The Brief Addiction Science Information Source (BASIS)

ASHES, Vol. 5(9) –Thinking of quitting smoking "cold turkey?" Chew on it for a while.

Most smokers interested in quitting do not feel ready for an abrupt cessation, often referred to as “cold turkey.” Most smokers prefer to gradually quit by reducing the number of cigarettes they smoke (Cheong, Yong, & Borland, 2007). This method, however, fails more often than not (Levinson, Shapiro, Schwartz, & Tursky, 1971). It has been hypothesized that nicotine–replacement therapy might aid smoking cessation by permitting smokers to gradually reduce the number of cigarettes they smoke. This week’s ASHES reviews a randomized double-blind placebo-controlled clinical trial that tests the efficacy of nicotine gum to assist cessation (Shiffman, Ferguson, & Strahs, 2009).

Methodology

• Investigators enrolled 3297 participants (57% women) using print and radio advertisements. The investigators randomized participants to nicotine gum or placebo treatment.
• Participants self-selected their nicotine gum dosage (2 mg (n = 1636) vs. 4 mg (n = 1661))
• A user-guide instructed participants to add an additional hour of abstinence and substitute one additional piece of gum daily. Once smokers had quit completely (i.e., for 24 hours), clinicians instructed participants to gradually reduce use of gum consistent with the current FDA-approved labeling.
• The investigators compared the number of smokers who achieved and maintained abstinence—self-reported, verified by two CO readings <= 10 ppm in average—for the placebo condition and for each dose level of nicotine gum.

Results

• As Figure 1 illustrates, smokers using nicotine gum were more likely to achieve initial cessation (i.e., no smoking for 24 hours) compared to the placebo group. They also were more likely to maintain abstinence 28 days and six months later (p < .05).
• Participants using nicotine gum were more likely to experience an adverse event (e.g., nausea, hiccups, heartburn) than participants using placebo gum (48% vs. 37%, p < .001)

Figure 1. Initial cessation and continuous smoking abstinence percentages.

                          Initial abstinence  28-day abstinence   6-month abstinence

Limitations

• Low absolute cessation rates
• Specific sample (i.e., smokers who are ready to quit on a relatively time-limited program) restricts the ability to generalize these results to other groups.
• The absence of a procedural measure of fidelity reveals that it is not clear, how precisely smokers followed the instructions.
• As Table 1 shows, the study encountered non-completers; it is not evident whether these completers were systematically different from completers; therefore, it is not possible to determine whether the treatment yielded differential impact upon the participants both within and across groups.

Table 1. Number of non-completers per condition, summarized across 2mg and 4 mg self-selected groups

	Placebo (N = 1648)	Nicotine gum (N = 1649)
28-day follow-up: number of drop outs	238	262
6-month follow-up: number of drop outs	8	21

Conclusions

Nicotine gum is widely used to help smokers maintain abstinence (Silagy, Lancaster, Stead, Mant, & Fowler, 2004). This study is the first to investigate the effect of nicotine gum on graduated abstinence from smoking. Although the absolute rate of quitting was low, it is comparable to similar “cold turkey” studies (Shiffman et al., 2002). The results suggest that, for smokers quitting gradually, using nicotine gums facilitates tobacco abstinence compared to a placebo. Investigators should conduct more studies to compare its safety and effectiveness against other smoking cessation methods and to include fidelity measure to ensure the participants followed the instructions.

References

Cheong, Y., Yong, H.-H., & Borland, R. (2007). Does how you quit affect success? A comparison between abrupt and gradual methods using data from the International Tobacco Control Policy Evaluation Study. Nicotine & Tobacco Research, 9(8), 801-810.

Levinson, B. L., Shapiro, D., Schwartz, G. E., & Tursky, B. (1971). Smoking elimination by gradual reduction. Behavior Therapy, 2(4), 477-487.

Shiffman, S., Ferguson, S. G., & Strahs, K. R. (2009). Quitting by Gradual Smoking Reduction Using Nicotine Gum: A Randomized Controlled Trial. American Journal of Preventive Medicine, 36(2), 96-104.e101.

Shiffman, S., Rolf, C. N., Hellebusch, S. J., Gorsline, J., Gorodetzky, C. W., Chiang, Y.-K., et al. (2002). Real-world efficacy of prescription and over-the-counter nicotine replacement therapy. Addiction, 97(5), 505-516.

Silagy, C., Lancaster, T., Stead, L., Mant, D., & Fowler, G. (2004). Nicotine replacement therapy for smoking cessation. Cochrane Database Systematic Reviews, 3, CD000146.

 

The DRAM, Vol. 5 (9) - Binging Then and Now – 30 Years of Data on Youth Drinking

Public policy initiatives, such as zero tolerance laws and raising the minimum legal drinking age (MLDA) to 21 years old (circa 1985), have likely contributed to the reduction in alcohol-related traffic fatalities among youth and young adult populations seen in the past two decades.  However, it is not clear that the reduction in fatalities corresponds to change in binge drinking among young people. In this DRAM issue, we review Grucza and colleagues’ evaluation of 30 years of data from the National Survey on Drug Use and Health (NSDUH) reflecting patterns of binge drinking (Grucza, Norberg, & Bierut, 2009).

Methodology

• Grucza et al. analyzed data specific to binge drinking behavior (i.e., consuming five or more drinks on one occasion during the past 30 days) from 20 administrations of the NSDUH survey conducted between 1979 and 2006 with more than 500,000 individuals.
• Researchers grouped participant data into four youth categories, 12-14, 15-17, 18-20, and 21-23 years of age; they also created an adult control group 24-34 years old.
• This study combined information from all survey years—except 1982, for which there was no data about binge drinking—into a regression analysis to estimate the influence of gender, age, survey year and their interactions on binge drinking.

Results

• As Figure 1 demonstrates, binge drinking prevalence decreased over time for males under 21, but increased for males over 21 and all female age groups.
• Results of the regression indicated that:
• • All male age groups experienced a significant decrease in relative risk (compared to the 24-34 male age group) for binge drinking across time, ORs range from 0.96 (95% CI: 0.96-0.97; p < .001) to 0.99 (95% CI: 0.98-1.00; p < .05).
  • Females age 21-23 experienced a significant increase in relative risk (compared to the 24-34 female age group) for binge drinking across time, OR = 1.02 (95% CI: 1.01-1.03; p < .001).

Figure 1 Binge Drinking Prevalence (Percentages) by Age & Gender (adapted from Grucza et al., 2009).


Limitations

• Data were derived from self-report during in-person interviews
• There were multiple survey design changes during the 30 years of this project, potentially making it difficult to compare data over time.

Conclusions
Though the increase of the MLDA to 21 might partially be responsible for the overall reduction in binge drinking behavior among males under 21, this analysis of the NSDUH data suggests that female participants have not responded similarly and that binge drinking among both genders has increased among older youth. It is possible that young women, attempting to close the gender gap, have ignored the messages in these initiatives, a point that the authors highlight. Future research needs to investigate the reasons why certain groups (e.g., women and older youth) fail to benefit from the initiatives already in place, and explore new ways to influence binge drinking behavior within these groups.

References
Grucza, R. A., Norberg, K. E., & Bierut, L. J. (2009). Binge drinking among youths and young adults in the United States: 1979-2006. Journal of the American Academy of Child Adolescent Psychiatry, 48(7), 692-702.

The WAGER, Vol. 14(8) - Video Game Playing and Gambling-Related Problems: Is there a Connection?

Research has begun to focus on how gambling impacts adolescents (e.g., Delfabbro, Lahn, & Grabosky, 2005; Welte, Barnes, Tidwell, & Hoffman, 2009). However, researchers rarely have studied the correlation between adolescent video game playing and disordered gambling behavior. This week’s WAGER reviews a study (Delfabbro, King, Lambos, & Puglies, 2009) that examined associations between gambling-related problems and types and frequency of video game play among Australian adolescents.

Methods

• Participants (N= 2,669) were students between the ages 12 and 17 attending six secondary schools (i.e., grades 8-13) in South Australia.
  • Delfabbro et al. purposely sampled schools; they selected four schools to obtain representation from each of the four major statistical districts in the metropolitan area and selected two from major regional centers.
  • All students who were present on the survey’s administration day completed the survey with the exception of students who were withdrawn by parents (i.e., withdrawers). The absentee rate reported by the schools was approximately 10%; the withdrawer rate and completion rate were not reported.
• The survey measured video-game involvement during the past year and DSM-IV Pathological Gambling (PG) items.
  • Video-game involvement measures included frequency of play, number of hours played, and type of game played, including: TV games (e.g., Xbox, Nintendo, Playstation); phone-based games; hand-held games (e.g., Nintendo, Gameboy); PC games; and arcade games.
  • Researchers assessed PG using the DSM-IV Juvenile Criteria (DSM-IV-J; Fisher, 1992); a screening instrument for gambling-related problems among adolescents modified from the DSM-IV criteria for PG.
    • A 12-item yes/no scale.
      • According to Fisher (1992), a total score of four or more indicates pathological gambling.
      • The screen asked about current problems. Five items specify “in the past year,” six items are asked in the present tense, and one item was more ambiguous (Have you fallen out with members of your family, or close friends, because of your gambling behavior?).
• The participants’ DSM-IV-J scores permitted the investigators to establish three groups: Not at risk (i.e., endorsed no items on the scale); At-risk (i.e., endorsed 1-3 items on the scale); and PGs (i.e., endorsed 4 or more items on the scale).
• The authors conducted a series of hierarchical multiple regression analyses to examine associations between gambling-related problems and the frequency and type of video game playing.

Results

• Table 1 indicates the gambling frequency (i.e., the number of video-game sessions per year) of various types of video game play among the three gambling groups.
  • The results indicate that the PG and At Risk groups played arcade, phone, hand-held, and TV games more frequently than the Not at Risk group and the At Risk group played PC games more frequently than the Not at Risk group.
• Participants who played video-games daily (n=574) had significantly higher DSM-IV-J scores (p<.001) than their counterparts who did not play or played video games less frequently (M=0.41, SD=1.25 and M=0.16, SD=0.78 respectively).

Table 1 Gambling Groups in Relation to Frequency of Video-Game Play in the Past Year (adapted from Delfabbro et al., 2009).

Click image to enlarge, or adjust your browser's zoom setting.
*p<0.01 and **p<0.001
Effect sizes: η²: 0.01-0.06 = small effect size; 0.07-0.13 = moderate effect size; 0.14+ = large effect size

Limitations

• This survey relies on self-report.
• The researchers used a cross-sectional design that does not allow for exploration of causal relationships between video-game playing and gambling. This does not take common factors (e.g., interaction between players and technology, and structural characteristics of the game such as lighting and sounds) into consideration that might account for the association between video gaming and gambling.
• Because of the selection procedures, the participants might not be representative of their schools, their classes, youthful gamblers, or youthful video game players.
• The authors used the number of students as the total degrees of freedom in their statistical analyses.  However, the cluster sampling within schools yields fewer degrees of freedom and without adjustment, the power of the statistical tests is exaggerated.
• The DSM-IV-J items shift time frames (i.e., past year, presently, and lifetime).  As a result, it is not possible to determine if gambling-related problems are current or in the past.

Conclusion

The results indicated significant associations between various types of video-gaming and gambling-related problems. However, the effect sizes were very small; this suggests that frequent video-game playing accounts for only a small part of the relationship between video game playing and gambling-related problems among adolescents. The inconsistency within the results (e.g., PC games were protective of PG, but hand-held games did not show a difference between no risk and at risk) suggests that other factors might better explain the association between video games and gambling-related problems. Future research should consider exploring additional factors (e.g., social/family influences, personality, beliefs etc.) that extend beyond frequency of playing video games that may explain why some adolescents experience gambling-related problems.

-Tasha Chandler

What do you think?  Please use the comment link below to provide feedback on this article.

References

Delfabbro, P. H., King, D., Lambos, C., & Puglies, S. (2009). Is video-game playing a risk factor for pathological gambling in Australian adolescents? Journal of Gambling Studies, 25(3), 391-405.

Delfabbro, P. H., Lahn, J., & Grabosky, P. (2005). Adolescent gambling: A report on recent ACT research. Canberra: ACT Gambling and Research Commission.

Welte, J. W., Barnes, G. M., Tidwell, M.-C. O., & Hoffman, J. H. (2009). The association of form of gambling with problem gambling among American youth. Psychology of Addictive Behaviors, 23(1), 105-112.

STASH, Vol. 5(8) – Straight dope: A randomized controlled trial of diacetylmorphine (heroin) treatment for opioid addiction

Opioid abuse affects more than 1 million North Americans and is a serious public health concern contributing to ill health and fatal overdose (United Nations Office on Drugs and Crime, 2008).  Although maintenance treatment with oral methadone is standard for opioid dependence, some opioid-dependent individuals do not respond well, have difficulty maintaining treatment, and/or continue to use illicit opioids while receiving methadone. This week’s STASH reviews the first North American randomized controlled trial of diacetylmorphine, the active component in heroin, versus methadone treatment for opioid dependence in individuals for whom standard treatments have been unsuccessful (Oviedo-Joekes et al., 2009).

Methods

• Researchers enrolled 251 opioid users in Montreal, Quebec and Vancouver, British Columbia, into an open-label, randomized controlled trial. Participants all had injected opioids daily for the past year and had at least two previous treatment attempts including one attempt with methadone treatment.
• Participants were randomized into three* groups for the 12-month study period.
• methadone (N=111): taken orally and dispensed once daily at clinic sites.
• diacetylmorphine (N=115): self-administered by injection up to three times daily by participants under medical supervision at study clinic sites.
• The primary outcomes of interest, assessed at 3, 6, 9, and 12 months were:
• Retention in treatment or drug-free status outside of the study context.
• Reduction in illicit drug use and/or other illegal activity assessed by the European Addiction Severity Index.

Results

• Figure 1 shows that treatment retention for participants receiving injectable diacetylmorphine was 87.8% compared to 51.4% of participants receiving oral methadone at 12-month follow-up, a significant difference (p < .0001).
• Figure 1 also shows that rates of illicit drug use and/or other illegal activities were reduced by 67.0% in the diacetylmorphine treatment group compared to 47.7% in the methadone treatment group (p < .01).
• The mean number of days of illicit heroin use per month was reduced from 26.6 to 5.3 in the diacetylmorphine group compared to a reduction from 27.4 to 12.0 days per month in the methadone treatment group (p < 0.001).

Figure 1 Primary Outcomes at 12 Months (adapted from Oviedo-Joekes et al., 2009).
  

Limitations

• The study relies on self-report data of illicit opioid use.
• Given the nature of the treatments under investigation, it is not possible to perform a double-blind study.
• Authors fail to acknowledge or control for the possible confounding of treatment outcome due to route of administration (i.e., injection use versus oral ingestion), social setting within which people use these drugs, with psychoactive effects of the drug.

Conclusion
This study shows that individuals treated for opioid dependence with injectable diacetylmorphine were more likely to remain in treatment and were less likely to engage in illicit drug use or other criminal behaviors compared to those treated with oral methadone.  The study authors conclude that for the subgroup of opioid-dependent individuals for which traditional (i.e., methadone) treatment options are not successful, clinically supervised treatment with diacetylmorphine appears to be a safe and effective treatment option. This interpretation must be tempered, however, because the researchers failed to account for the potential confounding effect of route of administration (i.e., oral vs. injected) on the study outcomes. It is likely that injection heroin users would prefer injection to another treatment (i.e., oral methadone) that requires significant behavioral change and places them on a different physiological and therefore psychosocial schedule. Perhaps diacetylmorphine administration is more appropriately understood as a harm-reduction strategy similar to needle exchange programs.  Further research is needed to assess the impact of diacetylmorphine administration on the lives of opioid dependent individuals. 

-Erica Marshall

What do you think?  Please use the comment link below to provide feedback on this article.

References

Haasen, C., Verthein, U., Degkwitz, P., Berger, J., Krausz, M., & Naber, D. (2007). Heroin-assisted treatment for opioid dependence: randomised controlled trial. Br J Psychiatry, 191, 55-62.

Oviedo-Joekes, E., Brissette, S., Marsh, D. C., Lauzon, P., Guh, D., Anis, A., et al. (2009). Diacetylmorphine versus methadone for the treatment of opioid addiction. N Engl J Med, 361(8), 777-786.

United Nations Office on Drugs and Crime. (2008). World drug report. Vienna.

van den Brink, W., Hendriks, V. M., Blanken, P., Koeter, M. W., van Zwieten, B. J., & van Ree, J. M. (2003). Medical prescription of heroin to treatment resistant heroin addicts: two randomised controlled trials. Bmj, 327(7410), 310.

* Twenty-five study participants were assigned to receive a treatment option not addressed in this review.