The Brief Addiction Science Information Source (BASIS)

Op-Ed/Editorials: Toward a Public Health Approach to Improve Parameters for Safer Gambling

Allyson J. Peller, M.P.H.
Research Associate
Harvard Medical School, Division on Addictions, Cambridge Health Alliance

Debi A. LaPlante, Ph.D.
Instructor of Psychology
Harvard Medical School, Division on Addictions, Cambridge Health Alliance

Howard J. Shaffer, Ph.D., C.A.S.
Director
Harvard Medical School, Division on Addictions, Cambridge Health Alliance

During recent years, there has been considerable media attention devoted to the potential harms associated with emerging gambling technology. Some advocates claim that emerging gambling technology is hazardous to the public health (i.e., leads to disordered gambling) while other advocates claim that technological interventions can prevent or alleviate gambling-related problems (e.g., reduce users’ spending by limiting the availability and denominations of bill acceptors) (Bulkeley, 1995; Federal Trade Commission, 2003; Nova Scotia Gaming Corporation, 2006; Wood, Griffiths, & Parke, 2007). Although these advocates have voiced strong opinions, these discussions have not utilized a public health approach to conduct a balanced assessment of factors that contribute to disordered gambling or strategies to control contributing factors.

Korn and Shaffer (1999, 2002) first proposed placing the study of gambling behavior within a public health framework to improve policy, prevention, and treatment practices.  More specifically, public health practitioners and researchers can use the classic three-part public health model, that is the Epidemiologic Triangle of host, agent, and environment, to understand the full spectrum of factors that contribute to a range of public health phenomena (e.g., an epidemic or the subjective effects of gambling). The goal of public health interventions often is to control at least one of the three factors to prevent or stop a challenge to the public’s health (Centers for Disease Control and Prevention, 2007). Applied to the study of disordered gambling, the Epidemiologic Triangle can help researchers to examine a comprehensive range of factors related to host (i.e., player characteristics (e.g., psycho-physiologic characteristics, cognitive characteristics)), agent (i.e., new gambling technology (e.g., Internet gambling, audiovisual game features)), and environment (i.e., context of gambling behavior (e.g., factors within gambling environments, new gambling technology environments)).

There is evidence that utilizing this approach can have some success in improving public health. Zinberg previously applied the Epidemiologic Triangle to the study of drug use; his approach yielded an enduring framework for understanding drug effects (Zinberg, 1984; Zinberg & Shaffer, 1985). Using this framework, Zinberg and others have been able to provide a way of understanding disordered drug use as a result of a complex interaction among drug use, the user’s psychological expectations of such use, and the setting within which the user has these experiences. According to this approach, comprehensive public health intervention strategies to control disordered drug use need to consider more dynamic and comprehensive ways to alter the factors relating to drug (i.e., agent), set (i.e., host), and setting (i.e., environment) rather than just focusing interventions to control the drug.

Using a public health approach to examine the field of gambling-related technology has the potential to yield similarly valuable suggestions about how to create parameters for safer gambling behavior. To fill knowledge gaps about gambling-related technology and help researchers to design innovative studies, we have completed a critical examination of study methodology and findings from the extant empirical literature (Peller, LaPlante, & Shaffer, under review). This work investigates empirical studies that examine game features associated with new gambling technology (i.e., agent) and player characteristics (i.e., host). For example, several studies have investigated how certain audiovisual game features (e.g., game speed, presence of sound, visual complexity (i.e., number of symbols that appear on the screen)) affect self-reported enjoyment and motivation to continue gambling (Christopherson & Weatherly, 2006; Ladouceur & Sevigny, 2005; Loba, Stewart, Klein, & Blackburn, 2001). Contrary to the opinion of some advocates, results of some studies show that complex game features (i.e., number of symbols appearing on screen, rate of speed or sound) did not lead participants to engage in greater gambling persistence (Christopherson & Weatherly, 2006; Loba, Stewart, Klein, & Blackburn, 2001).

Overall, our systematic review of gambling and technology-related literature found disproportionately fewer studies addressing the environment component of the Epidemiologic Triangle than the agent and host components; those studies that do exist show promise for the development and implementation of safety features for new gambling technology. Unfortunately, these studies  have been methodologically rudimentary and limited in scope (Peller, LaPlante, & Shaffer, under review). However, the history of safety feature manufacturing for other industries reminds us that it is possible for safety devices to become safer as they evolve and mature. For example, the effectiveness of automobile safety features has improved dramatically over time (Armes, 2005; Hasbrook, 1956). Empirical research investigating the causes of passenger injuries has been essential to improving the engineering of automobile safety features (Green & Woodrooffe, 2006; Motao, Cummings, Haitao, & Cook, 2007; Shladover & Tan, 2006; Woodruff & Gregory, 2005). Ultimately, the creation of parameters that promote safer gambling for new gambling technology will need to involve collaboration between researchers, industry, and policymakers.

Acknowledgments

The Division on Addictions receives funding for its studies of Internet gambling from bwin.com, Interactive Entertainment AG. The Division also receives funding from the National Center for Responsible Gaming, National Institute of Mental Health (NIMH), National Institute of Alcohol Abuse and Alcoholism (NIAAA), National Institute on Drug Abuse (NIDA), the Massachusetts Council on Compulsive Gambling, the State of Nevada Department of Public Health, the Massachusetts Family Institute, and others. The authors of this editorial take responsibility for its content and do not personally benefit (i.e., stocks, etc.) from gaming interests.

References

Armes, A. (2005). National seat belt usage at record 82 percent. Safety & Health, 172(6), 16-16.

Bulkeley, W. M. (1995, August 16). Feeling Luck? Electronics is bringing gambling into homes, restaurants and planes. Wall Street Journal, pp. 1, A7.

Centers for Disease Control and Prevention. (2007). Understanding the Epidemiologic Triangle through Infectious Disease.   Retrieved September 20, 2007, from http://www.bam.gov/teachers/activities/epi_1_triangle.pdf

Christopherson, K. M., & Weatherly, J. N. (2006). The effect of visual complexity when playing a slot-machine simulation: the role of computer experience, computer anxiety,and optimism. Computers in Human Behavior, 22(6), 1072-1079.

Federal Trade Commission. (2003). Online gambling and kids: a bad bet. Retrieved December 20, 2003, from http://www.ftc.gov/gamble

Green, P. E., & Woodrooffe, J. (2006). The estimated reduction in the odds of loss-of-control type crashes for sport utility vehicles equipped with electronic stability control. Journal of Safety Research, 37(5), 493-499.

Hasbrook, A. H. (1956). The historical development of the crash-impact engineering point of view. Clinical Orthopaedics 8, 268-274.

Korn, D. A., & Shaffer, H. J. (1999). Gambling and the health of the public: Adopting a public health perspective. Journal of Gambling Studies, 15(4), 289-365.

Ladouceur, R., & Sevigny, S. (2005). Structural Characteristics of Video Lotteries: Effects of a Stopping Device on Illusion of Control and Gambling Persistence. Journal of Gambling Studies, 21(2), 117-131.

Loba, P., Stewart, S. H., Klein, R. M., & Blackburn, J. R. (2001). Manipulations of the features of standard video lottery terminal (VLT) games: Effects in pathological and non-pathological gamblers. Journal of Gambling Studies, 17(4), 297-320.

Motao, Z., Cummings, P., Haitao, C., & Cook, L. J. (2007). Association of rear seat safety belt use with death in a traffic crash: a matched cohort study. Injury Prevention, 13(3), 183-185.

Nova Scotia Gaming Corporation. (2006). NSGC Announces Pilot Test of Internet Gambling Software.   Retrieved September 20, 2007, from http://www.nsgc.ca/news.php?news_id=7

Peller, A. J., LaPlante, D. A., & Shaffer, H. J. (under review). Review of Experimental Research Studies Examining Parameters for Safer Gambling Behavior.

Shaffer, H. J., & Korn, D. A. (2002). Gambling and related mental disorders: a public health analysis. In J. E. Fielding, R. C. Brownson & B. Starfield (Eds.), Annual Review of Public Health (Vol. 23, pp. 171-212). Palo Alto: Annual Reviews, Inc.

Shladover, S., & Tan, S.-K. (2006). Analysis of Vehicle Positioning Accuracy Requirements for Communication-Based Cooperative Collision Warning. Journal of Intelligent Transportation Systems, 10(3), 131-140.

Wood, R. T., Griffiths, M. D., & Parke, J. (2007). Acquisition, development, and maintenance of online poker playing in a student sample. Cyberpsychology and Behavior, 10(3), 354-361.

Woodruff, C., & Gregory, S. (2005). Profile of Internet Gamblers: Betting on the Future. UNLV Gaming Research & Review Journal, 9(1), 1-14.

Zinberg, N. E. (1984). Drug, set, and setting: the basis for controlled intoxicant use. . New Haven: Yale University Press.

Zinberg, N. E., & Shaffer, H. J. (1985). The social psychology of intoxicant use: the interaction of personality and social setting. In H. B. Milkman & H. J. Shaffer (Eds.), The Addictions: Multidisciplinary Perspectives and Treatments. Lexington, MA: Lexington Books.

ASHES Vol. 3(10) - Beyond Cigarettes: The Prevalence of Polytobacco Use in the United States

Much tobacco research and public health reporting has focused on cigarettes, but seven billion dollars worth of cigars, pipes, and smokeless tobacco are sold in the United States each year (U.S.D.A.).  The few studies of polytobacco use (i.e., cigarette use in combination with other tobacco products) suggest that such use can lead to increased risk of tobacco-related diseases and nicotine addiction (Gilpin & Pierce, 2003; Wetter et al., 2002).  This week’s ASHES reviews a study which explores the prevalence and characteristics of polytobacco users across the United States.

Bombard, Pederson, Nelson, and Malarcher (2007) analyzed the Behavioral Risk Factor Surveillance System (BRFSS) data from the ten states (i.e., Arkansas, Colorado, Delaware, Indiana, Nebraska, New Jersey, North Carolina, North Dakota, Texas, and Wyoming) whose surveys incorporated a module focusing on the consumption of tobacco products.  The random-digit-dialed telephone survey included items about whether participants (n=56,099) ever smoked cigarettes (i.e., smoked 100 or more cigarettes), used cigars, smokeless tobacco, pipes, and bidis, and currently smoked cigarettes and used cigars, smokeless tobacco, pipes, and bidis. 

Results indicated that 22.4% of adults currently used cigarettes (i.e., using every day or most days) but only 3.4% of adults were polytobacco users. Table 1 shows that the predictive value of many characteristics included in the multivariate analysis is weak.  However, gender, education level, and “more-than-moderate” alcohol use predicted both cigarette and polytobacco use.  Men reported a slightly higher likelihood of smoking cigarettes than women and were 9.6 times more likely than women to be polytobacco users.  Respondents with less than a high school education were equally likely to be cigarette smokers as polytobacco users; respondents who reported “more-than-moderate” alcohol use were more likely to be cigarette smokers and significantly more likely to be polytobacco users. 

Table 1. Prevalence and characteristics associated with cigarette use among adults and current polytobacco use among adult smokers (adapted from Bombard et al., 2007).

This study is limited to respondents from the ten states that included a tobacco product module within their BRFSS surveys; findings from the study might not generalize well to populations in other states.  Also, responses were self-report, possibly underestimating or biasing the prevalence of tobacco use.

This study is one of the first to examine the prevalence of, and characteristics associated with, polytobacco use; the findings provide significant insight into tobacco use beyond cigarettes.  Although these results indicate a low prevalence of polytobacco use, especially among women, clinicians and scientists should not overlook this phenomenon.  More information is needed about the escalated risk profile associated with polytobacco use. Hopefully, a better understanding of the extent of polytobacco use and the population of polytobacco users will stimulate enhanced intervention and prevention programs.

What do you think? Comments can be addressed to Sara Kaplan.

References

Bombard, J. M., Pederson, L. L., Nelson, D. E., & Malarcher, A. M. (2007). Are smokers only using cigarettes?  Exploring current polytobacco use among an adult population. Addictive Behaviors, 32, 2411-2419.

Gilpin, E. A., & Pierce, J. P. (2003). Concurrent use of tobacco products by California adolescents. Preventive Medicine, 36, 575-584.

U.S.D.A. Briefing Room.  Tobacco:  Data tables.  Expenditures for tobacco products and disposable personal income, 1989-2005. Washington, D.C.: U.S. Department of Agriculture, Economic Research Service.

Wetter, D. W., McClure, J. B., de Moor, C., Cofta-Gunn, L., Cummings, S., Cinciripini, P. M., et al. (2002). Concomitant use of cigarettes and smokeless tobacco:  Prevalence, correlates, and predictors of tobacco cessation. Preventive Medicine, 34, 638-648.

The DRAM, Vol. 3(10) - Comorbidity among Repeat Driving Under the Influence of Alcohol Offenders

Psychiatric comorbidity is elevated among people guilty of driving under the influence of alcohol (DUI; Lapham et al., 2001). Consequently, understanding the psychiatric profiles of repeat DUI offenders is important for developing and delivering effective treatment (Nelson et al., 2007).  This week’s DRAM presents epidemiological information about the frequency of psychiatric disorders among repeat DUI offenders attending a treatment program compared to the general US adult population (Shaffer, Nelson, LaPlante, LaBrie, & Albanese, 2007).

Massachusetts’ judges can offer adults who have received two or more DUI convictions participation in a two-week inpatient treatment program as an alternative to incarceration.  As part of the program’s intake procedure, trained counselors administer a truncated version** of the Composite International Diagnostic Interview (CIDI) to all eligible admissions (i.e., those who spoke English and had no cognitive difficulties). The CIDI is a comprehensive, standardized, and computer guided instrument to assess interviewees for the presence of substance use and other mental disorders according to ICD-10 and DSM-IV criteria.  The investigators compared the psychiatric profiles of 729 eligible  and consenting consecutive admissions during a period of 15 consecutive months (79% of eligible admissions) to data from the National Comorbidity Survey Replication (NCS-R; Kessler, Berglund, Demler, Jin, & Walters, 2005). The NCS-R used the same version of CIDI to survey a nationally representative household sample of adults (N = 9,282).

The sample was 81% male, 88% Caucasian, and the average age was 39.7 (SD = 11.6).  Thirty-two percent had an income lower than $20,000, 72% had a high school education or less, 63% were employed, and 97.6% qualified for a substance use disorder during their lifetime.   Figure 1 shows the prevalence of 1, 2, and 3 or more different disorders occurring during participants’ lifetime and within the past year. The DUI treatment population consistently had a significantly greater likelihood of disorder than the general population.

Figure 1.  Comparing the prevalence of psychiatric comorbidity between repeat DUI offenders and a general population community sample

Adapted from Shaffer, Nelson, LaPlante, LaBrie, & Albanese, 2007
Note:  Includes only disorders measured by both this study and the NCS-R. 
*p<.001

One limitation of the Shaffer et al. study is that the study sample might not generalize to all repeat DUI offenders in Massachusetts or elsewhere.  The treatment program only served 53% of those repeat offenders who were sentenced to treatment and agreed to that option instead of prison time.  Therefore, those who did not attend this program might have a different diagnostic profile of addictive and psychiatric disorders.  Furthermore both the DUI treatment group and the NCS-R group self-reported psychiatric symptoms, which might reflect a reporting bias.

Despite these limitations, these results demonstrate that multiple DUI offenders are more likely than the general population to suffer from comorbid psychiatric disorders in addition to a substance use disorder.  Therefore, DUI treatment programs will need to include a comprehensive assessment of pathology and develop treatment plans that account for complex psychiatric profiles.

**The truncated CIDI assessed past-year and lifetime alcohol abuse and dependence, drug abuse and dependence, nicotine dependence, pathological gambling, major depression, mania, hypomania, dysthymia, generalized anxiety disorder, posttraumatic stress disorder, and conduct disorder.  Authors added assessment for intermittent explosive disorder and attention-deficit hyperactivity disorder after 107 people had completed the CIDI.

What do you think?  Comments can be addressed to Leslie Bosworth

References

Kessler, R. C., Berglund, P., Demler, O., Jin, R., & Walters, E. E. (2005). Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry, 62, 593-602.

Lapham, S. C., Smith, E., C'de Baca, J., Chang, I., Skipper, B. J., Baum, G., et al. (2001). Prevalence of psychiatric disorders among persons convicted of driving while impaired. Archives of General Psychiatry, 58, 943-949.

Nelson, S. E., LaPlante, D. A., Peller, A. J., LaBrie, R. A., Caro, G., & Shaffer, H. J. (2007). Implementation of a computerized psychiatric assessment tool at a DUI treatment facility: A case example. Administration and Policy in Mental Health, 34, 489–493.

Shaffer, H. J., Nelson, S. E., LaPlante, D. A., LaBrie, R. A., & Albanese, M. (2007). The epidemiology of psychiatric disorders among repeat DUI offenders accepting a treatment-sentencing alternative option. Journal of Consulting and Clinical Psychology, 75(5), 795-803.

What do you think?  Comments can be addressed to Leslie Bosworth

The WAGER Vol. 12(10): Another DUI: Deciding Under the Influence of Emotion

Somatic markers (i.e., affective states associated with former experience) guide healthy decision-making (Bechara & Damasio, 2002). Research suggests that poor decision-making among disordered gamblers (Goudriaan, Oosterlaan, de Beurs, & van den Brink, 2005) might originate from prefrontal cortex dysfunction that impairs the use of somatic markers to guide decisions (Brand et al., 2005; Cavedini, Riboldi, Keller, D'Annucci, & Bellodi, 2002). However, recent research (Preston, Buchanan, Stansfield, & Bechara, 2007) suggests an alternative source of decision-making difficulties on gambling tasks: anticipatory stress.

Preston and colleagues (2007) assigned forty university students to either an experimental condition, in which they thought they would have to give a public speech after completing the Iowa Gambling Task (IGT: Bechara, Damasio, Damasio, & Anderson, 1994; Bechara, Tranel, Damasio, & Damasio, 1996), or a control condition (i.e., IGT only). The IGT has subjects choose between four card decks; each card is associated with either a win or a loss, and the decks differ in payoff rate. Disadvantageous decks give large immediate rewards but also large or frequent losses, whereas advantageous decks give smaller rewards but also smaller overall losses. 

Figure 1. Improvement on IGT as a Function of Group and Learning Block

Note. The figure displays the improvement from block 1 -2, and from 2-3. Participants in the experimental group show significant (p<.05) delayed and diminished learning compared to controls. The negative score of the controls from block 2-3 is due to the fast learning from block 1-2, which makes further improvement difficult.

Individuals in the experimental group learned the task more slowly than controls (See Figure 1), more often failed to correct disadvantageous choices, and had suboptimal outcomes.

Though this study is limited by its small sample size, it might hold important implications for our understanding of disordered gambling. The findings suggest that anticipatory stress significantly influences the emotional guidance of decision-making; other emotional interference might have a similar effect. Therefore, impaired decision-making among disordered gamblers might not be due only to a neurological deficit as some have suggested elsewhere. Several studies have shown high co-morbidity between disordered gambling and emotional problems (e.g., depression, mania, or anxiety -- Crockford & el-Guebaly, 1998; Petry, Stinson, & Grant, 2005). It is possible that these emotional problems have a similar influence to that of anticipatory stress on disordered gamblers’ decision-making, perhaps interfering with the somatic markers that ought to guide that decision-making and thereby exacerbating gambling problems. For example, research has found that depressed patients show impaired performance on the IGT (Must et al., 2006).

The potential influence of these emotional problems on decision-making in gambling situations reinforces the importance of recognizing and addressing these co-morbid disorders when treating people with gambling-related problems.

What do you think? Comments can be addressed to Line Gebauer.

References
Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50, 7-15.

Bechara, A., & Damasio, H. (2002). Decision-making and addiction (part I): Impaired activation of somatic states in substance dependent individuals when pondering decisions with negative future consequences. Neuropsychologia, 40(10), 1675-1689.

Bechara, A., Tranel, H., Damasio, H., & Damasio, A. R. (1996). Failure to respond autonomically to anticipated future outcomes following damage to prefrontal cortex. Cerebral Cortex, 6, 215-225.

Brand, M., Kalbe, E., Labudda, k., Fujiwara, E., Kessler, J., & Markowitsch, H. J. (2005). Decision-making impairments in patients with pathological gambling. Psychiatry Research, 133(1), 91-99.

Cavedini, P., Riboldi, G., Keller, R., D'Annucci, A., & Bellodi, L. (2002). Frontal lobe dysfunction in pathological gambling patients. Biological Psychiatry, 51, 334-341.

Crockford, D. N., & el-Guebaly, N. (1998). Psychiatric comorbidity in pathological gambling: a critical review. Can J Psychiatry, 43(1), 43-50.

Goudriaan, A. E., Oosterlaan, J., de Beurs, E., & van den Brink, W. (2005). Decision making in pathological gambling: A comparison between pathological gamblers, alcohol dependents, persons with Tourette syndrome, and normal controls. Cognitive Brain Research, 23(1), 137-151.

Must, A., Szabó, Z. n., Bódi, N., Szász, A., Janka, Z. n., & Kéri, S. (2006). Sensitivity to reward and punishment and the prefrontal cortex in major depression. Journal of Affective Disorders, 90(2), 209-215.

Petry, N. M., Stinson, F. S., & Grant, B. F. (2005). Comorbidity of DSM-IV Pathological Gambling and Other Psychiatric Disorders: Results From the National Epidemiologic Survey on Alcohol and Related Conditions. Journal of Clinical Psychiatry, 66(5), 564-574.

Preston, S. D., Buchanan, T. W., Stansfield, R. B., & Bechara, A. (2007). Effects of anticipatory stress on decision making in a gambling task. Behavioral Neuroscience, 121(2), 257-263.

STASH Vol. 3(9) - The Tortoise or the Hare: Comparing Physiological & Subjective Effects of Methamphetamine and Cocaine

Conventional wisdom asserts that drugs of abuse have different patterns of action and that a drug of choice among abusers is the drug that best fits abusers’ preferred course of action (Khantzian & Shaffer, 1981; Mathias, 2001; Shaffer & Simoneau, 2001). However, empirical evidence about the matter also indicates that an abuser’s drug of use also depends on its ease of access, ingestion, and surrounding social circumstances (Harford, 1978). In this issue of STASH, we discuss Newton, De La Garza II, Kalechstein, and Nestor’s (2005) comparison of the subjective and physiologic effects of cocaine and methamphetamine. 

In this single-blind experiment, researchers administered saline placebo and 40mg of cocaine to 14 non-treatment seeking cocaine dependent volunteers and saline placebo and 30mg of methamphetamine to 11 non-treatment seeking methamphetamine dependent volunteers. Researchers had detoxed patients for 3 days prior to the study. They measured how “high” the subjects were by obtaining their subjective ratings of adjectives. The researchers recorded cardiovascular responses by automatically monitoring heart rate, and systolic and diastolic blood pressure (BP). Both the physiological and subjective measures were obtained at -15, 3, 6, 10, 15, 20, and 30 minutes after injection of saline placebo followed by the active drug of choice. Heart rate also was measured at 60 minutes post injection. Researchers used repeated measures analyses of variance (ANOVA) and Huynh-Feldt sphericity correction to calculate time and group by time effects from the observed data. 

Figure 1 shows that the participants’ description of “highs” correlated with heart rates; this was evidenced for both cocaine (red lines) and methamphetamine (blue lines). Methamphetamine produced a later onset and longer surge (square markers) than cocaine (diamond markers). Methamphetamine’s cardiovascular effects were felt at five minutes, peaked at 10 minutes, and remained relatively high for an extended period of time. Cocaine, on the other hand, caused an immediate reaction that peaked after10 minutes for heart rate. However, after the peak, the effects of cocaine diminished rapidly and the users returned to almost their initial, baseline state.

Figure 1: Cardiovascular and Subjective Effects: Methamphetamine vs. Cocaine (Adapted from: Newton et al, (2005))

This study has some limitations. First, the cocaine and methamphetamine groups might not be representative of the cocaine and methamphetamine dependent populations. (Substance Abuse and Mental Health Services Administration, 2007a, 2007b; Winslow, 2007). Second, researchers administered both drugs intravenously, which can result in a different rate of absorption than other routes of administration for these users. Users often have several routes of administration (e.g., oral, inhaled, intravenous). Third, laboratory settings are very different from the in vivo contexts within which users administrate their drugs. Research has shown that the social setting influences subjective state more than most observers recognize (Zinberg, 1984).

Despite these limitations, the mean heart rate changes and the participants’ descriptions of subjective effects indicate a correlation between substance user perceptions, drug induced physiological effects, and the need that these circumstances create for drug use. An understanding of the preferred type or attributes of drug effect could help researchers and practitioners to better redirect craving and find healthier substitutes. For example, a methamphetamine user could redirect addictive tendencies towards another stimulating activity (e.g., cross country running), which somewhat mimics, but is healthier, than stimulant use.

What do you think? Comments can be addressed to Ingrid Maurice

References

Harford, R. J. (1978). Drug Preferences of multiple drug abusers. Journal of Consulting and Clinical Psychology, 46(5), 908-912.

Khantzian, E. J. (1975). Self selection and progression in drug dependence. Psychiatry Digest, 36, 19-22.

Khantzian, E. J. (1997). The self-medication hypothesis of substance use disorders: A reconsideration and recent applications. Harvard Review of Psychiatry, 4(5), 231-244.

Mathias, R. (2001). Even Modest Cocaine Use May Cause Brain Changes That Could Contribute to Addiction. NIDA Notes, 16(3).

Newton, T. F., De La Garza II, R., Kalechstein, A.D. , Nestor, L. (2005). Cocaine and methamphetamine produce different patterns of subjective and cardiovascular effects. Pharmacology, Biochemistry and Behavior, 82(1), 90-97.

Simon, S. L., Richardson, K., Dacey, J, Glynn, S, Domier, C.P., Rawson, R.A., et al. (2002). A comparison of patterns of methamphetamine and cocaine use. Journal Addictive Disorders, 21(1), 35-44.

Substance Abuse and Mental Health Services Administration. (2007a). The NSDUH Report: Demographic and Geographic Variations in Injection Drug Use.Rockville.

Substance Abuse and Mental Health Services Administration. (2007b). The NSDUH Report: Worker Substance Use, by Industry Category.Rockville.

Winslow, B. T., Voorhees, K.I, Pehl K.A. (2007). Methamphetamine Abuse. American Family Physician, 76(8), 1169-1174.

Zinberg, N. E. (1984). Drug, Set, and Setting: The Basis for Controlled Intoxicant Use. New Haven: Yale University Press.