The Brief Addiction Science Information Source (BASIS)

Addiction & the Humanities Vol. 3(9) - Holidays: A Time to Celebrate, a Reason to Drink, or a Time to Celebrate Drinking?

Growing up, many people think of Halloween as a time for costumes and candy. Birthdays provide a reason to have cake and receive presents. Thanksgiving is a chance to see family and watch football.  But research suggests that college students view holidays in a more limited way:  as opportunities to engage in heavy drinking.  This week’s Addiction and the Humanities examines college students’ celebratory drinking habits.

Previous studies of the American general population found that certain holidays (e.g., Christmas, New Year’s Eve) correlate with greater alcohol consumption (Poikolainen, Leppanen, & Vuori, 2002; Weir, 2003).  Considering the large body of research describing college students’ excessive alcohol use, it seems reasonable to ask whether this association extends to additional holidays on college campuses.  However, few studies have scrutinized students’ drinking behavior during holiday festivities. 

When surveyed, college students report “special events” as one of the principal reasons for drinking (Klein, 1992), and “fun and celebration” as the essential motive for playing drinking games (Johnson, Hamilton, & Sheets, 1999).  These studies also found that those who endorsed the “fun and celebration” rationale drank greater quantities of alcohol and viewed drinking as a way to have fun. These attitudes might contribute to binge drinking and other serious abuses of alcohol.

To measure the alcoholiday effect (i.e., holidays and celebrations that center on alcohol) without relying on self-reported consumption, which might bias the results, Glindemann, Wiegand, and Geller (2007) performed three similar studies comparing students’ blood alcohol concentrations (BACs) during holiday celebrations and ordinary weeknights. In the first study, for five consecutive Thursdays, the fourth Thursday being Halloween 2002, research assistants in different sections of downtown Blacksburg, Virginia, executed brief surveys. Using handheld breathalyzers, they measured pedestrians’ BACs, 87.8% of whom were students.  Research assistants followed the same procedures around St. Patrick’s Day in 2003 (Study 2) and 2005 (Study 3), during which 84.5% and 92.7% of pedestrians were students. 

Study 1 revealed a statistically significant main effect for costume; participants wearing costumes had an average BAC of .089, compared to those in everyday clothes who had a mean BAC of .058.  Furthermore, Chi-square comparisons indicated that the number of participants with a BAC of .08 (i.e., threshold of intoxication) or greater was significantly higher for those in costume (i.e., 60.3% vs. 35.7%).  Data from Studies 2 and 3 revealed that BACs were significantly higher for participants reporting a celebration motive (.096) as opposed to those not reporting the motive (.074).  Analyses from all three studies revealed a main effect of holiday: participants’ BACs were significantly higher during celebratory occasions than the non-celebrating occasions.

Figure 1. “Costumes for Kids”("Costumes for Kids" 2006)

Glindemann, Wiegand and Geller’s (2007) findings are similar to the results of a study conducted earlier by Miller, Jasper and Hill (1993).  The 1993 study not only found a significant association between dressing in costume and alcohol consumption, but also revealed that 85% of participants who were involved in Halloween activities, regardless of costume wearing, had used alcohol. 

These studies provide important insight into circumstances that are associated with risky drinking behavior by students on college campuses.  The studies are limited, however, because the participants are from a specific and narrow segment of the population.  Moreover, those who agreed to participate might represent only a segment of the original sample, biasing the results.  Future research should expand upon these studies by examining the effects of different holidays (e.g., July 4th, New Year’s Eve) and the potential moderating effects of geographic locations on celebratory drinking patterns.  Better understanding of the alcoholiday phenomenon could lead to better prevention and intervention programs.

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

References

Costumes for Kids. (2006).   Retrieved October 30, 2007, from http://cip.plsinfo.org/bulletin/CIPRB0610.htm

Glindemann, K. E., Wiegand, D. M., & Geller, E. S. (2007). Celebratory drinking and intoxication. Environment and Behavior, 39(3), 352-366.

Johnson, T. J., Hamilton, S., & Sheets, V. L. (1999). College students' self-reported reasons for playing drinking games Addictive Behaviors, 24(2), 279-286.

Klein, H. (1992). Self-reported reasons for why college students drink. Journal of Alcohol and Drug Education 37, 14-28.

Miller, K. A., Jasper, C. R., & Hill, D. R. (1993). Dressing in costume and the use of alcohol, marijuana, and other drugs by college students. . Adolescence 28(109), 189-198.

Poikolainen, K., Leppanen, K., & Vuori, E. (2002). Alcohol sales and fatal alcohol poisonings:  A time-series analysis Addiction 97, 1037-1040.

Weir, E. (2003). Seasonal drinking:  Let's avoid the "January effect". Canadian Medical Association Journal 169(11), 1186.

ASHES Vol. 3(9): Varenicline vs. Bupropion SR - Two pharmacotherapy options for smoking cessation go head to head in a clinical trial

Most everyone in the developed world knows the health effects of tobacco smoke and more than 70% of smokers report that they want to quit (Schroeder & Cox, 2006). Those wishing to quit have a variety of tools to choose from, including, Nicotine Replacement Therapy (NRT) (see ASHES Vol. 3(6)), smoking cessation treatment programs (see ASHES Vol. 3(5), ASHES Vol. 3(4), and ASHES Vol. 2(9)), and drug therapy, which until recently was limited to bupropion SR (ALA, 2006).  This week ASHES reviews a clinical trial that studied the safety and efficacy of varenicline, a drug treatment option recently approved by the FDA, to help people with nicotine dependence.

Gonzalez et al. (2006) used a randomized double-blind, parallel-group, placebo-and active-treatment-controlled, phase 3, multi-site clinical trial of varenicline to study its effectiveness in helping people to quit smoking.  Participants, recruited through media advertisements, included healthy people 18 to 75 years of age, who smoked at least 10 cigarettes per day, were not abstinent during the previous 3 months, and who had a desire to quit.  Researchers randomized 1,025 eligible participants in a 1:1:1 ratio to receive either varenicline 1mg twice daily, bupropion SR 150mg twice daily, or matching placebo for 12 weeks, and then followed participants for 40 weeks.  Gonzalez et al. reported similar compliance rates for the groups, with the median treatment time being 84 days.   Participant completion rates varied from 60.5% for varenicline, 56% for bupropion SR, and 54% for placebo. 

Researchers measured continuous abstinence through exhaled carbon monoxide. As Table 1 shows, at weeks 9-12 and weeks 9-24 the group taking varenicline had a higher continuous abstinence rate than both bupropion SR and placebo.  At week 52, the group taking varenicline had a continuous abstinence rate of 21.9% that was no longer statistically different from the 16.1% continuous abstinence rate of the group taking bupropion SR.  However, both groups were statistically different from the 8.4% continuous abstinence rate of the placebo group.

Note. P<.001 for all comparisons except varenicline vs. bupropion SR at weeks 9-24 (P=.007),
varenicline vs. bupropion SR at weeks 9-52 (P=.057), and bupropion SR vs. placebo at weeks 9-52 (P=.001).

Limitations to this study include restricting the study to only healthy individuals.  The study also excluded those who had previously used bupropion SR for either depression or to help them quit smoking.  The researchers did this to study the true difference between the drugs and prevent a negative bias against bupropion SR.  Consequently, it is possible that both groups might be more motivated to quit. This extra motivation might increase the continuous abstinence rate.

Despite these limitations, this study suggests that, for some, varenicline is an effective treatment for smoking cessation.  Results showed that 22% of participants who used varenicline were able to remain abstinent for the full 52 weeks of the study, a rate two-and-a-half times that of the placebo.  Importantly, the results indicate that people in the varenicline group quit smoking more quickly. Improving health more quickly is an important effect even if over the long-term rates of improvement equal out.  That said, there is no cure-all, no magic pill for everyone, only options that people should use with trial and error until they are able to maintain, in the case of tobacco, abstinence.  When discussing smoking cessation options, doctors, psychiatrists, and patients should remain open to other forms of treatment or consider taking varenicline in concert with nicotine replacement, counseling, or online help.  Although quitting smoking is a difficult task, it is possible, and many people succeed everyday. Perhaps most importantly, they succeed using a variety of methods.

What do you think? Comments can be addressed to John Kleschinsky.

References

ALA. (2006). Nicotine Replacement Therapy (NRT) and Other Medications Which Aid Smoking Cessation, Quit Smoking.

Gonzales, D., Rennard, S. I., Nides, M., Oncken, C., Azoulay, S., Billing, C. B., et al. (2006). Varenicline, an alpha4beta2 nicotinic acetylcholine receptor partial agonist, vs sustained-release bupropion and placebo for smoking cessation: a randomized controlled trial. Jama, 296(1), 47-55.

Schroeder, S. A., & Cox, H. C. (2006). Trials that Matter: Varenicline: A Designer Drug to Help Smokers Quit. Annals of Internal Medicine, 145(10), 784-785.

Op-Ed/Editorials: Toward a Balanced Discussion of Exposure to Gambling: The Importance of Social Context

Discussions about gambling expansion, actual and virtual, are often emotion laden. Rarely do such conversations take place within a public health approach to the issue, which requires a consideration of both benefits and harms. Rather, advocates on both sides tend to gravitate toward evidence that supports only their position. For example, anti-gambling advocates might highlight instances of increased crime, and pro-gambling advocates might highlight instances of improved economics. Alternatively, considering both the pros and cons of such situations has the potential to expand the discussion to include existing theories and models of behavior related to gambling. When this happens, a full consideration of potential gambling outcomes prompts consideration of both exposure and adaptation effects.

 Claims about the harmful effects of exposure to gambling have circulated over time, but Shaffer, LaBrie, & LaPlante (2004) were the first to conceptualize gambling as a possible social toxin. Advancing from earlier related work, Shaffer et al. argued that if gambling is indeed a social toxin, researchers should be able to accurately estimate its effects by extent of exposure. Shaffer et al. found support for this argument; but, they also found preliminary support for the idea that environmental factors modify exposure effects.[1] Through this work they expanded common conceptualizations of exposure (i.e., that increased exposure leads to a proportionate increase in harms) to suggest the existence of adaptation (i.e., that some individuals and societies develop adaptations to gambling exposure, and therefore do not succumb to prototypical exposure effects).

Recently, like earlier work with intoxicant use (e.g., Shaffer & Zinberg, 1985; Zinberg & Fraser, 1979; Zinberg & Shaffer, 1985), research has made it apparent that the social context is extremely important to any understanding of exposure effects. Exposure-related research is often inconsistent. Depending on the sample, the location of a study, and the historical time at which a study occurred, very different patterns emerge. For example, areas that have had more exposure with greater intensity and for longer periods of time can evidence fewer problems than anticipated (Shaffer & Hall, 2002; Shaffer, Vander Bilt, & Hall, 1999; Volberg, 2002). Over time, gambling-related behavior patterns in the community appear similar to the prototypical adaptation curves apparent for numerous biological toxins (e.g., viruses and bacterial infections, LaPlante & Shaffer, in press). This is important because anticipating such patterns of infection can facilitate the development of prevention and/or catalyst public health strategies or public policies.

As researchers, public policy makers, and advocates of both stripes continue to consider this issue, they should keep in mind that social contextual factors make any one-size-fits-all approach to gambling expansion likely to be insufficient. Until we identify the many social contextual factors that moderate gambling exposure, however, it is important to progress in a conservative manner. Taking a conservative approach might create some inconveniences, but ultimately will provide a measure of protection for those who remain at risk.

References

LaPlante, D. A., & Shaffer, H. J. (in press). Understanding the influence of gambling opportunities: Expanding exposure models to include adaptation. American Journal of Orthopsychiatry.

Shaffer, H. J., & Hall, M. N. (2002). The natural history of gambling and drinking problems among casino employees. Journal of Social Psychology, 142(4), 405-424.

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.

Shaffer, H. J., Vander Bilt, J., & Hall, M. N. (1999). Gambling, drinking, smoking, and other health risk activities among casino employees. American Journal of Industrial Medicine, 36(3), 365-378.

Shaffer, H. J., & Zinberg, N. E. (1985). The social psychology of intoxicant use: The natural history of social settings and social control. Bulletin of the Society of Psychologists in Addictive Behaviors, 4, 49-55.

Volberg, R. A. (2002). Gambling and problem gambling in Nevada: Report to the Nevada Department of Human Resources. Northampton, MA: Gemini Research Ltd.

Zinberg, N. E., & Fraser, K. M. (1979). The role of the social setting in the prevention and treatment of alcoholism. In J. Mendelson & N. Mello (Eds.), The Diagnosis & Treatment of Alcoholism (pp. 359-385). New York: McGraw-Hill Book Company.

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

 

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[1] Related work suggests that the social setting moderates alcohol effects. Alcohol is a central nervous system depressant. However, in certain social situations, low dose alcohol use often results in stimulation rather than depression (e.g., the party effect). The same dose of beverage alcohol taken alone might encourage sleep.

The DRAM Vol. 3(9) - How Protective are Protective Factors: The Complexity of Risky Drinking Behavior

For young people, the use and abuse of alcohol is alarming because of their still developing neuroanatomy, and the ease with which they can develop a dependency to alcohol (Molina, 2007; Padget, 2006). Using scientific studies to understand what leads adolescents toward future problematic alcoholic use can facilitate the development of more effective public health initiatives targeting underage alcohol consumption. This week, The DRAM discusses research that examines how adverse childhood experiences and the age of onset for first alcohol use contribute to young adults’ current use of alcohol.

Young, Hansen, Gibson, and Ryan (2006) surveyed 18-20 year old Marine Corps recruits with the Recruit Assessment Program (RAP) Questionnaire to gather data about their demographic, family and general history, and childhood experiences. They administered these surveys at the Marine Corps Recruit Depot in San Diego California from June 2002 to April 2006.  After reviewing the initial 65,178 surveys, the researchers determined that 41,482 surveys were suitable for analysis; these surveys contained complete outcome and covariate data, and fit the criteria set forth by the researchers (i.e., 18-20 years old with no contradictory responses). The researchers conducted multivariate logistic regression analyses to assess associations between childhood factors (e.g., onset age, adverse childhood experiences) and young adult risky drinking patterns. The investigators Identified risky drinkers by using the AUDIT Alcohol Consumption Questionnaire.

Approximately one in seven (14.8%) of participants met the researchers’ criteria for risky drinking, and 45.1% satisfied their criteria for the non-risky drinker category; the other 40.2% were self-reported non-drinkers. The risky drinkers reported only slightly higher prevalence of experiencing child abuse or witnessing domestic violence compared to their non-risky drinking counterparts (see Table 1). However, those recruits who reported first drinking around 13 years were 5.5 times more likely to engage in riskier drinking behavior than recruits who reported first drinking after age 13. Other significant and anticipated predictors of young adult drinking were smoking, having a rural or small town background, having grown up with someone who was a problem drinker or having grown up with someone who suffered from mental illness. Some unexpected correlates of risky drinking were achieving a higher educational level, having more close family members or friends, and being raised by two parents.

This study of young adult drinking had three key limitations: (1) self-reported data collection; (2) the large number of excluded surveys, potentially limiting the study's representativeness; (3) the narrow sample (i.e., male military recruits). Despite these concerns, Young et al. (2006) provide support for the importance of age of onset to young adult drinking habits; however, they did not find adverse childhood experiences to be equally strong predictors of young adult drinking. Interestingly Young et al. (2006) noted that they did not expect to find that risky drinkers had a number of experiences that one might expect to be protective, such as, a higher number of close family and friends, a higher level of education, along with being slightly more likely to be raised by two parents. Young et al’s (2006) research shows that multiple and interactive factors, whether prototypically protective or detrimental, can be associated with harmful drinking behavior. The presence of protective childhood experiences does not guarantee a young adult life without substance abuse problems.

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

References

Molina, J. C., Spear, N.E., Mennella, J.A., Lewis, M.J. (2007). The International society for developmental psychobiology 39th annual meeting symposium: Alcohol and development: beyond fetal alcohol syndrome. Developmental Psychology, 49(3), 227-242.

Padget, A., Bell, M.L., Shamblen, S.R., Ringwalt, C.L. (2006). Does learning about the effects of alcohol on the developing brain affect children's alcohol use? Prevention Science, 7(3), 293-302.

Young, S. Y. N., Hansen, C.J, Gibson, R.L, Ryan, M.A.K. (2006). Risky Alcohol Use, Age at Onset of Drinking, and Adverse Childhood Experiences in Young Men Entering the US Marine Corps. Archives Pediatrics & Adolescent Medicine, 160(12), 1207-1214.

The WAGER 12(9) : The Pleasure of Gambling: Is it in the Winning and the Losing?

Research indicates that the dopaminergic reward system is one of the key mechanisms in learning and reinforcement of adaptive behaviors. Upon receipt of a reward, dopamine is released in the brain, which leads to pleasurable feelings and reinforces behavior. However, dopamine also reinforces maladaptive behaviors, such as substance-induced and behavioral addictions (Reuter, Raedler, & Rose, 2005; Zack & Poulos, 2007). In this edition of the WAGER, we review research by Fiorillo, Tobler & Schultz (2003) that examines the role of dopamine in uncertain situations.

Fiorillo and colleagues presented 2 primates with visual stimuli associated with a 0%, 25%, 50%, 75%, or 100% chance of receiving a reward (liquid) and recorded the activity of these primates’ midbrain dopamine neurons. Once the primates had learned the associations, the dopamine neurons showed increased activity between the stimuli presentation and the delivery of reward for stimuli predicting uncertainty of reward (25%, 50%, 75%). Activation was highest for stimuli predicting a 50% probability of reward (i.e., the highest uncertainty). See Figure 1.

Figure 1. Dopaminergic Activity in Response to Uncertain Stimuli in Two Primates (reproduced from Fiorillo et al., 2003, Figure 3c).

There are some limitations to this study. The reward system in primates might differ from humans’ in important ways. The study also used a very simple conditioning paradigm, whereas a real gambling situation is much more complex.

This increased dopaminergic activation under uncertainty appears to direct attention to the predictive stimulus to facilitate learning. The anticipatory activity might also reinforce risk taking in uncertain situations. Though potentially adaptive, this increased activation under uncertainty also might reinforce maladaptive behaviors such as excessive gambling in some people (Fiorillo, 2004). Most games rely on chance, so gamblers constantly face uncertain situations. Disordered gamblers might be particularly sensitive to the sustained anticipatory dopamine activation produced by these uncertain situations, making them feel good and continue to gamble regardless of whether they win or lose. Future research needs to look directly at this group.

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

References

Fiorillo, C. D. (2004). The uncertain nature of dopamine. Molecular Psychiatry, 9(2), 122-123.

Fiorillo, C. D., Tobler, P. N., & Schultz, W. (2003). Discrete coding of reward probability and uncertainty by dopamine neurons. Science, 299(5614), 1898-1902.

Reuter, J., Raedler, T., & Rose, M. (2005). Pathological gambling is linked to reduced activation of the mesolimbic reward system. Nature Neuroscience, 8(2), 147-148.

Zack, M., & Poulos, C. X. (2007). A D2 antagonist enhances the rewarding and priming effects of a gambling episode in pathological gamblers. Neuropsychopharmacology, 32(8), 1678-1686.

Op-Ed/Editorials: World Series Proves Poker Game Of Skill

The final table of the World Series of Poker’s main event shows, once again, that poker tournaments are games of skill.

Courts have developed tests over the last couple of hundred years to determine whether a particular game is predominantly chance or skill.  If courts and prosecutors were honest in applying these tests, at least No Limit Texas Hold 'em tournaments would have to be considered skill contests and not gambling.

Let’s take a look at the most common tests and what happened on July 18, 2007.

1)  A skillful player will win more than an unskillful one.  The tournament started 12 days earlier, with 6,358 paying $10,000 each to enter.  All the chips that were lost by players went to other players, not the house.

2)  Skill can be learned from experience, from real or mock play.  Here’s how the Associate Press described the nine players at the final table, in the order in which they were eliminated:

• Lee Childs, a 35-year-old software engineer from Reston, Va., who quit his job a month ago to play poker for a living.
• Philip Hilm, a 31-year-old Dane making a living from poker in England.
• Lee Watkinson, a 40-year-old poker pro from Cheney, Wash.
• Hevad Khan, an Internet poker pro from Poughkeepsie, N.Y.
• Jon Kalmar, a 34-year-old poker pro from Chorley, England.
• Alex Kravchenko, 36.  No other information given.  AP did not mention that Kravchenko has been in the money in numerous poker events, including winning three European tournaments.
• Raymond Rahme, a South African retiree.  AP did not mention that Rahme had previously came in first, second and fourth in major South African poker tournaments.
• Tuan Lam, a 40-year-old Vietnamese Canadian online poker pro from Mississauga, Ontario.
• California psychologist Jerry Yang.

Question:  If poker is not a game of skill, how can there be professional poker players?  No one makes a living playing lotteries.

3)  Skill games usually require a knowledge of mathematics and psychological skill.  Here’s how Yang described his playing style:  “I study my opponents very carefully, and when I sensed something, when I sensed some weakness, I took a chance.  Even if I had nothing, I decided to raise, reraise, push all-in or make a call.”

When courts or attorneys general want to declare a game, such as poker, is predominantly luck, they focus on the fact that cards are involved.  The most common argument is that even a complete novice could beat a professional if the amateur were dealt better cards.

This shows a fundamental lack of understanding of how poker is played.  Nobody ever sits down to a single hand of poker.  And even if they did, the rules of elimination tournaments require that there be more than a single hand.

And poker is not just about being dealt the best cards.  We do not yet have the wonderful 20-20 hindsight provided by the cameras that show TV viewers the players’ down cards.  But we do know at least one important hand.

On the ninth hand of play at the final table, the flop was seven, four and deuce.  Yang declared an all-in reraise.  His opponent, Lee Childs, folded, showing pocket queens, face up.

Now, maybe Yang had the better hand, with two pair.  But maybe not.

It is very possible that Childs’ queens were the best cards before the flop.  He might still have had the best hand after that flop of little cards.  And he might have had the best hand if he had stayed in to the end.  But Yang won. 

Because it is fundamental to the game of poker that the best hand does not necessarily win.

© Copyright 2007.  Professor I Nelson Rose is recognized as one of the world’s leading experts on gambling law.  His latest books, INTERNET GAMING LAW and GAMING LAW: CASES AND MATERIALS, are available through his website, www.GamblingAndTheLaw.com.

STASH Vol. 3(8) - Does Homelessness contribute to Drug Use? Analyzing the Social Adaptation Model

Research shows higher lifetime and six month prevalence of substance use disorders among the homeless than found among non-homeless populations (Fischer, Shapiro, Breakey, Anthony, & Kramer, 1986; Koegel, Burnam, & Farr, 1988).  The social adaptation model (Stark, 1987) suggests that homelessness might be a risk factor for substance use.  This week’s STASH reviews a study that examines the relationship between homelessness and substance use.

Using a multistage probability design, trained interviewers from the University of Illinois at Chicago Survey Research Laboratory surveyed English speaking adults between the ages of 18 and 40 living in the city of Chicago between June 2001 and January 2002 (Johnson & Fendrich, 2007).  Participants (n=627, response rate = 40%) reported their lifetime and most recent substance use (i.e., tobacco, alcohol, marijuana, cocaine, crack, heroin, hallucinogens, inhalants, stimulants, tranquilizers, sedatives, and pain relievers), frequency and age of onset of substance use, and onset and recency of homelessness.  Researchers used bivariate and multivariate statistics to analyze the association between early homelessness (i.e., before age 19) and recent substance use.

Table 1: Association between First Homeless Experience and Recent Drug Use 



Adapted from (Johnson and Fendrich, 2007)

Analysis revealed that 66.7% of participants who had experienced homelessness prior to 19 years of age (n=45) also had used substances within the past year. In comparison, only 31% of those lacking early homeless experiences (n=583) reported substance use within the past year (χ₂=23.76, df=1, p<.001; cf. Table 1). Multivariate analyses, controlling for the age of first substance use, also yielded a significant link between early homelessness and recent substance use (unstandardized coefficient=0.23, standard error=0.08, p<0.01).This study has several limitations. First, self-report often results in participants underestimating their socially undesirable behaviors (e.g., substance use, homelessness). Second, as a result of the low response rate (40%) and exclusion of currently homeless people, the participants might not accurately represent the Chicago homeless population.

Third, other factors known to be related to homelessness (e.g., childhood abuse and history of mental illness; Koegel, Melamid, & Burnam, 1995), were not measured; without accounting for these risk factors, the study cannot measure the independent effect of early homelessness on substance use.

Despite the limitations, this study does indicate an association between homelessness and substance use. Perhaps homeless people start using drugs to better deal with their daily struggles on the street or they might be introduced to drugs within homeless shelters. Alternatively, excessive substance use might lead to financial ruin or involvement in other illegal activities, resulting in homelessness.  Whatever the reasons for the association between drug use and homelessness, findings from this study suggest that public health interventions should include efforts to address both the potential for homelessness among drug users and the potential for drug use among homeless people.

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

References

Fischer, P. J., Shapiro, S., Breakey, W. R., Anthony, J. C., & Kramer, M. (1986). Mental health and social characteristics of the homeless:  a survey of mission users. American Journal of Public Health, 76, 519-524.

Johnson, T. P., & Fendrich, M. (2007). Homelessness and Drug Use:  Evidence from a community sample. American Journal of Preventative Medicine, 32(6S), S211-S218.

Koegel, P., Burnam, A., & Farr, R. K. (1988). The prevalence of specific psychiatric disorders among homeless individuals in the inner city of Los Angeles. Archives of General Psychiatry, 45, 1085-1092.

Koegel, P., Melamid, E., & Burnam, A. (1995). Childhood risk factors for homelessness among homeless adults. American Journal of Public Health, 85, 1642-1649.

Stark, L. (1987). A century of alcohol and homelessness:  demographics and stereotypes. Alcohol Health and Research World, 11, 8-13.