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. Author manuscript; available in PMC: 2019 Aug 1.
Published in final edited form as: Behav Pharmacol. 2018 Aug;29(5):462–468. doi: 10.1097/FBP.0000000000000379

Pilot Investigation: Randomized Controlled Analog Trial for Alcohol and Tobacco Smoking Co-Addiction Using Contingency Management

Michael F Orr 1,2,3,4, Crystal Lederhos Smith 1,2,3,4, Myles Finlay 1,2,3,4, Samantha C Martin 1,2,3,4, Olivia Brooks 2,3,4, Oladunni A Oluwoye 1,2,3, Emily Leickly 1,2,3, Michael McDonell 1,2,3, Ekaterina Burduli 1,2,3, Celestina Barbosa-Leiker 2,3,4, Matt Layton 1,2,3, John M Roll 1,2,3, Sterling M McPherson 1,2,3
PMCID: PMC6035091  NIHMSID: NIHMS933026  PMID: 29561290

Abstract

Contingency management (CM) is associated with decreases in off-target drug and alcohol use during primary target treatment. The primary hypothesis for this trial was that targeting alcohol use or tobacco smoking would yield increased abstinence in the opposite, non-targeted drug. We used a 2 (CM versus non-contingent control [NC] for alcohol) x 2 (CM versus NC for smoking tobacco) factorial design, with alcohol intake (via urinary ethyl-glucuronide) and tobacco smoking (via urinary cotinine) as the primary outcomes. Thirty-four heavy drinking smokers were randomized into 1 of 4 groups wherein they received CM, or equivalent NC reinforcement, for: alcohol abstinence, smoking abstinence, both drugs, or neither drug. The CM for alcohol and tobacco group had only 2 participants and therefore was not included in analysis. Compared to the NC for alcohol and tobacco smoking group, both the CM for tobacco smoking group (OR=12.03; 95% CI: 1.50–96.31) and the CM for alcohol group (OR=37.55; 95% CI: 4.86–290.17) submitted significantly more tobacco abstinent urinalyses. Similarly, compared to the NC for alcohol and tobacco group, both the CM for smoking (OR= 2.57; 95% CI: 1.00–6.60) and the CM for alcohol groups (OR= 3.96; 95% CI: 1.47–10.62) submitted significantly more alcohol abstinent urinalyses. These data demonstrate cross-over effects of CM on indirect treatment targets. While this is a pilot investigation, it could help to inform the design of novel treatments for alcohol and tobacco co-addiction.

Keywords: alcohol use disorder treatment, smoking cessation, contingency management, ethyl glucuronide, treatment biomarkers, randomized clinical analog trial, human

Introduction

Together, tobacco and alcohol kill more than a half a million people in the United States every year (Li et al., 2007; Kalman et al., 2010; Sacks et al., 2013; Apollonio et al., 2016; Xu et al., 2017), and addiction to these substances represents the leading causes of preventable death (Johnson, 2004; Li et al., 2007). Some tobacco smoking prevalence estimates are reported to be as high as 80% among clinical populations with an alcohol use disorder (Kalman et al., 2005; Hendrickson et al., 2013). There is also a synergistic risk when both of these substances are used concurrently, which multiplies many health risks already at work when each substance is used independently (Hurt et al., 1996; Castellsagué et al., 1999; Pelucchi et al., 2006).

Both drugs exert their reinforcing effects through mesolimbic pathways and complex interactions with dopamine and other neurotransmitters (Soderpalm et al., 2000; Hendrickson et al., 2013; Tolu et al., 2017), including nicotinic acetylcholine receptors (Gianoulakis, 2004; Chatterjee and Bartlett, 2010; Nocente et al., 2013). Unfortunately, the current understanding of these biological factors has not yet improved the effective treatment of tobacco smoking and alcohol use disorders. Understanding the comparative reinforcement provided by both substances is necessary to test hypotheses regarding the integration of alcohol use disorder treatment with smoking cessation treatment and other types of co-addiction, e.g., sequencing of treatments, timing, lead-in periods.

Contingency management (CM) is one of the most effective behavioral interventions for initiating abstinence from illicit and non-illicit drugs (Stitzer et al., 1979; Higgins et al., 1986, 1994; Budney et al., 2000; Robles et al., 2002; Roll, 2007; Dutra et al., 2008). Using operant conditioning, an alternative, non-drug reinforcing reward (prizes or monetary incentives) is given in exchange for negative urine samples, thus reinforcing abstinence (Bigelow and Silverman, 1999). Our group has found that CM has decreases in off-target drug and alcohol use (McDonell et al., 2013) and off-target increases in treatment attendance (McPherson et al., 2016; McDonell et al., 2017).

At the same time, CM has not been widely applied as a behavioral treatment for alcohol use disorders given the methodological difficulty of detecting abstinence using standard breath alcohol test procedures (M. McDonell et al., 2011; Lowe et al., 2015). This randomized controlled analog trial (RCaT) addresses this critical methodological barrier by using a superior alcohol measure, ethyl glucuronide (EtG) urine tests, which can detect alcohol use for up to 5 days after drinking has occurred (Wurst and Metzger, 2002; Helander et al., 2009; Litten et al., 2010; M. G. McDonell et al., 2011; Leickly et al., 2015). We also used cotinine as our primary smoking measure.

Given previous evidence of the synergistic biochemical relationship between alcohol and tobacco use, we hypothesized that targeting one substance would increase abstinence in the other non-targeted substance. We conducted this RCaT in a population of heavy drinking tobacco smokers, targeting both alcohol and tobacco smoking with CM concurrently, to directly compare the impact of equivalent reinforcement levels on abstinence from these neurobiologically and behaviorally linked substances. Analog trials are a commonly used method of examining an experimental behavioral treatment with a small sample of non-treatment seeking patients (Packer et al., 2012; McPherson et al., 2014). These trials can be helpful in the design of future, potentially promising treatment that may be ready for a larger clinical trial. The primary aim of this investigation was to understand how equivalent reinforcement levels would impact both on- and off-target substances (i.e., alcohol and tobacco use) in a population of heavy drinking smokers who were non-treatment seeking.

Methods

Participants

Our target demographic for recruitment were participants with a Diagnostic and Statistical Manual, Fourth Edition Text Revision (DSM-IV-TR) diagnosis of alcohol dependence, abuse or reported heavy drinking (6 or more drinks, 4 or more days in a week for males; and 5 or more drinks, 4 or more days in a week for females) with co-occurring DSM-IV-TR diagnosis of nicotine dependence, abuse or report heavy use (10+ cigarettes per day and smokerlyzer carbon monoxide value of >9 ppm) who were not seeking treatment. Recruiting those who are not seeking treatment is a common method for examining experimental behavioral therapeutics, such as the one under investigation in this study, to 1) maintain the highest ethical standards of not providing an untested treatment to individuals who are seeking evidence-based care, 2) provide the most rigorous examination of efficacy, and 3) provide homogeneity among participants in their motivation to change their use of the substances under investigation. Participants were recruited through advertisements, Craigslist, and classified ads around a city in the northwestern United States. Interested individuals contacted study staff, who explained the study and screened for recent alcohol and tobacco use. Eligible participants (see section Participant Eligibility) were scheduled for an in-person visit and they provide written informed consent. This study was approved for human subjects by the Washington State University Institutional Review Board. Informed consent was obtained from all participants prior to participation in study activities.

Design

We chose a 2×2 factorial design for this RCaT, with alcohol and tobacco smoking as our co-primary, biochemically-verified outcomes. Participants were screened via phone for initial criteria, and if found to be eligible, were invited to come to the laboratory for a baseline visit. After completing the baseline visit, eligible participants were randomized into 1 of 4 groups (see Supplemental Figure 1 for reinforcement schedule, potential earnings and the range of reinforcement provided): 1) NC (Control) for Alcohol and Tobacco, 2) CM for Alcohol and NC (Control) for Tobacco, 3) NC (Control) for Alcohol and CM for Tobacco, and 4) CM for Alcohol and Tobacco. No participants in this group attended more than one visit and this group therefore was not included in analysis. Importantly, the planned and actual reinforcement provided across all four of the conditions was equivalent, as shown in Supplemental Figure 1.

This is a notable observation in itself because several participants, upon finding out that they were going to be incentivized to be abstinent from both smoking and alcohol consumption, decided that they did not want to participate. While these are all non-treatment seeking participants, this observation speaks to a likely need for high intensity treatment when targeting both alcohol and smoking.

Experimental Behavioral Intervention

All groups were scheduled for 3 visits per week for a 4-week period where they provided urine and breath samples, and completed questionnaires to assess several aspects related to recent alcohol use, tobacco smoking, withdrawal, and cravings. Participants in the CM groups received escalating monetary reinforcement, based on the duration of abstinence, biochemically verified by urine samples from the targeted substance or substances. Positive samples earned the participant no rewards, and upon the next negative sample rewards were reverted back to the start amount. Participants in the NC for alcohol and tobacco group received monetary reinforcement regardless of the status of their urine sample. Participants received monetary reinforcement one visit after the visit wherein they provided a urine sample. This delay was due to the time required for samples to be sent to a local lab and processed for EtG. This methodology has been used previously, and demonstrated to be effective (McDonell et al., 2012).

Measures

Alcohol and tobacco abstinence was biochemically verified through provisioned urine samples from participants. Recent, 24-hour alcohol use and tobacco smoking was assessed by a breathalyzer and a Smokerlyzer. The past 72 hours of use was assessed through self-reported at each visit.

Baseline Measures

At baseline, participants completed the Addiction Severity Index (ASI) (McLellan et al., 1992; Appleby et al., 1997; Thomas McLellan et al., 2006) to assess self-reported alcohol and drug use and the impact of alcohol and drug use on psychiatric, legal, medical, and family functioning. Tobacco and alcohol use disorders were assessed using the Mini Interventional Neuropsychiatric Interview (MINI) (Sheehan et al., 1998), the Fagerström Test for Nicotine Dependence (FTND) (Korte et al., 2013), and the Alcohol Use Disorders Identification Test (AUDIT) (Piccinelli et al., 1997). General physical and mental health was captured by Brief Symptom Inventory (BSI) (Preston and Harrison, 2003), HIV Risk-Taking Behavior Scale (Darke et al., 1991), and RAND 36-Item Health Survey 1.0 Questionnaire (Vander Zee et al., 1996).

Alcohol Use and Tobacco Smoking Biomarkers

At each study visit, urine samples were collected and analyzed for EtG and cotinine using Diagnostic Reagents Incorporated (DRI) EtG and DRI cotinine semi-quantitative enzyme immunoassay tests performed by a local independent company, Absolute Drug Testing. Alcohol use was determined by detection of EtG, using a cutoff level of 200 ng/mL, as this level has been shown to accurately detect 80% of clinically significant drinking for up to five days (McDonell et al., 2012; Leickly et al., 2015; Lowe et al., 2015; McDonell et al., 2015). Tobacco smoking was determined by detection of cotinine using a threshold of 100 ng/mL, an established cutoff for detecting cigarette smoking in the past 5–7 days (Bernert et al., 1997; Benowitz, 1999; Cooke et al., 2008).

Recent alcohol and tobacco use were measured by breath samples. A Micro Smokerlyzer (Bedfont Scientific Ltd., Rochester, U.K.) assessed recent tobacco smoking by measuring carbon monoxide levels, and Alco-Sensor III Intoximeters Inc. (Saint Louis, Missouri, USA) measured breath alcohol concentration (Gibb et al., 1984; Al-Sheyab et al., 2015).

Self-Reported Alcohol and Drug Measures

During the baseline assessment, demographic characteristics were collected via self-report using the ASI noted above. At baseline, self-reported alcohol and tobacco use was assessed by the Alcohol and Tobacco Timeline Followback method (Sobell and Sobell, 1992; Pedersen and LaBrie, 2006), in which participants report the frequency and quantity of their daily drinking and smoking for the 30 days prior to the baseline visit. During the study visits, Timeline Followback method was used to assess alcohol and tobacco daily use. At each visit alcohol and tobacco “craving” were assessed via Visual Analog Scales (VAS) and Questionnaire of Smoking Urges (QSU) respectively, with a 10 cm visual analog scale anchored at 0 (no craving) and 100 (most intense craving possible) (Toll et al., 2006).

Participant Eligibility

Eligibility criteria included: 1) age 18 years and older, 2) DSM-IV-TR diagnosis of current alcohol abuse or dependence per the MINI (a semi-structured clinical interview) or heavy alcohol use as defined by 6 or more drinks, 4 or more days in a week for males; and 5 or more drinks, 4 or more days in a week for females (McDonell et al., 2015), 3) DSM-IV-TR diagnosis of current tobacco abuse or dependence per the MINI or heavy tobacco use as defined by 10+ cigarettes per day and smokerlyzer carbon monoxide value of >9 ppm (McDonell et al., 2015), 4) not seeking treatment for either substance, 5) ability to read and speak English, and 6) ability to provide written informed consent.

Exclusion criteria included: 1) significant risk of dangerous alcohol withdrawal, defined as a history of alcohol detoxification or seizure in the last 12 months, 2) expressed concern about dangerous withdrawal, 3) attempted suicide in the past year, 4) any other medical or psychiatric condition that the Principal Investigator or Medical Director determined would compromise safe study participation, 5) breath alcohol content above 0.00 at the time of informed consent, 6) positive baseline urine analysis for Benzodiazepines (due to Benzodiazepines being prescribed for alcohol withdrawal).

Statistical Analysis

We computed means and standard deviations for continuous variables, and percentages and frequencies for categorical variables, as shown in Table 1 for all baseline characteristics. We performed ANOVA analyses to determine whether participants differed across continuous variables at baseline, and we conducted chi-square statistics on categorical variables at baseline to determine whether participants differed across categorical variables. For the primary outcome analyses that used the above noted cutoffs to determine whether an individual’s sample was positive or negative, we utilized generalized estimating equations to examine change in alcohol use and smoking across the 4-week experimental intervention period. Because we removed one of our four experimental groups, due to the lack of participants and the other groups covering the substances, we converted our original 2×2 factorial to a 3-group categorical variable that was used as the primary independent variable. The control group (NC for Alcohol Tobacco) was the referent group for all analyses.

Table 1.

Baseline characteristics of participants by experimental randomization group.

NC Alcohol, NC Tobacco (n=8) CM Alcohol, NC Tobacco (n=8) NC Alcohol, CM Tobacco (n=10) CM Alcohol, CM Tobacco (n=8)
Outcome Mean SD Mean SD Mean SD Mean SD
Age (years) 37.0 9.9 29.3 8.6 39.8 12.4 32.9 10.7
Race/Ethnicity %, n
 Caucasian 75.0 6 75.0 6 90.0 9 75.0 6
 Hispanic 12.5 1 0 0 0 0 12.5 1
 American Indiana/Alaskan Native 0 0 12.5 1 10 1 12.5 1
 Multi-Racial 12.5 1 12.5 1 0 0 0 0
Sex (Male) %, n 62.5 5 62.5 5 80.0 8 50.0 4
Standard drinks per episode 6.6 1.7 7.3 2.0 5.6 1.5 5.9 1.4
Average days per week drinking 5.1 1.4 5.4 1.1 4.9 1.3 5.3 1.2
Cigarettes per day 14.9 5.4 17.9 4.4 20.0 8.2 16.8 10.4
FTND Total 5.4 2.6 5.8 1.7 5.0 2.5 4.6 1.5
AUDIT Total 17.5 5.0 19.5 5.9 15.2 7.1 18.12 7.4
Average EtG (ng/mL) 981.6 992.6 1072.2 865.5 685.9 883.4 805.8 959.3
  Positive %, n 100.0 8 87.5 7 100.0 10 87.5 7
Average Cotinine (ng/mL) 1061.2 537.1 1198.2 462.7 1009.7 450.6 698.0 555.2
  Positive %, n 62.5 5 75.0 6 50.0 5 50.0 4
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We also conducted secondary analyses on quantitative change in EtG and cotinine using generalized estimating equations, but instead of the logit link for binary outcomes, as needed for the primary outcomes, we used the identity link for examining change in the continuous versions of EtG and cotinine. Missing data were handled in a manner consistent with standard generalized estimating equation procedures; if a participant contributed 2 or more data points in the longitudinal analysis, they were included in the model i.e., individuals with 2 or more outcome values were included in the analysis, which was everyone who made it to the first visit. This meant that all of our intention to treat sample was included for all of our analyses. We used an alpha threshold of 0.05 as the cutoff for determining statistical significance. All analyses were performed in Stata 14.2 (College Station, TX).

Results

Baseline Characteristics

Five hundred potential participants form the surrounding community were screened, resulting in 34 eligible and randomized participants (see Supplemental Figure 2). Demographic data of randomized participants revealed that 68% of our sample was male, 79% were Caucasian, and 11% were multi-racial. The average age was 36 years (range 20–59 years). At baseline, 97% of participants tested positive for cotinine and 65% tested positive for EtG. Participants had an average carbon monoxide value of 17.7 ppm (range 3–47 ppm). Among randomized participants, 9% tested positive for methamphetamine and 50% tested positive for cannabis. Lastly, 3% tested positive for methadone and 6% tested positive for opioids. Baseline characteristics by group are presented in Table 1. All participants in the CM for both alcohol and tobacco group (n=8) dropped out by study visit 3 and thus were not included in the analysis. The 3 remaining experimental groups did not significantly differ across any of the baseline characteristics (p > 0.05).

Primary Binary Biochemical Outcomes

The CM for tobacco NC for alcohol group submitted significantly more tobacco-abstinent urinalyses (UAs) when compared to the NC for alcohol and tobacco group (odds ratio [OR] = 12.03, p < 0.05; 95% CI: 1.50 – 96.31; see Figure 1). Likewise, the CM for alcohol NC for tobacco group (OR = 37.55, p < 0.05; 95% CI: 4.86 – 290.17) also submitted significantly more tobacco-abstinent UAs when compared to NC for alcohol and tobacco group. The CM for tobacco, NC for alcohol group, compared to the control (i.e., NC for alcohol and tobacco) group, also submitted significantly more alcohol-abstinent UAs (OR = 2.68, p < 0.05; 95% CI: 1.33 – 5.38). The CM for alcohol, NC for tobacco group (OR = 4.07, p < 0.05; 95% CI: 1.95 – 8.46) submitted significantly more alcohol-abstinent UAs when compared to NC for both drugs.

Figure 1.

Figure 1

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Average number of negative urine samples submitted during follow up across groups (missing visits coded as positive).

Secondary Quantitative Biochemical Outcomes

The CM for tobacco, NC for alcohol group did not submit significantly lower EtG urine levels when compared to the NC for alcohol and tobacco group (B = − 151.95 ng/mL, p > 0.05; 95% CI: − 414.16 ng/mL - 110.26 ng/mL). However, the CM for alcohol, NC for tobacco group did submit significantly lower EtG urine levels when compared to NC for alcohol and tobacco group (B = − 386.57 ng/mL, p < 0.05; 95% CI: − 668.01 ng/mL - − 105.12 ng/mL). The CM for tobacco, NC for alcohol group, compared to the control group, also submitted significantly lower cotinine urine levels (B = − 214.58 ng/mL, p < 0.05; 95% CI: − 373.39 ng/mL - − 55.76 ng/mL). Lastly, the CM for alcohol, NC for tobacco group (B = − 580.06 ng/mL, p < 0.05; 95% CI: − 750.53 ng/mL - − 409.59 ng/mL) submitted significantly lower cotinine urine levels when compared to NC for alcohol and tobacco group. See Supplemental Figure 3 for a depiction of both EtG and cotinine negative sample submissions over time during the experimental intervention.

Discussion

We conducted an RCaT to examine the potential on-and-off-target effects of CM on co- occurring tobacco smoking and alcohol use. While both the CM for tobacco NC for alcohol group and the CM for alcohol NC for tobacco submitted significantly more abstinent UAs for both substances when analyzing the biomarkers in a binary fashion, the CM for tobacco group submitted significantly more negative urine samples for the off-target substance of alcohol, while the CM for alcohol group saw smaller off-target effects for tobacco. Thus, based on our findings, CM for tobacco was more effective for decreasing both alcohol and tobacco, than CM for alcohol. Future behavioral studies should consider focusing on the behavioral link between tobacco and alcohol when optimizing interventions for this co-occurring addiction. Specifically, future interventions designs should consider whether to ‘weight’ the intervention in question more heavily towards the treatment of tobacco or more towards alcohol. Previous research found that increases in tobacco prices increased alcohol use, while increases in alcohol use resulted in decreases in both substances (Decker and Schwartz, 2000).

While the study has a small sample size and has a large range of confidence intervals, we believe it provides an important signal, warranting further study of co-occurring substance abuse with CM. This novel design and analog methodology will allow for the examination of the comparative, or relative reinforcement between two related, but distinct, drugs of abuse that are linked neurobiological and behaviorally (Söderpalm et al., 2000; Gianoulakis, 2004; Chatterjee and Bartlett, 2010; Nocente et al., 2013). Such co-occurring addictions are ubiquitous in the realm of substance use disorder treatment and are in need of significantly increased understanding with regard to their relative reinforcement in order to optimize co-morbid treatment options. Our analog methodology is proposed as a possible first step for understanding the basics of comparative reinforcement between two drugs when used in tandem and how behavior change can or cannot be achieved when there is an attempt to exploit their inherent linkage. A better understanding of how to quantify and exploit this linkage may help inform a model that will be applicable to the treatment of several other co-occurring addictions. Also, while the reinforcement we provided was monetarily equivalent across groups and across the substances, establishing what equivalent reinforcement units are between different substances can be challenging and further complicates understanding and treating co-addiction.

Another complicating factor to consider in the design of novel treatments for this population is psychiatric co-morbidity. As noted previously, we have found off-target effects of CM on smoking in both psychostimulant use disorder and alcohol use disorder populations with psychiatric co-morbidities (McDonell et al., 2012, 2014). Overall, CM has demonstrated robust effects as part of a treatment package for drug and alcohol use disorder patients who also have serious mental illness (Ries et al., 2004; Bellack et al., 2006; McDonell et al., 2013; Kelly et al., 2014). This is further justification for examining variants of CM as a method of increasing abstinence among patients with a co-addiction.

Importantly, a few months prior to the implementation of this study, Washington State legalized recreational cannabis use. Therefore, the exclusion of those who tested positive for tetrahydrocannabinol (THC) at baseline was removed from the protocol before implementation. Given the ubiquity of cannabis use throughout the state and the increased prevalence rates post-legalization, this is the standard for several on-going studies. This was evidenced in the current investigation, as 50% of the participants thus far have tested positive for THC. This change makes the study more applicable to a community setting as more states legalize recreational cannabis use.

During the initiation of this investigation, and in an effort to better capture the population of non-treatment seeking individuals, the inclusion criteria of a DSM-IV-TR diagnosis of Alcohol Dependence or Abuse and Nicotine Dependence or Abuse was widened to heavy users of both substances. Clinically, this change indicates our willingness to recruit participants who smoke or drink high amounts, but are not classified as dependent. This is an important group to include because our findings will likely generalize to this population for future treatment design considerations. Indeed, as more and more treatment agencies rely less on DSM diagnoses and more on self-reported use thresholds, RCaT studies or similar studies will more readily translate to treatment settings after collecting experimental treatment evidence. Moreover, this is a high risk group of smokers at severe risk for several tobacco-associated health problems, which are magnified when accounting for their heavy drinking. Special attention needs to be paid to those with co-addiction, as no participant attended more than one visit in our CM for both alcohol and smoking group, indicating a need for higher intensity treatment among individuals wanting to receive treatment for two addictions.

Compared to other, previously reported RCaT-type studies conducted in our laboratory that usually enroll between 25% and 50% of screened participants, our 12% enrollment rate in this experiment is notably low, even among non-treatment seeking populations(McDonell et al., 2012; Packer et al., 2012; McPherson et al., 2014). This may be indicative of a need to increase the amount of reinforcement delivered during future, RCaT experiments. As noted above, the development of experimental, co-occurring addiction treatments should start with either this or a similar design in order to first understand the basic comparative reinforcement properties when two or more substances are used in tandem (McDonell et al., 2012; Packer et al., 2012).

Supplementary Material

Supplemental Figure 1
Supplemental Figure 2
Supplemental Figure 3

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