Alcohol and drug involvement after adolescent treatment and functioning during emerging adulthood
Introduction
Although it is becoming increasingly clear that heavy alcohol and drug (AOD) involvement during adolescence carries substantial risk (Brown and Ramo, 2006, Spear, 2004), little is known of the adult outcomes of youth treated for alcohol and drug use disorders (A/SUDs). Past investigations have shown that adolescents treated for A/SUDs return to substance use at rates consistent with their adult counterparts (e.g., Brown et al., 1990, Chung and Maisto, 2006, Cornelius et al., 2003, Latimer et al., 2000). While similar in rates of return to use, the impact of AOD use after treatment are different for youth (Brown, 2004, Schulenberg et al., 1996) and may have far reaching developmental consequences (Brown et al., 2008). While advances have been made in characterization of long-term substance involvement patterns and its impact on youth in community samples (e.g., Schulenberg and Maggs, 2002), little is known regarding the unfolding patterns of alcohol and drug use and impact on development for youth who received treatment for A/SUDs.
The vast majority of the existing work examining trajectories of AOD involvement in teens after treatment has focused on the early period after treatment (e.g., 1 year: Chung et al., 2004, Chung et al., 2005; 3 years: Chung et al., 2008; 2.5 years: Godley et al., 2004). For example, in a longitudinal study, Chung and colleagues (2008) used latent class growth modeling to characterize patterns of alcohol, marijuana and other drug dependence symptoms across 3 years in adolescents who had experienced a treatment episode (in- or outpatient). They identified six classes for alcohol and marijuana symptoms including Low Improver (or No Symptoms [Marijuana]), High Improver, Stable Low, Moderate Improver, Increasing and Stable High. Four trajectories emerged for other drug symptoms representing No Symptoms, Improving, Increasing, and Stable High. To a large degree, these patterns were concordant across individuals assigned to these classes (only one of four subtypes were discordant). Subtypes differed on the basis of gender, ethnicity, age, conduct disorder and depression, and the patterns identified demonstrated some consistency across the other studies cited above. These studies demonstrate the usefulness of empirically based AOD trajectories and highlight the need for longer term follow-ups to understand the course of substance use into early adulthood.
Examination of longer term substance use patterns among treated teens has used clinical categorization, rather than empirical strategies such as latent class growth analysis (LCGA). For example, work in our lab has characterized the substance involvement, social and behavioral functioning in the years after treatment (Anderson et al., 2007, Brown et al., 1994, Brown et al., 2001, Chung et al., 2003). Based on quantity/frequency of use and associated problems exhibited over multiple follow-up time points after treatment, youth were clinically categorized into five groups: Abstainers (7%), Users (8%), Slow improvers (10%), Worse with time (27%), and Continuous heavy users (48%; Brown et al., 2001). Based on frequency of use and SUD diagnostic criteria at three follow-up time points up to 5.5 years, Winters and colleagues (2007) found that treated youth had consistently better outcomes than wait list controls, while community controls demonstrated lower substance use than the other two group at all three assessed time points. While this initial conceptual approach to treatment outcome was critical, the latent trajectory approach may further our understanding of both post-treatment substance involvement patterns and developmentally important functioning as treated adolescents transition into adulthood.
In an early effort to apply latent growth analysis to classify teens’ alcohol use in the 8 years following treatment, Abrantes described four trajectories of teen alcohol patterns in 140 who had an inpatient treatment episode (Chung et al., 2003). Based on these 8-year trajectories, teens were labeled as Abstainers (22%), Infrequent users (24%), Worse with time (36%), and Frequent users (18%). Worse alcohol trajectories were associated with more severe alcohol dependence symptoms, severe drug use, and poorer psychosocial functioning in late adolescence. This longitudinal work underscores the importance of extending the trajectory analysis approach beyond the 8-year period as well as incorporating joint consideration of multiple substances (i.e., marijuana, other drugs) which are so often used in conjunction with alcohol among youth in substance abuse treatment (Substance Abuse and Mental Health Services Administration and Office of Applied Studies, 2007).
This investigation has two primary goals: (1) to identify 10-year patterns of AOD use for youth after treatment for A/SUDs in adolescence, and (2) examine the developmental outcomes for youth expected to best represent these patterns of consumption in young adulthood. For full consideration of long-term outcomes, use of alcohol and other drugs were modeled simultaneously using latent class growth analysis (LCGA). Joint approaches, integrating two separate trajectory models, have been used successfully to characterize the comorbidity between alcohol and tobacco use from late adolescence to young adulthood (Jackson et al., 2005) and 1-year alcohol use and AUD symptomatology for youth after treatment (Chung et al., 2005). Further, we examined demographic characteristics (age, sex and ethnicity) and pre-treatment substance use, which have commonly been investigated as predictors or associates of substance use trajectory classes in treatment samples (Brown et al., 1989, Chung and Maisto, 2006, Chung et al., 2004, Latimer et al., 2000, Richter et al., 1991).
Given our interest in understanding the developmental impacts of AOD use across time, we examined how youth with differing longitudinal patterns of AOD involvement following treatment managed the developmental transitions of emerging adulthood. Emerging adulthood, the period of development from 18 to 25 years of age, is characterized by transitions to independent living, educational and occupational attainment, and deeper intimacy in interpersonal relationships (Arnett, 2000, Scales et al., 2003). We targeted the domains of independence (financial/residential), industry (education/work), and intimacy (marriage/family) as indicators of developmental attainment in this investigation. As past research has demonstrated that continued treatment (Kelly et al., 2000, Kelly et al., 2008) and living in environments where alcohol or drugs are difficult to acquire (i.e., juvenile hall, jail) influence post-treatment course (Godley et al., 2004), class differences on these variables were compared at each follow-up time point.
Based on recent findings, we expected that an abstaining/non-using trajectory and chronic use trajectory would emerge across the 10 years after treatment (Brown et al., 2001, Chung et al., 2005, Clark et al., 2006). In addition, we expected that developmentally relevant shifts in use patterns might emerge around developmental transition points identified in past studies, such as the move to independent living and graduations from high school and college (Aseltine and Gore, 2005, Kypri et al., 2004, Zucker et al., 2000) and transition to legal consumption of alcohol (age 21). We also expected that males and younger teens at treatment entry might be more likely to experience more severe patterns of alcohol and drug use engagement (Chung and Maisto, 2006, Wiesner et al., 2007). Consistent with young adult development, patterns characterized by lower levels of AOD use across time were expected to demonstrate higher levels of educational and occupational attainment, age appropriate independence from families-of-origin, and greater intimacy and responsibility within their interpersonal relationships.
Section snippets
Participants
One hundred and seventy-one youth (40.9% girls), aged 13–18 years of age, were recruited from alcohol and drug use treatment centers in San Diego County between 1988 and 1994, as a part of a longitudinal study of adolescent alcohol and drug use treatment outcomes (Brown et al., 1994). Recruited youth had a primary diagnosis of alcohol or substance use disorder without concurrent Axis I psychopathology (DSM-III-R; American Psychiatric Association, 1987) exclusive of conduct disorder (94% with CD
10-Year patterns of post-treatment substance use and DSM-IV dependence symptoms
As described above, the series of LCGA models were tested to determine the best fit to the longitudinal data. While the Adjusted BIC dropped with each increase in number of classes and all class models were significant using the BLRT (Table 2), the 6 class solution maximized the interpretability of the longitudinal use patterns within the data. The parameter estimates for this selected model are presented in Table 3. Fig. 1, Fig. 2 highlight the topography of alcohol and other drug use within
Discussion
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
Role of funding source
This research was supported by National Institute on Alcohol Abuse and Alcoholism Grants AA07033 and AA12171. Preparation of this manuscript was supported in part by DA019960 (K. Anderson), a Predoctoral Fellowship, DA219412 (D. Ramo), and an Institutional Training Grant T32 DA007250 (D. Ramo) from the National Institute on Drug Abuse. Dr. Anderson is also supported by ABMRF: The Alcohol Research Foundation. The NIAAA, NIDA and ABMRF had no further role in study design; in the collection,
Contributors
Dr. Brown designed the studies and wrote the protocol. Dr. Anderson and Mr. Cummins completed the analyses. Drs. Anderson and Ramo completed the first draft and subsequent drafts of the manuscript, and Dr. Brown, reviewed and revised subsequent drafts of the manuscript. All authors contributed to and have approved the final manuscripts.
Conflict of interest
All four authors declare that they have no conflict of interest.
Acknowledgements
The authors wish to thank the programs, staff, and participants in this study. Special thanks to Blair Priest for her work on the family development section.
References (58)
- et al.
Substance use treatment outcomes for youth: integrating personal and environmental predictors
Drug Alcohol Depend.
(2007) - et al.
Correlates of success following treatment for adolescent substance abuse
Appl. Prev. Psychol.
(1994) - et al.
Characteristics of relapse following adolescent substance abuse treatment
Addict. Behav.
(1989) - et al.
Relapse to alcohol and other drug use in treated adolescents: review and reconsideration of relapse as a change point in clinical course
Clin. Psychol. Rev.
(2006) - et al.
Joint trajectory analysis of treated adolescents’ alcohol use and symptoms over 1 year
Addict. Behav.
(2005) - et al.
What were they thinking? Adolescents’ interpretations of DSM-IV alcohol dependence symptom queries and implications for diagnostic validity
Drug Alcohol Depend.
(2005) - et al.
Substance use disorder trajectory classes: diachronic integration of onset age, severity, and course
Addict. Behav.
(2006) - et al.
Rapid relapse generally follows treatment for substance use disorders among adolescents
Addict. Behav.
(2003) - et al.
The impact of social support and self-esteem on adolescent substance abuse treatment outcome
J. Subst. Abuse
(1991) - et al.
Long-term outcome of substance-dependent youth following 12-step treatment
J. Subst. Abuse Treat.
(2007)
Diagnostic and Statistical Manual of Mental Disorders
Diagnostic and Statistical Manual of Mental Disorders
Am. Psychol.
Work, postsecondary education, and psychosocial functioning following the transition from high school
J. Adolesc. Res.
Observations on the use of growth mixture models in behavioral research
Multivariate Behav. Res.
Developmental trajectories of physical aggression from school entry to late adolescence
J. Child Psychol. Psychiatry
Measuring youth outcomes from alcohol and drug treatment
Addictions
Four-year outcomes from adolescent alcohol and drug treatment
J. Stud. Alcohol.
A developmental perspective on alcohol and youth ages 16–20
Pediatrics
Adolescent alcohol and drug treatment outcome
Psychometric Evaluation of the Customary Drinking and Drug Use Record (CDDR): a measure of adolescent alcohol and drug involvement
J. Stud. Alcohol.
Clinical course of youth following treatment for alcohol and drug problems
Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach
Trajectories of alcohol and drug use and dependence from adolescence to adulthood: the effects of familial alcoholism and personality
J. Abnorm. Psychol.
Adolescents’ alcohol and drug use trajectories in the year following treatment
J. Stud. Alcohol.
Course of alcohol problems in treated adolescents
Alcohol. Clin. Exp. Res.
Concurrent change in alcohol and drug problems among treated adolescents over three years
JSAD
Maximum Likelihood Estimation: Logic and Practice
G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences
Behav. Res. Meth.
Cited by (40)
Gradient of association between parenting styles and patterns of drug use in adolescence: A latent class analysis
2017, Drug and Alcohol DependenceCitation Excerpt :These underlying unobserved groups of adolescents are inferred from a set of measured dichotomous questions about use (or not) of alcohol, tobacco, marijuana, and inhalants and could be treated as homogeneous, and the benefits and drawbacks of various parenting styles could then be tested in a more methodologically robust manner (Percy and Iwaniec, 2007). Latent class analysis (LCA) is a mixture model (Collins and Lanza, 2009; Lazarsfeld and Henry, 1968) that allows to better understanding profiles of behavior outcomes such as substance use profiles (Anderson et al., 2010; Scheier et al., 2008). The breakthrough of LCA is the use of an analytical methodology focused on the person, in contrast to past data analysis techniques that have focused on the variables (Lanza and Rhoades, 2013), where for each observed outcome, a regression was build up and as consequence and increasing in the false discovery rates might occur (Simmons et al., 2011).
Socioeconomic differences in adolescent substance abuse treatment participation and long-term outcomes
2017, Addictive BehaviorsCitation Excerpt :Thus, we should pay close attention as to whether changes in payment plans for treatment services and increased costs for services lead to greater treatment barriers among adolescents. Long-term alcohol or drug abstinence was not related to parent SES which is consistent with two other adolescent treatment studies that accounted for parent SES (Anderson et al., 2010; Chung et al., 2008). Both studies reported non-significant effects of parent SES (via Hollingshead index, a composite measure of parent's occupation and education) on adolescent treatment outcomes of alcohol and drug use.
Marijuana use and service utilization among adolescents 7 years post substance use treatment
2016, Drug and Alcohol DependencePolysubstance use patterns and trajectories in vocational students - A latent transition analysis
2016, Addictive BehaviorsCitation Excerpt :So far, LCA and LTA have been used to identify a variety of substance use patterns from smoking (Guo, Aveyard, Fielding, & Sutton, 2009), and alcohol use (Connell, Gilreath, & Hansen, 2009) to drug use (James, McField, & Montgomery, 2013). Nevertheless, LTA studies of substance use trajectories are scarce, and mostly limited to certain groups, e.g., adolescents in treatment (Anderson, Ramo, Cummins, & Brown, 2010), or children in foster care (Shin, Hong, & Hazen, 2010). In addition, most LCA and LTA studies include substance-related problems like sexual risk behavior or financial troubles as latent class indicators.
Family history density predicts long term substance use outcomes in an adolescent treatment sample
2015, Drug and Alcohol DependenceCitation Excerpt :The age period examined in the present study is particularly significant given the amount of developmental change occurring across neurologic, cognitive and social domains (e.g., Brown et al., 2008). Understanding the salience of important long-term risks is particularly significant for adolescents as they have yet to transition through the peak periods of alcohol and drug dependence and greater use can lead to more severe long-term trajectories and adverse consequences for the individual and society (Anderson et al., 2010; Brown and Ramo, 2006). Additionally, adolescents with a comorbid Axis I disorder have also been shown to have worse treatment outcomes, using substances more frequently (Tomlinson et al., 2004).