Adolescent Substance Use in the Multimodal Treatment Study of Attention-Deficit/Hyperactivity Disorder (ADHD) (MTA) as a Function of Childhood ADHD, Random Assignment to Childhood Treatments, and Subsequent Medication Brooke S.G. Molina, Ph.D., Stephen P. Hinshaw, Ph.D., L. Eugene Arnold, M.D., James M. Swanson, Ph.D., William E. Pelham, Ph.D., Lily Hechtman, M.D., Betsy Hoza, Ph.D., Jeffery N. Epstein, Ph.D., Timothy Wigal, Ph.D., Howard B. Abikoff, Ph.D., Laurence L. Greenhill, M.D., Peter S. Jensen, M.D., Karen C. Wells, Ph.D., Benedetto Vitiello, M.D., Robert D. Gibbons, Ph.D., Andrea Howard, Ph.D., Patricia R. Houck, M.S.H., Kwan Hur, Ph.D., Bo Lu, Ph.D., Sue Marcus, Ph.D., on behalf of the MTA Cooperative Group Objective: To determine long-term effects on substance use and substance use disorder (SUD), up to 8 years after childhood enrollment, of the randomly assigned 14-month treatments in the multisite Multimodal Treatment Study of Children with Attention-Deficit/Hyperactivity Disorder (MTA; n ¼ 436); to test whether medication at follow-up, cumulative psychostimulant treatment over time, or both relate to substance use/SUD; and to compare substance use/SUD in the ADHD sample to the non-ADHD childhood classmate comparison group (n ¼ 261). Method: Mixed-effects regression models with planned contrasts were used for all tests except the important cumulative stimulant treatment question, for which propensity score matching analysis was used. Results: The originally randomized treatment groups did not differ significantly on substance use/SUD by the 8-year follow-up or earlier (mean age ¼ 17 years). Neither medication at follow-up (mostly stimulants) nor cumulative stimulant treatment was associated with adolescent substance use/SUD. Substance use at all time points, including use of two or more substances and SUD, were each greater in the ADHD than in the non-ADHD samples, regardless of sex. Conclusions: Medication for ADHD did not protect from, or contribute to, visible risk of substance use or SUD by adolescence, whether analyzed as randomized treatment assignment in childhood, as medication at follow-up, or as cumulative stimulant treatment over an 8-year follow-up from childhood. These results suggest the need to identify alternative or adjunctive adolescent-focused approaches to substance abuse preven- tion and treatment for boys and girls with ADHD, especially given their increased risk for use and abuse of multiple substances that is not improved with stimulant medication. Clinical trial registration information—Multimodal Treatment Study of Children With Attention Deficit and Hyperactivity Disorder (MTA); http://clinical trials.gov/; NCT00000388. J. Am. Acad. Child Adolesc. Psychiatry; 2013;52(3):250-263. Key Words: attention-deficit/hyperactivity dis- order (ADHD), substance use R isk of substance use or substance abuse or dependence disorders for children with attention-deficit/hyperactivity disorder (ADHD) is a topic of substantial interest for researchers, practitioners, and parents. Recent meta-analytic reviews of pertinent longitudinal studies have reported moderately higher rates of substance abuse and dependence by early adult- hood for children with the disorder in relation to non-ADHD comparison groups. 1,2 For example, although results across studies are heterogeneous, children with ADHD are more than twice as likely This article is discussed in an editorial by Dr. Benjamin I. Goldstein on page 225. JOURNAL OF THE AMERICAN ACADEMY OF CHILD & ADOLESCENT PSYCHIATRY 250 www.jaacap.org VOLUME 52 NUMBER 3 MARCH 2013 NEW RESEARCH |
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NEW RESEARCH|
Adolescent Substance Use in the MultimodalTreatment Study of Attention-Deficit/HyperactivityDisorder (ADHD) (MTA) as a Function of Childhood
ADHD, Random Assignment to Childhood Treatments,and Subsequent Medication
Brooke S.G. Molina, Ph.D., Stephen P. Hinshaw, Ph.D., L. Eugene Arnold, M.D.,James M. Swanson, Ph.D., William E. Pelham, Ph.D., Lily Hechtman, M.D., Betsy Hoza, Ph.D.,
Jeffery N. Epstein, Ph.D., Timothy Wigal, Ph.D., Howard B. Abikoff, Ph.D.,Laurence L. Greenhill, M.D., Peter S. Jensen, M.D., Karen C. Wells, Ph.D., Benedetto Vitiello, M.D.,
Robert D. Gibbons, Ph.D., Andrea Howard, Ph.D., Patricia R. Houck, M.S.H., Kwan Hur, Ph.D., Bo Lu, Ph.D.,Sue Marcus, Ph.D., on behalf of the MTA Cooperative Group
Thison p
250
Objective: To determine long-term effects on substance use and substance use disorder (SUD),up to 8 years after childhood enrollment, of the randomly assigned 14-month treatments in themultisite Multimodal Treatment Study of Children with Attention-Deficit/HyperactivityDisorder (MTA; n¼ 436); to test whether medication at follow-up, cumulative psychostimulanttreatment over time, or both relate to substance use/SUD; and to compare substance use/SUDin the ADHD sample to the non-ADHD childhood classmate comparison group (n ¼261). Method: Mixed-effects regression models with planned contrasts were used for alltests except the important cumulative stimulant treatment question, for which propensity scorematching analysis was used. Results: The originally randomized treatment groups did notdiffer significantly on substance use/SUD by the 8-year follow-up or earlier (mean age ¼17 years). Neither medication at follow-up (mostly stimulants) nor cumulative stimulant treatmentwas associated with adolescent substance use/SUD. Substance use at all time points, including useof two or more substances and SUD, were each greater in the ADHD than in the non-ADHDsamples, regardless of sex. Conclusions: Medication for ADHD did not protect from, orcontribute to, visible risk of substance use or SUD by adolescence, whether analyzed as randomizedtreatment assignment in childhood, as medication at follow-up, or as cumulative stimulanttreatment over an 8-year follow-up from childhood. These results suggest the need toidentify alternative or adjunctive adolescent-focused approaches to substance abuse preven-tion and treatment for boys and girls with ADHD, especially given their increased risk for useand abuse of multiple substances that is not improved with stimulant medication. Clinical trialregistration information—Multimodal Treatment Study of Children With Attention Deficit andHyperactivity Disorder (MTA); http://clinical trials.gov/; NCT00000388. J. Am. Acad. ChildAdolesc. Psychiatry; 2013;52(3):250-263. Key Words: attention-deficit/hyperactivity dis-order (ADHD), substance use
R isk of substance use or substance abuseor dependence disorders for childrenwith attention-deficit/hyperactivity disorder
(ADHD) is a topic of substantial interest for
article is discussed in an editorial by Dr. Benjamin I. Goldsteinage 225.
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researchers, practitioners, and parents. Recentmeta-analytic reviews of pertinent longitudinalstudies have reported moderately higher rates ofsubstance abuse and dependence by early adult-hood for children with the disorder in relation tonon-ADHD comparison groups.1,2 For example,although results across studies are heterogeneous,children with ADHD are more than twice as likely
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as comparison peers to meet diagnostic criteria formarijuana abuse or dependence by early adult-hood.2 The included studies do not, however,provide data about the developmental course ofsubstance use, and they tend to emphasize diag-nosis of substance abuse or substance dependenceeven for adolescents whose addiction risk is oftenbetter measured by more developmentally sensi-tive, continuous variables (e.g., frequency ofalcohol use).3,4 Nevertheless, the results raiseimportant questions about factors, such as treat-ment history, that might affect ADHD-relatedaddiction vulnerability.
Although not the only evidence-based treat-ment for ADHD,5 stimulants (e.g., methylpheni-date, amphetamine) are the most commonly usedand widely accessible treatment, and prescriptionrates have increased markedly in recent years.6
These medications reliably decrease ADHDsymptoms for more than 80% of diagnosedchildren, sometimes dramatically.7,8 To the extentthat symptoms of ADHD contribute to addictionvulnerability, treatment with stimulant medica-tion should theoretically decrease risk. Indeed, agrowing number of studies have shown thatADHD symptom persistence correlates with sub-stance use.9–12 In other accounts, however, risk ofsubstance abuse has been hypothesized to increasefor children medicated with stimulants.13 Thispossibility, potentially linked to the overlap inbrain-based mechanisms for both addictive beha-viors and stimulant medication efficacy,14 hasbeen supported, with mixed results, in animalstudies, showing an enhanced response to drugsof abuse following methylphenidate exposure.15
However, route of administration, relativedosages, developmental timing of medicationreceipt, and limits of cross-species parallels (orlack thereof) complicate interpretation.
An early, small meta-analytic review of stimu-lant treatment and risk of substance abusereported a protective effect of medication use inadolescence and, to a lesser extent, in earlyadulthood.16 However, considering recently pub-lished studies, the accumulated data appear to bemixed. Some studies have found protective asso-ciations;17–20 one reported predisposing associa-tions (i.e., more medication treatment, moresubstance use),13,21,22 and three reported no asso-ciation.23–25 Barkley et al.25 reported one positiveassociation for adulthood cocaine use, which fellto nonsignificance after controlling for conductdisorder. A retrospective chart review of a birth
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cohort in Rochester, MN, reported protectionfrom substance abuse by age 18 years.26 Twoother prospective longitudinal studies recentlyreported no association between years of stimu-lant treatment and substance use severity ordisorder for 16- to 22-year-olds,27 and no associa-tion between childhood treatment with stimulantmedication (including course of medication) andSUD or tobacco use to age 24 years28.
Substantial and important variability in themethodologic features of these studies makes itdifficult to synthesize the findings. Samples varyconsiderably in age both within and acrossstudies, and age is a potentially confoundingvariable that is strongly related to both treatmentand substance use (W. Pelham, unpublishedconference presentation, 2004 October). Com-pounding this problem is that most relevantresearch is based on sample sizes of about 100,which can be insufficient for examining theassociation between stimulant treatment and avariable that has a low to moderate frequencyoccurrence even in high-risk samples (i.e., sub-stance abuse). For example, marijuana abuse anddependence peak at 5.6% of 18- to 25-year-oldsnationally and are even less frequent at youngerages.29 A few of the relevant studies measuredactual substance use,21,24,25,27 whereas othersrelied on diagnostic assessments of abuse ordependence (which, as mentioned earlier, maynot identify teens on a developmental trajectorytoward abuse/dependence in adulthood). Studiesalso vary in their operationalization of psycho-active treatment history, which has resulted infundamentally different questions being address-ed across studies comprising a relatively smallbody of literature.
The longitudinal follow-up of the children inthe Multimodal Treatment Study of ADHD (MTA)provides an opportunity to examine the associa-tion between ADHD treatment and substanceabuse with a large, diverse, narrow age-bandsample recruited in childhood, treated for14 months in a randomized clinical trial, andfollowed prospectively with substance use assess-ments through adolescence.14 The randomizedtrial design of the MTA in childhood (see below)provided partial protection from the influences ofself-selection into treatment at an age whenmedication treatment is at its peak.30,31 In thefirst published MTA report of substance use24 at3 years after study baseline, no protective orpredisposing associations were found between
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substance use and early medication exposure,measured as either randomly assigned treatmentor proportion of days medicated in the yearleading up to the substance use assessment.However, substance use was nascent in the firstfollow-up report, with limited opportunity todetect treatment effects because ages ranged from10 to 13 years. We now turn to the 8-year follow-up (mean age, 16.8 years), when substance use ismore prevalent.32
Hence, one aim of the current study was to testthe association between medication treatment(both as randomly assigned during childhoodand naturalistically used following the clinicaltrial) and adolescent substance use/SUD by meanage 17 years. We opted for a three-prongedapproach to examine substance use/SUD asa function of treatment: random assignment tomedication in childhood (at a mean age of8.5 years); concurrent medication use across themultiple follow-up assessments; and, mostimportantly, duration of medication (specificallypsychostimulant treatment) from study entry tothe 8-year follow-up. Thus, we expanded ouranalyses to consider putative long-term exposureeffects of stimulant medication that have beenhypothesized to either protect children withADHD from or increase risk for substance use/SUD.14 Although the initial randomizationaffected subsequent parental choices about med-ication and therefore provided only partial pro-tection against the effects of confoundingvariables underlying natural selection into med-ication over time, initial group differences inmedication use were lost by the 3-year follow-up.33 To control for these and other individualdifferences in propensity to medicate over time,we selected an analytic approach—propensityscore matching—favored for managing the effectsof confounding variables.34 A similar propensityanalysis was conducted at the 3-year follow-upfor effects of medication on ADHD symptoms,without significant results.35
A second aim was to compare substance use/SUD at the 8-year follow-up between the childrenwith ADHD and an MTA-recruited and followedcomparison group without ADHD. This analysistested an extension of our prior findings24 ofADHD-related risk for substance use experimen-tation to heavier levels of use/SUD for the highschool age range. Polysubstance use is highlyprevalent among teen substance users, but ques-tions have arisen about ADHD-related drug
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specialization, especially for tobacco because ofits distinct pharmacologic (stimulant) proper-ties.36 Thus, we examined use of other drugs bytobacco users. We also tested whether our failureto find sex differences in early adolescent sub-stance use24 would extend to this older age range.Because of the larger research literature on ADHDfor boys than for girls, it is not surprising that littleresearch has been conducted on use/SUD in girlswith ADHD histories (for exceptions, see Babinskiet al.,37 Biederman, et al.38 and Hinshaw et al.39,40).
METHODParticipantsThe participants with ADHD in the MTA were 579children with DSM-IV ADHD combined type. Each ofthe six participating sites randomized 95 to 98 children toone of four treatment groups: Medication Management(MedMgt), Behavior Therapy (Beh), Combined MedMgtplus Beh (Comb), and Community Comparison (CC). Atbaseline (pretreatment), participants were 7.0 to 9.9 yearsof age (mean ¼ 8.5 years, SD ¼ 0.8 years). The MTArecruitment strategy, procedures for diagnosing ADHD,treatment specifics, and sample demographics have beendescribed elsewhere.41–46
The children were reassessed at 3 and 9 months, atcompletion of the 14-month treatment phase, at 24 and36 months after randomization, and again at 6 and8 years after randomization. Participation rates datingfrom 14 months were 97%, 93%, 84%, 78%, and 75% ofthe original 579 enrolled, respectively. There were nosignificant differences in any baseline characteristicsbetween participants and non-participants for the 36-month assessment.33 However, children lost to the8-year follow-up assessment, compared with thoseretained, were more often male and had youngermothers, less-educated parents, lower parent income,and parents more likely to have antisocial personalitydisorder at baseline (p o .025 corrected for experiment-wise error). There were no significant differences on theremaining sociodemographic or adversity variables(e.g., age, grade, ethnicity/race, parent marital status,stable residency, on welfare, parent job loss, childhealth, birthweight) or on baseline measures of intellectand achievement, parent and teacher report of ADHDand oppositional defiant disorder (ODD) symptoms,parent-reported aggression and conduct problems,parental diagnoses of mood, anxiety, alcohol, or otherdrug use disorders, or randomized treatment groupassignment. Mean ages at the 6- and 8-year follow-upassessments were 14.9 (SD ¼ 1.0) and 16.8 (SD ¼ 1.0)years, respectively.
A local normative comparison group (LNCG,N ¼ 289) was recruited at 24 months to reflect the localpopulations from which the ADHD sample was drawn.
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The LNCG children were randomly selected from thesame schools and grades and in the same sex proportionsas the children with ADHD. LNCG children were notexcluded from recruitment because of symptoms ofADHD, but those found to have diagnosable ADHD(n ¼ 31) were excluded from the present analyses as wehave done previously.47 The assessment battery includedthe Diagnostic Interview Schedule for Children–VersionIV (DISC-IV48) and teacher-reported ratings of ADHDsymptoms, which afforded examination of DSM-IVdiagnoses and ADHD symptom severity. The LNCGhad the same entry criteria as the children with ADHDexcept for ADHD diagnosis and age; they were matchedto the age of the ADHD sample at 24 months afterrandomization. Thus, data for the LNCG are availablestarting at the 24-month assessment. At that time,average age of the LNCG (mean ¼ 10.4 years, SD ¼1.08 years) did not differ significantly from that of theADHD sample (t811¼1.04, p¼ .36) at that time point. Thepercentage of female subjects was similar in the LNCG(18.7%, n¼ 54/289) and the ADHD samples (19.7%, n¼114/579, w2
1 ¼ 0.13, NS). The percentage of retainedLNCG participants by 6 and 8 years was 87% (252/289)and 90% (261/289), respectively. The LNCG participantslost to the 8-year assessment had less stable residencyand younger mothers than those retained, but all otherbaseline variables were nondiscriminating. LNCG meanages at the 6- and 8-year assessments were 14.5 (SD¼ 1.2)and 16.6 years (SD ¼ 1.2), respectively.
MeasuresThe substance use outcomes were measured at allinterviews beginning with the 24-month assessment.Use (e.g., consumption) of substances was measuredseparately from DSM symptoms of abuse or depen-dence.49 The primary analyses made use of two vari-ables: a single substance use variable and a singleabuse/dependence variable, each composited acrosssubstances. Secondary analyses explored substance-specific associations.
Substance Use. Substance use was assessed with achild/adolescent-reported questionnaire50 adapted forthe MTA. The measure included items for lifetime andcurrent (past 6 months) use of alcohol, cigarettes,chewing tobacco, marijuana, and other street drugs.Also included were items for nonprescribed use orother misuse of psychoactive medications, includingstimulants. The measure was modeled after similarsubstance use measures in longitudinal or nationalsurvey studies of alcohol and other drug use51–53 thatalso rely on confidential youth self-report as the bestsource of data.54 A National Institutes of Health (NIH)Certificate of Confidentiality further strengthened theassurance of privacy.
Our primary substance use variable was a binarycomposite that allowed consideration of multiple sub-stance use classes simultaneously. Analysis of
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substance use as an ordinal variable is a commonapproach—particularly for studying adolescents.55
Substance use was coded positive if any of the followingbehaviors, selected after examining distributions, wereendorsed as occurring in the participant’s lifetime:drank alcohol (more than just a sip) more than fivetimes or drunk at least once; smoked cigarettes or triedchewing tobacco more than a few times; used marijuanamore than once; or used inhalants, hallucinogens,cocaine, or any of amphetamines/stimulants, barbitu-rates/sedatives, opioids/narcotics without a prescrip-tion or misused a prescription (used more in quantity ormore often than prescribed). Each of the four types ofsubstances, as well as daily use of tobacco and thenumber of substance use classes endorsed (0, 1, 2 ormore), were explored in secondary analyses.
For the analysis of stimulant treatment duration inrelation to substance use at the 8-year follow-up, theprimary outcome was number of substances used in thepast 6 months, to ensure that most stimulant treatmentreceived would have preceded substance use. Compo-nent variables included the following: ‘‘drunk’’ once ormore or drank alcohol three to four times or more; oneor more cigarettes/day in the past month (time frameexception specific to tobacco); marijuana two or moretimes; and any other illicit drug use or prescriptionmedication misuse. Secondary analyses explored eachclass of substances separately.
Substance Abuse or Dependence (SUD). DSM-IV abuseor dependence was based on a positive parentor child report with the Diagnostic Interview Schedulefor Children Version 2.3/3.0 (DISC48) at the 6- and 8-year follow-up assessments. The DISC includes bothlifetime and past-year diagnoses. The DiagnosticInterview Schedule–IV56 was used at the 8-year follow-up for participants 18 years or older (n ¼ 111). SUDwas defined as presence or absence of any abuse ordependence in the lifetime (excluding tobaccodependence, due to differences in the meaning ofabuse/dependence for tobacco versus othersubstances). Additional analyses explored SUD foralcohol, tobacco, and marijuana/other drugs (illicit ormisuse of prescription medications) separately.
For the analysis of stimulant treatment exposure overtime in relation to substance use at the 8-year follow-up,the primary outcome variable was SUD in the past yearfor any substance (excluding tobacco). Secondary ana-lyses explored alcohol and marijuana/other drug usedisorders separately.
Psychoactive Medication Treatment. In addition tooriginal randomized assignment to treatment, twomedication treatment variables were calculated fromresponses to the Services for Children andAdolescents–Parent Interview,57,58 administered at allassessments to collect a cumulative history ofmedication treatment since enrollment into the study.Detailed information about the children’s psychoactive
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medication over the follow-up period has beenpreviously reported.47 One variable was the proportionof days medicated with psychoactive medications inthe past year at each follow-up assessment. Thesemedications were predominantly stimulants butoccasionally included others (atomoxetine, guanfacine,and typical alternative medications such as clonidineand amitriptyline). A second medication variable wascalculated for the current study to reflect thecumulative number of days stimulant-treated sinceenrollment into the study.
Statistical AnalysisThe analyses addressed the following aims: first,ADHD versus LNCG group differences in substanceuse and SUD; second, prediction of substance use andSUD from original randomized treatment assignment,including covariation with concurrent (past-year) med-ication at each assessment; and third, prediction ofsubstance use and SUD from cumulative days stimu-lant treated since study entry (using propensity score-matching analysis).
The main analytic approach for the first and seconditems above was generalized estimating equation (GEE)analyses. As one method for mixed-effects regressionwith repeated measures, the GEE analysis is an exten-sion of the general linear model (for a relevant over-view, see Hedeker and Gibbons,59 especially pp. 131–146). These analyses tested whether groups (subjectgroup; assigned treatment group) differed as a functionof time. In contrast to traditional repeated measuresanalysis of variance, mixed-effects regression allowsinclusion of subjects with complete data on covariatesbut partial data on the outcome variable. Thus, allsubjects with substance use data from at least onefollow-up assessment between 24 months and the 8-year follow-up were included in tests of group � timeand treatment � time effects (n ¼ 520 ADHD; n ¼ 258LNCG). For the second aim, the ADHD sample size wasreduced to n ¼ 515 because of missing data formedication use. We tested individual point-in-timecontrasts, treating group and time as fixed effects andthe intercept as a random effect, to test the significanceof group differences overall and at each follow-upassessment. Because of some variability in age at eachassessment, age was controlled as a time-varyingcovariate (as expected, it was always a statisticallysignificant correlate). Power was sufficient (Z0.80) todetect small treatment group differences (effect sizeof Z0.28) at p o .05 or less. We used binary logitmodels for dichotomous outcomes and multinomialcumulative logit models for secondary analyses withordinal count variable outcomes. We explored whetherparticipant sex moderated results.
For the ADHD group only, effects of treatment weretested with three orthogonal contrasts following statis-tically significant treatment � time interactions (or
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for endpoint-only analyses, following statistically sig-nificant main effects of treatment): CombþMedMgtversus BehþCC, termed the MTA Medication Algo-rithm effect; Comb versus MedMgt, the Multimodalityeffect; and Beh versus CC, the Behavioral Substitutioneffect.60 Consistent with prior analyses,24,47 we alsotested an alternate set of planned contrasts in whichbehavioral treatment rather than medication algorithmwas the primary divider: CombþBeh versusMedMgtþCC (Intensive Behavioral effect); Comb ver-sus Beh (Medication Addition effect); and MedMgtversus CC (Intensity of Medication effect). Covariateswere site, age at each assessment and proportion ofdays medicated in the past year (time varying forrepeated-measures analyses); and p values greater than.025 are not reported as significant to adjust for alphainflation.
For the third aim (i.e., prediction of substance use andSUD from cumulative days stimulant treated since studyentry) , to test prediction of substance use and SUD fromthe number of days stimulant treated since study entry,we used propensity score matching analyses for ordinaltreatment variables.34 Propensity scores are calculated torepresent each subject’s propensity (e.g., probability) to bestimulant medicated over time as a function of baselinecharacteristics (e.g., original randomly assigned treat-ment, ADHD symptom severity, parent education, andfamily income). Participants with higher versus lowerstimulant medication exposure are matched on the basisof these scores to statistically control for observedcharacteristics that may influence the likelihood of naturalselection into treatment over time. We used matched pairanalyses to compare the substance use and SUD variablesbetween subjects with higher versus lower stimulantmedication exposure.
RESULTSMedication Use Over TimeTo explicate the medication variable, we firstexamined medication use over time. As pre-viously reported, proportion of days medicatedin the past year declined over time to mean ¼0.31(SD ¼ 0.42) by the 8-year follow-up. Incontrast, at 14 months, when study-deliveredtreatment ended, the mean (SD) was 0.71 (0.22)for Comb; 0.71 (0.24) for MedMgt; 0.54 (0.41) forCC; and 0.16 (0.28) for Beh. At the 8-year follow-up, only 32.5% (132/406 with complete medica-tion data) were medicated more than 50% of daysin the past year (versus 63.3% or 257/406 at 14months). Treatment was still predominantly withstimulants (83%) or stimulants plus nonstimulanttreatment (8%). Cumulative number of daysstimulant treated since enrollment into the study,
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with mean (SD) ¼ 1,402 (985) days (mean [SD] ¼3.84 [2.70] years), was correlated 0.91 with cumu-lative dose (p o .01).
ADHD Versus LNCGSubstance Use. There was a main effect of ADHDversus LNCG group between the 24-month and8-year follow-ups (w2
¼ 20.59, p o .0001). Figure 1depicts this finding as well as the expectedincrease in substance use with time. There wasa barely significant group � time interaction (p ¼.05), indicating variable group effects with time.Group differences were significant at each follow-up: 24 months (w2[1] ¼ 12.54, p o .01; 36 months,w2[1]¼ 6.65, p¼ .01; 6 years, w2[1]¼ 11.11; p o .01,8 years, w2
¼ 4.24, p ¼ .04). However, the largestgroup difference was at the 6-year follow-up,when 35% (n¼ 155/443) of ADHD and 19.5% (n¼44/226) of LNCG participants reported substanceuse. Results were similar for number of sub-stances used, with the ADHD versus LNCGgroup difference greatest at the 6-year follow-
FIGURE 1 Proportion of participants using substances (by nfollow-up assessments. ADHD ¼ attention-deficit/hyperactivity
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up, with 17.8% (n ¼ 79/443) of ADHD and 8%(n ¼ 18/226) of LNCG participants reporting useof two or more substances (Figure 1).Type of Substance. ADHD/LNCG group differenceswere apparent for all substances. There was amain effect of ADHD versus LNCG group foralcohol use between the 24-month and 8-yearfollow-ups (w2[1] ¼ 5.99, p ¼ .01), but nogroup� time interaction. There was a main effectof ADHD versus LNCG group for tobacco use(w2[1] ¼ 23.98, p o .01 [group � time interactionnot tested because no LNCG subjects reportedsmoking at 24 or 36 months]). Few participantswere sole tobacco users (i.e., did not also reportother substance use), comprising 3.6% ofparticipants with ADHD (n ¼ 15/420) versus 0%of those with LNCG. There were more dailysmokers at 8 years for ADHD (16.7%) than forLNCG (7.9%, w2[1] ¼ 10.58, p o .01). There was amain effect of ADHD versus LNCG group formarijuana and other drug use (w2[1] ¼ 16.47,p o .01), but no group � time interaction. Figure 2
umber of substances) from the 24-month through 8-yeardisorder; LNCG ¼ local normative comparison group.
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FIGURE 2 Percentage of participants reporting alcohol, tobacco, marijuana, and other drug use at each of the 6- and8-year follow-up assessments. ADHD ¼ attention-deficit/hyperactivity disorder; LNCG ¼ local normative comparisongroup.
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shows the expected age-related increases insubstance use and the high prevalence of alcoholand marijuana use for both groups at a mean ageof 17 years. The bar graphs also illustrate therelatively stronger contribution of tobacco andmarijuana use to the ADHD versus LNCG groupdifferences.
Rates of illicit drug use other than marijuana,including misuse of prescription medications,were low and not significantly different by group.At the 8-year follow-up, the following categoriesof substance use had base rates below 5%:amphetamines/ stimulants, barbiturates/seda-tives, opioids/narcotics, inhalants, hallucino-gens/ psychedelics, and cocaine. These ratesincluded misuse (more use than prescribed oruse without a prescription) of amphetamine/stimulant medications for the ADHD (8/422,1.9%) and LNCG (4/230, 1.7%) groups.SUD. There was a main effect of group (ADHDversus LNCG) on SUD, but no main effect oftime or group � time interaction. The groupeffect was statistically significant at 6 years[w2(1) ¼ 11.01, p o .01) but not at 8 years [w2(1) ¼1.45, p ¼ .23] (for rates, see Table 1).Type of SUD. For alcohol abuse or dependence,there was no main effect of group, no maineffect of time, and no group � time interaction(for all rates, see Table 1). For marijuana/other
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drug abuse or dependence, there was a maineffect of group, but no main effect of time andno group � time interaction. For nicotinedependence, there was a main effect of group,but no main effect of time. In each case, theADHD group had greater use. There were toofew nicotine dependence diagnoses amongLNCG subjects to evaluate a group � timeinteraction.Sex. There was no main effect of sex (w2[1] ¼.49, p ¼ .49), and no sex � ADHD groupinteraction (w2[1] ¼ 0.30, p ¼ .59) for substanceuse. Results were parallel for number ofsubstances used (w2[1] ¼ 0.82, p ¼ .37, and w2[1] ¼.19, p ¼ .66, respectively). There was a main effectof sex for SUD (w2[1] ¼ 4.21, p ¼ .04), with moreboys (17.9%) than girls (11.5%) meeting diagnosticcriteria. There was no group � sex interaction(w2[1] ¼ 0.02, p ¼ .89).
Associations With Treatment: Randomly Assignedin Childhood and Naturalistic Medication Use atEach Follow-up AssessmentSubstance Use. Neither treatment assignment atbaseline nor proportion of days medicated in thepast year was significantly associated with sub-stance use at any follow-up assessment. For theorthogonal treatment contrasts, p values ranged
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TABL
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Perc
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(n)of
Parti
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nts
Repo
rting
Subs
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ADOLESCENT SUBSTANCE USE IN MTA
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from .03 to .96 (with an average p ¼ .52). Theoverall effect of the proportion of days medicatedwas not significant (p ¼ .68), and there were nostatistically significant medication � time inter-actions. Removal of the treatment group assign-ment variables (because of their correlationwith medication treatment at the earlier assess-ments) did not change the pattern of results(p ¼ .59). Results were similar for number ofsubstances used.Type of Substance. There were no statisticallysignificant associations between originalrandomized treatment assignment and alcoholuse (average p ¼ .60), tobacco use (average p ¼.26), or marijuana/drug use (average p ¼ .58; pvalues ranged from .07 to .99). There were also noassociations between these variables and ADHDmedication use at each assessment for alcoholuse (average p ¼ .55), for tobacco use (averagep ¼ .25), or for marijuana/drug use (average p ¼.46; p values ranged from .22 to .82).SUD. Neither randomized treatment assign-ment nor proportion of days medicated inthe past year was a statistically significantpredictor (correlate) of SUD at the 6- and 8-yearassessments. Across all orthogonal treatmentcontrasts and across assessments, the average pvalue for the treatment group comparisons wasp ¼ .51 (range ¼ 0.03 to 0.89). The associationbetween proportion of days medicated andSUD was also not statistically significant (p ¼.37), and did not interact with assessment point(p ¼ .48). There were no statistically significantassociations for any specific type of SUD(alcohol, marijuana/drug) or for nicotinedependence.
Cumulative Psychostimulant Treatment andSubstance Use/SUD at 8 yearsFor the propensity score–matching analyses, andfollowing Lu et al.,34 the distribution of days thatparticipants had been stimulant treated sincebaseline was divided into five ordinal groupsusing quintile cut points. Using an ordinal logitmodel, quintile group membership was regressedon 36 variables selected for their potential topredict stimulant treatment. These includeddemographics (e.g., parent education and income;marital status; child sex), receipt of medication orschool assistance before study enrollment, ADHDsymptom severity (parent and teacher report),parent-reported impairment, other behavior
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problems (e.g., parent and teacher ratings ofoppositionality and aggression), social skills rat-ings by parent and teacher, achievement testscores and IQ estimate, parental psychopathology(mood, anxiety, alcohol, or drug use disorder),and original randomly assigned treatment group(references for these variables are reported else-where45). A propensity score was produced foreach participant, estimated from baseline char-acteristics, as an index of the likelihood of recei-ving more or fewer days of stimulant treatmentbetween baseline and the 8-year follow-up. Parti-cipants were subsequently matched in pairs tominimize their differences in propensity scoresand to maximize their differences in days ofstimulant treatment. These matched pairs pro-duced two groups distinguished by more versusfewer days treated. As planned, the groupsdiffered significantly in days stimulant treated(mean ¼ 2071.10, SD ¼ 728.87, versus mean ¼763.08, SD ¼ 765.00; p o .01), but not in any ofthe 36 baseline variables (all p values 4.10;average p value ¼ .70).
Matched-pair analyses were performed foreach of the primary (composited number ofsubstances used; any SUD excluding nicotinedependence) and secondary (alcohol; marijuana;cigarettes; SUD for alcohol and SUD for mari-juana/other drugs) variables. There were nostatistically significant associations for any ofthese variables. For the primary outcomes, theWilcoxon signed-rank test for number of sub-stances used was z ¼ �0.34 (p ¼ .73); McNemar’sw2 test for any SUD was w2(1) ¼ .44 (p ¼ .62). Forthe exploratory analyses, the following valueswere observed: alcohol use, z ¼ 0.42, p ¼ .68;cigarettes, z ¼ �.54, p ¼ .59; marijuana, z ¼ .45,p¼ .65, alcohol abuse or dependence, w2(1)¼ 0.00,p ¼ 1.00; marijuana or other drug abuse ordependence, w2(1) ¼ 1.29, p ¼ .35.
DISCUSSIONThe current study first tested the hypothesis thatchildren with combined type ADHD haveincreased risk of substance use and SUD inadolescence. We found a significantly higherprevalence of substance use by adolescents with,versus former classmates without, ADHD his-tories. Overall group differences for any SUD(DSM-IV abuse or dependence) were alsoobserved, but these occurred at comparativelylower rates and only at certain ages—for
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marijuana and nicotine only—highlighting theimportance of measuring levels of certain types ofsubstance use in adolescence that may foresha-dow later SUD in adulthood. The multi-sitenature of the MTA sample, including demo-graphic diversity (e.g., 20% female) and otherunique sample features (discussed below), under-scores for the first time the significance of thesubstance abuse risk for both boys and girls withchildhood ADHD. This finding renders all themore important our examination of treatmenteffects on substance use/SUD risk. Across allanalyses, we found no associations to suggesteither harm or benefit from medication (mostlystimulant) treatment in regard to rates of adole-scent substance use or SUD. As this is the firststudy to include a period of randomized assign-ment to treatment and the first to consider a largenumber of potential confounding variables, thesefindings are the strongest test to date of theassociation between medication for ADHD andadolescent substance abuse.
About one-third of the ADHD group was athigh risk of adult substance abuse because ofteenage substance use. At the 6-year follow-up(mean age ¼ 15 years), group differences weregreatest with 35% of the ADHD versus 20%of the LNCG (non-ADHD) groups reportinguse of one or more substances. We alsodetected group differences in SUD at the 6-yearfollow-up, but for fewer participants, which mayexplain other smaller studies’ failure to findstatistically significant group differences inadolescent SUD.50,61 A large body of researchhas shown that substance use at a young ageis prognostic of later abuse/dependence inadulthood.62,63
Marijuana and tobacco use most clearly dis-tinguished the ADHD from LNCG groups. Only afew longitudinal studies of children diagnosedwith ADHD have examined marijuana use, asopposed to abuse/dependence, and the resultshave been mixed.50,64,65 In the current study,approximately one-third of the children withADHD (32%) had used marijuana more than onceby the 8-year follow-up (mean age ¼ 17 years)versus 24% of the LNCG. These high rates for bothgroups reflect the increasing use of marijuana byteens in the United States,66 but they also indicatea relatively higher risk of marijuana use for teenswith ADHD histories. This risk was barely visiblein early adolescence but was more pronounced bythe 6-year follow-up assessment (mean age ¼
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15 years). At this age, nearly one-fifth of ourparticipants with ADHD histories reported mar-ijuana use more than once, indicating that thisoutcome variable should be directly targeted forfuture research. Marijuana use at a young age hasbeen associated with a host of negative cognitivesequelae such as neurocognitive decrements,decreased academic achievement, and poor phy-sical health.67–72 Given the long-term impact ofchildhood ADHD on educational, occupational,and psychosocial outcomes of adulthood,9 earlyand chronic marijuana use has the potential tosignificantly undermine later adult accomplish-ments for this population.
Tobacco use (mostly cigarette smoking) wasstrongly associated with childhood ADHD.Nearly one-quarter (24%) of the ADHD samplehad smoked cigarettes or used other forms oftobacco more than a few times by the 8-yearfollow-up, and 17% were already daily smokers.These are high rates compared with both theLNCG (8% were daily cigarette users) and dailysmoking rates for high school students nationally,(11.2%).73 Combined with other reports of highrates of daily smoking from single-site sam-ples,9,74 these findings suggest that the MTAchildren are headed toward a previously pro-jected4 doubling of the 19.3% adulthood preva-lence of smoking currently found in the UnitedStates.75 Cigarette use that begins in adolescenceis associated with an increased likelihood ofnicotine addiction into adulthood and with alower likelihood of successful quitting.76,77 Ado-lescent cigarette use is also associated with useand abuse of other drugs78—a finding stronglysupported by the current data for both the ADHDand non-ADHD groups. In fact, we found extre-mely few adolescents who reported using onlycigarettes.
Assignment to study treatments in childhooddid not predict any high school–aged substance-related outcomes. This result parallels our pre-viously reported failure to find treatment groupdifferences for any of the other 24 symptom,behavioral, and functioning (e.g., arrests, psychia-tric hospitalizations) variables examined at thesame age.48 Many of these variables (e.g., impul-sivity, conduct problems, academic achievement)are long-established predictors of substance usefor teens in the general population,79–81 and inother studies they correlate with substance use foradolescents with ADHD histories.12,82,83 To theextent that these variables were not differentially
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affected by assigned treatment group in child-hood,47 it follows that substance use/SUD inadolescence should not be differentially affected.Collectively, these findings are in stark con-trast to the widely held belief that starting treat-ment early (here, mean age ¼ 8 years) will, inand of itself, change long-term outcomes thatalso include SUD vulnerability. Instead, treat-ments found to be efficacious for adolescentsubstance abuse84,85 may hold some promisefor this population because of their targeting ofthe same behavioral, academic, and familial/peer vulnerabilities known to accompany adole-scent ADHD.
Our findings did not provide any evidence thatADHD medication protects from, or increases riskfor, adolescent substance use or SUD. This findingheld for recent medication and for days cumula-tively treated with stimulants. Unmeasured con-founders may have been operating because of thenaturalistic follow-up study design, and we didnot statistically control for psychopathology andfunctioning at the follow-up assessments. How-ever, we conducted the most carefully controlledanalysis to date using a statistical method (pro-pensity score matching analysis) that allowedconsideration of multiple baseline factors thatmight contribute to long-term medication treat-ment or termination. Moreover, our prior reportthat medication at high school age does not relateto multiple indicators of symptoms and dailyfunctioning47 revealed that addition of thesecovariates was not indicated. Our findings conflictwith anecdotal clinical experience with long-termpatients who appear to derive benefit fromcontinued medication. We speculate that theseexperiences reflect a subset of well-monitoredteens who, unlike the majority of our sample,continue to be accepting of medication recom-mendations and other concurrent protective stra-tegies and/or may have a low risk profile fordeveloping substance use.
The observed lack of associations betweenstimulant exposure over time and adolescentsubstance use/SUD do not discount the possibi-lity that brain-based changes in neural mechan-isms underlying addiction vulnerability areoccurring as a function of prolonged stimulanttreatment. Volkow et al. recently demonstrated,for stimulant-naive adults with ADHD, an up-regulation of the dopamine transporter anddecreased D2 receptor availability in the striatumafter 1 year of methylphenidate treatment.86,87
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Although demonstrated with adults, these find-ings heighten concern about potential stimulant-related adjustments in dopaminergic mechanismspertinent to drug abuse vulnerability. On theother hand, Castellanos et al. suggested neuro-protection from stimulant use in childhood andadolescence for youth with ADHD.88 Studies ofdifferential responsivity to drug challenges inadulthood as a function of prior stimulant treat-ment would be useful. In adolescence, as in thepresent study, other factors that increase sub-stance abuse risk, such as parental and peerinfluences, may outweigh developing biologicalvulnerability.
The substance use/SUD outcomes for the MTAshould be considered in the context of severalunique study features and limitations. All of thechildren in the MTA were diagnosed with thecombined type of DSM-IV ADHD, and general-ization of study results should generally notextend beyond this subtype. It is interesting thatconduct disorder, a common comorbidity for thecombined subtype that is also a strong proximalmediator of ADHD-related substance use/SUD,was infrequent. Compared with other clinic-based longitudinal studies of ADHD, for whichCD rates range from 25% to 30% for boys64,83 and18% to 23% for girls,38,39 the MTA rate of 8% at the8-year assessment,47 and even the rate of 14% atbaseline,44 is low. The sample recruitment thatincluded, in addition to psychiatric clinics, pedia-trician offices and school settings41 may havecontributed to this finding89. Our follow-upassessments, which relied on self-report and oftenwith 2-year windows, may have missed episodesof substance use, and rates may be underesti-mated. Similarly, the precision of our treatmentdata relies on the accuracy of parent recall at eachassessment, which no doubt includes somedegree of error.
Thus, children with ADHD have increased riskof substance use, in particular tobacco and mar-ijuana, against a backdrop of high rates of alcoholuse for all teens, and our standard treatments(short-term psychosocial, medication) for ADHDhave not, to date, countered this risk. Given theubiquity of medicinal treatment for ADHD andour failure to find relations between ADHDmedication treatment and adolescent substanceuse/SUD, the findings argue for additionalresearch on neurobiological and psychosocialfactors that fuel the vulnerability and can informthe types of treatment so urgently needed. &
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Accepted December 19, 2012.
Dr. Molina is with the University of Pittsburgh. Dr. Hinshaw is with theUniversity of California–Berkeley. Drs. Arnold and Lu are with Ohio StateUniversity. Drs. Swanson and Wigal are with the University of California–Irvine. Dr. Pelham is with Florida International University. Dr. Hechtman iswith McGill University. Dr. Hoza is with the University of Vermont. Dr.Epstein is with the University of Cincinnati. Dr. Abikoff is with New YorkUniversity. Drs. Greenhill and Marcus are with Columbia University. Dr.Jensen is with the REsource for Advancing Children’s Health (REACH)Institute and the Mayo Clinic. Dr. Wells is with Duke University. Dr. Vitiellois with the National Institute of Mental Health (NIMH). Dr. Gibbons is withthe University of Chicago. Dr. Howard is with the University of NorthCarolina. Dr. Houck is with the University of Pittsburgh Medical Center.Dr. Hur is with Hines Veterans Affairs Hospital and the University ofIllinois–Chicago.
The work reported was supported by cooperative agreement grantsand contracts from NIMH and the National Institute on Drug Abuse(NIDA) to the following: University of California–Berkeley: U01MH50461, N01MH12009, and HHSN271200800005-C;DA-8-5550; Duke University: U01 MH50477, N01MH12012,and HHSN271200800009-C; DA-8-5554; University of California–Irvine:U01MH50440,N01MH12011,andHHSN271200800006-C; DA-8-5551; Research Foundation for Mental Hygiene (New YorkState Psychiatric Institute/Columbia University): U01 MH50467, N01MH12007, and HHSN271200800007-C; DA-8-5552; LongIsland–Jewish Medical Center U01 MH50453; New York University:N01MH 12004, and HHSN271200800004-C; DA-8-5549;University of Pittsburgh: U01 MH50467, N01 MH 12010, andHHSN271200800008-C; DA-8-5553; and McGill UniversityN01MH12008, and HHSN271200800003-C; DA-8-5548. TheOffice of Special Education Programs of the U.S. Department ofEducation, the Office of Juvenile Justice and Delinquency Prevention ofthe Justice Department, and NIDA also participated in funding.
Drs. Gibbons, Howard, Hur, Lu, and Marcus served as the statisticalexperts for this research.
The Multimodal Treatment Study of Children with Attention-Deficit/Hyperactivity Disorder (ADHD) (MTA) was an NIMH cooperativeagreement randomized clinical trial involving six clinical sites. Colla-borators from NIMH: Peter S. Jensen, M.D., of the Mayo Clinic; L.Eugene Arnold, M.D., M.Ed., of Ohio State University; Joanne B.Severe, M.S., of the Clinical Trials Operations and Biostatistics Unit,Division of Services and Intervention Research; Benedetto Vitiello,M.D., of the Child and Adolescent Treatment and Preventive Interven-tions Research Branch; Kimberly Hoagwood, Ph.D., of ColumbiaUniversity; previous contributors from NIMH to the early phase: JohnRichters, Ph.D., of the National Institute of Nursing Research; andDonald Vereen, M.D., of NIDA. Principal investigators and co-investigators from the clinical sites are: University of California–Berkeley/San Francisco: Stephen P. Hinshaw, Ph.D., of Berkeley;and Glen R. Elliott, Ph.D., M.D., of San Francisco; Duke University: C.Keith Conners, Ph.D.; Karen C. Wells, Ph.D.; John March, M.D.,M.P.H.; and Jeffery Epstein, Ph.D.; University of California–Irvine/LosAngeles: James Swanson, Ph.D., of Irvine; Dennis P. Cantwell, M.D.,deceased, of Los Angeles; and Timothy Wigal, Ph.D., of Irvine; LongIsland Jewish Medical Center/Montreal Children’s Hospital: HowardB. Abikoff, Ph.D., of New York University School of Medicine; and LilyHechtman, M.D., McGill University; New York State PsychiatricInstitute/Columbia University/Mount Sinai Medical Center: LaurenceL. Greenhill, M.D., of Columbia University; and Jeffrey H. Newcorn,M.D., of Mount Sinai School of Medicine; University of Pittsburgh:William E. Pelham, Ph.D., of Florida International University; BetsyHoza, Ph.D., of the University of Vermont; and Brooke Molina, Ph.D.Original statistical and trial design consultant: Helena C. Kraemer,Ph.D., of Stanford University. Follow-up phase statistical collaborators:Robert D. Gibbons, Ph.D., of the University of Illinois–Chicago; SueMarcus, Ph.D., of Mt. Sinai College of Medicine; and Kwan Hur,Ph.D., of the University of Illinois–Chicago. Collaborator from theOffice of Special Education Programs/US Department of Education:Thomas Hanley, Ed.D. Collaborator from Office of JuvenileJustice and Delinquency Prevention/Department of Justice: KarenStern, Ph.D.
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JOVO
The opinions and assertions contained in this report are the privateviews of the authors and are not to be construed as official or asreflecting the views of the Department of Health and Human Services,the National Institutes of Health (NIH), or NIMH.
Disclosure: Dr. Hinshaw has received an honorarium from the AmericanPsychological Association for his editorship of Psychological Bulletin. Dr.Arnold has received research funding from Curemark, Eli Lilly and Co.,and Shire; advisory board honoraria from Biomarin, Noven, SeasideTherapeutics, and Shire; and travel support from Noven. Dr. Swansonhas served on the advisory board of Noven Pharmaceuticals, and hasreceived travel support from Shire and Jannsen to attend separate,professional meetings. Dr. Pelham has received a research grant fromand has served on the advisory board of Noven Pharmaceuticals. Dr.Hechtman has received research funds from and has served on theadvisory boards and speakers’ bureaus for Eli Lilly and Co., Janssen,Ortho, Purdue, and Shire. Dr. Wigal has received research support andconsulting honoraria from, and has served on the speakers’ bureau forEli Lilly and Co., Noven, Rhodes, Otsuka, and Shire. Dr. Abikoff has
URNAL OF THE AMERICAN ACADEMY OF CHILD & ADOLESCENT PSYCHIATRY
LUME 52 NUMBER 3 MARCH 2013
received royalties from Multi-Health Systems regarding the Children’sOrganizational Skills Scale. Dr. Greenhill has received grant supportfrom Shire and Rhodes, and has served as a member of the ScientificAdvisory Board of BioBDX LLC. Dr. Jensen has received honoraria forthree keynote addresses given at European conferences on attention-deficit/hyperactivity disorder treatment outcomes (two from Shire andone from Janssen-Cilag), and has received a charitable donation fromShire. Dr. Wells has received royalties from Multi-Health Systems, thepublisher of Conners’ Rating Scales. Drs. Molina, Hoza, Epstein,Vitiello, Gibbons, Howard, Hur, Lu, and Marcus, and Ms. Houck reportno biomedical financial interests or potential conflicts of interest.
Correspondence to Brooke Molina, Ph.D., Department of Psychiatry,University of Pittsburgh School of Medicine, 3811 O’Hara Street,Pittsburgh, PA 15213; e-mail: [email protected]
0890-8567/$36.00/C 2013 Published by Elsevier Inc on behalf ofAmerican Academy of Child and Adolescent Psychiatry
http://dx.doi.org/10.1016/j.jaac.2012.12.014
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