The Brief Addiction Science Information Source (BASIS)

The DRAM Vol. 6(1) – Drosophila study shows you have to be handsome to stay sober

Animal models help scientists to understand human behavior and develop new drugs to treat pathologies. This week’s DRAM reviews a study that examines an animal model of drinking behavior. Devineni and Hebertein (2009) sought to determine if the drinking behavior of drosophila flies resembles human drinking behavior and if genetic mutations modify this behavior.

Method

• Investigators gave flies the opportunity to choose between ethanol-containing and nonethanol-containing food. Researchers calculated a preference index (PI) as (ethanol consumption – nonethanol consumption) / total consumption. The index ranged from -1 to 1, with positive numbers indicating preference for ethanol.
• Researchers measured the following:
• • the basic preference of flies
  • the pattern of preference change across time and ethanol concentration shifts
  • the preference change after periods of starvation or ethanol deprivation
  • ethanol preference of different mutations.

Results

• At baseline, the flies consistently showed a statistically significant preference for the ethanol-containing food, even when the caloric ratio between ethanol and nonethanol food was balanced.
• The preference for ethanol-containing food increased for the first few days but leveled off after 4 – 5 days.
• The level of preference increased with larger ethanol dose concentration.
• Starved flies were willing to consume ethanol at levels sufficient to produce behavioral intoxication.
• After being deprived of ethanol for several days, flies quickly resumed ethanol consumption when given an opportunity.
• Previous testing of 27 different types of mutations by the researchers had found one mutant -- krasavietz – or handsome in Russian – that exhibited a significantly lower preference for alcohol compared to the non-mutated controls. The “handsome” flies were studied in this experiment (see Figure 1).During the first two days the “handsome” flies showed no preference for alcohol, a behavior that was significantly different from controls who took to alcohol immediately.

Figure 1. Five day ethanol preference for control group and mutant fly krasavietz (handsome). N = 25.

Limitations

• No animal model provides a perfect analog to human alcohol consumption or alcoholism. For example, social and cultural factors influence human alcoholism, and these influences are difficult to create within animal studies.

Conclusions

This study indicates that drosophila’s drinking behavior resembles human drinking patterns in many facets. Similar to humans, drosophila’s initial preference to ethanol containing food is low and apt to vary,however gradually it increases and becomes consistent. Drosophila gradually are attracted to ethanol despite its aversive taste and intoxicating effect. One characteristic of human alcohol dependence is relapse – a return to previous levels of alcohol consumption after a period of abstinence (Hunt, Barnett, & Branch, 1971). Flies also exhibit a relapse-like behavior after ethanol deprivation. Devineni and Hebertein (2009) showed that a mutation, specific to a single gene, is related to low preference for ethanol. Identifying this and potentially other mutations help scientists to understand the potential molecular mechanisms underlying alcohol preference. No animal model can explain all facets of alcohol dependence among humans. However, a better scientific understanding of ethanol preference in both animals and humans may lead to the discovery of features common to a general reward pathway involved with addiction.

 -Julia Braverman

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References

Devineni, A. V., & Hebertein, U. (2009). Preferential Ethanol Consumption in Drosophila Models Features of Addiction. Current Biology, 19, 2126-2132.

Hunt, W. A., Barnett, L. W., & Branch, L. G. (1971). Relapse rates in addictions programs. . Journal of Clinical Psychology 27, 455 - 456.

 

DRAM Vol. 5(11) – Which comes first – the drink or the distress?

Previous studies have demonstrated a higher prevalence of psychiatric disorders among individuals with alcohol use disorder (AUD) than among the general population (Grant et al., 2004; Kessler et al., 1996). However, there is limited literature that examines both the co-occurence and the temporal ordering (i.e., which disorder occurs first) of AUD and other psychiatric disorders. This week’s DRAM reviews a study that aims to fill this gap in the literature (Flensborg-Madsen et al., 2009).

Method

• Investigators analyzed 25 years of follow up data for 18,146 individuals from Copenhagen, Denmark.
• The data reflected all admissions to Danish hospitals since 1976 and the data from the Danish Psychiatric Central Register since 1969.
• The investigators classified individuals by diagnostic record for mood disorders, psychotic disorders, anxiety disorders, personality disorders, drug use disorder, and alcohol use disorder (AUD). 
• The researchers analyzed the data by means of Cox regression analysis, which is a type of survival analysis.

Results

o          Among individuals with AUD (N = 1,756), 50% had a lifetime comorbid psychiatric disorder.

• Personality disorders were the most common comorbid disorders (24%, N = 421), followed by mood disorders (16.8%, N = 295) and drug abuse (16.6%, N = 291).
• As Table 1 shows, the risk of developing a psychiatric disorder among AUD individuals (N = 1,200) was larger than the risk of developing an AUD among individuals who were already registered with another psychiatric disorder (N = 2812).

 

Table 1. Risk of AUD for individuals with psychiatric disorders and risk of psychiatric disorders for individuals with AUD adjusted for sex, smoking, cohabitation status and educational level.

Initial                  disorder     Future   disorder	AUD	Mood disorder	Psychotic disorder	Anxiety disorder	Personality disorder	Drug abuse
AUD		4.5	3.2	3.4	4.3	5.1
Mood disorder	4.8
Psychotic disorder	4.5
Anxiety disorder	6.4
Personality disorder	8.1
Drug abuse	18.3

Note: Data are presented in the form of odd-ratios.

Limitations

• The time of disorder onset is based on a diagnostic record and not a report of onset; consequently, the timing of AUD and other psychiatric disorders might reflect the order of treatment seeking or clinician recognition of a problem, not the actual onset.
• The sample contains only individuals who were diagnosed with a psychiatric disorder.
• Even if alcohol orders tend to precede other psychiatric disorders, this study does not provide evidence for a causal link between the two.

 

Conclusion

Comorbidity of AUD with other psychiatric disorders is the rule rather than the exception. These findings show that, for individuals who have been diagnosed with AUD, the risk of subsequently receiving a diagnosis for other psychiatric disorders is higher than the risk for individuals who have been diagnosed with other psychiatric disorders to receive a subsequent diagnosis of AUD. However, previous findings have shown that among people with both a psychiatric and an addictive disorder, psychiatric disorders typically occur first (Kessler et al., 2008; Kessler et al., 1996). This contradiction might be explained by the fact that the researchers in the current study used the onset of treatment as the definition of disorder onset. It might be that individuals with both AUD and psychiatric disorder register to the hospital to receive treatment for the AUD first. It also might be that individuals with comorbid AUD register at the hospital more frequently than individuals without comorbid AUD. Overall, these findings imply the need to develop improved and integrated treatment for individuals meeting criteria of AUD and other psychiatric disorders.

 

-Julia Braverman

 

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References

Flensborg-Madsen, T., Mortensen, E. L., Knop, J., Becker, U., Sher, L., & Grønbæk, M. (2009). Comorbidity and temporal ordering of alcohol use disorders and other psychiatric disorders: Results from a Danish register-based study. Comprehensive Psychiatry, 50(4), 307-314.

Grant, B. F., Stinson, F. S., Dawson, D. A., Chou, S. P., Dufour, M. C., Compton, W., et al. (2004). Prevalence and co-occurrence of substance use disorders and independent mood and anxiety disorders. Archives of General Psychiatry, 61, 807-816.

Kessler, R. C., Hwang, I., LaBrie, R. A., Petukhova, M., Sampson, N. A., Winters, K. C., et al. (2008). DSM-IV pathological gambling in the National Comorbidity Survey Replication. Psychological Medicine 38(9), 1351-1360.

Kessler, R. C., Nelson, C. B., McGonagle, K. A., Edlund, M. J., Frank, R. G., & Leaf, P. J. (1996). The epidemiology of co-occurring addictive and mental disorders: Implications for prevention and service utilization. American Journal of Orthopsychiatry, 66(1), 17-31.

 

The DRAM, Vol. 5(10) – New data for an old debate: Does moderate alcohol intake really lower the risk of adverse health outcomes?

The harmful effects of heavy alcohol drinking are well documented (e.g., World Health Organization, 2004). Recent research, however, suggests that there are potential health benefits associated with moderate alcohol intake (e.g., physical functionality, reduced disability and mortality: Cawthon et al., 2007; Karlamangla et al., 2009; Lang, Guralnik, Wallace, & Melzer, 2007). However, these studies have not fully controlled for variables that might account for the apparent relationship between alcohol and health. This week, the DRAM reviews a longitudinal study that explores the possibility of a causal relationship between alcohol intake and physical mobility among older adults, controlling for multiple lifestyle factors (Maraldi et al., 2009).

Methods

• The current study included 3,061 participants enrolled in the Health Aging and Body Composition (Health ABC) study.
  • Participants were well-functioning adults, aged 70-79 at intake, recruited from the metropolitan areas of Pittsburgh, PA and Memphis, TN 
• Self-reported measures included:
  • At baseline, alcohol intake (i.e., number of drinks consumed in a typical week during the previous 12 months), as well as demographic, lifestyle, health, and cognitive variables.  
  • Mobility assessments every 6 months for 78 months coded as: No limitation; Mobility limitation (two consecutive reports of difficulty walking ¼ of a mile or climbing stairs without rest); or Mobility disability (two consecutive reports of severe difficulty or inability to walk ¼ mile or climb stairs).  
• Researchers performed Cox proportional hazard regression analyses (a form of survival analysis) on the data to determine the relationship between alcohol intake at baseline and mobility status while controlling for the baseline variables.

Results

• Table 1 shows the results of two models.
  • Model 1 predicted mobility status from baseline alcohol intake, controlling for demographics (i.e., age, sex, race and site location).
  • Model 2 replicated Model 1, but also controlled for lifestyle characteristics (i.e., education, family income, smoking status, physical activity and body mass index).
  • In Model 1, moderate alcohol intake was associated with a lower risk of mobility limitation and mobility disability compared to never or occasional alcohol intake, as shown in blue in Table 1.
  • As the green section of Table 1 shows, however, Model 2 found that moderate alcohol intake was no longer significantly associated with a lower risk of mobility limitation or mobility disability compared to never or occasional alcohol intake.  

Table 1: Risk of Mobility Problems According to Alcohol Intake

Click image to enlarge, or adjust your browser's zoom setting.
*Number of drinks in a typical week during the 12 months previous to baseline.
Note: Two participants are excluded from this analysis because they did not have follow-up data. Follow-up was from baseline to the time of the first of two consecutive reports of mobility measures. For participants who died and did not experience mobility disability prior to death, data were censored at time of death.

Limitations

• This study used self-report for the alcohol intake variables.
• Researchers assessed alcohol intake only once at the beginning of the study. Changes in alcohol intake over the study period could have affected the results.
• Researchers controlled for physical activity in Model 2 of the analysis. This control variable is so similar to the mobility measure that controlling for it might distort the results of the analysis.
• The age range and geographic locations might reduce the generalizability of the results.

Discussion

This study indicates that, among a cohort of well-functioning older adults, moderate and light alcohol intake was associated with a lower rate of reduced mobility. However, a causal relationship between alcohol intake and physical functionality could not be determined. After adjusting the results for lifestyle characteristics, the association between alcohol use and mobility was largely diminished. These results suggest that lifestyle choices, rather than alcohol intake, might be more important influences on physical functionality among older adults.

-Tasha Chandler

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References

Cawthon, P. M., Fink, H. A., Barrett-Connor, E., Cauley, J. A., Dam, T. T., Lewis, C. E., et al. (2007). Alcohol use, physical performance, and functional limitations in older men. Journal of the American Geriatrics Society, 55(2), 212-220.

Maraldi, C., Harris, T. B., Newman, A. B., Kritchevsky, S. B., Pahor, M., Koster, A., et al. (2009). Moderate alcohol intake and risk of functional decline: The health, aging, and body composition study. Journal of the American Geriatrics Society, 57(10), 1767-1775.

World Health Organization. (2004). Global status report on alcohol 2004: World Health Organization.

Cawthon, P. M., Fink, H. A., Barrett-Connor, E., Cauley, J. A., Dam, T. T., Lewis, C. E., et al. (2007). Alcohol use, physical performance, and functional limitations in older men. Journal of the American Geriatrics Society, 55(2), 212-220.

Karlamangla, A. S., Sarkisian, C. A., Kado, D. M., Dedes, H., Liao, D. H., Kim, S., et al. (2009). Light to moderate alcohol consumption and disability: Variable benefits by health status. American Journal of Epidemiology, 169(1), 96-104.

Lang, I., Guralnik, J., Wallace, R. B., & Melzer, D. (2007). What level of alcohol consumption is hazardous for older people? Functioning and mortality in U.S. and English national cohorts. Journal of the American Geriatrics Society, 55(1), 49-57.

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 DRAM Vol. 5(8) – Tell me who your friends are and I'll tell you who you are: Romantic relationships and alcohol use by adolescents.

Some people suggest that you can tell a lot about people by the company that they keep. It is unclear, however, whether behavioral similarities of friends and partners are due to influence (people becoming like their friends or partners) or selection (people gravitating toward similar friends or partners). Addressing this issue, this week’s The DRAM reviews a study that examines the associations between adolescent alcohol use and adolescents’ perception of their partners’ alcohol use (van der Zwaluw et al., 2009).

Method

Participants and procedure:

• Investigators interviewed parents and adolescents from 428 Dutch families once a year for four years (T1-T4). Each family consisted of two parents and two siblings. The retention rate was 81% across the study.
• Both siblings participated. The mean age was 13.4 years for the younger siblings and 15.2 years for older siblings.
• Only participants who reported romantic involvement at a given time point were used in these analyses (e.g., 36% of older siblings and 26% of younger siblings at T4).

Main measures:

• Participants self-reported: (1) their frequency of alcohol use during the past 4 weeks; (2) whether they currently were in a steady romantic relationship; and (3) their estimation of their partner’s frequency of alcohol use during the past 4 weeks.
  

Results

• According to participants’ estimation of their own and their partner’s alcohol use frequency, participants and romantic partners were relatively similar in their alcohol use (r = .42, p <. 01).
• Partners’ alcohol use (as estimated by participants) did not significantly predict later participant alcohol use if prior participant alcohol use was taken into account (Table 1, first row).
• However, adolescents tended to select partners with similar drinking habits: participants’ use of alcohol at a given time point predicted their partner’s alcohol use one year later (Table 1, second row).

Table 1 Prospective relations between partner and participant alcohol use frequency (adapted from van der Zwaluw et al., 2009).

	T1→T2 (N)	T2→T3 (N)	T3→ T4(N)
PartneràParticipant1	0.09 (149)	0.18† (181)	−0.02 (241)
ParticipantàPartner2	0.17 (149)	0.25** (181)	0.26** (241)

Notes: ¹  The numbers in this row reflect the correlation between the partner’s alcohol use and the participant’s alcohol use one year later, controlling for the prior levels of participant’s alcohol use. ^† p < 0.10; ² The numbers in this row reflect the correlation between the participant’s alcohol use and the partner’s alcohol use one year later. ** p < .01

Limitations

• Self-reported results (both for own and partner’s alcohol consumption)
• Specific sample (adolescents who start dating early)

Conclusion

Adolescents and their romantic partners are similar in how much alcohol they consume. Results from this study suggest that this similarity is best explained by the initial selection of a partner rather than by the influence of partners’ drinking habits on adolescents. The lack of correlation between a partner’s alcohol consumption and the adolescent’s alcohol consumption one year later might be explained, as the authors suggest, by the fact that most adolescent relationships are short-lived. Together, these findings suggest that research and intervention efforts addressing peer influence on drinking behaviors in adolescence ought to focus on peer groups, not romantic partners.

- Julia Braverman

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References

van der Zwaluw, C. S., Scholte, R. H. J., Vermulst, A. A., Buitelaar, J., Verkes, R. J., & Engels, R. C. M. E. (2009). The crown of love: Intimate relations and alcohol use in adolescence. European Child & Adolescent Psychiatry, 18(7), 407-417.

The DRAM Vol. 5(7) – To eat or to drink? That is the question!

Both physiological and behavioral evidence suggest that we might be able to view excessive eating as an expression of addiction. Previous studies suggest that food and alcohol consumption might compete for the same biological pathways (Kleiner et al., 2004). If so, one would expect that engagement in one of these behaviors might lessen engagement in the other. This week’s DRAM reviews a paper that examines the relationship between excessive food and alcohol consumption (Gearhardt & Corbin, 2009).

Method

• Participants and procedure
  • Investigators interviewed more than thirty five thousand participants as a part of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC: Grant, 1997), a nationally representative general population survey.
• Measures of interest
  • Investigators assessed two measures of alcohol consumption: frequency of use and blood alcohol level. Typical blood alcohol level was estimated based on the reported frequency of use (number of drinking days in the past year) and quantity of drinking (drinks per drinking day), sex and weight.
  • In addition, the researchers collected data on family history of alcohol problems and BMI (Body Mass Index) for each participant. See Figure 1 for weight categorization.   

Results

• Obese participants consumed alcohol significantly less frequently than normal weight and overweight participants (F (4, 37502) = 34.47, p < .001).
• Family history of alcohol problems predicted frequency of alcohol consumption for nonobese participants (F (1, 28544) = 97.26, p < .001), but not for obese participants (F (1, 8971) = 2.577, p = .28) See Figure 1.

Figure 1. The effect of weight category and family history of alcohol problems on the frequency of alcohol use

 *Click image to enlarge, or adjust your browser's zoom setting.

• Family history of alcohol problems predicted blood alcohol level both for obese and nonobese participants; however its effect for nonobese participants was much higher (2= .012 vs. 2= .001)

Limitations

• Self-reported results
• No direct test of excessive eating. Obesity suggests but does not require excessive eating behavior. 
• The findings suggest food and alcohol consumption might share the same biological pathway; however, this mechanism was not tested directly.

Conclusion

The authors conclude that the results provide additional support for the hypothesis that alcohol consumption and excessive eating share the same neurobiological pathways and both might be categorized as expressions of addiction, a conclusion consistent with the Syndrome Model of Addiction (Shaffer et al., 2004). However, the inverse relation between obesity and alcohol consumption is contradictory to other findings that demonstrate the multimorbidity of different expressions of addiction (McKee et al., 2009; also see DRAM 5(5)). Future research needs to investigate both the mechanisms through which multiple expressions of addiction arise and the course of those expressions within individuals who develop more than one expression.

References

Gearhardt, A. N., & Corbin, W. R. (2009). Body mass index and alcohol consumption: Family history of alcoholism as a moderator. Psychology of Addictive Behaviors, 23(2), 216-225.

Grant, B. F. (1997). Prevalence and correlates of drug use and DSM-IV alcohol dependence in the United States:  Results of the National Longitudinal Alcohol Epidemiologic Survey Journal of Studies on Alcohol 58, 464-473.

Kleiner, K. D., Gold, M. S., Frost-Pineda, K., Perri, M. G., Jacobs, W. S., & Lenz-Brunsman, B. (2004). Body Mass Index and Alcohol Use. Journal of Addictive Diseases, 23(3), 105-118.

McKee, S. A., Harrison, E. L., O'Malley, S. S., Krishnan-Sarin, S., Shi, J., Tetrault, J. M., et al. (2009). Varenicline reduces alcohol self-administration in heavy-drinking smokers. Biological Psychiatry (In Press).

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 DRAM Vol. 5(6) – High School Limits and College Drinking: Parental Influence on College Students’ Alcohol Use

Research indicates that alcohol abuse is linked to negative consequences (e.g., drunk driving, physical altercations, etc.) among college students (Hingson, Heeren, Winter, & Wechsler, 2005). Although previous research has shown that parental influence diminishes when adolescents enter college, more recent research suggests that parents still have some influence (Galotti & Mark, 1994; Lehr, Dilorio, Dudley, & Lipana, 2000). This week the DRAM reviews a study that examined the impact of parental permissibility of alcohol use during late high school on alcohol use and negative drinking consequences experienced among college students (Abar, Abar, & Turrisi, 2009).

Methods:

• The researchers randomly recruited 500 freshmen college students from a northeastern university to complete an online survey assessing their parent-teen communication and alcohol use behaviors.
• Participants (N=290, 58% response rate) were at least 18 years of age, were 61% female and 88.9% Caucasian; 97.2% lived in dormitories.
• The survey consisted of demographics and questions designed to measure:
  • parent permissibility of alcohol (i.e., student report of parent limit setting in high school and maternal and paternal acceptance of alcohol);
  • student drinking behavior (i.e., weekend drinking, peak drinking, and frequency of being drunk in the past month); and
  • negative consequences of alcohol within the last year (26 items from the Young Adult Alcohol Problem Screening Test; Hurlbut & Sher, 1992).  

Table 1: Parent Permissibility and Students’ Drinking Behavior and Negative Consequences – unstandardized B weights and SEs (adapted from Abar et al., 2009).

*p < .001. Note. These weights reflect a saturated structural equation model that also included other family variables, none of which contributed significantly. For student gender, 1=male and 0=female. For parental limit setting, a high score indicates permissive limits.

Results:

• Students reported consuming an average of 9.14 drinks per weekend (SD=7.98), an average peak of 7.04 drinks (SD=5.77) on one occasion, being drunk an average of 1.86 (SD=1.65) times, and experiencing an average 18.90 (SD=16.44) negative consequences from alcohol in the past month.
• As Table 1 shows, parent permissibility was predictive of college drinking behaviors.
  • Permissive parental limits for alcohol use in high school were associated with greater college drinking but not negative consequences. 
  • Higher paternal acceptability of alcohol use in high school was associated with fewer negative consequences of college drinking. 
• Separate analyses found that students whose parents had allowed no drinking during high school reported less college drinking (M diff weekend drinks = -4.99, t (254) = -5.14, p<.001;  M diff peak drinks = -3.56, t (256) = -5.10, p<.001; M diff frequency of drunkenness = -1.08, t (256) = -5.51, p<.001) and experienced fewer negative consequences (M diff= -10.10, t (256) = -5.03, p<.001) than their college peers.

Limitations:

• The study used self-report and retrospective data.
• Parental variables reflected student perceptions of parent behavior.

Conclusion:

This study does not support the theory that allowing adolescents to experiment with drinking will lead to a later reduction in alcohol misuse. Instead, the study indicates that having parents who allowed any drinking during high school was a significant risk factor for alcohol misuse during college. The size of those limits, if greater than zero, did not affect negative consequences, and paternal acceptance of alcohol use had a negative effect on consequences, suggesting that among students whose parents allowed some drinking in high school, the relationship between parent permissiveness and alcohol misuse is more complicated.

-Tasha Chandler

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

References:

Abar, C., Abar, B., & Turrisi, R. (2009). The impact of parental modeling and permissibility on alcohol use and experienced negative drinking consequences in college. Addictive Behaviors, 34(6-7), 542-547.

Galotti, K. M., & Mark, M. C. (1994). How do high school students structure an important life decision? A short term longitudinal study of the college decision-making process. Research in Higher Education, 35, 589-607.

Hingson, R., Heeren, T., Winter, M., & Wechsler, H. (2005). Magnitude of alcohol-related mortality and morbidity among U.S. college students ages 18-24: Changes from 1998 to 2001. Annual Review of Public Health, 26, 259-279.

Hurlbut, S. C., & Sher, K. J. (1992). Assessing alcohol problems in college students. Journal of American College Health, 41(2), 49-58.

Lehr, S. T., Dilorio, C., Dudley, W. N., & Lipana, J. (2000). The relationship between parent-adolescent communication and safer sex behaviors in college students. Journal of Family Nursing, 6, 180-196.

The DRAM Vol. 5(5) - One Drug, Two Treatments

Research has shown that alcohol and tobacco dependence are often comorbid disorders (Grant, 1998; McKee, Falba, O'Malley, Sindelar, & O'Connor, 2007). Research also shows that some of the chemical treatments used for smoking cessation may be used to treat alcohol consumption and craving. This week the DRAM reviews a study that tested the effects of varenicline, a new FDA approved treatment for smoking cessation, on alcohol consumption in heavy smokers and drinkers (McKee et al., 2009).

Methods:

• The researchers conducted a double-blind, placebo controlled study examining the effect of varenicline on alcohol self-administration.
• Participants (N=20) were eligible if they were ≥ 21 years old, smoked ≥ 10 cigarettes daily, and drank multiple drinks multiple days per week but were not alcohol dependent.
• Participants took varenicline or a placebo for 7 days prior to completing a 14-hour laboratory session that included two 1-hour alcohol self-administration sessions.
• The researchers measured alcohol craving prior to and during the alcohol self-administration sessions.

Figure 1: Alcohol Craving Before and During Alcohol Self-Administration Sessions (Adapted from McKee et al., 2009).

*VAS is a visual analogue scale, i.e., a continuous scale on which participants mark a point indicating their response

Results:

• Figure 1 shows that alcohol cravings decreased in the varenicline group but increased in the placebo group across time (time x medication, F = 2.57, p < .05).
• Participants who took varenicline consumed significantly fewer drinks than participants who took a placebo during the self-administration periods (varenicline M = 0.5, SD = .40; placebo M = 2.60, SD = .9; p < .05).

Limitations:

• The sample size was small.
• The results might not generalize to all drinking populations because the drug was not tested on people with alcohol dependence or people who did not smoke.
• The effects of this drug were tested in a laboratory, not in actual social drinking situations.

Conclusion:

This study demonstrated that varenicline was able to inhibit alcohol consumption and craving in people who are heavy drinkers and smokers. Varenicline use had minimal adverse effects. Given that the majority of drinkers smoke, varenicline seems like a promising drug to treat people with dual alcohol and tobacco disorders.

-Tasha Chandler

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

References:

Grant, B. F. (1998). Age at smoking onset and its association with alcohol consumption and DSM-IV alcohol abuse and dependence: Results from the National Longitudinal Alcohol Epidemiologic Survey. Journal of Substance Abuse, 10(1), 59-73.

McKee, S. A., Falba, T., O'Malley, S. S., Sindelar, J., & O'Connor, P. G. (2007). Smoking status as a clinical indicator for alcohol misuse in US adults. Archives of Internal Medicine, 167(7), 716-721.

McKee, S. A., Harrison, E. L., O'Malley, S. S., Krishnan-Sarin, S., Shi, J., Tetrault, J. M., et al. (2009). Varenicline reduces alcohol self-administration in heavy-drinking smokers. Biological Psychiatry (In Press).

The DRAM Vol. 5(4) – Benefits of Housing the Homeless

The chronically homeless, especially those with substance use problems, represent a tremendous cost to states’ public health care, criminal justice, and other social welfare systems. Abstinence-based housing programs do not always prove effective in dealing with this population sector and sometimes fail to prevent substance abuse relapses (Culhane & Metraux, 2008). This week’s DRAM reviews a study about the cost-effectiveness of a housing program, Housing First, which does not require sobriety, allowing residents to drink in their rooms while still receiving food, shelter, healthcare, and substance abuse counseling (Larimer et al. 2009). 

Methods

• Researchers recruited participants by rank-ordering a list of known chronically homeless people by their total monthly costs to public services in 2004. Housing First’s staff offered housing to these individuals in order on a first come first serve basis. Ninety-five individuals received housing (n=95; housed); the remaining willing participants were placed on a wait-list (n=39; controls).
• Researchers estimated average monthly costs by collecting data from participants including: jail days, jail bookings, shelter nights, EMS contacts, detoxification days, sobering center visits and Medicaid charges.
• Interviewers administered the Current Substance Use Assessment of the Addiction Severity Index to all participants at baseline, three, six, nine, and twelve months after the study began.

Results

• Regression analyses revealed that housed participants incurred significantly fewer costs (53% less) than the controls during the first six months of the study.
• Housed participants also showed a significant decrease in average number of daily drinks and average number of days intoxicated. (See Figure 1.)

Figure 1. Median Number of Drinks per Day and Days per Month Spent Intoxicated at Baseline, Six, Nine and Twelve Months after Housing Intervention (Adapted from Larimer et al. 2009)

Limitations

• Participants were not randomly assigned to the experimental or control conditions.
• Researchers did not compare the results from this study to the results from an abstinence based program.
• Researchers used self report measures and interviews to obtain alcohol abuse data.
• Not all of the participants completed the study.

Conclusions
Despite its limitations and need for further research, the study suggests that providing housing (without requiring substance abstinence) to chronically homeless individuals with alcohol abuse problems not only decreases their monthly cost to the public system, but might also contribute to the individuals’ alcohol addiction recovery.

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

References
Culhane, D. P., & Metraux, S. (2008). Rearranging the deck chairs or reallocating the lifeboats? homelessness assistance and its alternatives. JAMA, 74(1), 111-121.

Larimer, M., Garner, M. D., Atkins, D. C., Burlingham, B., Lonczak, H. S., Tanzer, K., et al. (2009). Health Care and Public Service Use and Costs Before and After Provision of Housing for Chronically Homeless Persons With Severe Alcohol Problems. JAMA, 301(13), 1349-1357.

The DRAM, Vol. 5(3) - College Women Trying to Drink Men under the Table: The Effects of Reflective Norms

Though men consume more alcohol than women, women’s alcohol intake has increased steadily during the past thirty years (Johnston, O'Malley, Bachman, & Schulenberg, 2007; Wechsler et al., 2002). Research has begun to explore unique influences on women’s consumption. This week’s DRAM reviews a study that explores the effects of reflective norms (i.e., individuals’ beliefs about what behaviors the opposite sex prefers them to do) on college women’s drinking behavior (LaBrie, Cail, Hummer, & Lac, 2009).

Methods:

• 3,616 college students (53.6% of the initial representative sample targeted for recruitment from two west coast universities) completed a Web-based survey.
• 62% of participants were female and 55% were White.
• The Web-based survey assessed:
  • females’ perceptions of how much their male peers prefer females to drink (i.e., reflective norms), 
  • males’ self-report of how much they prefer females to drink, and 
  • females’ self-report of weekly alcohol consumption. 

Results:

• Figure 1 shows that women significantly overestimated the amount of alcohol their male peers wanted them to drink; 71% of women estimated the preferred number of drinks to be more than the average male preference.
• Women’s reflective norms positively correlated to their drinking behavior after controlling for demographics and perceived same-sex norms (i.e., beliefs about typical behavior of female peers). 

Figure 1- Means of Women’s Perceived Reflective Normative Preferences and Men’s Actual Normative Preferences (adapted from LaBrie et al., 2009).

 

t’s(3615)=19.1 and 14.53, respectively, p’s < .001

Limitations:

• The data was obtained by self report.
• The study was cross-sectional; consequently, this research was not able to determine whether perceived norms influenced drinking or drinking influenced perceived norms.

Discussion:

The study suggests that women who drink more do so because they think their male peers prefer women to drink more. This motivation and misperception is particularly troubling because women are more likely than men to suffer sexual aggression while intoxicated (Parks & Fals-Stewart, 2004). College prevention programs should consider informing women about the disconnect between what they think men want and what men do want and address the dangers associated with drinking to impress one’s peers.
 
What do you think?  Comments can be addressed to Tasha Chandler.
 
References:

Johnston, L., O'Malley, P., Bachman, J., & Schulenberg, J. E. (2007). Monitoring the future national survey results on drug use, 1975-2006: Volume II. College students and adults ages 19-45. Bethesda, MD: National Institute on Drug Abuse.

LaBrie, J., Cail, J., Hummer, J. F., & Lac, A. (2009). What men want: The role of reflective opposite-sex normative preferences in alcohol use among college women. Psychology of Addictive Behaviors, 23(1), 157-162.

O'Malley, P. M., & Johnston, L. D. (2002). Epidemiology of alcohol and other drug use among American college students. Journal of Studies on Alcohol - Supplement(14), 23-39.

Parks, K. A., & Fals-Stewart, W. (2004). The temporal relationship between college women's alcohol consumption and victimization experiences. Alcoholism: Clinical and Experimental Research, 28(4), 625-629.

Wechsler, H., Lee, J. E., Kuo, M., Seibring, M., Nelson, T. F., & Lee, H. (2002). Trends in college binge drinking during a period of increased prevention efforts: Erratum. Journal of American College Health, 51, 37-37.