| healthy behavior through technology Tailoring Prevention Strategies: Are There Subgroups That We Have Not Considered? Emil Chiauzzi, Ph.D. VP, Research and Innovation Inflexxion, Inc. Pronabesh DasMahapatra, M.P.H. Biostatistician Inflexxion, Inc. Beth DeRicco, Ph.D. DeRicco Consulting January 19, 2012 NASPA Alcohol & Other Drug Abuse Prevention & Intervention Conference

| healthy behavior through technology First Year Transition Why focus on first year students? particularly high risk period students likely increase alcohol use during first year pre-college drinkers are more likely to continue expect that drinking will be part of college experience alcohol used to select and socialize with new peer group first year use may establish alcohol use patterns that lead to future difficulties promising target re: social norms, protective behaviors

| healthy behavior through technology Identifying Subgroups Why are subgroups important? Psychotherapy literature – key question is not what works, but what works for whom We all know that students are not all the same - yet we tend to educate and intervene the same Understanding of complex relationships limited due to focus on individual variables – examples in college alcohol use studies: –Gender –Race –Sensation seeking –Religiosity –Parental influence –Pre-college alcohol use

| healthy behavior through technology Latent Class Analysis Is there a way that we can account for more variables? LCA advantages –identify subgroups (“classes”) based on multiple risk factors –follow classes over time –tailor interventions based on class LCAs in the literature –subgroups of risky drinkers based on drinking patterns and beliefs –perceptions of college alcohol policies based on drinking levels and age of onset

| healthy behavior through technology Subgroup Differences What can we compare in a latent class model? Get the “OD” back in “AOD” –High risk drinkers have higher risk of marijuana and nonmedical prescription medication use –Past LCAs restricted to alcohol or drugs –No college LCA data re: nonmedical prescription medication use (mainly study older substance abusers) Protective behaviors Harmfulness perceptions Social norms awareness

| healthy behavior through technology Study Goals Identify classes of incoming first year college students based on –alcohol consumption –behavioral characteristics associated with alcohol use –use of marijuana and nonmedical prescription medications Compare demographic composition of classes Compare class beliefs and behaviors –social norms awareness –harmfulness perceptions related to a variety of illicit drugs and prescription medications –use of protective strategies Question: Can LCA model be used to classify and compare incoming first year student groups based on aggregate data?

| healthy behavior through technology Latent Class Analysis Statistical technique to identify latent classes or subgroups based on certain observed characteristics The classes are “latent” because they are not immediately identifiable from the data Addresses challenges in subgroup analysis including high error rate and low statistical power Steps 1.Identify measures that distinguish students based on behavioral patterns 2.Derive classes based on response to these measures 3.Explore and compare classes characteristics

| healthy behavior through technology Latent Class Analysis Variables Alcohol consumption Typical week - # drinks High risk drinking – past 2 weeks Peak BAC – past 2 weeks Behavioral components Physiological consequences Drinking and driving/Riding with driver who has been drinking Impulsivity/Aggression Illegal drug use Marijuana Non-medical prescription medication Demographics Gender Race Norms and perceptions Social norms awareness Perceived drug harmfulness Protective behaviors

| healthy behavior through technology Study Inclusion Criteria Incoming first year college students Used alcohol in the past year 18 to 25 years old Completed assessments in MyStudentBody ® Essentials Course, an online alcohol and drug risk assessment and prevention program

| healthy behavior through technology Sample Characteristics Total students = 21,945 Number of colleges = 89

| healthy behavior through technology Four Classes BAC – Peak blood alcohol concentration DUI/RWDD – Drinking and driving/Riding with driver who has been drinking NMUPM – Nonmedical use of prescription medications

| healthy behavior through technology Demographics and Drug Use Low Risk Drinkers Lower Intake Drinkers with Identified Risks Moderate Risk Drinkers High Risk Drinkers Class prevalence 46%20%14%20% Demographics Race - Caucasians Gender - Female Drug use (past year) Marijuana (36%) Nonmedical use: Prescription opioids (7%) Prescription stimulants (6%) Data expressed as percentage; except age, shown as Mean

| healthy behavior through technology Social Norms and Perceived Drug Harmfulness Low Risk Drinkers Lower Intake Drinkers with Identified Risks Moderate Risk Drinkers High Risk Drinkers Social norms perceptions Typical week consumption (# of drinks) * % of students using drugs in past 30 days Marijuana * 32.1%36.5%33.0%37.2% Prescription opioids (nonmedical) * 17.4%18.6%15.7%17.4% Prescription stimulants (nonmedical) * 19.9%22.8%19.5%23.4% Perceived drug harmfulness (any use) Marijuana 31.4%20.9%24.1%13.6% Prescription opioids (nonmedical) 64.2%57.4%64.1%53.5% Prescription stimulants (nonmedical) 61.7%51.6%59.0%45.5% Data expressed as Mean, except perceived drug harmfulness, shown as percentage

| healthy behavior through technology Protective Behaviors Low Risk Drinkers Lower Intake Drinkers with Identified Risks Moderate Risk Drinkers High Risk Drinkers Personal protective behavior Setting limits on how much to drink on a particular occasion Avoiding situations where heavy drinking is likely to occur Consuming drinks with a lower alcohol content Limiting access to money before going out to drink Keeping track of how many drinks consumed Spacing drinks over time (no more than one per hour) Alternating alcoholic and nonalcoholic drinks Choosing to socialize with people who don't drink excessively Finding activities that do not include alcohol consumption Drinking in less risky locations Using alternative transportation instead of driving Peer protective behavior Seek out help when you see illegal or dangerous behavior Data expressed as Mean of ordinal scale scores (1 = never 2 = rarely 3 = sometimes 4 = frequently 5 = always)

| healthy behavior through technology Key Findings Most incoming first year students either don’t drink or drink at low levels Females are very prominent in the Lower Intake Drinkers with Identified Risks group compared to other drinking groups Classes with greater drinking also show significantly higher past year drug use Lower Intake Drinkers with Identified Risks - past year drug use at least 2X that of Low Risk Drinkers – approaches that of High Risk Drinkers Four-class model generally follows stepwise patterns: –Social norm perceptions are more inaccurate as drinking increases –Classes with higher risks perceive drug use as less harmful –Protective factors are lower in high drinking groups

ACTION STEPS

Traditional Analysis Identify areas of concern based on overall incidence and consequences – “everything” is concerning Examines binary groups and data (e.g. age or gender, low risk or moderate) PROBLEM: when doing an analysis like this you miss important conclusions about sub group difference (e.g. low intake risky consequences *not* like low risk group)

Typology Matrix Areas of Strategic Intervention Program and Policy Levels (The social ecological framework) IndividualGroupInstitutionCommunityPublic Policy* Knowledge, attitudes; and behavioral intentions Environmental change (alcohol- free options; normative environment; alcohol availability, policy, and enforcement) Health protection Intervention and treatment DeJong, William & Langford, Linda M. A typology for campus-based alcohol prevention: Moving toward environmental management strategies. Journal of Studies on Alcohol, Vol Suppl14, Mar 2002,

Program & Policy Level: Individual Effective evidence-based screening practices re: drug use in high risk groups in campus counseling services There are some students who may screen negative for high risk drinking but evidence alcohol-related consequences and other substance use risks Require female students who reach judicial/health service threshold to receive assessment related to alcohol and other drug use Tailored BASICSish intervention that uncovers other drug use and seeks to reduce both rather than a narrowly focused intervention

Program & Policy Level: Group Education for first year students regarding protective behaviors Social Norms Marketing Campaign targeting high risk drinkers Address drug use perceptions Social Norms Marketing Campaign targeting females Importance of using protective factors especially related to consequences that are of concern to women Assess interventions based on subgroups Target interventions where women are (e.g. clubs, sororities, etc.) Training and education of use of protective factors by focus on a peer to peer intervention Identifying and gaining consensus around positive group norm

Program & Policy Level: Institution Clearly enforce underage drinking policies and laws Develop training mechanisms for those who work directly with students Drug use – esp. marijuana and nonmedical prescription medication use Females: sorority advisors, first year student advisors

Program & Policy Level: Community Education about: First year transition risks Drug use – esp. marijuana and nonmedical prescription medication use Engage social and community groups to mentor positive and protective behavior Big Brothers/Big Sisters model for campus and community – pair first year females with others exhibiting positive behavior

Program & Policy Level: Public Policy Integrate more drug prevention education into AOD prevention Identify, implement and enforce effective polices to promote healthy behavior sorority rush drink specials that focus on women

Going Forward Use data to predict relative student classifications based on college characteristics – size, location, type, on/off campus, % on scholarship, cost SOLUTION: this type of data analysis allows for critical and defined strategic planning development that is proactive and can be implemented What happens to these groups over time? How do they respond to prevention and intervention programs?