The BASIS provides a forum for the free exchange of information related to addiction, and public access to the latest scientific developments and resources in the field.
Our aim is to strengthen worldwide understanding of addiction and minimize its harmful effects.
The Division on Addiction, Cambridge Health Alliance, a Harvard Medical School teaching affiliate.
States and the federal government have enacted public policies that assist with smoking cessation. A popular line of treatment is over-the-phone counseling via quitline. Quitlines can and often do serve as the first and sometimes the only line of help for smokers who want to quit. Since 2004, every state in the U.S., the District of Columbia, and Puerto Rico has operated a tobacco cessation quitline (American Lung Association, 2011). This week’s ASHES focuses on these state quitlines by examining data presented in the American Lung Association’s State of Tobacco Control Report (2011).
Methods
The American Lung Association’s State of Tobacco Control Report (2011) provides information at the state level about the following quitline statistics:
1. Dollars spent, per smoker, on quitlines; 2. percentage of all smokers who receive treatment via the quitlines
We calculated an additional variable, quitline cost effectiveness, operationalized as % of smokers who received treatment / money spent per smoker
If states spend large amounts of money but serve few of their smokers, their score on this variable will be low; if, conversely, they spend less money but serve more of their smokers, their score will be high.
Results
Table 1. State Spending on Quitlines and Smokers Served
There was a highly significant correlation (r = .667, p < .001), between the money spent per smoker, and the percentage of smokers who received treatment via quitline counseling.
The five states that spent the most money per smoker were South Dakota, Wyoming, Maine, North Dakota, and Delaware.
The five states that served the greatest number of smokers were South Dakota, New York, Oklahoma, Montana, and Iowa.
The five most “cost effective” states (% served / spending) were Connecticut, New York, Wisconsin, Iowa, and Missouri. There was little overlap between the highest-spending states and the most cost-efficient states.
Limitations
This is a correlational study; therefore it cannot inform us about the causal connection between spending and treatment.
The fact that more smokers are receiving treatment in a given state does not tell us about the quality of that treatment. Greater dollars spent might equate to better care, not just more care.
Discussion
Though dollars spent was positively associated with treatment engagement, states that were most cost effective in their quitline treatment programs were not the same states that reported the most spending. The CDC and the North American Quitline Consortium report that quitline services are not receiving the optimal rate of funding ($10.53 per tobacco user) to provide best practice-level services. However, appeals for increased funding must be considered carefully. Throwing more money into state-funded quitlines might not necessarily be predictive of better overall treatment efficiency.
- Kat Belkin and Sarah Nelson
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References
American Lung Association (2011). Helping smokers quit: Tobacco cessation coverage 2011. Accessible at http://www.lungusa.org/assets/documents/publications/smoking-cessation/helping-smokers-quit-2011.pdf
Research shows that the emergency room (ER) might be an important location for mental health screening and intervention, particularly drug and alcohol screening (Fleming et al., 2007). However, most ERs have limited time and resources to conduct such interventions. This week, the DRAM reviews a study examining the implementation and efficacy of an ER-based screening and brief intervention for young adults delivered through text messaging (Suffoletto, Callaway, Kristan, Kraemer, & Clark, 2011).
Method
Participants were 45 individuals (41% of those initially recruited; 87% of those who screened in) recruited during emergency room visits. Participants were 18-24 years old and screened positive for hazardous drinking according to a past-three month version of the Alcohol Use Disorder Identification Test – Consumption (AUDIT-C: Dawson, Grant, & Stinson, 2005).
At baseline and 12-week follow-up, all participants completed a computerized timeline follow-back (TLFB) assessment of their past-three month drinking, as well as the Rutgers Alcohol Problems Index to assess past-three month alcohol related problems.
During the study, all participants received weekly texts for 12 weeks. Researchers randomized participants into one of three groups:
Intervention (n = 15): Participants received weekly texts inquiring about their alcohol consumption. If they drank at hazardous levels during that week, they received additional texts asking them to set a goal to reduce their alcohol consumption during the next week and providing tips for reducing or assessing their drinking levels.
Assessment (n = 15): Participants received the same text inquiry about their consumption as the intervention group, but no follow-up texts about reducing or assessing their drinking.
Control (n = 15): Participants received weekly text messages reminding them that the researchers would ask them to complete a final survey at the end of the study.
Results
Almost all (97%) of the young adult ER patients approached for the study willingly completed the AUDIT-C screen, and approximately half screened positive for hazardous drinking. All but one potential participant had access to a phone with text messaging capabilities. Retention was high: 70-80% of the participants in the assessment and intervention conditions responded to all 12 weekly texts, and more than 90% responded to at least one.
According to weekly responses, participants who received the text-based intervention were less likely to have heavy drinking days (i.e., drinking more than 3 [women] of 4 [men] drinks in a single day) over the 12 weeks compared to participants in the assessment condition. Specifically, participants in the intervention condition had fewer weeks with a heavy drinking day (Median=2, IQR=1-7, compared to Median=7, IQR=3-9), and were less likely to have back-to-back weeks with heavy drinking days (58%, 95% CI=47-68, compared to 69%, 95% CI=58-79) than participants in the assessment condition.
As Figure 1 shows, according to comparisons between the baseline and follow-up TLFB assessments of prior month drinking, participants in the intervention condition decreased their heavy drinking days and drinks per day, whereas participants in the assessment condition increased their heavy drinking days and drinks per day.
Figure 1. Change in drinks per day and heavy drinking days from baseline to 12-week follow-up (adapted from Suffoletto et al., 2011).
*p<.05 between conditions for change in number of heavy drinking days; **p<.001 between conditions for change in number of drinks per drinking day Note. Negative numbers represent decreases.
Limitations
The small sample size and short follow-up period limit the strength of the study.
Control participants’ 12-week follow-up data might have been less accurate than other participants’ follow-up data, because researchers did not prompt them to remember their alcohol consumption on a weekly basis.
Conclusions
Suffoletto et. al’s study suggests that text messaging might be a promising brief intervention strategy, particularly for young adults. As communication channels become more advanced and innovative, public health approaches should explore the value of utilizing contemporary channels for promoting positive health behavior.
-Sarah Nelson
What do you think? Please use the comment link below to provide feedback on this article.
References
Dawson, D. A., Grant, B. F., & Stinson, F. S. (2005). The AUDIT-C: screening for alcohol use disorders and risk drinking in the presence of other psychiatric disorders. Compr Psychiatry, 46(6), 405-416.
Fleming, E. A., Gmel, G., Bady, P., Yersin, B., Givel, J. C., Brown, D., et al. (2007). At-risk drinking and drug use among patients seeking care in an emergency department. J Stud Alcohol Drugs, 68(1), 28-35.
Suffoletto, B., Callaway, C., Kristan, J., Kraemer, K., & Clark, D. B. (2011). Text-Message-Based Drinking Assessments and Brief Interventions for Young Adults Discharged from the Emergency Department. Alcohol Clin Exp Res.
Gambling-related problems often co-occur with other risky behavior patterns, such as excessive alcohol or substance use (Kessler et al., 2008). However, it is unclear whether efforts to address one behavior (e.g., gambling) will also lead to decreases in other problematic behaviors, or, instead, result in addiction-hopping, substituting one excessive behavior for another (Shaffer et al., 2004). This week, the WAGER reviews a study that examined the weekly alcohol use trajectories of treatment-seeking pathological gamblers to understand more fully the drinking patterns in that population (Rash et al., 2011).
Method
Participants were 163 individuals taking part in one of two randomized clinical trials of psychotherapy for pathological gambling who reported any alcohol consumption in the 36-week period of data collection (56% of trial participants).
Participants completed the timeline followback (TLFB) procedure at baseline, 8 weeks post-treatment, and 24 weeks post-treatment to report their alcohol use in the 12 weeks prior to gambling treatment, as well as the 24 weeks post-treatment.
Based on the alcohol use data, researchers split participants into one of two groups:
Ever-risky drinkers (n = 76) drank more than 14 standard drinks per week or four per day for males, and more than seven drinks per week or three per day for females on at least one occasion during the 12 weeks prior to treatment or 24 weeks post-treatment.
Non-risky drinkers (n = 87) never drank at the above-specified risky levels during the course of the study.
Results
Particularly among risky drinkers, alcohol use decreased prior to treatment entry, but then escalated again post-treatment.
There were significant changes in at-risk status from the pretreatment period to the period following treatment entry. Fifty-five participants qualified as at-risk drinkers prior to treatment entry; 26 of those (47%) did not drink at risky levels after treatment entry. One hundred eight participants did not qualify as at-risk drinkers prior to treatment; however, 21 of those (19%) exhibited at-risk drinking after treatment entry.
Figure 1. Drinks per day and drinking days pre-, during, and post-gambling treatment (adapted from Rash et al., 2011).
Limitations
The alcohol use data rely on retrospective self-report. Participants may have under- or over-estimated their alcohol use intake.
Results are limited to gamblers-seeking treatment and do not provide information about whether the gambling treatment itself influenced alcohol-use patterns.
Conclusions
The reviewed study suggests that entry into gambling treatment might be associated with reductions in alcohol use. According to the syndrome model of addiction (Shaffer et al., 2004), various risky behavior patterns like excessive drinking or gambling may indicate an underlying addiction syndrome. However, the behaviors associated with that syndrome often follow different trajectories. Most risky gamblers who also drank reduced their drinking behavior post-treatment, but a small subset of the population persisted and actually worsened, suggesting that efforts to address one behavior may sometimes result in addiction-hopping.
- Kat Belkin
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References
Kessler, R. C., Hwang, I., LaBrie, R., Petukhova, M., Sampson, N. A., Winters, K. C., & Shaffer, H. J. (2008). DSM–IV pathological gambling in the National Comorbidity Survey Replication. Psychological Medicine, 38, 1351–1360. doi:10.1017/S0033291708002900
Rash, C. J., Weinstock, J., & Petry, N. M. (2011). Drinking patterns of pathological gamblers before, during, and after gambling treatment. Psychology Of Addictive Behaviors, 25(4), 664-674.
Shaffer, H., LaPlante, D., LaBrie, R., Kidman, R., Donato, A., & Stanton, M. (2004) Toward a syndrome model of addiction: Multiple expressions, common etiology. Harvard Review of Psychiatry, 12(6), 367-374.
Children who score relatively high on intelligence tests become relatively healthy adults, in some important respects. For instance, White and Batty (2011) noted that they are less likely to smoke, more likely to engage in physical activity, and more likely to eat healthy foods than their peers. However, research is equivocal regarding a link between childhood IQ scores and adult use of illegal drugs (as reviewed by White & Batty, 2011). In this week’s STASH, we review a recent longitudinal, population-based study investigating the connection between childhood IQ and adult use of illegal drugs (White & Batty, 2011).
Methods
The authors used data from the 1970 British Cohort Study, an ongoing longitudinal study of children born in Great Britain during April, 1970.
The children in the study were enrolled at birth and were invited to participate at ages 5, 10, 16, 26, and 29-30.
At age 5, participants completed four tests purported to measure general cognitive ability (e.g., the Human Figure Drawing test, shown at right). The researchers transformed scores to the standard IQ distribution (mean = 100, SD = 15). Also at these assessments, the researchers made note of parents’ social class.
At age 16, participants indicated their lifetime use of cannabis and cocaine (yes/no).
At age 30, participants indicated their past-year use of several illegal drugs (yes/no).
The researchers completed logistic regression to estimate the relation between childhood IQ and participant gender with illegal drug use at ages 16 and 30. They modeled childhood IQ as tertiles, with the lowest IQ group as the reference group. Positive odds ratios therefore indicate that a higher risk of illegal drug use is associated with moderate or high childhood IQ.* In the results presented here, researchers statistically controlled for parental social class at age 5.
Results
The researchers present drug use outcomes separately for men and women at both 16 and 30 years.**
The strongest effects emerged from comparisons between the highest childhood IQ tertile and to the lowest IQ tertile. Figure 1 illustrates only these comparisons.***
Male and female participants with relatively high childhood IQ were more likely to report having used cannabis but not cocaine at age 16.
Male participants with relatively high childhood IQ were more likely to report past-year use of cannabis and cocaine at age 30.
Female participants with relatively high childhood IQ were more likely to report past-year polydrug use and use of cannabis and cocaine at age 30.
Associations between childhood IQ and adult illegal drug use were stronger for women than for men.
Figure 1 Odds ratios and 95% confidence intervals for illegal drug use outcomes at 16 and 30 years, separately for men and women. Adapted from White and Batty, 2011. (Click to enlarge.)
Limitations
Participants did not report important features of their drug use, such as frequency, quantity, and consequences. Accordingly, these results do not speak to the potential association between childhood IQ and substance use disorders (e.g., dependence, abuse).
As with most longitudinal studies, this research was marked by some attrition. For instance, only 46% of the participants at the 30-year follow-up completed all earlier surveys. Indeed, participants who completed the 30-year follow-up had slightly higher childhood IQ scores than participants who did not. It is unclear how this selective attrition might have influenced observed relationships between childhood IQ and adult drug use.
Conclusion In this large, population-based cohort study, the authors observed a consistent pattern: higher childhood IQ predicted greater likelihood of illegal drug use during adulthood, independent of parental social class. The authors speculate about a potential pathway involving personality. According to this reasoning, high IQ individuals use drugs because they are characterized by traits linked with drug use, such as stimulation seeking and openness to experience. The presence of stronger associations for women than men requires further exploration, as does the potential association between childhood IQ and adult substance dependence and abuse.
-Heather Gray
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Reference
White, J., & Batty, G. D. (2011). Intelligence across childhood in relation to illegal drug use in adulthood: 1970 British Cohort Study. Journal of Epidemiology and Community Health, Online first: November 14, 2011.
* Preliminary analyses suggested that modeling IQ as a linear term was inappropriate. ** The interaction term between childhood IQ and participant gender for drug use outcomes was significant at 30 years but not at 16 years. *** Comparisons between the moderate IQ tertile and the lowest IQ tertile were generally in the same direction. Readers interested in more details can see White and Batty (2011).
Addictive behavior is an ongoing public health problem. Fortunately, there are many possible pathways to health and recovery – including somatic healthcare. However, practitioners’ interest in specializing in addiction often drops as their medical training progresses (Ritson, 1991).
This circumstance prompts a reconsideration of the teaching methods used to educate medical students about addiction. In this edition of Addiction & the Humanities, we present a paper that discusses potential benefits of using movies to educate medical students regarding drug addiction, and, hopefully, to facilitate students’ interest in addiction treatment (Cape, 2009).
Cape (2009) suggests that using movies to familiarize medical students with the major concepts of drug addiction problems could help deconstruct myths regarding addiction treatment and addiction-related behaviors. To illustrate, Cape suggests:* Educators might use the movie, The Lost Weekend (Wilder, 1945), to discuss the myth that it is possible to treat addiction just by “love”.
Instructors might show the movie Drugstore Cowboy (Gus Van Sant, 1989) to illustrate some reasons why people start using drugs.
Course instructors also could use movies to launch a discussion related to the nature of withdrawal. Many movies portrait an exaggerated and dramatic illustration of withdrawal symptoms (e.g., French Connection II (Frankenheimer, 1975) or Trainspotting (Boyle, 1996)) in response to stopping drug use, and provide an opportunity to highlight how they differ from reality.
Similarly, movies might be useful educational devices for initiating a discussion of the consequences of addiction. For example, many movies suggest that drug users tend to commit violent crimes, (e.g., Nil by Mouth (Oldman, 1998)). However, drug users more often commit non-violent, rather than violent crimes (Gossop, Marsden, Stewart, & Kidd, 2003).
Cape (2009) also suggests that movies that emphasize and clarify different concepts of addiction might encourage students’ interest in addiction medicine.
Discussion
Cape (2009) concludes his review with the suggestion that using movies in medical student education could help to increase interest in addiction medicine. However, science has not yet determined whether this is possible. As the authors note, although most of these movies do not provide an accurate picture of drug-related reality, such movies might facilitate the relevant discussion and by doing this, increase motivation to become addiction specialists.
It also is important to note that the paper does not report evidence that establishes that “myths” about addictions generate a lack of interest in becoming addiction specialists among medical students. Similarly, the authors do not raise other possible factors (e.g., low payment or job conditions compared to more popular medical specialties) that might influence such a relationship. Therefore, it is still premature to assume that watching and discussing addiction-related movies will improve the situation.
The effectiveness of using movies to educate medical students is also not clear. Entertainment education (e.g., education by watching movies) is a popular persuasive strategy that is especially effective at influencing behavior and overcoming counterarguments and resistance to persuasion (Moyer-Guse, 2008). Unfortunately, there is no empirical evidence that entertainment education is effective to provide factual knowledge about medical conditions, such as alcohol or drug addiction. Although the idea to incorporate addiction related movies into medical education is intriguing, there is not yet sufficient evidence to confirm that this would be an effective practice.
-Julia Braverman
What do you think? Please use the comment link below to provide feedback on this article.
References
Cape, G. (2009). Movie was a vehicle to teach addiction medicine. International review of psychiatry. 21, 213 – 217
Moyer-Guse, E. (2008). Toward a theory of entertainment persuasion: Explaining the persuasive effects of entertainment-education messages. Communication Theory 18, 407 - 425.
Gossop, M., Marsden, J., Stewart, D., & Kidd, T. (2003). The National Treatment Outcome Research Study (NTORS). Addiction, 98(3), 291–303.
Ritson, E. B. (1991). Undergraduate alcohol education. Alcohol & Alcoholism, 26, 273–275.
Many people refer to marijuana as a gateway drug to other illicit drug use. However, some researchers have questioned whether this presumption has distracted researchers from considering the possibility that other more easily attainable substances might act as gateway drugs – for example, nicotine (Volkow, 2011). The week’s ASHES reviews an animal model study that explores whether nicotine exposure is associated with later cocaine use behavior (Levine et al., 2011).
Methods
Researchers separated mice into three groups: control group (exposed to saline); cocaine only group (exposed to cocaine for 7 days); and nicotine and cocaine group (exposed to nicotine for 7 days followed by cocaine for 4 days).
The researchers monitored cocaine-related behavior and physiological changes/differences among experimental groups following substance exposure.
Conditioned place preference (CPP) measured a study subject’s preference (i.e., time spent in an area) for the area where they received a reward (e.g., a drug), compared to a control area (Levine, et al., 2011).
Brain ΔFosB levels – ΔFosB is a protein in the brain that increases in response to cocaine use. Researchers administered nicotine and/or cocaine and tested for the extent of ΔFosB among the experimental groups.
Long Term Potentiation (LTP) – Long-term cocaine administration decreases LTP (Levine, et al., 2011). Researchers observed group-based differences for neuronal LTP responses.
Results
As Figure 1 shows, the cocaine only group displayed significantly greater CPP than the control group (76.5s versus 247.1s, p < 0.05, 223.2% difference). Similarly, the mice pretreated with nicotine showed significantly greater CPP than the cocaine only group (247.1s versus 441.2s, p < 0.05, 78.6% difference).
The nicotine and cocaine group had 74.9% greater ΔFosB levels compared with the cocaine only group (from cocaine only at 3.5 ± 0.69 to nicotine and cocaine at 6.12 ± 0.8, P < 0.05).
Neurons in brain slices from the nicotine and cocaine group differed, evidencing a 46.6% LTP decrease compared with the control group and by a 27.3% decrease compared with the cocaine only group (control: 129 ± 6%, cocaine only: 112 ± 5%, nicotine and cocaine: 88 ± 6%).
Figure 1 – Conditioned Place Preference (CPP) Results among Groups
Limitations
This research was conducted with mice. There is preliminary evidence to suggest that nicotine could have the same priming effects for enhancing cocaine’s effects among humans (Grant, Kaplan, Shepard, & Moore, 2003; Kandel, Yamaguchi, & Chen, 1992), but more research is needed to determine if this is accurate.
The researchers did not control for the priming effects of other substances prior to cocaine.
Discussion
Nicotine exposure altered the brain’s response to cocaine for the above behavioral and physiological/molecular measures. This suggests that nicotine primes the brain to enhance cocaine-associated rewards. The research design could be stronger if the researchers added an additional group, such as saline followed by cocaine, to control for the experiential effects of consuming a substance prior to cocaine. Without additional information it is difficult to attribute the priming to nicotine only. Nevertheless, this preliminary research could facilitate research for the development of new medication for cocaine addiction based on an understanding of the brain’s response to cocaine following nicotine exposure. It is also possible to speculate that smoking cessation could have an effect for cocaine addiction among those who are comorbid (Volkow, 2011).
-Tasha Chandler and Daniel Tao
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References
Grant, B. F., Kaplan, K. D., Shepard, J., & Moore, T. (2003). Source and Accuracy Statement for Wave I of the 2001-2002 National Epidemiologic Survey on Alcoohl and Related Conditions. Bethesda, MD: National Institute on Alcohol Abuse and Alcoholism.
Kandel, D. B., Yamaguchi, K., & Chen, K. (1992). Stages of progression in drug involvement from adolescence to adulthood: Further evidence for the gateway theory. Journal of Studies on Alcohol, 53, 447-457.
Levine, A., Huang, Y., Brisaldi, B., Griffin, E. A., Jr., Pollak, D. D., Xu, S., . . . Kandel, E. R. (2011). Molecular mechinisms for a gateway drug: Epigenetic changes initiated by nicotine prime gene expression by cocaine. Science Translational Medicine, 3(107), 107ra109. doi: 10.1126/scitranslmed.3003062
Volkow, N. D. (2011). Epigenetics of nicotine: Another nail in the coughing. Science Translational Medicine, 3(107), 107ps143. doi: 10.1126/scitranslmed.3003278
Many studies have described an association between academic attainment and alcohol use. For example, Crum et al. (2006) found that drinking among adolescents is associated with more educational problems and Kessler et al. (2005) found that alcohol and substance use disorders are more prevalent among those with lower education levels. It is unclear, however, what role genetic or environmental influences play in this association. This week’s DRAM reviews a study exploring influence of genetic and environmental factors on alcohol use and academic attainment (Latvala, Dick, Tuulio-Henriksson, Suvisaari, Viken, Rose & Kaprio, 2011).
Methods
4,858 Finnish twins (1,546 monozygotic [MZ] twins and 3,312 dizygotic [DZ] twins) comprised the sample.
Participants completed the Rutgers Alcohol Problem Index (RAPI) and reported the maximum number of drinks ever consumed within a 24-hour period, as well as their highest level of education.
Researchers tested ACE models (GLOSSARY LINK) predicting education, alcohol use problems, and maximum drinks, as well as their correlation, from a combination of three variables derived from twin type (i.e., MZ vs. DZ): genetic influences (A), common environmental influences (C; e.g., family environment), and unique environmental influences (E; e.g., friends).
Figure 1. Schematic of models predicting alcohol use and education attainment (Lavata et al. 2011)
Results
As Figure 1 shows, genetic factors (A) and common environmental factors (C), as well as unique environmental factors (E), predicted educational attainment; however, only genetic factors and unique environmental influences predicted the alcohol use outcomes.
Genetic factors (A) were significantly related to the negative association between educational attainment and alcohol use problems, which was small but significant in the full sample (r = -.06 to -.09). Put simply, MZ twins had higher correlations between educational attainment and alcohol use outcomes than DZ twins. This pattern was not found for common (C) or unique environmental (E) influences.
Limitations
The education system in Finland is very different from that in the United States: schooling is free and mandatory through 9th grade (middle school). Schools are of relatively uniform quality (Latvala et al., 2011). This may limit generalizability to countries such as the US, where income and social status can greatly influence education accessibility and quality Twin studies are limited in their generalizability, given the unique context within which twins develop.
This study cannot determine causality; we cannot know whether genetic or environmental factors directly influence educational attainment or alcohol use, only their level of association.
Conclusions
The current study suggests that genetic factors (A) influence both educational attainment and alcohol use, as well as their relationship, suggesting that there may be a common genetic underpinning for both these phenotypes. Common environmental influences (C), however, only influenced educational achievement. These results suggest a genetic influence on both educational attainment and alcohol use. Environmental influences play a greater role in educational attainment than alcohol use.
-Daniel Tao
What do you think? Please use the comment link below to provide feedback on this article.
References
Crum, R.M., Juon, H.S., Green, K.M., Robertson, J., Fothergill, K., Ensminger, M. (2006). Educational achievement and early school behavior as predictors of alcohol-use disorders. Journal of Studies on Alcohol, 67, 75-85.
Kessler, R.C., Berglund, P., Demler, O., Jin, R., Merikangas, K.R., Waters, E.E. (2005). Lifetime prevalence and age-of-onset distributions of DSM-IV disoders in the National Comorbidity Survey Replication. Archives of General Psychiatry, 62, 593-602.
Lavata, A., Dick, D.M., Tuulio-Henriksson, A., Suvisaari, J., Viken, R.J., Rose, R.J., Kaprio, J. (2011). Genetic correlation and gene-environment interaction between alcohol problems and educational level in young adulthood. Journal of Studies on Alcohol and Drugs, 72, 210-220.
The goal of most gambling treatment is abstinence from gambling. Though not everyone who engages in treatment achieves this “cold turkey” goal, it is possible that the goal itself deters some individuals from seeking treatment. This week’s WAGER reports results of a study that examined how common abstinence versus controlled gambling are among problem gamblers in recovery (Slutske, Piasecki, Blaszczynski, & Martin, 2010).
Method
Participants were 104 individuals who qualified for DSM-IV pathological gambling (PG) among a general population sample of 2,382 twin pairs drawn from the Australian twin registry.1
Researchers classified these life-time PG gamblers into three groups according to their symptoms during the past year:
Past-year PG (N = 28): Gamblers who qualified for PG within the past year
Some problems (N = 32): Gamblers who met 1 - 4 PG criteria within the past year
Recovery group (N = 44): Gamblers who had no symptoms of PG within the past year.
Researchers assessed the following indicators of gambling involvement during the heaviest gambling period and during the past year:
Number of days spent gambling
Number of hours spent on a typical gambling day
Percentage of yearly income spent on gambling
Number of different gambling activities –gambling versatility (e.g., horse and dog betting, casino table games, keno, bingo etc.).
Results
Almost all of the recovered gamblers (90%) gambled in the past year, and less than 20% received any formal treatment.
As Figure 1 shows, recovered gamblers demonstrated greatly reduced gambling activity, compared both to other non-recovered participants and to their own previous activity.
Figure 1. Gambling involvement of recovered, partially recovered, and symptomatic PGs
PY=past year; PG=pathological gambling
Limitation
This is a correlational study, based on a cross-sectional survey about past behavior. Therefore we cannot conclude that controlled gambling caused PG recovery.
Measures of gambling activity are based on retrospective self-report and may not reflect actual gambling.
Conclusion
This study does not support the assumption that abstinence is required for PG recovery. Only 10% of those who recovered from gambling-related problems did not gamble during the past year. However, the recovery group demonstrated substantial decreases in gambling involvement compared to non-recovery groups. As with any correlational study, this study does not conclude causality. Future controlled experiments or longitudinal studies are required to examine if abstinence or controlled gambling lead more effectively to PG recovery. However, this study does suggest that allowing for the possibility of a controlled gambling, or “warm turkey”2 goal within treatment might increase treatment engagement
-Julia Braverman & Sarah Nelson
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References
Miller, W. R., & Page, A. C. (1991). Warm turkey: other routes to abstinence. Journal of Substance Abuse Treat, 8(4), 227-232.
Slutske, W. S., Piasecki, T. M., Blaszczynski, A., & Martin, N. G. (2010). Pathological gambling recovery in the absence of abstinence. Addiction, 105(12), 2169-2175.
------------------ 1 The 104 participants in this study were derived from 102 different twin pairs: only two pairs of related twins were included in the sample.
2 Slutske and colleagues (2010) refer to the goal of engaging in controlled gambling as a “warm turkey” approach. The term was first used by Miller & Page (1991) to describe controlled drinking.
About 10 million Americans report that they have driven under the influence of an illicit drug at least once during the past year (US Department of Health and Human Services, Substance and Mental Health Services Association, Office of Applied Studies, 2007). Marijuana is the most commonly used illicit drug (ibid.). This week’s STASH reviews a randomized control study that examines the effect of smoking marijuana versus a placebo on a driving simulator (Anderson, Rizzo, Block, Pearlson & O’Leary, 2010).
Method
Investigators recruited 50 men and 35 women 18 - 31 y/o who used marijuana at least once but fewer than 10 times per month during the past year. The researchers excluded participants whose urine-screening test detected any drug other than tetrahydrocannablnol (THC) the psychoactive ingredient in marijuana.
During the baseline session, participants became familiarized with a math task that would serve as a distracting task during the driving session.
During the smoking session, participants smoked a cigarette that contained 2.9% (active) or 0% (placebo) of THC. The instructions encouraged participants to consume the entire cigarette, but participants could stop anytime if they felt uncomfortable.
Using a driving simulator, participants then performed a driving assessment (see Figure 1). Each participant drove for approximately 15 miles. An uneventful section of the drive lasted one minute. Then, the driving was interrupted by the following events:
Multitasking. Drivers completed a math test designed to distract them from the driving. The investigators measured the number of math errors made compared to the baseline, as well as the speed and the steering wheel position.
Response to emergency vehicle. The task assessed attention to the appearance of a police car. The dependent measures were speed, steering position and reaction time.
Go/NoGo. The task measured safe driving through the yellow light. Safe driving was operationalized as no hesitation in making a decision.
Dog incursion avoidance. The task measured safe avoidance of a dog, measured as the ability to stop the car or steer clear of the dog.
Intersection incursion avoidance. The task measured the speed at first contact and avoidance tactic.
Figure 1. View of a driving simulator (copied from Anderson, Rizzo, Block, Pearlson & O’Leary, 2010)
The researchers assessed participants’ heart rate, self-reported level of “highness” (0: no effect – 10:highest) and sleepiness (Stanford Sleepiness Scale; Hoddesetal. 1973) at baseline, after smoking and after driving.
Results
The results include only those participants who completed the entire cigarette. The analytic sample included 49 men (25 in the active group) and 24 women (9 in the active group)
As expected, marijuana smoking significantly increased heart rate (F = 66.4, p < .001) immediately after smoking, and subjective feeling of “highness” compared to placebo (F = 65.1, p < .001) immediately after smoking.
Women rated themselves as being “higher” than men for both active marijuana and placebo (F = 4.6, p < .05).
Men were less sleepy than women after driving (F = 4.6, p < .04). Women who smoked active marijuana reported to be more sleepy than men after driving (F = 6.0, p < .02), but not immediately after smoking.
Both marijuana and placebo groups performed similarly on all driving tasks; there were no sex differences.
Limitations
The driving simulator results are not necessary directly applicable to real life driving situations.
This study only investigated the first 15 miles of driving under certain conditions. It is still possible that marijuana affects prolonged driving, or driving under conditions that were not investigated in the study (e.g., slippery road or obscure vision).
This study uses a small sample, especially in the active marijuana female group. This might be a reason for a failure to find sex X drug effect interaction for all measures, but sleepiness.
Conclusion
A meta analytic study concluded that there was a subtle effect of marijuana on driving performance (Berghaus, Sheer, & Shmidt, 1995). Anderson et al. (2010) provided results that did not support these earlier findings. Using a modern driving simulator might have influenced these results; in addition, the different finding might have emerged because of Anderson et al. investigated different driving tasks and driving evaluation methods.
Results obtained in a lab are not always generalizable to real life. Specifically, previous studies showed that marijuana influenced driving performance impairments are more likely to be manifest within a driver stimulator test compared to on road settings (US Department of Transportation, 1993). The present study did not find any marijuana effect on simulated driving. However, we should interpret this null finding with caution because there are many methodological (e.g., large measurement error) and analytical (e.g., small sample size; small effect size) reasons for failing to finding differences between groups. In addition, it is possible that smaller undetected effects exist or that marijuana impacts actual driving.
-Julia Braverman
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References
Anderson, B.M., Rizzo, M., Block, R. I., Pearlson, G. D., & O’Leary, D.S. (2010) Sex differences in the effects of marijuana on simulated driving performance. Journal of Psychoactive Drugs, 40, 19 – 30.
Berghaus G, Sheer N, & Schmidt P. (1995) Effects of cannabis on psychomotor skills and driving performance–A meta-analysis of experimental studies. In CN Kloeden and AJ McLean (eds.), Proceedings of the 13th International Conference on Alcohol, Drugs and Traffic Safety. Adelaide, Australia: The University of Adelaide, NHMRC Road Accident Research Unit, 403–409.
Quantificalion of sleepiness: A new approach. Psvchophysiology 10, 11-36.
US Department of Transportation, National Highway Traffic Safety Administration. (1993) It appears performance is more affected by THC in laboratory (settings) than (in) actual driving tests. Marijuana and Actual Driving Performance: Final Report.
US Department of Health and Human Services, Substance and Mental Health Services Association, Office of Applied Studies. (2007) 2006 National Survey on Drug Use and Health: National Results.
During June of this year, Time Magazine and the British newspaper The Independent published stories about a disturbing new trend in drug use: the steady rise in Russia of “krokodil” consumption (Shuster, 2011; Walker, 2011). Krokodil (or crocodile in English) is “homemade” desomorphine, referred to as a very powerful synthetic opiate that is significantly less expensive than heroin. Its name comes from the scaly appearance of users’ skin. This cheap, homemade alternative to heroin has shocking effects on users’ minds and bodies. The story about krokodil was picked up in several blogs, and people began posting graphic videos on YouTube documenting the drug’s destructive consequences.
These stories tended to focus on krokodil’s physical effects, and no wonder. Derived from a combination of codeine, which is available over the counter in Russia, gasoline, paint thinner, iodine, hydrochloric acid, red phosphorus, lighter fluid, and industrial cleaning oil, krokodil causes scarring, gangrene and bone exposure at the site of injection. Other consequences include amputation, brain damage, blood poisoning, burst arteries, tooth loss, HIV from using dirty needles, and early death. Users who try to stop, face an excruciating month-long withdrawal process, marked by seizures, fever, and vomiting and often necessitating powerful tranquilizers for pain.
Please note that the following video is very graphic and contains footage of rotting flesh, needle injection in the groin area, and disturbing images. Please use your discretion before viewing.
The Time Magazine story mentioned that, so far, Russia is the only country in the world to experience krokodil use at epidemic proportions. Estimates of its use in Russia range from 100,000 (Walker, 2011) to 1,000,000 users (Miller, 2011; Shuster, 2011). Regional governors report that krokodil accounts for approximately half of all their addiction and drug-related deaths (Shuster, 2011). As recently as June of this year, officials at the U.S. National Institute on Drug Abuse had no awareness of the drug (Miller, 2011).
In this edition of Addiction & the Humanities, we consider the Russian epidemic of krokodil use from an intersectional lens (Smye, Browne, Varcoe, & Josewski, 2011); in other words, we ask how krokodil use might be shaped by intersecting variables associated with social identity and health. Though krokodil has yet to attract empirical attention, available information indicates that including geography, class, age, and stigma all might play important roles in determining krokodil use.
Geography: Many users report resorting to krokodil at times when heroin becomes too expensive (Walker, 2011), indicating that heroin addiction is a primary factor in krokodil use. Indeed, according to unofficial estimates, Russia has more heroin users than any other country in the world: about two million (Walker, 2011). Russia’s high consumption of heroin could be attributed in part to a quirk of geography (Grau et al., 2009). Russia is closely proximate to Afghanistan, where there was a sharp rise in poppy cultivation following U.S.-led invasion (Stack, 2009). The Russian government’s efforts to try to limit the amount of heroin entering the country inadvertently might have increased the street value of heroin in many areas. In parts of Russia where supplies of heroin are low and prices are high, krokodil use is especially high (Walker, 2011).
Class: Social disadvantage (stemming from poverty, unemployment, homelessness) increases risk for problematic substance use, in part, by creating stress (Mulia, Ye, Zemore, & Greenfield, 2008). During early 2009, Russian poverty rates rose one third compared to late 2008 – 24.5 million compared to 18.5 million, respectively (Huffington Post, 2009). This stressful economic change, along with the inexpensive manufacturing of krokodil (which has been called “a drug for the poor”) - just over $3 US (Walker, 2011) – might be a reason why this drug has increased at epidemic proportions among Russians.
Age: Some observers speculate that one reason Russian teenagers begin using drugs is because of extreme boredom during intense winter months. U.S. research indicates that boredom is a risk factor for drug use (National Center on Addiction and Substance Use, 2003). It appears that getting together with other teenagers to use drugs is normal among bored teens (Shuster, 2011), and perhaps acts as a means of stimulation.
Stigma/Attitudes toward Treatment: Research in other areas of the world has indicated that attitudes toward treatment and addiction-related stigma are treatment-seeking barriers for substance abusers (e.g., (Myers, Fakier, & Louw, 2009). Russians might fear seeking treatment because they want to avoid being identified, targeted, and stigmatized for their addiction. There are several fundamental differences between the psychiatric treatment of addiction in the U.S. and the narcological treatment of addiction in Russia (Mendelevich, 2011). According to Moscow’s Chief Drug Addiction Specialist, “the drug addict is feared and loathed. People… don’t like drug addicts. So the addict feels like a pariah and has no reason to get healthy” (Stack, 2009, para. 31). Most Russian treatment professionals view addiction as deviance and not as a disease, believe that the underlying cause is dissoluteness, and identify religion as an effective treatment option. People with addiction are often stigmatized, viewed as criminals, forced to register at treatment facilities, have few rights, do not have control over their treatment or other options including harm reduction (i.e., Russian treatment is generally abstinence-based), receive convoluted informed consent forms, have their confidentiality infringed upon when specialists turn over information to authorities, and some patients lose their license after registering at treatment clinics in some regions (Mendelevich, 2011). This cultural attitude might be a primary reason why addiction continues to increase in Russia.
Education and Governmental Response: Approximately 83 heroin users die from overdose every day in Russia (Stack, 2009), but most Russians are unaware of the risks (Grau, et al., 2009; Stack, 2009). Educating the public and developing national strategies for preventing drug abuse could have a significant impact on the Russian public health. Russia’s government largely was unprepared to deal with population-wide heroin addiction, as their country was previously unaffected during the Soviet Union. Methadone, a drug often used for treating opioid dependence in the U.S., is illegal in Russia (Stack, 2009).
The Russian government has considered some steps toward curbing the krokodil epidemic. These are short-term solutions, such as banning the websites that describe how to make it, making codeine available by prescription only, and confiscating doses (Shuster, 2011; Walker, 2011).
But krokodil will likely continue to plague the Russia unless and until the government and people address some of the broader social issues described above. Solutions could include, for example, opening federally funded treatment centers that incorporate ethical treatment and more up-to-date psychiatric techniques and education for citizens regarding the risks of krokodil and other drugs.
-Tasha Chandler
What do you think? Please use the comment link below to provide feedback on this article.
References
Grau, L. E., Green, T. C., Torban, M., Blinnikova, K., Krupitsky, E., Ilyuk, R., . . . Heimer, R. (2009). Psychosocial and contextual correlatees of opioid overdose risk among drug users in St. Petersburg, Russia. Harm Reduction Journal, 6(17). doi: 10.1186/1477-7517-6-17
Mendelevich, V. D. (2011). Bioethical differences between drug addiction treatment professionals inside and outside the Russian Federation. Harm Reduction Journal, 8(15). doi: 10.1186/1477-7517-8-15
Mulia, N., Ye, Y., Zemore, S. E., & Greenfield, T. K. (2008). Social disadvantage, stress, and alcohol use among Black, Hispanic, and White Americans: Findings from the 2005 U.S. National Alcohol Survey. Journal of Studies on Alcohol and Drugs, 69, 827-833.
Myers, B., Fakier, N., & Louw, J. (2009). Stigma, treatment beliefs, and substance abuse treatment use in historically disadvantaged communities. African Journal of Psychiatry, 12(3), 218-222.
Smye, V., Browne, A. J., Varcoe, C., & Josewski, V. (2011). Harm reduction, methadone maintenance treatment and the root causes of health and social inequities: An intersectional lens in the Canadian context. Harm Reduction Journal, 8(17). doi: 10.1186/1477-7517-8-17
