Effectiveness and Availability of Treatment for Substance Use Disorders
Summary and Keywords
Drug and alcohol use disorders, also called substance use disorders (SUD), are among the major health problems facing many countries, contributing a substantial burden in terms of mortality, morbidity, and economic impact. A considerable body of research is dedicated to reducing the social and individual burden of SUD.
One major focus of research has been the effectiveness of treatment for SUD, with studies examining both medication and behavioral treatments using randomized, controlled clinical trials. For opioid use disorder, there is a strong evidence base for medication treatment, particularly using agonist therapies (i.e., methadone and buprenorphine), but mixed evidence regarding the use of psychosocial interventions. For alcohol use disorder, there is evidence of modest effectiveness for two medications (acamprosate and naltrexone) and for various psychosocial treatments, especially for less severe alcohol use disorder syndromes. An important area for future research is how to make treatment more appealing to clients, given that client reluctance is an important contributor to the low utilization of effective treatments.
A second major focus of research has been the availability of medication treatments, building on existing theories of how innovations diffuse, and on the field of dissemination and implementation research. In the United States, this research identifies serious gaps in both the availability of SUD treatment programs and the availability of effective treatment within those programs. Key barriers include lack of on-site medical staff at many SUD treatment programs; restrictive policies of private insurers, states, and federal authorities; and widespread skepticism toward medication treatment among counseling staff and some administrators. Emerging research is promising for providing medication treatment in settings other than SUD treatment programs, such as community mental health centers, prisons, emergency departments, and homeless shelters.
There is still considerable room to make SUD treatment approaches more effective, more available, and—most importantly—more acceptable to clients.
Keywords: substance use disorders, treatment effectiveness, treatment availability, diffusion of innovations, implementation research, comparative effectiveness, research agendas, addiction medications, behavioral therapies
Drug and alcohol use disorders, also called substance use disorders (SUD), are among the major health problems facing many countries. SUD accounts for 1.5% of the worldwide burden of disability-adjusted life years, and one quarter of the burden is due to mental and behavioral disorders, according to a study by the World Health Organization (Murray et al., 2012; Whiteford et al., 2013). In the United States, studies have estimated that the economic cost of excessive drinking in 2010 was $249 billion (Sacks, Gonzales, Bouchery, Tomedi, & Brewer, 2015), and the cost of drug abuse in 2007 was $193 billion (National Drug Intelligence Center, 2011). Opioid use has been classified a national public health emergency, with 61% of drug overdose deaths in the United States in 2014 involving some type of opioid, including heroin, and the age-adjusted rate of opioid overdose deaths has tripled since 2000 to 9.0 per 100,000 (Rudd, Aleshire, Zibbell, & Matthew Gladden, 2016). Despite the large negative impact of SUD, only a small minority of individuals with SUD are receiving treatment. In the United States in 2016, an estimated 19 million individuals met criteria for having a SUD, but only 11% of them received treatment for SUD (Park-Lee, Lipari, Hedden, Copello, & Kroutil, 2017). Similar low rates of treatment are observed worldwide: Among 14 high-income countries, on average only 10% of those with SUD received minimally adequate treatment, defined as four visits for people reporting treatment from a specialty mental health or general medical provider, and six for those receiving treatment from non-medically trained professionals (Degenhardt et al., 2017).
This article reviews selected research programs which have evolved to improve the availability and quality of SUD treatment. The article focuses on two broad research areas within the field of substance use services research, summarized by the questions: (a) Are effective treatments available for SUD? (b) If there are effective treatments, why are they so infrequently used? The first question addresses the evaluation of efficacy and effectiveness of existing treatments (whether pharmacological or psychosocial), and innovations in developing new SUD treatments. A key goal of this effort is to determine whether the field has effective treatments to offer patients. The second question examines the uptake of treatment options by the health care system, assessing how treatment programs decide which innovations to adopt and to whom these are provided. Some of this work is referred to as “dissemination and implementation” research.
For each of these two research areas, this article summarizes major findings, but also considers more broadly how the research agenda has evolved in recent years, including what might be the important next questions to address. The article focuses mostly on the U.S. experience due to the authors’ greater familiarity with that setting, and because the United States also accounts for a disproportionate share of substance use services research internationally. The focus is mainly on the past 30 years. Other recent articles have reviewed the earlier history of SUD services research (VanGeest, Johnson, & Alemagno, 2017; Sloboda, 2012). The article’s focus is on health services research, not biomedical research, looking specifically at treatment (not prevention or detoxification) of SUD involving alcohol and drugs other than tobacco. Finally, although alcohol and drug use disorders are often accompanied by other cooccurring conditions, the treatment of those conditions is not addressed in this review.
Before describing the research activities of interest, the article briefly addresses two questions about the context. Who are the key funders of substance use services research? What are their research agendas in relation to treatment effectiveness and availability?
Funders of Research
A key player in the treatment effectiveness research effort has been the U.S. National Institutes of Health (NIH), in particular the National Institute on Drug Abuse (NIDA) and the National Institute on Alcohol Abuse and Alcoholism (NIAAA). NIDA is the dominant funder of drug abuse treatment research in the United States. It receives about $1 billion each year from the federal government. In 2015, NIDA allocated around one third of its $1.02 billion total funding to the two divisions where most treatment research takes place: the Division of Epidemiology, Services, and Prevention Research (26%), and the Division of Pharmacotherapies and Medical Consequences (13%). Most of the rest went to basic neuroscience and behavioral research, as well as research management and intramural research (NIDA, 2017). In 2015, NIAAA had a considerably smaller budget of $447 million. Its publicly reported budget did not break out the Institute’s spending by division. As with the other NIH institutes, NIDA and NIAAA fund research primarily by awarding grants to outside researchers. Those researchers submit investigator-initiated applications in response to Funding Opportunity Announcements (FOAs) from the institutes which describe their research priorities, while leaving the design of specific interventions and study questions up to the investigators.
In addition to NIDA and NIAAA, other organizations have played an important role in funding studies and in shaping SUD research priorities in the United States. These include other federal agencies, such as the U.S. Centers for Disease Control and Prevention (CDC), the U.S. Department of Justice (DOJ), the Substance Abuse and Mental Health Services Administration (SAMHSA), and the U.S. Department of Veterans Affairs (Sloboda, 2012). In addition, private foundations have played an increasing role since the 1960s and have been credited with helping to stabilize research support, as well as promoting the integration of evidence into treatment (VanGeest et al., 2017). For example, from 1994 to 2009, the Robert Wood Johnson Foundation’s Substance Abuse Policy Research Program supported policy research related to alcohol, tobacco, and drug use and abuse. Through this program, the foundation made 368 awards for nearly $60 million, covering addiction treatment as well as alcohol and drug prevention and tobacco control (McCarty, McConnell, & Schmidt, 2010).
The development of new medications and psychosocial treatments for SUD, and the evaluation of their efficacy and effectiveness, have long been research priorities for both NIDA and NIAAA. Over time, however, the institutes have expanded their portfolios to look more closely at the diffusion of those treatments into practice. One stimulus to this expansion was a 1998 report from the Institute of Medicine (now called the National Academy of Medicine) which noted substantial gaps in the translation of substance use services research into practice (McCarty, Greenlick, & Lamb, 1998). The report also laid out several recommendations for closing this gap by pursuing improvements in implementation, dissemination, and knowledge transfer. Subsequently, translation issues were increasingly prominent in successive versions of the institutes’ research plans. NIDA’s 2000 plan listed one goal to “ensure that science-based treatments are translated to community settings” (Leshner, 2000), and in the most recent plan (2016–2020), this had expanded to “develop and test strategies for effectively and sustainably implementing evidence-based treatments” (NIDA, 2016). The progression indicates growing attention to the need to understand the process by which innovations spread in order to accelerate it. In 2005, NIAAA and NIDA joined six other NIH institutes in issuing the first multi-institute funding opportunity announcement on Dissemination and Implementation (D&I) Research in Health (Ducharme, Chandler, & Harris, 2016). The result was a steady growth in dissemination and implementation studies of SUD treatment. In the Veterans Health Administration, similar research was supported through its Quality Enhancement Research Initiative (Ducharme et al., 2016).
For alcohol treatment, NIAAA’s latest strategic plan (2017–2021) includes the following objective: “Evaluate the effectiveness, accessibility, affordability, and appeal of alcohol use disorder treatments and recovery models, and test strategies to increase their adoption in real-world settings.” The subsequent detailed text references the need to optimize the dissemination and implementation of treatments, not just to measure effectiveness. This is in addition to statements of other objectives addressing the development of new treatments and evaluating which treatments work best for which individuals (NIAAA, 2017).
Finding Effective Treatments
One common SUD treatment approach involves the use of medications to help stabilize dysregulated neural circuitry associated with chronic substance use. For opioid use disorder, three medications approved by the U.S. Food and Drug Administration (FDA) are available in the United States: (1) methadone, a synthetic opioid agonist (activates receptor) with a long duration of action, which substitutes fully for illicit opioid use and, due to its prolonged activity, allows the individual to have relief from opioid craving without experiencing euphoria or other effects of illicit opioid use; (2) buprenorphine, a partial opioid agonist which also substitutes fully for illicit opioid use but is safer to use than methadone since it does not fully activate opioid receptors (i.e., greatly reduced risk of overdose and respiratory depression); and (3) naltrexone, an opioid antagonist (blocker) which binds to opioid receptors and prevents other opioids from activating the receptor, without itself activating the receptor. Multiple formulations are available for all three medications. In the United States, methadone is dispensed as supervised dosing of oral liquid formulation and is restricted to delivery within a federally regulated opioid treatment program, with strict policies to oversee dispensation and diversion prevention. In contrast, both buprenorphine and naltrexone may be provided in typical office-based care settings, although prescribers of buprenorphine must complete 8 hours of regulatory training (or have other approved dispensation to prescribe) to qualify for a waiver from the U.S. Drug Enforcement Administration to prescribe buprenorphine for treatment of opioid use disorder. Buprenorphine is frequently diverted and diversion prevention initiatives include the formulations combined with naloxone (a potent opioid antagonist that has little transmucosal or oral absorption but is potent if injected) and newer extended release formulations that may be surgically implanted or subcutaneously delivered. Naltrexone, being a full opioid antagonist, has no street value and thus no diversion prevention formulation is necessary, but the extended release intramuscular injection formulation is the standard of care due to poor patient adherence with oral naltrexone. It may be worth noting that heroin-assisted treatment has an evidence base for use outside the United States for opioid use disorder unresponsive to agonist and antagonist therapies, but it is not available or FDA indicated within the United States.
For alcohol use disorders, the FDA has approved three medications: (a) disulfiram, an enzyme inhibitor that blocks the final metabolic breakdown of alcohol and causes toxic accumulation of acetaldehyde if the person consumes alcohol, resulting in an aversive “disulfiram reaction” consisting of sweating, vomiting, difficulty breathing, and risk of arrhythmia and cardiovascular compromise; (b) naltrexone, an opioid antagonist which reduces some of the pleasurable effects of alcohol resulting from opioid receptor activation by alcohol; and (c) acamprosate, which partially substitutes for the effects of alcohol on the brain, reducing hyperarousal and alcohol craving during alcohol abstinence. Two other medications, topiramate and gabapentin, have an evidence base and frequent use for treatment of alcohol use disorder but are not FDA indicated in the United States due to being generic at the time of study.
No medications have been approved to treat marijuana, cocaine, methamphetamine, or other classes of SUD (US DHHS Surgeon General, 2016). Some of the SUD medication innovation has focused on finding new delivery routes with improved safety and adherence (e.g., 4-week depot formulations of naltrexone and buprenorphine) rather than new molecular entities.
A number of psychosocial or behavioral therapies are also available for treating SUD, either alone or combined with medication treatment. These therapies typically aim to teach and motivate patients to change their behaviors in order to reduce substance use and its ill effects. Cognitive behavioral therapy (CBT) seeks to modify maladaptive behaviors and improve coping skills by emphasizing the identification and modification of dysfunctional thinking (Carroll, 1998). Contingency management provides patients with tangible rewards such as gift cards or lottery drawings, with the goal of supporting positive behavior change (Stitzer, Iguchi, Kidorf, & Bigelow, 1993). Motivational enhancement therapy uses motivational interviewing techniques to help individuals resolve any uncertainties they have about reducing substance use (Miller & Rollnick, 2002). Other evidence-based therapies promoting commitment to substance use reduction and behavioral change include the Community Reinforcement Approach (Meyers & Smith, 1995), the Matrix Model (Rawson et al., 1995), Twelve-Step Facilitation Therapy (Ries, Galanter, & Tonigan, 2008), and family therapies including Community Reinforcement and Family Training (CRAFT) and Behavioral Couples Therapy for substance use disorders (BCT) (US DHHS Surgeon General, 2016; Meyers, Roozen, & Smith, 2011).
The primary historical focus of SUD services research has been the quest to evaluate the efficacy and effectiveness of medications and psychosocial treatments developed to treat SUD. A PubMed search identifies 581 studies with keywords including “randomized controlled trial” and either “substance use disorder treatment” or “substance abuse treatment.” Over 60% of those studies were published since 2006, and around that time the number of papers started exceeding 30 per year. This measure is imperfect but provides some indication of the accelerating growth in the research effort in this field. Key questions posed by this research area include the following: Which medications and psychosocial treatments are efficacious in treating patients with SUD? How does the efficacy of a treatment vary with patient characteristics, severity of the disorder, and other variables? What length of retention in treatment is needed to result in improved outcomes?
Over the same time, research focused on the effectiveness of SUD treatments, as distinct from their demonstrated efficacy in clinical trial settings, has likewise grown. Effectiveness refers to the performance of an efficacious treatment provided in real-world natural settings to patients with complex comorbidities and social determinants of health, who are frequently excluded from participation in controlled clinical trials. As with other medical treatments, efficacy results from a clinical trial may overstate the potential of a SUD treatment because they describe services delivered by highly qualified clinicians with modest caseloads in academic or specialized centers, who receive intensive training and a manualized protocol and treat a patient pool that has been shaped by multiple inclusion and exclusion criteria (Miller, Zweben, & Johnson, 2005). Results from such studies may not generalize well to community treatment needs, hence the importance of effectiveness research to test “true efficacy” of SUD treatments.
For pharmacological treatments, the dominant theory regarding their efficacy is necessarily neurobiological, based on how brain activity is altered by a given medication. Of course, how effective medications may be in real-world natural settings will also depend on factors such as patient adherence, which may in turn reflect factors such as social support, patient beliefs, side effects, and financial or other barriers. Placebo effects for some medications, such as for alcohol use disorder, may contribute to behavioral change as well (Weiss et al., 2008); however, other medications, such as agonist treatment of opioid use disorder, demonstrate little placebo effect in controlled trials. Nonetheless, the dominant “medical model” that drives both new drug development and treatment efficacy research tends to view the medication itself as the active ingredient in treatment, whereas there are actually no controlled clinical studies of SUD medication demonstrating efficacy or effectiveness in the absence of a platform of psychosocial treatment aimed at substance reduction (e.g., manualized medication management, supportive therapy, motivational enhancement, group drug counseling, and 12-step attendance); this suggests a false dichotomy in the interpretation of research results (Kelly, Bergman, & O’Connor, 2017).
Kelly et al. (2017) describe an ongoing effort to improve the stature of psychotherapy research by making it fit the highly regarded medical model. Examples of this would be the push for manualization of therapeutic approaches and the practice of classifying treatments as evidence-supported based solely or mainly on clinical trials. However, Wampold and Imel (2015) and others have challenged the applicability of the medical model to psychosocial treatments, arguing that the therapist is not simply a technician who administers the preset therapy, but also has roles as an ally, coach, etc.—and the therapist’s effectiveness in those roles is another key determinant of outcomes. This “contextual model” provides an alternative paradigm for thinking about treatment effectiveness and why the effectiveness of a given psychosocial approach may differ widely across settings. It is also not unique to psychosocial research; there is a long-standing literature on the “white coat effect” in medication response (Sheppard, Fletcher, Gill, Martin, Roberts, & McManus, 2016).
One way that effectiveness research is used is to define certain treatments as “evidence- based practices” (EBPs). Researchers, governments, and other stakeholders can then track how rapidly treatment programs are making EBP treatments available.
Much of the research on SUD treatment efficacy and effectiveness has followed the randomized controlled trial model, with a control group randomly assigned to placebo (for drug trials) or to “treatment as usual” (for trials of psychosocial treatments). For medications, testing proceeds through a well-established sequence required for FDA approval, running from testing the drug in nonhuman subjects (preclinical) to testing the drug on patients to assess efficacy, effectiveness, and safety (Phase 3). For new psychosocial therapies, FDA approval is not required, but the National Institute on Drug Abuse (NIDA) has presented its own stage model, which proceeds from Stage I (feasibility and initial evaluation) and II (efficacy testing) research to Stage III (dissemination). Methodological standards have been developed for each phase, with the standards for Stage III drawing on work by the Clinical Trials Network (see “Understanding Gaps in the Availability of Treatments”) (Carroll et al., 2011). However, use of the Randomized Controlled Trial (RCT) approach to study SUD treatment efficacy and effectiveness has posed some methodological challenges, which are now described.
One much debated issue in efficacy and effectiveness research has been the choice of what outcome measure should be used to define success. One challenge is the lack of universally acceptable biological markers for the addictive state (NACDA, 2012). As a result, many studies have used the quantity and frequency of the client’s substance use as their main effectiveness measure, with success defined as either abstinence or reductions in substance use. For medication treatment, this corresponds to the approach of the U.S. FDA, which specifies abstinence as the only acceptable endpoint for clinical trials testing medications for drug use disorders, and either abstinence or reduction in heavy drinking days as the endpoints for alcohol use disorders. However, the heavy focus on use of the substance has been periodically challenged by researchers, including expert panels convened by the NIDA over the past 20 years. For drug use, one critique has been that abstinence may not be sustained unless the intervention also reduces craving and increases the client’s self-efficacy, so those too should be considered as components of effectiveness (Tiffany, Friedman, Greenfield, Hasin, & Jackson, 2012). Another critique is that personal and societal concerns about addiction are driven less by drug use per se than by its consequences for the individual user, significant others, and society in domains such as health, well-being, psychological functioning, relationships, productivity, and criminality (Tiffany et al., 2012). By implication, judging an intervention’s effectiveness should require examining its effect on those other outcomes. In 2012, a panel convened by the NIDA called for the Institute to promote research into the development of new outcome measures that are broader than abstinence. Based on these concerns and on a report by an advisory panel (Tiffany et al., 2012), the NIDA has adopted the validation of endpoints other than abstinence as a research priority, to be worked on with its academic and industrial partners and the FDA (NIDA, 2016). The FDA has announced that it intends to issue guidance for the development of novel clinical endpoints for use in future research, including for work on validated measures of “craving” or “urge to use” illicit opioids (FDA, 2018). Finally, for medication treatment of opioid use disorder, a number of studies have specified retention in treatment, rather than abstinence, as the main outcome (Jerry & Collins, 2013). This approach corresponds to a “chronic care” model of addiction in which the goal is to sustain recovery with evidence-based treatment for opioid use disorder (OUD), including agonist or antagonist therapy that will reduce opioid use and its harms and in only some cases achieve opioid abstinence.
One issue with the RCT method has been the fact that trial participants have been shown to diverge substantially in characteristics from the overall population of SUD patients. One study found that SUD treatment trial participants had more years of education and were more often in full-time work compared with people receiving care in usual care settings (Susukida, Crum, Stuart, Ebnesajjad, & Mojtabai, 2016). The differences result from use of exclusion criteria, but also from differential refusal to participate. These patterns raise questions about the generalizability of results from trials, particularly if trial participants are more responsive to treatment than the wider population. Various institutes at NIH have responded by encouraging “pragmatic” clinical trials that use fewer exclusion criteria (Wang, Ulbricht, & Schoenbaum, 2009). This addresses the exclusion issue, but not the issue of refusal to participate.
A second issue is that the measured effect of the intervention can vary depending on what care the “treatment as usual” group receives. This issue is often beyond the ability of study investigators to control or even measure (e.g., if patients are supplementing study medication with over-the-counter medications they purchase). This issue of “nonstudy” care is increasingly recognized as an issue in the evaluation of psychosocial interventions (Freedland, Mohr, Davidson, & Schwartz, 2011), and methodology is designed to include surveillance for such factors to be captured during the trial in order to control statistically for their impact during data analysis.
In addition to RCTs, there has been some research using observational (nonexperimental) data (e.g., administrative data and large electronic medical record databases from health plans and public payers). These studies potentially can be more generalizable than RCTs, as they can use entire client populations seen in naturalistic settings. However, the nonrandom assignment to treatment presents confounding bias when comparing outcomes among groups receiving different types of treatment (or none). For example, observed differences in outcomes may result from differences in patient characteristics or provider behavior rather than from the treatment type selected. Some studies have attempted to address this problem using propensity score matching or weighting, which seeks to minimize differences in observable variables between members of different study groups (e.g., Mojtabai & Graff Zivin, 2003; Ciesla & Mazurek, 2013). Others have used an instrumental variables approach, which exploits variation in an exogenous variable that is predictive of treatment assignment (e.g., Lu & McGuire, 2002; Rhodes, Pelissier, Gaes, Saylor, Camp, & Wallace, 2001). Observational studies are generally viewed with greater skepticism by clinical researchers and assigned lower weight in the “hierarchy of evidence” relative to RCTs.
The research question regarding optimal duration of treatment has been more difficult to address, since clients cannot be compelled to remain in treatment for prespecified periods, as an RCT design would require. Furthermore, randomized controlled trials enduring longer than 6 months are unaffordable and therefore unfunded.
This section discusses selected key findings from effectiveness research. The discussion focuses on the treatment of opioid and alcohol use disorders, given their prevalence and impact.
Opioid Use Disorder Treatment
The evidence base for medication treatment of OUD is generally regarded as strong, particularly for methadone, which has been available and studied for decades. A systematic review in 2009 concluded that methadone is effective in both retaining patients in treatment and decreasing their heroin use compared to treatments that do not use opioid agonist therapy (Mattick, Breen, Kimber, & Davoli, 2009). The review noted the difficulty of comparing methadone to drug-free treatment as dropout rates are much higher among trial participants assigned to drug-free treatment. In another systematic review, Fullerton et al. (2014) concluded that there was a high level of evidence for the positive impact of agonist therapy with methadone on treatment retention and illicit opioid use, particularly at doses greater than 60 mg. In the case of buprenorphine, another systematic review found it effective in the treatment of heroin dependence, although less effective than methadone at retaining patients in treatment (Mattick, Breen, Kimber, & Davoli, 2014). Similarly, Thomas et al. (2014) found a high level of evidence for a positive impact of buprenorphine therapy on treatment retention and illicit opioid use, with less risk of adverse events than for methadone. For oral naltrexone, a systematic review found it was no more effective than placebo or drug-free treatment in preventing relapse in opioid addicts after detoxification (Minozzi, Amato, Vecchi, Davoli, Kirchmayer, & Verster, 2011). Finally, only a few clinical trials (and no recent systematic review) have examined the effectiveness of injectable extended-release naltrexone due to its relative novelty. (The FDA approved it in 2010.) Results of those clinical trials have been summarized as finding injectable extended-release naltrexone to be superior to placebo treatment and to drug-free treatment-as-usual among participants not interested in opioid-agonist therapy (Lee et al., 2017). One direct comparison study finds that when successfully initiated, injectable extended-release naltrexone is as effective as buprenorphine in outpatient early recovery treatment (6 months postdetoxification) (Lee et al., 2017).
The evidence for the use of psychosocial interventions in OUD treatment is more mixed, possibly because of some of the methodological challenges already noted. A systematic review by Mayet and colleagues in 2004 only found five studies testing psychosocial treatment without medications (“drug-free”) against medication treatment and concluded that there was not enough evidence to support this approach (Mayet, Farrell, Ferri, Amato, & Davoli, 2004). Because medication management provides a significant psychosocial intervention—providing education about substance use disorders, facilitating substance reduction goal-setting, and supporting abstinence orientation through recommendation of peer supports such as Narcotics Anonymous—most studies have looked at the effect of augmenting medication treatment with more intensive psychosocial approaches. Amato, Minozzi, Davoli, and Vecchi’s (2011) systematic review concluded that adding psychosocial support to standard medication treatments did not seem to add further benefits in treatment retention or heroin abstinence, even for contingency management where the authors had expected some impact based on previous research with SUD more generally (Dutra, Stathopoulou, Basden, Leyro, Powers, & Otto, 2008). A more recent systematic review (Dugosh, Abraham, Seymour, McLoyd, Chalk, & Festinger, 2016) concluded that adding psychosocial therapy to medication treatment did result in improved clinical outcomes, although this varied for different outcomes across studies and within psychosocial intervention types. The authors suggested the variation may have been because the comparison groups were not consistent across studies (e.g., did not always consist of providing medication alone or included medication management that was more intensive than would typically be encountered in clinical practice). Carroll and Weiss (2017) reviewed psychosocial treatments added to buprenorphine and concluded that contingency management appears to have the strongest evidence supporting adjunctive psychosocial treatment response. There is also some evidence suggesting that subpopulations with greater severity (i.e., injection heroin use) may specifically benefit from additional counseling intensity (Weiss et al., 2014).
As far as the optimal duration of SUD treatment, a series of large-scale observational studies from the 1970s on have indicated better abstinence and lower relapse rates if clients stay in treatment longer, particularly beyond a cutoff of 90 days. These studies included the Drug Abuse Reporting Program (DARP) (Simpson, Joe, & Bracy, 1982), the Treatment Outcome Prospective Study (TOPS) (Hubbard, Marsden, Rachal, Harwood, Cavenaugh, & Ginzburg, 1989), and the Drug Abuse Treatment Outcome Studies (DATOS) (Fletcher, Tims, & Brown, 1997). As noted in a recent review by Swartz (2017), confounding is likely to have biased the findings of all these observational studies, as retention in treatment is likely to be positively correlated with other factors, such as motivation, that predict better outcomes. The difficulty in randomizing patients across treatment durations has complicated researchers’ ability to address this question. One study that used instrumental variables to address confounding did find that longer duration was associated with reductions in drug use, for durations up until 47 days in treatment for moderate users and 68 days for heavy users (Lu & McGuire, 2002).
Alcohol Use Disorder Treatment
Among medications to treat alcohol use disorder, disulfiram has been available the longest (since 1951 in the United States). However, in clinical trials, it has demonstrated “inconsistent results” (Suh, Pettinati, Kampman, & O’Brien, 2006), possibly in part due to low rates of patient adherence in trials. Acamprosate, a second medication option, has been found in systematic reviews to reduce the risk of any drinking and significantly increase the cumulative abstinence duration (Rosner et al., 2010b), and to reduce rates of returning to drinking and heavy drinking (Jonas et al., 2014). In the case of naltrexone, another systematic review based on 50 RCTs concluded that oral naltrexone is effective in both reducing the risk of heavy drinking and decreasing drinking days (Rosner et al., 2010a). Finally, in a meta-analysis, injectable naltrexone was not found effective in avoiding any drinking or heavy drinking, but was effective in reducing heavy drinking days (Jonas et al., 2014).
As for psychosocial treatments of alcohol use disorder, separate systematic reviews have found evidence for the effectiveness of motivational enhancement therapy (MET: Hettema, Steele, & Miller, 2005; Vasilaki, Hosier, & Cox, 2006), cognitive-behavioral therapy (CBT: Magill & Ray, 2009), the community reinforcement approach (CRA: Roozen, Boulogne, & van Tulder, 2004), and behavioral couples therapy (BCT: Powers, Vedel, & Emmelkamp, 2008), as summarized in a recent review by Martin and Rehm (2012). In addition, another systematic review used results from 381 studies to rank the evidence on effectiveness of 48 modalities (Miller & Wilbourne, 2002). The authors concluded that the approaches with most support from research were (in order) MET, CRA, behavioral self-control training (BSCT), and behavior contracting. However, the ranking approach has been questioned by other authors on grounds of inconsistencies across studies in the probability of detecting treatment effects and uncontrolled variations in the characteristics of clients treated (Finney, 2000). In addition to these studies, it is also worth noting a substantial literature examining brief interventions in primary care for patients identified with mild to moderate alcohol use disorder or risky drinking patterns. These are often found to be effective (O’Donnell et al., 2013).
Other Strands in Effectiveness Research
It is worth mentioning several other areas of focus within the field of SUD treatment effectiveness research, albeit without discussing the findings here. One focus is the study of how treatment effects vary with characteristics of patients, providers, and treatments, and the related development and testing of treatment interventions targeted at specific subpopulations, such as prisoners, pregnant women, or homeless individuals. Another focus area is “comparative effectiveness,” involving direct comparison of alternative treatment approaches rather than comparing each to placebo (e.g., Lee et al., 2017; Tanner-Smith, Wilson, & Lipsey, 2013). This includes consideration of individual-level heterogeneity in treatment effects, where different treatments may work for different individuals (Basu, 2011). Finally, many studies have built on effectiveness findings to also evaluate the cost-effectiveness and cost-benefit of various SUD treatment approaches (e.g., Murphy & Polsky, 2016). This research is relevant since resource-constrained payers cannot allocate funding solely based on comparing the effectiveness of different treatment approaches.
Future Directions for Effectiveness Research
Given that a substantial body of evidence has demonstrated the effectiveness of a variety of medications and psychosocial approaches for SUD, it may be timely to move beyond that initial agenda. As noted, research is increasingly focusing on what interventions work for specific subpopulations and severity levels, and so forth. Another important area for future research is how to make treatment more appealing to clients, given that client reluctance is an important contributor to the low utilization of effective treatments. One way to improve acceptability to clients might be through use of internet-based text messaging and smartphone applications. These could help by allowing clients to access the intervention at a convenient time and place, to review skills and other therapeutic activities at their own pace, and to have greater anonymity around sensitive issues (Campbell et al., 2015). Quanbeck, Chih, Isham, Johnson, and Gustafson (2014) report strong effectiveness for some texting-based applications in alcohol use disorder treatment, while noting some remaining challenges. In 2014, the Journal of Substance Abuse Treatment devoted a special issue to technology-based interventions for the treatment and recovery management of substance use disorders.
Acceptability of treatment could also be improved by greater use of client incentives outside the academic settings where most studies of contingency management (CM) have been conducted so far. Although most CM interventions have rewarded abstinence, there have been studies that rewarded more proximal outcomes such as treatment attendance (Ledgerwood, Alessi, Hanson, Godley, & Petry, 2008). It could also be promising to combine CM with other interventions such as the use of recovery navigators (Brolin et al., 2017).
Finally, another important emerging trend in SUD effectiveness research is the involvement of other stakeholders (e.g., patients, providers, and payers) in the development and dissemination of new interventions. The NIDA’s expert panel recommended such involvement as necessary to increase the likelihood that interventions will be implementable (NACDA, 2012). The panel also noted that SUD treatment research is less well connected to clinical practice than is research on some other diseases, in part because most SUD treatment has historically occurred in specialty SUD programs that are mostly not connected to academic research. Similarly, Ducharme et al. (2016) called for more research focused on shared decision making between patients, their families, and their healthcare providers to inform those developing new treatments about what features would make SUD care more attractive in the context of patients’ lives.
Understanding Gaps in the Availability of Treatments
Although multiple effective treatments have been developed to treat SUD, only a small minority of individuals with SUD receive any treatment (11% of those meeting criteria for having SUD; Park-Lee et al., 2017). This reflects client reluctance to enter or remain in treatment, which in turn reflects the episodic relapsing nature of SUD, the many socioeconomic barriers clients face regarding treatment, and the role of cooccurring mental health symptoms such as depressive isolation and anxious avoidance. For clients who actively seek care, there remains a striking lack of availability of evidence-based treatments within the specialty SUD treatment system. For example, in 2016, only 27% of substance use treatment facilities provided any buprenorphine services, and 21% provided any extended-release injectable naltrexone treatment, despite the emerging evidence base for both medications (SAMHSA, 2017, p. 13) Lack of access to evidence-based medication treatment has been highlighted in multiple studies, including an advisory panel report to the NIDA (NACDA, 2012). Availability of psychosocial treatments appears to be somewhat better, with 93% of facilities reporting in 2016 that they sometimes used cognitive behavioral therapy; 91% reporting this for motivational interviewing; and 77% for 12-step facilitation. The rate of use was considerably lower for contingency management and motivational incentives (55%) and community reinforcement approach plus vouchers (12%) (SAMHSA, 2017). These data must be evaluated conservatively, however, since studies of community-delivered psychosocial treatments observe significant clinician nonadherence to treatment protocols (Carroll, 2014), which poses concern for quality of care and effectiveness as well as inadvertent harms.
As noted, in the United States only 11% of individuals with a SUD report having received any treatment in the prior year (Park-Lee et al., 2017). This statistic has raised one of the key questions facing the SUD research field, namely: Why are so few people in treatment, given the existence of multiple treatments with strong evidence of effectiveness? Answering this question has occupied many researchers for years and become an important part of the funding priorities at NIDA and NIAAA.
Prompt access to effective treatment is particularly important for patients with SUD, as motivation to enter treatment may be hard to sustain if the patient faces a wait of weeks or even months or a travel distance of hours to the nearest treatment program. Withdrawal syndromes compete with treatment-seeking efforts when relapse will provide relief faster than treatment availability. Explaining the pace at which facilities adopt and implement new treatments has been an important question that researchers have sought to address along with other factors that affect treatment entry and retention.
In studies of what types of treatment SUD programs offer their clients, the dominant paradigm appears to be the “diffusion of innovation” approach drawn from organizational theory. The classic version of this theory predicts that the speed of an innovation’s diffusion depends on the characteristics of the innovation itself, the organization, and the organization’s environment. This includes the extent to which potential adopters perceive the innovation to be compatible with their goals and values, and its affordability and ease of use (Rogers, 1995). Subsequent adaptations have added emphasis on the importance of the organization’s external environment, such as access to networks that include other organizations or individuals who have already tested the innovation (Berwick, 2003). The framework has been widely used across the study of health services outside SUD treatment (Greenhalgh, Robert, Macfarlane, Bate, & Kyriakidou, 2004).
Over time, diffusion theory is increasingly being supplemented by theory drawn from implementation science, which studies methods to promote the uptake of research findings into routine healthcare practice (Peters, Adam, Alonge, Agyepong, & Tran, 2013). One strand focuses on better understanding how innovations diffuse into organizations, while another strand focuses on designing interventions to accelerate diffusion. Of relevance to the diffusion of evidence-based practices, in recent years Damschroder and colleagues (2009) developed a “consolidated framework for implementation research” (CFIR) intended to integrate concepts from diffusion theory with those of implementation science. The original framework was for health care in general, but Damschroder and Hagedorn (2011) also published a discussion of how it could apply to the implementation of EBPs in SUD treatment.
Much of the research on SUD treatment availability has used the standard toolkit of health services research. This toolkit has included qualitative studies to learn about access barriers; quantitative studies using multivariate approaches to evaluate the contribution of different barriers and facilitators toward treatment entry and retention; and mixed methods approaches. Quantitative studies have often used surveys of treatment facilities that ask respondents which SUD treatments they offer. Many studies of treatment availability use the National Survey of Substance Abuse Treatment Services (N-SSATS) collected by SAMHSA, which is an annual census of all known substance abuse treatment facilities in the United States, both public and private (SAMHSA, 2017). This dataset has advantages including a high response rate and consistency over many years. However, it also has potential limitations as a source of information on the types of treatment available in private facilities, as these are not obligated to reply and may have little incentive to conduct detailed data collection for which they are not reimbursed. In addition, N-SSATS excludes SUD treatment programs that are located in settings other than specialty facilities (e.g., health centers or integrated health plans).
In addition, the growing interest in implementation science has expanded the range of research methods used to address questions about availability of EBPs for SUD treatment. Peters et al. (2013) note that in addition to the methods commonly used in RCT research, implementation research draws particularly on some other methods. These include mixed methods, which combine qualitative with quantitative methods; effectiveness-implementation hybrid trials, which evaluate both the effectiveness of an intervention and of the strategy used to implement it; and quality improvement studies. In terms of measurement, Simpson and colleagues have developed instruments to measure organizations’ “readiness to change” (Lehman, Greener, & Simpson, 2002). These are used to identify what factors are hindering or encouraging the adoption of evidence-based interventions.
Funders have also supported initiatives designed to both encourage dissemination of EBPs and identify barriers and facilitators. The NIDA Clinical Trials Network is a cooperative network that includes treatment researchers and community-based service providers, which was created to facilitate the development and implementation of evidence-based treatments (both psychosocial and medication) in community practice settings (Tai, Straus, Liu, Sparenborg, Jackson, & McCarty, 2010). The Network for the Improvement of Addiction Treatment (NIATx) teaches participating treatment centers to use process improvement strategies, applying approaches from other service sectors to the SUD treatment system (McCarty et al., 2007). It was initiated with funding from the Robert Wood Johnson Foundation (RWJF) and the Center for Substance Abuse Treatment (CSAT) within SAMHSA. A third approach, called Advancing Recovery, sought to create partnerships between state authorities and treatment providers, aimed both at increasing the use of EBPs in treatment of addiction and at developing a framework for identifying and removing systems barriers to EBP adoption (Molfenter, McCarty, Capoccia, & Gustafson, 2013). This approach too was funded by RWJF and NIDA.
Geographic Availability of Treatment Programs
One limitation affecting patient access to treatment in the United States is the lack of SUD treatment programs in many areas, particularly rural ones. In 2004, there was no SUD treatment program in 31% of midsized nonmetro U.S. counties (those with populations between 5,000 and 80,000), and in 85% of small nonmetro counties (Freeborn & McManus, 2010). Over 90% of SUD treatment programs are in either a metro county or a nonmetro county adjacent to a metro county. In addition, only 9% of outpatient facilities have Opioid Treatment Programs (OTPs), which are required in order to provide methadone (Lenardson, Jennifer, Gale, & John, 2008).
In the case of buprenorphine, treatment may be provided outside OTPs, including in office settings—but only by physicians who have received a waiver from the Drug Enforcement Administration allowing them to prescribe and either have specialty addiction board certification or have undergone 8 hours of approved training. One key supply constraint for buprenorphine is therefore the availability of prescribers who have obtained waivers. One study found that 43% of counties had no waivered physicians in 2011, although the mean number per county had increased from five in 2008 to seven in 2011 (Stein et al., 2015). In December 2013, the number of buprenorphine-waivered physicians per 100,000 residents varied from 18.0 in Massachusetts to 1.7 in Iowa, with availability lowest in southern and midwestern states (Knudsen, 2015). An additional supply constraint is that waivered physicians may not treat more than 275 patients per year and are only allowed this number if they have previously prescribed buprenorphine to 100 patients for at least one year. However, research shows that waivered prescribers often do not prescribe buprenorphine or prescribe well below their limit capacity (Huhn & Dunn, 2017; Thomas et al., 2017).
Availability of Effective Treatment Within Programs
Even those who live close to a SUD treatment program are by no means assured of access to effective treatment approaches, given the low adoption rates already noted, particularly for medication treatment. Thus, a considerable amount of research has examined treatment programs’ adoption rates for emerging treatments.
One strand in this literature has examined the predictors of programs’ decisions to adopt novel treatments, particularly for medication treatment. For example, a systematic review reported that programs with the following organizational characteristics were more likely to have adopted buprenorphine: those that offered detoxification services, provided naltrexone services, were a for-profit organization, and were an accredited organization (Garner, 2009). Other studies have reported that private facilities are more likely than public ones to adopt naltrexone (Knudsen, Ducharme, & Roman, 2007). In addition, researchers have identified factors in the treatment program’s environment that influence adoption. For example, OTPs are more likely to provide buprenorphine services the more they rely on private insurance and if they have access to state subsidies to cover medication cost, and less likely to do so in local markets in which there is greater heroin and injection drug use, as opposed to use of prescription opioids (Andrews, D’Aunno, Pollack, & Friedmann, 2014). SUD treatment programs (not limited to OTPs) are more likely to adopt disulfiram, naltrexone, and buprenorphine if other nearby facilities have done so and if state policy makers adopt policies favoring diffusion, such as improving Medicaid coverage of medication treatment or requiring private insurers to cover it (Heinrich & Cummings, 2014). Interestingly, about one third of the facilities stopped offering these addiction medications in a given year (Heinrich & Cummings, 2014), a finding that challenges the focus of diffusion theory models on initial adoption as being decisive. This points to the need to track availability after initial adoption.
Research has documented several barriers to wider adoption of novel treatments, mostly focusing on medication treatment. Some of these barriers are beyond the control of individual treatment programs. First, most addiction treatment is provided separately from general medical settings, and few community-based treatment programs have full-time and on-site medical staff who could prescribe medications (NACDA, 2012). Second, federal regulations limit the delivery of methadone to programs approved as OTPs (which are only 9% of all treatment programs), and limit buprenorphine services to OTPs or physicians who have undergone special training. Some states add further restrictions (Sharma, Kelly, Mitchell, Gryczynski, O’Grady, & Schwartz, 2017), such as requiring buprenorphine to be accompanied by counseling beyond the basic national guidelines for treatment outlined in 2004 by SAMHSA/CSAT in TIP 40 (SAMHSA, 2004). These restrictions reflect in part concern about diversion of medications to nonmedical uses, which many clinicians worry about for buprenorphine (Schuman-Olivier et al., 2013). Third, insurance plans often lack coverage for one or more addiction medications. In a survey in 2014, 19 state Medicaid programs did not cover methadone and 16 did not cover oral naltrexone (Grogan et al., 2016), although these numbers may have improved since then. Private insurers are less likely than Medicaid to exclude addiction medications from coverage, but often apply other limitations such as prior authorization or high copayments and coinsurance (Reif, Horgan, Hodgkin, Matteucci, Creedon, & Stewart, 2016; Reif, Creedon, Horgan, Stewart, & Garnick, 2017).
In addition, researchers have documented other barriers within treatment programs. An important barrier is widespread skepticism toward medication treatment among counseling staff and some administrators, reflecting the abstinence paradigm which regards medication treatment as merely substituting one drug for another. In one survey of counselors in SUD treatment programs, 20% of counselors admitted not knowing enough about either buprenorphine or methadone’s effectiveness to rate them. Those counselors who felt able to comment rated methadone and buprenorphine significantly lower than they rated the psychosocial EBPs mentioned. Acceptance of medication treatment was higher among counselors with more training and those employed in programs that had adopted medication treatment (Aletraris, Edmond, Paino, Fields, & Roman, 2016). Sharma et al. (2017) document other barriers facing treatment programs, including logistical ones resulting from the need for clients to have from 7 to 10 days of opioid abstinence prior to administering injectable naltrexone, to avoid precipitated withdrawal.
An additional issue is that even if a given treatment is available at a program, the program staff may not routinely offer it to clients. Although 27% of facilities provided any buprenorphine services in 2016, only 5% of clients were receiving them. Similarly, 21% of facilities provided extended-release injectable naltrexone services, but only 5% of clients were receiving them (SAMHSA, 2017). These numbers highlights the importance of the type of implementation that follows initial adoption, which has been a focus of studies drawing on implementation science.
In terms of interventions to improve EBPs using implementation theory, this literature is newer and has not been subjected to systematic literature reviews. In 2009, Garner (2009) reported finding only nine research studies that examined implementation of EBTs for SUD treatment, although more have been published since then. From 1999 to 2009, the Clinical Trials Network completed 20 trials that tested pharmacological, behavioral, and integrated treatment interventions for adolescents and adults (Tai et al., 2010). At the same time, a NIDA advisory panel noted that despite its other strengths, the Clinical Trials Network faced limitations in its ability to fully investigate barriers to EBP implementation (NACDA, 2012). The Advancing Recovery initiative reported that all 12 of the participating state and provider partnerships increased the use of the clinical EBP they had selected, with five of these focusing on medication treatment uptake. Among the various “policy levers” involved, the two that most participating states endorsed as being important in the readiness-planning phase of implementation were interorganizational stakeholder planning and buyin, and readiness and planning trainings to raise awareness about the importance of the EBP in effective addiction treatment (Molfenter et al., 2013).
Waiting time is another important dimension of treatment availability. Even for clients living near a treatment program that provides medication treatment, long waiting times can be a formidable obstacle to entering treatment. Opioid-dependent individuals are at substantial risk for negative outcomes while they wait to start treatment, including illicit drug use, criminal activity, infectious disease, overdose, and mortality (Sigmon, 2014).
There are anecdotal reports that wait times have lengthened in recent years, which would not be surprising since the opioid epidemic has increased SUD prevalence at a much faster rate than any growth in treatment availability. For example, in recent years waiting times for residential rehabilitation reached 6 months in southern New Hampshire (Gotbaum, 2016) and 18 months in parts of Maine (Fisher, 2015). Research has documented longer waiting times in states where the Medicaid program covers methadone or buprenorphine (McKnight, 2015), presumably because this expands demand without automatically adding supply.
In addition to SUD treatment, many individuals with SUD often need treatment for a variety of cooccurring health problems. For some of these problems, such as mental health disorders, human immunodeficiency virus (HIV), and trauma, confidentiality and other issues suggest that patients may be better served if treatment is available in the same setting as their SUD treatment (“colocation”). However, the specialty SUD treatment sector is severely limited in its capacity to treat other medical conditions (Pating, Miller, Goplerud, Martin, & Ziedonis, 2012). For example, a national survey of OTPs found that fewer than one third of programs offered onsite testing for HIV, and fewer than 10% offered clients a dedicated HIV and AIDS treatment track at the baseline interview (Aletraris et al., 2016).
Future Directions for Research on Availability
There are opportunities to expand the scope of future research on availability of SUD treatments. One challenge is to move beyond examining availability in the specialty SUD treatment system, which has been the focus of this research to date. Ducharme et al. (2016) have called for more attention to the availability of medication treatment in primary care, where many individuals with SUD present, but are not offered evidence-based SUD treatment. A similar point could be made about other settings that often encounter individuals with SUD, including community mental health centers, prisons, emergency departments, and homeless shelters, although for each of these examples some research is underway. A second conceptual challenge is to focus more on sustaining the use of EBPs, not only on the organization’s initial adoption and implementation. The early research emphasis on initial adoption may in part be a result of using diffusion theory, which implicitly treats adoption as an end state rather than something potentially reversible. Finally, meaningful assessment of the availability of behavioral therapies may require spot audits to evaluate whether the services delivered truly correspond to the modality reported.
Substantial resources have been invested to evaluate the effectiveness of SUD treatments. The resulting research demonstrates that for alcohol and opioid disorders there is strong evidence for effectiveness of a variety of medications and psychosocial treatments. Research on the availability of SUD treatment has documented large gaps and provided evidence on the reasons for those gaps, including problems at the level of both treatment program and health system. There is still considerable room to make SUD treatment approaches more effective, more available, and—most importantly—more acceptable and responsive to client needs.
The authors acknowledge helpful comments on an earlier draft from Sarah Duffy and Sharon Reif, as well as support from the Brandeis/Harvard Center to Improve System Performance of Substance Use Disorder Treatment (NIDA grant P30 DA035772).
Carroll, K. M., & Onken, L. S. (2005). Behavioral therapies for drug abuse. American Journal of Psychiatry, 162(8), 1452–1460.Find this resource:
Carroll, K. M., & Rounsaville, B. J. (2003). Bridging the gap: A hybrid model to link efficacy and effectiveness research in substance abuse treatment. Psychiatric Services, 54(3), 333–339.Find this resource:
England, M. J., Butler, A. S., & Gonzalez, M. L. (Eds.). (2015). Psychosocial interventions for mental and substance use disorders: A framework for establishing evidence-based standards. Washington, DC: National Academy Press.Find this resource:
Kleber, H. D., Weiss, R. D., Anton Jr, R. F., George, T. P., Greenfield, S. F., Kosten, T. R., . . . & Hennessy, G. (2007). Treatment of patients with substance use disorders. American Journal of Psychiatry, 164(4), 5–123.Find this resource:
Kolodny A., Courtwright, D. T., Hwang, C. S., Kreinter, P., Eadie, J. L., Clark, T. W., & Alexander, G. Caleb (2015). The prescription opioid and heroin crisis: A public health approach to an epidemic of addiction. Annual Review of Public Health, 36, 559–574.Find this resource:
National Institute on Drug Abuse. (2018). Principles of drug addiction treatment: A research-based guide. (3rd ed.).
Providers Clinical Support System. A program to train primary care providers in the evidence-based prevention and treatment of opioid use disorders and treatment of chronic pain. Funded by the Substance Abuse and Mental Health Services Administration.
Rawson, R. A., Woody, G., Kresina, T. F., & Gust, S. (2015). The globalization of addiction research: Capacity building mechanisms and selected examples. Harvard Review of Psychiatry, 23(2), 147.Find this resource:
Saunders, E. C., & Kim, E. (2013). Substance abuse treatment implementation research. Journal of Substance Abuse Treatment, 44(1), 1–3.Find this resource:
Volkow N. D., Frieden, T. R., Hyde, P. S., & Cha, S. S. (2014). Medication-assisted therapies: Tackling the opioid-overdose epidemic. New England Journal of Medicine, 370(22), 2063–2066.Find this resource:
Wiessing, L., Ferri, M., Darke, S., Simon, R., & Griffiths, P. (2017). Large variation in measures used to assess outcomes of opioid dependence treatment: A systematic review of longitudinal observational studies. Drug and Alcohol Review, 37, S323–S338.Find this resource:
Aletraris, L., Edmond, M. B., Paino, M., Fields, D., & Roman, P. M. (2016). Counselor training and attitudes toward pharmacotherapies for opioid use disorder. Substance Abuse, 37(1), 47–53.Find this resource:
Amato, L., Minozzi, S., Davoli, M., & Vecchi, S. (2011). Psychosocial combined with agonist maintenance treatments versus agonist maintenance treatments alone for treatment of opioid dependence. London, UK: Cochrane Library.
Andrews, C. M., D’Aunno, T. A., Pollack, H. A., & Friedmann, P. D. (2014). Adoption of evidence-based clinical innovations: The case of buprenorphine use by opioid treatment programs. Medical Care Research and Review, 71(1), 43–60.Find this resource:
Basu, A. (2011). Economics of individualization in comparative effectiveness research and a basis for a patient-centered health care. Journal of Health Economics, 30(3), 549–559.Find this resource:
Benishek, L. A., Dugosh, K. L., Kirby, K. C., Matejkowski, J., Clements, N. T., Seymour, B. L., & Festinger, D. S. (2014). Prize‐based contingency management for the treatment of substance abusers: A meta‐analysis. Addiction, 109(9), 1426–1436.Find this resource:
Berwick, D. M. (2003). Disseminating innovations in health care. Journal of the American Medical Association, 289(15), 1969–1975.Find this resource:
Brolin, M., Torres, M., Hodgkin, D., Horgan, C., Lee, M., Merrick, E., . . . & Gewirtz, A. (2017). Implementation of client incentives within a recovery navigation program. Journal of Substance Abuse Treatment, 72, 25–31.Find this resource:
Campbell, A. N., Turrigiano, E., Moore, M., Miele, G. M., Rieckmann, T., Hu, M. C., . . . & Nunes, E. V. (2015). Acceptability of a web-based community reinforcement approach for substance use disorders with treatment-seeking American Indians/Alaska natives. Community Mental Health Journal, 51(4), 393–403.Find this resource:
Carroll, K. M. (1998). A cognitive-behavioral approach: Treating cocaine addiction. NIH Publication 98–4308. Rockville, MD: National Institute on Drug Abuse.Find this resource:
Carroll, K. M. (2014). Lost in translation? Moving contingency management and cognitive behavioral therapy into clinical practice. Annals of the New York Academy of Sciences, 1327, 94–111.Find this resource:
Carroll, K. M., Ball, S. A., Jackson, R., Martino, S., Petry, N. M., Stitzer, M. L., . . . & Weiss, R. D. (2011). Ten take home lessons from the first ten years of the CTN and ten recommendations for the future. American Journal of Drug and Alcohol Abuse, 37(5), 275.Find this resource:
Carroll, K. M., & Weiss, R. D. (2017). The role of behavioral interventions in buprenorphine maintenance treatment: A review. American Journal of Psychiatry, 174(8), 738–747.Find this resource:
Ciesla, J., & Mazurek, K. (2013). Length of stay and outcomes for adolescents treated for substance use disorder: An analysis of dose response using propensity scores. Value in Health, 16(7), A694.Find this resource:
Connery, H. S. (2015). Medication-assisted treatment of opioid use disorder: Review of the evidence and future directions. Harvard Review of Psychiatry, 23(2), 63–75.Find this resource:
Damschroder, L. J., Aron, D. C., Keith, R. E., Kirsh, S. R., Alexander, J. A., & Lowery, J. C. (2009). Fostering implementation of health services research findings into practice: A consolidated framework for advancing implementation science. Implementation Science, 4(1), 50.Find this resource:
Damschroder, L. J., & Hagedorn, H. J. (2011). A guiding framework and approach for implementation research in substance use disorders treatment. Psychology of Addictive Behaviors, 25(2), 194.Find this resource:
Degenhardt, L., Glantz, M., Evans-Lacko, S., Sadikova, E., Sampson, N., Thornicroft, G., . . . & Bruffaerts, R. (2017). Estimating treatment coverage for people with substance use disorders: An analysis of data from the World Mental Health Surveys. World Psychiatry, 16(3), 299–307.Find this resource:
Ducharme, L. J., Chandler, R. K., & Harris, A. H. (2016). Implementing effective substance abuse treatments in general medical settings: Mapping the research terrain. Journal of Substance Abuse Treatment, 60, 110–118.Find this resource:
Dugosh, K., Abraham, A., Seymour, B., McLoyd, K., Chalk, M., & Festinger, D. (2016). A systematic review on the use of psychosocial interventions in conjunction with medications for the treatment of opioid addiction. Journal of Addiction Medicine, 10, 91–101.Find this resource:
Dutra, L., Stathopoulou, G., Basden, S. L., Leyro, T. M., Powers, M. B., & Otto, M. W. (2008). A meta-analytic review of psychosocial interventions for substance use disorders. American Journal of Psychiatry, 165(2), 179–187.Find this resource:
Finney, J. W. (2000). Limitations in using existing alcohol treatment trials to develop practice guidelines. Addiction, 35, 1491–1500.Find this resource:
Fisher, M. (2015). Long waiting lists for drug treatment add to addicts’ desperation. Washington Post.Find this resource:
Fletcher, B. W., Tims, F. M., & Brown, B. S. (1997). Drug abuse treatment outcome study (DATOS): Treatment evaluation research in the United States. Psychology of Addictive Behaviors, 11(4), 216.Find this resource:
Food and Drug Administration. (2018). FDA takes new steps to advance the development of innovative products for treating opioid use disorder.
Freeborn, B. A., & McManus, B. (2010). Substance abuse treatment and motor vehicle fatalities. Southern Economic Journal, 76(4), 1032–1048.Find this resource:
Freedland, K. E., Mohr, D. C., Davidson, K. W., & Schwartz, J. E. (2011). Usual and unusual care: Existing practice control groups in randomized controlled trials of behavioral interventions. Psychosomatic Medicine, 73(4), 323.Find this resource:
Fullerton, C. A., Kim, M., Thomas, C. P., Lyman, D. R., Montejano, L. B., Dougherty, R. H., . . . & Delphin-Rittmon, M. E. (2014). Medication-assisted treatment with methadone: Assessing the evidence. Psychiatric Services, 65(2), 146–157.Find this resource:
Garner, B. R. (2009). Research on the diffusion of evidence-based treatments within substance abuse treatment: A systematic review. Journal of Substance Abuse Treatment, 36(4), 376–399.Find this resource:
Gotbaum, R. (2016). The wait for opioid treatment can mean life or death in New Hampshire. Kaiser Health News.Find this resource:
Greenhalgh, T., Robert, G., Macfarlane, F., Bate, P., & Kyriakidou, O. (2004). Diffusion of innovations in service organizations: systematic review and recommendations. Milbank Quarterly, 82(4), 581–629.Find this resource:
Grogan, C. M., Andrews, C., Abraham, A., Humphreys, K., Pollack, H. A., Smith, B. T., & Friedmann, P. D. (2016). Survey highlights differences in Medicaid coverage for substance use treatment and opioid use disorder medications. Health Affairs, 35(12), 2289–2296.Find this resource:
Heinrich, C. J., & Cummings, G. R. (2014). Adoption and diffusion of evidence‐based addiction medications in substance abuse treatment. Health Services Research, 49(1), 127–152.Find this resource:
Hettema, J., Steele, J., & Miller, W. R. (2005). Motivational interviewing. Annual Review of Clinical Psychology, 1, 91–111Find this resource:
Hubbard, R. L., Marsden, M. E., Rachal, J. V., Harwood, H. J., Cavenaugh, E. R., & Ginzburg, H. M. (Eds.). (1989). Drug abuse treatment: A national study of effectiveness. Chapel Hill: University of North Carolina Press.Find this resource:
Huhn, A. S., & Dunn, K. E. (2017). Why aren’t physicians prescribing more buprenorphine? Journal of Substance Abuse Treatment, 78, 1–7.Find this resource:
Jerry, J. M., & Collins, G. B. (2013). Medication-assisted treatment of opiate dependence is gaining favor. Cleveland Clinical Journal of Medicine, 80, 345–349.Find this resource:
Jonas, D. E., Amick, H. R., Feltner, C., Bobashev, G., Thomas, K., Wines, R., . . . & Garbutt, J. C. (2014). Pharmacotherapy for adults with alcohol use disorders in outpatient settings: A systematic review and meta-analysis. Journal of the American Medical Association, 311(18), 1889–1900.Find this resource:
Kelly, J. F., Bergman, B. G., & O’Connor, C. L. (2017). Evidence-based treatment of addictive disorders. In J. MacKillop, G. A. Kenna, L. Leggio, & L. A. Ray (Eds.), Integrating psychological and pharmacological treatments for addictive disorders: An evidence-based guide (p. 54). London, UK: Routledge.Find this resource:
Knudsen, H. K. (2015). The supply of physicians waivered to prescribe buprenorphine for opioid use disorders in the United States: A state-level analysis. Journal of Studies on Alcohol and Drugs, 76(4), 644–654.Find this resource:
Knudsen, H. K., Ducharme, L. J., & Roman, P. M. (2007). The adoption of medications in substance abuse treatment: Associations with organizational characteristics and technology clusters. Drug and Alcohol Dependence, 87(2), 164–174.Find this resource:
Ledgerwood, D. M., Alessi, S. M., Hanson, T., Godley, M. D., & Petry, N. M. (2008). Contingency management for attendance to group substance abuse treatment administered by clinicians in community clinics. Journal of Applied Behavior Analysis, 41(4), 517–526.Find this resource:
Lee, J. D., Nunes, E. V., Novo, P., Bachrach, K., Bailey, G. L., Bhatt, S., . . . & King, J. (2017). Comparative effectiveness of extended-release naltrexone versus buprenorphine-naloxone for opioid relapse prevention (X: BOT): A multicentre, open-label, randomised controlled trial. The Lancet, 391(10118), 309–318.Find this resource:
Lehman, W. E. K., Greener, J. M., & Simpson, D. D. (2002) Assessing organizational readiness for change. Journal of Substance Abuse Treatment, 22, 197–209.Find this resource:
Lenardson, M. H. S., Jennifer, D., Gale, M. S., & John, A. (2008). Distribution of substance abuse treatment facilities across the rural-urban continuum (Research & Policy Brief No. 35B). Portland, ME: University of Southern Maine, Muskie School of Public Service, Maine Rural Health Research Center.Find this resource:
Leshner, A. (2000). NIDA’s strategic plan for 2000–2005. NIDA Notes, 15(2), August 2000.
Lu, M., & McGuire, T. G. (2002). The productivity of outpatient treatment for substance abuse. Journal of Human Resources, 37(2), 309–335.Find this resource:
Magill, M., & Ray, L. A. (2009). Cognitive-behavioral treatment with adult alcohol and illicit drug users: A meta-analysis of randomized controlled trials. Journal of Studies on Alcohol and Drugs, 70, 516–527.Find this resource:
Martin, G. W., & Rehm, J. (2012). The effectiveness of psychosocial modalities in the treatment of alcohol problems in adults: A review of the evidence. Canadian Journal of Psychiatry, 57(6), 350–358.Find this resource:
Mattick, R. P., Breen, C., Kimber, J., & Davoli, M. (2009). Methadone maintenance therapy versus no opioid replacement therapy for opioid dependence. London, UK: Cochrane Library.Find this resource:
Mattick, R. P., Breen, C., Kimber, J., & Davoli, M. (2014). Buprenorphine maintenance versus placebo or methadone maintenance for opioid dependence. Cochrane Database of Systematic Reviews, 2, CD002207.Find this resource:
Mayet, S., Farrell, M., Ferri, M., Amato, L., & Davoli, M. (2004). Psychosocial treatment for opiate abuse and dependence. London, UK: Cochrane Library.Find this resource:
McCarty, D., Greenlick, M. R., & Lamb, S. (Eds.). (1998). Bridging the gap between practice and research: Forging partnerships with community-based drug and alcohol treatment. Washington, DC: National Academies Press.Find this resource:
McCarty, D., Gustafson, D. H., Wisdom, J. P., Ford, J., Choi, D., Molfenter, T., . . . & Cotter, F. (2007). The network for the improvement of addiction treatment (NIATx): Enhancing access and retention. Drug and Alcohol Dependence, 88(2), 138–145.Find this resource:
McCarty, D., McConnell, K. J., & Schmidt, L. A. (2010). Priorities for policy research on treatments for alcohol and drug use disorders. Journal of Substance Abuse Treatment, 39(2), 87–95.Find this resource:
McKnight, C. (2015). Assessing the impact of restrictions to Medicaid coverage of methadone and buprenorphine on opioid users’ access to and utilization of substance use treatment (Doctoral dissertation). City University of New York, NY.Find this resource:
Meyers, R. J., Roozen, H. G., & Smith, J. E. (2011). The community reinforcement approach: An update of the evidence. Alcohol Research & Health, 33(4), 380.Find this resource:
Meyers, R. J., & Smith, J. E. (1995). Clinical guide to alcohol treatment: The community reinforcement approach. New York, NY: Guilford.Find this resource:
Miller, W. R., & Rollnick, S. (2002). Motivational interviewing: Preparing people for change (2nd ed.). New York, NY: Guilford.Find this resource:
Miller, W. R., & Wilbourne, P. (2002). Mesa Grande: A methodological analysis of clinical trials of treatments for alcohol use disorders. Addiction, 97, 265–277.Find this resource:
Miller, W. R., Zweben, J., & Johnson, W. R. (2005). Evidence-based treatment: Why, what, where, when, and how? Journal of Substance Abuse Treatment, 29(4), 267–276.Find this resource:
Minozzi, S., Amato, L., Vecchi, S., Davoli, M., Kirchmayer, U., & Verster, A. (2011). Oral naltrexone maintenance treatment for opioid dependence. London, UK: Cochrane Library.Find this resource:
Mojtabai, R., & Graff Zivin, J. (2003). Effectiveness and cost‐effectiveness of four treatment modalities for substance disorders: A propensity score analysis. Health Services Research, 38(1p1), 233–259.Find this resource:
Molfenter, T., McCarty, D., Capoccia, V., & Gustafson, D. (2013). Development of a multilevel framework to increase use of targeted evidence-based practices in addiction treatment clinics. Public Health Frontier, 2(1), 11.Find this resource:
Murphy, S. M., & Polsky, D. (2016). Economic evaluations of opioid use disorder interventions. PharmacoEconomics, 34(9), 863–887.Find this resource:
Murray, C. J., Vos, T., Lozano, R., Naghavi, M., Flaxman, A. D., Michaud, C., . . . & Aboyans, V. (2012). Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010. The Lancet, 380(9859), 2197–2223.Find this resource:
National Advisory Council on Drug Abuse Workgroup. (2012). Adoption of NIDA’s evidence-based treatments in real world settings (A Report).Find this resource:
National Drug Intelligence Center. (2011). The economic impact of illicit drug use on American society. Washington, DC: United States Department of Justice.Find this resource:
National Institute on Alcohol Abuse and Alcoholism (NIAAA). (2017). Strategic plan 2017–2021. Washington, DC: NIAAA.Find this resource:
National Institute on Drug Abuse (NIDA). (2016). 2016–2020 NIDA strategic plan: Advancing addiction science. Rockville, MD: National Institute on Drug Abuse.Find this resource:
National Institute on Drug Abuse (NIDA). (2017). FY 2017 budget.
Nordstrom, B. R., Saunders, E. C., McLeman, B., Meier, A., Xie, H., Lambert-Harris, C., . . . & Lord, C. F. (2016). Using a learning collaborative strategy with office-based practices to increase access and improve quality of care for patients with opioid use disorders. Journal of Addiction Medicine, 10(2), 117–123.Find this resource:
O’Donnell, A., Anderson, P., Newbury-Birch, D., Schulte, B., Schmidt, C., Reimer, J., & Kaner, E. (2013). The impact of brief alcohol interventions in primary healthcare: A systematic review of reviews. Alcohol and Alcoholism, 49(1), 66–78.Find this resource:
Office of National Drug Control Policy (2004, November). The economic costs of drug abuse in the United States, 1992–2002. Washington, DC: Executive Office of the President.Find this resource:
Park-Lee, E., Lipari, R. N., Hedden, S. L., Copello, E. A. P, & Kroutil, L. A. (2017). Receipt of services for substance use and mental health issues among adults: Results from the 2016 national survey on drug use and health. NSDUH Data Review.
Pating, D. R., Miller, M. M., Goplerud, E., Martin, J., & Ziedonis, D. M. (2012). New systems of care for substance use disorders. Psychiatric Clinics, 35(2), 327–356.Find this resource:
Peters, D. H., Adam, T., Alonge, O., Agyepong, I. A., & Tran, N. (2013). Implementation research: What it is and how to do it. British Medical Journal, 347, f6753.Find this resource:
Powers, M. B., Vedel, E., & Emmelkamp, P. M. G. (2008). Behavioral couples therapy (BCT) for alcohol and drug use disorders: A meta-analysis. Clinical Psychology Review, 28, 952–962.Find this resource:
Quanbeck, A., Chih, M. Y., Isham, A., Johnson, R., & Gustafson, D. (2014). Mobile delivery of treatment for alcohol use disorders: A review of the literature. Alcohol Research: Current Reviews, 36(1), 111.Find this resource:
Rawson, R., Shoptaw, S. J., Obert, J. L., McCann, M. J., Hasson, A. L., Marinelli-Casey, P. J., Brethen, P. R., & Ling, W. (1995). An intensive outpatient approach for cocaine abuse: The Matrix model. Journal of Substance Abuse Treatment, 12(2), 117–127.Find this resource:
Reif, S., Creedon, T. B., Horgan, C. M., Stewart, M. T., & Garnick, D. W. (2017). Commercial health plan coverage of selected treatments for opioid use disorders from 2003 to 2014. Journal of Psychoactive Drugs, 49(2), 102–110.Find this resource:
Reif, S., Horgan, C. M., Hodgkin, D., Matteucci, A. M., Creedon, T. B., & Stewart, M. T. (2016). Access to addiction pharmacotherapy in private health plans. Journal of Substance Abuse Treatment, 66, 23–29.Find this resource:
Rhodes, W., Pelissier, B., Gaes, G., Saylor, W., Camp, S., & Wallace, S. (2001). Alternative solutions to the problem of selection bias in an analysis of federal residential drug treatment programs. Evaluation Review, 25(3), 331–369.Find this resource:
Ries, R. K., Galanter, M., & Tonigan, J. S. (2008). Twelve-step facilitation. In M. Galanter & H. D. Kleber (Eds.), The American psychiatric publishing textbook of substance abuse treatment (4th ed., pp. 373–386). Arlington, VA: American Psychiatric Publishing.Find this resource:
Rogers, E. M. (1995). Diffusion of innovations. New York: Free Press.Find this resource:
Roozen, H. G., Boulogne, J. J., & van Tulder, M. W. (2004). A systematic review of the effectiveness of the community reinforcement approach in alcohol, cocaine and opioid addiction. Drug and Alcohol Dependence, 74, 1–13.Find this resource:
Rösner, S., Hackl-Herrwerth, A., Leucht, S., Vecchi, S., Srisurapanont, M., & Soyka, M. (2010a). Opioid antagonists for alcohol dependence. London, UK: Cochrane Library.Find this resource:
Rösner, S., Hackl-Herrwerth, A., Leucht, S., Lehert, P., Vecchi, S., & Soyka, M. (2010b). Acamprosate for alcohol dependence. London, UK: Cochrane Library.Find this resource:
Rudd, R. A., Aleshire, N., Zibbell, J. E., & Matthew Gladden, R. (2016). Increases in drug and opioid overdose deaths—United States, 2000–2014. American Journal of Transplantation, 16(4), 1323–1327.Find this resource:
Sacks, J. J., Gonzales, K. R., Bouchery, E. E., Tomedi, L. E., & Brewer, R. D. (2015). 2010 national and state costs of excessive alcohol consumption. American Journal of Preventive Medicine, 49(5), e73–e79.Find this resource:
Schuman-Olivier, Z., Connery, H., Griffin, M. L., Wyatt, S. A., Wartenberg, A. A., Borodovsky, J., . . . & Weiss, R. D. (2013). Clinician beliefs and attitudes about buprenorphine/naloxone diversion. American Journal on Addictions, 22(6), 574–580.Find this resource:
Sharma, A., Kelly, S. M., Mitchell, S. G., Gryczynski, J., O’Grady, K. E., & Schwartz, R. P. (2017). Update on barriers to pharmacotherapy for opioid use disorders. Current Psychiatry Reports, 19(6), 35.Find this resource:
Sheppard, J. P., Fletcher, B., Gill, P., Martin, U., Roberts, N., & McManus, R. J. (2016). Predictors of the home-clinic blood pressure difference: A systematic review and meta-analysis. American Journal of Hypertension, 29(5), 614–625.Find this resource:
Sigmon, S. C. (2014). Access to treatment for opioid dependence in rural America: Challenges and future directions. Journal of the American Medical Association: Psychiatry, 71(4), 359–360.Find this resource:
Simpson, D. D., Joe, G. W., & Bracy, S. A. (1982). Six-year follow-up of opioid addicts after admission to treatment. Archives of General Psychiatry, 39(11), 1318–1323.Find this resource:
Sloboda, Z. (2012). The state of support for research on the epidemiology, prevention, and treatment of drug use and drug use disorders in the USA. Substance Use & Misuse, 47(13–14), 1557–1568.Find this resource:
Stein, B. D., Gordon, A. J., Dick, A. W., Burns, R. M., Pacula, R. L., Farmer, C. M., et al. (2015). Supply of buprenorphine waivered physicians: the influence of state policies. Journal of Substance Abuse Treatment, 48(1), 104–11.Find this resource:
Stitzer, M. L., Iguchi, M. Y., Kidorf, M., & Bigelow, G. E. (1993). Contingency management in methadone treatment: The case for positive incentives. In L. S. Onken, J. D. Blaine, & J. J. Boren (Eds.), Behavioral treatments for drug abuse and dependence (pp. 19–36). Rockville, MD: National Institute on Drug Abuse.Find this resource:
Substance Abuse and Mental Health Services Administration. Center for Substance Abuse Treatment. (2004). Clinical guidelines for the use of buprenorphine in the treatment of opioid addiction. Treatment Improvement Protocol (TIP) 40,” DHHS Publication 04–3939. Rockville, MD: SAMHSA.Find this resource:
Substance Abuse and Mental Health Services Administration (2017). National survey of substance abuse treatment services (N-SSATS): 2016. Data on Substance Abuse Treatment Facilities. BHSIS Series S-93, HHS Publication No. (SMA) 17–5039. Rockville, MD: Substance Abuse and Mental Health Services Administration.Find this resource:
Suh, J. J., Pettinati, H. M., Kampman, K. M., & O’Brien, C. P. (2006). The status of disulfiram: A half of a century later. Journal of Clinical Psychopharmacology, 26(3), 290–302.Find this resource:
Susukida, R., Crum, R. M., Stuart, E. A., Ebnesajjad, C., & Mojtabai, R. (2016). Assessing sample representativeness in randomized controlled trials: Application to the National Institute of Drug Abuse Clinical Trials Network. Addiction, 111(7), 1226–1234.Find this resource:
Swartz, J. A. (2017). Randomized controlled trials in substance abuse treatment research: Fundamental aspects and new developments in random assignment strategies, comparison/control conditions, and design characteristics. In J. B. VanGeest, T. P. Johnson, & S. A. Alemagno (Eds.), Research methods in the study of substance abuse (pp. 43–63). Cham, Switzerland: Springer.Find this resource:
Tai, B., Straus, M. M., Liu, D., Sparenborg, S., Jackson, R., & McCarty, D. (2010). The first decade of the National Drug Abuse Treatment Clinical Trials Network: Bridging the gap between research and practice to improve drug abuse treatment. Journal of Substance Abuse Treatment, 38, S4–S13.Find this resource:
Tanner-Smith, E. E., Wilson, S. J., & Lipsey, M. W. (2013). The comparative effectiveness of outpatient treatment for adolescent substance abuse: A meta-analysis. Journal of Substance Abuse Treatment, 44(2), 145–158.Find this resource:
Thomas, C. P., Doyle, E., Kreiner, P. W., Jones, C. M., Dubenitz, J., Horan, A., & Stein, B. D. (2017). Prescribing patterns of buprenorphine waivered physicians. Drug & Alcohol Dependence, 181, 213–218.Find this resource:
Thomas, C. P., Fullerton, C. A., Kim, M., Montejano, L., Lyman, D. R., Dougherty, R. H., . . . & Delphin-Rittmon, M. E. (2014). Medication-assisted treatment with buprenorphine: Assessing the evidence. Psychiatric Services, 65(2), 158–170.Find this resource:
Tiffany, S. T., Friedman, L., Greenfield, S. F., Hasin, D. S., & Jackson, R. (2012). Beyond drug use: A systematic consideration of other outcomes in evaluations of treatments for substance use disorders. Addiction, 107(4), 709–718.Find this resource:
U.S. Department of Health and Human Services (HHS), Office of the Surgeon General (2016, November). Facing addiction in America: The Surgeon General’s report on alcohol, drugs, and health. Washington, DC: Health and Human Services.Find this resource:
VanGeest, J. B., Johnson, T. P., & Alemagno, S. A. (Eds.). (2017). History of substance abuse research in the United States. In Research methods in the study of substance abuse (pp. 3–25). Cham, Switzerland: Springer.Find this resource:
Vasilaki, E. I., Hosier, S. G., & Cox, W. M. (2006). The efficacy of motivational interviewing as a brief intervention for excessive drinking: A meta-analytic review. Alcohol, 41, 328–335.Find this resource:
Wampold, B. E., & Imel, Z. E. (2015). The great psychotherapy debate: The evidence for what makes psychotherapy work (2nd ed.). New York: Routledge.Find this resource:
Wang, P. S., Ulbricht, C. M., & Schoenbaum, M. (2009). Improving mental health treatments through comparative effectiveness research. Health Affairs, 28(3), 783–791.Find this resource:
Weiss, R. D., Griffin, M. L., Potter, J. S., Dodd, D. R., Dreifuss, J. A., Connery, H. S., & Carroll, K. M. (2014). Who benefits from additional drug counseling among prescription opioid dependent patients receiving buprenorphine-naloxone and standard medical management? Drug and Alcohol Dependence, 140, 118–122.Find this resource:
Weiss, R. D., O’Malley, S. S., Hosking, J. D., LoCastro, J. S., Swift, R., & the COMBINE Study Research Group. (2008). Do patients with alcohol dependence respond to placebo? Results from the COMBINE study. Journal of Studies on Alcohol and Drugs, 69(6), 878–884.Find this resource:
Whiteford, H. A., Degenhardt, L., Rehm, J., Baxter, A. J., Ferrari, A. J., Erskine, H. E., . . . & Burstein, R. (2013). Global burden of disease attributable to mental and substance use disorders: Findings from the Global Burden of Disease Study 2010. The Lancet, 382(9904), 1575–1586.Find this resource: