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.
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
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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
What do you think? Please use the comment link below to provide feedback on this article.
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
Although research has shown a decline in the number of people smoking cigarettes in the United States, the annual premature death rate associated with smoking (440,000+) remains a cause for concern (U.S. Department of Health & Human Services). One potential strategy for promoting smoking cessation is to provide many readily accessible options for smokers who wish to quit. This week, ASHES reports on a study that tested the efficacy of a smoking cessation program delivered via video messages for cellular phones (cell phone) (Whittaker et al., 2011).
Methods
The researchers recruited participants using radio, Internet, cell phone, magazine, newspaper, and media release advertisements in New Zealand.
Eligibility: Participants had to be 16 years or older, smoke every day, have a desire to quit smoking, and have a cell phone that could receive video messages.
After selecting a quit date, the researchers randomly assigned participants to an intervention or control group: each received mobile messages1 for 6 months.
Control group – This group received one general health video message every 2 weeks.
Intervention group – This group received a tailored set of video messages, text messages, other video messages (e.g., “truth” campaign), and on-demand support via texting key words (e.g., crave, stress, drinking) and a website. The tailored set included more options and contact (e.g., the ability to select and change role model, chronological schedule of messages – 1 video and text message/day for 1 week prior to quit date, 3 video and text messages/day on quit date, 3 video and text messages/day for 4 weeks following quit date, 1 message or other video every 2 days for the next 2 weeks, and then 1 message or other video every 4 days for the rest of the study).
The researchers compared past week abstinence rates at 4 weeks, 12 weeks, and 6 months among the control and intervention groups.
Results
868 people registered to participate in the study; 642 people were ineligible, withdrew from the study, or did not complete a baseline interview; leaving 226 participants (control group n=116 and intervention group n=110).
Control group completion rates – 80.2% (n = 93) at 4 weeks, 75% (n = 87) at 12 weeks, and 77.6% (n = 90) at 6 months.
Intervention group completion rates – 73.6% (n = 81) at 4 weeks, 71.8% (n = 79) at 12 weeks, and 68.2% (n = 75) at 6 months.
Figure 1 illustrates abstinence rates over the course of the study for controls and the intervention group. The groups’ abstinence rates differed the most at 12 weeks, but were very similar at 4 weeks and 6 months.
The control and intervention groups failed to evidence statistically significant differences at these time points (P = 0.8 at 4 weeks, P = 0.3 at 12 weeks, and P = 0.99 at 6 months).
Researchers assumed 35 participants in the intervention group and 26 participants in the control group who had missing data were not abstinent.
Figure 1 – Past week abstinence rates during study
Limitations
The researchers aimed to recruit a much larger sample. The low participation rate suggests that these results might not be generalizable to the New Zealand population or other populations.
The researchers compare one type of video messaging intervention against a more complex type of video messaging intervention for smoking cessation. Without a “control” group of people who did not receive any video messages, it is difficult to determine whether either intervention system was effective or if people were quitting on their own.
Discussion
The results indicate that a more personalized smoking cessation video messaging system with additional resources was not more effective at helping people achieve abstinence than an automated video messaging system. It is unclear if this type of intervention is effective, and it is impossible to interpret these null findings. In this instance, it might be that the intervention was not successful, or that the selection of participants and low participation rate might be responsible or other methodological problems might be responsible for the absence of significant results.
However, video and text messaging intervention programs might hold promise because most people carry their cell phones with them at all times and this type of system could allow for intervention at any time and potentially when it is needed the most. Future research should test the efficacy of this type of intervention among different populations, age groups, and addictions (e.g., gambling, substance use, drinking).
-Tasha Chandler
What do you think? Please use the comment link below to provide feedback on this article.
References
U.S. Department of Health & Human Services. Tobacco prevention and control: New actions to end the tobacco epidemic Retrieved Jul. 13, 2011, from http://www.hhs.gov/tobaccocontrol/index.html
Whittaker, R., Dorey, E., Bramley, D., Bullen, C., Denny, S., Elley, R., . . . Salmon, P. (2011). A theory-based video messaging mobile phone intervention for smoking cassation: Randomized controlled trial. Journal of Medical Internet Research, 13(1), e10. doi: 10.2196/jmir.1553
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1The video messages were a diary of role models who shared their difficulties, techniques, and strategies for quitting.
