Transcript Reducing Hazardous Drinking by College Students: Lessons

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
“Man, I can’t wait to get to college and start drinking.”
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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?
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How are young adults “at risk”?
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Figure 3.1 Current, Binge, and Heavy Alcohol Use among Persons Aged
12 or Older, by Age: 2009 NSDUH
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% consuming ≥5 drinks in the last 2 weeks
by age group
37
40
35
30
%
23
25
16
20
15
15
7
10
5
0
8th grade
10th grade
12th grade
college
adult
Monitoring the Future 2010
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College student drinking patterns
ACHA/NCHA-II Spring 2010
139 campuses, > 94,000 respondents
frequent binge
drinkers
nondrinkers
occasional
binge drinkers
light
drinkers
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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)
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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
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What’s the Harm?
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Harm caused by college drinking
to the self
to others
to the institution
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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
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(Presley, Leichliter, & Meilman, 1999)
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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)
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Annual alcohol-related mortality & morbidity
Hingson, Zha, & Weitzman (2009)
1825 deaths
599,000
unintentional injuries
97,000 victims of alcohol-related sexual assault
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Harm to Others
had studies interrupted or lost sleep
took care of a drunken student
been insulted or humiliated
received an unwanted sexual advance
had property damaged
0%
10% 20% 30% 40% 50% 60% 70%
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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
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What is the developmental and social context
of drinking among young people?
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Emerging Adulthood
~ 18 – 25
(Arnett, 2000, 2005)
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Identity Exploration
-
Possibilities
-
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. . .
Relatively few constraints
Optimistic bias
“Playing the odds”
In-Between
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Self-Focus
-
Independence
Investing in self
Peer intensive settings
Weaker social controls
Not child but not adult
Freedom w/o responsibility
Less conformity to adult norms
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drinking patterns are evolving in the
college years . . .
0% 5% 10%15%20%25%30%35%40%45%
I enjoy drinking but sometimes I
drink too much
I have recently reduced my drinking
habits
I am trying to drink less than I used
to
My drinking is a problem sometimes
N = 682 students referred to SURE
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Exaggerated campus norms:
Use in last month. . .
100%
YOU
90%
typical student on campus
80%
70%
60%
50%
40%
30%
20%
10%
0%
cigarettes
hookah
alcohol
marijuana
cocaine
ACHA/NCHA-II Spring 2010
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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, ScottSheldon et al., 2007)
• Changes in perceived norms mediate intervention
effects (e.g, Carey, Henson et al., 2010)
• Students are interested in normative feedback
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What have we learned?
(after 7 RCTs and several meta-analyses)
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When we started in the late 90’s. . .
• Will they tell the truth?
YES
• Will college drinkers participate seriously in
YES
alcohol interventions?
• Can you reduce college students’ drinking?
YES
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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 metaanalytic review. Addictive Behaviors, 32, 2469-94.
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Weighted between-groups effect sizes (d+) for consumption
Carey, Scott-Sheldon, Carey, & DeMartini (2007)
0.45
0.4
0.35
0.3
0.25
0.2
0.15
ns ns
0.1
ns
0.05
ns
0
-0.05
quantity
binge freq
peak BAC
post-interv.
(<4 wks)
short-term (413 wks)
medium-term
(14-26 wks)
long-term (27195 wks)
-0.1
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Weighted between-groups effect sizes (d+) for problems
Carey, Scott-Sheldon, Carey, & DeMartini (2007)
0.25
post-interv.
(<4 wks)
0.2
★
0.15
medium-term
(14-26 wks)
0.1
0.05
short-term
(4-13 wks)
ns
long-term
(27-195 wks)
0
problems
 Heterogeneous
effect
 Fewer problems if:
Intervention was faceto-face & 1:1
Intervention used MI,
personalized
normative feedback
More women in
sample
Sample was not an “at
risk” group
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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
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Early Studies with Volunteer Students:
BMIs produced better outcomes than assessmentonly 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
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Drinks Per Drinking Day
6
Standard Drinks
5.5
5
control
TL/control
4.5
Basic BMI
TL/Basic BMI
4
3.5
3
baseline
1 month
6 months
12 months
(Carey, Carey, Maisto, & Henson, 2006)
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Peak BAC
0.24
0.22
0.20
Control
0.18
g/dL
TL/Control
Basic BMI
0.16
TL/Basic BMI
0.14
0.12
0.10
baseline
1 month
6 months
12 months
(Carey, Carey, Maisto, & Henson, 2006)
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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
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Focusing on high-risk students
•
•
•
•
•
Freshmen
Greeks
Athletes
Mandated students
Screening at counseling or health
centers
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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)
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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?
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SURE Study 3
(Carey et al., 2011, Addiction)
Recruitment
1207 Referrals
Consent
Randomization
Follow-ups
BMI
1M,
(n = 164)
6M, 12M
Alcohol 101+
1M,
Baseline
(n = 172)
6M, 12M
(N = 677)
Alcohol Edu
1M,
(n = 167)
6M, 12M
Delay
1M + choice,
(n = 174)
6M, 12M
(1096 eligible)
Alcohol Edu
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Baseline values - outcome variables
Sample
(N = 677)
Males
(n = 426)
Females
(n = 247)
Drinks per Week
13.4 (9.7)
14.8 (10.3)
10.9 (8.1) *
Drinks in Heaviest Week
18.6 (13.0)
21.1 (14.0)
14.3 (9.8) *
Drink per Drinking Day
4.6 (2.5)
5.1 (2.6)
3.8 (2.1) *
Heavy Drinking Frequency
5.1 (4.5)
5.3 (4.4)
4.6 (4.7)
Typical BAC
.08 (.06)
.08 (.05)
.09 (.06) *
Peak BAC
.16 (.09)
.16 (.09)
.16 (.09)
Consequences
5.0 (5.3)
5.0 (5.3)
4.9 (5.2)
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Female Participants - Consumption
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Drinks per Heaviest Week
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
15
Control
14
BMI
13
101
12
EDU
11
10
1
2
3
4
5
6
7
8
9
10
11
12
13
Study Months
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Male Participants - Consumption
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Drinks per Heaviest Week
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25
24
23
Control
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BMI
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101
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EDU
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18
17
1
2
3
4
5
6
7
8
9
10
11
12
13
Study Months
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Problems-Females


Problems-Males
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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
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Qualified support for hypothesis 1:
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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
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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
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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
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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
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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
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To be continued. . .
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