A behavioral economic analysis of polydrug abuse in alcoholics: asymmetrical substitution of alcohol and cocaine

Drug and Alcohol Dependence 62 (2001) 31 – 39 www.elsevier.com/locate/drugalcdep

A behavioral economic analysis of polydrug abuse in alcoholics: asymmetrical substitution of alcohol and cocaine Nancy M. Petry * Department of Psychiatry, Alcohol Research Center, Uni6ersity of Connecticut Health Center, 263 Farmington A6enue, Farmington, CT 06030 -1517, USA Received 19 October 1999; received in revised form 3 May 2000; accepted 16 May 2000

Abstract Economic concepts can be used to assess how drug prices affect consumption patterns. Increases in price for a commodity typically result in reductions in consumption. Demand is considered elastic if decreases in consumption are proportionally greater than increases in price, and inelastic if they are proportionally smaller than rises in price. The price of one commodity can also affect consumption of others. Commodities can function as substitutes, complements or independents, and these concepts refer to increases, decreases, or no change in the consumption of one item as the price of another increases. This study evaluated the effects of drug prices on hypothetical drug-purchasing decisions in 53 alcohol abusers. Experiments 1, 2, and 3 examined how alcohol, cocaine, and Valium prices, respectively, influenced purchases of alcohol, cocaine, Valium, heroin, marijuana and nicotine. As price of alcohol rose in Experiment 1, alcohol purchases decreased and demand for alcohol was inelastic. Cocaine was a complement to alcohol, but other drugs purchases were independent of alcohol prices. In Experiment 2, demand for cocaine was elastic as its price increased. Alcohol was a substitute for cocaine, but other drug purchases did not change significantly. In Experiment 3, demand for Valium was elastic as its price rose, and all other drug purchases were independent of Valium prices. Hypothetical choices were reliable between and within subjects and associated with urinalysis results and lifetime histories of drug abuse. These results suggest that, among alcohol abusers, cocaine is a complement to alcohol, but alcohol is a substitute for cocaine. © 2001 Elsevier Science Ireland Ltd. All rights reserved. Keywords: Polydrug abuse; Alcohol abuse; Behavioral economics

1. Introduction Alcohol abusers frequently abuse multiple drugs (e.g. Hesselbrock et al., 1985; Helzer and Pryzbeck, 1988). For example, estimates indicate that 30 – 60% of alcoholics abuse cocaine (Tsuang et al., 1994; Caetano and Weisner, 1995; Martin et al., 1996), 20 – 50% abuse marijuana (Caetano and Weisner, 1995; Martin et al., 1996), Tsuang et al., 1994), 12 – 20% abuse benzodiazepines (Ciraulo et al., 1988; Ross, 1993) and 7 –10% abuse heroin (Tsuang et al., 1994; Caetano and Weisner, 1995; Martin et al., 1996). Polydrug abuse presents a range of problems to treatment and public health initiatives. For example, the majority of drug-related emergency room visits involve combinations of alcohol * Tel.: +1-860-6792593; fax: + 1-860-6798090. E-mail address: [email protected] (N.M. Petry).

and illicit drug use (NIDA, 1991). Polydrug abuse also increases the likelihood of overdose and suicide (Ruttenber and Luke, 1984; Roy et al., 1990; Risser and Schneider, 1994), participation in HIV risk behaviors (Petry, 1999), and poor treatment outcomes (e.g. Schuckit, 1985; Rounsaville et al., 1987). Caulkins and Reuter (1996) discuss how economic models may explain polydrug abuse, but they point to the lack of empirical research in this area. In part, these data are lacking because illicit drugs are bought and sold at varying prices and purities, and the interrelationships between price and consumption variables are difficult to ascertain in natural settings. Behavioral economics applies consumer demand theory to the study of behavior (e.g. Bickel et al., 1990, 1991, 1995b). Several concepts derived from behavioral economics may assist in explaining relationships between drug prices and consumption. Cross-price elastic-

0376-8716/01/$ - see front matter © 2001 Elsevier Science Ireland Ltd. All rights reserved. PII: S 0 3 7 6 - 8 7 1 6 ( 0 0 ) 0 0 1 5 7 - 5

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ity quantifies how changing the price of one commodity affects the consumption of another. At one end of the spectrum, one commodity may substitute for another commodity that remains at a fixed price. For example, if the price of Coca-Cola soda increases from $0.50 to $2 per can and the price of Pepsi soda remains constant at $0.50 per can, Pepsi consumption is likely to increase. An increase in Pepsi purchases following a rise in Coca-Cola prices would demonstrate that Pepsi is a substitute for Coca-Cola. A reinforcer may also be a complement of another. As the price of soup increases [and soup consumption decreases], consumption of crackers may decrease, even though its price has not changed. Between these extremes are independents; the price of Coca-Cola is unlikely to affect consumption of crackers. Own-price elasticity quantifies the relationship between price and consumption of one commodity (e.g. Hursh, 1980, 1993; Samuelson and Nordhaus, 1985; DeGrandpre and Bickel, 1996). As price for a commodity increases, its consumption usually decreases. Increases in price markedly decrease consumption of some commodities. If reductions in consumption occur at rates proportionally greater than the increase in price, this pattern is termed elastic demand. Demand may be elastic for potato chips; a price increase of 20% may result in a 30% reduction in purchases. At the other extreme, price increases can result in marginal decreases in consumption, or inelastic demand. As price of gasoline increases 20%, a 10% reduction in purchases may result. If decreases in consumption are proportionally less than increases in price, demand is inelastic. These concepts of cross-price and own-price elasticity have been tested empirically in laboratory studies of drug self-administration, mainly in animals. Bickel et al. (1995a) reviewed 16 studies in which two reinforcers, one or both of which were drugs, were concurrently available and prices (number of lever presses) were altered. Cross-price elasticities indicated that some drugs were substitutes for others, some served as complements, and others were independents. These relationships were not always symmetrical. For example, while ethanol substituted for PCP, the reverse was not true (Carroll, 1987). In terms of own-price elasticities, demand for alcohol was more inelastic than demand for sucrose in rats with extensive alcohol histories (e.g. Petry and Heyman, 1995). These economic relationships may be useful in describing drug use in natural situations (Hursh, 1991; Bickel and DeGrandpre, 1995, 1996). For example, as alcohol price increases, alcohol abusers may substitute other drugs that abate alcohol withdrawal symptoms, such as benzodiazepines. Some drugs may be complements to others; when alcohol is very inexpensive, alcohol abusers may have enough resources to use cocaine concurrently. Demand for one’s primary drug

of abuse may be inelastic, with increases in price not greatly affecting consumption. Demand for secondary drugs may be elastic, and purchases may decrease rapidly as price increases. Although the relationships between drug prices and consumption could be studied in the laboratory, logistical and ethical considerations of providing drugs to drug abusers exist. Hypothetical behavioral experiments involve simulation of essential aspects of a situation to elicit the behavior in question (Epstein, 1986). These methods have been used in experimental economics such that the resultant data is predictive of real-world behavior (Plott, 1986). This paper describes the use of a paradigm that was developed to apply a behavior economic analysis to the phenomena of polydrug abuse. Drug-experienced subjects are given imitation money, and prices of drugs are indicated on paper. Subjects indicate the types and quantities of drugs they would buy. In one study with heroin abusers (Petry and Bickel, 1998), changes in the price of heroin significantly altered purchases of heroin as well as some other drugs, especially cocaine and Valium. At initial price increments, cocaine was a complement to heroin, but at higher price increments, cocaine became a substitute for heroin. Valium was a substitute for heroin, but the reverse was not true; heroin purchases were independent of Valium prices. Choices were reliable and correlated with urinalysis results of drug use during treatment. The present study sought to evaluate the effects of alcohol, cocaine, and Valium prices on polydrug use patterns in alcoholics. As shown in the previous study (Petry and Bickel, 1998), demand for the primary drug of abuse (in this case alcohol) was expected to be inelastic, while demand for secondary drugs (e.g. cocaine and Valium) was expected to be elastic. The study also evaluated how prices of alcohol, cocaine, and Valium affected polydrug use patterns. The hypotheses were that cocaine would be a complement to alcohol as these two drugs often are used together, while Valium may be a substitute for alcohol because benzodiazepines abate alcohol withdrawal symptoms.

2. Methods

2.1. Subjects Fifty-three subjects, recruited from newspaper advertisements and flyers distributed at low-income housing projects and social service agencies in the greater Hartford, Connecticut area, participated. A telephone screen (see Petry and Casarella, 1999, for details) was used to assess eligibility criteria, including alcohol abuse or dependence and a history of sampling at least three other types of drugs: marijuana, opioids, cocaine, and sedatives. Although they had varying levels of involve-

N.M. Petry / Drug and Alcohol Dependence 62 (2001) 31–39

ment with these other drugs, these subjects reported alcohol as their primary drug of abuse. Subjects provided written informed consent and received $50 for participation.

2.2. Assessments Subjects provided a breath sample that was screened for alcohol using an Alcosensor IV Alcometer (Intoximeters, St. Louis, MO) and a urine specimen that was screened for opioids, cocaine, and marijuana using EZScreen (Editek, Burlington, NC). The Addiction Severity Index (ASI; McLellan et al., 1985) was administered to evaluate problem areas in seven domains. Higher scores are indicative of more severe problems.

2.3. Apparatus and general methods Various drugs, in amounts typically used for a ‘hit’, were presented on a piece of paper. The prices for each drug were representative of Hartford street prices, as determined by informal survey: heroin was $15/bag, Valium was $1/pill, marijuana was $5/joint, alcohol was $1/drink, cocaine was $10/eighth g, and cigarettes were $2/pack. The Experimenter read instructions (see Appendix A), and handed subjects $35 of imitation money. In Experiment 1 (n= 53), the price of alcohol varied from $0.50, $1, $2.50, and $5 per drink, while other drug prices remained constant. In Experiment 2, cocaine prices varied from $2, $4, $10, and $20 (only 17 subjects participated in Experiment 2; no differences were noted between this subgroup and the full group). In Experiment 3 (n=53), the price of Valium varied from $0.33, $1, $3, and $10 per pill. The eight or 12 conditions were presented in a random order, which differed among all subjects tested.

2.4. Data analysis Cross-price elasticities (Ecross) were determined as prices for alcohol, cocaine, or Valium rose in Experiments 1–3, respectively. Ecross values were derived using Equation 1 from Allison (1983) in which Q is quantity purchased of drug A at price B1 or B2 (prices of alcohol, cocaine or Valium). Ecross = [log(QA2)−log(QA1)]/[log(PB2) − log(PB1)]

(1)

When price and purchase data are plotted on log –log coordinates, Ecross is simply the slope between two points. Positive slopes indicate drug A is a substitute for drug B, and negative slopes indicate drug A is a complement of drug B. Slopes near 0 indicate drug A is independent of drug B (e.g. Hursh, 1980; Samuelson and Nordhaus, 1985; Green and Freed, 1993). Slopes of ] 0.2, 5 −0.2, and between −0.2 and 0.2 were

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defined as substitutes, complements, and independents, respectively (Bickel et al., 1995a). Because some drugs were purchased infrequently (e.g. mean purchases of B 1 unit of drug), variations in purchases by just a few individual subjects who purchased a drug at high rates in some conditions could greatly affect slopes. Therefore, statistically significant changes in purchases, along with slopes of ] 0.2 or 5 − 0.2, were required in order for a drug to be labeled a substitute or a complement, respectively. In this manner, a substantial proportion of subjects tested would have to demonstrate the pattern in order for a drug to be labeled as a substitute or complement (Petry and Bickel, 1998). To determine demand for alcohol, Valium, and cocaine as their prices rose, own-price elasticity (Eown) was calculated by an equation from Allison (1983) in which Q is the quantity of drug A purchased at price (P) 1 or 2. Eown = [log(QA2)− log(QA1)]/[log(PA2)− log(PA1)]

(2)

When price and consumption data are plotted on log – log coordinates, the slope between any two points represents Eown. If the slope is B − 1, demand is elastic, and consumption decreases rapidly with price increases. Conversely, if the slope is between − 1 and 0, demand is inelastic, and increases in price are associated with proportionally smaller changes in consumption (e.g. Hursh, 1991, 1993; DeGrandpre et al., 1994). The slopes of the best fitting line across all conditions was calculated as an indicator of overall Ecross or Eown for each drug. Slopes were conducted for mean units purchased and for individual subjects. In the interests of brevity, individual subject data are not shown, but the number of subjects demonstrating trends is reported in the text. Statistical analyses for changes in purchases across price conditions were conducted using repeated measures ANOVA. When the overall F was significant, subsequent matched pairs t-tests were used to evaluate differences between two individuals points (Rosenthal and Rosnow, 1984). The alpha value was B 0.05, and p values refer to two-tailed tests. To evaluate the test –retest reliability of responses in the simulation, Pearsons correlations were conducted between the units of drugs purchases in repeated exposures to the same condition ($1 alcohol/$1 Valium/$10 cocaine). To evaluate whether drug use in real-life was associated with purchases during the hypothetical situations in these experiments, two types of analyses were conducted. First, t-tests compared the number of units of cocaine, heroin, and marijuana purchased for subjects submitting urine samples positive for the respective drug on the day of testing versus those negative for the drug. The expectation was that subjects who had recently used the drug would be likely to purchase greater quantities of that drug during the simulation. In

N.M. Petry / Drug and Alcohol Dependence 62 (2001) 31–39

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addition, the lifetime years of regular use for each drug was correlated with units of that drug purchased during the simulation.

3. Results

3.1. Demographic and drug use characteristics Table 1 shows demographic and drug use characteristics of the sample. The group consisted primarily of low-income Caucasian men, with an average age of 40. All had histories of polydrug use. Most had histories of regular abuse of cocaine and marijuana, but fewer used sedatives and heroin on a regular basis. Urinalysis results confirmed recent polydrug use in the sample, with cocaine and marijuana use being most prevalent.

Table 1 Demographic and drug use characteristics of subjectsa Variable

Alcohol abusers

N % Male Age (years) Race (%) Caucasian African American Hispanic/other Years of education Annual legal income Addiction severity index scores Medical Employment Alcohol Cocaine Opioids Legal Family/social Psychiatric Breath and urinalysis results (% positive) Alcohol Marijuana Cocaine Opioids Self-reported use in past 30 (days) Alcohol (to intoxication) Marijuana Cocaine Heroin Benzodiazepines Subjects with lifetime regular use (%) Marijuana Cocaine Heroin Benzodiazepines In treatment for substance abuse (%)

53 74 40 (10)

a

75 19 6 12 (2) 8386 (9848) 2.9 5.6 3.3 1.0 0.6 1.1 2.2 2.9

(4.9) (3.5) (1.9) (1.9) (1.5) (1.9) (2.1) (2.3)

6 17 23 8 8 2 3 1 1

(10) (6) (7) (5) (3)

57 36 16 13 43

All values are means and S.D.s, unless otherwise indicated.

Fig. 1. (Top panel) Mean units of alcohol purchased as alcohol increases in price from $0.50 to $5 per drink. Data are plotted in log– log coordinates such that the overall slope is equal to the Eown values listed in Table 2. Purchases that differ significantly from the $0.50 price condition are denoted by asterisks. (Bottom panel) Mean units of cocaine, Valium, marijuana, heroin, and nicotine purchased as alcohol increases in price from $0.50 to $5 per drink. Data are plotted in log– log coordinates such that the overall slope across points is equal to the Ecross values listed in Table 2. Purchases that differ significantly from the $0.50 alcohol price condition are denoted by asterisks.

3.2. Effects of alcohol price on polydrug purchases The top panel of Fig. 1 shows alcohol purchases as a function of its price in Experiment 1. Alcohol purchases differed significantly across the four price conditions, F= 13.51, 3/50 df, P5 0.001, with purchases in each of the three higher price conditions differing significantly from the $0.50 condition, t= 4.35, 4.80, and 5.39, 52 df, P50.001 for the three respective conditions. Values significantly different from the $0.50 condition are denoted by filled symbols. Data are plotted on log –log coordinates, such that the slope across conditions is equal to Eown shown in Table 2. Because the slope was greater than −1, decreases in alcohol purchases were proportionally less than the increases in prices, and demand for alcohol was inelastic. The effects of alcohol prices on other drug purchases are shown in the bottom panel of Fig. 1. No statistically significant changes in Valium purchases were

N.M. Petry / Drug and Alcohol Dependence 62 (2001) 31–39

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Table 2 Own-price elasticities of demand for alcohol, cocaine and valiuma Own-price

Experiment 1 Experiment 2 Experiment 3

Alcohol −0.62* Cocaine −1.15* Valium −1.30*

Cross-price Alcohol

Cocaine

Valium

Marijuana

Heroin

Nicotine

–

−0.42*

−0.02

−0.29

−0.04

−0.03

0.21*

–

0.26

0.45

0.00

−0.01

0.01

−0.09

0.05

0.11

−0.01

–

a

Cross-price elasticities of demand for other drugs as prices of alcohol, cocaine and valium changed. * Changes in purchases that are differ significantly different across price conditions, PB0.05.

noted as a function of alcohol price, and the Ecross value for Valium was −0.02 (Table 2). In contrast, cocaine purchases decreased significantly as alcohol prices rose, F = 3.18, 3/50 df, P5 0.05. Purchases of cocaine in the $2.5 and $5 alcohol price conditions were significantly lower than purchases of cocaine in the $0.50 alcohol price condition, t= 2.22 and 2.80, 52 df, P 5 0.05. Table 2 indicates that overall Ecross value for cocaine was −0.42, indicating that cocaine was a complement to alcohol. Although the Ecross value of − 0.29 for the group mean purchases showed that marijuana may be a complement to alcohol, the number of marijuana purchases did not change significantly with alcohol price. In fact, marijuana was a complement to alcohol in only three of the 53 subjects. Heroin and nicotine purchases were unaffected by alcohol price, and overall demand for these drugs was independent of alcohol prices, with Ecross values of − 0.04 and −0.03.

and 2 subjects, respectively, showing the substitution effect. Demand for heroin and nicotine was independent of cocaine prices.

3.4. Effects of Valium price on polydrug purchases Fig. 3 (top panel) shows Valium purchases as a function of Valium price. Purchases of Valium decreased significantly as its price rose, F= 2.24, 3/50 df, P5 0.05, with purchases in the two highest price conditions differing from the $0.33 condition, t= 1.95 and

3.3. Effects of cocaine price on polydrug purchases The top panel of Fig. 2 shows cocaine purchases as a function of cocaine price. Cocaine purchases decreased significantly as its price rose, F = 11.07, 3/14 df, P5 0.001, with each of the three conditions differing from the $2 condition; t values ranged from 5.20 to 5.87, 16 df, P 5 0.001. Eown values (Table 2) indicate that demand for cocaine was elastic, and purchases of cocaine decreased at rates proportionally greater than the price increases. The price of cocaine significantly affected purchases of alcohol, F =4.66, 3/14 df, P 50.02. Alcohol purchases in the $20 cocaine condition were significantly greater than purchases in the $2 cocaine condition. Table 2 shows that alcohol was a substitute for cocaine, with an Ecross value of 0.21. Cocaine prices did not significantly affect purchases of any other drugs. Although Ecross values for Valium and marijuana were positive (0.26 and 0.45), purchases of these drugs were not significantly affected by cocaine prices, with only 1

Fig. 2. (Top panel) Mean units of cocaine purchased as cocaine increases in price from $2 to $20 per eighth gram. See Fig. 1 for further details. (Bottom panel) Mean units of alcohol, Valium, marijuana, heroin, and nicotine purchased as cocaine increases in price. See Fig. 1 for further details.

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N.M. Petry / Drug and Alcohol Dependence 62 (2001) 31–39

average number of cocaine purchases was 0.992.5. These purchases were significantly different, t(51)= 5.30, PB 0.001. Similarly, the number of purchases of heroin was significantly higher among subjects with opioid-positive urine samples (5.896.2) compared to those without (0.69 2.3), t(51)= 3.63, PB 0.001. For marijuana, these differences did not reach levels of statistical significance, t(51)= 1.9, P= 0.70, with average purchases of 10.89 16.3 versus 4.29 8.0 for the THC-positive and THC-negative subjects. Spearman correlations were conducted between the number of purchases in the simulation and self-reported years of lifetime use. Lifetime years of cocaine use were correlated with number of units of cocaine purchased in the simulation (r=0.53, PB 0.001), as was lifetime years of heroin use associated with heroin purchases (r= 0.55, PB 0.001). Marijuana purchases in the simulation were associated with self-reported years of marijuana use (r= 0.28, PB 0.05), and Valium purchases in the simulation were associated with self-reported years of benzodiazepine use (r=0.31, PB0.05).

4. Discussion Fig. 3. (Top panel) Mean units of Valium purchased as Valium increases in price from $0.33 to $10 per pill. See Fig. 1 for further details. (Bottom panel) Mean units of alcohol, cocaine, marijuana, heroin, and nicotine purchased as Valium increases in price. See Fig. 1 for further details.

2.01, 52 df, P5 0.05. Table 2 shows demand for Valium was elastic, with Eown = − 1.30. The bottom panel of Fig. 3 shows other drug purchases as Valium price increased. No statistically significant changes in purchases were noted for any drug as a function of Valium price. Ecross values (Table 2) demonstrate independent relationships between the price of Valium and purchases of all other drugs.

3.5. Reliability and 6alidity of the simulation Reliability of choices was evaluated by examining correlations between purchases of all six of the drugs in the two exposures to the $1 alcohol/$1 Valium/$10 cocaine condition. These correlations were significant for every drug and ranged from 0.55 to 1.00, P B 0.05. To evaluate whether purchases of drugs in the simulation were associated with drug use in real-life, t-tests for independent samples were used to compare drug purchases between subjects testing positive for a drug compared to those testing negative. For subjects remitting a cocaine-positive urine specimen, the average number of cocaine purchases during Experiments 1 and 3 (in which all subjects participated) was 8.09 7.4. For subjects with cocaine-negative specimens, the

Data from this study replicate previous research showing high rates of polydrug use in alcoholics. Over half of the subjects reported regular use of marijuana, and over one-third reported at least weekly use of cocaine over their lifetime. Regular use of benzodiazepines and heroin was relatively rarer among these alcohol abusers (13 and 16%, respectively), a finding also concordant with past research (Ciraulo et al., 1988; Ross, 1993; Tsuang et al., 1994; Martin et al., 1996). Drug choices in the simulation tended to correlate with self-reports of lifetime abuse and urinalysis results. Drug choices in repeated exposures to the same condition were correlated as well. Thus, drug choices in the simulation were reliable and associated with objective indicators of drug use and with self-reported drug use histories among alcohol abusers in the present study, as well as with heroin addicts in a previous study (Petry and Bickel, 1998). In Experiment 1, price of alcohol significantly influenced purchases of cocaine. Specifically, cocaine and alcohol were purchased together when alcohol was inexpensive in the simulation. This result was hypothesized because data from patient populations demonstrate that alcohol and cocaine often are used together (Carroll et al., 1993; Higgins et al., 1994). Moreover, in a laboratory study with human subjects, alcohol pre-treatment increased preference for cocaine (Higgins et al., 1996). Concurrent use of these drugs may result in an enhanced or extended high (McCanceKatz et al., 1993) or an attenuation of the negative

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acute effects of cocaine such as nervousness and insomnia (Gawin and Kleber, 1986). As price of alcohol increased, both alcohol and cocaine purchases decreased significantly, and cocaine was a complement to alcohol, with an overall Ecross value of −0.42. In contrast, as the price of cocaine rose in Experiment 2, alcohol purchases increased significantly, and the overall Ecross value of 0.21 indicated that alcohol was a substitute for cocaine. Thus, an asymmetrical substitution effect occurred between alcohol and cocaine; while alcohol was a substitute for cocaine, cocaine was a complement to alcohol. In other words, among alcohol abusers, alcohol use may increase as cocaine becomes more expensive, but cocaine use may decrease when alcohol prices rise. Presumably, these reductions in cocaine use with increasing alcohol prices occurred because subjects were defending some minimal level of alcohol consumption. In fact, demand for alcohol was inelastic (Eown = − 0.62), suggesting that subjects defended alcohol purchases as its price increased. In contrast, demand for both cocaine and Valium was elastic (Eown = − 1.15 and − 1.30), and subjects did not defend their intake of either of these drugs. In another study (Petry, 2000), income elastic demand (purchases rising in greater proportion than increases in income) was found for one’s primary drug of abuse (alcohol, cocaine or heroin for alcohol, cocaine, and heroin abusers, respectively) but not for secondary drugs. Data from a previous study (Petry and Bickel, 1998) indicate that heroin addicts in VT abuse Valium at high rates and that Valium was a strong substitute for heroin. Because Valium and alcohol induce effects on the same receptor complex and cross-tolerance between these drugs occurs (Mehta and Ticku, 1988; Ticku and Kulkarni, 1988; Khanna et al., 1998), Valium was expected to be a substitute for alcohol in this study as well. Contrary to expectations, Valium was not a substitute for alcohol, and Valium purchases generally were independent of alcohol prices. No statistically significant changes in Valium purchases were noted as alcohol prices varied, and the Ecross value was near 0 as well. In part, the independent relationship between alcohol prices and Valium purchases may be related to the relatively low rates of Valium purchases in this sample compared to a sample of heroin addicts (c.f. Petry and Bickel, 1998). Although most of these subjects had sampled Valium in their lifetime, only 13% had a history of regular use. Demand for most other drugs tested was independent of the prices of alcohol, cocaine, or heroin. Neither heroin nor cigarette purchases were affected by alcohol prices. Cross-price demand elasticities indicated that marijuana may be a complement to alcohol (Ecross = − 0.29, Table 2), but no statistically signifi-

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cant changes in marijuana purchases occurred with changes in alcohol prices, and only a few subjects showed a complementary effect. Cocaine prices likewise did not significantly affect purchases of marijuana, heroin or nicotine. While Ecross values were greater than 0.2 for Valium and marijuana as cocaine prices increased, Valium and marijuana were substitutes for cocaine only among a couple of subjects. Demand for all drugs was independent of Valium prices, a finding consistent with that reported among heroin addicts (Petry and Bickel, 1998). Thus, in a sample of primary alcoholics with polydrug use histories, alcohol and cocaine are frequently used together. An increase in the price of alcohol seemingly reduces cocaine use, while other drug use remains constant or decreases marginally. A rise in the street price of cocaine, in contrast, may result in an increase in alcohol consumption. Comparing these results with the limited number of laboratory studies investigating consumption of two concurrently available drugs (Bickel et al., 1995b) reveals some consistencies and inconsistencies. Roll et al. (1997) found that cigarette use increased when cocaine was administered in a laboratory study and when a clinical sample of cocaine-dependent patients used cocaine. Mello et al. (1980) found that cigarettes were a complement to alcohol in one study, but that cigarette consumption was independent of alcohol price in a second study (Mello et al., 1987). In the present study, cigarette purchases were independent of the prices of alcohol, cocaine and Valium. Other published laboratory studies have rarely demonstrated substitution effects between drugs, except when the same drug is concurrently available at two different price requirements (Bickel et al., 1995a). However, most laboratory studies evaluate the interrelationships between drugs when only one or two drugs are present. This simulation methodology allows for examination of a variety of concurrently available drugs, and such a situation may be analogous to realworld settings in which a variety of drugs are accessible. The findings from this study must be interpreted with caution. First, they may only be generalized to populations of primary alcoholics with rather extensive polydrug use histories. Different effects may be noted among primary cocaine or heroin addicts, or among alcoholics without polydrug use experience. Only alcoholics who had sampled other drugs were included in this study to ensure the subjects had used the drugs tested. Nevertheless, few alcoholics screened for this study reported no polydrug use, and the prevalence of polydrug use in this sample is consistent with that reported in a variety of other studies of primary alcoholics (e.g. Ciraulo et al., 1988; Ross, 1993; Tsuang et al., 1994; Martin et al., 1996).

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A potential criticism of these findings is that drug preferences were evaluated over large changes in price conditions. Two- to 3-fold increases in prices were used to evaluate preferences under extreme conditions. In real-world settings, prices for illicit drugs also can vary markedly from day to day (e.g. when a large shipment comes in compared to after a police raid). Extreme changes in the price of alcohol can occur across settings as well (ranging from free drinks at a party, to moderately priced liquor store prices, and high restaurant or bar prices). Nevertheless, all choices were hypothetical in this study. Whether or not alcohol abusers actually would chose these same types and amounts of drugs in natural settings is not known. Furthermore, the instructions attempted to equate fear of legal sanction across subjects by instructing that drug ‘use’ during the experiment would not result in legal recourse. Whether these results would vary with individual differences in fear of sanctioning may be addressed in future studies. Future research must also demonstrate whether other factors (e.g. social, mood) affect drug choices using this methodology in the same manner that they affect drug choices in real life (e.g. Glautier, 1998; Reuter, 1998). In summary, this study demonstrates an asymmetrical substitution effect between alcohol and cocaine in alcohol abusers. Cocaine purchases decreased markedly as the price of alcohol rose, and cocaine was a complement to alcohol. In contrast, alcohol purchases increased significantly as the price of cocaine increased, and alcohol was a substitute for cocaine. These results suggest that alcoholics are unlikely to switch to harder drugs as alcohol prices increase, and use of cocaine may actually decrease as prices for alcohol rise. Similarly, using data from the National Household Survey of Drug Abuse, Saffer and Chaloupka (1999) found illicit drugs were complements to alcohol. Their results suggest that increased alcohol taxation may not only decrease alcohol use but illicit drug use as well in a general population. This study provides preliminary support for their conclusion in a sample of alcoholic individuals. If replicated in natural settings, data from studies such as these may assist in informing drug abuse policy.

Acknowledgements Jackie Tedford, Jaime Kelley, and Bonnie Martin are thanked for assistance in data collection and management. This work was supported by grants R29DA12056, R01-DA05862, and R01-DA05862-Supp from the National Institute of Drug Abuse, grant P50AA03510 from the National Institute of Alcohol Abuse and Alcoholism, and General Clinical Research Center Grant M01RR06192.

Appendix A ‘‘This is a series of questions designed to assess choices for drugs across changes in prices. This information is entirely for research purposes. First, think back to a typical day when you use(d) drugs. In an average day, which drugs, and how much, would you use?’’ (This section was included to frame the reference for the next instructions.) ‘‘Now, we’re going to use this sheet and fake money to play a type of game. Please answer the questions honestly and thoughtfully. Assume you have access to $35 a day that you can buy drugs with (The experimenter handed the subject the imitation money). The drugs you may buy and their prices are listed on this sheet (The experimenter pointed to the price sheet). You may buy any drugs you’d like with this money, and there are no consequences to using these drugs. So, assume this is a study that has been approved by the police and all other organizations. Also, assume that the only drugs you will receive are those you purchase with the allotted $35 per day. You have no other drugs available to you. You cannot purchase more drugs, or any other drugs except those you choose below. Therefore, assume you have no other drugs stashed away, you have no prescriptions for anything (including antabuse, naltrexone, methadone or Valium), and you cannot get drugs through any other source, other than those you buy with your $35 per day. Also, assume that the drugs you are about to purchase are for your consumption only. In other words, you can’t sell them or give them to anyone else. You also can’t save up any drugs you buy and use them another day. Everything you buy is, therefore, for your own personal consumption within a 24-h period. With this $35, please indicate what you would purchase, and I’m going to check off each drug as you buy it so you’ll know what you’ve purchased.’’ References Allison, J., 1983. Behavioral substitutes and complements. In: Mellgren, R.L. (Ed.), Animal Cognition and Behavior. North-Holland, Amsterdam, pp. 1 – 30. Bickel, W.K., DeGrandpre, R.J., 1995. Price and alternatives: suggestions for drug policy from psychology. Int. J. Drug Policy 6, 93 – 105. Bickel, W.K., DeGrandpre, R.J., 1996. Modeling drug abuse policy in the behavioral economics laboratory. In: Green, L., Kagel, J.H. (Eds.), Advances in Behavioral Economics, vol. 3. Ablex, Norwood, NJ, pp. 69 – 95. Bickel, W.K., DeGrandpre, R.J., Higgins, S.T., Hughes, J.R., 1990. Behavioral economics of drug self-administration. I. Functional equivalence of response requirement and drug dose. Life Sci. 47, 1501– 1510. Bickel, W.K., DeGrandpre, R.J., Hughes, J.R., Higgins, S.T., 1991. Behavioral economics of drug self-administration: II. A unit price analysis of cigarette smoking. J. Exp. Anal. Behav. 55, 145–154.

N.M. Petry / Drug and Alcohol Dependence 62 (2001) 31–39 Bickel, W.K., DeGrandpre, R.J., Higgins, S.T., 1995a. The behavioral economics of concurrent drug reinforcers: a review and reanalysis of drug self-administration research. Psychopharmacology 118, 250– 259. Bickel, W.K., DeGrandpre, R.J., Higgins, S.T., Hughes, J.R., Badger, G.J., 1995b. Effects of simulated employment and recreation on cigarette smoking: a behavioral economic approach. Exp. Clin. Psychopharmacol. 3, 467–476. Caetano, R., Weisner, C., 1995. The association between DSM-III-R alcohol dependence, psychological distress and drug use. Addiction 90, 351– 359. Carroll, M.E., 1987. Self-administration of orally delivered phencyclidine and ethanol under concurrent fixed ratio schedules in rhesus monkeys. Psychopharmacology 93, 1–7. Carroll, K.M., Rounsaville, B., Bryant, K., 1993. Alcoholism in treatment-seeking cocaine abusers: clinical and prognostic significance. J. Stud. Alcohol 54, 199–208. Caulkins, J., Reuter, P., 1996. The meaning and utility of drug prices. Addiction 91, 1261– 1264. Ciraulo, D.A., Sands, B.R., Shader, R.I., 1988. Critical review of liability for benzodiazepine abuse among alcoholics. Am. J. Psychiatry 145, 1501– 1506. DeGrandpre, R.J., Bickel, W.K., 1996. Drug dependence as consumer demand. In: Green, L., Kagel, J.H. (Eds.), Advances in Behavioral Economics, vol. 3. Ablex, Norwood, NJ, pp. 1 – 36. DeGrandpre, R.J., Bickel, W.K., Higgins, S.T., Hughes, J.R., 1994. A behavioral economic analysis of concurrently available money and cigarettes. J. Exp. Anal. Behav. 61, 191–201. Epstein, R., 1986. Simulation research in the analysis of behavior. In: Poling, A., Fuqua, R.W. (Eds.), Research Methods in the Applied Behavioral Analysis: Issues and Advances. Plenum, New York, pp. 127– 155. Gawin, F.H., Kleber, H.D., 1986. Abstinence symptomology and psychiatric diagnosis in cocaine abusers: clinical observation. Arch. Gen. Psychiatry 43, 107–113. Glautier, S., 1998. Costs and benefits of analysis. Addiction 93, 605– 606. Green, L., Freed, D.E., 1993. The substitutability of reinforcers. J. Exp. Anal. Behav. 60, 141–158. Helzer, J.E., Pryzbeck, T.R., 1988. The co-occurrence of alcoholism with other psychiatric disorders in the general population and its impact on treatment. J. Stud. Alcohol 49, 219–224. Hesselbrock, M.N., Meyer, R.E., Keener, J.J., 1985. Psychopathology in hospitalized alcoholics. Arch. Gen Psychiatry 42, 1050– 1055. Higgins, S.T., Budney, A.J., Bickel, W.K., Foerg, F.E., 1994. Alcohol dependence and simultaneous cocaine and alcohol use in cocainedependent patients. J. Addict. Dis. 13, 177–189. Higgins, S.T., Roll, J.R., Bickel, W.K., 1996. Alcohol pretreatment increases preference for cocaine over monetary reinforcement. Psychopharmacology 123, 1–8. Hursh, S.R., 1980. Economic concepts for the analysis of behavior. J. Exp. Anal. Behav. 34, 219–238. Hursh, S.R., 1991. Behavioral economics of drug self-administration and drug abuse policy. J. Exp. Anal. Behav. 56, 377–393. Hursh, S.R., 1993. Behavioral economics of drug self-administration: an introduction. Drug Alcohol Depend. 33, 165–172. Khanna, J.M., Kalant, H., Chau, A., Shah, G., 1998. Rapid tolerance and crosstolerance to motor impairment effects of benzodiazepines, barbiturates, and ethanol. Pharm. Biochem. Behav. 59, 511– 519. Martin, C.S., Clifford, P.R., Maisto, S.A., Earleywine, M., Kirisci, L., Longabaugh, R., 1996. Polydrug use in an inpatient treatment sample of problem drinkers. Alcohol Clin. Exp. Res. 20, 413– 417. McCance-Katz, E.F., Price, L.H., McDougle, C.J., Kosten, T.R., 1993. Concurrent cocaine ethanol ingestion in humans: pharmacology, physiology, behavior, and the role of cocaethylene. Psychopharmacology 111, 39–46.

39

McLellan, A.T., Luborsky, L., Cacciola, J., Griffith, J.E., 1985. New data from the Addiction Severity Index. Reliability and validity in three centers. J. Nerv. Ment. Dis. 173, 412– 423. Mehta, A.K., Ticku, M.K., 1988. Ethanol potentiation of GABAergic transmission in cultured spinal cord neurons involves GABAA gated chloride channels. J. Pharmacol. Exp. Ther. 246, 558–564. Mello, N.K., Mendelson, J.H., Sellars, M.L., Kuehnle, J.C., 1980. Effects of alcohol and marijuana on tobacco smoking. Clin. Pharmacol. Ther. 27, 202– 209. Mello, N.K., Mendelson, J.H., Palmeiri, S.L., 1987. Cigarette smoking by women: interactions with alcohol use. Psychopharmacology 93, 8 – 15. National Institute on Drug Abuse National Household Survey on Drug Abuse, Main Findings, 1991, DHHS Publication no. ADM 92-1887, Rockville, MD, 1991. Petry, N.M., 1999. Alcohol use in HIV patients: what we don’t know may hurt us. Int. J. STDS AIDS 10, 561– 570. Petry, N.M., 2000. Effects of income on polydrug abuse: a comparison of alcohol, cocaine and heroin abusers. Addiction 95, 706– 717. Petry, N.M., Bickel, W.K., 1998. Polydrug abuse in heroin addicts: a behavioral economic analysis. Addiction 93, 321– 335. Petry, N.M., Casarella, T., 1999. Substance abusers with gambling problems discount delayed rewards at extremely high rates. Drug Alcohol Depend. 56, 25 – 32. Petry, N.M., Heyman, G.M., 1995. Behavioral economic analysis of concurrent ethanol/sucrose and sucrose reinforcement in the rat: effects of altering variable-ratio requirements. J. Exp. Anal. Behav. 64, 331– 359. Plott, C.R., 1986. Laboratory experiments in economics: the implications of the posted-price institutions. Science 232, 732– 738. Reuter, P., 1998. Polydrug abuse in heroin addicts: a behavioral economic analysis — a reply to Petry & Bickel. Addiction 93, 604– 605. Risser, D., Schneider, B., 1994. Drug related deaths between 1985 and 1992 examined at the Institute of Forensic Medicine in Vienna, Austria. Addiction 89, 851– 857. Roll, J.M., Higgins, S.T., Tidey, J., 1997. Cocaine use can increase cigarette smoking: evidence from laboratory and naturalistic settings. Exp. Clin. Psychopharmacol. 5, 263– 268. Rosenthal, R., Rosnow, R.L. (Eds.), 1984. Essentials of Behavioral Research: Methods and Data Analysis, McGraw Hill, New York. Ross, H.E., 1993. Benzodiazepine use and anxiolytic abuse and dependence in treated alcoholics. Addiction 88, 209– 218. Rounsaville, B.J., Dolinsky, Z.S., Babor, T.F., Meyer, R.E., 1987. Psychopathology as a predictor of treatment outcome in alcoholics. Arch. Gen. Psychiatry 44, 505– 513. Roy, A., Lamparski, D., Dejong, J., Moore, V., Linnola, M., 1990. Characteristics of alcoholics who attempt suicide. Am. J. Psychiatry 147, 761– 765. Ruttenber, A.J., Luke, J.L., 1984. Heroin-related deaths: new epidemiological insights. Science 226, 14 – 16. Saffer, H., Chaloupka, F.J., 1999. Demographic differentials in the demand for alcohol and illicit drugs. In: Chaloupka, F., Bickel, W., Saffer, H., Grossman, M. (Eds.), Economic Analysis of Substance Use and Abuse: An Integration of Econometric and Behavioral Economic Research, University of Chicago Press, Chicago. pp. 187– 211. Samuelson, P., Nordhaus, W.D. (Eds.), Economics, 12th Edition, McGraw Hill, New York, 1985. Schuckit, M.A., 1985. The clinical implications of primary diagnostic groups among alcoholics. Arch. Gen. Psychiatry 42, 1043–1049. Ticku, M.K., Kulkarni, S.K., 1988. Molecular interactions of ethanol with GABA-ergic system and potential of Ro 15-4513 as an ethanol antagonist. Pharm. Biochem. Behav. 30, 501– 510. Tsuang, J.W., Shapiro, E., Smith, T.L., Schuckit, M.A., 1994. Drug use among primary alcoholic veterans. Am. J. Drug Alcohol Abuse 20, 483– 493.