We identified six longitudinal patterns of alcohol and other drug use over the decade following adolescent alcohol and drug treatment: Abstainers/Infrequent Users, Late Adolescent Resurgence, Emerging Adulthood Resurgence, Frequent Drinkers, Frequent Drinkers/Drug Dependent, and Chronic
. These trajectories reflect both the diversity of youth outcomes and dynamics of AOD involvement as adolescents transition into adulthood. Consistent with recent findings for youth in the first few years post treatment (Chung et al., 2003
; Chung et al., 2005
), the vast majority of this sample, approximately two thirds, dramatically improved after treatment. This early successful Abstaining/Infrequent
pattern of use has been found in prior investigations of post treatment outcomes (Brown et al., 2001
; Chung et al., 2004
). As youth transition out of adolescence, two trajectory classes emerged with distinct differences in the timing of post treatment accelerations in substance involvement (Late Adolescent Resurgence & Emerging Adulthood Resurgence). Similarly, Clark and colleagues (2006)
found six trajectory classes when modeling retrospective reports of SUD symptoms across early adolescence to mid-adulthood in SUD adults, including the presence of classes distinguished by developmental shifts in mid-adolescence, late adolescence and in emerging adulthood. These time periods correspond to important developmental transitions reflected in prior conceptualizations of development and empirical research (Aseltine and Gore, 2005
; Brown et al., 2008
; Chassin et al., 2004
; Kypri et al., 2004
). Additional measures of AOD involvement and DSM-IV diagnostic symptoms support the characterization of distinct patterns of AOD engagement and problems for teens following treatment.
Beyond the identification of these patterns, we were interested in how long term patterns of alcohol and other substance use after treatment related to attainment of the developmental milestones common to emerging adulthood (Arnett, 2000
). As expected, trajectory classes characterized by lowest rates and levels of use were most likely to have more optimal psychosocial outcomes as young adults. Interestingly, the Frequent Drinkers
trajectory class shared the characteristics of better occupational attainment, stable and intimate relationships, and fiscal responsibility for children with the Abstaining/Infrequent Use
trajectory class. This finding highlights that some individuals who enter SUD treatment during adolescence may resolve their drug involvement but come to drink alcohol relatively frequently as they transition into young adulthood while remaining sub-threshold for alcohol dependence into their mid twenties. By contrast, frequent use of alcohol in conjunction with other substance use during late adolescence and emerging adulthood were associated with poorer occupational and family functioning as well as important diagnostic and functioning problems. Our choice to include the Chronic
class, despite the small number of individuals exhibiting this use trajectory pattern (6%), seemed to capture a unique pattern of persistent AOD use which was qualitatively worse than others who had also received treatment and was associated with the most severe developmental consequences during young adulthood. Despite the lower prevalence rates, similar patterns have been demonstrated in other samples (e.g., Chung et al., 2008
Given the influence of treatment and incarceration on adolescent substance involvement (Anderson et al., 2007
; Brown and Ramo, 2006
; Chung and Maisto, 2006
; Godley et al., 2004
), we examined these factors that might impact the emergence or transitions in these trajectories. Youth assigned to classes did not differ on their exposure to alcohol and drug treatment across the first 8 years of assessment following treatment despite substantial proportions of the sample meeting criteria for A/SUD over the course of study. However, trajectory-related differences in treatment exposure emerged at the final time point. The overall low rates of treatment for youth with continued heavy AOD use and dependence in emerging adulthood raises questions about opportunities for assessment, intervention and the continuity of care for individuals with a history of A/SUD treatment during adolescence. Further, the difference in treatment status at the final assessment point may reflect more the current treatment system (access, opportunities, triggers for treatment) than need and argues for routine screening, as well as new venues and strategies for identification and intervention in the early twenties.
In comparison to other work explicitly modeling controlled environments and substance use after treatment (Godley et al., 2004
), we found that incarceration did vary between groups with the most severe longitudinal use patterns associated with the highest proportions being incarcerated, and largest number and longest duration of jail or prison terms. For example, individuals with a Chronic
pattern spent the longest period of time incarcerated and tended to have more frequent and longer individual stays. This suggests that living within a restricted environment was not the primary force in lower rates of use for the other groups and that such severe and protracted use is associated with more serious and costly offenses over time. Piquero et al. (2001)
identified the importance of incorporating incarceration as a time-varying covariate in longitudinal substance use research. While sample size limited doing so in the present study, these findings underscore the potential value of considering incarceration as well as treatment in modeling the mechanisms of change and consequences of substance involvement in adolescence and early adulthood.
This investigation had a number of strengths including the length of follow up, high follow up rates, use of an integrated modeling strategy to develop the descriptive classes considering both alcohol and other drugs and a priori hypotheses. To our knowledge, this is the first investigation that used LCGA to examine patterns across the ten years following adolescent inpatient substance use treatment. Our ability to examine both alcohol and substance use characteristics across 8 measurement time points during a period of rapid developmental change is a hallmark of this project; it allowed for examination of shifts in substance use at relevant adolescent/young adult transitions. The use of a dual trajectory LCGA approach provides a view of how both alcohol and other drug use fluctuate and reciprocally influence each other across the transition into adulthood and has been described as the most appropriate way to model “developmental comorbidity” (Jackson et al., 2005, p. 622
Clinicians working with SUD individuals and their families can use this developmental trajectory approach to inform treatment planning. For example, for some teens, alcohol and other drug use tends to change together across time (e.g., Late Adolescent Resurgence and Frequent Drinkers/Drug Dependent groups), while others clearly have a consistent heavier pattern of alcohol use compared to marijuana or other drug use after treatment (Abstainers/Infrequent users and Frequent Drinkers). Further, our work has identified a number of important developmental periods when youth treated for alcohol and drug problems may be particularly in need of or receptive to treatment. As clients in recovery reach developmental periods when risk for a resurgence of use is greatest (late adolescence and emerging adulthood), these time points should be seen as “critical” opportunities for screening, assessment, relapse prevention and supporting normal development through non-substance using behaviors (e.g., job training, eliciting help from non-using social supports; Brown et al., 2008
). Given the high rate of problems in core developmental arenas and cost of problems associated with heavy alcohol and drug involvement during this period, universal screening is called for. Future research should address the mechanisms by which these periods of developmental transition influence clinical course of AOD use and effectiveness of developmentally tailored interventions.
There are limitations on the conclusions drawn from this investigation. First, youth recruited into this study were primarily mid- and late adolescents (ages 15–17 years). The commonality that distinguished trajectories was in AOD frequency, not age of treatment. While trajectories were similar on age and distribution of youth across these categories, we cannot be specific as to age-related transitions. In addition, a number of different statistical strategies exist for examining longitudinal data of this type. Each strategy has its strengths and weaknesses (Bauer, 2007
). In the case of LCGA, it is important to remember that it is inherently a descriptive technique. The investigator chooses the best fitting model on the basis of both theory and statistical criteria; these decisions could be made differently on the basis of alternative theories. For example, we chose a model with fairly high posterior probabilities (). This could suggest that the model has been closely fit to the data and consequently, prediction error will need to be examined when this classification system is applied to new populations of youth (Burnham and Anderson, 2002
; Hastie et al., 2001
). As our goal was to best describe the dominant longitudinal patterns of use (including simultaneous use of different substances) over time, this approach seems most appropriate for initial examination of long term course. Further studies should consider the impact of covariates on the description of AOD patterns across development. Comparisons of these findings to other investigations using other operationalizations of use and alternative modeling strategies will be useful in the future to provide convergence of evidence for typical patterns of post treatment clinical course and developmental periods of risk. While intake substance use (severity of alcohol use episodes and frequency of marijuana use) varied between classes, the diagnoses at intake did not. It remains to be seen whether this reflects inadequacies in the current diagnostic system for youth (Chung and Martin, 2005
) or fact that severity of use rather than consequences (i.e., symptoms) are more associated with long term patterns of persistence or remission. Lastly, the sample was relatively small for conducting this type of longitudinal analyses, requiring further validation in other samples of alcohol or substance use disordered youth. Power analyses suggested our sample size was adequate to detect medium to large effect sizes; more subtle distinctions between groups might emerge in larger samples.
The goal of this investigation was to identify long term, developmentally relevant patterns in AOD use for treated youth. These patterns were associated with differential success in meeting the developmental milestones associated with the transition to adulthood. Clarification of the dispersion of outcomes following youth treatment can lead to better assessment of the effectiveness of treatments (e.g., Schulenberg and Maggs, 2002
; Witkiewitz and Marlatt, 2004
) and facilitate the development of new treatments or targeted timing of special interventions (Brown et al., 2008
). Outcomes from addiction treatment necessarily reflect dynamic processes (Brown, 2004
; Brown and Ramo, 2006
) and should be examined from a framework that can account for the interaction between the person and environment over time.