The Brief Addiction Science Information Source (BASIS)

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.

The DRAM Vol. 5 (2): Wet, Dry, Moist: DUI Characteristics based on Alcohol Sale Restrictions

Despite an overall decrease in arrests in the past 25 years, DUI continues to be one of the most frequently committed crimes in the United States (Webster, Pimentel, & Clark, 2008). This fact is made more frightening by the many victims DUI crimes can instantly claim: drivers, passengers, pedestrians, other motorists. Furthermore, it suggests that DUI behavior is still prevalent in our society despite current educational and preventative measures. This week, the DRAM reviews a study by Webster et al. (2008) that examines how the characteristics of convicted DUI offenders differ by the alcohol sales restrictions in their county of conviction.

Methods

• Researchers classified counties in Kentucky into the four following categories (see map in Figure 1):
  • Wet: allow alcohol sales; Dry: prohibit alcohol sales; Dry-exception: allow alcohol sales at only a small number of very specific businesses; Moist: Dry county that contains a ‘wet’ town or city
• Researchers obtained records of 21,647 DUI convictions occurring between 01/01/04 and 12/31/04 in Kentucky counties.
• These records included demographics and assessment results within 10 days after conviction from the Alcohol Use Disorders Identification Test (AUDIT: Babor, Higgins-Biddle, Saunders, & Monteiro, 2001), Drug Abuse Screening Test (DAST: Skinner, 1982), and the DSM-IV-TR’s alcohol abuse/dependence criteria (APA, 2000).

Results

• Wet counties had a significantly higher number of DUI convictions than dry counties, both before (11,000 and 3,496 respectively) and after (529.3 and 463.7 per 100,000 residents, respectively) controlling for population size.
• However, convicted DUI offenders in dry counties were more likely to be male and underage, more likely to have multiple DUI offenses, more likely to meet alcohol abuse and dependence criteria, and less likely to comply with treatment plans, than offenders in wet counties. (See Table 1.)

Table 1: Comparison of DUI offender characteristics across Wet and Dry Counties (adapted from Webster et al., 2008).

*p < .001

Limitations

• DUI assessment data came from over 100 different assessment programs
• Data was reported in counties of conviction, as opposed to counties of residence
• Assessment information was self-report
• It is difficult to generalize results because data was collected in a single state.

Conclusions
Webster et al’s comparison of wet and dry counties indicates that, though the absence of alcohol in dry counties appears to deter DUI behavior, the people who do commit DUIs in those counties represent a more serious class of offender. These findings, particularly those from the alcohol assessments, suggest that limiting exposure deters DUI behavior in many people, but has less effect on those struggling with alcohol use disorders and those resistant to traditional treatment and sanctions.

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

References
APA. (2000). Diagnostic and Statistical Manual of Mental Disorders, fourth ed., text revision. Washington, DC.

Babor, T. F., Higgins-Biddle, J. C., Saunders, J. B., & Monteiro, M. G. (2001). The Alcohol Use Disorders Identification Test: Guidelines for Use in Primary Care, second ed. Retrieved. from.

Skinner, H. A. (1982). The drug abuse screening test. Addict Behav, 7(4), 363-371.

Webster, J. M., Pimentel, J. H., & Clark, D. B. (2008). Characteristics of DUI offenders convicted in wet, dry, and moist counties. Accid Anal Prev, 40(3), 976-982.

The DRAM, Vol. 5(1) - Unexpectedly Associated: Childhood Mental Ability and Later Heavy Drinking

Cognitive epidemiology  is a small but growing area of research (Deary, 2005). Early studies in this field show a link between cognitive functioning and health (Deary & Batty, 2006); more recently, the field has connected mental ability to a variety of other behaviors, such as vegetarianism (Gale, Deary, Schoon, & Batty, 2007) and voting preferences (Deary, Batty, & Gale, 2008).  This week’s DRAM explores a study that links the mental ability of ten year old children to their drinking behavior as 40 year old adults (Batty, G.D., Deary, I.J., Schoon, I., Emslie, C., Hunt, K., et al. 2008).

Methods

• Investigators associated with the 1970 British Cohort Study assessed the cognitive ability of 11,040 10 year olds and generated a mental ability score for each child (Butler N. & J., 1997).
  • The average mental ability score for the sample was 100 points; the standard deviation for this sample was 15 points.
• Thirty years later, the researchers completed a follow-up survey with the participants (n=8,170; response rate = 74%), which assessed a number of alcohol-related variables, including participants’ responses to the CAGE  (Ewing, 1984).
• The authors used ANOVA to examine the relationship between mental ability and CAGE response scores.

Results

Figure 1.  Proportion of sample who ever had a drinking problem (2 or more endorsements of CAGE questions) and childhood IQ score, by gender.

(Please click image to enlarge.)
Adapted from Batty et al. (2008).
*p<.01; **p<.001

• Figure 1 shows that people who had higher mental ability scores as children were more likely to endorse CAGE questions than those who had lower mental ability scores as children.  This effect was stronger for women than men.
• Women who ever had an alcohol problem scored an average of 4.5 points higher on the mental ability assessment as girls than women without an alcohol problem; men with an alcohol problem scored an average of 1.7 points higher as boys (effect size of d=.31 for women and d=.11 for men).

Limitations

• The researchers collected drinking information using participants’ self report.
• The researchers measured a small number of potential mediators, so an unmeasured variable could influence the relationship between childhood mental ability and later drinking problems. 
• The researchers used mental ability scores, which were derived from a short-hand test of cognitive function and might not accurately represent the participants’ mental ability.
• The final analyses of alcohol problems did not include over a quarter of adults who, as children, enrolled in the study.  Those lost to follow-up might have had more – or less – severe alcohol problems than those included in the final analyses.
• Without a conceptual framework to explain the relationship between childhood mental ability and later drinking problems, the meaningfulness of this association is difficult to interpret.

Conclusion
The reason behind the mental ability-drinking relationship is not clear, and there is not yet a theoretical framework to offer an explanation. Those with a high mental ability might be more willing to report drinking patterns, have more expendable income, or are members of social groups that are more accepting of drinking. The relationship might be more complicated: mental ability merely might serve as a proxy for some other unmeasured variables.  A theoretical framework to explain this relationship is necessary to interpret the importance of this association and provide the guide for further research.

What do you think?  Comments can be addressed to Leslie Bosworth.

References

Batty, G. D., Deary, I. J., Schoon, I., Emslie, C., Hunt, K., & Gale, C. R. (2008). Childhood Mental Ability and Adult Alcohol Intake and Alcohol Problems: The 1970 British Cohort Study. American Journal of Public Health, 98(12).

Butler N., & J., B. (1997). 1970 British Cohort Study: Ten-Year Follow-Up. Colchester, England: UK Data Archive.

Deary, I. J. (2005). Intelligence, health and death: the new field of cognitive epidemiology. Psychologist, 18, 610-613.

Deary, I. J., Batty, D. G., & Gale, C. R. (2008). Childhood intelligence predicts voter turnout, voting preferences, and political involvement in adulthood: The 1970 British Cohort Study. Intelligence, 36(6), 548-555.

Deary, I. J., & Batty, G. D. (2006). Commentary: Pre-morbid IQ and later health—the rapidly evolving field of cognitive epidemiology. International Journal of Epidemiology, 35(3), 670-672.

Ewing, J. A. (1984). Detecting alcoholism: The CAGE questionnaire. Journal of the American Medical Association, 252(14), 1905-1907.

Gale, C. R., Deary, I. J., Schoon, I., & Batty, G. D. (2007). IQ in childhood and vegetarianism in adulthood: 1970 British cohort study. British Medical Journal, 334(7587), 245.

The DRAM Vol. 4(10): Wait 'till your parents hear about this!

Is there any correlation between the presence of prevention measures and a decrease in substance use behavior? In their study about college youth smoking risks, Weitzman et al. also examine the correlation between binge drinking risks, ease of alcohol purchase, and college campus prevention policies (Weitzman, Chen, & Subramanian, 2005).

Data Collection/Participants

• Researchers used data from the 2001 Harvard School of Public Health College alcohol study (HSPH-CAS)(Wechsler, Davenport, Dowdall, Moeykens, & Castillo, 1994; Wechsler et al., 2002)

• Total N = 10, 924 randomly selected full time undergraduate college students
• 2001 survey had a 50% general response rate

• Researchers asked participants to think about their drinking during the past two weeks using a gender-based definition of binge drinking:

• Five or more drinks on one occasion for males
• Four or more drinks on one occasion for females
• Specific definitions of “one drink” were provided in the HSPH-CAS Survey

Figure 1: Ease of alcohol availability at the contextual level vs Probability of binge drinking. *Cross Level interaction p<0.05 (Weitzman et al., 2005)

Figure 2: Policy enforcement at the contextual level vs Probability of binge drinking. *Cross Level interaction p<0.05 (Weitzman et al., 2005)

Results

• Prevalence of students who report binge drinking during the last two weeks =  42.9%
• Researchers found:

• A positive correlation between alcohol availability and binge drinking

• Schools where students said alcohol was easy to obtain had a binge drinking rate of 52.4%

• A negative correlation between policy enforcement and binge drinking

• For schools whose alcohol restriction policies were described as “strongly enforced” the binge drinking rate was 44.4% 

• A negative correlation between parental notification and binge drinking

• Schools “very likely” to notify parents of students caught using a fake ID had the lowest binge drinking rate (26.9 %)

Limitations

• Researchers used an anonymous mail-in self-report survey
• The response rate of 50% is lower than in previous years.
• The low response rates indicate possible bias within the data.

Conclusions
Weitzman et al’s study, found that the perception of strong alcohol regulation policy enforcement limited binge drinking. Parental notification of violations had the most pronounced effect on lowering binge drinking rates. The results suggest that colleges are not powerless to affect student behaviors, especially with regard to substance use. The implementation of regulatory policy and strict consequences to excessive substance use are a step in the right direction to prevent substance related problems.

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

References

Wechsler, H., Davenport, A., Dowdall, G., Moeykens, B., & Castillo, S. (1994). Health and behavioral consequences of binge drinking in college. A national survey of students at 140 campuses. Jama, 272(21), 1672-1677.

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. Findings from 4 Harvard School of Public Health College Alcohol Study surveys: 1993-2001. J Am Coll Health, 50(5), 203-217.

Weitzman, E. R., Chen, Y. Y., & Subramanian, S. V. (2005). Youth smoking risk and community patterns of alcohol availability and control: a national multilevel study. J Epidemiol Community Health, 59(12), 1065-1071.

The DRAM Vol. 4 (9) – College Students’ Alcohol Use: Do Parents Still Matter?

Alcohol abuse is a major problem on American college campuses (Knight et al., 2002). Researchers have suggested that peer influence might outweigh parental influence as adolescents develop into adulthood (Wood et al., 2001). This week’s DRAM reviews a study of how parents might indirectly shape college students’ alcohol use and abuse by influencing their children’s selection of peers (Abar & Turrisi, 2008).

Participants
•    Participants were 392 prospective college freshmen (57% female, 90% Caucasian, mean age = 18) who were part of a larger study examining college student drinking (Turrisi et al., 2001).
•    Researchers randomly selected participants from all public and private school yearbooks in the sampling areas. The authors do not report the response rate or the retention rate.

Measures
•    Once during the summer before college, participants completed by mail multi-item 3-point scales of:

Parental knowledge of behavior (e.g., “How well do your parents know where you go at night?”)
Parental monitoring of behavior (e.g., “How hard do your parents try to know where you go at night?”)
Parental approval of alcohol use (e.g., “How would your parents feel if you drank one or two drinks every day?”)

•    Once during the fall semester, participants described how many of their friends use and abuse alcohol using a 5-point scale ranging from “None” to “Nearly all.”
•    Once during the spring semester, participants completed a scale describing their own alcohol use (e.g., drinks per day, days drunk in the past month).

Method
•    The authors used structural equation modeling (SEM) to explore the relationships among the variables.

Figure 1. Hypothesized model of the relationships among parental involvement, friend alcohol use at college, and child’s alcohol use at college. Statistically significant pathways are indicated by text boxes.

(click image to enlarge)

Results
•    The data generally support the hypothesized model (chi square (205) = 446.65, p < 0.001, CFI = 0.96).
•    In particular, decreased parental knowledge and monitoring of the child’s behavior during the summer before college was associated increased selection of alcohol-using-peers during the fall semester, and increased selection of alcohol-using-peers during the fall semester was associated with increased child alcohol use during the spring semester.
•    Increased parental approval was associated with increased child alcohol use during the spring semester.

Limitations
•    Data, including estimates of parental involvement, were derived from participants’ self report.
•    The authors did not assess perceived quality of monitoring and cannot establish whether the child understood, appreciated, and internalized the parental involvement, or instead saw it as intrusive.
•    The sample has limited generalizability (i.e., was 90% Caucasian).
•    Because the authors do not provide information on retention rates or characteristics of dropout, it is unclear whether missing observations might confound the findings. 

Conclusion
Children with parents who were more approving of alcohol use were more likely to drink during the spring semester at college. Children with parents who were less involved in their children’s lives chose more first-semester friends who used and abused alcohol, and having friends more involved with alcohol predicted more use and abuse of alcohol during the spring semester. Because these results illustrate some ways in which parents and peers shape drinking behavior, they might be of interest to both parents and intervention scientists who are concerned with finding ways to reduce risky drinking behavior on campus.

What do you think?  Comments can be addressed to Heather Gray.

References

Abar, C., & Turrisi, R. (2008). How important are parents during the college years? A longitudinal perspective of indirect influences parents yield on their college teens’ alcohol use. Addictive Behaviors, 33, 1360-1368.

Knight, J. R., Wechsler, H., Kuo, M., Seibring, M., Weitzman, E. R., & Schuckit, M. A. (2002). Alcohol abuse and dependence among U. S. college students. Journal of Studies on Alcohol, 63, 263-270.

Turrisi, R., Jaccard, J., Taki, R., Dunnam, H., & Grimes, J. (2001). Examination of the short-term efficacy of a parent intervention to reduce college student drinking tendencies. Psychology of Addictive Behaviors, 15(4), 366−372.

Windle, M. (2000). Parental, sibling, and peer influences on adolescent substance use and alcohol problems. Applied Developmental Science, 4, 98-110.

The DRAM Vol. 4(8) - Falling off the Recovery Wagon: Drinking Trajectories

Understanding the drinking behavior that can occur immediately after a lapse from abstinence can help individuals prepare for obstacles as they work their way toward a more stable recovery. Using data from the Relapse Replication and Extension Project (RREP), researchers examined the behavior of recovering individuals from an alcohol-related disorder following an initial post-treatment lapse (Witkiewitz & Masyn, 2008).

Participants

• N (RREP sample) = 563 participants
• All participants met RREP recruitment criteria, which included the following:
  • aged 18 or older;
  • satisfied criteria for having alcohol use or dependence within the past 6 months;
  • with no record of additional concurrent substance use disorder diagnoses, intravenous drug use, or other major comorbid diagnoses.

Measures/Instruments
Using the Structured Assessment Interview for Drinking and Related Behaviors, self-report data from RREP baseline assessments were obtained during admission and bimonthly assessments of drinking for one year after admission.

Figure 1: Drinking Trajectories: Drinking Frequency after First Lapse
Adapted from Figure 3(A&B): Trajectory subgroups for three-class growth mixture model (Witkiewitz & Masyn, 2008)

Results

• 395 participants (70%) lapsed following treatment
• The authors identified three primary post first lapse drinking patterns (see Figure 1):
  • Moderate drinking, Heavy drinking, and Prolapsing (i.e., frequent drinking following the first lapse and a return to less frequent drinking)
  • The majority of participants had a moderate drinking trajectory or a prolapsing trajectory
• Years of drinking problems and family history of drinking problems did not negatively affect post-lapse drinking trajectories
• An increase in coping ability over time led to less frequent drinking at the time of a first lapse and lighter drinking in the time following the first lapse

Limitations
Self-report data

Discussion
Witkiewitz & Masyn’s results suggest that lapses can lead to multiple drinking trajectories and that successful recovery is affected by coping skills. Further, a family history of addiction and many years of alcohol related problems do not affect individuals’ post lapse recovery. The three trajectories indicate that both lapse and lapse recovery have to be addressed early and comprehensively in an individual’s treatment so that any possible future relapse is offset by a plan for recovery.

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

References
Witkiewitz, K., & Masyn, K. (2008). Drinking Trajectories Following an Initial Lapse. Psychology of Addictive Behaviors, 22(2), 157-167.

The DRAM Vol. 4(7) - Relapse After an Organ Transplant

Long term alcohol abuse and or dependence can lead to organ disease, sometimes creating a need for organ transplant. Returning to substance use after a transplant increases the risk of medical complications and even death (e.g., Kran & DiMartini, 2005). Organs for transplant are scarce (Organ Procurement and Transplantation Network, 2008).  Some observers believe recipient prioritization should take alcohol use history into account.  This week we examine a meta-analysis to measure relapse among people who require organ transplants and have a history of alcohol misuse (Dew, DiMartini, Steel, De Vito Dabbs, Myaskovsky, et al., 2008).

Data
Meta-analysis of 54 studies, included 3,651 participants and 13,821 person years of observation

Methods
Authors searched six databases (e.g., PsychInfo, MEDLINE) for studies addressing non-adherence to the medical regimen following organ transplantation

Inclusion criteria for studies

• Written in English language
• Published between 1981-2005
• Did not use the same dataset as any other study
• Sample size of 10 people or more
• Indication that prior to transplant, participants had histories of substance abuse, dependence, or diseases that indicated these disorders
• Includes substance use history, relapse, and treatment non-adherence behavior

Outcomes
Relapse to any alcohol use and to heavy alcohol use

Table 1.Pooled estimates of post-transplantation rates of relapse to alcohol use (cases per 100 persons per year)

Results

• Average number of observation years per study = 4 (SD=2.7, range=0.9-12.9)
• The Q-statistic in Table 1 indicates a wide range of relapse rates across studies
• Risk for post-operative relapse higher for patients of hospitals that had no requirement for 6-month pre-operative sobriety (average effect size: r = .21 (95% CI 0.11-0.31) p<.01)

Limitations

• Retrospective self report methodology
• No control group: unclear whether risk for post-operative drinking is greater among samples with an abuse history than other transplant populations

Discussion
This study reminds us about the likelihood for relapse among transplant patients who had histories of alcohol abuse or dependence. Although popular opinion seems to lean towards sanctions on those with alcohol misuse histories, hospitals can use empirical evidence to guide careful selection of transplant candidates.  This study’s results suggest one selection criteria could be a longer period of abstinence before the planned surgery.  Another guideline, not examined in this study, could be participation in substance abuse treatment before and after the surgery.

What do you think? Comments can be addressed to Leslie Bosworth.

References
Dew, M. A., DiMartini, A. F., Steel, J., De Vito Dabbs, A., Myaskovsky, L., Unruh, M., et al. (2008). Meta-analysis of risk for relapse to Substance use after transplantation of the liver or other solid organs. Liver Transplantation, 14, 159-172.

Kran, L. E., & DiMartini, A. (2005). Psychiatric and psychosocial aspects of liver transplantation. Liver Transplantation, 11, 1157-1168.

Organ Procurement and Transplantation Network. Data, View Data Reports, National Data. Retrieved June 4, 2008, from http://www.optn.org/latestData/step2.asp