Research Analysis

Soc Psychiatry Psychiatr Epidemiol (2015) 50:713–724

DOI 10.1007/s00127-014-0980-3

ORIGINAL PAPER

Nonmedical prescription drug use among US young adults by educational attainment

Silvia S. Martins • June H. Kim • Lian-Yu Chen •

Deysia Levin • Katherine M. Keyes •

Magdalena Cerdá • Carla L. Storr

Received: 19 May 2014 / Accepted: 10 November 2014 / Published online: 27 November 2014

© Springer-Verlag Berlin Heidelberg 2014

Abstract

Purpose Little is known about nonmedical use of pre-

scription drugs among non-college-attending young adults

in the United States.

Methods Data were drawn from 36,781 young adults (ages

18–22 years) from the 2008–2010 National Survey on Drug

Use and Health public use files. The adjusted main effects for

current educational attainment, along with its interaction

with gender and race/ethnicity, were considered.

Results Compared to those attending college, non-col-

lege-attending young adults with at least and less than a HS

degree had a higher prevalence of past-year nonmedical

use of prescription opioids [NMUPO 13.1 and 13.2 %,

respectively, vs. 11.3 %, adjusted odds ratios (aORs) 1.21

(1.11–1.33) and 1.25 (1.12–1.40)], yet lower prevalence of

prescription stimulant use. Among users, regardless of drug

type, non-college-attending youth were more likely to have

past-year disorder secondary to use [e.g., NMUPO 17.4 and

19.1 %, respectively, vs. 11.7 %, aORs 1.55 (1.22–1.98)

and 1.75 (1.35–2.28)]. Educational attainment interacted

with gender and race: (1) among nonmedical users of

prescription opioids, females who completed high school

but were not enrolled in college had a significantly greater

risk of opioid disorder (compared to female college stu-

dents) than the same comparison for men; and (2) the risk

for nonmedical use of prescription opioids was negligible

across educational attainment groups for Hispanics, which

was significantly different than the increased risk shown for

non-Hispanic whites.

Conclusions There is a need for young adult prevention

and intervention programs to target nonmedical prescrip-

tion drug use beyond college campuses.

Keywords Nonmedical prescription drug use · Drug use disorders · Educational attainment · Young adults · Gender differences

S. S. Martins (&) · J. H. Kim · D. Levin · K. M. Keyes · M. Cerdá Department of Epidemiology, Mailman School of Public Health,

722 West 168th Street, Rm. 509, New York, NY 10032, USA

e-mail: ssm2183@columbia.edu

L.-Y. Chen

Taipei City Psychiatric Center, Taipei City Hospital, Taipei,

Taiwan

C. L. Storr

Department of Family and Community Health, University of

Maryland School of Nursing, Baltimore, USA

123

Introduction

Nonmedical prescription drug use—use without a pre-

scription or use with a prescription but in a manner other

than how prescribed—is the fastest growing drug problem

in the US [1], driven primarily by nonmedical use of pre-

scription opioids (NMUPO) among younger cohorts [2].

While a large proportion of young adults (age 18–22) are

prescribed opiates (PO) and stimulants for legitimate health

conditions [3–7], NMUPO is second only to marijuana as

the most prevalent form of illegal drug use among young

adults, and a third of persons with opiate disorders sec-

ondary to PO use in 2011 were young adults [8]. Non-

medical use of prescription stimulants is also of concern

among young adults [5–7, 9]. Moreover, this age group is

particularly vulnerable to the development of adverse

substance using patterns, due in part to the process of

identity formation that emerges at this developmental

stage, and a greater level of independence compared to

adolescence [10].

A limitation of many studies on nonmedical prescription

drug use (particularly opiates) among young adults is that

their samples are limited to select segments of the young

adult population. For example, a few studies have exam-

ined NMUPO in community samples of high-risk young

adults (i.e., injection drug users) in urban settings, but none

of these studies have compared estimates to young adults in

the general population [11–16]. Problems related to sub-

stance use on college campuses have also been a central

focus of research on alcohol [17, 18], nonmedical stimulant

use [5–7, 19–22], and NMPO [7, 23, 24] use among college

students. However, many young adults are not seeking a

college education [25]. It has been estimated that among

those completing their secondary education, about 70 %

enroll in further education: 42 % enroll in 4-year institu-

tions and 28 % at 2-year institutions right after graduating

high school [26]. College-based studies also exclude sig-

nificant proportions of minority young adults [27]. The

National Center for Education Statistics indicates that high

school dropout rates are particularly high for non-Hispanic

(NH) black and Hispanic students, as well as for those who

are the first in their family to attend college, and those who

have limited English proficiency [28]. Nationally, about

75 % of all students graduate from high school on time

with a regular diploma, but barely half of non-Hispanic

black and Hispanic students earn diplomas with their peers

[29]. Thus, a substantial proportion of young adults fall

outside the purview of college-based studies and there is a

need to further compare the prevalence of nonmedical

prescription use of opiates and stimulants and disorders

secondary to their use by race/ethnicity between young

adults who attend college versus those who do not attend

college. Notably, most prevention programs to reduce

substance use among young adults are designed for college

settings; a comparison between college- and non-college-

attending young adults would illuminate specific issues that

need to be adapted for prevention programs targeting non-

college-attending youth [30].

It is also important to investigate whether there are any

racial/ethnic and male–female differences in NMUPO or

nonmedical stimulant use within subgroups of young adults

with similar educational attainment levels. Lifetime and

past-year drug use disorders have been consistently asso-

ciated with lower educational attainment and minority

status [31–35]. Studies suggest that individuals with less

years of formal education are at high risk of becoming drug

dependent [32, 34] and of experiencing persistent depen-

dence, in contrast to those with more years of formal

education [34]. Among college students, whites are more

likely than the students of other race/ethnicities to be

nonmedical stimulant and prescription opioid users [5, 7,

23, 24, 36, 37]. The evidence on potential gender differ-

ences in nonmedical prescription drug use among young

adults has been mixed—some studies find no difference,

others have found a higher prevalence in males, and others

in females [7, 8, 23, 24, 36–39]. Very few studies have

investigated male–female differences in prescription stim-

ulant and prescription opioid disorders secondary to non-

medical use in young adults [40, 41]. Thus, examining how

college attendance modifies gender differences might

inform the mixed results on the association between gender

and nonmedical prescription drug use. This study aims to

examine racial/ethnic and male–female differences in

nonmedical prescription use of opiates and stimulants as

well as on disorders secondary to their use among young

adults by different educational attainment.

The goals of this study are to explore whether non-

medical prescription drug use (specifically, opioids and

stimulants) and disorders secondary to the drug use varies

by education and examine race/ethnic and male–female

differences within educational subgroups using data

obtained from nationally representative samples of

18–22 year olds residing in the US. Specifically, we sought

to: (1) compare the 12-month prevalence of nonmedical

use of prescription opioids and stimulants as well as the

prevalence of opioid and stimulant disorder secondary to

nonmedical use among non-college-attending young adults

versus their college-attending peers adjusting for demo-

graphics and past-year serious psychological distress, and

(2) test for risk differences of nonmedical use and disorder

among males and females separately and racial/ethnic

groups stratified by educational attainment in this popula-

tion. Our models also adjust for the presence of psycho-

logical distress because POs have been found to be used

nonmedically to self-medicate negative emotions among

young adults [42] and several studies have shown that

NMUPO can be related to psychological distress in general

population samples [43–48].

Materials and methods

Study sample and measures

We analyzed data from 36,781 young adults between the

ages of 18 and 22 from the 2008 (n = 57,739), 2009

(n = 55,772), and 2010 (n = 57,873) NSDUH public use

files; three consecutive NSDUH years were combined to

increase the sample size. The NSDUH is an annual cross-

sectional survey sponsored by the Substance Abuse and

Mental Health Administration (SAMHSA) and is designed

to provide estimates of the prevalence of drug use and

disorders in the household population of the US among

those 12 years old and older [49]. Annually the survey

selects an independent multistage area probability sample

for each of the 50 states and the District of Columbia.

714 Soc Psychiatry Psychiatr Epidemiol (2015) 50:713–724

123

African-Americans, Hispanics, and young people were

over-sampled to increase the precision of estimates for

these groups. The response rate for household screening

was 88 % and the weighted response rate was 74.8 % for

completed interviews across 3 years [50]. Survey items

were administered by computer-assisted personal inter-

viewing (CAPI) conducted by an interviewer and audio

computer-assisted self-interviewing (ACASI). Use of

ACASI was designed to provide respondents with a highly

private and confidential means of responding to questions

and to increase the level of honest reporting of drug use and

other sensitive behaviors [51]. Respondents were offered a

$30 incentive payment for participation in the survey.

Detailed information about the sampling and survey

methodology of NSDUH are found elsewhere [8, 35, 49].

All respondents provided information about their drug

experiences and their sociodemographic characteristics.

The NSDUH questionnaire instrument has sensitivity val-

ues ranging from 0.8 to 0.97 for most substances, and

specificity values of 0.7–0.95 [35, 52].

Outcome variables: nonmedical prescription opioid

and stimulant use and disorders secondary to use

NMUPO was defined as any self-reported use of pre-

scription pain relievers that were not prescribed for the

respondent or that the respondent took only for the expe-

rience or feeling they caused [49]. To reduce false-positive

responses, all respondents were given the following

instructions: ‘‘These questions are about prescription pain

reliever use. We are not interested in your use of over-the-

counter pain relievers that can be bought in stores without a

doctor’s prescription.’’ Past-year NMUPO was defined

based on the response to the following question: ‘how long

has it been since you last used any prescription pain

reliever that was not prescribed for you or that you took

only for the experience or feeling it caused.’ If the

respondent answered positively, they were classified as a

lifetime NMUPO user. Then, if the response indicated that

nonmedical use occurred during the preceding 12 months,

the respondent was classified as a past-year NMUPO user.

The survey used discrete questions and a card with pictures

of many types of prescription opioids. The respondents

were asked which ones he/she had used, as well as the

frequency of use.

Similarly, the NSDUH used a screening question that

assessed whether the respondent had ever used a pre-

scription stimulant that was not prescribed, or taken one for

the experience or feeling it caused. If the response was

positive, the respondent was given a card with pictures of

many types of prescription stimulant sand was asked which

ones he/she had used, as well as the frequency of use. Then,

if the response indicated that nonmedical use occurred

during the preceding 12 months, the respondent was clas-

sified as a past-year prescription stimulant user.

Respondents with NMUPO in the past-year were asked a

set of 17 structured questions designed to operationalize

DSM-IV criteria [53] for past-year opioid abuse and

dependence secondary to NMUPO (referred together as

OD secondary to NMUPO in this manuscript). Similar

questions were asked to operationalize DSM-IV criteria

[50] for past-year stimulant abuse and dependence sec-

ondary to prescription stimulant use (referred together as

prescription stimulant disorder in this manuscript).

Primary exposure variable: educational attainment

Current educational attainment was operationalized in the

NSDUH as: (1) current college student, (2) high school

graduate/GED [general education certification], (3) did not

complete high school (this information was only asked for

18- to 22-year-old respondents). There was no information

on whether respondents were attending 2-year (community

colleges) or 4-year colleges.

Demographic covariates

Demographic variables selected for this study included

gender, race/ethnicity (non-Hispanic white, non-Hispanic

African American, Native American/Hawaiian/Pacific

Islander, Asian, more than one race, Hispanic), and whe-

ther they resided in a large metro, small metro or non-

metropolitan statistical area. We recognize that for some

racial/ethnic groups sample sizes of respondents (particu-

larly for disorders) will be small, but simply combining

these groups into an ‘‘Other’’ group would prohibit us from

exploring potential prevalence disparities that might exist.

Past-year serious psychological distress

Serious psychological distress (SPD) was measured using

the Kessler 6 (K6) screening instrument for nonspecific

psychological distress. The K6 scales were designed to

maximize the ability to discriminate cases of SPD from

non-cases [54]. The tool consists of six items, each with a

0–4 point rating scale that screens for general distress in the

past year. It has excellent internal consistency and reli-

ability (Cronbach’s alpha = 0.89). In all years, respon-

dents were classified as past-year SPD if the totaled

summed score was 13 or greater [54].

Statistical analyses

Data were weighted to reflect the complex design and

multiple years of the NSDUH sample and were analyzed

using Stata 11.0 [55] and SUDANN [59] software

Soc Psychiatry Psychiatr Epidemiol (2015) 50:713–724 715

123

(specifically used for interaction-testing). We used Taylor

series estimation methods to obtain proper standard error

estimates for the cross-tabulations and logistic regressions.

All percentages reported are weighted by study weights.

Because we analyzed data from three NSDUH years

combined, weights were divided by three (number of years

of data combined) as recommended by SAMHSA [8].

Exploratory data analyses did not show any statistically

significant differences in the NSDUH samples across years,

justifying combining the data from multiple years. After

basic contingency tables were created, we ran logistic

models that included covariates (adjusted for demographics

and past-year serious psychological distress) to compare

the prevalence of past-year NMUPO, past-year nonmedical

prescription stimulant use, past-year OD secondary to

NMUPO, and past-year stimulant dependence secondary to

nonmedical prescription stimulant use among college stu-

dents aged 18–22 vs. their non-college student counter-

parts. Then, we tested for interactive effects between

educational group status with both gender and race. Inter-

action was assessed on the additive scale by testing the

interaction contrast (IC), which represents the difference in

risk differences [56]. Adjusted ICs were calculated using

the PRED_EFF command in SUDAAN [57].

Results

Estimated past-year prevalence by educational

attainment (Table 1)

The prevalence estimates of past-year NMUPO among 18-

to 22-year-old college students, those with high school

diploma/GED and those with less than high school edu-

cation were 11.3, 13.1 and 13.2 %, respectively. Those

with less than high school [aOR 1.25 (1.12–1.40)] and

those who completed high school/GED [aOR 1.21

(1.11–1.33)] were more likely than college students to be

past-year NMUPO (Table 1). Women were less likely than

men to be past-year NMUPO [aOR 0.74 (0.68–0.81)]; NH

blacks, NH Asians and Hispanics were less likely to use PO

nonmedically in the past-year compared to NH whites; and

respondents who reported past-year serious psychological

distress were more likely than those without distress to

report past-year NMUPO [aOR 2.09 (1.89–2.31)]. There

were no differences in the prevalence of NMUPO or in the

prevalence of OD secondary to NMUPO by county type.

In contrast, the prevalence estimates for past-year non-

medical stimulant use among 18- to 22-year-old college

students, those with a high school diploma/GED, and those

with less than high school education were 4.8, 3.1, and

3.0 %, respectively. Those with less than high school [aOR

0.66 (0.54–0.80)] and those who completed high school/

GED [aOR 0.65 (0.55–0.77)] were less likely to have been

past-year nonmedical stimulant users compared to their

college-attending peers (Table 1). As with prescription

opioids, females, NH blacks, NH Asians, and Hispanics

were less likely to report past-year nonmedical stimulant

use, and respondents who reported past-year serious psy-

chological distress were more likely than those without

distress to report past-year nonmedical stimulant use.

Among young adults aged 18–22 year old with past-

year NMUPO, those with lower educational attainment

[less than high school education: 19.1 %, aOR 1.75

(1.35–2.28), completed high school/GED: 17.4 %, aOR

1.55 (1.22–1.98)] were more likely to have past-year OD

compared to college students (Table 1). NH blacks were

less likely than NH whites to have OD [aOR

0.60(0.41–0.89)], but there were no racial/ethnic differ-

ences in the prevalence of OD between NH whites and

those of other racial/ethnic groups. NMUPO users who

reported past-year psychological distress were more likely

to have OD than those with no psychological distress [aOR

3.05 (2.40–3.88), Table 1].

Among young adults with past-year nonmedical stimu-

lant use, a similar pattern with educational attainment was

seen. Past-year stimulant use disorder was more likely

among those with lower educational attainment [less than

high school education: 17.9 %, aOR 2.39 (1.35–4.12),

completed high school/GED: 14.0 %, aOR 1.75 (1.02-

3.02)] compared to their college-attending peers (Table 1).

However, among past-year nonmedical stimulant users,

NH Asians were more likely than whites to have developed

past-year stimulant use disorder [aOR 3.29 (1.17–9.24)],

and those living in a nonmetro county type were less likely

to develop past-year stimulant use disorder [aOR 0.54

(0.32–0.92)], compared to living in a large metro area.

Risk differences: educational attainment by gender

(Table 2)

For both males and females, having less than a high school

degree was associated with a greater risk of NMUPO use

compared to their college-attending counterparts (RD

3.4 %, p \ 0.001 for males, RD 1.5 %, p = 0.072 for females). While a greater risk difference was observed for

males, this association was not significantly different from

that of females (IC 2.0 %, p = 0.069). Further, the rela-

tionship between educational attainment and past-year OD

among PO users differed by gender. While the difference

between male college students and males with a high

school diploma/GED for past-year OD secondary to

NMUPO was negligible (RD 1.7 %, p = 0.445), females

with a high school diploma/GED had a much greater risk

compared to their college-attending counterparts (RD

8.2 %, p \ 0.001). In our test of additive interaction, the

716 Soc Psychiatry Psychiatr Epidemiol (2015) 50:713–724

123

T a

b le

1 P

as t-

y ea

r n

o n

m ed

ic al

p re

sc ri

p ti

o n

o p

io id

an d

st im

u la

n t

u se

an d

u se

d is

o rd

er am

o n

g y

o u

n g

ad u

lt s

ag ed

1 8

– 2

2 ,

b y

cu rr

en t

E d

u ca

ti o

n al

A tt

ai n

m en

t N

at io

n al

S u

rv ey

o n

D ru

g U

se an

d

H ea

lt h

(N S

D U

H ),

2 0

0 8

-2 0

1 0

d at

a

S o

ci o

d em

o g

ra p

h ic

ch ar

ac te

ri st

ic s

P as

t- y

ea r

n o

n m

ed ic

al p

re sc

ri p

ti o

n

o p

io id

u se

(N =

4 ,4

2 1

)

P as

t- y

ea r

n o

n m

ed ic

al p

re sc

ri p

ti o

n

st im

u la

n t

u se

(N =

1 ,3

2 0

)

P as

t- y

ea r

p re

sc ri

p ti

o n

o p

io d

u se

d is

o rd

er (N

= 6

8 2

)

P as

t- y

ea r

p re

sc ri

p ti

o n

st im

u la

n t

u se

d is

o rd

er (N

= 1

5 4

)

N a

% b

A O

R 9

5 %

C I

N a

% b

A O

R 9

5 %

C I

N a

% b

A O

R 9

5 %

C I

N a

% b

A O

R 9

5 %

C I

E d

u ca

ti o

n al

at ta

in m

en t

C o

ll eg

e st

u d

en ts

1 ,9

3 8

1 1

.3 1

.0 0

– 7

8 6

4 .8

1 .0

0 –

2 3

6 1

1 .7

1 .0

0 –

6 6

7 .4

1 –

C o

m p

le te

d h

ig h

sc h

o o

l/ G

E D

1 ,4

4 8

1 3

.1 1

.2 1

* *

1 .1

1 –

1 .3

3 3

1 0

3 .1

0 .6

5 *

* 0

.5 5

– 0

.7 7

2 4

4 1

7 .4

1 .5

5 *

1 .2

2 –

1 .9

8 4

8 1

4 .0

1 .7

5 *

1 .0

2 –

3 .0

2

L es

s th

an h

ig h

sc h

o o

l 1

,0 5

8 1

3 .2

1 .2

5 *

* 1

.1 2

– 1

.4 0

2 2

4 3

.0 0

.6 6

* *

0 .5

4 –

0 .8

0 2

0 4

1 9

.1 1

.7 5

* *

1 .3

5 –

2 .2

8 3

9 1

7 .9

2 .3

9 *

1 .3

5 –

4 .1

2

G en

d er

M al

es 2

,3 9

1 1

3 .3

1 .0

0 –

6 3

7 4

.1 1

.0 0

– 3

6 1

1 4

.7 1

.0 0

– 7

4 1

0 .7

1 .0

0 –

F em

al es

2 ,0

5 4

1 0

.9 0

.7 4

* *

0 .6

8 –

0 .8

1 6

8 3

3 .9

0 .8

5 *

0 .7

4 –

0 .9

8 3

2 3

1 5

.6 0

.8 7

0 .6

9 –

1 .1

0 7

9 1

0 .2

0 .7

4 0

.4 5

– 1

.2 0

R ac

e/ et

h n

ic it

y

W h

it e

3 ,1

6 8

1 5

.1 1

.0 0

– 1

,0 4

7 5

.4 1

.0 0

– 5

1 6

1 5

.7 1

.0 0

– 1

1 9

9 .7

1 .0

0 –

A fr

ic an

-A m

er ic

an 3

6 0

6 .7

0 .4

1 *

* 0

.3 4

– 0

.4 9

4 3

0 .7

0 .1

3 *

* 0

.0 8

– 0

.2 1

4 0

1 0

.8 0

.6 0

* 0

.4 1

– 0

.8 9

6 c

1 1

.0 0

.9 0

.2 6

– 3

.1 0

N at

iv e-

A m

er ic

an /H

aw ai

ia n

/P ac

ifi c

Is la

n d

er 1

1 9

1 4

.7 0

.9 2

0 .6

6 –

1 .2

8 3

2 4

.6 0

.9 3

0 .4

7 –

1 .8

4 1

9 1

5 .8

0 .8

1 0

.3 4

– 1

.9 3

1 c

7 .9

0 .5

0 .0

4 –

6 .3

2

A si

an 7

7 5

.8 0

.3 6

* *

0 .2

6 –

0 .4

9 2

6 1

.9 0

.3 1

* *

0 .1

8 –

0 .5

3 9

c 1

6 .0

1 .1

1 0

.4 2

– 2

.9 3

4 c

2 4

.3 3

.2 9

* 1

.1 7

– 9

.2 4

M o

re th

an 1

ra ce

n o

n -H

is p

an ic

1 6

4 1

2 .3

0 .7

4 0

.5 2

– 1

.0 6

3 4

4 .4

0 .7

6 0

.5 2

– 1

.0 9

2 2

9 .7

0 .5

7 0

.2 8

– 1

.1 3

6 c

9 .1

1 .1

4 0

.3 6

– 3

.5 9

H is

p an

ic 5

5 7

8 .1

0 .4

8 *

* 0

.4 1

– 0

.5 6

1 3

8 2

.4 0

.4 6

* *

0 .3

5 –

0 .6

0 7

8 1

4 .1

0 .8

6 0

.5 9

– 1

.2 6

1 7

1 3

.4 1

.1 3

0 .5

2 –

2 .4

6

M et

ro p

o li

ta n

st at

is ti

ca l

ar ea

L ar

g e

m et

ro 1

,7 9

1 1

1 .4

1 .0

0 –

5 4

3 3

.7 1

.0 0

– 2

6 3

1 4

.8 1

.0 0

– 6

6 1

1 .2

1 .0

0 –

S m

al l

m et

ro 1

,6 9

8 1

3 .4

1 .0

6 0

.9 3

– 1

.1 9

5 0

4 4

.4 1

.0 2

0 .8

6 –

1 .2

1 2

7 8

1 5

.9 1

.0 9

0 .8

3 –

1 .4

2 6

1 1

0 .9

0 .9

9 0

.5 9

– 1

.6 6

N o

n m

et ro

9 5

6 1

2 .4

0 .8

8 0

.7 6

– 1

.0 2

2 7

3 4

.1 0

.9 2

0 .7

3 –

1 .1

6 1

4 3

1 3

.9 0

.8 5

0 .6

1 –

1 .1

9 2

6 7

.3 0

.5 4

* 0

.3 2

– 0

.9 2

P as

t- y

ea r

se ri

o u

s p

sy ch

o lo

g ic

al d

is tr

es s

N o

3 ,1

1 0

1 0

.5 1

.0 0

– 8

8 4

3 .3

1 .0

0 –

3 2

8 1

0 .5

1 .0

0 –

6 1

6 .2

1 .0

0 –

Y es

1 ,3

3 5

1 9

.4 2

.0 9

* *

1 .8

9 –

2 .3

1 4

3 6

7 .0

2 .2

1 *

* 1

.8 1

– 2

.6 9

3 5

6 2

6 .0

3 .0

5 *

* 2

.4 0

– 3

.8 8

9 2

1 9

.1 3

.3 9

* *

1 .9

6 –

5 .8

8

S u

rv ey

y ea

r

2 0

0 8

1 ,5

3 7

1 2

.4 1

.0 0

– 4

0 4

3 .6

1 .0

0 –

2 3

9 1

4 .9

1 .0

0 –

5 2

1 1

.4 1

–

2 0

0 9

1 ,4

9 6

1 2

.4 1

.0 1

0 .9

0 –

1 .1

3 4

5 8

4 .2

1 .1

5 0

.9 6

– 1

.3 9

2 2

8 1

4 .6

1 .0

0 0

.7 8

– 1

.2 9

5 6

1 1

.4 0

.9 7

0 .5

8 –

1 .6

0

2 0

1 0

1 ,4

1 2

1 1

.8 0

.9 4

0 .8

1 –

1 .0

8 4

5 8

4 .1

1 .1

0 0

.9 1

– 1

.3 3

2 1

7 1

5 .7

1 .0

3 0

.7 7

– 1

.3 6

4 5

8 .7

0 .7

1 0

.4 0

– 1

.2 6

A O

R ad

ju st

ed fo

r g

en d

er ,

ra ce

/e th

n ic

it y

, co

u n

ty ty

p e,

an d

se ri

o u

s p

sy ch

o lo

g ic

al d

is tr

es s

* p

\ 0

.0 5

; *

* p \

0 .0

0 1

a B

as ed

o n

u n

w ei

g h

te d

d at

a b

B as

ed o

n w

ei g

h te

d d

at a

c S

m al

l ce

ll si

ze ,

fi n

d in

g sh

o u

ld b

e in

te rp

re te

d w

it h

ca u

ti o

n

Soc Psychiatry Psychiatr Epidemiol (2015) 50:713–724 717

123

risk of OD secondary to NMUPO associated with educa-

tional attainment did significantly differ by gender (IC

-6.5 %, p = 0.050). For past-year nonmedical stimulant

use, females with less than a high school degree were

significantly less likely to report past-year nonmedical

stimulant use than females with college education (RD

-2.2 %, p \ 0.001). There was less of an educational risk difference among males (RD -0.8 %, p = 0.080).

Table 2 Risk differences for nonmedical prescription opioid

and stimulant use and

nonmedical prescription opioid

and stimulant use disorders for

males and females by

educational attainment, and test

for differential effects

(difference in risk differences)

among young adults aged

18–22: National Survey on

Drug Use and Health,

2008-2010 data

Males

RD % (SE) p value

Reference

Females

RD % (SE) p value

IC % (SE) p value

Past-year nonmedical prescription opioid use

Educational attainment

College Reference Reference

High School Diploma/GED 1.6 (0.6 %) 0.011 2.4 (0.9 %) 0.007

-0.8 (1.1 %) 0.463

Less than HS degree 3.4 (0.8 %) \0.001 1.5 (0.8 %) 0.072 2.0 (1.1 %) 0.069

Past-year nonmedical prescription stimulant use

Educational attainment

College Reference Reference

High School Diploma/GED -1.5 (0.4 %) \0.001 -1.7 (0.5 %) \0.001 0.3 (0.6 %) 0.674

Less than HS Degree -0.8 (0.5 %) 0.080 -2.2 (0.5 %) \0.001 1.4 (0.7 %) 0.050

Past-year prescription opioid use disorder

Educational attainment

College Reference Reference

High School Diploma/GED 1.7 (2.2 %) 0.445 8.2 (2.2 %) \0.001 -6.5 (3.3 %) 0.050

Less than HS degree 6.6 (2.5 %) 0.009 7.3 (2.4 %) 0.003

-0.6 (3.5 %) 0.856

Past-year prescription stimulant use disorder

Educational attainment

College Reference Reference

High School Diploma/GED 1.6 (2.7 %) 0.549 8.3 (3.5 %) 0.020

-6.7 (3.6 %) 0.071

Less than HS Degree 5.1 (3.8 %) 0.181 12.3 (5.6 %) 0.034

-7.2 (7.2 %) 0.325

718 Soc Psychiatry Psychiatr Epidemiol (2015) 50:713–724

123

RD—adjusted for race/

ethnicity, annual family income,

county type, serious

psychological distress, and

survey year

IC—interaction contrast (in

italics)—represents difference

between risk differences for

females vs. males

Risk differences: educational attainment by race

(Table 3)

The relationship between educational attainment and

NMUPO was modified by race. Among NH whites, those

with lower educational attainment had a significantly

higher risk of NMUPO compared to college students

(completed high school/GED, RD: 2.7 %, p \ 0.001; less than a high school (HS) degree, RD: 3.0 %, p \ 0.001).

However, among Hispanics, NH blacks, NH more than one

race and Asians, there were no significant differences

between either of the lower educational attainment groups

and college students for risk of NMUPO (see Table 3). The

risk differences for Hispanic groups were significantly

different from those observed for their NH white counter-

parts (IC -3.2 %, p = 0.031, IC -3.0 %, p = 0.040, for

completed HS/GED and less than a HS degree, respec-

tively). Further, while the risk of NMUPO associated with

educational attainment was much more pronounced among

Native Americans/Pacific Islanders and Asians, these risk

differences were not significantly different from those

observed for their NH white counterparts. For past-year

OD among NMUPO, the risk associated with educational

attainment did not significantly differ by race.

For past-year nonmedical stimulant use, the risk differ-

ences between NH whites attending college compared to

NH whites with less than a high school diploma (RD

T a

b le

3 R

is k

d if

fe re

n ce

s fo

r n

o n

m ed

ic al

p re

sc ri

p ti

o n

o p

io id

an d

st im

u la

n t

u se

an d

n o

n m

ed ic

al p

re sc

ri p

ti o

n o

p io

id an

d st

im u

la n

t d

is o

rd er

s fo

r ra

ce b

y ed

u ca

ti o

n al

at ta

in m

en t,

an d

te st

fo r

d if

fe re

n ti

al ef

fe ct

s (d

if fe

re n

ce in

ri sk

d if

fe re

n ce

s) am

o n

g y

o u

n g

ad u

lt s

ag ed

1 8

– 2

2 :

N at

io n

al S

u rv

ey o

n D

ru g

U se

an d

H ea

lt h

, 2

0 0

8 -2

0 1

0 d

at a

N o

n -H

is p

an ic

W h

it es

R D

% (S

E )

p -v

al u

e

(r ef

er en

ce )

N o

n -H

is p

an ic

B la

ck s

R D

% (S

E )

p v

al u

e

IC %

(S E

) p

v al

u e

N at

iv e

A m

er ic

an /P

ac ifi

c

Is la

n d

er

R D

% (S

E )

p v

al u

e

IC %

(S E

) p

v al

u e

A si

an s

R D

% (S

E )

p v

al u

e

IC %

(S E

) p

v al

u e

N o

n -H

is p

an ic

: M

o re

th an

o n

e ra

ce

R D

% (S

E )

p v

al u

e

IC %

(S E

) p

v al

u e

H is

p an

ic s

R D

% (S

E )

p v

al u

e

IC %

(S E

) p

v al

u e

P as

t- y ea

r n o n m

ed ic

al p re

sc ri

p ti

o n

o p io

id u se

E d u ca

ti o n al

at ta

in m

en t

C o ll

eg e

R ef

er en

ce R

ef er

en ce

R ef

er en

ce R

ef er

en ce

R ef

er en

ce R

ef er

en ce

H ig

h S

ch o o l

D ip

lo m

a/ G

E D

2 .7

(0 .6

% ) \

.0 0 1

1 .0

(0 .9

% )

0 .2

6 8

- 1 .7

(1 .2

% )

0 .1

3 8

8 .7

(3 .3

% )

0 .0

1 0

6 .0

(3 .3

% )

0 .0

7 6

7 .1

(3 .8

% )

0 .0

6 8

4 .3

(3 .9

% )

0 .2

6 7

- 1 .8

(3 .8

% )

0 .6

3 5

- 4 .5

(3 .8

% )

0 .2

3 4

- 0 .4

(1 .4

% )

0 .7

5 9

- 3 .2

(1 .4

% )

0 .0

3 1

L es

s th

an H

S D

eg re

e 3 .0

(0 .9

% )

0 .0

0 1

2 .3

(1 .4

% )

0 .1

0 5

- 0 .7

(1 .8

% )

0 .7

1 2

4 .4

(4 .6

% )

0 .3

3 9

1 .4

(4 .6

% )

0 .7

5 5

5 .4

(3 .8

% )

0 .1

6 6

2 .4

(4 .0

% )

0 .5

4 8

- 0 .3

(3 .9

% )

0 .9

4 2

- 3 .2

(4 .0

% )

0 .4

1 9

- 0 .1

(1 .3

% )

0 .9

5 9

- 3 .0

(1 .4

% )

0 .0

4 0

P as

t- y ea

r n o n m

ed ic

al p re

sc ri

p ti

o n

st im

u la

n t

u se

E d u ca

ti o n al

at ta

in m

en t

C o ll

eg e

R ef

er en

ce R

ef er

en ce

R ef

er en

ce R

ef er

en ce

R ef

er en

ce R

ef er

en ce

H ig

h S

ch o o l

D ip

lo m

a/ G

E D

- 2 .4

(0 .4

% ) \

.0 0 1

- 0 .2

(0 .3

% )

0 .5

0 4

2 .2

(0 .6

% ) \

.0 0 1

- 0 .1

(2 .5

% )

0 .9

5 4

2 .2

(2 .6

% )

0 .3

8 8

- 1 .2

(0 .8

% )

0 .1

2 8

1 .2

(0 .9

% )

0 .1

7 4

- 5 .7

(1 .3

% ) \

.0 0 1

- 3 .3

(1 .3

% )

0 .0

1 6

0 .1

(0 .7

% )

0 .8

7 8

2 .5

(0 .8

% )

0 .0

0 4

L es

s th

an H

S D

eg re

e -

2 .2

(0 .5

% ) \

.0 0 1

- 0 .3

(0 .3

% )

0 .4

1 9

1 .9

(0 .6

% )

0 .0

0 2

5 .1

(4 .7

% )

0 .2

8 3

7 .3

(4 .8

% )

0 .1

3 1

1 .0

(2 .1

% )

0 .6

3 8

3 .2

(2 .1

% )

0 .1

3 4

- 4 .0

(1 .9

% )

0 .0

4 0

- 1 .8

(1 .9

% )

0 .3

5 8

- 0 .8

(0 .6

% )

0 .1

6 7

1 .4

(0 .6

% )

0 .0

3 1

P as

t- y ea

r p re

sc ri

p ti

o n

o p io

id u se

d is

o rd

er

E d u ca

ti o n al

at ta

in m

en t

C o ll

eg e

R ef

er en

ce R

ef er

en ce

R ef

er en

ce R

ef er

en ce

R ef

er en

ce R

ef er

en ce

H ig

h S

ch o o l

D ip

lo m

a/ G

E D

5 .3

(1 .6

% )

0 .0

0 1

6 .5

(4 .3

% )

0 .1

3 5

1 .2

(4 .4

% )

0 .7

8 3

- 2 .8

(1 2 .2

% )

0 .8

1 8

- 8 .1

(1 2 .2

% )

0 .5

1 1

4 .3

(1 8 .2

% )

0 .8

1 3

- 0 .9

(1 8 .5

% )

0 .9

6 0

1 4 .4

(7 .8

% )

0 .0

7 1

9 .1

(8 .0

% )

0 .2

6 1

3 .9

(4 .8

% )

0 .4

1 9

- 1 .3

(4 .9

% )

0 .7

8 6

L es

s th

an H

S D

eg re

e 7 .0

(2 .2

% )

0 .0

0 2

5 .5

(4 .9

% )

0 .2

6 4

- 1 .4

(5 .2

% )

0 .7

8 1

4 .0

(1 4 .5

% )

0 .7

8 3

- 2 .9

(1 4 .7

% )

0 .8

4 2

- 3 .0

(1 2 .5

% )

0 .8

1 2

- 1 0 .0

(1 2 .7

% )

0 .4

3 5

2 0 .9

(1 0 .1

% )

0 .0

4 2

1 4 .0

(1 0 .0

% )

0 .1

6 8

7 .2

(5 .0

% )

0 .1

5 6

0 .2

(6 .0

% )

0 .9

6 9

P as

t- y ea

r p re

sc ri

p ti

o n

st im

u la

n t

u se

d is

o rd

er

E d u ca

ti o n al

at ta

in m

en t

C o ll

eg e

R ef

er en

ce R

ef er

en ce

R ef

er en

ce R

ef er

en ce

R ef

er en

ce R

ef er

en ce

H ig

h S

ch o o l

D ip

lo m

a/ G

E D

4 .8

(2 .8

% )

0 .0

9 2

5 .8

(1 1 .1

% )

0 .6

0 4

0 .9

(1 1 .4

% )

0 .9

3 5

2 0 .1

(2 5 .4

% )

0 .4

1 7

1 5 .9

(2 5 .9

% )

0 .5

4 1

3 0 .2

(1 9 .2

% )

0 .1

2 1

2 5 .4

(1 8 .7

% )

0 .1

8 0

- 1 .4

(9 .1

% )

0 .8

7 7

- 6 .3

(9 .5

% )

0 .5

1 4

8 .2

(6 .3

% )

0 .2

0 0

3 .4

(6 .8

% )

0 .6

2 2

L es

s th

an H

S D

eg re

e 5 .9

(3 .1

% )

0 .0

6 1

1 0 .0

(1 4 .8

% )

0 .4

9 9

4 .1

(1 5 .0

% )

0 .7

8 5

2 0 .8

(2 9 .0

% )

0 .4

7 7

1 4 .9

(2 9 .2

% )

0 .6

1 3

2 6 .9

(2 3 .6

% )

0 .2

5 8

2 1 .0

(2 3 .4

% )

0 .3

8 3

1 6 .6

(1 7 .7

% )

0 .3

5 3

1 0 .7

(1 7 .4

% )

0 .5

4 2

2 2 .2

(1 2 .3

% )

0 .0

7 5

1 6 .3

(1 2 .6

% )

0 .2

0 3

R D

— ad

ju st

ed fo

r g

en d

er ,

co u

n ty

ty p

e, se

ri o

u s

p sy

ch o

lo g

ic al

d is

tr es

s, an

d su

rv ey

y ea

r

Soc Psychiatry Psychiatr Epidemiol (2015) 50:713–724 719

123

-2.2 %, p \ 0.001) and NH whites with a high school diploma/GED (RD -2.4, p \ 0.001), was significantly greater than the same educational attainment comparisons

among NH blacks (for less than high school degree, IC

1.9 %, p = 0.002; for at least a high school diploma/GED,

IC 2.2 %, p \ 0.001) and among Hispanics (for less than high school degree, IC 1.4 %, p = 0.031; for at least a high

school diploma/GED, IC 2.5 %, p = 0.004). For past-year

stimulant use disorder among past-year stimulant users, the

risk associated with educational attainment did not signif-

icantly differ by race.

Discussion

Findings from the study highlight the need to examine in

greater detail the determinants of NMPO and both pre-

scription stimulant and opioid disorders among young

adults who are not in college. Past-year prevalence rates of

NMUPO and OD secondary to NMUPO are higher in these

subpopulations than among college students. These find-

ings are in sharp contrast with the educational profile of

nonmedical stimulant users in this age group. Consistent

with the reports from other studies [37, 58, 59], past-year

prevalence of nonmedical prescription stimulant use is

higher among college-attending young adults than among

those who do not attend college. On the other hand, similar

to our prescription opioid disorders findings, prescription

stimulant disorders were more prevalent among nonmedi-

cal prescription stimulant users who were non-college-

attending young adults as compared to their college-

attending peers. These findings are in line with several

other studies (with a focus on other substances) suggesting

that individuals with lower levels of educational attainment

are at high risk of developing drug use disorders [32, 34,

60]. It is important to note that over 40 % of the non-

medical PO and stimulant users identified in the National

Epidemiologic Survey on Alcohol and Related Conditions

(NESARC) data who initiated use of these drugs at

18 years of age or younger went on to develop prescription

opioid and stimulant disorders [61]. Previous studies have

already shown that users with more years of formal edu-

cation tend to mature out of using drugs and may have

more resources to seek help and reestablish their life again

after becoming drug-involved, while that is often not the

case among populations with fewer years of formal edu-

cation [62, 63].

Despite the fact that women were less likely than men to

be past-year NMUPO, they were equally likely as men to

have OD secondary to NMUPO. These findings are con-

sistent with findings from general population studies [40].

Interestingly, the relationship between educational attain-

ment and OD among NMUPO was modified by gender. An

important finding of this study is that among NMPO users,

women with less years of formal education are at signifi-

cantly higher odds to progress to OD secondary to

NMUPO, but this same risk was not observed in males.

There was only weak evidence of a gender and educational

attainment interaction among stimulant users. Prevention

messages targeting women aged 18-22 who have high

school degrees but are not attending college and using POs

are needed to prevent escalation to OD.

Also noteworthy is that among NH whites, having lower

educational attainment was strongly associated with

increased risk of NMUPO. However, among Hispanic

young adults, all educational groups had similar low risk

for NMUPO. That is, having a college education protects

against NMUPO among NH whites but not among His-

panics. This is consistent with prior studies that show that

NMUPO is more prevalent in rural regions of the US with a

large proportion of NH whites where young adults have

lower educational attainment and fewer returns on aca-

demic investment [64–67]. Associations between educa-

tional attainment and disorders among past-year users of

either nonmedical prescription opiate or stimulants did not

significantly differ by race.

This study shows that, at least among young adults aged

18–22, the PO epidemic is not simply a phenomena

occurring among NH whites in the US. While in this age

group minorities seem to be less likely to use POs non-

medically than NH whites, past-year prevalence of OD

among users is similar among Hispanics, Native Ameri-

cans, Asians, NH of more than one race and NH whites.

There is evidence from other studies that young adults from

NH white racial/ethnic groups could be particularly vul-

nerable to the consequences of having an OD. Data from

national studies and from a sample of Midwestern college

students indicate that Hispanics and NH whites are more

likely to engage in NMUPO than NH blacks, and they are

more likely to be recent-onset opioid users than NH blacks

[43, 68–70]. Patterns of persistent NMUPO use among

Hispanics may be more severe than among NH whites: an

analysis of NSDUH 2002-2003 data showed that Hispanics

who recently began using PO nonmedically were almost

two times more likely to persist using these drugs com-

pared with NH whites [46]. Finally, recent urban data on

PO overdose mortality point to an increasing risk among

Hispanics: while the rate of unintentional PO poisoning

mortality increased 6 % among NH blacks and 8 % among

NH whites in New York City in 2005–2009, the rate

increased 75 % among Hispanics in the same time period

[71].

These findings have some strong implications as there

are few NMUPO prevention programs tailored for young

adults with less years of formal education—most pre-

scription drug use prevention messages are targeted

720 Soc Psychiatry Psychiatr Epidemiol (2015) 50:713–724

123

towards college students [30]. As such, prevention pro-

grams are also needed for non-college-attending young

adults, not only at the media level, but also in workplaces

and other sites that young adult congregate. One of the few

prevention programs designed to prevent nonmedical use

of prescription drugs is a web-based workplace program,

the SmartRx [72] that has been tested among working

women (mean age 44 years), but not specifically among

large diverse samples of young adults. The program pro-

vides the pharmaceutical properties of the medications,

instructions on the safe administration of the medications,

and alternatives to medications with suggestions on ways

to enhance users’ health and well-being [72]. Secondary

prevention efforts should target non-college-attending

young adults to prevent the transition from nonmedical use

to disorder among young adults who are already nonmed-

ical prescription opioid and prescription stimulant users.

Limitations are noted. While large epidemiologic data-

sets are useful for examining factors associated with non-

medical prescription opioid use and prescription opioid

disorder, we can at most infer associations using the cross-

sectional design of this study. The surveys were based on

self-report, but the use of computerized reporting system

minimizes the impact of social desirability bias on

reporting [73]. NSDUH data do not distinguish whether

respondents were attending 2-year (community colleges) or

4-year colleges, which could potentially influence findings

and need to be further investigated in future studies. In

addition, reasons for males do not attend college versus

reasons for females not to attend college might be different

[74], and these differences might be associated with the

development of nonmedical prescription drug use and

disorders. Also, we could not distinguish whether these

nonmedical prescription opioid users first started using

these drugs when legitimately prescribed (e.g., pain relief)

or when obtained illegally (e.g., to get high); such data

were unavailable in the NSDUH. Moreover, another limi-

tation of the NSDUH data is the fact that motives for use

are not included in the questionnaire [75–77]. In addition,

the lack of detailed data on psychiatric diagnosis is a

limitation of the NSDUH data, as the K-6 scale is a proxy

for psychological distress and does not reflect psychiatric

diagnoses per se. Gathering such data in future studies will

help us understand the profiles of these users, which may

be distinct. Small cell sizes for some racial/ethnic groups,

particularly when examining disorders, are a limitation.

However, this study has also had several substantial

strengths, including the rigorous NSDUH research design

and data collection methods, large sample size and gener-

alizability to the US young adult household population.

In conclusion, this study illustrates that young adults

who do not attend college are at particularly high risk for

nonmedical prescription opioid use and disorder. In

contrast, the nonmedical use of prescription stimulants is

higher among college-educated young adults. The influ-

ences of gender and race on these associations are impor-

tant to consider. Higher education may be a protective

factor for some race/ethnic groups but not for others. There

is a need for young adult prevention and intervention

programs to target nonmedical prescription drug use

beyond college campuses.

Acknowledgements Dr. Martins is currently a consultant for Pur- due Pharma. All other authors have no conflict of interest to declare.

The data reported herein come from the National Survey of Drug Use

and Health (NSDUH) public use files and made publicly available by

the Substance Abuse and Mental Health Services Administration

(SAMHSA). This study was partially funded by the National Institute

of Drug Abuse-National Institutes of Health (NIDA-NIH grant

DA023434, Martins; NIDA-NIH grant K01DA030449, Cerdá; NIDA-

NIH T32DA031099, Hasin), the Eunice Kennedy Shriver National

Institute of Child and Human Development- National Institutes of

Health, (NICHD- NIH grant HD020667, Martins); and the National

institute on Alcohol and Alcoholism, National Institutes of Health

(NIAAA grant K01AA021511, Keyes). NIDA, NICHD and SAM-

HSA had no further role in the data analysis or interpretation of

results.

References

1. Office of National Drug Control Policy (2011) Epidemic:

Responding to America’s Prescription Drug Abuse Crisis.

Retrieved on January 25, 2014 from: http://www.whitehouse.gov/

sites/default/files/ondcp/issues-content/prescription-drugs/rx_

abuse_plan.pdf

2. Miech R, Bohnert A, Heard K, Boardman J (2013) Increasing use

of nonmedical analgesics among younger cohorts in the United

States: a birth cohort effect. J Adolesc Health 52(1):35–41.

doi:10.1016/j.jadohealth.2012.07.016

3. Boyd CJ, Esteban McCabe S, Teter CJ (2006) Medical and

nonmedical use of prescription pain medication by youth in a

Detroit-area public school district. Drug Alcohol Depend

81(1):37–45. doi:10.1016/j.drugalcdep.2005.05.017

4. McCabe SE, West BT, Wechsler H (2007) Trends and college-

level characteristics associated with the non-medical use of

prescription drugs among US college students from 1993 to

2001. Addiction 102(3):455–465. doi:10.1111/j.1360-0443.2006.

01733.x

5. McCabe SE, Knight JR, Teter CJ, Wechsler H (2005) Non-

medical use of prescription stimulants among US college stu-

dents: prevalence and correlates from a national survey. Addic-

tion 100(1):96–106. doi:10.1111/j.1360-0443.2005.00944.x

6. McCabe SE, Teter CJ (2007) Drug use related problems among

nonmedical users of prescription stimulants: a web-based survey

of college students from a Midwestern university. Drug Alcohol

Depend 91(1):69–76. doi:10.1016/j.drugalcdep.2007.05.010

7. McCabe SE, West BT, Teter CJ, Boyd CJ (2014) Trends in

medical use, diversion, and nonmedical use of prescription

medications among college students from 2003 to 2013: con-

necting the dots. Addict Behav 39(7):1176–1182. doi:10.1016/j.

addbeh.2014.03.008

8. Substance Abuse and Mental Health Services Administration

(2011) Results from the 2010 National Survey on Drug Use and

Health: summary of national findings, NSDUH series H-41, HHS

PUBLICATION No. (SMA) 11-4658 Substance Abuse and

Soc Psychiatry Psychiatr Epidemiol (2015) 50:713–724 721

123

Mental Health Services Administration, Office of Applied Stud-

ies, Rockville

9. Fortuna RJ, Robbins BW, Caiola E, Joynt M, Halterman JS

(2010) Prescribing of controlled medications to adolescents and

young adults in the United States. Pediatrics 126(6):1108–1116.

doi:10.1542/peds.2010-0791

10. Stone AL, Becker LG, Huber AM, Catalano RF (2012) Review of

risk and protective factors of substance use and problem use in

emerging adulthood. Addict Behav 37(7):747–775. doi:10.1016/j.

addbeh.2012.02.014

11. Lankenau SE, Schrager SM, Silva K, Kecojevic A, Bloom JJ,

Wong C, Iverson E (2012) Misuse of prescription and illicit drugs

among high-risk young adults in Los Angeles and New York.

J Public Health Res 1(1):22–30

12. Bruneau J, Roy E, Arruda N, Zang G, Jutras-Aswad D (2012) The

rising prevalence of prescription opioid injection and its associ-

ation with hepatitis C incidence among street-drug users.

Addiction 107(7):1318–1327. doi:10.1111/j.1360-0443.2012.

03803.x

13. Silva K, Schrager SM, Kecojevic A, Lankenau SE (2013) Factors

associated with history of non-fatal overdose among young

nonmedical users of prescription drugs. Drug Alcohol Depend

128(1–2):104–110. doi:10.1016/j.drugalcdep.2012.08.014

14. Roy E, Arruda N, Bourgois P (2011) The growing popularity of

prescription opioid injection in downtown Montreal: new chal-

lenges for harm reduction. Subst Use Misuse 46(9):1142–1150.

doi:10.3109/10826084.2011.552932

15. Lankenau SE, Teti M, Silva K, Bloom JJ, Harocopos A, Treese M

(2012) Patterns of prescription drug misuse among young injec-

tion drug users. J Urban Health 89(6):1004–1016. doi:10.1007/

s11524-012-9691-9

16. Lankenau SE, Teti M, Silva K, Jackson Bloom J, Harocopos A,

Treese M (2012) Initiation into prescription opioid misuse

amongst young injection drug users. Int J Drug Policy

23(1):37–44. doi:10.1016/j.drugpo.2011.05.014

17. White A, Hingson R (2013) The burden of alcohol use: excessive

alcohol consumption and related consequences among college

students. Alcohol Res 35(2):201–218

18. Kypri K, Vater T, Bowe SJ, Saunders JB, Cunningham JA,

Horton NJ, McCambridge J (2014) Web-based alcohol screening

and brief intervention for university students: a randomized trial.

JAMA 311(12):1218–1224. doi:10.1001/jama.2014.2138

19. Weyandt LL, Marraccini ME, Gudmundsdottir BG, Zavras BM,

Turcotte KD, Munro BA, Amoroso AJ (2013) Misuse of pre-

scription stimulants among college students: a review of the lit-

erature and implications for morphological and cognitive effects

on brain functioning. Exp Clin Psychopharmacol 21(5):385–407.

doi:10.1037/a0034013

20. Garnier-Dykstra LM, Caldeira KM, Vincent KB, O’Grady KE,

Arria AM (2012) Nonmedical use of prescription stimulants

during college: four-year trends in exposure opportunity, use,

motives, and sources. J Am Coll Health 60(3):226–234. doi:10.

1080/07448481.2011.589876

21. Hartung CM, Canu WH, Cleveland CS, Lefler EK, Mignogna MJ,

Fedele DA, Correia CJ, Leffingwell TR, Clapp JD (2013) Stim-

ulant medication use in college students: comparison of appro-

priate users, misusers, and nonusers. Psychol Addict Behav

27(3):832–840. doi:10.1037/a0033822

22. DeSantis AD, Webb EM, Noar SM (2008) Illicit use of pre-

scription ADHD medications on a college campus: a multi-

methodological approach. J Am Coll Health 57(3):315–324.

doi:10.3200/jach.57.3.315-324

23. Arria AM, Caldeira KM, Vincent KB, O’Grady KE, Wish ED

(2008) Perceived harmfulness predicts nonmedical use of pre-

scription drugs among college students: interactions with

sensation-seeking. Prev Sci 9(3):191–201. doi:10.1007/s11121-

008-0095-8

24. McCabe SE, Teter CJ, Boyd CJ, Knight JR, Wechsler H (2005)

Nonmedical use of prescription opioids among U.S. college stu-

dents: prevalence and correlates from a national survey. Addict

Behav 30(4):789–805. doi:10.1016/j.addbeh.2004.08.024

25. Jekielek S, Brown B (2005) The transition to adulthood: Char-

acteristics of young adults ages 18 to 24 in America. Population

Reference Bureau, and Child Trends, 1-33. Retrieved May 25,

2011 from: http://www.prb.org/pdf05/TransitionToAdulthood.

pdf

26. Aud S, Hussar W, Kena G, Bianco K, Frohlich L, Kemp J, Tahan

K (2011) The condition of education 2011 (NCES 2011-033).

National Center for Education Statistics. U.S Government

Printing Office, Washington

27. Heckman JJ, Lafontaine PA (2010) The American high school

graduation rate: trends and levels. Rev Econ Stat 92(2):244–262.

doi:10.1162/rest.2010.12366

28. Digest of Education Statistics (2010) Chapter 3: postsecondary

education. Retrieved Feb 20 2012 from http://nces.ed.gov/pro

grams/digest/d10/ch_3.asp

29. Snyder TD, Dillow SA (2012) Digest of Education Statistics 2011

(NCES 2012-001). Institute of Education Sciences. U.S.

Department of Education, Washington

30. Dennhardt AA, Murphy JG (2013) Prevention and treatment of

college student drug use: a review of the literature. Addict Behav

38(10):2607–2618. doi:10.1016/j.addbeh.2013.06.006

31. Bergen SE, Gardner CO, Aggen SH, Kendler KS (2008) Socio-

economic status and social support following illicit drug use:

causal pathways or common liability? Twin Res Hum Genet

11(3):266–274. doi:10.1375/twin.11.3.266

32. O’Brien MS, Anthony JC (2005) Risk of becoming cocaine

dependent: epidemiological estimates for the United States,

2000-2001. Neuropsychopharmacology 30(5):1006–1018. doi:10.

1038/sj.npp.1300681

33. Compton WM, Thomas YF, Stinson FS, Grant BF (2007) Prev-

alence, correlates, disability, and comorbidity of DSM-IV drug

abuse and dependence in the United States: results from the

national epidemiologic survey on alcohol and related conditions.

Arch Gen Psychiatry 64(5):566–576. doi:10.1001/archpsyc.64.5.

566

34. Grant BF (1996) Prevalence and correlates of drug use and DSM-

IV drug dependence in the United States: results of the National

Longitudinal Alcohol Epidemiologic Survey. J Subst Abuse

8(2):195–210

35. Substance Abuse and Mental Health Services Administration

(2010) Results from the 2009 National Survey on Drug Use and

Health: volume I. Summary of National Findings. NSDUH Series

H-38A, HHS Publication No. SMA 10-4856. Substance Abuse

and Mental Health Services Administration, Office of Applied

Studies, Rockville

36. Lord S, Downs G, Furtaw P, Chaudhuri A, Silverstein A, Gam-

maitoni A (2003) Budman S (2009) Nonmedical use of pre-

scription opioids and stimulants among student pharmacists.

J Am Pharm Assoc 49(4):519–528. doi:10.1331/JAPhA.2009.

08027

37. Johnston LD, O’Malley PM, Bachman JG, Schulenberg JE

(2013) Monitoring the future national survey results on drug use,

1975–2012: volume 2, college students and adults ages 19–50.

Ann Arbor

38. Herman-Stahl MA, Krebs CP, Kroutil LA, Heller DC (2007) Risk

and protective factors for methamphetamine use and nonmedical

use of prescription stimulants among young adults aged 18 to 25.

Addict Behav 32(5):1003–1015. doi:10.1016/j.addbeh.2006.07.

010

722 Soc Psychiatry Psychiatr Epidemiol (2015) 50:713–724

123

39. Daniulaityte R, Falck RS, Wang J, Carlson RG (2009) Illicit use

of pharmaceutical opioids among young polydrug users in Ohio.

Addict Behav 34(8):649–653. doi:10.1016/j.addbeh.2009.03.037

40. Becker WC, LE Sullivan, Tetrault JM, Desai RA, Fiellin DA

(2008) Non-medical use, abuse and dependence on prescription

opioids among U.S. adults: Psychiatric, medical and substance

use correlates. Drug and Alcohol Depend 94(1-3):38–47

41. McCabe SE (2008) Screening for drug abuse among medical and

nonmedical users of prescription drugs in a probability sample of

college students. Arch Pediatr Adolesc Med 162(3):225–231.

doi:10.1001/archpediatrics.2007.41

42. Sullivan MD, Edlund MJ, Steffick D, Unutzer J (2005) Regular

use of prescribed opioids: association with common psychiatric

disorders. Pain 119(1–3):95–103. doi:10.1016/j.pain.2005.09.

020

43. Martins S, Storrs C, Zhu H, Chilcoat H (2009)Trends in recent-

onset extramedical opioid analgesic use in the US from 1991 to

2006. In: 70th college on problems of drug dependence annual

meeting, San Juan, Puerto Rico

44. Martins SS, Fenton MC, Keyes KM, Blanco C, Zhu H, Storr CL

(2012) Mood and anxiety disorders and their association with

non-medical prescription opioid use and prescription opioid-use

disorder: longitudinal evidence from the National Epidemiologic

Study on Alcohol and Related Conditions. Psychol Med

42(6):1261–1272. doi:10.1017/s0033291711002145

45. Fischer B, Lusted A, Roerecke M, Taylor B, Rehm J (2012) The

prevalence of mental health and pain symptoms in general pop-

ulation samples reporting nonmedical use of prescription opioids:

a systematic review and meta-analysis. J Pain 13(11):1029–1044.

doi:10.1016/j.jpain.2012.07.013

46. Dowling K, Storr CL, Chilcoat HD (2006) Potential influences on

initiation and persistence of extramedical prescription pain

reliever use in the US population. Clin J Pain 22(9):776–783.

doi:10.1097/01.ajp.0000210926.41406.2c

47. Katz C, El-Gabalawy R, Keyes KM, Martins SS, Sareen J (2013)

Risk factors for incident nonmedical prescription opioid use and

abuse and dependence: results from a longitudinal nationally

representative sample. Drug Alcohol Depend 132(1–2):107–113.

doi:10.1016/j.drugalcdep.2013.01.010

48. McCabe SE, Cranford JA (2012) Motivational subtypes of non-

medical use of prescription medications: results from a national

study. J Adolesc Health 51(5):445–452. doi:10.1016/j.jadohealth.

2012.02.004

49. Substance Abuse and Mental Health Services Administration

(2009) Results from the 2008 National Survey on Drug Use and

Health: national findings. NSDUH Series H-36, HHS Publication

No. SMA 09-4434. Substance Abuse and Mental Health Services

Administration, Office of Applied Studies, Rockville

50. National Survey on Drug Use and Health (2010) 2008-2010

National Survey on Drug Use and Health: overview and summary

of substate region estimation methodology. Retrieved from:

http://www.samhsa.gov/data/NSDUH/substate2k10/Methodology/

NSDUHsubstateMethodology2010.pdf

51. Gfroerer J, Eyerman J, Chromy J (2002) Redesigning an ongoing

national household survey: Methodological issues Substance

Abuse and Mental Health Services Administration, Office of

Applied Studies., Rockville, MD

52. Jordan BK, Karg RS, Batts KR, Epstein JF, Wiesen C (2008) A

clinical validation of the National Survey on Drug Use and

Health assessment of substance use disorders. Addict Behav

33(6):782–798. doi:10.1016/j.addbeh.2007.12.007

53. American Psychiatric Association (2000) Diagnostic and statis- ®tical manual of mental disorders, Fourth Edition: DSM-IV-TR .

American Psychiatric Association

54. Furukawa TA, Kessler RC, Slade T, Andrews G (2003) The

performance of the K6 and K10 screening scales for

psychological distress in the Australian National Survey of

Mental Health and Well-Being. Psychol Med 33(2):357–362

55. StataCorp (2009) Stata statistical software: release 11. College

station, StataCorp LP, TX

56. Rothman KJ, Greenland S, Lash TL (2012) Modern epidemiol-

ogy. Wolters Kluwer Health/Lippincott Williams & Wilkins

57. Research Triangle Institute (2005) SUDAAN: professional soft-

ware for survey data analysis

58. Johnston LD, O’Malley, P.M., Bachman, J.G., (2003) Monitoring

the Future national survey results on drug use, 1975-2002. Vol-

ume 1: Secondary school students (NIH Publication No.

03-5375). Bethesda, MD: National Institute on Drug Abuse

59. Substance Abuse and Mental Health Services Administration ®(2009) The NSDUH Report: Nonmedical Use of of Adderall

among Full-Time College Students. Substance Abuse and Mental

Health Services Administration, Rockville

60. Martins SS, Ko J, Kuwabara S, Clarke D, Alexandre P, Zandi P,

Mendelson T, Mortensen PBO, Eaton WW (2012) The relation-

ship of adult mental disorders to socioeconomic status, race/

ethnicity, marital status, and urbanicity of residence. In: Eaton

WW (ed) Public Mental Health. Oxford University Press, Oxford,

p 151

61. McCabe SE, West BT, Morales M, Cranford JA, Boyd CJ (2007)

Does early onset of non-medical use of prescription drugs predict

subsequent prescription drug abuse and dependence? Results

from a national study. Addiction 102(12):1920–1930. doi:10.

1111/j.1360-0443.2007.02015.x

62. Miech R, Chilcoat H (2007) The formation of a socioeconomic

disparity: a case study of cocaine and marijuana use in the 1990s.

Am J Prev Med 32(6 Suppl):S171–S176. doi:10.1016/j.amepre.

2007.02.021

63. Miech R (2008) The formation of a socioeconomic health dis-

parity: the case of cocaine use during the 1980s and 1990s.

J Health Soc Behav 49(3):352–366

64. Keyes KM, Cerda M, Brady JE, Havens JR, Galea S (2014)

Understanding the rural-urban differences in nonmedical pre-

scription opioid use and abuse in the United States. Am J Public

Health 104(2):e52–e59. doi:10.2105/ajph.2013.301709

65. Blackwell DL, McLaughlin DK (1999) Do Rural Youth Attain

Their Educational Goals? Rural Development Perspectives

13(3):37–44

66. Roscigno VJ, Crowle ML (2001) Rurality, institutional disad-

vantage, and achievement/attainment*. Rural Sociology

66(2):268–292. doi:10.1111/j.1549-0831.2001.tb00067.x

67. Young AM, Havens JR, Leukefeld CG (2012) A comparison of

rural and urban nonmedical prescription opioid users’ lifetime

and recent drug use. Am J Drug Alcohol Abuse 38(3):220–227.

doi:10.3109/00952990.2011.643971

68. Blanco C, Alderson D, Ogburn E, Grant BF, Nunes EV, Hat-

zenbuehler ML, Hasin DS (2007) Changes in the prevalence of

non-medical prescription drug use and drug use disorders in the

United States: 1991-1992 and 2001-2002. Drug Alcohol Depend

90(2–3):252–260. doi:10.1016/j.drugalcdep.2007.04.005

69. McCabe SE, Morales M, Cranford JA, Delva J, McPherson MD,

Boyd CJ (2007) Race/ethnicity and gender differences in drug use

and abuse among college students. J Ethn Subst Abuse

6(2):75–95. doi:10.1300/J233v06n02_06

70. Substance Abuse and Mental Health Services Administration

(2004) Results from the 2008 National Survey on Drug Use and

Health: National Findings. NSDUH Series H-28. DHHS Pub-

lication No. SMA 05-4062. Substance Abuse and Mental

Health Services Administration, Office of Applied Studies,

Rockville

71. New York City Department of Health and Mental Hygiene (2011)

Opioid analgesics in New York City: Misuse, morbidity and

mortality update. Data tables. New York, NY2011

Soc Psychiatry Psychiatr Epidemiol (2015) 50:713–724 723

123

72. Deitz DK, Cook RF, Hendrickson A (2011) Preventing pre-

scription drug misuse: field test of the SmartRx Web program.

Subst Use Misuse 46(5):678–686. doi:10.3109/10826084.2010.

528124

73. Turner CF, Ku L, Rogers SM, Lindberg LD, Pleck JH, Sonenstein

FL (1998) Adolescent sexual behavior, drug use, and violence:

increased reporting with computer survey technology. Science

280(5365):867

74. Ross T, Kena G, Rathbun A, KewalRamani A, Zhang J,

Kristapovich P, Manning E (2012) Higher education: gaps in

access and persistence study (NCES 2012-046). Government

Printing Office, Washington

75. McCabe SE, Boyd CJ, Cranford JA, Teter CJ (2009) Motives for

nonmedical use of prescription opioids among high school

seniors in the United States: self-treatment and beyond. Arch

Pediatr Adolesc Med 163(8):739–744. doi:10.1001/arch

pediatrics.2009.120

76. McCabe SE, Cranford JA, Boyd CJ, Teter CJ (2007) Motives,

diversion and routes of administration associated with nonmedi-

cal use of prescription opioids. Addict Behav 32(3):562–575.

doi:10.1016/j.addbeh.2006.05.022

77. Ghandour LA, El Sayed DS, Martins SS (2012) Prevalence and

patterns of commonly abused psychoactive prescription drugs in

a sample of university students from Lebanon: an opportunity for

cross-cultural comparisons. Drug Alcohol Depend

121(1–2):110–117. doi:10.1016/j.drugalcdep.2011.08.021

724 Soc Psychiatry Psychiatr Epidemiol (2015) 50:713–724

123