Continued detention involvement and adolescent marijuana use trajectories.

Article

Continued Detention Involvement and Adolescent Marijuana Use Trajectories

Journal of Correctional Health Care 2014, Vol 20(1) 31-44 ª The Author(s) 2013 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/1078345813505445 jcx.sagepub.com

Sarah W. Feldstein Ewing, PhD1, Sarah J. Schmiege, PhD2, and Angela D. Bryan, PhD3

Abstract Justice-involved youth have high rates of marijuana use. Less is known about what may drive these rates, particularly when justice-involved youth return to the community. One factor that has been implicated is continued detention involvement. Yet, it is unknown how this factor may influence marijuana use trajectories. Using longitudinal growth curve modeling, the researchers evaluated the association between continued detention involvement and marijuana use trajectories in two large, ethnically diverse samples of community-based, justice-involved youth. Across both samples, marijuana use decreased over time for youth with continued detention involvement but did not change for youth without continued detention involvement. These findings underscore the importance of attending to the influence of detention involvement in community-based, justice-involved adolescents’ marijuana use trajectories. This study also highlights the importance of coordinating prevention/intervention programming for justice-involved youth once they are in the community. Keywords detention, marijuana use, adolescents, juvenile justice

Introduction Marijuana is currently the most widely abused illicit substance (Centers for Disease Control and Prevention [CDC], 2010). By the start of high school (*age 14), 23% of American youth have used marijuana, a proportion that increases to 46% by the end of the 12th grade (*age 18; CDC, 2010). Surpassing the rates observed among mainstream youth, justice-involved adolescents have even greater rates of marijuana use and related consequences. Marijuana is the most abused substance among juvenile justice populations (e.g., Feldstein & Ginsburg, 2006), with approximately half

1

University of New Mexico, Albuquerque, NM, USA Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado–Denver, Aurora, CO, USA 3 Department of Psychology and Neuroscience, University of Colorado–Boulder, Boulder, CO, USA 2

Corresponding Author: Sarah W. Feldstein Ewing, PhD, Department of Psychiatry, University of New Mexico, MSC09 5030, Albuquerque, NM 87131, USA. Email: [email protected]

Downloaded from jcx.sagepub.com at SUNY HEALTH SCIENCE CENTER on April 12, 2015

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Journal of Correctional Health Care 20(1)

(45%) of justice-involved youth meeting criteria for marijuana use disorders (Aarons, Brown, Hough, Garland, & Wood, 2001). This is concerning, as adolescent marijuana use is strongly connected with a number of pediatric health risk behaviors (e.g., Chabrol, Chauchard, & Girabet, 2008; Feldstein & Miller, 2006; French & Dishion, 2003), sustained marijuana use (Perkonigg et al., 2008; Swift, Coffey, Carlin, Degenhardt, & Patton, 2008), and poorer longer term outcomes during adulthood (e.g., lower educational attainment, lower life satisfaction; Fergusson & Boden, 2008). Despite the ubiquity of marijuana use in this population, less is known about what factors may drive and sustain marijuana use over time. This is particularly true for adolescents who have prior involvement with the juvenile justice system but are currently living in the community; this includes both postrelease adolescents, who have recently returned from detention settings (Williams, 2009), and adolescents on probation. To capture the nature of both of these groups, we use the global term ‘‘community-based, justice-involved adolescents.’’ Research has indicated that there are limitations in the prevailing approach to treatment and rehabilitation in the juvenile justice system (e.g., Loughran et al., 2009). For example, many juvenile justice systems lack the resources needed to support the training and oversight of clinical substance abuse intervention staff (Pullmann & Heflinger, 2009), resulting in their inability to provide marijuana interventions for their high-risk and high-need youth. Additionally, studies have documented that once released, justice-involved youth are unlikely to seek intervention (Garland et al., 2005; Lennings, Kenny, & Nelson, 2006). Subsequently, many youth leave the justice system to face significant functional impairment once they return to the community (Abram, Choe, Washburn, Romero, & Teplin, 2009). One factor that appears to be strongly tied to functional outcomes is substance use (e.g., Abram et al., 2009; D’Amico, Ramchand, & Miles, 2009; Reynolds, Tarter, Kirisci, & Clark, 2011). Thus, examining the longitudinal progression of justice-involved youths’ marijuana use while they are residing in the community could yield critical data to guide improvements in prevention and intervention efforts.

Snares Hypothesis While many theories have been developed to explain the nature of high-risk adolescents’ substance abuse, recent attention has been drawn to the snares hypothesis (Hussong, Curran, Moffitt, Caspi, & Carrig, 2004; King, Chung, & Maisto, 2009). Originally posited to address how young adults become entrenched in sustained patterns of antisocial behavior, Hussong, Curran, Moffitt, Caspi, and Carrig (2004) suggest that a series of behaviors, including substance use and incarceration, contemporaneously ‘‘ensnare’’ youth into a continued pattern of risk behaviors, which consequently preclude the youths’ movement toward the assumption of prosocial adult roles (normative desistance). While detention involvement has been posited to be one of the factors likely to ensnare youth in a continued pattern of problem behavior (Hussong et al., 2004), empirical exploration of this theoretical relationship is lacking. Thus, this theory-based study sought to evaluate whether continued detention involvement served as a snare, or a vulnerability factor, in high-risk adolescents’ marijuana use trajectories.

Previous Research on Detention Involvement Consistent with the snares hypothesis, several studies have found continued detention involvement to be a catalyst for sustained and/or increased substance use (e.g., Keene, 1997; Mancha, RojasNeese, & Latimer, 2010; Tarter, Kirisci, Mezzich, & Patton, 2011; Tolou-Shams, Brown, Gordon, & Fernandez, 2007). Specifically, placement in detention clusters youth with a higher proportion of high-risk and delinquent peers, which could exacerbate substance use and risk behavior (e.g.,


Feldstein Ewing et al.

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Dishion, McCord, & Poulin, 1999; Wolfe & Shoemaker, 1999). Furthermore, fiscal and pragmatic constraints within the justice system (Pullmann & Heflinger, 2009) can limit treatment opportunities during detention, therefore hindering detention-involved youths’ potential opportunities for intervention and rehabilitation (and potentially compounding the problem). In contrast to the snares hypothesis, other studies have found that continued detention involvement either has no relationship to substance use (e.g., Chassin, Knight, Vargas-Chanes, Losoya, & Naranjo, 2009; D’Amico, Edelen, Miles, & Morral, 2008) or has been protective, with higher rates of substance use observed among youth who did not continue to interact with the justice system (e.g., Abram et al., 2009; Mauricio et al., 2009; Piquero et al., 2001). It has been argued that supervised settings protect adolescents from the environmental and social risk factors that promote and exacerbate substance use (e.g., antisocial peers, high-crime neighborhoods), and provide adolescents with needed formal monitoring, structured time, and physical security in a helpful way that might not exist in their day-to-day lives (e.g., Mauricio et al., 2009). However, many of the studies in this domain have been limited by design constraints (e.g., cross-sectional studies, single follow-up point) and sample selection approaches (e.g., males only, recruitment from a single site) that prevent a more nuanced understanding of the relationship between detention involvement and substance use. Thus, targeted evaluations with a longitudinal design and a strong statistical approach (e.g., Hussong et al., 2004) are needed to elucidate the nature of this relationship for community-based, justice-involved youth. Specifically, this type of evaluation will identify potential areas to target (e.g., potential snares), to guide the creation and adaptation of prevention and intervention programs to improve their efficacy with this population.

Hypotheses The goal of the study was to gain a better understanding of marijuana abuse trajectories (and potential treatment needs) among two independent samples of justice-involved youth who were residing in the community by comparing those with continued detention involvement to those without continued involvement. We implemented a longitudinal growth curve modeling approach to examine the association of continued detention involvement with marijuana use trajectories among two samples of community-based, justice-involved adolescents (original sample: postrelease detention youth; replication sample: youth on probation). Following the snares hypothesis, we posited that continued detention involvement would ensnare both samples of youth into a continued pattern of marijuana use. Thus, we predicted that detention involvement would be associated with increased marijuana use during the follow-up periods.

Original Sample: Sample 1 Methods Participants and Procedures Participants were 484 adolescents (83% male and 17% female) recruited from three detention facilities in the Denver, Colorado, metropolitan area. These facilities primarily house adolescents awaiting hearings and/or sentencing, along with a small proportion of youth with less severe offenses carrying out their disposition. The average length of stay for youth was 14 days. Gender proportions were consistent with the gender distribution in these facilities. The sample was ethnically diverse, consisting of approximately 38.5% Caucasian, 28.4% Hispanic, 11.5% African American, 3.4% Asian or Pacific Islander, 4.2% Native American, 2.2% other ethnicity, and 11.8% multiracial participants. Participants were 14 to 17 years old (M ¼ 15.82; SD ¼ 1.04) at the time


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of study enrollment. The participating institutional review board approved all study procedures and a federal certificate of confidentiality was obtained from the National Institutes of Health (NIH). Approval was also obtained from the Office for Human Research Protections. The sample for this analysis was taken from a larger, randomized controlled trial examining the efficacy of a sexual risk reduction intervention (PI: A.D.B.). Analyses were conducted using measures of marijuana use obtained 3 months, 6 months, and 9 months after baseline, that is, after the youth were released back into the community. The three conditions were combined for these analyses because (1) the intervention focused on sexual risk reduction and did not target marijuana use, which was measured at all follow-ups only as a secondary variable; (2) all participants were evaluated under identical conditions regardless of assignment to intervention condition; and (3) supplementary analyses demonstrated that condition assignment in no way affected the analyses described herein.1 To be eligible to participate in both the original intervention study and the subsequent secondary analyses evaluated here, adolescents had to be between the ages of 14 and 17, speak English, currently reside at one of the detention facilities, have parent/guardian consent, and provide their own assent. Eligibility criteria were kept purposefully broad so that the intervention would have the widest possible reach and so that subsequent results would have the greatest possible generalizability. One intake staff member from each facility assisted in recruitment, and all new, eligible detainees were offered the opportunity to participate. The intake staff member detailed that participation was voluntary and would not affect adolescents’ treatment by the justice system. Once assent was obtained, a parent/guardian was independently contacted by telephone by research staff (not justice staff) to provide his or her consent. All consent conversations were audiorecorded and logged for proof of consent. All measures were completed on a laptop computer using ACASI (audio computer-assisted selfinterview) procedures (Williams et al., 2000). ACASI was used to prevent confusion due to survey skip patterns and to provide audio versions of questions to assist youth with reading difficulties. Participants were told that they could opt out of any question they did not feel comfortable answering. The adolescents received US$25 for each survey completed. Retention rates ranged from 64.0% to 65.3% across follow-up waves. Consistent with similar studies that have highlighted the challenges in retaining high-risk youth throughout follow-ups (e.g., Montanaro, Ewell, Bryan, & Feldstein Ewing, 2012), the most common reason for missing data at each of the three follow-up points was that participants were unreachable despite repeated efforts of our staff.

Measures At baseline, adolescents completed an assessment querying basic demographic factors (age, gender, race/ethnicity, educational status, living arrangement, offense severity) and marijuana use (see Table 1 for details). Specifically, similar to other research in adolescent addiction studies (e.g., Hendershot, Magnan, & Bryan, 2010), marijuana use was measured at each time point with the item ‘‘In the last three months, how often did you smoke marijuana?’’ with response options on a 9-point scale (0 ¼ never to 8 ¼ every day). Youth completed follow-ups in the location in which they were residing at that time point. Thus, youth who were in detention at the follow-up stage completed their assessments within their detention center. This detention status (detention involvement at follow-up) was recorded in the data with a single item at each time point indicating whether participants were in detention at the time of follow-up. Detention status was aggregated across follow-up assessments to categorize participants as in ‘‘detention at any of the follow-ups’’ (DAF: 38.17%) versus ‘‘not in detention at any of the follow-ups’’ (NDAF: 61.83%).



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Table 1. Demographic and Behavioral Characteristics by Detention Status at Follow-Up Assessments— Sample 1. No Detention at Follow-Ups Detention at Follow-Ups (NDAF) (DAF) Age Gender (% male) Ethnicity

15.85 (1.05) 79.53% 27.18% Hispanic 36.58% Caucasian 10.07% African American 26.17% Other Educational status (% still in 66.78% school) Living arrangement 30.72% Both parents 51.19% One parent 18.09% Other guardian Offense severity 32.18% Mild 32.53% Moderate 35.29% Severe 3.43 (1.68) Baseline marijuana usea

Significance

15.70 (1.03) t(477) ¼ 1.48, n.s. 88.04% w2(1) ¼ 5.78, p < .05 w2(3) ¼ 2.58, n.s. 21.74% Hispanic 35.87% Caucasian 10.87% African American 31.52% Other 71.74% w2(1) ¼ 1.30, n.s. 28.42% Both parents 52.46% One parent 19.13% Other guardian 25.00% Mild 29.44% Moderate 45.56% Severe 3.65 (1.68)

w2(2) ¼ .30, n.s. w2(2) ¼ 5.26, n.s. t(452) ¼ 1.31, n.s.

a

Baseline marijuana use was measured on a 1–5 scale where 1 ¼ never, 2 ¼ once a month, 3 ¼ once a week, 4 ¼ more than once a week, 5 ¼ every day.

Results Analysis Overview We first compared the DAF and NDAF groups on a variety of baseline and demographic characteristics to determine whether there were differences between these two groups. To evaluate adolescents’ marijuana use trajectory once they had left the detention center and returned to the community (postrelease), we implemented a longitudinal growth curve modeling approach to model changes in marijuana use over the three follow-ups. The issue of detention status at follow-ups was examined by conducting the growth analyses in a multiple group framework, making it possible to examine the models within the DAF and NDAF groups simultaneously and compare results across groups. Growth models were estimated in Mplus Version 6 (Mutheń & Mutheń, 1998–2010) using a full information maximum likelihood estimator that makes use of all available data and is considered state-of-the art for addressing data that are missing at random and that display levels of missingness in line with those observed in this study (e.g., 36%; Enders & Bandalos, 2001).

Baseline Comparisons of DAF and NDAF Groups Table 1 presents descriptive information for the DAF and NDAF groups. The DAF and NDAF groups did not differ on the majority of demographic variables; the exception was gender, with males more likely to be in the DAF group.

Model Results Figure 1 presents mean marijuana use over the follow-up waves, by group. We estimated a model of the marijuana use trajectories simultaneously in the DAF and NDAF groups. The model demonstrated good fit to the data, w2(2) ¼ 2.17, n.s.; root mean square error of approximation (RMSEA) ¼ 0.02, 95% confidence intervals [0.00, 0.14]; comparative fit index (CFI) ¼ 0.99; standardized root


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3

Marijuana Use

2.5 2 1.5

NDAF DAF

1 0.5 0

3 Month

6 Month

9 Month

Follow-up Wave

Figure 1. Marijuana use means over the three waves of follow-up data collection in Sample 1, presented by DAF and NDAF groups. DAF ¼ detention at follow-ups; NDAF ¼ no detention at follow-ups.

mean square residual (SRMR) ¼ 0.02. Marijuana use did not change over time in the NDAF group, B ¼ 0.12 (SE ¼ 0.12), n.s., but the slope was negative and significant in the DAF group, B ¼ 0.36 (SE ¼ 0.12), p < .01. This indicates a decrease in marijuana use over time for individuals in detention during at least one of the follow-ups, but no change in marijuana use for those not in detention during the follow-ups. The marijuana use means did not differ between the DAF and NDAF groups at the 3-month follow-up, but the means were significantly lower in the DAF than in the NDAF groups at both the 6- and 9-month follow-ups. As a final step, we explored whether either age or gender influenced model results; no effects of age or gender on the marijuana use trajectories were observed.

Replication Sample: Sample 2 Method Participants and Procedures Sample 2 participants were youth who were on probation at recruitment and who were residing in the community. Specifically, 728 adolescents (66.62% male and 33.38% female) were recruited for a natural history study on adolescent risk behavior, where participants were surveyed every 6 months (PI: A.D.B.). To participate, adolescents had to be on probation, be between the ages of 14 and 18, and speak English. The sample included 40.9% Hispanic, 24.5% African American, 15.7% Caucasian, 3.4% Native American, 1% Asian/Pacific Islander, 2.9% other, and 11.6% multiracial youth. Average age at baseline was 15.71 (SD ¼ 1.05). The participating institutional review board approved all study procedures and a federal certificate of confidentiality was obtained from NIH. Participants were recruited through posters hung throughout two probation offices, as well as by research staff members who maintained a presence in the probation office waiting area. The research staff member clarified that participation was voluntary and would not affect youths’ treatment in the juvenile justice system. As with Sample 1, written assent was obtained from participants, and independent audiorecorded consent was obtained from the parent/guardian via telephone by research staff.



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Table 2. Demographic and Behavioral Characteristics by Detention Status at Follow-Up Assessments— Sample 2. No Detention at Follow-Ups (NDAF)

Detention at Follow-Ups (DAF)

Age

15.80 (1.06)

15.49 (.98)

Gender (% male)

61.28%

78.60%

Ethnicity

37.68% Hispanic 16.36% Caucasian 20.50% African American 25.47% Other 81.23%

36.89% Hispanic 12.62% Caucasian 24.27% African American 26.21% Other 82.08%

22.73% Both parents 60.47% One parent 16.80% Other guardian 16.06% Mild 45.41% Moderate 38.53% Severe 3.41 (3.54)

22.86% Both parents 56.19% One parent 20.95% Other guardian 11.40% Mild 51.30% Moderate 37.31% Severe 3.81 (3.53)

Educational status (% still in school) Living arrangement

Offense severity Baseline marijuana usea

Significance t(727) ¼ 3.74 p < .001 w2(1) ¼ 20.38 p < .001 w2(3) ¼ 2.33, n.s

w2(1) ¼ .07, n.s. w2(2) ¼ 1.88, n.s. w2(2) ¼ 3.02, n.s. t(694) ¼ 1.37, n.s.

a

0–8 scale where 0 ¼ never, 1 ¼ occasionally, 2 ¼ once a month, 3 ¼ 2–3 times a month, 4 ¼ 4–5 times a month, 5 ¼ once a week, 6 ¼ 2–3 times a week, 7 ¼ 4–5 times a week, 8 ¼ every day.

All survey administration procedures were supervised by study staff (vs. probation center personnel). As with Study 1, participants completed questionnaires on laptop computers using ACASI. Participants could opt out of any question. Marijuana use behavior was measured at the baseline wave of assessment as well as during each of the three follow-up time points (6, 12, and 18 months). Each session took 1 hour to complete. Participants were compensated US$20 for the baseline survey and US$50 for each follow-up survey. Retention rates across the 6- to 18-month follow-ups ranged from 78.6% to 90.1%.

Measures All measures paralleled Sample 1. At baseline, adolescents completed an assessment querying basic demographic factors (age, gender, race/ethnicity, educational status, living arrangement, and offense severity) and marijuana use (see Table 2 for details). Specifically, following prior research in the field of adolescent addiction (e.g., Hendershot et al., 2010), marijuana use was measured with the same 9-point item used in Sample 1 (‘‘In the past six months, how often did you smoke marijuana?’’), but placed within a 6-month (vs. 3-month) time frame, consistent with our follow-up interval. Detention status at follow-ups was assessed in a similar manner as Sample 1 with a single question at each follow-up, with 29.49% categorized in the DAF group and 70.51% in the NDAF group.

Results Analyses were designed to replicate those carried out in Sample 1. The DAF and NDAF groups did not differ on the majority of demographic variables, except the DAF group was significantly more likely to be younger and male than the NDAF group (see Table 2).


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4 3.5

Marijuana Use

3 2.5 2

NDAF DAF

1.5 1 0.5 0

6 Month

12 Month Follow-up Wave

18 Month

Figure 2. Marijuana use means over the three waves of follow-up data collection in Sample 2, presented by DAF and NDAF groups. DAF ¼ detention at follow-ups; NDAF ¼ no detention at follow-ups.

As with Sample 1, we estimated a model of the marijuana use trajectories simultaneously in the DAF and NDAF groups. Figure 2 presents mean marijuana use over the follow-up waves, by group. The model demonstrated good fit to the data, w2(2) ¼ 0.68, n.s.; RMSEA ¼ 0.00, 95% confidence intervals [0.00, 0.08]; CFI ¼ 1.00; SRMR ¼ 0.01. The Sample 1 results were largely replicated. The growth factor was negative and significant in the DAF group, B ¼ 0.70 (SE ¼ 0.16), p < .001, indicating a decrease in marijuana over time among individuals in detention during at least one of the follow-ups and was nonsignificant in the NDAF group, B ¼ 0.14 (SE ¼ 0.10), n.s., indicating no change in marijuana use over time. Marijuana use was significantly higher in the DAF group compared to the NDAF group at the 6-month follow-up, the groups were not significantly different at the 12-month follow-ups, and marijuana use was significantly lower in the DAF group at the 18-month follow-up. Again, we explored whether age or gender influenced the marijuana use trajectories. There was an effect of gender on the marijuana use slope in the DAF group; further examination showed that marijuana use decreased over time for both genders in this group, but that the decrease was more pronounced for males.

Discussion With a multiple time-point, longitudinal approach, this study sought to address the gap in the literature regarding the relationship between detention involvement and marijuana use for community-based, justice-involved youth (e.g., Abram et al., 2009; Dembo, Belenko, Childs, Greenbaum, & Wareham, 2010; Hussong et al., 2004). Based on the snares hypothesis (Hussong et al., 2004) and the emerging literature regarding the influence of detention exposure on substance use (e.g., Keene, 1997; Mancha et al., 2010; Tarter et al., 2011; Tolou-Shams et al., 2007), this study evaluated the influence of continued detention involvement on marijuana use trajectories among justice-involved youth residing in the community. To provide the greatest generalizability of results, we investigated this question with two large, ethnically diverse samples: a sample of youth recruited from short-term detention and a replication sample of youth recruited from probation. Consistent with the snares hypothesis, we expected to see greater marijuana use among the youth with continued detention involvement (DAF youth). In direct contrast to our predictions, the DAF



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group (in both Sample 1 and 2) evidenced significant decreases in the marijuana use trajectories, indicating that there was lower marijuana use among community-based youth who had continued detention involvement. Furthermore, this study demonstrated that across both samples, youth who did not have continued detention involvement (the NDAF group) displayed no change in their marijuana use over time (i.e., the growth factor was nonsignificant for the NDAF youth). Notably, previous studies in the college substance use literature (e.g., White, Mun, & Morgan, 2008) and the juvenile justice literature (e.g., Abram et al., 2009; Feldstein & Ginsburg, 2006; Mauricio et al., 2009; Piquero et al., 2001) provide support for the potential protective function of continued justice involvement on adolescent substance use. Specifically, supervised settings may protect adolescents from the environmental and social risk factors that promote and exacerbate substance use (e.g., antisocial peers, high-crime neighborhoods), and provide adolescents with needed formal monitoring, structured time, and physical security. Additionally, these studies suggest that interactions with the detention system may yield natural teachable moments whereby interaction with the justice system is, by itself, a strong catalyst for behavior change. Continued interactions with the detention system also might serve as natural booster sessions, reminding youth of the importance of not using marijuana and staying out of trouble. In terms of other possibilities, it is equally likely that our findings regarding continued detention involvement (DAF status) simply reflect the basic limitations of incarceration (e.g., limited access to substances and opportunities for substance use behavior) rather than true behavior change (Mauricio et al., 2009). Alternatively, it is also possible that the findings reflect that existing detention-setting treatment opportunities are effective in curtailing youths’ marijuana use (Chassin et al., 2009). Ultimately, additional studies are needed to further evaluate the mechanism and direction of this relationship.

Clinical and Policy Implications As highlighted by Hussong and colleagues (2004), it is critical to evaluate potential factors that may ensnare youth into continued patterns of problem behavior. Identification of these factors yields clear and addressable targets for prevention and intervention programming. While continued detention involvement has been posited as a potential snare (Hussong et al., 2004), results of this study indicate that it might serve as a protective (or at least) limiting factor, placing adolescents on a path toward decreasing marijuana use over time. Furthermore, the replication of this finding in two different samples of justice-involved youth (one recruited from detention facilities and another recruited from probation) suggests that the results are likely to be generalizable across adolescent justice contexts. In terms of practical implications, these results indicate that clinical prevention and intervention efforts may benefit from targeting other potential snares, both those that have demonstrated their proximal influence on increasing risk and antisocial behavior among high-risk adolescents (e.g., alcohol use, marijuana use, abstinence-related cognitions; Hussong et al., 2004; King et al., 2009) and factors that have been posited but not yet examined in this framework (e.g., academic continuity and investment, perception of peer use, deviant peer involvement, polysubstance abuse, severity of crime; D’Amico & McCarthy, 2006; Hussong et al., 2004; Poulin, Kiesner, Pedersen, & Dishion, 2011; Reynolds et al., 2011; Stouthamer-Loeber, Wei, Loeber, & Masten, 2004). Ultimately, we believe that these findings underscore the need for more information to understand the nature and context of detention involvement and its relationship with adolescent marijuana use trajectories once justice-involved youth are residing in the community. Consistent with the findings of King, Chung, & Maisto (2009), whereby continued treatment and residential placement were associated with decreased patterns of marijuana use, these findings also strongly implicate the importance of linking youth to effective marijuana use intervention services in the community immediately, during, and


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after their contact with juvenile justice (e.g., Abram et al., 2009; Stathis, Letters, Doolan, & Whittingham, 2006; Tripodi & Bender, 2011).

Summary and Limitations The results should be interpreted in light of the following considerations. First, due to the nature of the questionnaires, we were only able to assess whether youth were in detention at the time of the follow-up. Therefore, the NDAF youth could have been involved in detention between follow-up time points, but following the limitations of the employed measure, they would have been collapsed into the NDAF group. Notably, while this is a possibility, the significant differences between the two samples’ progressions (DAF and NDAF) suggest that this percentage was fairly small and/or that the influence of detention on these youth was not enough to interrupt their pattern of use throughout the follow-up periods (indicating that their pattern of use may have been more similar to the NDAF youth than the DAF youth). Second, we were unable to evaluate length of stay in detention at each follow-up point. This is relevant, as length of time spent in detention has been found to influence youth behavior (e.g., Zhou et al., 2012). Having a more precise measurement of detention would facilitate future examination of whether there is a dose–response relationship for detention involvement and marijuana use. We have subsequently incorporated a more detailed measure of detention involvement in our current work (including dates and length of stay between follow-up points, e.g., Chassin et al., 2009). Third, there were differences in retention across the two samples, with better retention rates for Sample 2 (youth recruited from probation) relative to Sample 1 (youth recruited from detention). There are several possible explanations for these effects; for example, it may have been easier for the research team to retain probation youth. More likely, however, is that this difference reflects the research team’s improved experience and proficiency in retaining high-risk youth in longitudinal trials. In terms of implications of these differences, while it is possible that the team may have been better able to reach less risky youth (meaning that observed rates of marijuana use could have been higher with better retention rates), because the team was able to access and retain youth in detention as well as those who were not in detention, we believe that it is unlikely that retention influenced the patterns observed for the DAF/NDAF analyses. Fourth, gender differences in trajectories were only found in Sample 2. In terms of the reason for this observed difference, it is important to note that there was an oversampling of females in Sample 2, but not in Sample 1. Similar to the predominance of justice research being conducted with allmale or predominantly male samples (e.g., Chassin et al., 2009; D’Amico et al., 2009), we may have been underpowered to detect gender differences in Sample 1. At the same time, there have been mixed findings on gender differences within justice-involved samples, with some studies suggesting that justice-involved females are much more severe across a number of indicators (e.g., Abram, Teplin, McClelland, & Dulcan, 2003), and others suggesting a more complex picture that includes lower rates of marijuana use among females (e.g., Dembo et al., 2010). Thus, these findings appear to reflect the mixed nature of these findings (Sample 1) and to provide some support for the severity argument (Sample 2); ultimately, future research is needed to deconstruct these gender patterns. Fifth, marijuana use was queried with an item-based approach. While recent studies support the validity of adolescent self-report in reporting substance use behavior (Clark & Winters, 2002; Marlatt et al., 1998), additional work highlights the benefit of employing interview approaches (e.g., the Timeline FollowBack; Sobell & Sobell, 1992) to gather greater detail regarding patterns of use during the prior 30 to 90 days (Donohue et al., 2004). Sixth and finally, these data suggest that we need to know more about the context in which adolescents are using marijuana. Specifically, these findings suggest that we need to learn more about other factors, such as the settings in which youth live and the nature of peer interactions for high-risk



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adolescents, and how they may inform access to and decisions around marijuana use (Danielsson, Wennberg, Tengstrom, & Romelsjo, 2010; Epstein, Botvin, & Doyle, 2009; Gardner & Steinberg, 2005). In sum, this study evaluated the association of detention involvement with marijuana use trajectories among two samples of community-based, justice-involved youth. Our findings indicate the importance of examining the influence of detention involvement on marijuana use trajectories. Additionally, these data highlight the importance of gaining a greater understanding of the factors that underlie or are highly related to detention involvement (e.g., the nature of peer relationships, potential family risk factors). Together, this information will assist in the development of efficacious community-based prevention and intervention efforts for this high-risk and high-need population. Acknowledgments We wish to thank the detention and probation facilities, the youth who participated in our study, and our contacts at each of the facilities. We also thank the Colorado Department of Youth Corrections for their cooperation and support. Finally, we thank our research assistants, Katy Seals, Sarah Taylor, Michael Levin, Roger Martin-Pressman, Helaine Powell, Patrick Finan, Justin Corrocher, Neil Cline, Ashleigh Golub, Jacob Lee, Lynette Smith, Angela Hendricks, Kathryn Stoddard, Miranda Dettmann, and Kerry Trachsel, as well as Amber McEachern and Hilary Mead for their review of this article.

Authors’ Note The study sponsors did not play a role in study design, data collection, data analysis, interpretation of results, writing of the reporting, or decision to submit the article for publication.

Declaration of Conflicting Interests The authors disclosed no conflicts of interest with respect to the authorship and/or publication of this article. For information about JCHC’s disclosure policy, please see the Self-Study Exam.

Funding The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by NIH Grants RO1 AA013844 and RO1 DA019139 (PI: Bryan), RO1 AA017878-01A2 (PI: Feldstein Ewing), and NIH/NCRR Colorado CTSI Grant Number UL1 RR025780 (PI: Schmiege).

Note 1. We conducted several analyses to justify combining the intervention conditions (two active treatment conditions and an education control group). First, we examined whether there were condition differences in the mean marijuana use scores at each follow-up time point and no significant differences emerged. Next, we verified that the distribution of the detention status at follow-ups variable was consistent across the three conditions. Finally, we confirmed that there were no significant differences in the longitudinal growth models across the three conditions.

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