Introduction
Approximately 50% of youth who have been arrested, are on probation post-adjudication, or are otherwise involved with the justice system (justice-involved youth; JIY) have a mental health disorder [Reference Wasserman, McReynolds, Ko, Katz and Carpenter1,Reference Wasserman, McReynolds, Schwalbe, Keating and Jones2]. More than a third meet diagnostic criteria for a substance use disorder (SUD) [Reference Wasserman, McReynolds, Schwalbe, Keating and Jones2]. Behavioral health disorders (i.e., both mental illness and SUD) are associated with an increased likelihood of recidivism and additional involvement with the justice system [Reference Henggeler, Clingempeel, Brondino and Pickrel3,Reference Kandel4]; further justice system involvement, in turn, is associated with higher rates of behavioral health disorders [Reference Wasserman, McReynolds, Schwalbe, Keating and Jones2]. While treatment completion has been associated with positive health and life outcomes (i.e., employment, housing) in substance use treatment [Reference Mutter, Ali, Smith and Strashny5] and with reduced recidivism among JIY youth specifically [Reference Kretschmar, Butcher, Flannery and Singer6], JIY often do not complete available treatment for behavioral health disorders [Reference Aarons, Brown, Garland and Hough7,Reference Yonek, Dauria, Kemp, Koinis-Mitchell, Marshall and Tolou-Shams8], even in the context of well-resourced treatment studies [Reference Silovsky, Hunter and Taylor9–Reference Burrow-Sanchez, Minami and Hops11]. It is critical to understand factors involved in treatment completion among JIY.
Data suggest that JIY and their families experience challenges in completing treatment for behavioral health disorders [Reference Yeterian, Greene, Bergman and Kelly12]. For example, 2017 data from the Substance Abuse and Mental Health Services Administration indicate that only 45% of youth aged 12–20 years who were referred to publicly funded substance abuse treatment by a criminal justice organization successfully completed treatment [13] – most dropped out of treatment early or were discharged by the service provider due to lack of compliance with treatment. A recent cross-sectional analysis of administrative data from the Florida Department of Juvenile Justice suggests that, although 32% of the sample met criteria for SUD treatment, only 11.5% completed a SUD treatment program [Reference Johnson and Tran14].
While administrative data may show the extent of the problem, they provide little understanding of how to address the problem. However, treatment studies for JIY may provide more insight. These studies often provide high-quality, evidence-based care, or are testing new interventions. In these contexts, researchers often make considerable efforts to help participants and their families maintain participation [Reference Cohen, Crabtree and Etz15], and may therefore represent the best-case scenario for measuring treatment completion among JIY. A better understanding of the factors that contribute to successful completion in these contexts may help inform future research and practice.
Influences on treatment completion among JIY
Existing research suggests a number of possible moderators of behavioral health treatment completion among JIY. A recent systematic review examined empirical evidence on the effects of three types of interventions designed to improve engagement in behavioral health treatment among adolescents (not exclusively focused on JIY): systems-level (e.g., offering treatment services in-home), family-level (e.g., informing family members about treatment topics), or individual-level (e.g., utilizing contingency management interventions) [Reference Kim, Munson and McKay16]. Findings suggested that any type of intervention designed to increase behavioral health treatment engagement has positive influences on attendance at varying stages of treatment. Type of treatment (i.e., group, individual, family) may also have an influence on the extent to which youth are able to engage in and complete treatment; existing research suggests that family-based treatments are associated with greater engagement in substance use treatment among adolescents [Reference Dakof, Tejeda and Liddle17]. JIY who are members of ethnic and/or racial minority groups may be less likely to have access to or utilize mental health treatment [Reference Yonek, Dauria, Kemp, Koinis-Mitchell, Marshall and Tolou-Shams8]; research also finds that JIY of color have lower rates of treatment completion [Reference Johnson and Tran14]. Some have argued that these disparities might reflect a difference in needs – for example, Black JIY are at an increased risk of experiencing poly-victimization, defined as having experienced many different types of traumatic victimization in their lifetime including assault, family/community violence, physical or sexual abuse, and trauma from racially driven encounters [Reference Ford and Blaustein18,Reference Loyd, Hotton, Walden, Kendall, Emerson and Donenberg19]. JIY of color are also more likely to experience a wide array of comorbid mental disorders [Reference Alegria, Carson, Goncalves and Keefe20], further demonstrating their need for high quality treatment. Researchers have argued that current treatment options may not be properly poised to address the complex stressors that JIY of color experience [Reference Loyd, Hotton, Walden, Kendall, Emerson and Donenberg19,Reference Rawal, Romansky, Jenuwine and Lyons21], thus discouraging engagement with treatment. Despite this, many youth of color do not have access to quality treatment options due to the fact that these youth tend to live in poorer communities with fewer resources, a systematic barrier [Reference Alegria, Carson, Goncalves and Keefe20]. Other barriers unique to engaging JIY youth of color include cultural mistrust of healthcare services rooted in historical oppression [Reference Whaley22], greater logistical challenges (e.g., transportation, insurance issues) [Reference Alegria, Carson, Goncalves and Keefe20], and difficulties engaging family members in treatment (e.g., language barriers, competing demands) [Reference Alegria, Carson, Goncalves and Keefe20].
Low rates of treatment completion among JIY may be surprising, since JIY often enter treatment because of legal mandates (e.g., as a condition of probation). However, the effect of juvenile drug courts – one of the most common examples of mandated treatment – has been highly variable, especially in comparison to more commonly successful adult drug courts [Reference Marlowe23]. Current evidence suggests that most juvenile drug courts minimally engage parents and youth; a meta-analytic review found that slightly more than half of all youth who initially enroll in a juvenile drug court program end up graduating and that youth who enroll but do not graduate (i.e., are terminated unsuccessfully) do not appear to benefit from participating in the program based on later measures of substance use and recidivism [Reference Stein, Deberard and Homan24]. Given these findings, researchers conclude that it is necessary to implement additional efforts to engage youth in treatment, beyond or instead of court mandates [Reference Stein, Deberard and Homan24]. Especially given the increase in diversion of youth away from the justice system, it is critically important to understand how to constructively engage youth without the force of court mandates [Reference Abrams25]; the first step in this process is understanding complexities in current rates of treatment completion among JIY and identifying potential moderators.
Purpose of Study
Longitudinal studies of behavioral health treatment among JIY report inconsistent findings, with wide ranges in rates of treatment completion [Reference Tolou-Shams, Dauria, Conrad, Kemp, Johnson and Brown26–Reference Henggeler, McCart, Cunningham and Chapman28] even in the context of additional resources available to researchers to help engage participants. The current study aims to conduct a meta-analysis to quantify treatment completion among JIY enrolled in behavioral health treatment studies. In addition, the current study aims to determine whether demographic variables (i.e., gender, race/ethnicity) and methodological variables (i.e., intervention focus, type of treatment, the presence or absence of interventions to increase treatment engagement) moderate the prevalence of treatment completion among JIY.
Methods
Procedures and results are reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) Statement [Reference Liberati, Altman and Tetzlaff29,Reference Moher, Liberati, Tetzlaff, Altman and Group30], which is available in Table 1.
Table 1. PRISMA reporting checklist for systematic reviews and meta-analysis
Inclusion and Exclusion Criteria
Studies were included if: 1) they were available in English, 2) the sample included youth who are involved in the juvenile justice system and who resided in the community at the time of the study (e.g., youth on probation, youth who have been arrested and then diverted from the justice system through a diversion program), 3) the study included an assessment to determine eligibility for treatment and the provision of a behavioral health treatment, and 4) the authors reported criteria for treatment completion. Studies in which all JIY were eligible (i.e., the primary goal of services was to prevent recidivism) were excluded; however, studies targeting behavioral problems (i.e., youth adjudicated through drug court, youth who sexually offend) indicative of a specific behavioral health disorder being treated were included. Book chapters and dissertations were included in literature searches. Studies were excluded if they were cross-sectional, were primarily studies of behavioral health service utilization (i.e., assessing whether youth access treatment not provided as part of the study), or represented evaluations of treatment services in which only participants who completed treatment were included in analyses. Studies that combined youth who were and were not JIY or included both adult and juvenile participants were excluded unless information for only the JIY participants could be obtained.
Literature Search
We identified studies meeting criteria for inclusion using four concurrent methods. First, we identified 2,850 studies through database literature searches (see Table 2 for databases and search terms). Second, because the terminology to identify JIY is quite heterogeneous (e.g., justice-involved, juvenile offender, probationers, delinquent, justice-referred), we identified 701 articles through extensive forward- and backward-literature searching of reviews on behavioral health treatments for adolescents and reviews of interventions for JIY [Reference Belenko, Knight and Wasserman31–Reference Woolfenden, Williams and Peat47]; this is an established method of conducting literature searches in areas where the terminology is not standardized [Reference Briscoe, Bethel and Rogers48]. Third, subject matter experts were contacted (i.e., authors of published articles on studies funded by JJTrials) to inquire about data from unpublished studies, but no additional studies were obtained from these contacts. Finally, we created email alerts in Google Scholar for the above search; no newly eligible studies were identified prior to manuscript submission. After duplicates were removed, we reviewed the title and abstract of 2,700 articles with the full text of 253 articles being reviewed (see Fig. 1 for PRISMA flow chart and article exclusions). Thirteen studies were coded for inclusion in the data analysis.
Table 2. Database search terms
Fig. 1. PRISMA flow chart.
Coding
We coded studies according to a fixed coding protocol, following guidelines from Lipsey, Wilson [Reference Lipsey and Wilson49], and Card [Reference Card50]; variables included study characteristics, primary outcomes of interest, moderators, and study quality. Each study was coded independently by the first, second, and third authors; after coding, the authors met to identify and resolve discrepancies in coding.
Prevalence Estimates
We coded the number of JIY who began treatment and the number who completed treatment according to the study’s stated treatment completion criteria. This prevalence rate was coded as treatment completion. If studies reported sufficient data on multiple treatment conditions, separate effect sizes were estimated for each treatment condition. For RCTs where “treatment completion” criteria were not specified for the control condition (usually “services as usual”), the treatment completion prevalence rate was only coded for the experimental condition(s).
Moderators
Racial and ethnic minority participants
The prevalence of racial and ethnic minority participants was coded as a percentage for each study or treatment group and included in analysis of moderators.
Intervention Focus
We assessed intervention focus as a moderator; this variable was coded as 1 = substance use disorders and 2 = other behavioral health for analyses.
Type of Treatment
Type of treatment was coded as 1 = individual, 2 = group, and 3 = family for analyses. When treatments included multiple components (e.g., individual sessions with the youth as well as sessions with youth and parent(s), individual therapy with group skills training classes), it was coded according to the study’s identified type of treatment (e.g., a family-based treatment) and the highest level of clinical intensity in the treatment. Thus, if a treatment included both individual and family components and was described as family-based treatment, it was coded as 3 = family. If a treatment included individual therapy sessions and less-frequent group skill-building classes, it was coded as 1 = individual.
Interventions to Increase Treatment Engagement
The presence or absence of reported interventions to increase treatment engagement was coded as a moderator (1 = present, 2 = not present). Interventions to increase engagement commonly included contingency management programs, engaging family in the treatment planning process, providing treatment in the youth’s home or other convenient locations, providing transportation to treatment, or a comprehensive assessment of barriers to engagement and subsequent problem solving that focuses on the whole ecology of youths and families, as is standard in multisystemic therapy (MST). Basic phone or text-message reminders of treatment appointments, as are commonly provided in behavioral healthcare, were not coded as interventions to increase engagement.
Treatment Mandates
Whether or not youth were mandated by a court to participate in outpatient treatment was coded as the percentage of youth in the study who were required to participate in outpatient treatment.
Primary Study Quality
Standardized measures for assessing primary study quality in systematic reviews and meta-analyses typically focus on specific study designs (e.g., quasi-experimental studies, noncontrolled longitudinal studies) and are geared toward assessing risk of bias in the study’s main outcome variable. Because we were primarily interested in judging the quality of measures of attendance and attrition (rather than bias in outcome) across studies with varied designs, we developed a checklist after reviewing existing measures of study quality [Reference Ma, Wang, Yang, Huang, Weng and Zeng51–Reference Moja, Telaro, D’Amico, Moschetti, Coe and Liberati53] as well as review articles on the measurement of treatment engagement [Reference Kim, Munson and McKay16,Reference Dakof, Tejeda and Liddle17,Reference McMurran and Ward54,Reference Garnick, Lee and O’Brien55]. The checklist was composed of 5 yes/no questions for all primary studies and 2 additional questions for studies with randomized designs; the checklist is available as a Supplementary file.
Data Analysis
Effect Size Calculations
All analyses were performed in R (Version 4.1.3) with packages Metafor and Meta [Reference Balduzzi, Rücker and Schwarzer56–58]. To assess proportional data, an analysis of binary outcomes was pooled in the form of proportions with a generalized linear mixed model (GLMM) using the logit link function with Clopper-Pearson intervals to stabilize the variance [Reference Schwarzer, Chemaitelly, Abu-Raddad and Rücker59,Reference Clopper and Pearson60]. Simulation studies indicate that the GLMM model provides the most accurate estimate in proportional meta-analysis because GLMM models do not require data transformations within studies [Reference Trikalinos, Trow and Schmid61,Reference Lin and Chu62], fully account for uncertainties, and produce confidence intervals with satisfactory coverage probabilities [Reference Hamza, van Houwelingen and Stijnen63]. We implemented all parameters via the maximum likelihood approach. Random effects models were selected to calculate effect sizes because they represent a more conservative estimate of mean prevalence and to account for heterogeneity between studies.
We examined forest plots to identify potential outliers, i.e., studies whose individual 95% CI did not overlap with the 95% CI for the mean effect [Reference Baker and Jackson64,Reference Viechtbauer and Cheung65]. Potential outliers were removed from calculation of effect size if overall prevalence rates were affected [Reference Viechtbauer and Cheung65]. Heterogeneity of the studies was assessed using Cochran’s Q-Test, which tests for the presence of heterogeneity across studies [Reference Huedo-Medina, Sánchez-Meca, Marín-Martínez and Botella66], and Higgins I2, which describes the percent of variation in prevalence across studies due to heterogeneity rather than chance [Reference Fazel, Doll and Långström67]. Low heterogeneity was defined as Q scores < critical chi square values and I2 < 25%; moderate if Q > critical chi square values and I2 around 50%; and high if Q > critical chi square values and I2 > 50%. Statistical significance was defined as P < 0.05 [Reference Fazel, Doll and Långström67].
Moderator Analyses
If significant heterogeneity of individual prevalence estimates was found via the two criteria, mixed-effect meta regression was used to attempt to explain the between-study heterogeneity based on study-level fixed-effect covariates (i.e., subgroups defined by categorical covariates or continuous covariates). Specifically, candidate variables were tested to identify significant moderators (i.e., candidate variables that account for a significant proportion of variability in individual prevalence across studies) in nonlinear mixed-effects models, such that random-effect terms were used to combine studies within each subgroup, and fixed-effect terms were used to combine subgroups and yield the overall effect [Reference Borenstein and Higgins68]. Study-to-study variance (T 2) was not assumed to be the same for all subgroups; the value was computed within subgroups [69]. The Q between statistic (analogous to analysis of variance) tested categorical variables to report between-study variance explained by moderators. We calculated mean effect sizes within all variable levels [Reference Sagie and Koslowsky70]. Variables were considered moderators if the mixed(random)-effects model indicated statistical significance (P < 0.05) on the Q between statistic [Reference Borenstein, Hedges, Higgins and Rothstein71]. Interactions among moderator variables were not tested due to insufficient power.
Publication Bias
Publication bias was assessed with funnel plot symmetry both visually and statistically by using Egger’s linear regression method to assess any relationship between sample size and prevalence [Reference Egger, Davey Smith and Altman72]. If significant funnel plot asymmetry was present, the trim and fill method was used to determine the number of missing studies that would be needed to correct the asymmetry [Reference Duval and Tweedie73]. An additional quantitative assessment of bias used the Begg’s rank method [Reference Begg and Mazumdar74] to identify relationships between effect sizes and sample sizes. Low publication bias was deemed present if funnel plots were visually symmetrical and were not statistically significant. Finally, given the gaps in the ability to assess publication bias in proportional meta-analyses using established statistical methods, we offer qualitative assessments of the role of publication bias in these analyses.
Results
Description of Included Studies
Altogether, 13 studies [Reference Silovsky, Hunter and Taylor9–Reference Burrow-Sanchez, Minami and Hops11,Reference Tolou-Shams, Dauria, Conrad, Kemp, Johnson and Brown26,Reference Henggeler, McCart, Cunningham and Chapman28,Reference Borduin, Schaeffer and Heiblum75–Reference Walker, Duong and Hayes82] representing 20 treatments (e.g., services as usual, Multisystemic Therapy) met inclusion criteria (see Table 3). Three of the 13 studies included services as usual treatment conditions in which they did not specify treatment completion criteria (see Table 4 for details); adolescents in these groups were not included in the tables or in the descriptions below. All studies were peer-reviewed published articles and were located in the United States. Complete descriptions of all 13 studies are included in Tables 3 and 4.
Table 3. Effect size variables and demographics
Note: In some cases, authors reported demographics for all participants (rather than by treatment group) after finding that there were no significant demographic differences between treatment groups.
Abbreviations: Multisystemic Therapy (MST), Services as Usual (SAU), Cognitive Behavioral Therapy (CBT), Multidimensional Family Therapy (MDFT), Motivational Enhancement Therapy (MET), Adolescent Group Treatment (AGT), Adolescent Community Reinforcement Approach (ACRA), Contingency Management (CM).
* SAU treatment groups that did not define or report treatment completion data were not included in analyses.
Table 4. Methodological variables
Abbreviations: Multisystemic Therapy (MST), Services as Usual (SAU), Cognitive Behavioral Therapy (CBT), Multidimensional Family Therapy (MDFT), Motivational Enhancement Therapy (MET).
* In some studies, Services as Usual (SAU) conditions did not report treatment completion criteria or data. These conditions were not included in analyses.
The 20 eligible treatment conditions included a total sample size of 1,269 adolescents, with a mean sample size of 74.7 (SD = 70.7, range = 24–320). Samples averaged 15.2 years of age, were predominantly male (80.2%), and were predominantly from minority ethnic or racial populations (68.1%). Studies were primarily focused on substance use disorders (n = 10, 76.9%). Individual treatment conditions utilized different types of treatment, categorized as family (n = 9, 52.9%), individual (n = 4, 23.5%), or group (n = 4, 23.5%). When treatment length and completion criteria were reported, treatments were designed to last an average of 18.4 weeks (SD = 8.6).
Studies employed a wide range of strategies to increase treatment completion; of 20 individual treatment groups (including services as usual and experimental treatment conditions, which often used different treatment engagement strategies between groups), 11 (65%) reported employing interventions to increase treatment engagement; see Table 4. Six treatment groups provided services in locations convenient to the youth or family (e.g., home, school, community spaces). Four treatment groups offered financial assistance with transportation to treatment, three made on-call therapists available to families at all times, and 4 included family in-treatment planning (e.g., contacting family each week to describe the group session topic, encouraging regular contact between therapists and families). Two treatment groups modified services to be culturally adapted, e.g., by recruiting providers from the local community. Finally, only one treatment group utilized a contingency management intervention to increase youth attendance in treatment. Nine treatment groups did not specify any treatment engagement strategies. See Table 4 for descriptions of engagement strategies used in each study.
Supervision or JIY court involvement varied widely both between and within studies; youth were on probation, arrested and entering treatment pre-adjudication, in formal diversion programs, or enrolled in drug court. In three studies (23.1%), participants were recruited entirely from juvenile drug courts. Whether or not youth had been mandated to participate in treatment also varied widely both within and between studies; 7 studies (53.8%) did not report information on treatment mandates; see Table 4.
Treatment Completion
A total of 13 studies yielded 20 individual prevalence estimates; see Fig. 2 for the forest plot of effect sizes and Table 5 for associated model statistics. Although there were two significant outliers from the mean prevalence estimate [Reference Silovsky, Hunter and Taylor9,Reference Henggeler, Pickrel and Brondino77], neither of these individual estimates had a significant effect on the overall estimate when removed from analyses, so individual estimates from all studies were retained. The main effect size for treatment completion was pr = 82.6 (CI = 70.6, 90.3), indicating that approximately 82.6% of JIY completed treatment, as defined by each study’s individual treatment completion criteria. The heterogeneity in individual prevalence estimates across studies was high (Q = 221.3, I2 = 91.4), meeting criteria for moderation analyses.
Fig. 2. Treatment completion by treatment type. Note: Treatment groups are marked as T (treatment group), C (control group), or T1 and T2 for multiple treatments in comparative effectiveness studies.
Table 5. Effect sizes and associated statistics
Moderator Analyses
Subgroup analyses revealed that treatment completion was higher in studies that provided family- or group-based treatment (pr = 87.8, CI = 78.9, 93.2, k = 14) compared to studies providing individual treatment (pr = 61.1, CI = 30.4, 84.9, k = 6); see Fig. 2 for a forest plot of effect sizes by type of treatment and Table 5 for full results from moderator analyses. No other significant moderators were identified.
Primary Study Quality
Studies included a range of designs: 9 studies (52.9%) were RCTs (some of these employed a cluster-randomized design), one (7.7%) was a quasi-experimental trial with a control group, and three (23.1%) were noncontrolled longitudinal trials of treatment effectiveness (see Supplemental Table). All but one study conducted intent-to-treat analyses, in which all participants who entered treatment were included in outcome analyses. Only three studies (23.1%) provided a detailed description of youth who did not complete treatment, including reasons for attrition and demographic characteristics of those who did not complete treatment.
Publication bias
Visual assessment of the funnel plot for treatment completion suggests moderate publication bias (see Fig. 3 for the funnel plot with unpublished studies imputed). Quantitative assessments using Begg’s rank correlation analysis trended toward significance (p = 0.08), while assessment of Egger’s test of the intercept was statistically significant (p < 0.01), suggesting some publication bias. We used the trim-and-fill method to determine the number of missing studies that would be needed to correct the asymmetry (see Fig. 3); this method suggests 5 imputed studies; however, current literature indicates that substantial heterogeneity in effect sizes (as is present in this analysis) seriously impairs the power of the trim-and-fill method, since the plot’s asymmetry may be confounded by heterogeneity [Reference Peters, Sutton, Jones, Abrams and Rushton83–Reference Ioannidis and Trikalinos85]. Therefore, these results should be interpreted with caution. Although established statistical methods for assessing publication bias in meta-analysis are traditionally used to examine bias in treatment effects, these results may also have implications for examining bias in study completion rates. In particular, studies with low completion rates may be likely to have fewer positive treatment effects, increasing the probability that results will not be published.
Fig. 3. Funnel plot of treatment completion with imputed studies. Note: Imputed studies are shown in black.
Discussion
This systematic review and meta-analysis represent the first attempt to quantitatively review and synthesize behavioral health treatment completion data among JIY enrolled in treatment studies. This meta-analysis aimed to derive a more accurate estimate of treatment completion among JIY and better understand issues related to treatment engagement strategies and barriers to treatment completion. Findings indicated relatively high rates of treatment completion, with 82.6% of youth who initiated treatment completing treatment according to the treatment’s specified completion criteria. Our findings suggest when some systemic barriers to treatment (i.e., low treatment availability, difficulty identifying appropriate treatment, treatment cost) are removed by the presence of providing treatment within a study, JIY may not be any more difficult to engage in treatment than the general adolescent population.
One aim of this investigation was to begin to disentangle the influence of barriers to behavioral health treatment from the challenge of helping JIY complete treatment. That is, when behavioral health treatment is available and accessible (i.e., where treatment is provided through the study and JIY have been determined to be in-need of treatment), is it still difficult for JIY to complete treatment? This does not seem to be true in these studies, as the majority did complete treatment. Another aim of this investigation was to identify moderators of treatment completion. Type of treatment (i.e., family, group, individual) was a significant moderator, with treatments that provided family- or group-based treatment having higher rates of treatment completion compared to treatment groups that provided individual treatment. This is consistent with prior research findings that family-based treatments are associated with greater engagement in care [Reference Dakof, Tejeda and Liddle17,Reference Coatsworth, Santisteban, McBride and Szapocznik86]. Some have argued that the greater engagement associated with family and group-based interventions for adolescents may relate to the social components inherent to these types of interventions [Reference Bolton Oetzel and Scherer87]. Adolescence, marked by many important socio-developmental milestones, typically includes dynamic changes to the role and identification of supportive others in their lives [Reference Kim, Munson and McKay16,Reference Noam, Esman, Flaherty and Horowitz88,Reference Remschmidt89], and this may be particularly true for JIY [Reference Zwecker, Harrison, Welty, Teplin and Abram90]. For adolescents who may not have a strong, existing support network, group- and family-based interventions may be one desirable method of facilitating or improving these social connections, making them more engaging and desirable. These types of interventions may align more with helping adolescents meet important developmental milestones than individual modalities might. Even further, qualitative literature examining perspectives from youth, caregivers, treatment providers, and juvenile justice personnel consistently suggests that caregiver involvement is essential to achieve youth uptake in treatment and maintain engagement [Reference Elkington, Lee, Brooks, Watkins and Wasserman91,Reference Mauro, McCart, Sheidow, Naeger and Letourneau92]. Thus, family-focused treatments may increase rates of treatment completion by increasing caregiver involvement and support.
Influence of Publication Bias and Study Quality
Analyses indicated some likelihood that publication bias has influenced the results; funnel plots show imputed studies with lower effect sizes than those reported by published studies, which suggests rates of treatment completion may be lower. While funnel plots should be interpreted with caution given the high level of heterogeneity present in the data [Reference van Assen, van Aert and Wicherts93,Reference Terrin, Schmid, Lau and Olkin94], 6 studies that otherwise met eligibility criteria for inclusion in these analyses did not report full data on treatment completion and therefore could not be included in these analyses. Further, the majority of studies did not sufficiently describe withdrawals and dropouts, making it difficult to identify reasons or demographic characteristics predictive of treatment noncompletion.
Moderators not Supported by the Current Investigation
Hypotheses regarding the intervention focus (i.e., substance use disorders, conduct disorders, etc.) and racial or ethnic minority participants were not supported as moderators. In the case of intervention focus, the majority of studies (n = 15, 71.4%) were focused on addressing substance use disorders or problematic substance use, so it is possible that our analyses were underpowered to detect differences between studies with different intervention foci. It is less likely that moderator analyses of the prevalence of racial or ethnic minority participants were underpowered, given that there was a wide range of study participants who identified as racial or ethnic minorities. Previous research suggests that minority youth face significant barriers to accessing behavioral health treatment but fewer barriers when that treatment is available and accessible [Reference Alegria, Carson, Goncalves and Keefe20]. For example, a systematic review of literature on referrals to behavioral health services from the juvenile justice system [Reference Spinney, Yeide, Feyerherm, Cohen, Stephenson and Thomas95] finds that a majority of the 26 articles reviewed reveal at least some evidence of racial disparities in decisions to refer youth. Thus, disparities in “utilization” may be more appropriately named disparities in access.
The inclusion of interventions to increase treatment engagement was not a significant moderator of treatment completion. Given the broad heterogeneity in treatment engagement interventions provided by studies included in this analysis, it may be important to conduct additional research assessing the success of such interventions.
Another potentially important moderator that should be examined in future studies is mandated treatment (i.e., when youth are required by the court to seek outpatient behavioral health treatment). We did not examine this as a moderator in the current study since only 6 studies reported information about whether or not youth were court-mandated to engage in behavioral health treatment. Of the 6 that reported on treatment mandates, results were highly heterogeneous (i.e., 42–100%). Literature on the effectiveness of court mandates for outpatient treatment is mixed and may depend in particular on the variability of court mandates by jurisdiction [Reference Stein, Deberard and Homan24,Reference Stein, Homan and Berard96]. However, recent research [Reference Johnson and Tran97] found that youth who attended treatment at court-direction (compared to voluntarily) demonstrated higher rates of SUD treatment completion; this variable should be further examined as a possible moderator in future research. One limitation of this analysis is that, due to the small number of control groups providing services as usual that reported data on treatment completion (k = 2), we were unable to consider experimental treatment group as a moderator of treatment completion. It is important for studies examining treatment completion in JIY to report this data for all treatment groups. Future systematic reviews should consider this factor in their analyses.
Implications and Recommendations for the Future
Overall, our findings suggest that existing high-quality studies of behavioral health treatment among JIY have generally achieved high rates of treatment completion. While included studies were not limited by presenting problem, all the studies included in this meta-analysis examine treatment of either substance use or problematic sexual behavior. Notably, the majority of treatments provided in these studies (Multisystemic Therapy, MET-CBT, Multidimensional Family Therapy) are not specific to inappropriate sexual behavior or substance use and are frequently provided to youth and families with a broad range of diagnoses. Thus, we expect that these results are generalizable to many JIY and families receiving behavioral healthcare. However, it is likely helpful for the field to consider what the effectiveness of other evidence-based practices (e.g., behavioral activation) may look like for JIY. Expanding access to evidence-based treatment and helping youth and their families remain engaged in services will both be critical challenges for researchers, policymakers, juvenile justice professionals, and community mental health administrators. Based on the results of our systematic review and meta-analysis, we make three recommendations for future research, implementation, and practice of behavioral health interventions for JIY:
1. Researchers should place a greater focus on measuring and reporting treatment completion. This includes thoroughly describing dropouts and withdrawals and reporting treatment completion or “dose” criteria, to ensure that estimates of treatment efficacy are not confounded by youth engaging in different amounts of treatment.
2. Given the potentially important role that treatment mandates play in the referral of JIY to behavioral health treatment, researchers should attempt to document the nature of youth’s justice involvement and whether they have been mandated to participate in outpatient treatment (even if study participation is not mandatory).
3. Researchers should consider utilizing interventions that include family- or group-based services to improve rates of treatment completion.
Supplementary Material
To view supplementary material for this article, please visit https://doi.org/10.1017/cts.2022.418.
Acknowledgements
This project was made possible in part by a pre-doctoral fellowship awarded to AJK by the National Institute on Drug Abuse (F31DA054721). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Disclosures
All authors have no conflict of interest to declare.
Open access
