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. Author manuscript; available in PMC: 2021 Mar 1.
Published in final edited form as: Drug Alcohol Depend. 2020 Jan 20;208:107772. doi: 10.1016/j.drugalcdep.2019.107772

The drug purity discounting task: Ecstasy use likelihood is reduced by probabilistic impurity according to harmfulness of adulterants

Sean B Dolan a, Matthew W Johnson a
PMCID: PMC7156028  NIHMSID: NIHMS1569518  PMID: 31974022

Abstract

Background:

Ecstasy typically contains adulterants in addition to, or in lieu of, MDMA which may pose a greater risk to users than MDMA itself. The current study aimed to evaluate the effectiveness of adulterant-related informational prompts in reducing Ecstasy use using a novel probability discounting task.

Methods:

An online sample of past-month Ecstasy users (N = 278) were randomized to one of four different framing prompt conditions: no prompt; a prompt describing MDMA’s effects; a prompt describing adulterants as inert “filler”; or a prompt describing adulterants as pharmacologically-active, potentially-harmful compounds. Each prompt contained general, potential public-health information that was not specifically related to subsequent behavioral tasks. All participants then completed an identical Drug Purity Discounting Task, in which they indicated the likelihood of using a sample of Ecstasy across different probabilities of the sample being impure, and then completed a hypothetical Ecstasy purchasing task.

Results:

Likelihood of Ecstasy use decreased as impurity probability increased across conditions. Ecstasy use likelihood was highest in the “inert” prompt condition, whereas pharmacologically-active adulterant or adulterant-nonspecific prompts resulted in comparably low likelihood of use. Ecstasy-use likelihood did not differ among conditions when the likelihood of sample impurity was 0. Ecstasy purchasing did not differ among groups. Inelastic purchasing was associated with greater likelihood of using potentially-impure Ecstasy.

Conclusions:

Altogether, these data highlight the necessity of education regarding pharmacologically-active, rather than inert, adulterants in Ecstasy, and suggest that increased access to drug checking kits and services may mitigate some of the harms associated with Ecstasy use.

Keywords: Ecstasy, MDMA, behavioral economics, probability discounting, demand, drug checking

1. Introduction

“Ecstasy” refers to recreational drugs frequently used in club or rave settings (Palamar et al., 2017; SAMHSA, 2017; UNODC, 2017). Ecstasy is commonly associated with 3,4-methylenedioxymethamphetamine (MDMA), which produces prosocial, euphoric, and energizing effects (de la Torre et al., 2004). MDMA’s biobehavioral effects have been well-characterized (de la Torre et al., 2004; Green et al., 2003; Parrott, 2013); however, street Ecstasy is commonly adulterated with substances in addition to or in lieu of MDMA. Despite the rebranding of Ecstasy as “Molly”, crystalline Ecstasy commonly marketed as “pure” MDMA, various cathinone, piperazine, and amphetamine analogs are frequently detected in Ecstasy and “Molly” samples (Brunt et al., 2017; Saleemi et al., 2017). Recent data demonstrate that MDMA content did not differ across Ecstasy samples marketed as “MDMA”, “Molly”, or “E” (Saleemi et al., 2017), and that individuals who reported only using MDMA, but not novel psychoactive substances, tested positive for novel psychoactive substances (Palamar et al., 2016). These data highlight that perceptions of “pure” MDMA in “Molly” have led to unintentional consumption of potentially-dangerous compounds.

Aside from the violations of informed consent posed by unintentional consumption of a drug, many Ecstasy adulterants have psychological and health-related consequences exceeding the severity of those posed by MDMA. Methamphetamine, for example, is more reinforcing than MDMA in both animals (Wang & Woolverton, 2007) and humans (Kirkpatrick et al., 2012). Many of the synthetic cathinones found in Ecstasy are more potent (Gatch et al., 2019; Rickli et al., 2015; Simmler et al., 2014) and more-robustly self-administered than MDMA in rodents (Dolan et al., 2018; Vandewater et al., 2015). Additionally, adulterant pharmacokinetics may differ substantially from MDMA and potentially lead to redosing with faster pharmacokinetics, or delayed onset from slower pharmacokinetics, which is considered a driving factor in Ecstasy-related fatalities associated with para-methoxymethylamphetamine (Kraner et al., 2001; Lin et al., 2007; Nicol et al., 2015; WHO, 2015). Adulterated Ecstasy produces greater adverse subjective effects than MDMA, which can be highly-distressing, even in the absence of the threat of addiction or overdose (Brunt, et al., 2012).

Despite the high incidence of Ecstasy adulteration, the myth of “Molly” as pure MDMA persists. Perhaps perpetuating this perception is the low frequency of drug-purity checking, as only 5–24% of Ecstasy users utilize these services (Day et al., 2018; Palamar & Barratt, 2019). Furthermore, individuals report reduced drug-use likelihood contingent upon an MDMA-negative or adulterant-positive reading in both real (Saleemi et al., 2017) and hypothetical drug-checking scenarios (Day et al., 2018; Palamar & Barratt, 2019). Drug-checking services can be highly-effective harm-reduction tools; however, novel approaches are necessary to understand drug-related decision-making processes to optimize strategies for increasing engagement in drug checking.

Behavioral economics provides a theoretical framework for evaluating decision making under different constraints, and has been successfully utilized to assess drug use and dependence. Two frequently-used behavioral economic concepts for evaluating drug use are probability discounting and demand (Bickel et al., 2014). Probability discounting classically refers to devaluation of a reinforcer as the probability of its receipt decreases (Rachlin et al., 1991), but novel approaches demonstrate a reduction in the likelihood of engaging in an activity as the probability of a co-occurring punisher increases, such as engagement in condomless intercourse as a function of STI risk (e.g., Johnson & Bruner, 2013). Responding on these tasks is sensitive to framing conditions, such that condom-use likelihood increases when the hypothetical STI is considered more dangerous (i.e. HIV vs. chlamydia) (Berry et al., 2019) and that potentially-addictive medication-use likelihood increases with longer pain relief (one year vs. 3 days) (Tompkins et al., 2018). Demand, referring to changes in consumption as a function of cost, is commonly assessed using hypothetical purchase tasks, in which participants indicate how much of a commodity they would purchase across prices. Demand is also sensitive to framing effects, as evidenced by a study indicating an inverse relation between hypothetical alcohol consumption and next-day responsibility load (Skidmore & Murphy, 2011). Three studies have assessed hypothetical Ecstasy demand, albeit alongside other, simultaneously-available drugs, and demonstrated Ecstasy demand increases with perceived quality (Sumnall et al., 2004; Goudie et al., 2007; Cole et al., 2008). These data highlight that context can strongly influence decision-making processes. The current study aimed to utilize these principles to investigate how Ecstasy-use behaviors may be influenced by information related to adulteration.

2. Methods

Participants were recruited via Amazon Mechanical Turk (mTurk), an online crowdsourcing platform commonly used for substance-abuse research (Strickland & Stoops, 2019), to complete an anonymous survey (Qualtrics, Provo, UT). The survey was released in 3 waves between November 2018 and February 2019.

2.1. Participants

Participants needed to be ≥18 years old, reside in the United States, have ≥95% mTurk approval rating, and have completed ≥100 previously-approved mTurk tasks, and endorse using MDMA/“Ecstasy”/“Molly” (hereafter referred to as Ecstasy) in the past 30 days to qualify for the study. Participants who met the aforementioned inclusion criteria were provided a link to the main survey, which took about 15 minutes to complete. Participants read a consent page describing the study and their rights as participants. Beginning the survey after reading this page was considered provision of consent. Participants were paid $1.00 for completing the survey and a $1 bonus for passing an embedded attention check and providing systematic discounting data (described below).

Participants provided demographic information (age, gender, race, marital status, educational history, employment status) and Ecstasy-use data, including use frequency and preferred formulation (e.g., pressed tablets or powder).

2.2. Probability Discounting Questionnaire

Participants completed the Probability Discounting Questionnaire (PDQ) (Madden et al., 2009) for both monetary gains and losses. The PDQ is a 10-item questionnaire which, in the gains condition, asks participants whether they would prefer to win $200 for sure or a chance of winning $800 at each of the following probabilities:10%, 13%, 17%, 20%, 25%, 33% 50% 67%, 75% and 83%. The loss condition assessed preference for losing $200 for sure or probabilistically losing $800 at each aforementioned probability. Probabilities were presented in random order and whether losses or gains were completed first was randomized. An attention check in the gains condition assessed preference for winning $0 for sure or a 100% chance of winning $800. Participants endorsing $0 were excluded from further analysis.

2.3. Ecstasy Severity of Dependence Scale

Participants completed the 5-item Ecstasy Severity of Dependence Scale (ESDS) (Bruno et al., 2009). Each question contained a 4-point Likert scale with anchors ranging from “Never/almost never” to “Always/nearly always” or “Not difficult”, to “Impossible”.

2.4. Framing Prompts

Participants were randomly assigned to one of four experimental framing conditions containing different Ecstasy-related prompts (supplemental methods). The No Frame condition served as the negative control, and participants were asked to click the ‘next’ button to continue with the survey. In the Drug Effects condition, participants read a brief paragraph about MDMA’s structural similarity to amphetamine, common routes of administration, and subjective adverse effects. This condition was designed to describe general aspects of MDMA likely known to recreational users. The Inert Impurities condition described impurities used as “cutting” agents in Ecstasy, such as baking soda, to increase weight, stretch out supply, and maximize dealer profits. It was made explicit that these impurities did not produce any deleterious effects. In the Active Impurities condition, the prompt described that Ecstasy samples are often “laced” with pharmacologically-active compounds, such as methamphetamine, “bath salts,” caffeine, and para-methoxymethlyamphetamine, that produce subjective effects comparable to MDMA, some of which may have worse adverse effects. Unlike the Inert Impurities frame, the Active Impurities frame explicated that these adulterants can cause serious harm. Following the prompt (excluding No Frame), participants completed a brief, three-question quiz specific to their condition, and were not allowed to move on to the next section until they correctly answered each question. The quiz ensured participants were exposed to and understood the information in the prompt.

2.5. Purity Discounting Task

All participants then completed an identical probability discounting task that was independent of any prompt condition. Participants used a sliding visual analog scale ranging from 0–100, in one-point, whole-integer increments, to indicate their likelihood of using a sample of Ecstasy based on the probability that it contained an unspecified impurity. Participants first indicated their likelihood of using a sample with a 0% chance of containing an impurity, defined as “pure MDMA”, and then indicated use likelihood at 1%, 10%, 25%, 50% 75%, and 99% chance of sample impurity. Each probability was presented sequentially on separate pages.

2.6. Ecstasy Purchase Task

Participants read a brief instructional set (supplemental methods) asking to imagine a typical month when they would use Ecstasy and to consider the following: the Ecstasy was of their normal quality; they could not get Ecstasy elsewhere; they could not use Ecstasy saved from previous use episodes; they could not spend more money than they actually had; they would consume all of the purchased Ecstasy in the next month; they should consider each price individually; and that one dose was defined as either one pressed tablet, capsule of powder, or small baggie of powder. Participants endorsed how many doses they would purchase for one month across the following prices: $0.01, 0.10, 0.25, 0.50, 1.00, 2.50, 5.00, 10.00, 25.00, 50.00, 100.00, 250.00, 500.00, and 1,000.00 per dose (USD). The purchase task was not included in the initial release, but participants in waves 2 and 3 completed the task.

2.7. Data Analysis

Most statistical analyses were performed using SPSS (Version 25, Armonk, NY: IBM Corp). Due to non-normality across variables, nonparametric statistical tests were used.

Demographic variables were compared among groups to evaluate demographic matching. When possible, non-continuous demographic variables were dichotomized for statistical comparison. Continuous variables were analyzed using Kruskal-Wallis H tests, whereas categorical variables were analyzed using chi-squared analysis.

Responses on the ESDS were coded from 0–3 and summed across the 5 questions to create a composite score ranging from 0 (no/low dependence) to 20 (severe dependence).

PDQ scores (h value) were calculated the same way for gains and losses, as previously described (Madden et al., 2009). Larger h values indicate greater probability discounting (e.g., choosing the certain outcome). Individual h values were compared among groups using a Kruskal-Wallis H test.

Responses on the Purity Discounting Task were considered systematic if (1) use likelihood at one probability did not exceed use likelihood at the previous, lower probability by ≥20% and (2) use likelihood at 99% chance of impurity did not exceed use likelihood at 0% chance of impurity by ≥10% (Johnson & Bickel, 2008; Johnson, Herrmann, & Johnson, 2015). Datasets violating these criteria were excluded from analysis. Drug-use likelihood at 0% chance of impurity was used as an index of pure MDMA reinforcement. In order to isolate the effect of impurity on responding, responses on each non-zero probability were divided by the likelihood of use at 0% chance of impurity (Herrmann, Johnson, & Johnson, 2015; Johnson et al., 2016). Using these standardized data points, area under the curve (AUC) was calculated for each participant as previously described (Myerson et al., 2001). Greater AUC indicates a greater drug-use likelihood. Pure MDMA use-likelihood and AUC values were compared among groups using a Kruskal-Wallis H test. In the event of a significant group difference, pairwise comparisons using Mann-Whitney U tests were conducted to assess differences between each group.

Orderliness of purchasing was determined according to previously-established methods (Bruner & Johnson, 2014; Stein et al., 2015). Responses were considered systematic if (1) consumption at one price did not exceed consumption at the previous, lower price by ≥20%, (2) consumption at the highest price was ≥10% less than consumption at the lowest price, and (3) consumption >0 was not reported at higher prices after 0 consumption was endorsed at a lower price. Datasets with consumption exceeding 100 doses were excluded from analysis. Individual responses were modeled in GraphPad Prism version 8 (GraphPad software, La Jolla, CA) using the exponential demand equation (Hursh & Silberberg, 2008): logQ=logQ0+k(e(−α*Q0*C)−1). In this equation, Q represents consumption, Q0 represents demand intensity (consumption at near-0 prices), C represents price, k represents the range of log-transformed consumption, and α provides a global description of demand elasticity (price-sensitivity). To allow for log transformation, the first instance of 0 consumption was converted to 0.1 and subsequent prices were not analyzed (e.g., Johnson et al., 2017; MacKillop et al., 2012). Reported consumption at $0.01 was used for demand intensity, and k was set to 3 based on the observed range of logarithmic consumption. Observed Omax (maximum expenditure) was determined by calculating the maximum money spent on Ecstasy at any single price for each participant. Money spent at each price was determined by multiplying the number of Ecstasy units purchased by the price per unit. Because model-fitting was not possible in such cases, datasets with 0 or unchanging consumption across prices were excluded from demand elasticity, but not intensity, analyses. Intensity and elasticity were compared among groups using a Kruskal-Wallis H test.

Bivariate Spearman Rank correlational analyses were performed to determine relations between measures.

3. Results

In total, 22,081 people completed the screener, and 320 (1.4%) completed the survey. This is comparable to the prevalence of past-30-day Ecstasy use among persons aged 26–34 in the United States (0.7% (SAMHSA, 2017)). Thirteen participants missed the attention check and 30 participants endorsed using Ecstasy 0 times after endorsing past-30-day use in the screener, and were not included in the final analysis, yielding a total sample of N=278. Sample sizes among the framing conditions were approximately equal with n=72 in No Frame, n=68 in Drug Effects, n=68 in Inert Impurities, and n=70 in Active Impurities.

Participant demographics are presented in Table 1. Participants were predominately young (Mage=29.54, SD=7.14), white (60.1%), college-educated (65.5% associate’s degree or higher), and male (54.7%). Demographics were comparable among conditions with a few exceptions. Participants in the Active Impurities group were more likely to be college-educated (χ2(3)=12.938, p=.005); however, the majority in each group had an associate’s degree or higher. Unemployment was higher in the Inert Impurities group (χ2(3)=9.966, p=.019), but ≥72% were employed across groups. Tablet Ecstasy use was more common in the No Frame group (χ2(3)=8.771, p=.032), but the majority of participants primarily used tablets across groups. All ecstasy-use-related variables (ESDS, past-30-day use, and drug-testing frequency) were similar among groups (all ps >.10).

Table 1:

Participant demographics for the total sample and each framing condition. Bolded numbers indicate significant differences (p < .05) among framing conditions.

Demographic Variable Total (N = 278) No Frame (n = 72) Drug Effects Frame (n = 68) Inert Impurities Frame (n = 68) Active Impurities Frame (n = 70) H or χ2 value p value
Age in years (median, Q1–Q3) 28 (25–32) 27 (25–31) 28 (25–31) 28 (25–32) 28 (25–33) 0.898 .826
Ecstasy Severity of Dependence Score (median, Q1–Q3) 1 (0–4) 1 (0–3) 1 (0–3) 0.5 (0–3) 0.5 (0–3) 0.428 .934
Past 30 Day Ecstasy use (days) (median, Q1–Q3) 1 (1–3) 1 (1–2) 1 (1–3) 1 (1–3) 1 (1–3) 0.272 .965
Primary Ecstasy formulation, N (%) 8.771 .032
Tablets 175 (62.9) 55 (76.4) 41 (60.3) 36 (52.9) 43 (61.4)
Powder 103 (37.1) 17 (23.6) 27 (39.7) 32 (47.1) 27 (38.6)
Gender, N (%) 8.646 .195
Male 152 (54.7) 35 (48.6) 44 (64.7) 37 (54.4) 46 (51.4)
Female 123 (44.2) 36 (50.0) 22 (32.4) 31 (45.6) 34 (48.6)
Transgender (M to F) 3 (1.1) 1 (1.4) 2 (2.9) 0 (0.0) 0 (0.0)
Race, N (%) 0.007 1.000
White 167 (60.1) 43 (59.7) 41 (60.3) 41 (60.3) 42 (60.0)
Nonwhite 111 (39.9) 29 (40.3) 27 (39.7) 27 (39.7) 28 (40.0)
Education, N (%) 12.938 .005
Associate’s Degree or Higher 182 (65.5) 48 (66.7) 37 (54.4) 40 (58.8) 57 (81.4)
Below Associate’s Degree 96 (34.5) 24 (33.3) 31 (45.6) 28 (41.2) 13 (18.6)
Employment, N (%) 9.966 .019
Employed (FT/PT) 232 (83.5) 65 (90.3) 60 (88.2) 49 (72.1) 58 (82.9)
Unemployed 46 (16.5) 7 (9.7) 8 (11.8) 19 (27.9) 12 (17.1)
Frequency of Drug Testing, N (%) 11.587 .479
Never 121 (44.2) 29 (40.8) 30 (45.5) 34 (50.0) 28 (40.6)
Rarely 76 (27.7) 16 (22.5) 18 (27.3) 17 (25.0) 25 (36.2)
Sometimes 17 (6.2) 4 (5.6) 7 (10.6) 3 (4.4) 3 (4.3)
Most of the Time 26 (9.5) 11 (15.5) 5 (7.6) 6 (8.8) 4 (5.8)
Always 34 (12.4) 11 (15.5) 6 (9.1) 8 (11.8) 9 (13.0)
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PDQ h values were greater for losses (Mdn=0.87, IQR=1.43) than gains (Mdn=0.68, IQR=0.34) (Z=−4.643, p<.001), but did not differ among groups for either gains (H(3)=3.182, p=.364) or losses (H(3)=0.797, p=.850).

Drug purity discounting is illustrated in Figure 1. Discounting responses were generally orderly. Forty-five nonsystematic datasets (16.2%) were removed, yielding n=233 for analysis (15 removed from No Frame, 7 from Drug Effects, 9 from Inert Impurities, 14 from Active Impurities). Drug-use likelihood at 0% chance of impurity did not differ among groups (H(3)=0.440, p=.932). A Kruskal-Wallis H test on AUC determined a significant difference among groups (H(3)=34.896, p<.001), with a mean rank of 98.14 for No Frame, 103.35 for Drug Effects, 161.63 for Inert Impurities, and 104.04 for Active Impurities. Planned Mann-Whitney U tests determined a significantly greater AUC in the Inert Impurities condition relative to all other conditions (all ps < .001), and no differences among the other conditions (all ps > .64).

Figure 1:

Figure 1:

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Ecstasy Purity Discounting. Median (±IQR) likelihood (%) of using pure MDMA (0% chance of sample impurity) for each group (left). Median standardized (i.e. divided by pure MDMA use likelihood) likelihood of using Ecstasy across probabilities of sample impurity for the No Frame (white circles, solid line), Drug Effects (filled circles, dotted line), Inert Impurities (white squares, dashed line), and Active Impurities (filled squares, dotted dashed line) groups (middle). Median (±IQR) area under the curve based on standardized use likelihood for each group (right). * p < .05 compared to all other groups.

Ecstasy demand is illustrated in Figures 2 and 3. Because the purchase task was not included in the initial survey release, only 181 participants completed it. Demand responses were generally orderly. Twenty-six datasets (25.4%) were removed from analysis for nonsystematic responding and 8 (4.4%) were removed for purchasing >100 doses (11 removed from No Frame, 7 from Drug Effects, 12 from Inert Impurities, 4 from Active Impurities). Kruskal-Wallis tests determined no significant differences in demand intensity (H(3)=1.368, p=.713), Omax (H(3)=3.781, p=.286), or elasticity among groups (H(3)=2.050, p=.562).

Figure 2:

Figure 2:

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Hypothetical Ecstasy demand curves. Median hypothetical Ecstasy doses purchased across prices and best-fit demand curves for the No Frame (white circles, solid line), Drug Effects (filled circles, dotted line), Inert Impurities (white squares, dashed line), and Active Impurities (filled squares, dotted dashed line) groups.

Figure 3:

Figure 3:

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Hypothetical Ecstasy demand metrics. Median (±IQR) Q0 values (demand intensity) for each group (left). Larger values indicate greater consumption. Median (±IQR) Omax values (maximum expenditure) for each group (middle). Larger values indicate greater expenditure. Median (±IQR) α values (demand elasticity) for each group (right). Larger values indicate greater price-sensitivity.

Spearman correlations among measures are presented in Table 2. Significant correlations were determined between AUC and pure-MDMA use likelihood, pure-MDMA use likelihood and ESDS score, pure-MDMA use likelihood and Omax, AUC and elasticity, intensity and Omax, elasticity and Omax, elasticity and ESDS score, and ESDS score and past-30-day Ecstasy use. All other correlations were not significant (all ps >.05).

Table 2:

Bivariate correlations among task metrics and Ecstasy use measures. Spearman rho values are presented in each cell.

1 2 3 4 5 6 7 8
1. AUC (↑ AUC = ↑ Drug Use Likelihood)
2. Use Likelihood of Pure MDMA .289**
3. Demand Intensity (Consumption at lowest price) .150 .081
4. Demand Elasticity (Price-Sensitivity) −.205* −.004 −.053
5. Omax (maximum observed expenditure) .120 .195* .490** −.490**
6. Money Discounting Gains −.083 −.088 I −.070 −.043 −.037
7. Money Discounting Losses −.011 −.029 .026 .056 .044 .002
8. ESDS SCORE −.068 −.222** .164 −.198* .105 .032 −.007
9. Past-30-Day Ecstasy Use .068 −.006 .069 .067 .097 −.069 .125 .399**
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*

p < .05,

**

p < .01

4. Discussion

These findings underscore the importance of information regarding Ecstasy sample purity in making informed decisions related to Ecstasy use. Although hypothetical, these data mirror reports in which individuals who test their Ecstasy samples and detect an impurity report decreased likelihood of using the tainted sample (Day et al., 2018; Palamar & Barratt, 2019; Saleemi et al., 2017). Given the harms associated with pharmacologically-active adulterants, the growing literature demonstrating reduced use likelihood with knowledge of sample impurity highlight the public-health benefit of drug-checking services. These data also inform best practices for messaging about drug-checking services. When impurities were discussed generally (No Frame/Drug Effects) or when harms were explicated (Active Impurities), participants reported reduced use likelihood than when described as inert and harmless. Strategies to advocate drug-checking services could most-effectively promote drug-checking by emphasizing dangerous effects of pharmacologically-active adulterants in Ecstasy, or simply stressing the high incidence of adulteration, given the reduction in use likelihood without explicitly defining impurities. Conversely, focusing on pharmacologically-inert adulterants may be counterproductive for effective utilization of drug-checking services.

These data contribute to the emerging framing-effects literature in behavioral economics. Individuals reading about dangers of pharmacologically-active adulterants responded similarly to individuals receiving no adulterant-related information. Conversely, those reading about inactive adulterants indicated a greater likelihood of using potentially-impure Ecstasy relative to the other conditions. Assessments of framing effects have demonstrated reduced delay discounting when delays are presented as dates rather than temporal distances (e.g., 1 year) (e.g., Naudé et al., 2018; DeHart & Odum, 2015), when the outcome is in clear dollar amounts rather than equivalent, unclear amounts (e.g., “handfuls of quarters”) (DeHart et al., 2018), and when activities allowed during the delay are unrestricted (Johnson et al., 2015). Relatedly, individuals exhibited less sexual probability discounting (greater condom-use likelihood) when responding about HIV/AIDS or an unspecified STI than for gonorrhea or herpes, which were perceived as less harmful (Berry et al., 2019). When considered with the current data, the similar responding between unspecified and explicitly-injurious outcomes suggest individuals may default to worst-case scenarios when provided limited information about a potentially-harmful outcome.

These data also underline the importance of probability on decision-making processes, as use likelihood between groups under ideal circumstances of pure MDMA (0% impurity) did not differ, demonstrating the importance of evaluating multiple probabilities to ascertain the influence of adulterant-related information on Ecstasy-use behavior. Simply assessing general Ecstasy use may have led us to conclude that adulterant-related information did not influence Ecstasy-use behaviors. Additionally, probability discounting for Ecstasy use and money showed near-zero correlation coefficients, indicating independent underlying probability-discounting processes for the two commodities. The relation of Purity, but not monetary, discounting with Ecstasy demand adds to the literature demonstrating the importance of domain-specific discounting, rather than “impulsivity” generally, when evaluating clinically-relevant outcomes, such as sex- (Johnson et al., 2015; Lawyer & Schoepflin, 2013; Mahoney & Lawyer, 2018) and cannabis-based discounting (Strickland et al., 2017).

This study was the first to assess hypothetical purchasing of Ecstasy alone. Demand responses were generally orderly with monotonic decreases in consumption with increasing price, and suggests the utility of this approach for assessing Ecstasy demand. Although discounting differed among groups, hypothetical purchasing was similar among prompt conditions. The lack of a framing effect here contrasts with previous studies evaluating framing effects on demand, which have demonstrated that alcohol demand can be influenced by next-day responsibility load (Skidmore & Murphy, 2011), duration of access (Kaplan et al., 2017), and happy-hour specials (Kaplan & Reed, 2018), and that cigarette demand is altered by various pre-task narratives (Kaplan et al., 2019; DeHart et al., 2019). The reason for the lack of framing effects on hypothetical demand is unclear. Because the purchasing task was not included in the initial survey release, the smaller sample size may have been insufficient for detecting significant group differences. The temporal distance between the framing prompt and the purchasing task, which were separated by the purity discounting task, may have diminished the salience of the prompt information. Overall past-30-day Ecstasy use was very low (Mdn = 1 day across groups), and this floor effect may have contributed to the lack of framing effects on demand, given that neither past-30-day use or demand metrics differed across groups. Additionally, the purchase task did not explicitly reference purity, whereas adulteration was the crux of the discounting task. Because the instructions specified the Ecstasy was “of your usual quality”, participants may have assumed their usual supply is pure MDMA and did not generalize the prompt information to the purchasing task. Studies incorporating purity-related information into the instructions, such as specifying buying from an unknown/untrusted source, may provide insight into how perceived purity influences Ecstasy demand.

Despite the lack of framing effects on purchasing, there were significant relations between Ecstasy demand, Ecstasy use, and Purity discounting. The negative relation between elasticity and AUC indicates that greater Ecstasy demand may be related to riskier Ecstasy use patterns, as individuals indicating more inelastic purchasing are more likely to use impure Ecstasy. Elasticity was negatively related to ESDS scores, suggesting that more inelastic purchasing patterns are also associated with greater dependence severity. Curiously, a similar relation between ESDS and AUC did not emerge, suggesting the greater impure sample use likelihood in inelastic purchasers was not necessarily reflective of greater dependence, but may be indicative of a stronger desire to use Ecstasy, regardless of price or purity. Omax was positively correlated Pure MDMA use likelihood, suggesting those willing to spend more on Ecstasy were more likely to use unadulterated MDMA. There were no relations between Ecstasy demand metrics and use frequency, which diverges from the associations with rates of drug consumption demonstrated with other drugs, including alcohol (Murphy & MacKillop, 2006) and cannabis (Strickland et al., 2017). This may stem from the low use frequency in the sample, as 53% endorsed using only once in the past month. Similarly, alternative assessments of use, such as episodic consumption (i.e. tablets/session), may have yielded an association between use and demand, as previously reported for daily cigarettes (MacKillop et al., 2008), daily cocaine use (Bruner & Johnson, 2014), and, in a hypothetical purchase task assessing multiple, simultaneously-available drugs, self-reported number of pills used in an Ecstasy-use episode (Cole et al., 2008). Perhaps the most-unexpected finding is that pure MDMA use likelihood was negatively related to ESDS score, suggesting participants demonstrating greater Ecstasy dependence were less likely to use pure MDMA. The reasons for this relation are not apparent. There was substantial variability in pure-MDMA use likelihood and 63% of participants had ESDS scores ≤1 (out of 20), so the variability in use likelihood among the large number of low-ESDS participants may have confounded the correlation and this effect is spurious; however, replication is necessary for determining whether this phenomenon is reliable.

The most salient outcomes from this study should support harm-reduction education and public-health campaigns regarding Ecstasy adulteration and the benefits of drug-checking services. Narratives simply informing the public of the presence of adulterants may be as effective as emphasizing the potentially-dangerous compounds present in Ecstasy, given that purity discounting was comparable between the pharmacologically-active impurities framing condition and the two, impurity-nonspecific groups. However, messaging specifying the types of adulterants encountered should focus on pharmacologically-active adulterants rather inert products used to “cut” samples, as the inert-impurities group was more willing to ingest potentially-impure samples than the other conditions. Additionally, these data expand upon previous findings demonstrating that individuals will use information regarding Ecstasy sample purity to decide whether to use the drug (Day et al., 2018; Palamar & Barratt, 2019; Saleemi et al., 2017). Despite the willingness to avoid impure samples, drug-checking services are heavily underutilized, given that 44% of this sample reported never using these services. These numbers are higher than previous reports (Day et al., 2018; Palamar & Barratt, 2019); however, those studies evaluated drug checking in individuals at festivals and clubs, which may have differed from the current, broader sample. Altogether, increased public awareness of the relative impurity of Ecstasy and its associated harms alongside increased access to drug-checking services has the potential to save lives by reducing the inadvertent use of tainted Ecstasy.

Supplementary Material

1

Highlights.

  • Ecstasy use likelihood decreases as perceived sample impurity increases

  • Framing conditions about inert adulterants increased impure Ecstasy use likelihood

  • Framing for unspecified or potentially-harmful adulterants decreased use likelihood

  • Hypothetical Ecstasy purchasing was not influenced by framing conditions

  • Inelastic purchasing was associated with greater impure Ecstasy use likelihood

Funding:

This work was supported by the National Institute on Drug Abuse [grant numbers T32DA007209, R01DA042527].

Footnotes

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Role of Funding Source:

Nothing declared.

Conflict of Interest:

No conflict of interest declared.

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