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: [email protected]
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
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.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.
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- Nonmedical prescription drug use among US young adults by educational attainment
- Abstract
- Purpose
- Methods
- Results
- Conclusions
- Introduction
- Materials and methods
- Study sample and measures
- Outcome variables: nonmedical prescription opioid and stimulant use and disorders secondary to use
- Primary exposure variable: educational attainment
- Demographic covariates
- Past-year serious psychological distress
- Statistical analyses
- Results
- Estimated past-year prevalence by educational attainment (Table 1)
- Risk differences: educational attainment by gender (Table 2)
- Risk differences: educational attainment by race (Table 3)
- Discussion
- Acknowledgements
- References