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 Addictions, Cambridge Health Alliance, a Harvard Medical School teaching affiliate.
For decades, public health advocates have developed and tested smoking cessation programs. One method, looming vulnerability, involves a perceived increase of susceptible dangers due to smoking, which heightens state anxiety. Researchers hypothesize that this change will stimulate efforts to quit. This week the ASHES reviews a study that examined the effects of looming vulnerability and smoking cessation (McDonald, D., O’Brien, J., Farr, E., & Haaga, D. A. F. 2010).
Methods
Researchers used a randomized control study design and surveyed 72 adult smokers.
At baseline, researchers included questions for demographics, smoking history, nicotine dependence, and state anxiety.
For both conditions, participants listened to four three-minute audiotape-guided imagery exercises.
The experimental condition focused on the act of smoking and the associated health consequences. The scene also connected the continuation of smoking with other impending dangers such as riding a conveyor belt that ultimately leads to a diagnosis of lung cancer.
The control condition elicited relatively calm images that included movement, but were not associated with smoking, such as riding an escalator in an empty mall.
Immediately after the manipulation, participants completed measures for state anxiety, motivation, and contemplation to quit.
During a one-month follow-up interview, participants again completed measures of motivation, contemplation, nicotine dependence, current smoking behavior, and quit attempts.
Table 1: Immediate effects of looming manipulation (adapted from McDonald et al., 2010)
Note: For means, standard deviations are given in parentheses. State anxiety = Visual Analogue Scale (0-100). t(70) compares looming and control conditions. NS parameters not reported.
Results
As Table 1 shows, the post-test anxiety ratings for the looming condition were significantly higher than for the control condition. No significant difference was found between groups in the follow-up interview for motivation, contemplation, nicotine dependence, quit attempts, or attempts to seek formal help for cessation.
Looming condition participants on average (M = 9.10, SD = 6.72) did smoke fewer cigarettes than control participants (M=12.90, SD = 9.02). ANCOVA on smoking rate, with baseline rate as covariate and experimental condition as independent variable, was significant, F (1, 58) = 4.24, p<.05, partial eta squared = .068.
Limitations
Self-reported measures.
Measures of contemplation and motivation to quit were completed post-test only.
Small sample size limits statistical power.
Discussion
Looming vulnerability was found to successfully increase a participant’s state anxiety immediately after the manipulation. While it did have a significant impact on the number of cigarettes smoked during a one-month follow-up survey, there were no other significant findings. These results illustrate how an induced behavioral change might not penetrate or affect internal motivations for such behavior. The results also suggest that scare tactics are ineffective as a smoking cessation strategy in the long term. A more comprehensive approach that addresses personal and psychological history and physical dependence might result in more successful cessations.
-Aaron Lim
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References
McDonald, D., O’Brien, J., Farr, E., & Haaga, D. A. F. (2010). Pilot study of inducing smoking cessation attempts by activating a sense of looming vulnerability. Addictive Behaviors, 35, 599-605.
Riskind, J. H., & Williams, N. L., & Joiner, T. E., jr. (2006). The looming cognitive style: A cognitive vulnerability for anxiety disorders. Journal of Social & Clinical Psychology, 25, 2006.
Our peers – best friends, enemies, acquaintances, coworkers, etc. – have considerable influence on our behaviors, both good and bad. This influence is observable as early as preschool, and appears to peak during adolescence, when peer influence begins to trump parental influence (Dishion & Owen, 2002; Hawkins, Catalano, & Miller, 1992). This week, the DRAM reviews a study by Rosenquist and colleagues (2010) that uses innovative new techniques to measure alcohol use patterns within social networks.
Methods
Participants (n=5,124) were members of the offspring cohort (i.e., children of the original participants) of the Framingham Heart Study (FHS), assessed across 7 waves of data collection from 1972 to 2000.
Participants identified family members, other contacts, and friends at each wave; researchers also identified participants’ neighbors and coworkers through address and employment information collected at each wave. Only contacts who also were involved in the FHS were included in this study.
Participants self-reported alcohol use (i.e., average drinks per day) at each wave.
Researchers categorized participants as heavy drinkers (>1 / 2 drinks per day for women / men), moderate drinkers (any drinking less than heavy), or abstainers.
Results
Participants’ drinking behavior varied according to the behavior of members of their social network, for up to three degrees of separation (i.e., friends’, friends’ of friends, and friends’ of friends of friends drinking behavior all relate to participants’ drinking behavior); geographical distance between contacts did not influence this effect.
As Table 1 shows, having a social network with a concentration of heavy drinkers at one time point predicted an increase in participant drinking by the next time point; similarly, concentrations of abstainers predicted decreases in participant drinking.
Friends and spouses, in particular female friends and wives, had the strongest effect on future drinking behavior; neighbors and coworkers had no discernable effect.
Table 1. Effect of Social Network Drinking Behavior on Participants’ Future Alcohol Consumption (adapted from Rosenquist et al., 2010)
Note. Regression models controlled for age, sex, education, and wave. ***p<.001; **p<.01; *p<.05
Only contacts within the FHS were used; it is possible that participants’ friends and acquaintances not involved in the FHS had unmeasured influence.
Alcohol use was self-reported and might be subject to the biases associated with self-report.
Rosenquist et al. examined social network influence on future drinking behavior, but did not examine the influence of drinking behavior on the future development or maintenance of social networks.
Discussion Similar to other studies investigating the spread of phenomena across social networks (e.g., Christakis & Fowler, 2007, 2008), the current study demonstrated that the alcohol using patterns of those around you tend to influence your own use. This, in itself, is not surprising, but the reach of that influence is: our drinking patterns reflect those of people separated from us by three degrees in our social networks. These findings highlight the importance of social network research capable of analyzing social influences beyond direct peer or aggregate group effects.
-Sarah Nelson
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References
Christakis, N. A., & Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. New England Journal of Medicine, 357, 370-379.
Christakis, N. A., & Fowler, J. H. (2008). The collective dynamics of smoking in a large social network. New England Journal of Medicine, 358(2249-2258).
Dishion, T. J., & Owen, L. D. (2002). A longitudinal analysis of friendships and substance use: Bidirectional influence from adolescence to adulthood. Developmental Psychology, 38(4), 480-491.
Hawkins, J. D., Catalano, R. F., & Miller, J. Y. (1992). Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood: Implications for substance abuse prevention. Psychological Bulletin, 112(1), 64-105.
Rosenquist, J. N., Murabito, J., Fowler, J. H., & Christakis, N. A. (2010). The spread of alcohol consumption behavior in a large social network. Annals of Internal Medicine, 152, 426-433.
Conventional wisdom suggests that specific gambling activities, such as Internet gambling, are especially “addictive.” However, recent research suggests that the relationship between gambling and disordered gambling is more complicated than playing specific types of games. Using a United States youth sample, research suggests that gambling involvement (e.g., the number of games one plays), is a better predictor of disordered gambling than participation in any particular game (Welte, Barnes, Tidwell, & Hoffman, 2009). This week the WAGER reviews a study that further explored this phenomena by examining the association between disordered gambling and gambling involvement within the 2007 British Gambling Prevalence Survey (LaPlante, Nelson, LaBrie, & Shaffer, 2009).
Methods
LaPlante et al. (2009) conducted secondary data analyses of the British Gambling Prevalence Survey (BGPS)1 using weighted data of 8968 observations characteristic of the general population.
The researchers operationally defined the following variables.
Disordered gambling (i.e., endorsing 3+ DSM-IV pathological gambling symptoms in the past year).
Gambling involvement (i.e., the number of types of gambling for which an individual reported being involved during the past year).
The authors conducted a series of logistic regressions using participation in each gambling type to predict past year disordered gambling.
Consistent with Welte et al’s. analytic strategy (2009), these logistic regressions were conducted first without controlling for involvement and then added involvement as a control. Table 1 lists the corresponding odds ratios.
Table 1: Odd ratios for predicting disordered gambling from type of game with and without controlling for involvement (adapted from LaPlante et al. 2009)
Results
When not controlling for involvement, participation in nearly every gambling type was statistically significant and positively associated with disordered gambling.
When controlling for involvement, gambling via virtual gaming machines (e.g., virtual roulette, virtual bingo, virtual keno) was the only gambling type that remained significantly and positively associated with disordered gambling.
Limitations
This BGPS gathers self-reported data without corroboration; therefore, this study is subject to the problems commonly associated with self-report.
The authors only used one measure of gambling involvement.
Discussion
LaPlante et al. (2009) examined associations between participation in a particular gambling type and disordered gambling. The results indicated that when the authors controlled for gambling involvement, the association between participation in a particular gambling type and disordered gambling weakened for all types, and for 13 of 14 types, this association was no longer meaningful. These findings are consistent with an emerging body of research that suggests gambling involvement is a better predictor of gambling problems than participation in a particular game (e.g., Welte et al., 2009). One limitation of this research is that the authors used only one measure of gambling involvement (i.e., the number of types of gambling for which an individual reported being involved during the past year). More research is necessary to examine other -- and multiple -- measures of involvement to more accurately refine the meaning of gambling involvement.
-Ryan Martin
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References
American Psychiatric Association. (1994). DSM-IV: Diagnostic and statistical manual of mental disorders (Fourth ed.). Washington, D.C.: American Psychiatric Association.
LaPlante, D. A., Nelson, S. E., LaBrie, R. A., & Shaffer, H. J. (2009). The relationships between disordered gambling, type of gambling, and gambling involvement in the British Gambling Prevalence Survey 2007. European Journal of Public Health, epub ahead of print.
National Center for Social Research. (2008). British gambling prevalence survey 2007. Colchester, Essex: UK Data Archive.
Wardle, H., Sproston, K., Orford, J., Erens, B., Griffiths, M., Constantine, R., et al. (2007). British Gambling Prevalence Survey 2007. London: National Center for Social Research.
Welte, J., Barnes, G., Tidwell, M., & Hoffman, J. (2009). The association of form of gambling with problem gambling among American youth. Psychology of Addictive Behaviors, 23(1), 105-112.
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1The National Centre for Social Research (2008) produced the 2007 British Gambling Prevalence Survey (BGPS). The BGPS is a publicly available dataset representing interviews from 9,003 residents, randomly selected from households in England, Scotland and Wales; the response rate was 52% (Wardle et al., 2007). The BGPS assessed various gambling-related and demographic measures, including the following: past year gambling participation for 15 gambling types (e.g., lottery, online gambling, etc.) and past year DSM-IV pathological gambling criteria (American Psychiatric Association, 1994).
The previous edition of STASH highlighted the complex relationship between subjective distress and substance abuse. We continue to explore this theme in this week’s STASH by reviewing a new study about the illicit use of buprenorphine, an opioid analgesic, among opioid-dependent treatment seekers. Schuman-Olivier and colleagues (2010) explored whether treatment seekers typically use buprenorphine as an attempt to attain euphoria or as self-treatment for pain and symptoms of opioid dependence. Additional research goals were to describe the prevalence of illicit buprenorphine use and to explore and how illicit buprenorphine use decreases as people become engaged in comprehensive substance abuse treatment.
Methods • An outpatient addiction service in New England served as the study site. All participants met DSM-IV criteria for opioid dependence. • Stage 1 participants included 44 individuals seeking outpatient treatment with buprenorphine (i.e. “new intakes”) and 34 patients already receiving outpatient treatment with buprenorphine (i.e. “existing patients”). Participants reported, on average, 12.7 years of opioid use.1 • Among other measures not described here, participants completed: the Buprenorphine Beliefs and Behaviors Questionnaire, to measure reasons for buprenorphine use (e.g., to get high, to save money, to reduce pain); and the Illicit Buprenorphine Use Questionnaire, to measure whether participants used buprenorphine illicitly (e.g., by buying illicit “street” buprenorphine). • For Stage 2, researchers administered the same surveys to 42 of the Stage 1 participants three months later.2
Results • The Stage 1 past 90-day prevalence of illicit buprenorphine use was 49%. Most patients who used buprenorphine illicitly (61%) were new intakes – those who did not yet have access to a doctor’s prescription. • Patients reported that they used buprenorphine to reduce distress, not to get high. As Figure 1 indicates, the most common reasons for buprenorphine use among all Stage 1 participants were to self-treat opioid dependence, anxiety, and pain. Illicit users were more likely than non-illicit users to use buprenorphine to prevent withdrawal, reduce pain, treat depression, and save money (all χ2 > 3.50, all p ≤ .05). • At Stage 1, only 32% of existing patients, compared with 61% of new intakes, reported illicit buprenorphine use during the past 90 days (χ2 = 6.46, p < .05). Among completers of Stage 2, illicit buprenorphine use dropped by 70% from Stage 1 to Stage 2 (repeated measures χ2 = 4.89, p < .05).
Figure 1: Percent of participants in each group (illicit users, non-illicit users, and all users) who endorsed each potential reason for buprenorphine use. Asterisks indicate significant differences between illicit users and non-illicit users, as revealed by chi-square analysis. Based on data reported by Schuman-Olivier et al. (2010). Limitations • This study had a small sample size, which limits the power to detect significant differences; it also employed a homogenous sample obtained from one clinic, which limits conclusions about the generalizability of the findings. • The follow-up time frame was only 3 months, which precludes understanding whether and how people use buprenorphine to prevent long-term relapse. • These data, collected at intervals separated by 3 months, do not speak to the effectiveness of buprenorphine in self-treatment for short-term states such as cravings and depressed moods.
Discussion About half of patients in this sample used buprenorphine illicitly at baseline. The fact that these individuals were most likely to use buprenorphine to self-treat their opioid dependence, pain, and depression counters the notion that most people use this drug illicitly to mimic the euphoric effects of opiates. Further, these data reveal that once engaged in an outpatient addiction treatment setting, and given a legal prescription for buprenorphine, most patients tend to reduce their illicit use of this drug. Overall this study suggests that people turn to buprenorphine to deal with recurrent feelings of distress and symptoms of opioid dependence, and that they are less likely to do so illicitly when given an affordable prescription. Future studies using more frequent assessments (e.g., ecological momentary assessment) might shed light on the effectiveness of buprenorphine for alleviating short-term distress. We suggest that policy makers take these data and the results from larger future studies into consideration when making decisions about the prescription use of buprenorphine as part of comprehensive substance abuse and mental health treatment programs.
--Heather Gray
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References Schuman-Olivier, Z., Albanese, M., Nelson, S. E., Roland, L., Puopolo, F., Klinker, L., & Shaffer, H. J. (2010). Self-treatment: Illicit buprenorphine use by opioid-dependent treatment seekers. Journal of Substance Abuse Treatment, 39, 41-50.
Notes. 1. New intakes did not differ from existing patients on any demographic variables measured (e.g., gender composition, salary, age). However, new intakes reported greater depression severity and more frequent lifetime benzodiazepine use at Stage 1. 2. Researchers chose to track the first three-quarters of participants who were eligible for Stage 1.
Many researchers suspect that exposure to public health toxins, such as gambling, alcohol, and tobacco, can lead to changes in engagement with those toxins (LaPlante & Shaffer, 2007; Shaffer, LaBrie, & LaPlante, 2004). Consistent with this perspective, many studies have examined the health effects of toxin-related imagery in movies, music, literature, and the like (Addiction and the Humanities, Vol. 6(3); 3(2); and 1(11). This week, for Addiction and the Humanities we review a study that examined the association between adolescents’ exposure to cannabis in popular music, and their cannabis use (Primack et al., 2009).
Methods
The researchers examined the baseline survey data of an anti-smoking clinical trial involving all students (N=959; 80% of those eligible) enrolled in 9th grade health classes at three large Pittsburgh, PA high schools.
The baseline survey included socio-demographic, parenting style, sensation seeking, rebelliousness, and alcohol-use scales and variables.
The study estimated students’ cannabis lyric exposure in two ways:
First, by asking students the number of hours per day they listen to music and their favorite musician. The researchers used this information, in combination with the results of a separate content analysis of musicians’ songs (Primack, et al., 2008), to determine each student’s unique exposure to cannabis lyrics (i.e., the product of students’ hours per week listening to music by the number of cannabis references per hour in their favorite artist’s songs).
The researchers categorized students’ exposure scores into tertiles: low, medium, and high.
Second, by determining students’ favorite artist’s number of songs that contained cannabis lyrics (i.e., 0 songs, 1-2 songs, or 3 or more songs with cannabis lyrics).
The primary outcome variables of interest were: (1) ever cannabis use, even a puff (yes or no) and (2) past 30 day cannabis use, even a puff (yes or no).
**p<.05; multivariate regression analyses controlling for all covariates (i.e., socio-demographic, parenting style, sensation seeking, and rebelliousness scales and variables)
Results
As displayed in Table 1, multivariate logistic regressions, controlling for all covariates (i.e., socio-demographic, parenting style, sensation seeking, and rebelliousness scales and variables), showed that compared to those with the lowest exposure to cannabis lyrics, those with the highest exposure had higher odds of past 30 day cannabis use.
Regression analyses also showed that compared to those with the lowest song exposure, those with the highest exposure had higher odds of past 30 day cannabis use and ever cannabis use.
Limitations
The study relied on self-reported data without corroboration; therefore, this study is subject to the problems commonly associated with self-report.
The study does not consider the role of individual resiliency factors (e.g., positive social networks), which might influence exposure processes.
The measures of exposure assume that students’ favorite artist also is their most played artist, which might not be the case.
The study design is cross-sectional; therefore, it cannot establish whether the cannabis exposure and engagement relationship is causal.
Discussion
The study supports research that suggests that exposure to specific public health toxins is associated with increased engagement with those toxins. It is the first study to illustrate this phenomenon with cannabis, using exposure estimation calculations. Future research in the area might consider the role of resiliency factors, which might change the nature and shape of the relationship between exposure and engagement. Also, research on exposure effects for other public health toxins suggests that exposure effects often are attenuated over time, through adaptation processes (LaPlante & Shaffer, 2007; Shaffer et al., 2004). Researchers and others should consider whether such processes might apply here, and/or how to facilitate such processes and minimize prototypical exposure effects.
-Debi LaPlante
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References
LaPlante, DA & Shaffer, H. J. (2007). Understanding the influence of gambling opportunities: Expanding exposure models to include adaptation. American Journal of Orthopsychiatry, 77, 616-623.
Primack, B. A., Dalton, M.A., Carroll, M. V. Agarwal., A. A. Fine, M. J. Content analysis of tobacco, alcohol and other drugs in popular music. Archives of Pediatric Adolescent Medicine, 162, 169-175.
Primack, B. A., Douglas, E. L., & Kraemer, K. L. (2009). Exposure to cannabis in popular music and cannabis use among adolescents. Addiction, 105, 515-523.
Shaffer, H.J., LaBrie, R.A., LaPlante, D.A. (2004). Laying the foundation for quantifying regional exposure to social phenomena: Considering the case of legalized gambling as a public health toxin. Psychology of Addictive Behaviors, 18(1), 40–48.
A previous BASIS DRAM described a study about the relationship between friendship and addiction-related behavior, specifically, alcohol abuse. Continuing this topic, this week’s ASHES reviews a study that examined how social networks predict long-term changes in smoking behavior (Pollard, Tucker, Green, Kennedy, & Go, 2010).
Methods
Pollard et al. used data from the National Longitudinal Study of Adolescent Health (Add Health). The research sample included students who were interviewed three times (i.e., wave I, wave II, wave III) during a six year period. The students were in grades 9 to 11 at the last wave of interviews (N = 837 for the final analysis).
First, the researchers calculated the average number of cigarettes smoked during the last 30 days of each wave using the reported frequency of smoking days, that is, the reported number of cigarettes smoked each day. Next, the researchers applied latent class growth analysis to estimate the smoking trajectory for each individual. These trajectories describe how the individual’s smoking behavior changes over time (i.e., whether it decreases, increases or stays steady).
Then, the researchers applied a network analysis strategy to classify the wave I network position of an individual (i.e., belongs to smoking group, belongs to a non-smoking group, is a liaison to a smoking group, is a liaison to only nonsmoking groups). They also calculated other network related variables (e.g., number of best friend who smoke).
Finally, the researchers built a statistical model to estimate how well the network position of the individual predicted smoking trajectory. The model controlled for other concomitants of smoking behavior (i.e., gender, race/ethnicity, depressed affect, coping, self esteem, parental education, and resident parent who smoked).
Results
As Figure 1 shows, the study identified 6 unique smoking trajectories ranked from the “lowest” to the “highest” in tobacco use as follows: never smokers, steady lows, delayed uptake to regular use, early uptake, decreasers, and steady highs.
Adolescents with a greater number of smoking friends were more likely to belong to the higher use trajectories.
Membership in a smoking group and being a liaison of a smoking group increased the likelihood of belonging to the higher use trajectories; this finding was obtained beyond the effect of the perceived number of smoking friends.
Figure 1. Trajectories of average cigarettes per day: grades 9–11 at Wave 1 in the entire Add Health sample (N = 6096).*
* Note: The picture is copied from (Pollard et al., 2010) .
Limitations
Self-reported measures.
It is not clear, why the authors defined “decreasers” as higher tobacco use trajectory, compared to the “delayed uptake” and “early uptake”.
It is not clear, exactly how the social network affects smoking behavior. The mechanism is unknown.
Conclusion
The main contribution of this experiment is that the investigators used both network analysis and trajectory analysis within the same study. By using these strategies within the same study, they were able to find that the position within the social network affects the dynamic of smoking behavior. Another important strength is that this study used a nationally representative sample of adolescents, so the findings likely will generalize to other groups of adolescents. The major conclusion –that the peer network is associated with smoking behavior – seems to be intuitive. However, this is the first study to demonstrate that the network position predicts the changes in adolescent’s smoking behavior over time above and beyond simple number of smoking friends.
References
Pollard, M. S., Tucker, J. S., Green, H. D., Kennedy, D., & Go, M.-H. (2010). Friendship networks and trajectories of adolescent tobacco use. Addictive Behaviors, 35(7), 678-685.
It takes three oxygen molecules for humans to metabolize one molecule of alcohol (i.e., ethanol). This week the DRAM reviews a study that compares the blood alcohol concentration (BAC) levels of participants after drinking oxygen-enriched drinks to their BAC level after drinking normal drinks (Baek, Lee, & Kwon, 2010).
Methods
Participants (n=49) were healthy adults who volunteered for up to three experiments that comprised this research. Investigators used thirty participants for each experiment; ten participants participated in one experiment, nineteen participated in two, and eleven participants participated in all three.
In each experiment, participants completed two phases in which they consumed 40 ml every 5 minutes of a drink that was 19.5% ethanol by volume and enriched with varying amounts of dissolved oxygen concentration. In one phase, the drink was “normal” (oxygen concentration of 8 parts per million [ppm]); in the other, it was oxygen-enriched. Drink volume and oxygen concentration of the enriched drink varied as follows:
Experiment 1 – 240 ml drink, 20 ppm (25 minutes to drink)
Experiment 2 – 360 ml drink, 20 ppm (40 minutes to drink)
Experiment 3 – 360 ml drink, 25 ppm (40 minutes to drink)
The researchers measured BAC levels by collecting breath samples at regularly timed intervals.
Results
Participants’ maximum BAC levels and time to reach those levels did not differ significantly between phases for any of the experiments. Average maximum BACs ranged from .040% to .041% for 240ml drinks, and from .056% to .066% for 360ml drinks.
As Table 1 shows, in all experiments, it took less time for participants to reach 0.000% BAC (i.e., completely metabolize the ingested alcohol) after drinking an oxygen-enriched drink than a normal drink.
The researchers did not observe BAC level differences between the drinks until approximately 150 minutes after consumption.
Table 1. Time to Reach 0.000% BAC in Each Experiment after Consuming Normal and Oxygen-Enriched Drinks (adapted from Baek et al., 2010)
Limitations
The researchers did not measure the subjective effects of each drink (e.g., how intoxicated did participants feel).
The study used a small sample and several participants were used in multiple experiments.
The maximum BAC levels measured in this study are all below the US legal limit of 0.08 BAC to drive (Insurance Institute for Highway Safety & Highway Loss Data Institute, 2010).
Discussion
The results indicate that oxygen-enriched drinks reduced BAC significantly faster than normal drinks. Based on these results, one could argue that oxygen-enriched drinks reduce BAC levels rapidly and might have the potential to lead to fewer alcohol-related negative consequences, such as traffic accidents. However, maximum BAC levels were similar for both types of drinks and did not diverge until more than 2 hours after consumption. There might also be unintended consequences of oxygen-enriched drinks; for example, people who enjoy feeling intoxicated might consume more alcohol than they otherwise would in an attempt to maintain their BAC levels. Future research should examine the subjective effects of oxygen-enriched drinks as well as their effects at higher alcohol concentrations.
-Tasha Chandler
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References
Baek, I. H., Lee, B. Y., & Kwon, K. I. (2010). Influence of dissolved oxygen concentration on the pharmacokinetics of alcohol in humans. Alcoholism: Clinical and Experimental Research, 34(5), 834-839.
Insurance Institute for Highway Safety, & Highway Loss Data Institute. (2010). DUI/DWI laws. Retrieved May 13, 2010, from http://www.iihs.org/laws/dui.aspx
On April 15, 2010, the European Elite Athletes Association, in conjunction with the European Gaming and Betting Association and the European Sports Security Association, announced the development of a sports betting code of conduct and educational campaign for professional athletes ("EU Athletes & European betting industry launch educational campaign to support integrity in sports," 2010). In the U.S., the National Collegiate Athletic Association (NCAA) has a long-standing policy regarding gambling activities for its student athletes (National Collegiate Athletic Association). This week, the WAGER examines the newly released policy targeting European athletes and the NCAA policy governing sports wagering by student-athletes.
European Code of Conduct Policy for Athletes
Development of the code was a coordinated effort of three organizations:
The European Elite Athletes Association (i.e., a federation of independent European athlete associations including athletes from many countries and a wide variety of sports)
The European Sports Security Association (i.e., a betting integrity watchdog organization)
The European Gaming & Betting Association (i.e., a gaming and betting operators association) (EU Athletes, The European Gaming and Betting Association, & The European Sports Security Association, 2010).
The campaign targets professional athletes across Europe.
The code includes the following five guiding principles:
Know the rules. This principle encourages athletes to be familiar with the rules/regulations governing gambling for their particular sports in their home countries.
Never bet on yourself or the opponent.
Play safe – don’t bet on your own sport at all.
Be careful about handling sensitive information.
Fixing a competition: an absolute No-No (EU Athletes et al., 2010).
An educational component will expound upon the code and will consist of online resources coupled with in-person presentations aimed at informing athletes about how they can maintain sports betting integrity ("EU Athletes & European betting industry launch educational campaign to support integrity in sports," 2010). The educational materials are not yet available.
NCAA Gambling Policy for Athletes
The NCAA gambling policy applies to all student-athletes, the staff of NCAA member athletic departments, and NCAA staff. The NCAA policy prohibits the following gambling activities:
Providing information to individuals involved in organized gambling activities concerning intercollegiate competition.
Soliciting a bet on any intercollegiate team.
Accepting a bet on any team representing the institution.
Soliciting or accepting a bet on any intercollegiate competition for any item (e.g., cash, shirt, dinner) of value.
Participating in any gambling activity that involves intercollegiate athletics or professional athletics, a bookmaker, a parlay card or any other method employed by organized gambling (National Collegiate Athletic Association).
The following sanctions apply to all NCAA student-athletes found to be in violation of the NCAA gambling policy:
Betting on sports contests at either the college or professional level results in the loss of eligibility to participate in collegiate sports for one year.
Engaging in activities designed to change the outcome of a sporting event or betting on sports contests at ones own educational institution results in permanent loss of eligibility (National Collegiate Athletic Association).
The cornerstone of the NCAA “Don’t Bet on It” educational campaign is an interactive website which provides information on NCAA sports wagering policies and other gambling related resources. In addition, the campaign includes information sessions with players, coaches and officials and printed materials distributed to campuses (National Collegiate Athletic Association).
Table 1: Side-by-side comparison of the European and NCAA policies
*Athletes are subject to the rules and laws governing sports wagering in their home countries. **Not currently implemented.
Discussion
In many ways, the new sports betting policy governing European athletes and the policy of the NCAA are very similar (see Table 1). Both policy strategies strive to protect the integrity of athletic competition by limiting behavior that can influence the outcomes of competition. Both are centered on educational campaigns with similar goals and methods of delivering their messages. There are, however, some key differences. In particular, the EU policy pertains to professional athletes and does not include sanctions on behavior, only guidelines for what is considered appropriate. In contrast, the NCAA policy has strict punitive consequences for amateur athletes that violate the gambling policy, including loss of the ability to engage in athletic competition at the collegiate level. The newly implemented EU policy and that of the NCAA seek to protect the integrity of athletic competition from the negative influences of sports wagering by athletes. It is still unclear whether the broad guidelines of the European policy or the sanctions of the NCAA will be more successful in meeting their intended goals.
-Erica Marshall
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References
EU Athletes, The European Gaming and Betting Association, & The European Sports Security Association. (2010). Code of conduct on sports betting for athletes.
Needle-exchange programs (NEPs) hold the potential to reduce drug-related harms by promoting safer drug-injection practices and by serving as a bridge to treatment for substance abuse. Yet, few NEP participants (5-11%) take advantage of available treatment referral services (Kidorf et al., 2005; Riley et al., 2002). This week’s STASH reviews a recent study that investigated a potential moderator of substance abuse treatment enrollment among NEP participants (Kidorf et al., 2010). Based on data from national epidemiological studies (e.g., Cohen, Feinn, Arias, & Kranzler, 2007), Kidorf and colleagues (2010) hypothesized that NEP participants who have concurrent psychiatric distress are more likely to enroll in treatment for substance abuse than their counterparts who have relatively little concurrent psychiatric distress.
Methods
This study was conducted in collaboration with the Baltimore Needle Exchange Program.
Data for this study came from a larger randomized evaluation of strategies designed to promote substance abuse treatment enrollment (Kidorf et al., 2009). The evaluation study compared the effectiveness of two experimental conditions against standard enrollment practices (i.e. treatment as usual).
Study participants were 281 opioid-dependent individuals with an average age of 41 years. The study population was largely male (71.2%), unmarried (89.7%), and African-American (72%).
Researchers used the Symptom Checklist 90-Revised to generate a measure of participants’ overall psychiatric distress. On this scale participants indicate the extent to which they feel a variety of forms of distress (e.g., depression, anxiety, hostility).
The central outcome was whether participants enrolled in substance abuse treatment (inpatient or outpatient) during the 4-month study period.
The authors used a frequency distribution to classify participants into three groups of roughly the same size (i.e. low, medium, or high baseline psychiatric distress). Then, they used logistic regression to evaluate the relationship between baseline psychiatric distress and substance abuse treatment enrollment (with the low baseline psychiatric distress group serving as the reference group), after controlling for demographic variables, randomized treatment enrollment condition, baseline drug use severity, and syringe exchange sites.
Results
Overall, 40% of participants enrolled in substance abuse treatment during the course of the study.
As Figure 1 shows, participants with high baseline psychiatric distress were more likely to enroll in substance abuse treatment (M = 52% enrollment) than participants with low levels of baseline psychiatric distress (M = 33% enrollment) (adjusted OR = 2.15, CI = 1.10-4.23, p < .05). Participants with low levels of baseline psychiatric distress did not differ from participants with medium levels of baseline psychiatric distress (M = 36% enrollment).
Figure 1: Proportion of participants who did and did not enroll in substance abuse treatment as a function of baseline psychiatric distress. Based on data reported in Kidorf et al. (2010). Limitations
The levels of substance abuse treatment enrollment were higher than what typically is reported (Kidorf et al., 2005; Riley et al., 2002). This was likely the result of at least two aspects of the study. First, only participants who were not already receiving substance abuse treatment at baseline were included in the study. Second, about two-thirds of participants were exposed to an intervention designed to promote treatment enrollment. These aspects of the study might limit the generalizability of the study’s findings.
Different forms of concurrent psychiatric illness might differentially be related to the decision to seek treatment for substance abuse. However, the authors did not explore this possibility.
It appears the authors did not also measure enrollment in psychiatric services.
Discussion
People who use needle-exchange programs are more likely than the general population to suffer from psychiatric disorders in addition to their substance abuse. Most commonly, these disorders include major depression, post-traumatic stress disorder, and antisocial personality disorder (Kidorf et al., 2004). These disorders create distress, impair functioning, and exacerbate substance abuse problems. However, the current study suggests that higher levels of psychiatric distress might be beneficial to the extent that they motivate people to seek treatment for substance abuse. The authors speculate that psychiatric distress might motivate treatment-seeking because it increases self-perceived functional impairment, reduces the reinforcement value of substance use, or reflects a combination of these processes. Knowledge about this potential moderator of treatment seeking might be used to better match NEP participants to appropriate referral services.
--Heather Gray
What do you think? Please use the comment link below to provide
feedback on this article.
References
Cohen, E., Feinn, R., Arias, A., & Kranzler, H. R. (2007). Alcohol treatment utilization: Findings from the National Epidemiologic Survey on Alcohol and Related Conditions. Drug and Alcohol Dependence, 86, 214-221.
Kidorf, M., Disney, E., King, V., Kolodner, K., Beilenson, P., & Brooner, R. K. (2005). Challenges in motivating treatment enrollment in community syringe exchange participants. Journal of Urban Health, 82, 456-467.
Kidorf, M., Disney, E. R., King, V. L., Neufeld, K., Beilenson, P. L., & Brooner, R. K. (2004). Prevalence of psychiatric and substance use disorders in opioid abusers in a community syringe exchange program. Drug and Alcohol Dependence, 74, 115-122.
Kidorf, M., King, V. L., Neufeld, K., Peirce, J., Kolodner, K., & Brooner, R. K. (2009). Improving substance abuse treatment enrollment in community syringe exchangers. Addiction, 104, 786-795.
Kidorf, M., King, V. L., Peirce, J., Burke, C., Kolodner, K., & Brooner, R. K. (2010). Psychiatric distress, risk behavior, and treatment enrollment among syringe exchange participants. Addictive Behaviors, 35, 499-503.
Riley, E. D., Safaeian, M., Strathdee, S. A., Brooner, R. K., Beilenson, P., & Vlahov, D. (2002). Drug user treatment referrals and entry among participants of a needle exchange program. Substance Use & Misuse, 37, 1869-1884.
Although evidence suggests that that natural history of
addiction is similar across people and expressions of addiction (Shaffer et al., 2004), personal stories about the
development of addiction remain compelling and informative. This week’s Addiction and the Humanities presents
annotated excerpts from an intensely gripping story
about the gradual metamorphosis of the psychological state, thoughts, and
emotions of a physician who became addicted to morphine. The fictional story,Morphine, is based on the real
experiences of the author, a famous Russian writer Michael Bulgakov*, who became morphine addicted early in his life.
Background: The story is set in Russia,
during January 1917. This is a period involving the Russian Revolution and the
First World War. The protagonist, a young doctor Polyakov, not being eligible
for active military service after graduation from a medical school in St
Petersburg, is beginning an assignment to practice
medicine in a remote country village. Below we present annotated excerpts from
his diary, as described in, Morphine.
The excerpts are unmodified quotes from the book; we give the annotations in
italic.
Jan. 20. 1917. Perpetual blizzards… I’m sick of it. Alone every
evening. <…>
Social isolation could
have protective or risk-related implications for addiction. Here, social
isolation does not appear to provide protection from exposure to potential
objects of addiction and leaves the person with no social support.The diary portraits a
lonely and distressed person.
Feb. 15.Last night an interesting thing happened. I
was just going to bed, when I suddenly felt pain in the region of my stomach.
And what pain! Groaning, I reached the kitchen, and called Anna K., the nurse.
She came to my room and had to give me a morphine injection. <…> I must
give due praise to the man who first extracted morphine from poppyheads. The
pain stopped seven minutes after the injection. It would be a good thing if a
doctor were able to test many more drugs on himself. He would then have a
completely different understanding of their effect. After the injection I slept
soundly and well for the first time in months – and I forgot completely about
the woman who deceived me.
The first experience
of morphine injection is extremely rewarding. It relieves not only physical
pain, but also emotional distress.
Feb 16. I am
alone in my quarters. Fearing a recurrence of yesterday’s pain attack, I
injected myself in the thigh with .01 gram. The pain cease almost
instantaneously. A good thing Anna K. left the phial behind.
Being alone, previous
rewarding experience and availability of drug - all become factors in the
addiction progress.
Feb 18. Four
injections. No harm in that.
Tolerance is evident
as he increases the dose to reach the same effect previously obtained at a
lower dose.
Feb 21. Anna K.
is behaving very oddly – just as though I weren’t a doctor at all! 1.5 syringes
= .0.015 grammas morph.? Yes. It is two weeks now since I last thought about
the woman who deceived me. I am very proud of that. I am a man.
Morphine injection
“protects” against condemnation of other people. The character feels pride
rather than shame.
For the
first minute there is a sensation of being touched on my neck. The touch grows
warmer and spreads. In the second minute there is a sudden surge of cold in the
pit of my stomach, after which I start to think with unusual clarity and
experience a burst of mental energy. If I had not been spoiled by my medical
training, I would say that a man can only work normally after an injection of
morphine. <…> Anna K. is frightened. Calmed her, saying that since
childhood I have been remarkable for having tremendous will power.
Exposure to morphine
can produce extremely rewarding psychological states that are accompanied by
misleading feelings of self-confidence. Medical training and knowledge about
consequences are not enough to protect the person from continuing morphine use.
March 2 I shall
go to bed very early <…> And my sleep will be sweet.
Morphine is a central
nervous system depressant, often leading to deep relaxation and sleep.
March 19. Last
night I had a quarrel with Anna K. “I’m not going to make up the solution any
more. ‘ It was then that I first discovered in myself a nasty tendency to lose
my temper and, worse, to shout at people when I am in the wrong. <…> And
I snatched the keys out of her hand.
Personality change and
episodes of violent mood swings can be symptoms of addiction. This often leads
to deterioration of interpersonal relationship with other people (e.g., his
nurse, who also became Dr. Polyakov’s mistress at that time).
April 9. The
devil is in this phial! This is its effect: on injecting one syringe of a 2%
solution, you feel almost immediately a state of calm, which quickly grows into
a delightful euphoria. This lasts for only a minute or two, and then it
vanishes without a trace as though it had never been. Then comes pain, horror,
darkness.
This is a common
description of euphoria and withdrawal effects. Increasingly shorter periods of
euphoria are followed by longer periods of physical suffering.
May 6. Two
syringes of a 3% solution. My previous notes must sound somewhat hysterical. In
fact there is nothing particularly unusual or alarming about my condition. It
does not in the least affect my capacity to work. On the contrary….
People with addiction
often deceive themselves by suggesting that the drug injection may even improve
their work ability.
May 18. A
book is open in front of me… “.. morbid anxiety, a nervous depressed condition”
<...> “Depressed condition” indeed! The body which moves, agonises and
suffers is a corpse. It wants nothing, can think of nothing but morphine. The
feeling must be something like that of a man buried alive, groaning and
writhing as the first tongues of flame lick at his feet. Death. That is what
lurks behind that clinical, academic phrase “a depressed condition’. I can’t
hold out.
I have just had injected myself. Three syringes of a 3%
solution. That will last me until midnight... Nonsense. That last entry was nonsense.
It’s not as bad as that. Sooner or later I’ll give it up…but now I need sleep,
sleep.
Dr. Polyakov
experiences mood swing episodes. The dysphoria and physical discomfort
associated with withdrawal are accompanied by denial; Another symptom of
addiction is irregular sleeping patterns that encourage tiredness and physical
distress.
<…The pages in
between describe Dr. Polyakov’ attempts to be treated in a clinic in Moscow
that ended by his running away and stealing morphine from the hospital, his
coming back to the country, the relationship with the nurse, gradual increase
of the injection dose, and his descent into madness…>
Nov 18. <…>
I may be in a state of moral decay, but I am nevertheless making an effort to
arrest it. This morning, for instance, I did not have an injection.
Attempts of self-cure
are difficult and made even more difficult in almost complete social isolation.
Jan. 17. 1918. During
abstinence I am terrified by the slightest sound and I find people detestable.
I am afraid of them. In the euphoric phase I love everyone, although I prefer
solitude.
People with opioid
addiction often experience fear and tension during the withdrawal periods. To
reduce the discomfort of sounds, lights, and people, one sign of addiction is
seeking isolation or trips to an isolated place like the bathroomor bedroom.
Feb. 13. 1918
<…> I have decided against treatment. It’s hopeless. And I don’t want to
torment myself any longer. NO ONE IS TO BE BLAMED FOR MY DEATH.
Dr. Polyakov commits
suicide and leaves his notebook as a “pathological history” to “warn others.”
Discussion
In 1927, Medical Worker (Medicinsky
rabotnik) journal first published this story as an impressive and sincere
description of addiction. As described in the annotations, the story contains
the initial rewarding experience associated with morphine use, the progression
of increasing doses, later self-excuses for using, additional need to increase
the dose (tolerance), a switch between self-confidence and disparity, and
finally, the agonizing suffering and complete destruction of life. This story
raises questions about the role of personality and social settting in
addiction. Which is the “devil” here: The person, the environment, or morphine
itself? The tale is based on the real experience of the author, who was a
physician in a rural area, who developed an addiction to morphine despite his
medical training and knowledge. This is a bright illustration of how knowledge
itself does not prevent or limit addiction. Unlike his character, Bulgakov
managed to overcome his addiction after coming back to Kiev
(a big city) from the province and getting support from his family and friends.
-- Julia Braverman
What do you think? Please use the comment link below to provide
feedback on this article.
References cited
M. Bulgakov (1975). A country doctor’s notebook. Morphine.
pp.114 – 144. London : Collins,
1975.
Shaffer, H. J., LaPlante, D. A., LaBrie, R. A., Kidman, R.
C., Donato, A. N., & Stanton, M. V. (2004). Toward a syndrome model of
addiction: Multiple expressions, common etiology. Harvard Review of Psychiatry, 12, 367-374.
The tale is a part of a book of stories that is available on
Amazon,
ebay,
and LibraryThing
The pictures are taken from the story-based movie
Morphia (2008) presented at Cannes
fest, 2009.
*M.Bulgakov is the author of a novel “Master and Margarita” that critiques often name
among the 100
best books of 20th century..
