Psychology of Addictive Behaviors 2016, Vol. 30, No. 1, 29 –38

© 2015 American Psychological Association 0893-164X/16/$12.00 http://dx.doi.org/10.1037/adb0000117

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Change Over Time in Adolescent and Friend Alcohol Use: Differential Associations for Youth With and Without Childhood Attention-Deficit/ Hyperactivity Disorder (ADHD) Katherine A. Belendiuk

Sarah L. Pedersen

University of California, Berkeley

University of Pittsburgh

Kevin M. King

William E. Pelham

University of Washington

Florida International University

Brooke S. G. Molina University of Pittsburgh Individuals with attention-deficit/hyperactivity disorder (ADHD) are at increased risk for experiencing alcohol-related problems by adulthood. However, few studies have examined contextual factors that may contribute to this risk. The current study examined 1 widely investigated social-contextual risk factor, friend alcohol use, in a sample of adolescents with and without a history of ADHD. One hundred and 59 adolescents (14 –17 years old) with childhood ADHD and 117 demographically similar youth without ADHD were interviewed annually in the Pittsburgh ADHD Longitudinal Study. Adolescents reported the frequency of their own alcohol use in the prior 12 months and the number of friends who used alcohol regularly or occasionally (perceived friend alcohol use). Multiple-group parallel process models indicated that increases in friend alcohol use were more strongly associated with increases in adolescent alcohol use over time for individuals with ADHD (r ⫽ .15, SE ⫽ 0.04; 95% confidence interval [CI] ⫽ [0.08, 0.22]) than for those without ADHD (r ⫽ .06, SE ⫽ 0.03; 95% CI [0.00, 0.11]). These results suggest that social factors are an important part of escalating alcohol use among adolescents with ADHD histories, and they highlight the possibility that interventions focused on the peer context could be important for these at-risk youth. Additional social network research on adolescent alcohol use within the larger context of other relationships (e.g., family and romantic relationships) is indicated. Keywords: adolescence, alcohol use, friend socialization, ADHD

According to the National Survey on Drug Use and Health, 9.5% of 14- and 15-year-olds have used alcohol in the past month, and this prevalence increases across adolescence, such that 22.7%

of adolescents have used alcohol in the past month by ages 16 and 17 (Substance Abuse and Mental Health Services Administration, 2014). Prior to age 18, a majority of American adolescents (59%) have consumed alcohol at least once in their lives (Substance Abuse and Mental Health Services Administration, 2014). Moreover, individual differences in alcohol use escalation are prognostic of later alcohol-related difficulties. Frequent and escalating alcohol use through adolescence is associated with poorer longterm outcomes in adulthood, including alcohol use disorders (Colder, Campbell, Ruel, Richardson, & Flay, 2002; Duncan, Alpert, Duncan, & Hops, 1997; Li, Duncan, & Hops, 2001), impaired neurocognitive functioning (Brown, Tapert, Granholm, & Delis, 2000), and aggression, theft, and suicidality (Duncan et al., 1997). Therefore, it is important to understand risk factors that contribute to these escalating patterns of drinking through adolescence to develop targeted interventions aimed at curtailing these negative outcomes. Individuals diagnosed with attention-deficit/hyperactivity disorder (ADHD) in childhood are vulnerable to heightened consequences from adolescent alcohol use. Recent meta-analyses suggest that these children are 1.35 to 1.74 times more likely to develop an alcohol use disorder by young adulthood (Charach, Yeung, Climans, & Lillie, 2011; Lee, Humphreys, Flory, Liu, &

This article was published Online First October 5, 2015. Katherine A. Belendiuk, Institute of Human Development, University of California, Berkeley; Sarah L. Pedersen, Department of Psychiatry, University of Pittsburgh; Kevin M. King, Department of Psychology, University of Washington; William E. Pelham, Department of Psychology, Florida International University; Brooke S. G. Molina, Departments of Psychiatry and Psychology, University of Pittsburgh. This research was supported by National Institute on Alcohol Abuse and Alcoholism (AA) and National Institute on Drug Abuse (DA) Grants AA12342, AA011873, DA12414, and AA00202. Additional support was provided by National Institute of Mental Health (MH), National Institute of Environmental Health Sciences (ES), and KAI Research (KAI) Grants MH40564, MH50467, MH12010, ES05015, AA12342, DA016631, MH065899, KAI-118-S1, DA85553, MH077676, K01 AA021135, and T32 MH015442. Katherine A. Belendiuk holds stock/equity in Shire Pharmaceuticals. Correspondence concerning this article should be addressed to Brooke S. G. Molina, Departments of Psychiatry and Psychology, University of Pittsburgh, 3811 O’Hara Street, Pittsburgh, PA 15213. E-mail: molinab@ upmc.edu 29

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BELENDIUK, PEDERSEN, KING, PELHAM, AND MOLINA

Glass, 2011). In addition, although there is some variability in the results across studies, ADHD in childhood predicts adolescent alcohol use as well (Barkley, Fischer, Edelbrock, & Smallish, 1990; Molina & Pelham, 2003; Molina, Pelham, Gnagy, Thompson, & Marshal, 2007; Sibley et al., 2014). Prospective longitudinal studies of children without ADHD also find associations between early appearing personality features or other behaviors consistent with ADHD (e.g., impulsive, restless, distractible) and later alcohol-related outcomes (Caspi, Moffitt, Newman, & Silva, 1996; Mâsse & Tremblay, 1997; Niemela et al., 2006; Tarter, Kirisci, Habeych, Reynolds, & Vanyukov, 2004). In contrast to research on ADHD, multivariable vulnerability pathways to adolescent alcohol use have been well-studied in community samples (Chassin, Colder, Hussong, & Sher, 2013). Such research is only just beginning to accumulate for children with ADHD (Molina & Pelham, 2014). Impairments in daily life functioning experienced by individuals with ADHD provide clues to candidate explanatory variables for alcoholism vulnerability in this population. Between 50% and 80% of children diagnosed with ADHD experience problems from their symptoms through adolescence and into adulthood (Barkley, 1998). Common impairments include educational (e.g., Loe & Feldman, 2007), occupational/vocational (Kuriyan et al., 2013), cognitive (Martinussen, Hayden, Hogg-Johnson, & Tannock, 2005; Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005), and social (Hoza, 2007) impairments. Deficits in social functioning may be particularly important for alcohol vulnerability among adolescents with ADHD histories because the peer context is especially salient for teen drinking. Alcohol use in adolescence is largely a social behavior (Johnson, O’Malley, Bachman, & Schulenberg, 2006), with approximately 75% of adolescent alcohol users reporting that they drink with friends (Creswell, Chung, Clark, & Martin, 2013). Due to their difficulties with acquiring and maintaining healthy friendships (Hoza, 2007), adolescents with ADHD may be more likely than those without to befriend peer drinkers and to share in the drinking activities. A well-developed literature exists outside of research on ADHD on the contribution of the peer environment to adolescent drinking behavior. Having a friend who gets drunk is the strongest predictor of binge drinking behavior in 8th and 10th graders (Patrick & Schulenberg, 2010), and the alcohol use of a social network predicts changes in alcohol use from adolescence to early adulthood controlling for genetic and shared environmental effects (Cruz, Emery, & Turkheimer, 2012). Peer alcohol use, including perceived alcohol use by peers (often referred to as descriptive norms) also predicts early and escalating alcohol use for adolescents who are at elevated risk for substance use and is associated with subsequent alcohol dependence (Chassin, Pitts, & Prost, 2002; Voogt, Larsen, Poelen, Kleinjan, & Engels, 2013). In addition to the importance of peer drinking behavior (i.e., descriptive norms), peer attitudes toward alcohol (i.e., injunctive norms) have also been shown to be important predictors of adolescent alcohol use (e.g., Elek, Miller-Day, & Hecht, 2006; Voogt et al., 2013). Thus, the importance of peer alcohol use, and especially perceived alcohol use norms, have been shown in many non-ADHD studies to be a crucial proximal indicator of alcohol vulnerability for adolescents, including high-risk adolescents. Our research group has tested the hypothesis that childhood ADHD predicts more friends who drink alcohol, and that the

association between peer drinking and adolescent alcohol use is stronger in the presence of an ADHD history. We found initial cross-sectional support for these hypotheses (Marshal, Molina, & Pelham, 2003). Specifically, among 142 teens with, and 100 teens without, childhood ADHD, (a) those with ADHD reported more friends who used and tolerated alcohol and other substance use, and (b) the association between friend substance use (and friend tolerance of substance use) and the adolescent’s own substance use was stronger for the ADHD than non-ADHD group (Marshal et al., 2003). These findings suggest that ADHD-related alcoholism vulnerability may be partly explained by a socially mediated context in which drinking begins in adolescence. However, that prior study did not examine the degree to which these variables change over time, limiting the inferences that may be drawn. For example, as children with ADHD age through adolescence and opportunities for autonomous decision-making increase, they may acquire friendships that support drinking behavior at a faster rate than children without ADHD. Moreover, given their difficulties with inhibitory control and coping skills, the extent to which their own drinking increases alongside an alcohol-infused peer context is unknown. A longitudinal study of these variables over time would address this concern. The current study provides a much-needed expansion of Marshal and colleagues’ (2003) findings by using a different and longitudinally followed sample of children with ADHD to examine the prospective association between descriptive (i.e., perceived friend alcohol use) and injunctive (i.e., perceived friend tolerance of adolescent alcohol use) norms and adolescent alcohol use for individuals with and without childhood ADHD. Based on prior findings (Marshal et al., 2003), we have the following hypotheses. We expect that adolescents with childhood ADHD will report more friends who use alcohol and who tolerate its consumption, we expect a faster rate of growth in friend alcohol use and friend tolerance of adolescent alcohol use, and we expect that there will be a stronger association over time (i.e., a tighter linkage) between friend alcohol use (and friend tolerance of adolescent alcohol use) and adolescent alcohol use for adolescents with versus without ADHD histories.

Method Participants Participants with ADHD. Three hundred sixty-four individuals with ADHD were recruited for follow-up from a pool of 516 children (70.5% participation rate) previously diagnosed with DSM–III–R or DSM–IV ADHD at the Attention-Deficit Disorder (ADD) Clinic at the Western Psychiatric Institute and Clinic in Pittsburgh, PA from 1987 to 1996. Age at initial evaluation ranged from 5 to 17 years old (M ⫽ 9.40, SD ⫽ 2.27), with 90% in their elementary school-aged years (ages 5 to 12). All participants with ADHD attended the Summer Treatment Program for children with ADHD, an 8-week intervention that included behavioral modification, parent training, and psychoactive medication trials where indicated (Pelham & Hoza, 1996). Of the 516 potential participants, 23 could not be located at follow-up and 129 refused or failed to participate. Participating individuals with ADHD were compared to nonparticipating individuals with ADHD on demographic (e.g., age at first treatment, race, parental education level

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GROWTH IN TEEN AND FRIEND DRINKING BY ADHD STATUS

and marital status) and diagnostic (e.g., parent and teacher ratings of ADHD and related symptomatology) variables. Only one of 14 comparisons was significant at the p ⬍ .05 significance level; participants had a slightly lower average conduct disorder (CD) symptom rating than nonparticipants (participants M ⫽ .43, nonparticipants M ⫽ .53, Cohen’s d ⫽ .30). ADHD participants were between the ages of 11 and 28 at the time of their first follow-up interview in the Pittsburgh ADHD Longitudinal Study (PALS), with the majority (99%) falling between 11 and 25 years of age with an average of 8.35 (SD ⫽ 2.79) years having elapsed since the ADHD participants’ initial assessment in the ADD program. Diagnostic information for the ADHD participants was collected in childhood using several sources, including the parent and teacher DBD Rating Scale, which assesses the DSM–III–R and DSM–IV symptoms of the disruptive behavior disorders (Pelham, Gnagy, Greenslade, & Milich, 1992). Parents completed a semistructured diagnostic interview with doctoral-level clinicians consisting of the DSM–III–R or DSM–IV descriptors for ADHD, oppositional defiant disorder (ODD), and CD, with supplemental probe questions regarding situational and severity factors. The interview also included queries about other comorbidities to determine whether additional assessment was needed (instrument available at http://ccf.buffalo.edu/resources_downloads.php). Following DSM guidelines, diagnoses were made if a sufficient number of symptoms were endorsed (considering information from both parents and teachers) to result in diagnosis. Two doctorallevel clinicians independently reviewed all ratings and interviews to confirm the DSM diagnoses. When the two clinicians disagreed, a third clinician reviewed the file and the majority decision was used. Exclusionary criteria for participation in the follow-up study were also assessed in childhood: a full scale IQ less than 80, a history of seizures or other neurological problems, and/or a history of pervasive developmental disorder, schizophrenia, or other psychotic or organic mental disorders. Participants without ADHD. Two hundred forty participants without ADHD were recruited from the Pittsburgh area between 1999 and 2001 for their demographic similarity to the participants with ADHD at follow-up (e.g., age range between 11 and 25). Most of the minors were recruited through several large pediatric practices in Allegheny County serving patients from diverse socioeconomic backgrounds (40.8% of the non-ADHD sample). The remaining participants without ADHD were recruited via advertisements in local newspapers and the university hospital staff newsletter (27.5%), local universities and colleges (20.8%), and other methods (Pittsburgh Public Schools, word of mouth, etc.). A telephone screening interview administered to parents gathered basic demographic characteristics, history of diagnosis and treatment for ADHD and other behavior problems, presence of exclusionary criteria as previously listed for participants with ADHD, and a checklist of ADHD symptoms. ADHD symptoms were counted as present if reported by either the parent or young adult. Individuals who met DSM–III–R criteria for ADHD (presence of eight or more symptoms)— either currently or historically—were excluded. Participants with subthreshold ADHD symptomatology, or other psychiatric disorders, were retained. The participants without ADHD were selected to ensure that the two groups were equivalent in proportion on several demographic characteristics. As a result, the ADHD and non-ADHD participants did not differ in age (for ADHD, M ⫽ 17.74, SD ⫽ 3.38; for

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non-ADHD, M ⫽ 17.17, SD ⫽ 3.16), gender (for ADHD, 89.6% male; for non-ADHD, 88.7% male), racial minority (for ADHD, 18.4% were minority race; for non-ADHD, 15.4% were minority race), the last grade completed in school (for ADHD, M ⫽ 11.34, SD ⫽ 2.79; for non-ADHD, M ⫽ 11.78, SD ⫽ 3.36), and highest parent education (for ADHD, M ⫽ 7.14, SD ⫽ 1.62; for nonADHD, M ⫽ 7.41, SD ⫽ 1.65, on a scale of 1 (⬍7th grade education), 7 (associate’s or 2-year degree), to 9 (graduate professional training). A higher percentage of participants with ADHD than without ADHD were from single-parent households (33.2% of ADHD vs. 23.6% of non-ADHD, p ⬍ .05), parents of participants with ADHD had lower incomes than parents of participants without ADHD (for ADHD, M ⫽ $62,959, SD ⫽ 47,971; for non-ADHD, M ⫽ $76,091, SD ⫽ 58,140, p ⬍ .01), and more participants with than without ADHD had been adopted (8.0% of ADHD vs. 0.4% of non-ADHD, p ⬍ .01). Subsample for the current study. Data were selected from the first 8 annual follow-up interviews of the PALS for participants who were 14 to 17 years old at any of these assessments (n ⫽ 276; 159 ADHD, 117 non-ADHD). Younger teens were excluded because of their smaller numbers and low rates of drinking (Molina et al., 2007); 18-year-olds were excluded because of the associated educational and residential transitions at that age that have implications for alcohol use. Because of age at first interview, some participants did not have any data between ages 14 and 17 (e.g., a participant whose baseline assessment occurred at age 18) and some had partial data for ages 14 to 17 (e.g., a participant whose baseline assessment was at age 9 would complete their eighth follow-up assessment at age 14 and would not have data for ages 15–17); therefore, data were missing by study design. As in Molina et al. (2012), we modeled alcohol use by age (Bollen & Curran, 2006). For example, for those who were 15 years old at the first annual interview, their alcohol use at ages 15, 16, and 17 was included in the analyses. This resulted in the following numbers of participants providing data one (42 ADHD, 23 non-ADHD), two (38 ADHD, 23 non-ADHD), three (46 ADHD, 31 non-ADHD), or four (38 ADHD, 43 non-ADHD) times. The procedure also resulted in the following numbers of participants in the ADHD and non-ADHD groups providing data for alcohol use at ages 14 (54 ADHD, 50 non-ADHD), 15 (92 ADHD, 77 non-ADHD), 16 (113 ADHD, 91 non-ADHD), and 17 (140 ADHD, 116 nonADHD). There were no statistically significant differences between the ADHD groups on gender (␹2 ⫽ 0.01, ns) or racial minority status (␹2 ⫽ 2.64, ns), but highest parental education was lower in the ADHD group (␹2 ⫽ 16.07, p ⬍ .01) and youth with ADHD were more likely to have single parent-headed households (␹2 ⫽ 11.47, p ⬍ .001).

Procedure Follow-up interviews in adolescence were conducted by postbaccalaureate research staff. Interviewers were not blind to recruitment source (i.e., presence or absence of ADHD in childhood), but they were trained to avoid bias in data collection. Moreover, many of the PALS questionnaires were completed privately by participants (e.g., alcohol consumption measures), which helps to minimize interviewer contamination. Informed consent was obtained and all participants were assured confidentiality of all disclosed material except in cases of

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BELENDIUK, PEDERSEN, KING, PELHAM, AND MOLINA

impending danger or harm to self or others (reinforced with a DHHS Certificate of Confidentiality). In cases where distance prevented participant travel, information was collected through a combination of mailed and telephone correspondence; home visits were offered as need dictated. Self-report questionnaires were completed either with paper and pencil or web-based versions on a closed-circuit Internet page.

100 friends (n ⫽ 9) were recoded to 100 with the resulting variable (M ⫽ 33.2, SD ⫽ 27.57) having skew below 3 (skew ⫽ 1.38). Means, standard deviations, skewness, and correlations between outcome variables for each group (non-ADHD and ADHD) are presented in Table 1.

Measures

Descriptive analyses were conducted with SPSS 20.0, and latent growth curve modeling with MPlus 6.0 (Muthén & Muthén, 2010) was used to test study hypotheses. All data were analyzed using bias-corrected 95% bootstrapped confidence intervals (CIs) to account for nonnormal data. Because we were interested in changes in alcohol use and friend alcohol use across adolescence, we arranged our data according to age rather than by year of the annual interview to explicitly model the trajectories of study variables across ages 14 –17. Initial models were estimated separately for friend alcohol use and friend alcohol tolerance. As the results for these models were similar, the results for friend alcohol use are primarily presented below with important model differences in alcohol tolerance presented secondarily. To examine the association between adolescent and friend alcohol use, unconditional growth models were first tested to examine the growth pattern in each study variable from ages 14 to 17. We estimated linear growth curve models (i.e., loadings for the slope factors were specified as 0, 1, 2, and 3 for ages 14, 15, 16, and 17, respectively) to estimate the level of adolescent alcohol use or friend alcohol use at age 14 (i.e., intercept factor) and the growth rate per year based on the repeated measures from ages 14 to 17 (i.e., slope factor). We then estimated a parallel process latent growth curve model to examine the relations between growth in adolescent and friend alcohol use. We allowed the slope and intercept factors to covary. We modeled the concurrent relations between the intercept factors (i.e., adolescent alcohol use intercept with friend alcohol use intercept) and between the slope factors (i.e., adolescent alcohol use slope with friend alcohol use slope; see Figure 1). In addition, we modeled the association between adolescent alcohol use intercept and friend alcohol use slope, and the association between friend alcohol use intercept and adolescent alcohol use slope. Covariates (gender, race, number of friends) were included as predictors of both the intercept and slope parameters in all models. Inclusion of parental education and marital status worsened model fit, but model parameter estimates did not change; thus, these covariates were excluded. To examine the differences in the relations between parameters for the ADHD and non-ADHD groups, each of the models were tested in a multiple group framework using childhood ADHD diagnosis as the grouping variable. First, to avoid capitalizing on multiple comparisons, a fully constrained model was compared to a fully unconstained model. Next, following a significant decrement in model fit for the fully constrained model (presented in the Results), multiple group comparisons began with an unconstrained model where all of the parameter estimates were allowed to vary across groups. Subsequently, each parameter estimate was individually equated across groups while using Wald chi-square testing (Chou & Bentler, 1990) to determine whether constraining the specified parameter estimate produced a significant decrement in model fit. This decrement indicated that the strength of the asso-

Frequency of alcohol use. Alcohol use was evaluated annually with a structured paper-and-pencil substance use questionnaire (SUQ) that was an adaptation of existing measures, including the Health Behavior Questionnaire (Jessor, Donovan, & Costa, 1989) and National Household Survey of Drug Abuse interview (United States Department of Health and Human Services, Public Health Service, Alcohol, Drug Abuse and Mental Health Administration, 1992). The SUQ includes both lifetime exposure and use questions (e.g., have you ever had a drink, have you ever been drunk, age of first drink) and recent quantity/frequency questions. Frequency of alcohol use was used as the outcome of interest in the current study with the following question: “In the past 12 months, how often did you drink beer, wine, wine coolers, or liquor?” Response options were 0 (not at all), 1 (1–3 times), 2 (4 –7 times), to 11 (several times a day). The percent of adolescents who used alcohol in the past year was 14% for 14-year-olds, 25% for 15-year-olds, 40% for 16-year-olds, and 52% for 17-year-olds. As previously reported, ADHD group differences were not observed for frequency of alcohol use in the past year (Molina et al., 2007, 2012). Friend alcohol use and tolerance. Adolescents reported how many of their friends used alcohol occasionally (one item) and regularly (one item). They also reported how their close friends would feel about them using alcohol occasionally (one item) and regularly (one item). These items were adapted from the Monitoring the Future Study (Johnston, O’Malley, & Bachman, 1988). Response options for the two friend alcohol use variables ranged from 1 (none) to 6 (all) on a 6-point scale. The average response across these two items was analyzed. Across time, correlations between regular and occasional alcohol use ranged from 0.75 to 0.81. Response options for the two friend tolerance variables ranged from 1 (strongly disapprove) to 5 (strongly approve) on a 5-point scale. The average response across these two items was analyzed. Across time, correlations between regular and occasional alcohol tolerance ranged from 0.75 to 0.85.

Covariates Demographic variables. Participants provided self-report of their gender (0 ⫽ female; 1 ⫽ male) and race (0 ⫽ non-White; 1 ⫽ White). Number of friends. The number of friends in an adolescent’s social network, used to control for network size, was assessed at each wave using the open-ended question, “About how many friends do you have?” from the six-item Parents and Peers Questionnaire (Loeber, 1989). The response from the first administration of the questionnaire was used; the number of friends reported did not change over time (F ⫽ 0.83, ns) and the change in number of friends over time did not vary as a function of childhood ADHD/non-ADHD group (F ⫽ 1.521, ns). Reports of greater than

Data Analytic Strategy

.73 .42 .17 ⫺.17 1.76 (.82) 2.22 (1.00) 2.56 (1.02) 2.78 (.97)

Note. Values for individuals without ADHD are presented in the left two columns and below the diagonal. Values for individuals with ADHD are presented in the top two rows and above the diagonal. p ⬍ .05. ⴱⴱ p ⬍ .01. ⴱ

.19 .57ⴱⴱ .46ⴱⴱ — .30ⴱ .55ⴱⴱ — .66ⴱⴱ .09 .41ⴱⴱ .66ⴱⴱ .68ⴱⴱ .37ⴱⴱ .61ⴱⴱ .46ⴱⴱ .42ⴱⴱ .45ⴱⴱ .32ⴱ .31ⴱ .18

1.90 1.32 .50 .36 1.54 (.69) 2.08 (1.20) 2.57 (1.25) 2.84 (1.32)

.26 .32ⴱⴱ .45ⴱⴱ .45ⴱⴱ .36ⴱⴱ .30ⴱⴱ .29ⴱ .30ⴱⴱ .34ⴱ .37ⴱⴱ .18 .22

.22 .44ⴱⴱ .43ⴱⴱ .50ⴱⴱ

.20 .29ⴱ .68ⴱⴱ .56ⴱⴱ

— .19 .34ⴱ .22

.50ⴱⴱ — .47ⴱⴱ .40ⴱⴱ

.32ⴱ .55ⴱⴱ .55ⴱⴱ .67ⴱⴱ .49ⴱⴱ .53ⴱⴱ .52ⴱⴱ .36ⴱⴱ .34ⴱ .52ⴱⴱ .63ⴱⴱ — .33ⴱ — .53ⴱⴱ .58ⴱⴱ — .58ⴱⴱ .44ⴱⴱ .29ⴱ .26 .37ⴱⴱ .49ⴱⴱ .52ⴱⴱ .50ⴱⴱ .49ⴱⴱ .33ⴱⴱ .39ⴱⴱ .22 .30ⴱ .12 .22

.37ⴱⴱ .41ⴱⴱ .44ⴱⴱ .52ⴱⴱ

.25 .68ⴱⴱ — .69ⴱⴱ

.49ⴱⴱ .33ⴱ .27ⴱ .26

.36ⴱⴱ .67ⴱⴱ .64ⴱⴱ .46ⴱⴱ

.11 .46ⴱⴱ .42ⴱⴱ .54ⴱⴱ .37ⴱⴱ .38ⴱⴱ .43ⴱⴱ .45ⴱⴱ .31ⴱⴱ .32ⴱⴱ .55ⴱⴱ .48ⴱⴱ .44ⴱⴱ .36ⴱⴱ .34ⴱ .36ⴱⴱ .33ⴱⴱ .51ⴱⴱ .47ⴱⴱ .54ⴱⴱ .20 .50ⴱⴱ .57ⴱⴱ .52ⴱⴱ .34ⴱ .42ⴱⴱ .53ⴱⴱ .50ⴱⴱ .65ⴱⴱ .63ⴱⴱ .33ⴱⴱ .48ⴱⴱ .19 .62ⴱⴱ .60ⴱⴱ — .27 .72ⴱⴱ — .68ⴱⴱ .65ⴱⴱ — .59ⴱⴱ .62ⴱⴱ — .71ⴱⴱ .50ⴱⴱ .43ⴱⴱ 3.45 2.49 1.65 1.00

ADHD M (SD) Skewness Adolescent alcohol use Age 14 Age 15 Age 16 Age 17 Peer alcohol use Age 14 Age 15 Age 16 Age 17 Peer alcohol tolerance Age 14 Age 15 Age 16 Age 17

.26 (.75) .62 (1.34) .96 (1.52) 1.67 (1.94)

.49 (1.44) .70 (1.52) 1.35 (2.15) 1.74 (2.15) 1.59 (1.03) 1.86 (1.14) 2.22 (1.31) 2.57 (1.45) 1.95 (1.10) 2.11 (1.09) 2.27 (1.11) 2.40 (1.09) 3.06 2.53 1.48 1.45 2.43 1.66 1.00 .77 .86 .62 .38 .17

Age 16 Age 15 Age 14 Age 17 Age 16

Peer alcohol use

Age 15 Age 14 Age 17 Age 16 Age 15

Adolescent alcohol use

Age 14 Skewness

Non-ADHD

M (SD)

Table 1 Descriptive Statistics of and Correlations for Outcome Variables by Attention-Deficit/Hyperactivity Disorder (ADHD) Status

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Peer alcohol tolerance

Age 17

GROWTH IN TEEN AND FRIEND DRINKING BY ADHD STATUS

Adolescent Alcohol at age 14

Adolescent Intercept

Adolescent Alcohol at age 15

33

Adolescent Alcohol at age 16

-0.33 (0.13); [-0.59, -0.07]

Adolescent Alcohol at age 17

Adolescent Slope

-0.09 (0.06); [-0.20, 0.05] 0.61 (0.25); [0.16, 1.14]

0.11 (0.03); [0.06, 0.15]

0.03 (0.12); [-0.18, 0.30] Friend Use Slope

Friend Use Intercept -0.13 (0.05); [-0.21, -0.04]

Friend Use at age 14

Friend Use at age 15

Friend Use at age 16

Chi-square: 51.59, p = 0.04 RMSEA= 0.04 CFI= 0.97 TLI=0.96

Friend Use at age 17

Figure 1. Parallel process latent growth curve model for adolescent alcohol use and friend alcohol use. All parameter estimates and standard errors are standardized. 95% bias corrected bootstrap confidence intervals are included after the semicolon. Gender, race, and number of friends were included as covariates in the model but are not represented in the figure. RMSEA ⫽ root-mean-square error of approximation; CFI ⫽ comparative fit index; TLI ⫽ Tucker-Lewis index.

ciation for the specified parameter significantly differed as a function of ADHD status. If a group difference was found, then the parameter was freed to vary across groups in all subsequent iterations of model testing; if not, then the parameter was constrained to be equal across groups in all subsequent iterations of model testing. Therefore, a model building approach was used where each path was tested individually but in the context of the other parameters in the model. We assessed model fit using chi-square as an indicator of exact fit. Where exact fit was not achieved (as chi-square is sensitive to violations of normality and sample size, Hu & Bentler, 1999), we used relative fit indices, specifically the Tucker-Lewis index (TLI), comparative fit index (CFI), and root-mean-square error of approximation (RMSEA). Using these indices, we judged model fit with reference to standards provided by Hu and Bentler (1999) and the cautions of Marsh, Hau, and Wen (2004), and we examined modification indices and model residuals (with caution) to examine sources of model mis-fit. Examining modification indices and model residuals did not result in changes to the final models.

Results Unconditional Latent Growth Curve Models Adolescent alcohol use latent growth curve model. The model fit the data well, ␹2(6) ⫽ 4.41, p ⫽ .62; RMSEA ⫽ 0.00; CFI ⫽ 1.00; TLI ⫽ 1.00. The mean and the variance of the intercept factor differed significantly from zero (M ⫽ .41, SE ⫽

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BELENDIUK, PEDERSEN, KING, PELHAM, AND MOLINA

.11, 95% CI [0.20, 0.64]; variance ⫽ 1.57, SE ⫽ .53, 95% CI [0.60, 2.65], indicating that, on average, adolescents had used alcohol less than once a year at age 14, and there were significant individual differences in the frequency of alcohol use at age 14. The mean and the variance of the alcohol use slope factor were also significantly different from zero (M ⫽ .43, SE ⫽ .06, 95% CI [0.31, 0.53]; variance ⫽ .47, SE ⫽ .11, 95% CI [0.26, 0.71]), indicating that on average alcohol use frequency increased significantly with age and there were individual differences in this rate of change (i.e., some adolescents increased more than others). Perceived friend alcohol use latent growth curve model. The model fit the data well, ␹2(6) ⫽ 20.49, p ⬍ .05; RMSEA ⫽ 0.09; CFI ⫽ 0.93; TLI ⫽ 0.93. The mean and the variance of the intercept factor differed from zero (M ⫽ 1.71, SE ⫽ .09, 95% CI [1.54, 1.88]; variance ⫽ .87, SE ⫽ .21, 95% CI [0.48, 1.29]), indicating that on average, adolescents reported that some of their friends drank occasionally or regularly at age 14, and there was significant variability in their friend’s alcohol use at age 14. The mean and the variance of the slope factor were also different from zero (M ⫽ .62, SE ⫽ .09, 95% CI [0.41, 0.78]; variance ⫽ .15, SE ⫽ .02, 95% CI [0.11, 0.19]), suggesting that over time, more adolescents reported that their friends drank regularly or occasionally, although adolescents differed in the rate of change. The intercept and slope factors were negatively but weakly associated (r ⫽ ⫺.17, SE ⫽ .06, 95% CI [⫺0.26, ⫺0.04]), suggesting that adolescents with more drinking friends at age 14 had somewhat slower increases in the number of drinking friends over time.

Parallel Process Model (See Figure 1) The model fit the data well, ␹2(36) ⫽ 51.59, p ⫽ .04; RMSEA ⫽ 0.04; CFI ⫽ 0.97; TLI ⫽ 0.96. Controlling for gender, race, and number of friends, the residual correlation between adolescent alcohol use at age 14 and friend alcohol use at age 14 was positive and large (r ⫽ .61, SE ⫽ .25, 95% CI [0.16, 1.14]). Moreover, sharper increases in adolescent alcohol use from age 14 to 17 were significantly associated with sharper increases in friend alcohol use from age 14 to 17 (r ⫽ .11, SE ⫽ .03, 95% CI [0.06, 0.15]). On the other hand, there was no association between adolescent alcohol use at age 14 (intercept parameter) and the change in friend alcohol use (slope parameter) from age 14 to 17 (␤ ⫽ ⫺0.09, SE ⫽ 0.06, 95% CI [⫺0.20, 0.05]) or vice versa (␤ ⫽ 0.03, SE ⫽ 0.12, 95% CI [⫺0.18, 0.30]). Higher intercept values at age 14 predicted slower rates of increase in the associated slope for both adolescent alcohol use (r ⫽ ⫺.33, SE ⫽ .13, 95% CI [⫺0.59, ⫺0.07]) and friend alcohol use (r ⫽ ⫺0.13, SE ⫽ .05, 95% CI [⫺0.21, ⫺0.04]).

Multiple Group Parallel Process Model (See Figure 2) Next, we compared a multiple group parallel process model with all parameters constrained to be equal across groups to a multiple group parallel process model with all parameters free to vary across groups. The fully constrained model resulted in a significantly worse model fit compared to the fully unconstrained model, ␹2(22) ⫽ 89.57, p ⬍ .05 and therefore justified testing which parameters of the parallel process model differed across the ADHD groups. The final multiple group parallel process model for friend alcohol use fit the data well, ␹2(91) ⫽ 152.77, p ⬍ .001; RMSEA ⫽

Adolescent Alcohol at age 14

Adolescent Alcohol at age 15

Adolescent Alcohol at age 16

Adolescent Intercept

Adolescent Alcohol at age 17

Adolescent Slope

-0.03 (0.07); [-0.17, 0 10]/ . -0.11 (0.09); [-0.25, 0.10] 0.06 (0.03); [0.00, 0.11]/ 0.15 (0.04); [0.08, 0.22]

Friend Use Slope

Friend Use Intercept

Non-ADHD/ADHD Chi-square: 125.76, p < 0.05 RMSEA= 0.07 CFI= 0.90 TLI=0.89

Friend Use at age 14

Friend Use at age 15

Friend Use at age 16

Friend Use at age 17

Figure 2. Multiple group parallel process latent growth curve model for adolescent alcohol use and friend alcohol use. All parameter estimates and standard errors are standardized. Parameter estimates, standard errors, and 95% bias corrected bootstrap confidence intervals for individuals without attention-deficit/hyperactivity disorder (ADHD) are listed first and parameter estimates, standard errors, and 95% bias corrected bootstrap confidence intervals for individuals with ADHD are listed second. Bold lines indicate paths that were significantly different between groups; gray paths are not significantly different between non-ADHD and ADHD groups. Gender, race, and number of friends were included as covariates in the model but are not represented in the figure. RMSEA ⫽ root-mean-square error of approximation; CFI ⫽ comparative fit index; TLI ⫽ Tucker-Lewis index.

0.07; CFI ⫽ 0.90; TLI ⫽ 0.89. The intercepts and slopes were first compared across groups. Group differences were found for the adolescent alcohol use intercept and peer alcohol use slope. Although both groups, on average, used alcohol less than once a year at age 14, adolescents without ADHD reported significantly lower levels of alcohol use at age 14 (M ⫽ 0.27) than adolescents with ADHD (M ⫽ 0.59), Wald’s chi-square (df ⫽ 1) ⫽ 5.60, p ⫽ .02. In addition, both groups, on average, had increases in the number of friends who used alcohol and the increase between ages 14 and 17 was greater for non-ADHD (M ⫽ 0.38) than for ADHD adolescents (M ⫽ 0.29), Wald’s chi-square (df ⫽ 1) ⫽ 4.94, p ⫽ .03. These parameters were free to vary in all models; there were no group differences in peer alcohol use at age 14, Wald’s chisquare (df ⫽ 1) ⫽ 0.01, p ⫽ .93 or growth in adolescent alcohol use from ages 14 to 17, Wald’s chi-square (df ⫽ 1) ⫽ 3.58, p ⫽ .10 so these parameters were constrained to be equal in subsequent iterations of model testing. Associations among the intercepts and slopes were subsequently compared across groups. Model fit was decreased by constraining the residual covariance between the slope factors to be equal, Wald’s chi-square (df ⫽ 1) ⫽ 6.29, p ⫽ .01, indicating that the relation between slopes differed across groups. Specifically, the

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GROWTH IN TEEN AND FRIEND DRINKING BY ADHD STATUS

association between the rate of growth in friend alcohol use and the rate of growth in adolescent alcohol use was moderate and positive for those with ADHD (r ⫽ .15, SE ⫽ 0.04, 95% CI [0.08, 0.22]), and nonsignificant for those without ADHD (r ⫽ .06, SE ⫽ 0.03, 95% CI [0.00, 0.11]). Adolescent alcohol use at age 14 was also more strongly related to change in friend alcohol use over time for adolescents with ADHD (␤ ⫽ ⫺0.11, SE ⫽ 0.09, 95% CI [⫺0.25, 0.10]) than for adolescents without ADHD (␤ ⫽ ⫺0.03, SE ⫽ 0.07, 95% CI [⫺0.17, 0.10]), Wald’s chi-square (df ⫽ 1) ⫽ 4.96, p ⫽ .03. No other model paths were different between the ADHD and non-ADHD groups. Specifically, there were no group differences in the association between average adolescent and friend alcohol use at age 14, Wald’s chi-square (df ⫽ 1) ⫽ 1.83, p ⫽ .18, or the association between friend alcohol use at age 14 and change in adolescent use across adolescence, Wald’s chi-square (df ⫽ 1) ⫽ 0.00, p ⫽ .97. Furthermore, there were no group differences in the association between age 14 adolescent alcohol use and the rate of growth in adolescent alcohol use, Wald’s chi-square (df ⫽ 1) ⫽ 0.00, p ⫽ .99, or age 14 friend alcohol use and the rate of growth in friend alcohol use, Wald’s chi-square (df ⫽ 1) ⫽ 0.00, p ⫽ .99. The final multiple group parallel process model for friend alcohol tolerance also fit the data well, ␹2(90) ⫽ 127.41, p ⫽ .06; RMSEA ⫽ 0.06; CFI ⫽ 0.93; TLI ⫽ 0.92. Findings for friend alcohol tolerance were similar to the findings for friend alcohol use with the exception that there were no significant group differences in change in friend tolerance over time, Wald’s chi-square (df ⫽ 1) ⫽ 3.10, p ⫽ .07, or in the association between adolescent alcohol use at age 14 and change in friend alcohol tolerance over time, Wald’s chi-square (df ⫽ 1) ⫽ 0.00, p ⫽ .99.

Discussion Peer alcohol involvement and adolescent self-reported alcohol use have been prospectively associated in many studies (e.g., Curran, Stice, & Chassin, 1997; Li, Barrera, Hops, & Fisher, 2002), but this study was the first to examine these associations prospectively for adolescents with childhood ADHD. Specifically, this study directly tested whether alcohol use by one’s friends changes in concert with adolescent alcohol use. Importantly, a stronger association was found for those with an ADHD history compared to those without. Thus, this study implicates the social context in ADHD-related vulnerability to alcohol use. It also replicated and extended our similar earlier but cross-sectional finding with a separate sample (Marshal et al., 2003) into a longitudinal framework spanning four years of adolescence when alcohol use is known to escalate. The tighter connection between peer and adolescent alcohol use for adolescents with ADHD suggests that the social context of adolescent drinking is especially important for this population. The vast majority of adolescent alcohol use occurs in a social context (Johnson et al., 2006), and our findings suggest that features of that environment, such as social motives for alcohol use, might contribute importantly to drinking vulnerability for youth with ADHD histories. There are some data to suggest that youth with ADHD may be more likely to drink for social reinforcement. Although the research did not involve alcohol use, Kohls, Herpertz-Dahlmann, and Konrad (2009) found that youth with ADHD, compared to youth without ADHD, are more susceptible to social rewards (i.e.,

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happy faces); thus, they may be more likely to drink for social reinforcement. Relatedly, youth with ADHD may drink to avoid negative social reactions to alcohol abstention. Individuals who refuse alcohol in a social context are more likely to be offered alcohol (Rabow & Duncan-Schill, 1995) and may report teasing that results in feelings of inferiority (Borsari & Carey, 2001). The present study did not examine conformity motives, but male youth who are vulnerable to using alcohol (e.g., children of alcoholics) exhibit enhanced conformity motives (Chalder, Elgar, & Bennett, 2006) to drink alcohol to avoid social ostracism. Future studies that examine alcohol-related cognitions in socially impaired youth, such as those with ADHD, may inform interventions and treatment for elevated alcohol use. Research from our group (Pedersen, Harty, Pelham, Gnagy, & Molina, 2014), however, has shown that adolescents with ADHD report lower explicit expectancies about social facilitation resulting from alcohol use compared to adolescents without ADHD. In other words, they are less likely to endorse social reasons for drinking, which conflicts somewhat with the results of the current study. More research examining a wider range of alcohol-related cognitions (e.g., social motives, implicit alcohol cognitions) is needed to further understand how the social context relates to decisions to drink for individuals with ADHD. This may be especially relevant because of well-documented perceptual biases including a discrepancy between objective and subjective ratings of responsiveness to social reinforcement (Kohls et al., 2009). Social processes that play a role in multiple risky behaviors including delinquent or other externalizing behaviors beyond alcohol consumption, may also explain our findings. These behaviors, as a group, are highly likely to have social motivations in adolescence (Bradizza, Reifman, & Barnes, 1999), to occur in the presence of peers (Cohen & Prinstein, 2006; Gardner & Steinberg, 2005), and when social reward is anticipated (Goodnight, Bates, Newman, Dodge, & Pettit, 2006), including increased social status (Cohen & Prinstein, 2006). Prospective longitudinal studies have shown that both social mimicry (e.g., mimicking the behaviors of individuals in power to increase social standing by appearing similar to those in power) and unreciprocated attraction (i.e., engaging in behaviors similar to a person whom an adolescent wants to befriend) increase antisocial behavior. Therefore, risky and antisocial behaviors, including alcohol use, are often highest for those with persistent unmet social needs (Juvonen & Ho, 2008), which are common in youth with ADHD. Furthermore, adolescents with ADHD are likely to affiliate with deviant peers due to rejection by normative peers, which can further exacerbate negative social influence and increase likelihood of engagement in risky behaviors including alcohol use (Marshal et al., 2003). In sum, youth with ADHD may be vulnerable to elevated drinking due to a general tendency toward friendships with peers who engage in multiple risky and/or deviant behaviors that increase through adolescence. Youth with ADHD reported early and elevated drinking (i.e., a higher frequency of drinking alcohol at age 14) compared to same-aged youth without ADHD. This finding is consistent with other prospective longitudinal studies of children with ADHD that show that youth with ADHD more often use alcohol at an early age (Barkley, 1990; Molina, Flory, et al., 2007; Molina & Pelham, 2003). Our initial reports of alcohol use in this sample failed to find group differences in adolescent alcohol use frequency (Molina

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et al., 2012; Molina et al., 2007). However, there is considerable heterogeneity in the sample, with some teens having elevated risk whereas others have reduced risk and factors such as delinquency, social impairment, and parental monitoring appear to play a role (Molina et al., 2012). The current findings may result from a slightly different modeling technique (e.g., controlling for friend alcohol use at age 14) and combination of variables in the present analysis. Taken together, our findings as well as other publications from our group (Molina et al., 2012) suggest that social functioning and response to the social environment are potentially important factors to consider when developing interventions that target alcohol use by adolescents with ADHD. Interventions that target management or avoidance of peer contexts that promote drinking may be needed. Clues may be taken from social influence and competence enhancement approaches such as those used in drug prevention programs (e.g., Botvin, 2000). However, the challenge of changing social skills per se in the ADHD population (de Boo & Prins, 2007; Pfiffner & McBurnett, 1997) may demand some adaptations specific to this population. Importantly, normative feedback programs that target adolescent perception of peer substance use have been shown to be effective in reducing a variety of alcohol-related behaviors (Schulte, Monreal, Kia-Keating, & Brown, 2010; Spijkerman et al., 2010); correcting perceptions about peer alcohol use may be a fruitful area of future research for developing interventions for youth with ADHD who are using alcohol. Novel psychosocial interventions may be particularly necessary for this vulnerable population given the limitations of stimulant medication for ameliorating ADHD-related risk of drug abuse (Humphreys, Eng, & Lee, 2013; Molina et al., 2013). Although the current study was characterized by certain methodological strengths (e.g., prospective longitudinal design), there were also limitations. First, similar to the majority of studies of adolescent substance use (Chassin et al., 2004), the current study examined adolescent perception of their friends’ alcohol use. Adolescents with ADHD are prone to reporting bias (Hoza, Pelham, Dobbs, Owens, & Pillow, 2002). If adolescents with ADHD misestimate their friends’ behaviors then the current study may also under- or overestimate the true association between adolescent and peer alcohol use for youth with ADHD. Importantly, the perception of friend behavior, even if inaccurate, is an important predictor of alcohol and drug use among teens (Bauman & Fisher, 1986; Iannotti, Bush, & Weinfurt, 1996) and has been shown to mediate the association between delinquent behavior and aggression on alcohol use outcomes in adolescents (Barnow et al., 2004). Still, improved characterization of peer alcohol use will be important, particularly characterizing any differences between individuals with and without ADHD. In addition, although we posit that some of our findings are due to social impairments experienced by individuals with ADHD, this was not assessed directly. Given the prior finding from this same sample (Molina et al., 2012) that mother-rated teen difficulty with peer relationships had both direct and indirect effects on adolescent alcohol use, examining the contribution of social impairment with more comprehensive measurement approaches is an important future direction. Furthermore, the current study examined only the frequency of adolescent alcohol use as opposed to frequency of heavy or problem drinking. Whether social context plays the same role for youth with serious alcohol problems remains to be tested. Finally, these models

controlled for gender but were examined in a sample that was largely male, and clinic-based; whether these findings generalize to other populations is an important empirical question. In sum, the current study demonstrated, with prospective longitudinal data, a potentially important role for friend alcohol use in ADHD-related drinking in the adolescent years. The types of friends with whom adolescents with ADHD socialize appears to be even more important for adolescents with ADHD histories than for those without. This finding raises important questions about the ways in which children with ADHD establish their peer networks and manage the risks that accompany these choices. Given the limited intervention literature that exists for this specific population (adolescents with ADHD), our findings indicate a need for additional research on social pathways to risk and resilience to alcohol use disorder as well as novel approaches to intervene in this social context pathway.

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Received November 14, 2014 Revision received July 14, 2015 Accepted July 15, 2015 䡲