Hazardous Drinking by College Students: Lessons Learned and Future Directions

1. Hazardous Drinking by College Students: Lessons Learned and Future Directions Kate B. Carey, Ph.D. Department of Behavioral & Social Sciences Center for Alcohol & Addiction Studies

2. “Man, I can’t wait to get to college and start drinking.”

3. Roadmap for talk: • How are young adults “at risk”? • What is the harm? • What is the developmental and social context of drinking among college students? • What have we learned over a dozen years of college alcohol intervention studies?

4. How are young adults “at risk”?

5. Figure 3.1 Current, Binge, and Heavy Alcohol Use among Persons Aged 12 or Older, by Age: 2009 NSDUH

6. % Monitoring the Future 2010

7. College student drinking patterns ACHA/NCHA-II Spring 2010 139 campuses, > 94,000 respondents frequent binge drinkers nondrinkers occasional binge drinkers light drinkers

8. Percentage reporting heavy drinking episodes in the last month, ages 18-20 and 21-24 for college- and noncollege-attending young adults Hingson, Zha, & Weitzman (2009)

9. As with all behavior, there is variability in college drinking: • Students who drink the least attend: • Two-year schools • Religious schools • Commuter schools • Historically Black schools • Students who drink the most include: • Students at residential colleges • First semester, first year students • Men • Whites • Members of fraternities and sororities • Athletes

10. What’s the Harm?

11. Harm caused by college drinking to the self to others to the institution

12. Common Consequences to Self Among college drinkers: • 62% had a hangover • 31-36% report doing something later regretted • 27-35% reported some memory loss • 22% report driving while under the influence • 15-18% report physically injuring self or another • 28% missed a class • 21% performed more poorly on a test or project 2008 CORE Survey & 2009 NCHA

13. (Presley, Leichliter, & Meilman, 1999)

14. But is College Drinking “Problem Drinking”? • 31% meet DSM criteria for Alcohol Abuse • 6 - 15% meet DSM criteria for Alcohol Dependence (Dawson et al., 2004; Grekin & Sher, 2006; Knight et al., 2002; Slutske, 2005)

15. Annual alcohol-related mortality & morbidity Hingson, Zha, & Weitzman (2009) 1825 deaths 599,000 unintentional injuries 97,000 victims of alcohol-related sexual assault

16. Harm to Others

17. Harm to the Institution • Failure and dropout rates • Property damage • Burden on security, judicial, & student services • 16% of university ambulance calls are alcohol-related: 171 x $600 = $102,600/year (Carey et al., 2009) • “town/gown”relationships • Reputation of the institution

18. What is the developmental and social context of drinking among young people?

19. Emerging Adulthood~ 18 – 25 (Arnett, 2000, 2005)

20. Identity Exploration • Love and work • Try on possible selves • Seek range of experiences • Identity confusion • Instability • Frequent moves • Changing friends • Education/jobs Emerging Adulthood is the age of. . . • Possibilities • Relatively few constraints • Optimistic bias • “Playing the odds” • Self-Focus • Independence • Investing in self • Peer intensive settings • Weaker social controls • In-Between • Not child but not adult • Freedom w/o responsibility • Less conformity to adult norms

21. drinking patterns are evolving in the college years . . .

22. Exaggerated campus norms:Use in last month. . . ACHA/NCHA-II Spring 2010

23. Why are norms important? • Perceived norms correlate with student drinking • Self-other discrepancy predicts increases in future drinking (Carey et al., 2006) • Meta-analyses reveal that interventions with normative education produce larger effects (Carey, Scott-Sheldon et al., 2007) • Changes in perceived norms mediate intervention effects (e.g, Carey, Henson et al., 2010) • Students are interested in normative feedback

24. What have we learned? (after 7 RCTs and several meta-analyses)

25. When we started in the late 90’s. . . • Will they tell the truth? • Will college drinkers participate seriously in alcohol interventions? • Can you reduce college students’ drinking? YES YES YES

26. Lessons learned from meta-analyses Carey et al. (2007) • Individual-level alcohol risk reduction interventions • Target population = college students • Design = Random assignment with control • Outcomes = alcohol consumption and/or problems • 62 published RCTs Carey, K. B., Scott-Sheldon, L., Carey, M. P., & DeMartini, K. (2007). Individual-level interventions to reduce college student drinking: A meta-analytic review. Addictive Behaviors, 32, 2469-94.

27. Weighted between-groups effect sizes (d+) for consumptionCarey, Scott-Sheldon, Carey, & DeMartini (2007)

28. Weighted between-groups effect sizes (d+) for problemsCarey, Scott-Sheldon, Carey, & DeMartini (2007) • Heterogeneous effect • Fewer problems if: Intervention was face-to-face & 1:1 Intervention used MI, personalized normative feedback More women in sample Sample was not an “at risk” group ★

29. Brief Motivational Interventions (BMIs) Assessment + a 60-minute session Motivational interviewing style BMI content • Personalized feedback (DPW, typical/peak BAC, heavy drinking, consequences) • Normative comparisons (DPW, heavy drinking frequency) • Alcohol information (BAC, tolerance, protective strategies) • Risk reduction goals and strategies

30. Early Studies with Volunteer Students: BMIs produced better outcomes than assessment-only controls (Borsari & Carey, 2000; Marlatt et al., 1998) BMI equivalent to multi-session group intervention (Baer et al., 1992) SURE I: Carey, Carey, et al. (2006) • N = 509 heavy drinking volunteers • RQ: does BMI improve outcomes over extended assessment effect? • RCT design: BMI v. AO X TLFB v. AO, 12M follow-up

31. (Carey, Carey, Maisto, & Henson, 2006)

32. (Carey, Carey, Maisto, & Henson, 2006)

33. Take home messages I • Heavy drinking students can be engaged in brief, feedback-based interventions • Many students are actively sorting out their attitudes and behaviors towards alcohol • Opportunities to engage in nonjudgmental discussion about risks/benefits can shape those behaviors towards less risk • Single-session BMI reduces drinking & consequences rapidly • Risk reduction maintains over 12M

34. Focusing onhigh-risk students • Freshmen • Greeks • Athletes • Mandated students • Screening at counseling or health centers

35. Early Studies with Mandated Students: • Mandated students did reduce drinking after intervention • Peer-led groups = professionally-led groups (Fromme & Corbin, 2004) • BMI > individualized alcohol education session (Borsari & Carey, 2005) • BMI = CDI (Barnett et al., 2007) • BMI > CDI (Carey et al., 2009) • Is intervention needed? (Carey et al., 2009; Hustad et al., 2011; Morgan et al., 2008)

36. Sure 3 Research Questions • RQ1: is any intervention better than no intervention? • RQ2: does face-to-face BMI produce better outcomes than 2 commonly employed CDIs? • RQ3: how long do intervention effects last for mandated students? • RQ4: how does gender influence response to intervention in short- and long-term?

37. SURE Study 3 (Carey et al., 2011, Addiction)

38. Baseline values - outcome variables

39. Female Participants - Consumption

40. Male Participants - Consumption

41.  Problems-Females  Problems-Males

42. SURE3 Conclusions – Part I • Sanction effect seen for females but not males • Female students reduced drinking and problems waiting for delayed intervention • Male students don’t change without an intervention • Qualified support for hypothesis 1: • Any intervention is better than no intervention for male students • For female students, 101 was less effective than no intervention in reducing problems • Reliable and rapid response to brief FTF intervention

43. SURE3 Conclusions – Part II • Qualified support for hypothesis 2: • BMIs produced greater initial change than either CDI for females only • BMI suppressed drinking and problems for longer than either CDI for both genders • Gender moderation: • Initial change & maintenance are different processes • Mode of intervention delivery is less important for male students • Female students maintain BMI-induced risk reduction longer • Trend lines go UP over 12M of follow-up

44. More lessons learned from meta-analysis: Carey, Scott-Sheldon et al. (under review) • Face-to-face interventions (FTFI) vs computer-delivered interventions (CDI) • N= 49 studies • Small ES compared to AO for both • FTFI affect more outcomes and for longer intervals • Limited # direct comparisons favor FTFI (d+s = 0.12-0.21) • FTFI effects larger with mandated vs. non-mandated samples

45. Extensions and Next Steps:Current SURE Project Goal: improve the efficacy of BMIs Focus on maintenance of initial behavior change • E-booster (low threshold) Build upon known mechanisms of change • Remind and expand upon behavioral norms feedback • Extend with attitudinal norms feedback

46. Supported by NIAAA R01 AA012518 UConn Center for Health Intervention & Prevention Brown Center for Alcohol & Addiction Studies Investigators Kate Carey Michael Carey Seth Kalichman Project Coordinator Sarah Lust Referral Partners Community Standards Wellness & Prevention

47. To be continued. . .