treatment of co-occurring medical and psychiatric disorders either through on-site provision of services (see Parthasarathy et al., 2003; Umbricht-Schneiter et al., 1994) or through better client-problem matching and case management (see McLellan et al., 1997, 1999, 2005b),
adoption of a long-term rather than an acute-care model of treatment for drug dependence (see Dennis et al., 2003; McKay, 2005; McLellan et al., 2005a; Scott et al., 2005b), and
outcomes-based accountability for treatment funding (see McLellan et al., 2008).
Research would be needed to determine how much additional improvement in outcomes could be expected with implementation of any of these innovations or combinations thereof and the extent to which the innovations are cost-beneficial.
Modeling Potential Policy Changes
Policy changes that would be expected to affect treatment include increased funding to expand treatment and improve availability, expansion of pretrial (e.g., drug courts), in-prison, and postincarceration aftercare treatment programs in compulsory treatment, and more funding per client to improve quality of care. The case for treatment expansion is often based on the broad social benefit that might result (see, e.g., Meara and Frank, 2005), as well as cost-benefit calculations that include the offset of criminal justice costs, lost productivity, and reductions in health care costs.
It may also be possible to characterize the direct impact of treatment expansion on drug demand by using data on drug purchases, which is information that drug users in treatment are routinely asked to report. For example, if a user spends on average $30 per day ($10,950 per year) on drug purchases, the direct effect on demand reduction can be seen for each day on which that person does not purchase and use drugs. Following this logic, the positive impact of treatment can be estimated directly by the number of abstinent days observed during and after treatment for each drug user enrolled in treatment compared with that user’s days of abstinence in a comparable time frame without treatment.4 As noted, information about money spent on drugs is often collected, while data on days of drug use (versus abstinence) in the past 30 days (Cacciola et al., 2007; McLellan et al., 1992) is one of the most common self-report measures collected at treatment entry and follow-up in studies that examine treatment outcome.
This direct approach to understanding treatment effects on demand reduction would predict that the demand reduction benefits of residential or pharmacological treatment for opioid dependence may be more readily apparent than the benefits of psychosocial counseling interventions. Opiate (e.g., heroin) abusers usually seek treatment when they are physically dependent and using the drug on a daily basis, thus directly fueling a high demand for illicit drugs. Residential treatment, which temporarily removes drug users from the marketplace, can be a relatively cost-effective strategy for demand reduction relative to incarceration. However, efficacious outpatient pharmacotherapy treatments, notably methadone and buprenorphine, which can suppress or eliminate on-going use of opiate drugs (National Consensus Development Panel on Effective Medical Treatment of Opiate Addition, 1998; Strain and Stitzer, 2006), would have an even lower per patient cost. Furthermore, to the extent that these phar-
macological treatments are delivered in a chronic care model of opioid substitution (i.e., methadone maintenance), they address the life-long risk of relapse that is characteristic of drug use disorders. This reasoning suggests that expansion of pharmacotherapy treatment for opiate users would be an especially effective demand reduction strategy, at least with regard to this drug class. The caveat, as demonstrated from the Baltimore experience, is that the extent of expansion may need to be substantial in order to affect the local prevalence of opiate use and consequent reduction in drug demand.
The demand reduction calculation is a bit muddier for alcohol, stimulant, and marijuana users who enter the large network of psychosocial counseling programs. Not only is this a more heterogeneous group in terms of drugs used, but much of the use may have been sporadic rather than daily, and the users may have stopped use for some time prior to treatment entry. An episodic pretreatment drug use pattern would complicate estimates of treatment-associated improvement in abstinence rates.
It may nevertheless be possible to model the impact of treatment expansion on demand for non-opioid drugs if appropriate datasets are available. The Services Research Outcome Study (Substance Abuse and Mental Health Services Administration, 1998), for example, has reported days of use for each drug in a large cohort (N = 2,222) of drug users enrolled in outpatient psychosocial counseling programs who entered treatment in 1989-1990. This is a useful dataset because it reports days of use per month for a variety of drugs, including alcohol, marijuana, and stimulants, during the 5 years before and after treatment. Although still imperfect (due to recall bias and the long time frame of recall), such data could be used to model treatment-associated demand reductions.
Another useful approach to understanding the impact of treatment expansion or improvement policies on demand would be to conduct experiments in the natural environment. For example, block grant funds could be manipulated (e.g., doubled or tripled) in specified locations. The effects on treatment utilization and outcomes could then be followed closely in a comparative research design (i.e., including locations where funding is not altered). Ideally, changes in drug demand could be simultaneously monitored using ethnographic techniques to study street-level drug sales. Such an experiment would be complex, but it is possible and would provide invaluable data on the question of optimal levels of funding for drug treatment.
As noted by Reuter and Pollack (2006), drug abuse treatment is imperfect and does not “work” comprehensively in the way that patients,
clinicians, or society would like. Nevertheless, there is a compelling argument to be made that treatment-facilitated abstinence from drugs, even if abstinence is not permanent, will have a direct impact on demand, reducing that demand. Whether or not the behavior changes associated with treatment are sufficient to detect demand reduction will depend on a myriad of factors, including the population being treated and the quality and amount of treatment being delivered.
This argument is compelling only if the extent of expansion is sufficient to make significant inroads on the problem. Furthermore, as discussed above, it may be more likely to see an impact on demand for illicit opiates (heroin) with expansion of opioid substitution programs than for reduction in other types of illicit drug demand (stimulants and marijuana) from expansion of psychosocial counseling programs. Despite the caveats, treatment expansion, both within and outside of the criminal justice system, as well as treatment improvement, need to be seriously considered in any policy discussions about demand reduction.
As always, the debate would be better informed by advances in a variety of research areas. Questions that need to be addressed include (but are not restricted to) the following:
How do multiple treatment episodes influence patterns and amounts of drug use over the lifetime of drug users?
Are there ways to shift treatment entry to an earlier time in a drug use career?
Under what conditions can treatment increase the number of drug users who begin a long-term abstinence trajectory?
What are the costs and benefits of expanding existing treatments for voluntary clients?
What are the feasibility and effectiveness of various strategies to improve the attractiveness of treatment to users?
What are the costs and benefits of expanding behavioral treatment delivered through the criminal justice system, including pretrial diversion programs (such as drug courts), in-prison treatment (such as therapeutic communities), and various kinds of residential and outpatient aftercare treatment?
What are the costs and benefits of expanding medication treatments for offenders with opioid dependence histories?
What are the costs and benefits of specific treatment improvement strategies and strategy combinations, including adoption of evidence-based practices and inclusion of such services such as employment and housing and longer-term models of care?
How can the demand reduction impact of treatment best be examined and modeled?
How many users would need to be taken out of the marketplace through treatment to make a perceptible impact in the demand for drugs?
How would different strategies and amounts of funding for drug treatment affect demand reduction?
We take up the design of a research agenda in Chapter 5.
Aos, S., M. Miller, and E. Drake. (2006). Evidence-Based Adult Corrections Programs: What Works and What Does Not. Olympia: Washington State Institute for Public Policy.
Baltimore City Health Department. (2006). Drug Treatment in Baltimore: 2005. Available: http://www.baltimorehealth.org/snapshots/DRUG_FINAL_6_6_06.pdf [accessed August 2010].
Belenko, S. (1998). Research on drug courts: A critical review. National Drug Court Institute Review, 1, 1-14.
Belenko, S. (2000). Research on drug courts: A critical review. National Drug Court Institute Review, 2, 1-58.
Belenko, S.R. (2001). Research on Drug Courts: A Critical Review 2001 Update. New York: The National Center on Addiction and Substance Abuse at Columbia University.
Bhati, A., J. Roman, and A. Chaifin. (2008). To Treat or Not to Treat: Evidence on the Prospects of Expanding Treatment to Drug-Involved Offenders. Washington, DC: Urban Institute. Available: http://www.urban.org/publications/411645.html [accessed August 2010].
Bickel, W.K., L.A. Marsch, A.R. Buchhalter, and G.J. Badger. (2008). Computerized behavior therapy for opioid-dependent outpatients: A randomized controlled trial. Experimental and Clinical Psychopharmacology, 16, 264-274.
Booth, R.E., C. Kwiatkowski, M.Y. Iguchi, F. Pinto, and D. John. (1998). Facilitating treatment entry among out-of-treatment injection drug users. Public Health Report, 113(S1), 116-128.
Bureau of Justice Statistics. (2009). Prisoners in 2008. Washington, DC: U.S. Department of Justice. Available: http://bjs.ojp.usdoj.gov/content/pub/pdf/p08.pdf [accessed August 2010].
Butler, C.B., and S. Swanton. (2006). The mobile health experience: A blueprint for expanding access to substance abuse treatment. Journal of Maintenance in the Addictions, 3, 17-36.
Cacciola, J.S., M.I. Alterman, A.T. McLellan, Y.T. Lin, and K.G. Lynch. (2007). Initial evidence for the reliability and validity of a “lite” version of the Addiction Severity Index. Drug and Alcohol Dependence, 87, 297-302.
Carballo, J.L., J.R. Fernández-Hermida, L.C. Sobell, M. Dum, R. Secades-Villa, O. García-Rodríguez, J.M. Errasti-Pérez, and S. Alhalabí-Diaz. (2008). Differences among substance abusers in Spain who recovered with treatment or on their own. Addictive Behaviors, 33, 94-105.
Carroll, K.M., and L.S. Onken. (2005). Behavioral therapies for drug abuse. American Journal of Psychiatry, 162, 1,452-1,460.
Carroll, K.M., S.A. Ball, S. Martino, C. Nich, T.A. Babuscio, K.F. Nuro, M.A. Gordon, G.A. Portnoy, and B.J. Rounsaville. (2008). Computer-assisted delivery of cognitive-behavioral therapy for addiction: A randomized trial of CBT4CBT. American Journal of Psychiatry, 165, 881-888.
Caulkins, J.P., S.S. Everingham, C.P. Rydell, J. Chiesa, and S. Bushway. (1999). An Ounce of Prevention, a Pound of Uncertainty: The Cost-Effectiveness of School-Based Drug Prevention Programs. Santa Monica, CA: RAND.
Chandler, R.K., B.W. Fletcher, and N.D. Volkow. (2009). Treating drug abuse and addiction in the criminal justice system. Journal of the American Medical Association, 301, 183-190.
Cornish, J.W., G.E. Woody, D. Wilson, A.T. McLellan, B. Vandergrift, and C.P. O’Brien. (1997). Naltrexone pharmacotherapy for opioid dependent federal probationers. Journal of Substance Abuse Treatment, 14, 529-534.
Daley, M., C.T. Love, D.S. Shepard, C.B. Petersen, K.L. White, and F.B. Hall. (2004). Cost-effectiveness of Connecticut’s in-prison substance abuse treatment. Journal of Offender Rehabilitation, 39, 69-92.
Dawson, D.A., B.F. Grant, F.S. Stinson, P.S. Chou, B. Huang, and W.J. Ruan. (2005). Recovery from DSM-IV alcohol dependence: United States, 2001-2002. Addiction, 100, 281-292.
DeFulio, A., W.D. Donlin, C.J. Wong, and K. Silverman. (2009). Employment-based abstinence reinforcement as a maintenance intervention for the treatment of cocaine dependence: A randomized controlled trial. Addiction, 104, 1,530-1,538.
Dennis, M., C.K. Scott, and R. Funk. (2003). An experimental evaluation of recovery management checkups (RMC) for people with chronic substance use disorders. Evaluation and Program Planning, 26, 339-352.
Dennis, M., C.K. Scott, R. Funk, and M.A. Foss. (2005). The duration and correlates of addiction and treatment careers. Journal of Substance Abuse Treatment, 28, S51-S62.
Dennis, M.L., M.A. Foss, and C.K. Scott. (2007). An eight-year perspective on the relationship between the duration of abstinence and other aspects of recovery. Evaluation Review, 31, 585-612.
Ettner, S.L., D. Huang, E. Evans, D.R. Ash, M. Hardy, M. Jourabchi, and Y.I. Hser. (2006). Benefit-cost in the California treatment outcome project: Does substance abuse treatment “pay for itself”? Health Services Research, 41, 192-213.
Faggiano, F., F.D. Vigna-Taglianti, E. Versino, A. Zambon, A. Borraccino, P. Lemma. (2005).(2005). School-based prevention for illicit drugs’ use. Cochrane Database of Systematic Reviews 18(2). Available: http://www2.cochrane.org/reviews/en/ab003020.html [accessed August 2010].
Glaze, L.E., and T.P. Bonczar. (2006). Probation and Parole in the United States, 2005. NCJ #215091. Washington DC: U.S. Department of Justice, Office of Justice Programs.
Godfrey, C., D. Stewart, and M. Gossop. (2004). Economic analysis of costs and consequences of the treatment of drug misuse: 2-year outcome data from the National Treatment Outcome Research Study (NTORS). Addiction, 99, 697-707.
Gordon, M.S., T.W. Kinlock, R.P. Schwartz, and K.E. O’Grady. (2008). A randomized clinical trial of methadone maintenance for prisoners: Findings at 6-months’ post-release. Addiction, 103, 1,333-1,342.
Gossop, M., J. Marsden, D. Stewart, and T. Kidd. (2003). The National Treatment Outcome Research Study (NTORS): 4-5 year follow-up results. Addiction, 98, 291-303.
Greenfield, L., J.V. Brady, K.J. Bestman, and A. De Smet. (1996). Patient retention in mobile and fixed-site methadone maintenance treatment. Drug and Alcohol Dependence, 42, 125-131.
Grella, C.E., L. Greenwell, M. Prendergast, D. Farabee, E. Hall, J. Cartier, and W. Burdon. (2007). Organizational characteristics of drug abuse treatment programs for offenders. Journal of Substance Abuse Treatment, 32, 291-300.
Higgins, S.T., G.J. Badger, and A.J. Budney. (2000). Initial abstinence and success in achieving longer term cocaine abstinence. Experimental and Clinical Psychopharmacology, 8, 377-386.
Hser, Y., V. Hoffman, C.E. Grella, and M.D. Anglin. (2001). A 33-year follow-up of narcotics addicts. Archives of General Psychiatry, 58(5), 503-508.
Hser, Y.I., M.E. Stark, A. Paredes, D. Huang, M.D. Anglin, and R. Rawson. (2006). A 12-year follow-up of a treated cocaine-dependent sample. Journal of Substance Abuse Treatment, 30(3), 219-226.
Hser, Y.I., D. Longshore, and M.D. Anglin. (2007a). The life course perspective on drug use: A conceptual framework for understanding drug use trajectories. Evaluation Review, 31, 515-547.
Hser, Y.I., D. Huang, C.P. Chou, and M.D. Anglin. (2007b). Trajectories of heroin addiction: Growth mixture modeling results based on a 33-year follow-up study. Evaluation Review, 31, 548-563.
Hubbard, R.L., M.E. Marsden, J.V. Rachal, H.J. Harwood, E.R. Cavanaugh, and H.M. Ginzberg. (1989). Drug Abuse Treatment: A National Study of Effectiveness. Chapel Hill: University of North Carolina Press.
Hubbard, R.L., S.G. Craddock, P.M. Flynn, J. Anderson, and R.M. Etheridge. (1997). Overview of 1-year follow-up outcomes in the Drug Abuse Treatment Outcome Study (DATOS). Psychology of Addictive Behaviors, 11(4), 261-278.
Hulse, G.K., N. Morris, D. Arnold-Reed, and R.J. Tait. (2009). Improving clinical outcomes in treating heroin dependence: Randomized, controlled trial of oral or implant naltrexone. Archives of General Psychiatry, 66, 1,108-1,115.
Kinlock, T.W., M.S. Gordon, R.P. Schwartz, and K.E. O’Grady. (2008). A study of methadone maintenance for male prisoners: 3-month post-release outcomes. Criminal Justice and Behavior, 35, 34-47.
Laudet, A.B., and W. White. (2010). What are your priorities? Identifying service needs across recovery stages to inform service development. Journal of Substance Abuse Treatment, 38, 51-59.
Marsch, L.A., W.K. Bickel, and M.J. Grabinski. (2007). Application of interactive, computer technology to adolescent substance abuse prevention and treatment. Adolescent Medicine State of the Art Review, 18, 342-356.
McAuliffe, W.E., M. Hsu, E. Artigiani, and E.D. Wish. (2008). Need for Substance Abuse Treatment in Maryland: Final Report. Supported by a grant from the Maryland Department of Health and Mental Hygiene and the Alcohol and Drug Abuse Administration to the Center for Substance Abuse Research at the University of Maryland, College Park. Available: http://maryland-adaa.org/content_documents/NeedAssessmentFINAL.pdf [accessed August 2010].
McCarty, D., Y. Caspi, L. Panas, M. Krakow, and D.H. Mulligan. (2000). Detoxification centers: Who’s in the revolving door? Journal of Behavioral Health Services and Research, 27(3), 245-256.
McCollister, K.E., M.T. French, M.L. Prendergast, E. Hall, and S. Sacks. (2004). Long-term cost-effectiveness of addiction treatment for criminal offenders. Justice Quarterly, 21, 659-679.
McKay, J.R. (2005). Is there a case for extended interventions for alcohol and drug use disorders? Addiction, 100, 1,594-1,610.
McLellan, A.T., and K. Meyers. (2004). Contemporary addiction treatment: A review of systems problems for adults and adolescents. Biological Psychiatry, 56, 764-770.
McLellan, A.T., H. Kushner, D. Metzger, R. Peters, I. Smith, G. Grissom, H. Pettinati, and M. Argeriou. (1992). The fifth edition of the Addiction Severity Index. Journal of Substance Abuse Treatment, 9, 199-213.
McLellan, A.T., G.R. Grissom, D. Zanis, M. Randall, P. Brill, and C.P. O’Brien. (1997). Problem-service “matching” in addiction treatment: A prospective study in 4 programs. Archives of General Psychiatry, 54, 730-735.
McLellan, A.T., T.A. Hagan, M. Levine, K. Meyers, F. Gould, M. Bencivengo, J. Durell, and J. Jaffe. (1999). Does clinical case management improve outpatient addiction treatment? Drug and Alcohol Dependence, 55, 91-103.
McLellan, A.T., D. Carise, and H.D. Kleber. (2003). Can the national addiction treatment infrastructure support the public’s demand for quality care? Journal of Substance Abuse Treatment, 25, 117-121.
McLellan, A.T., R.L. Weinstein, Q. Shen, C. Kendig, and M. Levine. (2005a). Improving continuity of care in a public addiction treatment system with clinical case management. American Journal on Addictions, 14, 426-440.
McLellan, A.T., J.R. McKay, R. Forman, J. Cacciola, and J. Kemp. (2005b). Reconsidering the evaluation of addiction treatment: From retrospective follow-up to concurrent recovery monitoring. Addiction, 100, 447-458.
McLellan, A.T., J. Kemp, A. Brooks, and D. Carise. (2008). Improving public addiction treatment through performance contracting: The Delaware experiment. Health Policy, 87(3), 296-308.
Meara, E., and R. Frank. (2005). Spending on substance abuse: How much is enough? Addiction, 100, 1,240-1,248.
Mitchell, O., D.B. Wilson, and D.L. MacKenzie. (2007). Does incarceration-based drug treatment reduce recidivism? A meta-analytic synthesis of the research. Journal of Experimental Criminology, 3, 353-375.
Moos, R.H., and B.S. Moos. (2007). Treated and untreated alcohol-use disorders. Course and predictors of remission and relapse. Evaluation Review, 31, 564-584.
Mumola, C.J. (1999). Substance Abuse and Treatment, State and Federal Prisoners. NCJ #172871. Washington, DC: Bureau of Justice Statistics.
Mumola, C.J., and J.C. Karberg. (2006). Drug Use and Dependence, State and Federal Prisoners, 2004. NCJ #213530. Washington, DC: Bureau of Justice Statistics.
National Consensus Development Panel on Effective Medical Treatment of Opiate Addiction. (1998). Effective medical treatment of opiate addiction. Journal of the American Medical Association, 280, 1,936-1,943.
National Research Council. (2001). Informing America’s Policy on Illegal Drugs: What We Don’t Know Keeps Hurting Us. Committee on Data and Research for Policy on Illegal Drugs. C.F. Manski, J.V. Pepper, and C.V. Petrie (Eds.). Committee on Law and Justice and Committee on National Statistics, Commission on Behavioral and Social Sciences and Education. Washington, DC: National Academy Press.
Nurco, D.N., J.C. Ball, J.W. Shaffer, and T.E. Hanlon. (1985). The criminality of narcotic addicts. Journal of Nervous and Mental Disease, 173, 94-102.
Office of National Drug Control Policy. (2001). What America’s Users Spend on Illicit Drugs, 1988-2000. Prepared by Abt Associates. Available: http://www.ncjrs.gov/ondcppubs/publications/pdf/american_users_spend_2002.pdf [accessed August 2010].
Olmstead, T.A., J.A. Johnson, P.M. Roman, and J.L. Sindelar. (2005). What are the correlates of substance abuse treatment counselor salaries? Journal of Substance Abuse Treatment, 29, 181-189.
Parthasarathy, S., J. Mertens, C. Moore, and C. Weisner. (2003). Utilization and cost impact of integrating substance abuse treatment and primary care. Medical Care, 41, 357-367.
Patapis, N.S., and B.R. Nordstrom. (2006). Research on naltrexone in the criminal justice system. Journal of Substance Abuse Treatment, 31, 113-115.
Pelissier, B., N. Jones, and T. Cadigan. (2007). Drug treatment aftercare in the criminal justice system: A systematic review. Journal of Substance Abuse Treatment, 32, 311-320.
Perron, B.E., and C.L. Bright. (2008). The influence of legal coercion on dropout from substance abuse treatment: Results from a national survey. Drug and Alcohol Dependence, 92, 123-131.
Reuter, P., and H. Pollack. (2006). How much can treatment reduce national drug problems? Addiction, 101, 341-347.
Scott, C.K., and M.L. Dennis. (2009). Results from two randomized clinical trials evaluating the impact of quarterly recovery management checkups with adult chronic substance users. Addiction, 104, 959-971.
Scott, C.K., M.A. Foss, and M.L. Dennis. (2003). Factors influencing initial and longer-term responses to substance abuse treatment: A path analysis. Evaluation and Program Planning, 26, 287-296.
Scott, C.K., M.A. Foss, and M.L. Dennis. (2005a). Pathways in the relapse-treatment-recovery cycle over 3-years. Journal of Substance Abuse Treatment, 28, S63-S72.
Scott, C.K., M.L. Dennis, and M.A. Foss. (2005b). Utilizing recovery management checkups to shorten the cycle of relapse, treatment reentry and recovery. Drug and Alcohol Dependence, 78, 325-338.
Sells, S.B., and D.D. Simpson. (1980). The case for drug abuse treatment effectiveness based on the DARP research program. British Journal of Addiction, 75, 117-131.
Silverman, K., D. Svikis, C.J. Wong, J. Hampton, M.L. Stitzer, and G.E. Bigelow. (2002). A reinforcement-based therapeutic workplace for the treatment of drug abuse: Three-year abstinence outcomes. Experimental Clinical Psychopharmacology, 10, 228-240.
Silverman, K., C.J. Wong, M. Needham, K.N. Diemer, T. Knealing, D. Crone-Todd, M. Fingerhood, P. Nuzzo, and K. Kolodner. (2007). A randomized trial of employment-based reinforcement of cocaine abstinence in injection drug users. Journal of Applied Behavior Analysis, 40, 387-410.
Simpson, D.D., L.J. Savage, and M.R. Lloyd. (1979). Follow-up evaluation of treatment of drug abuse during 1969 to 1972. Archives of General Psychiatry, 36, 772-780.
Sindelar, J., and D. Fiellin. (2001). Innovations in treatment for drug abuse: Solutions to a public health problem. Annual Review of Public Health, 22, 249-272.
Sindelar, J.D., and B. Kilmer. (2007). Would More Treatment Reduce Use of Illicit Drugs and the Associated Social Harms? Presented at the Committee on Law and Justice Workshop on Understanding and Controlling the Demand for Illegal Drugs. October, Irvine, CA.
Sobell, L.C., J.A. Cunningham, and M.B. Sobell. (1996). Recovery from alcohol problems with and without treatment: Prevalence in two population surveys. American Journal of Public Health, 86, 966-972.
Stitzer, M.L., and N.M. Petry. (2006). Contingency management for treatment of substance abuse. Annual Review of Clinical Psychology, 2, 17.1-17.24.
Strain, E.C., and M.L. Stitzer. (2006). The Treatment of Opioid Dependence. Baltimore, MD: Johns Hopkins University Press.
Strathdee, S.A., E.P. Ricketts, S. Huettner, L. Cornelius, D. Bishai, J.R. Havens, P. Beilenson, P.C. Rapp, J.J. Lloyd, and C.A. Latkin. (2006). Facilitating entry into drug treatment among injection drug users referred from a needle exchange program: Results from a community-based behavioral intervention trial. Drug Alcohol Dependence, 83, 225–232.
Substance Abuse and Mental Health Services Administration. (1998). Services Research Outcomes Study, 1995-1996. ICPSR study 2691. Rockville, MD: U.S. Department of Health and Human Services.
Substance Abuse and Mental Health Services Administration. (2007). Substate Area Estimates of Substance Use Disorders for Maryland, 2005 and 2006. Rockville, MD: U.S. Department of Health and Human Services.
Substance Abuse and Mental Health Services Administration. (2008). National Survey of Drug Abuse Treatment Services. Available: http://wwwdasis.samhsa.gov/dasis2/nssats.htm [accessed August 2010].
Sullivan, L.E., and D.A. Fiellin. (2008). Narrative review: Buprenorphine for opioid-dependent patients in office practice. Annals of Internal Medicine, 148, 662-670.
Taxman, F.S., M.L. Perdoni, and L.D. Harrison. (2007). Drug treatment services for adult offenders: The state of the state. Journal of Substance Abuse Treatment, 32(3), 239-254.
Teesson, M., J. Ross, S. Darke, M. Lynskey, A. R. Ali, A. Ritter, and R. Cooke. (2004). Twelve-Month Outcomes of the Treatment of Heroin Dependence: Findings from the Australian Treatment Outcome Study (ATOS). National Drug and Alcohol Research Centre, Technical report #196. Available: http://notes.med.unsw.edu.au/NDARCWeb.nsf/resources/TR_3/$file/TR.196.pdf [accessed August 2010].
Umbricht-Schneiter, A., D.H. Ginn, K.M. Pabst, and G.E. Bigelow. (1994). Providing medical care to methadone clinic patients: Referral vs. on-site care. American Journal of Public Health, 84, 207-210.
U.S. Department of Justice. (1995). The Drug Court Movement. Washington, DC: Office of Justice Programs.
U.S. Department of Justice. (1998). Looking at a Decade of Drug Courts. Washington, DC: Office of Justice Programs.
U.S. Department of Justice. (2006). Drug Courts: The Second Decade. Washington, DC: Office of Justice Programs.
U.S. Government Accountability Office. (2005). Adult Drug Courts: Evidence Indicates Recidivism Reductions and Mixed Results for Other Outcomes. Publication #GAO 05-219. Report to Congressional Committees. Washington, DC: U.S. Government Accountability Office.
Vaillant, G.E. (1973). A 20-year follow-up of New York narcotic addicts. Archives of General Psychiatry, 29(2), 237-241.
Weisner, C., G.T. Ray, J.R. Mertens, D.D. Satre, and C. Moore. (2003a). Short-term alcohol and drug treatment outcomes predict long-term outcome. Drug and Alcohol Dependence, 10, 281-294.
Weisner, C., H. Matzger, and L.A. Kaskutas. (2003b). How important is treatment? One-year outcomes of treatment and untreated alcohol-dependent individuals. Addiction, 98, 901-911.