Two studies published yesterday point to predictors of teens using drugs. Researchers in one study found that teens who used opioids when they were not prescribed for medical reasons were more likely to later use heroin. Of the nearly 3,300 high schoolers who were included in the study, those who previously used opioids were about 11% more likely to use heroin, while those who currently used opioids were about 13% more likely to use heroin. Another study found that there was no increase in teenagers using marijuana if they lived in a state that had laws legalizing the drug. In fact, in states with recreational marijuana laws, the odds of teen marijuana use were about 10% less following legalization.

1. Association of Nonmedical Prescription Opioid Use
With Subsequent Heroin Use Initiation in Adolescents
Lorraine I. Kelley-Quon, MD, MSHS; Junhan Cho, PhD; David R. Strong, PhD; Richard A. Miech, PhD;
Jessica L. Barrington-Trimis, PhD; Afton Kechter, MS; Adam M. Leventhal, PhD
IMPORTANCE There is concern that nonmedical prescription opioid use is associated with an
increased risk of later heroin use initiation in adolescents, but to our knowledge, longitudinal
data addressing this topic are lacking.
OBJECTIVE To determine whether nonmedical prescription opioid use is associated with
subsequent initiation of heroin use in adolescents.
DESIGN, SETTING, AND PARTICIPANTS This prospective longitudinal cohort study conducted in
10 high schools in Los Angeles, California, administered 8 semiannual surveys from 9th through
12th grade that assessed nonmedical prescription opioid use, heroin use, and other factors from
October 2013 to July 2017. Students were baseline never users of heroin recruited through
convenience sampling. Cox regression models tested nonmedical prescription opioid use
statuses at survey waves 1 through 7 as a time-varying and time-lagged regressor and
subsequent heroin use initiation across waves 2 to 8 as the outcome.
EXPOSURES Self-reported nonmedical prescription opioid use (past 30-day [current] use vs past
6-month[prior]usewithoutpast30-dayusevsnopast6-monthuse)ateachwavefrom1to7.
MAIN OUTCOMES AND MEASURES Self-reported heroin use initiation (yes/no) during
waves 2 to 8.
RESULTS Of 3298 participants, 1775 (53.9%) were adolescent girls, 1563 (48.3%) were
Hispanic, 548 (17.0%) were Asian, 155 (4.8%) were African American, 529 (16.4%) were
non-Hispanic white, and 220 (6.8%) were multiracial. Among baseline never users of heroin
in ninth grade with valid data (3298 [97% of cohort enrollees]; mean [SD] age, 14.6 [0.4]
years), the number of individuals with outcome data available at each follow-up ranged from
2987 (90.6%) to 3200 (97.0%). The mean per-wave prevalence of prior and current
nonmedical prescription opioid use from waves 1 to 7 was 1.9% and 2.7%, respectively.
Seventy students (2.1%) initiated heroin use during waves 2 to 8. Prior vs no (hazard ratio,
3.59; 95% CI, 2.14-6.01; P < .001) and current vs no (hazard ratio, 4.37; 95% CI, 2.80-6.81;
P < .001) nonmedical prescription opioid use were positively associated with subsequent
heroin use initiation. For no, prior, and current nonmedical prescription opioid use statuses at
waves 1 to 7, the estimated cumulative probabilities of subsequent heroin use initiation by
wave 8 (42-month follow-up) were 1.7%, 10.7%, and 13.1%, respectively. In covariate-adjusted
models, associations were attenuated but remained statistically significant and current
nonmedical prescription opioid use risk estimates were stronger than corresponding
associations of nonopioid substance use with subsequent heroin use initiation.
CONCLUSIONS AND RELEVANCE Nonmedical prescription opioid use was prospectively
associated with subsequent heroin use initiation during 4 years of adolescence among Los
Angeles youth. Further research is needed to understand whether this association is causal.
JAMA Pediatr. doi:10.1001/jamapediatrics.2019.1750
Published online July 8, 2019.
Supplemental content
Author Affiliations: Division of
Pediatric Surgery, Children's Hospital
Los Angeles, Los Angeles, California
(Kelley-Quon); Department of
Preventive Medicine, University of
Southern California, Los Angeles
(Kelley-Quon, Cho, Barrington-Trimis,
Kechter, Leventhal); Department of
Surgery, Keck School of Medicine of
the University of Southern California,
Los Angeles (Kelley-Quon);
Department of Family Medicine and
Public Health, University of California,
San Diego School of Medicine,
La Jolla (Strong); Institute for Social
Research, University of Michigan,
Ann Arbor (Miech).
Corresponding Author: Adam M.
Leventhal, PhD, Keck School of
Medicine, University of Southern
California, 2250 Alcazar St,
Los Angeles, CA 90033
(adam.leventhal@usc.edu).
Research
JAMA Pediatrics | Original Investigation
(Reprinted) E1
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2. M
any adolescents access prescription opioids from
friends or relatives for nonmedical reasons.1-3
Con-
cern is heightened if adolescent nonmedical pre-
scription opioid use is associated with an increased risk of
subsequently initiating heroin, a drug with substantial addic-
tion potential that poses extensive medical, psychological,
social, and legal consequences.4-7
Prescriptionopioidsandheroinshareneuropharmacologi-
cal actions through a stimulation of endogenous opioid
receptors and the activation of the brain’s reward circuit.8,9
Experiencing the euphoric effects of nonmedical prescription
opioid use could be associated with an increased inclination
for youths to try other opioid drugs, including heroin. Cross-
sectional analyses of adolescents’ retrospective reports indi-
cateanassociationbetweenpriornonmedicalprescriptionopi-
oid use and later heroin use initiation.10-12
As cross-sectional
researchislimited,theabsenceoflongitudinal,prospectivedata
on this topic is an important gap in the literature. This prospec-
tive longitudinal cohort study estimated the association be-
tween nonmedical prescription opioid use and subsequent
heroin use initiation during 42 months of follow-up in high
school students in Los Angeles, California.
Methods
Participants and Procedures
Data were drawn from a longitudinal cohort survey of behav-
ioral health that included students from 10 Los Angeles–area
suburban and urban high schools recruited by convenience
sampling and described previously.13
Approximately 40 pub-
lic high schools in the Los Angeles metropolitan area were
approached for participation. Schools were chosen because
of their diverse demographic characteristics and proximity.
Ten schools agreed to participate; of these, 8 (80%) were in
urban areas and 2 (20%) were in suburban areas. Ninth-grade
students not enrolled in special education at the participating
schools in 2013 with written active student assent and paren-
tal consent were enrolled (N = 3396). The data collection
involved 8 assessments conducted every 6 months from
baseline (wave 1; fall 2013, 9th grade; 3383 [99.6%] surveyed;
mean [SD] age, 14.5 [0.40] years) through 42-month
follow-up (wave 8; spring 2017, 12th grade; 3140 [92.5%] sur-
veyed; mean [SD] age, 17.9 [0.39] years). Paper-and-pencil
surveys were administered in students’ classrooms. Students
not in class completed surveys by telephone, internet, or mail
(numbers of phone/internet/mail surveys across follow-ups
ranged from 49-468). The University of Southern California
institutional review board approved the study.
Measures
Nonmedical Prescription Opioid and Heroin Use
Past 6-month (yes/no) and past 30-day (forced choice with 9
options ranging 0-30 days) use of prescription opioids (de-
scribed as “prescription painkillers to get high [eg, Vicodin,
Oxycontin, Percocet, Codeine]”) and other substances were
measured in separate questions derived from previously vali-
dated surveys.14,15
Nonmedical prescription opioid use sta-
tuses at each wave were coded into a trichotomous variable
(past 30-day [current] use vs past 6-month [prior] use with-
out past 30-day use vs no past 6-month use). Participants re-
ported ever heroin use (yes/no) at baseline and past 6-month
heroin use (yes/no) at each semiannual follow-up.
Covariates
Factors previously associated with nonmedical prescription
opioid or heroin use considered peripheral to the putative
risk pathway were included as a priori covariates. Each of the
measures described hereafter have demonstrated adequate
psychometric properties in youth.16-26
Nonopioid Substance Use and Sociodemographic and Environmen-
tal Factors | Marijuana, alcohol, cigarettes, and other sub-
stance (eg, cocaine, methamphetamine, inhalants, and non-
medical prescription stimulants) use were assessed and coded
in the same fashion as nonmedical prescription opioid use as
time-varying covariates. Baseline age, sex, highest parental
education level, and family living situation were measured
using investigator-defined forced-choice items (Table 1).17,20
Because opioid use may differ by race/ethnicity,11
self-
reportedrace/ethnicity(AmericanIndian/AlaskaNative,Asian,
black/African American, Hispanic/Latino, Native Hawaiian/
Pacific Islander, white, multiethnic/multiracial, or other) was
included. A family history of smoking, alcohol problems, or
drug problems was also measured (yes/no). A 4-item parental
monitoring questionnaire was administered at wave 3
(α = .82),21-23
yielding a composite score ranging from 1 (no
monitoring) to 4 (regular monitoring).
Intrapersonal Factors | Baseline emotional symptoms were
assessed using the Center for Epidemiologic Studies Depres-
sion Scale24
(α = .81) and Revised Child Anxiety and Depres-
sion Scale generalized anxiety disorder25,26
subscale (6 items;
α = .91), which were dichotomously coded as “symptomatic”
(scoring at or higher than clinical cutoff values) vs “non-
symptomatic.” Subscales of the UPPS Impulsive Behavior
Scale,18
a measure of impulsive personality traits assessing the
tendency to act rashly during negative (negative urgency [12
items; eg, “I do impulsive things that I later regret”; α = .89])
Key Points
Question Is nonmedical prescription opioid use associated with
later heroin use initiation in adolescents?
Findings In this 8-wave cohort study of 14-year-old and
15-year-old high school students in Los Angeles, California, who
had never used heroin at baseline, youth reporting no, prior, and
current nonmedical prescription opioid use during high school
exhibited estimated cumulative probabilities of subsequent heroin
use initiation by end of the 42-month follow-up of 1.7%, 10.7%,
and 13.1%, respectively.
Meaning Nonmedical prescription opioid use was prospectively
associated with subsequent heroin use initiation in adolescents;
future research is needed to evaluate whether this association
is causal.
Research Original Investigation Association of Nonmedical Prescription Opioid Use With Heroin Use Initiation in Adolescents
E2 JAMA Pediatrics Published online July 8, 2019 (Reprinted) jamapediatrics.com
© 2019 American Medical Association. All rights reserved.
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3. and positive (positive urgency [14 items; eg, “I act without
thinking when I am really excited”; α = .94]) emotional states,
were included. Delinquent behavior was measured with mean
frequency ratings for engaging in 11 behaviors (eg, stealing,
lying to parents; each item was rated 1 [never] to 6 [≥10 times];
α = .79) in the past 6 months.19
Statistical Analysis
After descriptive analyses, Cox regression models tested
nonmedical prescription opioid use statuses at waves 1 to 7 as
having a time-varying and time-lagged association with heroin
useinitiationatwaves2to8inbaselinenever–heroinusers.27,28
This approach incorporated all waves of data on nonmedical
prescription opioid use occurring before heroin use for every
student (up to 7 waves for students who never used heroin by
wave 7). For each available wave of nonmedical prescription
opioid use data, heroin use initiation data were used at all
ensuing waves spanning from the immediately subsequent
wave (6 months later) to the last follow-up (up to 42 months
of follow-up). Follow-up heroin use data during waves 2
through 8 were regressed on wave 1 nonmedical prescription
opioid use data, follow-up heroin use in waves 3 to 8 was re-
gressed on wave 2 nonmedical prescription opioid use, and so
on. When a student had initiated heroin use, nonmedical pre-
scription opioid use data at that wave and ensuing waves were
not additionally incorporated into the model estimates. We
tested a univariable unadjusted model that included time-
varying past 6-month nonmedical prescription opioid use sta-
tus as the sole regressor. We also tested a multivariable model
that included nonmedical opioid, alcohol, cigarette, cannabis,
and other substance use for waves 1 through 7 as simultaneous
time-varying regressors that additionally adjusted 12 time-
invariant covariates listed previously. Substance use regres-
sorsweremodeledcategorically,producinghazardratios(HRs)
and95%confidenceintervalsforassociationsofcurrent(vsno)
and prior (vs no) use status contrasts with subsequent heroin
use initiation. We also tested head-to-head comparisons of
whetherthemagnitudeofHRsfornonmedicalprescriptionopi-
oiduseweresignificantlydifferentfromcorrespondingHRsfor
other substance use regressors from the multivariable model
using the χ2
difference test based on the log likelihood values
derived from the maximum likelihood robust estimator.
Analyses were conducted in Mplus, version 7 (Muthén &
Muthén), including school random effects to account for
Table 1. Sample Characteristics of Baseline Never Users of Heroin and Comparisons by Heroin Use Initiation Over Follow-upa
Characteristics
Total Analytic
Sample
(N = 3298)b
Comparisons by Heroin Use Initiation Over Follow-up
Never Used Heroin (n = 3228) Initiated Heroin Use (n = 70) P Value
Female sex, No. (%) 1775 (53.9) 1754 (54.4) 21 (30.0) <.001c
Age, mean (SD), y 14.6 (0.4) 14.61 (0.40) 14.6 (0.4) .50d
Race/ethnicity, No. (%)
Hispanic 1563 (48.3) 1527 (48.3) 36 (52.2)
.97c
Asian 548 (17.0) 537 (17.0) 11 (15.9)
African American 155 (4.8) 153 (4.8) 2 (2.9)
Non-Hispanic white 529 (16.4) 518 (16.4) 11 (15.9)
Multiracial 220 (6.8) 215 (6.8) 5 (7.2)
Othere
218 (6.7) 214 (6.8) 4 (5.8)
Parent(s) without high school diploma, No. (%) 376 (13.2) 367 (13.2) 9 (14.3) .71c
Living with both parents, No. (%) 2080 (63.7) 2032 (63.6) 48 (69.6) .37c
Family substance use history, No. (%) 2190 (70.0) 2149 (70.1) 41 (64.1) .18c
Parental monitoring, mean (SD)f
3.06 (0.69) 3.06 (0.69) 2.84 (0.83) .03d
Delinquent behavior, mean (SD)g
1.43 (0.47) 1.42 (0.45) 1.81 (0.91) <.001d
Depressive symptoms, No. (%)h
1161 (35.7) 1131 (35.5) 30 (42.9) .21c
Generalized anxiety symptoms, No. (%)i
701 (22.4) 683 (22.3) 18 (27.7) .30c
UPPS, mean (SD)j
Negative urgency 1.78 (0.60) 1.77 (0.59) 2.01 (0.72) .002d
Positive urgency 1.78 (0.61) 1.77 (0.60) 1.98 (0.75) .01d
a
Unless otherwise specified, wave 1 data reported. Variables depicted were also
time-invariant covariates in the multivariable-adjusted regression model.
b
Of the 3298 baseline never users of heroin, the number of users with available
data (and corresponding denominator for % values) ranged from 2845
(86.3%) to 3296 (99.9%).
c
P values from the χ2
test for comparisons of proportions by group.
d
P values from the analysis of variance test of mean scores by group.
e
American Indian/Alaska Native, Native Hawaiian/Pacific Islander, or other
responses constituted the “Other” race/ethnicity category.
f
Score ranges from 1 to 4, with higher scores indicating greater perceived
parental monitoring. The mean rating from 1 (no monitoring) to 4 (regular
monitoring) across 4 items. Data from wave 3.
g
Score ranges from 1 to 6, with higher scores indicating a greater average
frequency of engaging in 11 different delinquent behaviors. Each behavior is
rated from 1 (never) to 6 (10 or more times) for 11 behaviors.
h
Screen results were positive (vs negative) for mild to moderate depressive
symptoms or higher on the Center for Epidemiologic Studies Depression Scale.
i
Screen results were positive (vs negative) for subclinical or clinical generalized
anxiety symptoms on the Revised Anxiety and Depression Scale.
j
Score ranges from 1 to 4, with higher scores indicating an impulsive tendency
to act rashly during negative emotional (negative urgency) or positive
emotional (positive urgency) states for respective subscales of the UPPS
measure of impulsive personality.
Association of Nonmedical Prescription Opioid Use With Heroin Use Initiation in Adolescents Original Investigation Research
jamapediatrics.com (Reprinted) JAMA Pediatrics Published online July 8, 2019 E3
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4. clustering.29,30
Missing data were addressed using a full infor-
mation maximum likelihood estimation (available sample
sizes of each variable are presented in Figure 1; eTables 1 and 2
in the Supplement).31
Statistical significance was determined
after Benjamini-Hochberg multiple-testing corrections to raw
P values (2-tailed) of each substance use regressor estimate to
control studywise false-discovery rates at .05.32
Results
Sample
Of 4100 eligible 9th grade students, 3396 (82.8%) provided as-
sent and parental consent for enrollment (Figure 1). Following
priorstrategiesforminimizinginvalidresponses,33
studentsre-
porting questionable substance use patterns (ie, everyday use
of 6 substances in the past 30 days) or biologically implausible
body mass indexes (calculated as weight in kilograms divided
by height in meters squared) were excluded (77 [2.3%]). Base-
line ever users of heroin (21 [0.6%]) were excluded, resulting in
an analytic sample of 3298. Cohort enrollees who were ex-
cluded from the analysis differed from the analytic sample on
several characteristics (eTable 3 in the Supplement). The num-
bers of individuals with outcome data available at each
follow-up wave ranged from 2987 (90.6%) to 3200 (97.0%).
Descriptive
The sample (3298 [97% of cohort enrollees]; 1775 (53.9%) ado-
lescent girls; mean [SD] age, 14.6 [0.4] years) was sociodemo-
graphic diverse (Table 1). Seventy students (2.1%) initiated
heroin use during the 42-month follow-up. Students who
initiated heroin use were more likely to be male, report lower
parental monitoring, and report higher baseline delinquent
behavior, negative urgency, and positive urgency.
Across waves 1 through 7, 596 students (18.1%) reported
nonmedical prescription opioid use at 1 or more waves. The
mean per-wave percentages of prior and current nonmedical
prescription opioid use during waves 1 to 7 were 1.9% (range,
25[0.8%]to114[3.5%])and2.7%(range,56[1.7%]to112[3.5%])
(eTable 2 in the Supplement). Most students reporting past
30-day nonmedical prescription opioid use reported use for
9 days or fewer (eFigure 1 in the Supplement).
Associations Between Nonmedical Prescription Opioid Use
and Subsequent Heroin Use Initiation
The univariable unadjusted Cox regression model found that
prior vs no (HR, 3.59; 95% CI, 2.14-6.01) and current vs no (HR,
4.37; 95% CI, 2.80-6.81) time-varying nonmedical prescrip-
tion opioid use statuses for waves 1 through 7 were associ-
ated with an increased likelihood of subsequent heroin use ini-
tiation for waves 2 to 8 (Table 2). For no, prior, and current
nonmedical prescription opioid use statuses, the estimated
unadjusted cumulative probabilities of subsequent heroin use
initiation by the final 42-month follow-up (wave 8) were 1.7%,
10.7%, and 13.1%, respectively (estimated hazard curves can
be found in Figure 2).
In the multivariable model adjusted for time-varying past
6-month use of nonopioid substances and time-invariant
covariates, the associations of prior vs no (HR, 2.09; 95% CI,
1.14-3.83) and current vs no (HR, 3.18; 95% CI, 1.68-6.02) time-
varying nonmedical prescription opioid use statuses with
subsequent heroin use initiation remained statistically sig-
nificant but were attenuated in association with the unad-
justed results. In this model, the only time-varying non-
opioid substance use status variable significantly associated
with subsequent heroin initiation was the prior vs no use con-
trast for the “other substance” variable (HR, 2.20; 95% CI, 1.45-
3.33; Table 2). Female sex and a family history of substance use
were associated with heroin use initiation in the multivari-
able model (eTable 4 in the Supplement).
The HR estimates of the association of current vs no use
statues with subsequent heroin use initiation was signifi-
cantly stronger for nonmedical prescription opioids than for
alcohol, cannabis, cigarettes, or other nonopioid drugs (Δχ2
/
df ≥ 3.91,P ≤ .03;Table2;eTable5intheSupplement).Forprior
vs no use contrasts, nonmedical prescription opioid HRs were
significantly stronger than the cigarette smoking HRs but not
different than alcohol, cannabis, and other drug use HRs
(eTable 5 in the Supplement).
Sensitivity Analyses
Sensitivity analyses found that the primary results were not
substantively changed if different approaches to handling
missing data and data clustering were applied or if the
results were retested, including invalid responders in the
analytic sample. The hierarchical model adjusting for clus-
tering within students, schools, and urban vs (sub)urban
Figure 1. Study Accrual Flowchart
4100 Eligible students
3874 Assented to participate
3319 Students
3396 Enrolled in the larger study cohort
at baseline (fall, ninth grade)
226 Did not provide assent
77 Excluded for potential of invalid
reporting
21 Excluded for baseline ever use
of heroin
478 Did not receive parental consent
439 Consent declined by parent
39 Did not return consent form
or parent was unreachable
3298 Analytic sample of heroin never-users at baseline (fall 2013, ninth grade;
wave 1)
3200 Data available at 6-mo follow-up (spring 2014, ninth grade; wave 2)
3186 Data available at 12-mo follow-up (fall 2014, tenth grade; wave 3)
3158 Data available at 18-mo follow-up (spring 2015, tenth grade; wave 4)
3138 Data available at 24-mo follow-up (fall 2015, eleventh grade; wave 5)
2987 Data available at 30-mo follow-up (spring 2016, eleventh grade; wave 6)
3077 Data available at 36-mo follow-up (fall 2016, twelfth grade; wave 7)
3052 Data available at 42-mo follow-up (spring 2017, twelfth grade; wave 8)
Research Original Investigation Association of Nonmedical Prescription Opioid Use With Heroin Use Initiation in Adolescents
E4 JAMA Pediatrics Published online July 8, 2019 (Reprinted) jamapediatrics.com
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5. areas did produce slightly more conservative yet similarly
significant estimates (eResults and eTables 6-8 in the
Supplement). After eliminating 14 youths reporting opioid
use before high school (eTable 9 and eFigure 2 in the Supple-
ment), youth reporting no, prior, and current nonmedical
prescription opioid use exhibited similar estimated cumula-
tive probabilities of subsequent heroin use initiation to the
primary analysis.
Discussion
This study provides new evidence of a prospective associa-
tion between nonmedical prescription opioid use and an in-
creased risk of future heroin use initiation among adoles-
cents. Graded patterns of association with heroin use were
observed for nonmedical prescription opioid use (ie, the risk
was successively higher for nonuse, followed by prior use and
then current use); graded associations were not observed for
the use of nonopioid substances. To our knowledge, the only
other published prospective longitudinal study examining the
association of nonmedical prescription opioid use and later
heroin use initiation was conducted in military veterans and
also found evidence of a positive association.34
During the age period captured in this study (age 14-18
years), teenagers are exposed to new peers and often enter the
workforce alongside adult coworkers, both of whom may pro-
vide access to licit and illicit drugs.35-37
Furthermore, this pe-
riod is marked by imbalanced neural development in which
brain pathways underlying pleasure seeking mature more
rapidly than those underpinning decision-making skills.38
For these reasons, the risk of nonmedical prescription opioid
exposure and heroin use initiation may escalate during mid to
late adolescence.10
Prior studies of the association between nonmedical pre-
scription opioid use and heroin use in adolescents used
cross-sectional designs and retrospective reports of sub-
stance use.10-12
Such methods are subject to recall errors and
reporting biases that may produce imprecise association
estimates and inaccurate inferences regarding the ordering
Table 2. Associations of Nonmedical Prescription Opioid and Nonopioid
Substance Use With Subsequent Heroin Use Initiationa
Time-Varying Regressors,
Waves 1 to 7
Associations With Subsequent
Heroin Use Initiation, Waves 2 to 8
Hazard Ratio (95% CI) P Value
Univariable Unadjusted Modelb
Prior (vs no) nonmedical
prescription opioid usec
3.59 (2.14-6.01) <.001d
Current (vs no) nonmedical
prescription opioid usec
4.37 (2.80-6.81) <.001d
Multivariable Adjusted Modele
Prior (vs no) usec
Nonmedical prescription
opioid use
2.09 (1.14-3.83) .02d
Cannabis use 1.54 (0.89-2.65) .12
Alcohol use 1.76 (1.04-2.98) .04
Cigarette use 0.93 (0.52-1.67)f
.82
Other substance useg
2.20 (1.45-3.33) <.001d
Current (vs no) usec
Nonmedical prescription
opioid use
3.18 (1.68-6.02) <.001d
Cannabis use 1.68 (1.02-2.82)h
.04
Alcohol use 2.04 (1.10-3.92)h
.03
Cigarette use 0.75 (0.35-1.59)h
.45
Other substance useg
1.54 (0.92-2.62)h
.10
a
Baseline never users of heroin (N = 3298).
b
Cox regression hazards model including only nonmedical prescription opioid
use as a time-varying (time-lagged) regressor with school random effects.
c
No use = no past 6-month use; prior use = past 6-month use without past
30-day use; current use = past 30-day use.
d
Statistically significant after Benjamini-Hochberg corrections for multiple
testing to control the false-discovery rate at .05 (based on a 2-tailed corrected
P value).
e
Cox regression hazards model that included 5 simultaneous time-varying
(time-lagged) substance use regressors adjusted for time-invariant covariates
(ie, baseline age, sex, race/ethnicity, parental education level, family living
situation, family substance use history, delinquent behavior, depressive
symptoms, anxiety, negative urgency, and positive urgency and parental
monitoring at wave 3) with school random effects.
f
Statistically significant difference in the magnitude of the hazard ratio for the
prior (vs no) use contrast of a respective substance and the hazard ratio for the
prior (vs no) use contrast of nonmedical prescription opioid use from χ2
difference test using the log-likelihood values with the maximum likelihood
robust estimator (detailed statistics from the tests are presented in eTable 5 in
the Supplement).
g
Use of any substance to get high other than opioids, marijuana, cigarettes, or
alcohol.
h
Statistically significant difference in the magnitude of the hazard ratios for the
current (vs no) use contrast of a respective substance and the hazard ratio for
the current (vs no) use contrast of nonmedical prescription opioid use from χ2
difference test using the log-likelihood values with the maximum likelihood
robust estimator (detailed statistics from the tests are presented in eTable 5 in
the Supplement).
Figure 2. Estimated Hazard Curves for Heroin Use Initiation
by Nonmedical Prescription Opioid Use Status in Preceding Waves
0
14
8
10
12
CumulativeHeroinUseInitiationProbability,%
6
4
2
1 4 5 6 7 8 9
Baseline and Follow-up Assessment Waves
2 3
No opioid useNo opioid use
Current opioid use
Prior opioid use
The horizontal axis depicts 8 semiannual assessments from wave 1 (fall 9th
grade, 2013; mean [SD] age, 14.5 [0.40] years) to wave 8 (spring 12th grade,
2017; mean [SD] age, 17.9 [0.39] years). The vertical axis depicts the estimated
unadjusted cumulative probability of heroin use initiation at each follow-up
wave; the estimates were reported by opioid use status at the preceding waves.
The cumulative estimated probabilities of heroin use initiation at the final
assessment (wave 8) were 1.7%, 10.7%, and 13.1% for no, prior, and current
nonmedical prescription opioid use statuses, respectively, in the preceding
waves. No use = no past 6-month use; prior use = past 6-month use without
past 30-day use; and current use = past 30-day use.
Association of Nonmedical Prescription Opioid Use With Heroin Use Initiation in Adolescents Original Investigation Research
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6. of nonmedical prescription opioid and heroin use. By using a
prospective longitudinal design that excluded ever users of
heroin at baseline, to our knowledge, this study is the first to
establish the temporal precedence of adolescent nonmedical
prescription opioid use in association with subsequent
heroin use. Further, recall errors were reduced by repeated
assessments and brief interwave intervals. Additionally, pre-
vious adolescent studies10-12
excluded salient covariates that
may be associated with the risk of heroin use, including
impulsive personality traits, parental monitoring, and
delinquency. Finally, prior youth studies of this topic ana-
lyzed data collected in 2014 or earlier.10-12
Follow-up in this
study spanned from 2013 to 2017, a recent historical period
in which opioid-associated overdoses in US teenagers
increased after several prior years of decline.39,40
The observed association is subject to 2 explanations:
(1) a common liability to using nonmedical prescription opi-
oids and heroin, or (2) nonmedical prescription opioid use
directly increases the risk of heroin use initiation. Sources of
common liability to using both drugs include an exposure to
environments in which access to prescription opioids and
heroin is high and parental restrictions are absent.41
An
endogenous psychological or genetic disposition toward
impulsive behavior or engaging in rebellious acts may also
produce a common liability.42
Possible confounding influ-
ences were addressed by adjusting for covariates indicative
of common liability. While adjusted estimates were reduced,
suggesting common liability accounted for some of the asso-
ciation, current and prior nonmedical prescription opioid
use remained significantly associated with 3.18 (95% CI,
1.68-6.02) and 2.09 (95% CI, 1.14-3.83) greater HRs of heroin
use initiation. Although unmeasured confounding cannot be
ruled out, the results suggest that a common liability may
not entirely explain the association between nonmedical
prescription opioid use and the subsequent heroin use initia-
tion observed in this study.
Specificity in the study results also points toward a
direct risk pathway from nonmedical prescription opioid use
to heroin use initiation. As well as remaining robust to con-
trol for nonopioid substance use, the risk estimate for time-
varying current nonmedical prescription opioid use was sig-
nificantly stronger than the corresponding risk estimate for
time-varying current use of nonopioid drugs in head-to-
head comparisons. Thus, it is unlikely that the observed
association is entirely a by-product of a nonspecific liability
toward any drug use, including vulnerabilities that may vac-
illate across adolescence (ie, those that are time-varying).
Additionally, the association between nonmedical prescrip-
tion opioid use and heroin use initiation followed a graded
(dose response–like) pattern, with larger HRs for current
rather than prior use statuses. For the prior vs no use HRs,
the HRs were comparable for other substances and opioids.
For current use, the HR became larger for opioids but did not
for other substances. Thus, graded patterns of association
with heroin use were largely not observed for the use of
other substances. As recency of use may be a proxy for the
extent of exposure, these findings imply that adolescents
with higher exposure to nonmedical opioid use are more
likely to initiate heroin than those with lower exposure to
nonmedical prescription opioids and imply that the recency
of nonmedical prescription opioid use is also associated with
later heroin use initiation. Finally, incidence of the reverse
sequence was negligible; only 3 youths used heroin and later
initiated prescription opioid use, precluding formal tests of a
reverse association.
Although the observational design of this study pre-
cludes definitive causal inferences, the pattern of the results
warrants considering plausible mechanisms of risk from
nonmedical prescription opioid exposure to subsequent
heroin use initiation. Adults with long-term prescription opi-
oid use who develop opioid dependence report transitioning
to heroin to alleviate opioid withdrawal symptoms when
access to prescription opioids becomes difficult or costly.5,6
In the current adolescent sample, the daily use patterns
characteristic of severe opioid use disorder were uncom-
mon, suggesting that the desire to alleviate opioid with-
drawal mediated by opioid dependence was not a highly
common mechanism of the transition to heroin use. Pre-
scription opioids can produce powerful euphoric effects,
particularly when used nonmedically and at higher doses.8,9
It is possible that youths who enjoy the euphoric effects
from nonmedical prescription opioid use may become
inclined to try heroin because of a desire to experience simi-
lar opioidergic intoxicating effects at a higher potency.5
If evidence of a direct risk pathway from nonmedical opioid
use to heroin use initiation in adolescence were to be identi-
fied in future research, measures to prevent youths from
accessing prescription opioids merit consideration as a
public health priority.
Limitations
First, exposure was operationalized by survey questions
concatenating the use of several opioid compounds, leaving
unclear which opioid drug and whether polyopioid use was
associated with heroin use initiation. Second, medical use of
prescription opioids was not assessed; whether nonmedical
use stemmed from medical use of opioids was not captured
in this study. While nonmedical use was defined in the sur-
vey at several points, it is possible that some students may
have misread the survey instructions. Although the most
common prescription opioids were included as examples in
the survey item, it is also possible that some of the opioid
compounds students used were not mentioned (eg, trama-
dol). Third, characteristic of high school student samples,34
the prevalence of frequent nonmedical prescription opioid
use was low, precluding analyses of whether daily vs non-
daily use differentiates the likelihood of heroin use initia-
tion. Future studies of nonmedical prescription opioid use in
youth will need to be enriched for frequent users using a dif-
ferent sampling strategy (eg, clinical populations seeking
substance use treatment). Fourth, the method of drug
administration was also not assessed; whether different risk
estimates are found for opioid smoking, inhalation, or injec-
tion is worthy of study.5
There may also be limitations to the generalizability of
this study’s findings. The prevalence of nonmedical prescrip-
Research Original Investigation Association of Nonmedical Prescription Opioid Use With Heroin Use Initiation in Adolescents
E6 JAMA Pediatrics Published online July 8, 2019 (Reprinted) jamapediatrics.com
© 2019 American Medical Association. All rights reserved.
Downloaded From: https://jamanetwork.com/ by Giorgos Kassapis on 07/13/2019

7. tion opioid and heroin use in this study was larger than
figures observed in some nationally representative cross-
sectional surveys.10-12
In this study, all youth were followed
up after 9th grade, including teenagers frequently absent
from class or who later discontinued school and might be
underrepresented in national surveys. The repeated assess-
ment in this study may capture the incidence of reported
substance use missed in single point surveys. Like any obser-
vational study, there is a potential risk of bias when consider-
ing participant dropout, making our estimates more conser-
vative, as dropouts likely have a higher risk of substance use.
Additionally, this Los Angeles area youth sample is a conve-
nience sample that is more sociodemographically diverse
than the overall US population, and the (sub)urban backdrop
of this study diverges from some rural areas where opioid use
is widespread. Although prior cross-sectional research has
found that the association between nonmedical prescription
opioid use and heroin use initiation does not differ by race/
ethnicity or income,10
further investigation in geographically
heterogenous samples is warranted.
Conclusions
Nonmedical prescription opioid use was prospectively asso-
ciated with subsequent heroin use initiation during 4 years of
adolescence among Los Angeles youth. Further research is
needed to understand whether this association is causal.
ARTICLE INFORMATION
Accepted for Publication: April 17, 2019.
Published Online: July 8, 2019.
doi:10.1001/jamapediatrics.2019.1750
Author Contributions: Drs Kelley-Quon and Cho
had full access to all of the data in the study and
take responsibility for the integrity of the data and
the accuracy of the data analysis. Dr Leventhal was
the principal investigator.
Concept and design: Kelley-Quon, Cho, Strong,
Leventhal.
Acquisition, analysis, or interpretation of data:
Kelley-Quon, Cho, Barrington-Trimis, Kechter,
Leventhal.
Drafting of the manuscript: Kelley-Quon, Cho.
Critical revision of the manuscript for important
intellectual content: Kelley-Quon, Cho, Strong,
Barrington-Trimis, Kechter, Leventhal.
Statistical analysis: Cho, Strong, Kechter.
Administrative, technical, or material support: Cho.
Supervision: Leventhal.
Other: Kechter.
Conflict of Interest Disclosures: Drs Kelley-Quon
and Leventhal receive grant support from the
National Institutes of Health (NIH). Dr Miech
receives grant support from the National Institute
on Drug Abuse, the National Cancer Institute, and
the National Institute on Alcohol Abuse and
Alcoholism. No other disclosures are reported.
Funding/Support: This research was supported by
NIH grants R01-DA033296 and K24 DA048160. Dr
Kelley-Quon is supported by grant KL2TR001854
from the National Center for Advancing
Translational Science of the NIH.
Role of the Funder/Sponsor: The funding
organizations had no role in the design and conduct
of the study; collection, management, analysis, and
interpretation of the data; preparation, review, or
approval of the manuscript; and decision to submit
the manuscript for publication.
Disclaimer: The content is solely the responsibility
of the authors and does not necessarily represent
the official views of the NIH.
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