Behavior Analysts Serving Veterans
By Rebecca D. Woolbert, Kiki Yablon, & Robin M. Kuhn, University of Kansas
Rebecca D. Woolbert
Rebecca is currently enrolled in the University of Kansas’ MA in Applied Behavioral Science program. Most recently, she is an RBT in an EIBI clinic, but has worked with individuals of all ages in an RBT capacity for the past five years.
Kiki Yablon is a second-year master’s student in applied behavior analysis at the University of Kansas and a professional dog trainer. She is the former editor of the Chicago Reader and a former editor of Chicago and Outside magazines.
Robin M. Kuhn, PhD, BCBA, LBA
Robin Kuhn is an Assistant Professor of Practice and Practicum Supervisor for graduate students enrolled in the Online Applied Behavior Analysis Program within the Department of Applied Behavioral Science at the University of Kansas.
Americans show support for military veterans in many ways: We hang flags outside our homes, tie yellow ribbons around trees, celebrate Memorial Day and Veterans Day as federal holidays, wear red on Fridays, and donate more than $2.5 billion dollars annually to charities that support the military and its 19.6 million veterans (Charity Navigator, 2019; U.S. Department of Veteran Affairs [VA], 2019b). However, a different sort of support may be needed. Approximately 11% of the six million veterans using the 1,255 VA medical facilities meet diagnostic criteria for substance abuse disorder (SUD; Teeters, Lancaster, Brown, & Back, 2017; VA, 2017). What’s more, this is likely a gross underrepresentation of actual VA-related SUD, as not all veterans use VA healthcare (i.e., six million is less than half of the total veteran population) and not all veterans self-report behavioral health issues (VA, 2017; Held & Owens, 2013). Fortunately, behavior analysts have made a big contribution to veteran care over the past decade, introducing contingency management (CM) for substance abuse in VA clinics across the nation (Rash & DePhilippis, 2019). As the first large-scale CM implementation, it may provide a model not only for helping even more veterans but also for helping other populations with behavior analysis.
Contingency Management: Behavior Analysis’ Successful Application
In their seminal book describing the broad application of CM to target substance abuse, Higgins, Silverman, and Heil (2008) defined CM as the systematic application of consequences to promote changes in some aspect of behavior. Key components of CM include an objective monitoring system (e.g., urine or breath testing) and a delivery system for the reinforcement of desired behaviors. Delivery systems typically use either vouchers (e.g., to cash in for larger rewards) or prizes (e.g., “draws” from a bowl good for prizes ranging from $1-$100; Petry, Alessi, Marx, Austin, & Tardif, 2005). Also important to consider are the frequency of assessment and immediacy of reinforcement: Assessment needs to occur frequently enough to detect drug use, and reinforcement should be delivered as soon as possible following the target behavior (Rash & DePhilippis, 2019). CM has been successful across a variety of target behaviors, including drug abstinence, attendance to therapy sessions, job performance, and compliance with medication; substances, including alcohol, stimulants, and opioids; and populations, including adolescents, the homeless, and military veterans (Higgins et al., 2008).
Veterans are a population in which CM interventions have been demonstrated to be quite effective (Rash & DePhilippis, 2019). In 2011, the VA began a national rollout of the evidence-based yet underapplied treatment (Petry, DePhilippis, Rash, Drapkin, & McKay, 2014). The VA incorporated CM into its intensive outpatient substance abuse clinics and funded all aspects, including incentives and drug testing materials (Petry et al., 2014). The rollout specifically targeted stimulant use, encouraging clinicians to include patients abusing stimulants in CM procedures whenever feasible because pharmacotherapy options for stimulant abuse were limited (Petry et al., 2014). Some clinics targeted more than one substance per patient, although the literature indicates targeting polysubstance use results in smaller effect sizes (Rash & DePhilippis, 2019). In clinics where stimulant abstinence was the only target behavior, nontargeted substances were addressed clinically (e.g., talking with patients about how additional substances could hinder the progress made with CM, but not withholding reinforcement for stimulant abstinence; DePhilippis, Petry, Bonn-Miller, Rosenbach, & McKay, 2018). By the end of 2018, drug abstinence was targeted at 107 SUD programs (19 additional programs targeted attendance of therapy sessions due to a low rate of stimulant abuse among their patients), with 70% of the programs using prize CM and participants submitting 95% clean samples (Rash & DePhilippis, 2019).
Successful Application, Yet Limited Dissemination
Despite the success of the VA initiative, significant barriers to broader implementation, both within the VA and in the larger health care community, remain. After surveying 617 substance abuse treatment providers across 30 states using the author-created Contingency Management Beliefs Questionnaire, Rash, Petry, Kirby, Martino, Roll, and Stitzer (2012) found that although the majority of respondents viewed CM favorably, they were hesitant to implement it and concerned in particular about cost and long-term effects (i.e., what happens when contingencies are withdrawn). While higher costs are associated with CM when compared to standard care, CM results in a longer duration of abstinence (Olmstead, Sindelar, & Petry, 2007). It is possible the initial cost of CM interventions could be less than the potential future costs of SUDs, although further research is needed (Olmstead et al., 2007; Petry, Alessi, Olmstead, Rash, & Zajak, 2017).
Ruan, Bullock, and Reger (2017) reported financial concerns too, as well as other implementation challenges, most of which involved providers’ beliefs and attitudes. A lack of understanding of CM was listed as one of the biggest barriers to adoption among staff members at the VA Puget Sound Health Care System in Seattle during the VA’s initial roll out of CM, and this lack of understanding led to limited referrals to the CM program (Ruan et al., 2017). Ruan et al. (2017) implemented a quality improvement project designed to target the low referral rate by educating both staff and patients and tracking and monitoring the percentage of eligible patients enrolled in CM. Prior to the project, only 60% of patients who were eligible for CM were referred; that figure increased to 100% following the quality improvement project (Ruan et al., 2017). The authors also suggested that CM might have been more rapidly adopted if educational materials were distributed as a promotional campaign of sorts early in the implementation of CM, however, they did not indicate if this approach had worked for others.
In evaluating the adoption and effectiveness of CM during the VA initiative, DePhilippis et al. (2018) examined (a) treatment fidelity; (b) participation in coaching calls (i.e., clinicians receiving feedback from the authors) that were implemented 55 months after the first VA clinics adopted CM; and (c) clinical outcomes (i.e., attendance and drug abuse). While additional research is needed to determine the effect coaching might have on treatment outcomes, the authors suggested, based on anecdotal observation, that treatment fidelity may have been enhanced by participation in coaching calls. Perhaps implementing the quality improvement initiatives suggested by Ruan et al. (2017) and the coaching discussed by DePhilippis et al. (2018) as antecedent interventions as opposed to consequences of low acceptance would be helpful when disseminating CM interventions to the community on a broader scale.
Two important areas for future research on CM with veterans are (a) targeting the 4.7 million rural veterans, 24% of the total veteran population, and (b) targeting alcohol use disorder (AUD; VA, 2019a). Providing CM to rural veterans may require bringing CM to additional rural areas via remote monitoring and reinforcement delivery, and research has recently been funded to investigate remote monitoring, specifically through the use of mobile devices (U.S. Department of Health and Human Services, 2019). The VA currently utilizes a mobile application called Stay Quit Coach to support veterans receiving treatment for smoking cessation (VA.gov, n.d.); it does not currently include a monitoring component, but perhaps one could be integrated. Dallery, Defulio, and Meredith (2015) had three suggestions for monitoring patients with limited access to a clinic: (a) Secure Continuous Remote Alcohol Monitoring (SCRAM) bracelets that detect ingested alcohol expelled through the skin, (b) measurement of specific compounds that are detected in urine for two days after alcohol ingestion, and (c) video-based monitoring (e.g., blowing into a breathalyzer via HIPAA-compliant video monitoring).
In addition to remote monitoring of the target behavior, delivery of reinforcement to rural veterans may present challenges, specifically when it comes to the immediacy of reinforcement (e.g., mailing a voucher or check could weaken the contingency). Perhaps utilizing applications such as Venmo, PayPal, or Zelle could allow patients to see immediate deposits in their accounts. PayPal has been used for deposit contracts (i.e., programs in which participants pay a deposit to begin CM treatment and are then given back the deposit contingent on abstinence; Dallery, Raiff, Kim, Marsch, Stitzer, & Grabinski, 2016), but does not appear to have been used for delivering immediate reinforcement for drug abstinence. Some providers might be hesitant to provide this specific population with money that could potentially be used to purchase drugs, but research has not found that cash payments result in an increase in substance abuse (i.e., urine samples remained clean, suggesting the money was not spent on drugs; Dallery & Raiff, 2012).
Once the VA has the capacity to deliver CM to more veterans, a next logical step might be to target alcohol abstinence (Petry, Martin, Cooney, & Kranzler, 2000). Alcohol abuse is one of the most prevalent types of substance abuse among veterans (Hoggatt, Lehavot, Krenek, Schweizer, & Simpson, 2017); in a survey of 3,188 randomly sampled veterans, 14.8% reported abusing alcohol in the past year, and 40% reported having a lifetime history of alcohol abuse (i.e., had met the criteria for AUD at some point in their lives; Fuehrlein et al., 2016).
Veterans have sacrificed for their country; it is gratifying to see that the field of behavior analysis has given back to veterans by improving treatment of their substance abuse. Overall, the big picture implications of the VA’s CM initiative are profound: It successfully answered the question of what we can do to provide an effective treatment to veterans suffering from SUD (Rash & DePhilippis, 2019); served as a model for large-scale implementation of CM across a nationwide institution; and may serve as a model for how to disseminate our knowledge to others (military and nonmilitary) battling and suffering from the drug epidemic our country is experiencing (Rash & DePhilippis, 2019; Centers for Disease Control and Prevention, 2018). What’s more, it may provide insight into how to disseminate other well-researched yet underapplied behavioral technologies and recruit the funding essential for their implementation.
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