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. Author manuscript; available in PMC: 2014 Jul 1.
Published in final edited form as: Drug Alcohol Depend. 2012 Dec 28;131(0):71–77. doi: 10.1016/j.drugalcdep.2012.12.004

SIMULTANEOUS USE OF NON-MEDICAL ADHD PRESCRIPTION STIMULANTS AND ALCOHOL AMONG UNDERGRADUATE STUDENTS

Kathleen L Egan 1, Beth A Reboussin 2, Jill N Blocker 2, Mark Wolfson 1, Erin L Sutfin 1
PMCID: PMC3644523  NIHMSID: NIHMS431984  PMID: 23274057

Abstract

Background

Use of prescription stimulants used to treat Attention Deficit/Hyperactivity Disorder (ADHD) for reasons other than prescribed, known as non-medical use, is a growing problem among undergraduates. Previous studies show that non-medical prescription stimulant (NMPS) users consume more alcohol than individuals who do not use NMPS. However, research on simultaneous use of NMPS and alcohol is limited. The objectives of this study were to: (1) determine the prevalence of simultaneous use of alcohol and NMPS; (2) examine predictors and consequences of simultaneous NMPS and alcohol use among undergraduates.

Methods

In fall 2009, 4,090 students from eight North Carolina universities completed a web-based survey.

Results

Past year prevalence of NMPS use among this sample was 10.6% and simultaneous use of NMPS with alcohol was 4.9%. Among NMPS users, 46.4% used NMPS simultaneously with alcohol within the past year. Multivariable analysis revealed that simultaneous NMPS and alcohol use was associated with low grade point averages, use of other substances, and increased alcohol-related consequences. Simultaneous NMPS and alcohol users reported experiencing significantly more negative consequences than either past year drinkers who did not use prescription stimulants and concurrent NMPS and alcohol users (use over the past year but not at the same time).

Conclusions

Simultaneous use of NMPS and alcohol is high among NMPS users in our sample of undergraduate students. Simultaneous users are at increased risk of experiencing negative consequences. Thus, prevention and intervention efforts should include a focus on simultaneous NMPS and alcohol use.

Keywords: polydrug, alcohol, prescription stimulants, nonmedical use, college students

1. INTRODUCTION

Prescription stimulants, such as amphetamine, dexamphetamine and methylphenidate, are widely prescribed for the treatment of Attention Deficit/Hyperactivity Disorder (ADHD). ADHD prescription stimulants are classified as Schedule II drugs, signifying positive medical benefits but also potential for abuse (Greynadus and Patel, 2005). Young adults 18–25 years of age report the highest prevalence of non-medical prescription stimulant (NMPS) use (Substance Abuse and Mental Health Services Administration, 2009), and college students within this age group are twice as likely to report NMPS use in comparison to 18–25 year olds not enrolled in college (Johnston et al., 2008; Substance Abuse and Mental Health Services Administration, 2009). Prevalence of self-reported NMPS use among college students varies by study, with lifetime prevalence ranging from 8.1 to 34% (DeSantis et al., 2008; White et al., 2006; McCabe et al., 2006a; Carroll et al., 2006; Judson and Langdon, 2009; Teter et al., 2010; McCabe and Teter, 2007; Barrett et al., 2006), and past year prevalence ranging from 2.1 to 6.0% (McCabe et al., 2006a; Teter et al., 2006, 2010; Herman-Stahl et al., 2007; Shillington et al., 2006).

NMPS users report more substance use than individuals who do not misuse prescription stimulants (DeSantis et al., 2009; Novak et al., 2007; Substance Abuse and Mental Health Services Administration, 2009; Teter et al., 2005; Mccabe and Boyd, 2005; McCabe et al., 2006b; Arria et al., 2008). This is consistent with Problem Behavior Theory, which suggests that individuals who participate in one risky behavior are more likely to participate in another, often more risky behavior (Jessor et al., 1991). Specifically, NMPS users report more alcohol consumption and heavy episodic drinking than non-users (Teter et al., 2005).

A limited number of studies have examined simultaneous and concurrent use of NMPS with alcohol among college students. Concurrent use refers to the use of NMPS and alcohol in the same time period, but not necessarily at the same time (e.g., during the past year; Martin et al., 1992). Simultaneous use is a subset of concurrent drug use that occurs when NMPS and alcohol are ingested at the same exact time (e.g., during the same evening; Martin et al., 1992; Collins et al., 1998; Earleywine and Newcomb, 1997).

Two studies assessed prevalence of simultaneous use of NMPS and alcohol. Sepulveda (2011) found that out of 55 prescribed users of ADHD stimulants, 19% reported simultaneous use of prescription stimulants with alcohol. Barrett et al. (2006) found that out of 149 college students who reported using at least two drugs in their lifetime, not including alcohol, 35.7% reported simultaneous use of NMPS with alcohol. A larger study with a more representative sample, 4,580 undergraduates from a single university, found that 6.0% of college students reported using alcohol and NMPS concurrently and simultaneously, combined, within the past year (McCabe et al., 2006c). However, none of these studies examined the risk factors of simultaneous use of NMPS among a representative sample of undergraduate students by the fact that only students from a single university were included in each study.

There are known pharmacological risks of using NMPS alone. The Food and Drug Administration (FDA) recently required pharmaceutical manufacturers to add a “black box” warning to prescription stimulants. The warning informs users of the risks of “sudden death and serious cardiovascular events” as well as a “high potential for abuse” (Arria and DuPont, 2010). Additionally, mixing prescription stimulants with alcohol has pharmacological risks. Simultaneous administration of methylphenidate and alcohol produces the metabolite ethylphenidate (Markowitz et al., 1999, 2000; Koehm et al., 2010). Even though the toxicity of ethylphenidate has yet to be fully specified, ethylphenidate was reported in two overdose victims who had ingested large quantities of methylphenidate with evidence of alcohol consumption (Markowitz et al., 1999). To our knowledge, simultaneous administration of amphetamine with alcohol does not produce a known toxic metabolite. However, alcohol does compete with amphetamine for metabolizing enzymes which would enhance the bioavailability of amphetamine if co-ingested (Jiao et al., 2009). Additionally, during the ascending limb of the blood alcohol content curve when the blood alcohol content is rising, alcohol’s subjective effects are thought to be stimulating (Newlin and Thomson, 1990) and are associated with increased dopamine transmission (Boileau et al., 2003). Presumably, when individuals are at the beginning of a drinking session, they are more likely to experience stimulatory subjective effects. Thus, when prescription stimulants are co-administered with alcohol, they potentially exacerbate and lengthen the stimulatory effects that occur during the ascending limb of the blood alcohol content curve, while delaying the onset of alcohol’s sedative effects, which would typically lead to drinking cessation.

McCabe et al. (2006c) found that students who reported using non-medical prescription drugs (not stimulant specific) simultaneously with alcohol were significantly more likely than concurrent users to experience negative consequences. In another study, 18–34 year-old drinkers who report non-medical prescription drug use are more likely to experience alcohol-related consequences associated with “risk-taking behaviors” and “interpersonal troubles” (Hermos et al., 2009). Both studies examined consequences of simultaneous use of alcohol with prescription drugs rather than simultaneous use of alcohol with prescription stimulants, exclusively.

It is important to investigate the consequences associated with simultaneous use of prescription stimulants and alcohol for several reasons. Apart from the pharmacological effects, there are behavioral effects that vary by classification (e.g., stimulants tend to have stimulatory effects while sedatives tend to have depressive effects). In addition, undergraduate students have reported using prescription stimulants simultaneously with alcohol more frequently than simultaneous use of alcohol with other classes of prescription drugs (McCabe et al., 2006c). Studies on alcoholic energy drinks speculate that the consumption of energy drinks with alcohol alters drinkers’ perceptions of intoxication. Presumably, this alteration results in heightened risk of experiencing adverse consequences associated with alcohol consumption (O’Brien et al., 2008; Arria et al., 2011). Due to the shared stimulatory behavioral effects between prescription stimulants and energy drinks, it is plausible that prescription stimulants combined with alcohol may have similar effects. For these reasons, it is important to investigate the consequences associated with simultaneous use of prescription stimulants, in particular, and alcohol.

Although there has been extensive research on NMPS use, little is known about the predictors and consequences associated with the simultaneous use of NMPS with alcohol. In order to supplement the current literature, this study proposes to (1) determine prevalence of simultaneous use of alcohol with NMPS; and (2) examine predictors and consequences of simultaneous NMPS and alcohol use in a large, representative sample of undergraduate students at multiple institutions.

2. METHODS

2.1. Study Description

The Study to Prevent Alcohol Related Consequences (SPARC) was a group-randomized trial to assess the impact of an intervention using a community-based approach to implement environmental strategies to reduce high-risk drinking and alcohol-related consequences on college campuses and their surrounding communities (Wolfson et al., 2012). Every fall, a web-based College Drinking Survey (CDS) was emailed to a stratified random sample of undergraduate students to generate self-report data on high-risk drinking, alcohol-related consequences, and other behaviors (Sutfin et al., 2009; Wolfson et al., 2012).

In the fall 2009, the goal was to have 448 students (112 each of freshmen, sophomores, juniors, and seniors) from each of eight participating universities complete the CDS (n=3,584). The number of students invited to participate was based on power considerations for the overall SPARC trial, anticipated response rates based on previous web-based surveys of college students (McCabe et al., 2006d; Reed et al., 2007), and previous findings of the CDS. Shortly after the target number from the eight schools was met, the website was closed. The final sample consisted of over 4,000 students, with a 34.8% overall response rate across the eight schools, varying across universities (23.2%–46.5%). To estimate the potential for non-response bias, the observed proportions sampled by gender, freshman status and race/ethnicity were compared with the overall proportions, as reported in the University of North Carolina Statistical Abstracts. The means and standard deviations of the difference between the study sample and the population were 4% (2.5%) fewer males, 0.9% (6%) more freshmen, and 5.9% (3%) more white. A one-sample t-test was used to assess the difference between the mean population and sampled percentages for each school. The test showed no significant differences for freshman status, race/ethnicity, and gender (p >0.05).

2.2. Procedures

The Wake Forest School of Medicine (WFSM) Institutional Review Board (IRB) approved the study protocol. Students who were randomly selected to participate from an enrollment list provided by each participating college were sent an e-mail to their student e-mail account inviting them to participate in the survey. The e-mail included a link to a secured website where the survey could be completed. Students were sent up to four e-mailed reminders inviting them to participate (following Dillman, 2000). Students received $15 through PayPal for completing the survey. Additionally, one student from each institution was randomly selected to win $100.

2.3. Measures

The College Drinking survey queried on demographic characteristics, alcohol consumption behaviors, consequences experienced from alcohol use, and other health risk behaviors.

2.3.1 Past year alcohol use was measured with the following item: “Did you drink in the past year?”. Responses were used to characterize past year drinkers and concurrent NMPS users, as described below.

2.3.2 Past year prevalence of NMPS use was measured with “On how many occasions have you used stimulant medications, such as Ritalin, Dexedrine, Adderall, Concerta, methylphenidate, not prescribed to you or for reasons other than they were prescribed in the past 12 months?” (derived from Ashton, 2008). The measure was dichotomized as yes/no in order to separate NMPS users from non-NMPS users.

2.3.3 Simultaneous NMPS and alcohol use within the past year was assessed with: “On how many days have you used prescription stimulant medication, such as Ritalin, Dexedrine, Adderall, Concerta, methylphenidate, not prescribed to you by a doctor at the same time you were drinking alcohol?” (derived from McCabe et al., 2006c). Simultaneous NMPS and alcohol use was dichotomized as yes/no.

The following two groups were created to serve as a comparison to simultaneous NMPS/alcohol users: (1) concurrent NMPS/alcohol users, and (2) past year drinkers who did not use NMPS.

2.3.4 Concurrent NMPS/alcohol users were defined as individuals who responded yes to past year alcohol use and yes to past year NMPS use, but who were not simultaneous users.

2.3.5 Past year drinkers were defined as individuals who reported alcohol use but not NMPS use in the past year.

2.3.6 Demographics included gender, race (coded as White or Non-White), class year (coded as freshman, sophomore, junior, or senior/5th year senior), mother’s and father’s educational level (asked separately, coded as some college education or less vs. 4-year college degree), and membership in, or pledge of, a Greek organization (sorority/fraternity status; coded as yes or no). Students’ grade point averages (GPA) were measured as a self-reported continuous variable.

2.3.7 Other substance use, including past 30-day alcohol use, heavy episodic drinking (defined as drinking 4 or more drinks for females and 5 or more for males on a single occasion), smoking, and marijuana use (coded as yes or no) were measured. Past year non-medical prescription drug use other than prescription stimulants and past 30-day illicit drug use (not including marijuana) were measured (both coded as yes or no).

2.3.8 Alcohol-related consequences experienced within the past 30-days were grouped into moderate or severe categories as described by Wolfson et al. (2012). There were 16 moderate consequences, including: got drunk, did something you later regretted, strained a relationship, got sick/vomited, urinated in a public setting, damaged property, drove a car while under the influence of alcohol, rode with a driver who was under the influence of alcohol, got into a verbal argument, had a memory loss, passed out, was hurt or injured, hurt or injured someone else, had a hangover, missed a class, and performed poorly on a test or project. There were 9 severe alcohol-related consequences, including: received a ticket for a DUI/DWI, had a car/motorcycle crash, got into a physical fight, got into trouble with the police, was a victim of a crime, had sex later regretted, was taken advantage of sexually, and took advantage of another sexually. Responses to each alcohol-related consequence measured frequency: never, 1–2 times, 3–5 times, 6–9 times, and 10 or more times. A score was created for moderate alcohol-related consequences by summing the lower bounds of each frequency response. The Cronbach’s alpha for moderate alcohol-related consequences scale was 0.91. Due to the limited number of severe alcohol-related consequences reported, severe consequences were treated as a dichotomous variable (yes/no).

2.3.9 Sensation seeking was measured with the Brief Sensation Seeking Scale (Hoyle et al., 2002). The scale consists of eight items rated on a five-point Likert ordinal scale (1=strongly disagree to 5=strongly agree). The total sensation seeking score was derived by averaging responses for individuals who answered a minimum of five questions on the scale. The Cronbach’s alpha for the Brief Sensation Seeking Scale was 0.77.

2.3.10 Institution type (public or private) was reported based upon the institution the student attended.

2.4. Data Analysis

Descriptive statistics were calculated as means and standard deviations of continuous variables, percentages, and frequencies of discrete outcomes. Bivariate and multivariate analyses were performed to identify correlates of simultaneous NMPS/alcohol use as well as alcohol-related consequences. Variables found to be significantly associated with simultaneous NMPS/alcohol use in bivariate analyses (p<0.05) were included in the multivariate models. College was treated as a random effect with students nested within institution to adjust for within-college correlation.

Analyses for Table 3 were performed using clustered polytomous logistic regression using generalized linear mixed modeling (GLIMM) with maximum likelihood estimation. Multinomial models were used to compare simultaneous NMPS/alcohol users to past year drinkers, as well as simultaneous users to concurrent NMPS/alcohol users.

Table 3.

Multivariate Analysis of Demographics, Institutional Characteristics, and other Substance Use with NMPS Use

Substance Use Simultaneous Alcohol & NMPS use vs. Past Year Drinkers a (n = 202, 2,802) Simultaneous vs. Concurrent Alcohol & NMPS Use b (n = 202, 233)
AOR* 95% CI p-value AOR* 95%CI p-value
Gender
 Males vs. Females 0.83 0.51, 1.34 0.4487 1.30 0.76,2.24 0.3396
Academics Classification
 Freshman (referent)
 Sophomore 1.49 0.50, 4.48 0.4765 0.15 0.02, 1.40 0.0958
 Junior 1.57 0.53, 4.66 0.4114 0.16 0.02, 1.42 0.0979
 Senior 2.08 0.70, 6.15 0.1861 0.15 0.02, 1.34 0.0887
Race
 White vs. Non-White 1.11 0.48, 2.55 0.8049 1.00 0.39, 2.57 0.9989
Mother’s education level
 4 yr. College degree or higher vs. Some college or less 0.88 0.54, 1.46 0.6262 0.68 0.38, 1.20 0.1836
Father’s education level
 4 yr. College degree or higher vs. Some college or less 1.04 0.62, 1.74 0.8896 0.99 0.56, 1.78 0.9845
GPA 0.62 0.39, 0.99 0.0449 0.82 0.48, 1.39 0.4509
Sensation Seeking 1.16 0.81, 1.68 0.4156 0.94 0.62, 1.43 0.7764
Past 30-day drinking 0.43 0.05, 3.68 0.4376 0.99 0.09, 11.07 0.9971
Heavy-episodic drinking within the past 30 days 2.60 1.21, 5.56 0.0142 1.97 0.84, 4.63 0.1214
Past 30-day smoking 2.14 1.30, 3.54 0.0030 1.95 1.10, 3.44 0.0218
Past 30-day marijuana use 2.71 1.58, 4.64 0.0003 1.00 0.53, 1.88 0.9939
Past 30-day illicit drug use 3.85 2.13, 6.95 <.0001 2.03 1.07, 3.84 0.0300
Past year non-medical prescription drug use (excluding stimulants) 4.39 2.69, 7.16 <.0001 1.54 0.89, 2.68 0.1262
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*

AOR = Adjusted Odds Ratio; 95% CI = 95% Confidence Interval for AOR;

a

Polytomous clustered logistic regression with Past Year Drinkers as referent group;

b

Polytomous clustered logistic regression with Concurrent NMPS and alcohol users as referent group.

Multivariate mixed effects regression modeling (MIXED) was used to assess predictors of moderate alcohol-related consequences while generalized linear modeling (GENMOD) using generalized estimating equations (GEE) was used to assess correlates associated with severe alcohol-related consequences. Adjusted odds ratios and 95% confidence intervals (categorical outcomes) and beta estimates and standard errors (continuous outcomes) were reported. All analyses were performed using SAS v. 9.2 (Cary, NC).

3. RESULTS

3.1. Sample Characteristics

The overall sample consisted of 4,090 undergraduate students (Table 1). There were more females (63.5%) than males (36.5%), and the average age was 20 (SD = 2.87). The sample was evenly distributed among the four academic classifications, and the majority of the students were non-Hispanic white (81.4%). The majority of students had parents with at least a college education (53.2% and 53.2% for mothers and fathers, respectively). The average sensation seeking score was 3.38 (SD=0.52). In the overall sample, there were 2,805 (68.6%) past 30-day drinkers.

Table 1.

Characteristics of Concurrent and Simultaneous Alcohol and NMPS Users

Characteristics, n (%) or mean ± SD Overall n= 4090 Past Year Drinker n= 2,802 (68.5%) Concurrent Alcohol & NMPS use n=233 (5.7%) Simultaneous Alcohol & NMPS use n=202 (4.9%)
Gender
 Male 1486 (36.5%) 1017 (36.4%) 100 (42.9%) 98 (49.3%)
 Female 2583 (63.5%) 1774 (63.6%) 133 (57.1%) 101 (50.8%)
Academic Classification
 Freshman 1081 (26.5%) 703 (25.1%) 27 (11.6%) 33 (16.4%)
 Sophomore 957 (23.4%) 624 (22.3%) 58 (24.9%) 51 (25.4%)
 Junior 1057 (25.9%) 741 (26.5%) 69 (27.1%) 63 (31.3%)
 Senior 832 (24.3%) 703 (26.2%) 79 (36.4%) 52 (26.9%)
Age 20.48 ± 2.87 20.57 ± 2.84 20.47 ± 1.85 20.47 ± 2.01
Race/Ethnicity
 White 3309 (80.9%) 2309 (82.4%) 147 (63.1%) 197 (97.5%)
 Non-White 754(18.4%) 471 (16.8%) 86 (36.9 %) 5 (2.5%)
Mother’s Education
 Some College or Less 1869 (46.1%) 1269 (46.0%) 89 (38.2%) 79 (39.3%)
 4- Year College Degree 2157 (53.2%) 1491 (54.0%) 144 (61.8%) 120 (59.7%)
Father’s Education
 Some College or Less 1797 (44.4%) 1215 (37.6%) 93 (40.1%) 70 (34.8%)
 4-Year College Degree 2154 (53.2%) 1492 (46.2%) 137 (59.1%) 129 (64.2%)
Greek Affiliation (Sorority/Fraternity Status) 590 (14.4%) 468 (14.5%) 43 (18.5%) 31 (15.4%)
G.P.A. 3.24 ± 0.52 3.24 ± 0.52 3.13 ± 0.47 3.06 ± 0.55
College Campus
 Private 617 (15.1%) 441 (15.8%) 34 (14.6%) 38 (18.8%)
 Public 3465 (84.9%) 2354 (8.2%) 199 (85.4%) 164 (81.2%)
Sensation Seeking 3.38 ± 0.75 3.42 ± 0.73 3.74 ± 0.67 3.90 ± 0.66
Past 30-day Drinking 2805 (68.6%) 2333 (83.3%) 225 (96.6%) 196 (97.0%)
Heavy-episodic Drinking in the past 30-days 1723 (42.1%) 1337 (47.7%) 178 (76.4%) 178 (88.1%)
Past 30-day Smoking 860 (21.0%) 577 (20.6%) 112 (48.1%) 135 (66.8%)
Past 30-day marijuana Use 1029 (25.4%) 690 (24.6%) 152 (65.2%) 154 (76.2%)
Past 30-day Illicit Drug Use 206 (5.0%) 78 (2.8%) 46 (19.7%) 75 (37.1%)
Past year non-medical prescription drug use (excluding stimulants) 435 (10.6%) 207 (7.4%) 88 (37.8%) 116 (57.4%)
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*

Categorical totals may differ from sample totals due to missing responses.

3.2. Prevalence of NMPS Use and Simultaneous NMPS/Alcohol Use

The percentage of students who reported using NMPS within the past year was 10.6%, ranging from 4.0% to 15.7% by institution. Past year prevalence of simultaneous use of NMPS/alcohol was 4.9%, and past year prevalence of concurrent use of NMPS/alcohol was 5.7%. Among past year NMPS users, 46.4% reported simultaneous use of NMPS/alcohol, and 53.6% reported concurrent use of NMPS/alcohol. Demographic and institutional characteristics, as well as substance use by group, are reported in Table 1.

3.3. Bivariate Associations of Simultaneous NMPS/Alcohol Use with Demographics, Institutional Characteristics, and Substance Use

Bivariate analyses revealed significant differences between simultaneous NMPS/alcohol with respect to most demographic variables, contextual factors, and other substance use (Table 2).

Table 2.

Bivariate Analysis of Demographics, Institutional Characteristics, and other Substance Use with NMPS use

Simultaneous Alcohol & NMPS use vs. Past Year Drinkers a (n = 202, 2,802) Simultaneous vs. Concurrent Alcohol & NMPS Useb (n = 202, 233)
OR 95% CI p-value OR 95%CI p-value
Gender
 Males vs. Females 1.65 1.23, 2.20 0.0008 1.29 0.88, 1.89 0.1887
Academics Classification
 Freshman (referent)
 Sophomore 1.71 1.09, 2.70 0.0199 0.72 0.38, 1.36 0.3077
 Junior 1.85 1.20, 2.87 0.0056 0.75 0.41, 1.38 0.3509
 Senior 1.59 1.01, 2.49 0.0455 0.54 0.29, 0.99 0.0495
Race
 White vs. Non-White 1.95 1.14, 3.33 0.0149 1.24 0.64, 2.42 0.5298
Mother’s education level
 4 yr. College degree or higher vs. Some college or less 1.24 0.92, 1.67 0.1620 0.94 0.64, 1.38 0.7497
Father’s education level
 4 yr. College degree or higher vs. Some college or less 1.40 1.03, 1.91 0.0316 1.25 0.85, 1.85 0.2635
Greek Affiliation (Sorority/Fraternity Status) 0.95 0.62, 1.44 0.7994 0.80 0.48, 1.33 0.3901
GPA 0.52 0.39, 0.69 <.0001 0.82 0.57, 1.18 0.2864
College Campus
Public vs. Private 0.71 0.28, 1.85 0.4864 0.74 0.44, 1.22 0.2388
Sensation Seeking 2.54 1.50, 2.22 <.0001 1.39 1.05, 1.82 0.0200
Past 30-day drinking 8.87 3.27, 24.06 <.0001 1.74 0.52, 5.87 0.3707
Heavy-episodic drinking within the past 30-days 7.83 5.07, 12.11 <.0001 2.29 1.36, 3.87 0.0019
Past 30-day smoking 4.15 2.85, 6.05 <.0001 2.35 1.48, 3.72 0.0003
Past 30-day marijuana use 9.88 7.00, 13.95 <.0001 1.74 1.14, 2.67 0.0111
Past 30-day illicit drug use 20.18 13.88, 29.33 <.0001 2.40 1.56, 3.69 <.0001
Past year non-medical prescription drug use (excluding stimulants) 16.88 12.27, 23.21 <.0001 2.22 1.51, 3.27 <.0001
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a

Polytomous clustered logistic regression with Past Year Drinkers as referent group;

b

Polytomous clustered logistic regression with Concurrent NMPS and alcohol users as referent group.

3.4. Multivariate Associations of Simultaneous NMPS/Alcohol Use with Demographics, Institutional Characteristics, and Substance Use

3.4.1 Simultaneous NMPS/Alcohol Use vs. Past Year Drinking (Table 3)

Simultaneous users were significantly more likely than drinkers to have low grade point averages (AOR=0.62, CI=0.39, 0.99), be heavy-episodic drinkers (AOR=2.60, CI=1.21, 5.56), and be current smokers (AOR=2.14, CI=1.30, 3.54). Simultaneous users were almost three times more likely than past year drinkers to be current marijuana users (AOR=2.71, CI=1.58, 4.64). Simultaneous users were almost 4 times more likely than drinkers to have currently used illicit drugs (AOR=3.85, CI=2.13, 6.95), and prescription drugs (excluding stimulants) within the past year (AOR=4.39, CI=2.69, 7.16).

3.4.2 Simultaneous NMPS/Alcohol Use vs. Concurrent NMPS/Alcohol Use (Table 3)

Compared to concurrent users, simultaneous users were significantly more likely to be current smokers (AOR=1.95, CI=1.10, 3.44). Simultaneous users were twice as likely to have currently used illicit drugs (AOR=2.03, CI=1.07, 3.84).

3.5. Association of Simultaneous NMPS/Alcohol Use with Alcohol-Related Consequences

3.5.1 Moderate Alcohol-Related Consequences

Simultaneous users were five times more likely than drinkers to experience moderate consequences (β=5.29, p= <0.0001). Additionally, simultaneous users were 6 times more likely than concurrent users to experience moderate consequences (β=6.04, p= <0.0001). There was not a significant difference in moderate consequences between concurrent users and past year drinkers. However, concurrent users trended to experience fewer moderate consequences than drinkers. Other statistically significant predictors of moderate consequences are reported in Table 4.

Table 4.

Association of Moderate and Severe Alcohol-Related Consequences with Simultaneous and Concurrent Alcohol and NMPS Use, and Past Year Drinking

Model Moderate Alcohol-Related Consequences Severe Alcohol-Related Consequences
B* SE* p-value AOR* 95% CI* p-value
NMPS Use
 Concurrent NMPS & Alcohol Use vs. Past Year -0.75 1.06 0.4803 1.71 1.18, 2.48 0.0045
 Drinker (referent)
 Simultaneous NMPS & Alcohol Use Vs. Past Year 5.29 1.22 <0.0001 2.07 1.19, 3.63 0.0105
 Drinker (referent)
 Simultaneous vs. Concurrent (referent) 6.04 1.39 <0.0001 1.21 0.57, 2.56 0.6145
Gender
 Males vs. Females (referent) 2.16 0.72 0.0027 1.31 0.89, 1.94 0.1682
Academics Classification
 Freshman (referent) - - -
 Sophomore −1.28 1.62 0.4299 0.40 0.20, 0.81 0.0115
 Junior −1.00 1.58 0.5241 0.43 0.14, 1.31 0.1359
 Senior −0.82 1.58 0.6025 0.44 0.15, 1.3 0.1392
GPA −1.75 0.63 0.0053 0.73 0.63, 0.85 <0.0001
Sensation Seeking 2.11 0.53 <0.0001 1.42 1.01, 2.00 0.0463
Heavy-episodic drinking within the past 30 days 5.99 0.81 <0.0001 3.37 2.14, 5.33 <0.0001
Past 30-day smoking 0.79 0.71 0.2745 1.20 0.98, 1.49 0.0837
Past 30-day marijuana use 1.93 0.80 0.0155 1.12 0.75, 1.67 0.5790
Past 30-day illicit drug use 2.02 1.26 0.1110 0.81 0.41, 1.62 0.5597
Past year non-medical prescription drug use (excluding stimulants) 2.15 0.94 0.0231 0.94 0.52, 1.68 0.8267
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*

β = regression coefficient, SE = standard error for the regression coefficient, AOR = adjusted odds ratio; 95% CI = 95% confidence interval for AOR.

Adjusted for gender, academic classification, GPA, Sensation seeking, Past 30-day drinking, Heavy-episodic drinking within the past 30 days, Past 30-day smoking, Past 30-day marijuana use, Past 30-day illicit drug use, and Past year non-medical prescription drug use (excluding prescription stimulants).

3.5.2 Severe Alcohol-Related Consequences

Simultaneous users were two times more likely than past year drinkers to experience a severe consequence (β=2.07, p= 0.0105). There was not a significant difference in severe alcohol-related consequences between simultaneous and concurrent NMPS/ alcohol use. Concurrent users were significantly more likely than past year drinkers to experience a severe consequence (β=1.71, p= 0.0045). Other statistically significant predictors of severe consequences are reported in Table 4.

4. DISCUSSION

In a large sample of college students from eight universities in North Carolina, 4.9% of participants reported using NMPS simultaneously with alcohol within the past year. Additionally, almost half (46.4%) of the participants who reported NMPS use also reported simultaneous use with alcohol. Although 4.9% is lower than the prevalence of alcohol or marijuana use, it was greater than the prevalence of cocaine (4.2%), ecstasy (3.1%), heroin (0.4%), and methamphetamine (0.3%) reported nationally by college students (Johnston et al., 2010).

Even though ADHD prescription stimulants are prescribed substances, they have potential pharmacological side effects when used in conjunction with alcohol as well as when used alone. Concerns associated with simultaneous NMPS/alcohol use extend beyond pharmacological risks. Our results show that simultaneous NMPS/alcohol users have lower grade point averages, report more substance use, and experience more adverse consequences than drinkers who do not use NMPS and concurrent NMPS/alcohol users.

In comparison to drinkers, simultaneous NMPS/alcohol users reported lower grade point averages. These findings are consistent with the literature on grade point averages of NMPS users (Wilens, 2008; Arria, 2008; McCabe et al., 2006d; Shillington et al., 2006). Two of the moderate consequences (missing class and performing poorly on a test or project) could be related to this finding, since simultaneous NMPS/alcohol use is significant predictor of moderate consequences. There were no significant differences between simultaneous and concurrent use.

Simultaneous NMPS/alcohol users were significantly more likely than past year drinkers to report using other substances such as marijuana, illicit drugs, and non-medical prescription drugs. Also, simultaneous users were significantly more likely than concurrent users to be current smokers and to have currently used illicit drugs. These results are consistent with other studies showing that NMPS users are more likely to use other substances than non-NMPS users (DeSantis et al., 2009; Novak et al., 2007; Teter et al., 2005; McCabe et al., 2006e; Arria et al., 2008a). Since simultaneous NMPS/alcohol users are also engaging in other substance use, future research should expand beyond the focus of simultaneous use of NMPS/alcohol and examine the simultaneous use of NMPS with other substances (e.g., cigarettes, marijuana, etc.).

Concurrent use of NMPS/alcohol was not associated with an increased risk of experiencing moderate alcohol-related consequences. Unexpectedly, concurrent users of NMPS/alcohol were more likely than past year drinkers to experience a severe alcohol-related consequence. The following were the severe consequences: received a ticket for a DUI/DWI, had a car/motorcycle crash, got into a physical fight, got into trouble with the police, was a victim of a crime, had sex later regretted, was taken advantage of sexually, and took advantage of another sexually. Individual severe consequences should be examined in future studies to determine if one consequence is more common than another and if they are the same ones experienced by simultaneous NMPS/alcohol users.

Simultaneous users of NMPS/alcohol are significantly more likely than past year drinkers to experience both moderate and severe alcohol-related consequences, even after adjusting for other substance use and sensation seeking, which is consistent with the literature on simultaneous use of alcohol with prescription drugs (McCabe et al., 2006c). In addition, simultaneous users of NMPS/alcohol are significantly more likely than concurrent NMPS/alcohol users to experience moderate adverse consequences and tend to experience more severe adverse consequences than concurrent NMPS/alcohol users. The stimulatory effects of prescription stimulants may appear to mask alcohol intoxication, resulting in a delay in experiencing the depressive symptoms of alcohol which typically lead to cessation. Thus, simultaneous users may be exposing themselves to greater risks, such as injury, intoxicated driving, physical altercations, and sexual victimization, commonly associated with alcohol consumption.

These results may extend to the simultaneous use of alcohol and other substances. Undergraduates report simultaneous use of alcohol with marijuana, cocaine, MDMA, psilocybin, and tobacco (Barrett et al., 2006). Studies indicate that marijuana is commonly used simultaneously with alcohol (Midanik et al., 2007; Barnwell and Earleywine, 2006; Collins et al., 1998; Barrett et al., 2006), and simultaneous use has been associated with negative consequences (Midanik et al., 2007; Barnwell and Earleywine, 2006). Additionally, studies of alcoholic energy drink consumption (O’Brien et al., 2008; Arria et al., 2011) and simultaneous use of alcohol and prescription drugs (McCabe et al., 2006c) report associations between simultaneous use and adverse consequences. Due to substances’ biochemical differences, the specific consequences associated with simultaneous use of alcohol and other substances may vary by the substance used. For example, sedatives or anxiolytics may exacerbate the depressive effects of alcohol whereas stimulants may mask alcohol’s depressive effects. Thus, it would be of interest to examine if our results extend to other substances used simultaneously with alcohol, and if the consequences associated with use are the same.

This study has limitations that need to be considered. The cross-sectional nature of the data precludes assessment of causal relationships and directionality of the relationship between NMPS use and other health-risk behaviors. In addition, we rely on self-reported data. However, the use of a web-based survey should minimize social desirability bias (Shadish et al., 2002; McCabe et al., 2006d). Generalizability is limited due to sampling from one state. However, respondents were sampled from eight schools in North Carolina, including both public and private institutions. In addition, the southeast portion of the US is underrepresented in the literature on NMPS use among undergraduate students.

Also, it is important to note that this study focused on the simultaneous use of alcohol and NMPS that were not prescribed to an individual, thus, our findings do not include prescription holders who simultaneously use prescribed ADHD prescription stimulants. A previous study of prescribed users of ADHD prescription stimulants found that 19% of the sample had misused their prescription simultaneously with alcohol (Sepulveda, 2011). Thus, the prevalence of simultaneous use of NMPS with alcohol among our sample is likely an underestimate of the problem, and would likely increase by expanding the reach of our item to capture prescription holders who participate in this behavior. Future research should examine the prevalence of simultaneous use of prescription stimulants with alcohol among non-prescription holders and prescription holders, in order to get a better estimate of the problem.

This study was the first to examine predictors and consequences associated with the simultaneous use of NMPS with alcohol within a large multi-institution sample of undergraduate students. Our findings on the simultaneous use of NMPS/alcohol underscore the need for prevention efforts focused on the simultaneous use of NMPS with other substances, especially alcohol. Prevention efforts should focus on educating college students on the risks associated with mixing prescription stimulants with alcohol. In addition, practitioners should be made aware of the prevalence of this behavior in order to advise their patients about the adverse effects of combining alcohol with prescription stimulants, the dangers and legality of sharing their prescriptions with others, and to monitor the supply of prescription stimulants prescribed to their patients.

Acknowledgments

Role of Funding Source: This work was supported by funding from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) at the National Institutes of Health (RO1AA014007). NIAAA had no further role in study design; collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.

Footnotes

Contributors: All authors contributed to the conception of the research question. Ms. Egan, Ms. Blocker, and Dr. Reboussin were all involved in the statistical analysis. Ms. Egan wrote the first draft of the manuscript. All authors contributed to and have approved the final manuscript.

Conflict of Interest: All authors declare that they have no conflicts of interest.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

References

  1. Arria AM, Caldiera KM, Kasperski SJ, Vincent KB, Griffiths RR, O’Grady KE. Energy drink consumption and increased risk of alcohol dependence. Alcohol Clin Exp Res. 2011;35:365–375. doi: 10.1111/j.1530-0277.2010.01352.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Arria AM, DuPont RL. Nonmedical prescription stimulant use among college students: why we need to do something and what we need to do. J Addict Dis. 2010;29:417–426. doi: 10.1080/10550887.2010.509273. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Arria AM, Caldeira KM, O'Grady KE, Vincent KB, Fitzelle DB, Johnson EP, Wish ED. Drug exposure opportunities and use patterns among college students: results of a longitudinal prospective cohort study. Subst Abuse. 2008;29:19–38. doi: 10.1080/08897070802418451. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Ashton E. NIDA Notes. 2008. Alcohol abuse makes prescription drug abuse more likely; p. 21. [Google Scholar]
  5. Barnwell SS, Earleywine M. Simultaneous alcohol and cannabis expectancies predict simultaneous use. Subst Abuse Treat Prev Policy. 2006;1:1–29. doi: 10.1186/1747-597X-1-29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Barrett SP, Darredeau C, Pihl RO. Patterns of simultaneous polysubstance use in drug using university students. Hum Psychopharmacol. 2006;21:255–263. doi: 10.1002/hup.766. [DOI] [PubMed] [Google Scholar]
  7. Barrett SP, Darredeau C, Bordy LE, Pihl RO. Characteristics of methylphenidate misuse in a university student sample. Can J Psychiatry. 2005;50:457–461. doi: 10.1177/070674370505000805. [DOI] [PubMed] [Google Scholar]
  8. Boileau I, Assaad JM, Pihl RO, Benkelfat C, Leyton M, Diksic M, Tremblay RE, Dagher A. Alcohol promotes dopamine release in the human nucleus accumbens. Synapse. 2003;49:226–231. doi: 10.1002/syn.10226. [DOI] [PubMed] [Google Scholar]
  9. Carroll BC, McLaughlin TJ, Blake DR. Patterns and knowledge of nonmedical use of stimulants among college students. Arch Pediatr Adolesc Med. 2006;160:481–485. doi: 10.1001/archpedi.160.5.481. [DOI] [PubMed] [Google Scholar]
  10. Collins RL, Ellickson PL, Bell RM. Simultaneous polydrug use among teens: prevalence and predictors. J Subst Use. 1998;10:233–253. doi: 10.1016/s0899-3289(99)00007-3. [DOI] [PubMed] [Google Scholar]
  11. DeSantis A, Noar SM, Webb EM. Nonmedical ADHD stimulant use in fraternities. J Stud Alcohol Drugs. 2009;70:952–954. doi: 10.15288/jsad.2009.70.952. [DOI] [PubMed] [Google Scholar]
  12. DeSantis AD, Webb EM, Noar SM. Illicit use of prescription ADHD medications on a college campus: a multimethodological approach. J Am Coll Health. 2008;57:315–324. doi: 10.3200/JACH.57.3.315-324. [DOI] [PubMed] [Google Scholar]
  13. Dillman DA. Mail and Internet Surveys: The Tailored Design Method. John Wiley & Sons; New York: 2000. [Google Scholar]
  14. Earleywine M, Newcomb MD. Concurrent versus simultaneous polydrug use: prevalence, correlates, discriminant validity, and prospective effects on health outcomes. Exp Clin Psychopharmacol. 1997;5:353–364. doi: 10.1037//1064-1297.5.4.353. [DOI] [PubMed] [Google Scholar]
  15. Greynadus D, Patel D. The adolescent and substance abuse: current concepts. Dis Mon. 2005:91–431. doi: 10.1016/j.disamonth.2005.10.002. [DOI] [PubMed] [Google Scholar]
  16. Herman-Stahl MA, Krebs CP, Kroutil LA, Heller DC. Risk and protective factors for methamphetamine use and nonmedical use of prescription stimulants among young adults aged 18 to 25. Addict Behav. 2007;32:1003–1015. doi: 10.1016/j.addbeh.2006.07.010. [DOI] [PubMed] [Google Scholar]
  17. Hermos J, Winter M, Heeren T, Hingson R. Alcohol-related problems among younger drinkers who misuse prescription drugs: results from the national epidemiologic survey of alcohol and related conditions (NESARC) Subst Abuse. 2009;30:118–126. doi: 10.1080/08897070902802034. [DOI] [PubMed] [Google Scholar]
  18. Hoyle RH, Stephenson MT, Palmgreen P, Lorch EP, Donohew RL. Reliability and validity of a brief measure of sensation seeking. Pers Individ Dif. 2002;32:401–414. [Google Scholar]
  19. Jessor R, Donovan J, Costa F. Beyond Adolescence: Problem Behavior and Young Adult Development. Cambridge University Press; Cambridge, UK: 1991. [Google Scholar]
  20. Jiao X, Velez S, Ringstad J, Eyma V, Miller D, Bleiberg M. Myocardial infarction associated with Adderall XR and alcohol use in a young man. J Am Board Fam Med. 2009;22:197–201. doi: 10.3122/jabfm.2009.02.080003. [DOI] [PubMed] [Google Scholar]
  21. Johnston LD, O'Malley PM, Bachman JG, Schulenberg JE. NIH Publication No 10–7585. National Institute on Drug Abuse; Bethesda, MD: 2010. Monitoring the Future National Survey Results on Drug Use, 1975–2009: Volume II College Students and Young Adults Ages 19–50. [Google Scholar]
  22. Johnston LD, O'Malley PM, Bachman JG, Schulenberg JE. NIH Publication No 08-6418B. National Institute on Drug Abuse; Bethesda, MD: 2008. Monitoring the Future National Survey Results on Drug Use, 1975–2007: Volume II College Students and Young Adults Ages 19–45. [Google Scholar]
  23. Judson R, Langdon SW. Illicit use of prescription stimulants among college students: prescription status, motives, theory of planned behaviour, knowledge and self-diagnostic tendencies. Psychol Health Med. 2009;14:97–104. doi: 10.1080/13548500802126723. [DOI] [PubMed] [Google Scholar]
  24. Koehm M, Kauert GF, Toennes SW. Influence of ethanol on the pharmacokinetics of methylphenidate's metabolites ritalinic acid and ethylphenidate. Arzneimittel-Forschung. 2010;60:238–244. doi: 10.1055/s-0031-1296279. [DOI] [PubMed] [Google Scholar]
  25. Markowitz JS, DeVane CL, Boulton DW, Nahas Z, Risch SC, Diamond F, Patrick KS. Ethylphenidate formation in human subjects after the administration of a single dose of methylphenidate and ethanol. Drug Metab Dispos. 2000;28:620–624. [PubMed] [Google Scholar]
  26. Markowitz JS, Logan BK, Diamond F, Patrick KS. Detection of the novel metabolite ethylphenidate after methylphenidate overdose with alcohol coingestion. J Clin Psychopharmacol. 1999;19:362–366. doi: 10.1097/00004714-199908000-00013. [DOI] [PubMed] [Google Scholar]
  27. Martin CS, Clifford PR, Clapper RL. Patterns and predictors of simultaneous and concurrent use of alcohol, tobacco, marijuana, and hallucinogens in first-year college students. J Subst Use. 1992;4:319–326. doi: 10.1016/0899-3289(92)90039-z. [DOI] [PubMed] [Google Scholar]
  28. McCabe SE, Boyd CJ, Teter CJ. Subtypes of nonmedical prescription drug misuse. Drug Alcohol Depend. 2009;102:63–70. doi: 10.1016/j.drugalcdep.2009.01.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. McCabe SE. Misperceptions of non-medical prescription drug use: a web survey of college students. Addict Behav. 2008;33:713–724. doi: 10.1016/j.addbeh.2007.12.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. McCabe SE, Teter CJ. Drug use related problems among nonmedical users of prescription stimulants: a web-based survey of college students from a Midwestern university. Drug Alcohol Depend. 2007;91:69–76. doi: 10.1016/j.drugalcdep.2007.05.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. McCabe SE, Diez A, Boyd CJ, Nelson TF, Weitzman ER. Comparing web and mail responses in a mixed mode survey in college alcohol use research. Addict Behav. 2006a;31:1619–1627. doi: 10.1016/j.addbeh.2005.12.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. McCabe SE, Cranford JA, Boyd CJ. The relationship between past-year drinking behaviors and nonmedical use of prescription drugs: prevalence of co-occurrence in a national sample. Drug Alcohol Depend. 2006b;84:281–288. doi: 10.1016/j.drugalcdep.2006.03.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. McCabe SE, Cranford JA, Morales M, Young A. Simultaneous and concurrent polydrug use of alcohol and prescription drugs: prevalence, correlates, and consequences. J Stud Alcohol. 2006c;67:529–537. doi: 10.15288/jsa.2006.67.529. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. McCabe SE, Teter CJ, Boyd CJ. Medical use, illicit use and diversion of prescription stimulant medication. J Psychoactive Drugs. 2006d;38:43–56. doi: 10.1080/02791072.2006.10399827. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. McCabe SE, Teter CJ, Boyd CJ. Medical use, illicit use, and diversion of abusable prescription drugs. J Am Coll Health. 2006e;54:269–278. doi: 10.3200/JACH.54.5.269-278. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Mccabe S, Boyd C. Sources of prescription drugs for illicit use. Addict Behav. 2005;30:1342–1350. doi: 10.1016/j.addbeh.2005.01.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Midanik LT, Tam TW, Weisner C. Concurrent and simultaneous drug and alcohol use: results of the 2000 National Alcohol Survey. Drug Alcohol Depend. 2007;90:72–80. doi: 10.1016/j.drugalcdep.2007.02.024. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Newlin DB, Thomson JB. Alcohol challenge with sons of alcoholics: a critical review and analysis. Psychol Bull. 1990;108:383–402. doi: 10.1037/0033-2909.108.3.383. [DOI] [PubMed] [Google Scholar]
  39. Novak SP, Kroutil LA, Williams RL, Van Brunt DL. The nonmedical use of prescription ADHD medications: results from a national Internet panel. Subst Abuse Treat Prev Policy. 2007;2:32. doi: 10.1186/1747-597X-2-32. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. O'Brien MC, McCoy TP, Rhodes SD, Wagoner A, Wolfson M. Caffeinated cocktails: energy drink consumption, high-risk drinking, and alcohol-related consequences among college students. Acad Emerg Med. 2008;15:453–460. doi: 10.1111/j.1553-2712.2008.00085.x. [DOI] [PubMed] [Google Scholar]
  41. Reed MB, Wang R, Shillington AM, Clapp JD, Lange JE. The relationship between alcohol use and cigarette smoking in a sample of undergraduate college students. Addict Behav. 2007;32:449–464. doi: 10.1016/j.addbeh.2006.05.016. [DOI] [PubMed] [Google Scholar]
  42. Sepulveda DR, Thomas LM, McCabe SE, Cranford JA, Boyd CJ, Teter CJ. Misuse of prescribed stimulant medication for ADHD and associated patterns of substance use: preliminary analysis among college students. J Pharm Pract. 2011;24:551–560. doi: 10.1177/0897190011426558. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Shadish W, Cook T, Campbell D. Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin Company; Boston: 2002. [Google Scholar]
  44. Shillington AM, Reed MB, Lange JE, Clapp JD, Henry S. College undergraduate Ritalin abusers in Southwestern California: protective and risk factors. J Drug Issues. 2006;36:999–1014. [Google Scholar]
  45. Substance Abuse and Mental Health Services Administration. The NSDUH Report. Vol. 2009. Office of Applied Studies; Rockville, MD: 2009. Apr 7, Nonmedical Use of Adderall among Full-Time College Students. [Google Scholar]
  46. Sutfin EL, Reboussin BA, McCoy TP, Wolfson M. Are college student smokers really a homogeneous group? a latent class analysis of college student smokers. Nicotine Tob Res. 2009;11:444–454. doi: 10.1093/ntr/ntp006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Teter CJ, Falone AE, Cranford JA, Boyd CJ, McCabe SE. Nonmedical use of prescription stimulants and depressed mood among college students: frequency and routes of administration. J Subst Abuse Treat. 2010;38:292–298. doi: 10.1016/j.jsat.2010.01.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Teter CJ, McCabe SE, LaGrange K, Cranford JA, Boyd CJ. Illicit use of specific prescription stimulants among college students: prevalence, motives, and routes of administration. Pharmacotherapy. 2006;26:1501–1510. doi: 10.1592/phco.26.10.1501. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Teter CJ, McCabe SE, Cranford JA, Boyd CJ, Guthrie SK. Prevalence and motives for illicit use of prescription stimulants in an undergraduate student sample. J Am Coll Health. 2005;53:253–262. doi: 10.3200/JACH.53.6.253-262. [DOI] [PubMed] [Google Scholar]
  50. White BP, Becker-Blease KA, Grace-Bishop K. Stimulant medication use, misuse, and abuse in an undergraduate and graduate student sample. J Am Coll Health. 2006;54:261–268. doi: 10.3200/JACH.54.5.261-268. [DOI] [PubMed] [Google Scholar]
  51. Wilens TE, Adler LA, Adams J, Sgambati S, Rotrosen J, Sawtelle R, Utzinger L, Fusillo S. Misuse and diversion of stimulants prescribed for ADHD: a systematic review of the literature. J Am Acad Child Adolesc Psychiatry. 2008;47:21–31. doi: 10.1097/chi.0b013e31815a56f1. [DOI] [PubMed] [Google Scholar]
  52. Wolfson M, Champion H, McCoy TP, Rhodes SD, Ip EH, Blocker JN, Martin BA, Wagoner KG, O’Brien MC, Sutfin EL, Mitra A, Durant RH. Impact of a randomized campus/community trial to prevent high-risk drinking among college students. Alcohol Clin Exp Res. 2012;36:1767–1768. doi: 10.1111/j.1530-0277.2012.01786.x. [DOI] [PMC free article] [PubMed] [Google Scholar]

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