Abstract
Although research has documented the positive effects of family-based prevention programs, the field lacks specific information regarding why these programs are effective. The current study summarized the effects of family-based programs on adolescent substance use using a component-based approach to meta-analysis in which we decomposed programs into a set of key topics or components that were specifically addressed by program curricula (e.g., parental monitoring/behavior management, problem solving, positive family relations, etc.). Components were coded according to the amount of time spent on program services that targeted youth, parents, and the whole family; we also coded effect sizes across studies for each substance-related outcome. Given the nested nature of the data, we used hierarchical linear modeling to link program components (Level 2) with effect sizes (Level 1). The overall effect size across programs was .31, which did not differ by type of substance. Youth-focused components designed to encourage more positive family relationships and a positive orientation toward the future emerged as key factors predicting larger than average effect sizes. Our results suggest that, within the universe of family-based prevention, where components such as parental monitoring/behavior management are almost universal, adding or expanding certain youth-focused components may be able to enhance program efficacy.
Keywords: substance use, adolescence, family-based prevention, meta-analysis
Introduction
Considerable research has been devoted to preventing adolescent use of tobacco, alcohol, and marijuana. Substance use often starts among a small percentage of youth during early adolescence, and the percentage continues to increase throughout adolescence. Results from a recent national survey indicated that among eighth grade students, 30.3% had tried alcohol and 12.2% had been drunk in the past year, and 11.8% had used marijuana; however, by 12th grade, 47% of students had been drunk and 32.8% had used marijuana (Johnston, O’Malley, Bachman, & Schulenberg, 2010). These elevations in substance use have serious implications for adolescent health and well-being. For example, adolescent substance use has been linked to maladaptive behavior such as delinquency, school drop-out, and high-risk sexual behavior (DuRant, Smith, Kreiter, & Krowchuk, 1999; Ellickson, Tucker, & Klein, 2001; Krohn, Lizotte, & Perez, 1997; Tapert, Aarons, Sedlar, & Brown, 2001). Adolescent substance use also has significant longer-term implications; specifically, substance use in early adolescence is a strong predictor of later dependence (Brook, Brook, Zhang, Cohen, & Whiteman, 2002; Clark, Kirisci, & Tarter, 1998; Dewit, Adlaf, Offord, & Ogborne, 2000; Grant et al., 2006; Lynsky et al., 2003; Van Ryzin & Dishion, 2014). In turn, substance dependence is linked to a variety of maladaptive outcomes in adulthood, including increased unemployment, a greater likelihood of psychiatric disorder, and higher levels of involvement in violent crime and incarceration (Brook et al., 2002; Kandel, Davies, Karus, & Yamaguchi, 1986; Lennings, Copeland, & Howard, 2003; Soyka, 2000). The societal costs of substance use, including impacts on individual well-being, crime, and lost productivity, are estimated to be more than one half of a trillion dollars annually (NIDA, 2008). These findings underscore the importance of identifying specific approaches that can reduce substance use among adolescents and forestall the progression to substance dependence in adulthood, not only to protect the individual, but also to benefit society at large (Miller, 2004).
Research has consistently linked family-based factors with the initiation and escalation of substance use in adolescence. For example, effective parental monitoring of adolescent activities and peer groups can reduce risk for ubstance use (Dishion & McMahon, 1998; Dishion, Nelson, & Kavanagh, 2003; Duncan, Duncan, Biglan, & Ary, 1998; Griffin, Botvin, Scheier, Diaz, & Miller, 2000; Svensson, 2000; Van Ryzin, Fosco, & Dishion, 2012). Research also supports a link between parent-adolescent relationship quality and adolescent substance use (Farrell & White, 1998; Ledoux, Miller, Choquet, & Plant, 2002; Van Ryzin et al., 2012). As a result, numerous family-based prevention programs have been developed to address adolescent substance use (for review, see Van Ryzin, Kumpfer, Fosco, & Greenberg, 2015). Family-based programs emphasize the manner in which parenting practices and family interaction patterns can impact adolescent substance use and related problem behavior. These programs work with family members in an attempt to modify and manage emotions, cognitions, and behaviors within the family and create positive change in both individual behavior and family interaction patterns (e.g., increased parental monitoring of youth activities, more constructive parent-youth problem-solving, more positive parental involvement). These changes in the family can, in turn, reduce risk for adolescent substance use (Dusenbury, 2000; Kumpfer, Alvarado, & Whiteside, 2003; Lochman & van den Steenhoven, 2002).
Although research has documented the positive effects of family-based prevention programs, the field lacks specific information regarding why these programs have been successful. Liddle (2004) noted that although family-based programs have demonstrated favorable outcomes, we have a limited understanding of how these outcomes are achieved, and he called for more research on the exact “mechanisms of action” (p. 83). More recently, Sandler and colleagues (2011) reviewed the literature on family-based programs and noted that only a few studies examined mediational processes. In an era of increasing scarcity of resources, there is a critical need to closely examine existing family-based programs to identify the most effective program components (Lochman & van den Steenhoven, 2002; Westen, Novotny, & Thompson-Brenner, 2004). Existing meta-analyses have found significant heterogeneity of effect sizes across programs (e.g., Smit, Verdurmen, Monshouwer, & Smit, 2008), suggesting that not all family-based programs are equally effective, and this variance in effectiveness has not been explained by the amount of program time or the number of sessions specified by the program design (Lundahl, Risser, & Lovejoy, 2006). These meta-analyses evaluated programs as complete packages, and thus they cannot provide clear guidance on which components are most effective for given populations and circumstances. This is particularly true given the range of theoretical orientations, targeted recipients, and modes of delivery found in current programs (Van Ryzin et al., 2015).
Current Study
The goal of this study is to summarize the effects of family-based prevention programs on adolescent substance use using a component-based approach to meta-analysis. In addition to the traditional strengths of meta-analysis (i.e., the ability to generalize across research designs, sample characteristics, and operational definitions of variables; Cooper & Hedges, 1994), a components-based approach to meta-analysis enables us to identify the specific program aspects or characteristics that predict the largest reductions in adolescent substance use. This approach was based upon that taken by Kaminski and colleagues (2008), who conducted a meta-analysis of family-based programs for behavioral problems in children. The authors moved beyond an examination of complete programs to evaluate program strategies and processes of change. Characteristics of program content and delivery method were used to predict effect sizes on measures of parenting and children’s behavior, and their findings added significantly to our understanding of family-based programs for young children. Although this work was promising, however, it has not yet been extended to adolescence or substance use prevention.
We wished to extend the work of Kaminski and colleagues (2008) to include a component-based approach, so we first decomposed each family-based prevention program into a set of key topics or components that were specifically addressed by program curricula. This included, but was not limited to, parenting practices (e.g., monitoring and behavior management, managing peer influences), individual behavior and attitudes (e.g., self-regulation and stress management, future orientation, substance use knowledge and attitudes), family interaction patterns (e.g., problem solving, positive family relationships), and external factors (e.g., success at school, dealing with discrimination). To accurately gauge the contribution of each component to the overall success of each program, we quantified the extent to which each component was present in each program in terms of the amount of time dedicated to that component, which provided an accurate representation of the precise design of each program. We coded each component in terms of the amount of time spent delivering content to (1) youth without parents, (2) parents without youth, and (3) the whole family together. By linking components (rather than programs) to effect sizes, our results provide specific guidance regarding how to optimize family-based programs for adolescent substance use to achieve maximum effect.
Method
Studies
To ensure a broadly inclusive analysis of family-based prevention programs, our approach was to err on the side of over-inclusion. Thus, the population of primary studies included all available peer-reviewed studies in journals, books, dissertations, theses, and technical reporting on the effects of family-based prevention programs on adolescent substance use. The population of participants in the studies included all adolescents, defined inclusively from ages 11–12 (early adolescence) to 20–21 (late adolescence), regardless of gender, nationality, academic of physical abilities, or other characteristics. The population of studies included all family-based universal or selective programs aimed at preventing adolescent substance use.
Sampling Procedures
The initial document search was conducted in September 2012 using ERIC, PsycINFO, and MEDline databases. In order to error on the side of over-inclusion, we conducted the search in five steps, combining similar keywords (e.g., substance use, substance abuse) with the operator “OR,” and restricting the search in subsequent steps using the operator “AND”. Specifically, in Step 1, we searched for 36 keywords (e.g., substance, tobacco, marijuana, alcohol, chemical, narcotics, hard drug, dipsomania, etc.). In Steps 2–4, we limited the search using keywords prevention and intervention (Step 2), family, parenting, family-based, family-centered (Step 3), and adolescent, juvenile, teen, teenager, youth, children, child, middle school, junior high, elementary school (Step 4). In Step 5, we restricted the search to articles written in English. In addition to searching databases, we also reviewed lists of published work for known family-based programs, scanned citation lists for recent reviews, and perused databases of evidence-based prevention programs (e.g., SAMHSA’s National Registry Evidence-Based Programs and Practices).
These sampling procedures yielded 3,160 manuscripts from MEDline and 3,778 from ERIC and PsycINFO, resulting in 6,197 unique manuscripts after removing duplicates. We also identified an additional 485 unique manuscripts by way of reviewing citation lists and databases of evidence-based prevention programs. Thus, our initial sample included 6,682 manuscripts.
Inclusion Criteria
Review of all 6,682 manuscripts was completed by the investigators. To be included in the meta-analysis, the studies were evaluated against the following criteria: (1) The study must report on adolescent (ages 11–21) substance use, defined broadly to include tobacco (smoked or chewed), alcohol, marijuana, or “hard drugs” (e.g., cocaine, inhalants, etc.); (2) The family-program must include some component in which program staff actually communicate with the family, either face-to-face or by telephone or email (we excluded programs in which program staff simply mailed materials to families because it was not possible to assess whether the family actually made use of the material); (3) The prevention program must be aimed at limiting the number of youth who use substances or reducing the escalation of use, such as universal (Tier 1) and selective (Tier 2) interventions, rather than indicated (Tier 3) interventions aimed at youth already identified as having a substance use problem; (4) The study must include quantitative measures of substance use and report sufficient information to calculate an ES; and, (5) The study’s design must support inferences about the relative effectiveness of a family-based prevention program compared to a control group or baseline (i.e., studies comparing two family-based prevention programs were excluded).
In all, 6,551 manuscripts from the original sample were excluded. Of these, a large number of manuscripts (n = 2,829) were excluded because they did not include a quantitative measure of substance use, 326 reported on an indicated (Tier 3) intervention, 1,504 did not report sufficient quantitative information to calculate an effect size, and 3,058 did not support inferences about the relative effect of a family-based prevention program (some manuscripts were excluded for more than one reason). In addition, another five relevant manuscripts were discovered during the process of reviewing the original sample. Thus, in all, 136 manuscripts met the criteria to be included in this meta-analysis (see Appendix A); unfortunately, an inability to acquire the necessary program documentation from program authors forced us to exclude 20 of these manuscripts, yielding a final sample of 116 manuscripts (650 ESs).
Coding Program Components
Each manuscript in our sample reported on the results from a specific prevention program. For each program, we solicited program manuals or related materials from program authors. For situations where these materials could not be obtained, no coded could be generated and thus all studies using that program were necessarily excluded from our sample (see Appendix A for studies excluded for this reason).
Our approach to coding programs was guided by recent narrative reviews (i.e., Kumpfer et al., 2003; Lochman & van den Steen, 2002), consultation with Dr. Kaminski, and our own review of the literature, and reflects a top-down approach to coding the key aspects of prevention programs. We began at the top level by coding program design, which noted the presence of external components alongside the family-based content (e.g., school or community components), as well as specific program modalities (e.g., whether the program was delivered in-person or remotely, and whether it was delivered individually or in a group setting). We also coded the presence of role-playing with the curriculum (as this was found to be a key predictor of effect sizes in Kaminski et al., 2008), and the presence of optional booster sessions in the program design (the booster sessions were optional and thus not formally coded as described in the following paragraph, but their presence was captured by a dichotomous indicator).
We then coded the proportion of time allocated to each family-based component, which served to capture key differences across programs. To perform this coding, the first and third authors developed a coding system that reflects the diversity of components across different programs (see Table 1). This system evolved as program materials were reviewed but generally maintained its core design throughout the process. The codes corresponded to the types of activities, training, or information typically provided as part of family-based prevention programs. For each program session, we used documented times from program developers or, when necessary, estimated the amount of time (in minutes) spent on each topic in consultation with program developers, and these amounts were summed across all program sessions to arrive at a set of totals, by code, for each program. As discussed above, the time spent on each activity was broken down into whether it was delivered directly to parents without youth present, delivered to youth without parents present, or delivered to the whole family together.
Table 1.
Code (component) | Description |
---|---|
1. Parental Monitoring and Behavior Management | Training (generally targeting parents) that develops skills for effective monitoring and management of behavior. |
2. Fostering School Success | Behavior that relates specifically to school, which includes parental actions aimed at fostering the family’s involvement with school as well as youth actions that contribute to increased success in school, such as being productive and efficient with their time. |
3. Positive Family Relationships | Training, activities, and experiences that are designed to promote a warm, friendly, engaged relationship between parents and youth, including skills related to emotional closeness, sharing, listening, and disclosure. |
4. Substance Use Knowledge, Attitudes, and Values | Information and training that helps parents and youth to understand the facts regarding substance use and to clarify attitudes and values regarding substance use. |
5. Self-Regulation and Stress Management | Training that enables parents and youth to cope with stress and anger. |
6. Problem Solving | Training that assists parents and their youth with resolving on-going problems and sources of conflict. This entails facilitating conversations about problems where all sides contribute their own point of view and an agreement is reached that is equitable to all sides. |
7. Resisting Peer Risk | The development of skills and values that help youth to resist peer pressure to get involved in risky situations or engage in risky behavior. For parents, this code includes information and skills related to supporting their teen in avoiding or dealing with risky situations. |
8. Psycho-Education | Information and training that provides parents with insight into biological, cognitive, and social development during childhood and/or adolescence. |
9. Ethnic Identity | Activities designed to develop an awareness of or pride in one’s ethnic identity. This also includes development of skills for dealing with racial discrimination. |
10. Future Orientation | Envisioning dreams for the future and setting long-term goals related to these dreams. This includes youth working with their own goals, parenting thinking about goals for their youth and how to help youth attain them, and parents supporting and encouraging youth with their own goals. |
11. Other | All time taken by prevention programs that could not be allocated to a specific code (e.g., program introductions and overviews, icebreaker activities, discussion of rules, general reviews of material, program evaluations, celebrations, etc.). |
Coding Study Characteristics
We coded each study for the following descriptive variables: (a) publication mode, such as journal or book, (b) sample demographics, such as youth age, gender, and ethnicity, (b) sample size and rate of retention, (c) adolescent risk status (universal vs. selected), and (d) family risk status, such socioeconomic status and parent psychopathology. Because the methodological quality of primary studies also influences the validity of meta-analytic findings, we also coded methodological quality variables: (a) random assignment, (b) active vs. passive control groups, and (c) assessment of implementation fidelity. We also coded several aspects of the measures used to assess youth substance use, such as whether the measures assessed individual substances (i.e., tobacco, alcohol, marijuana/hard drugs) or general substance use (i.e., polysubstance use, or all substances combined).
Calculating Effect Sizes
We coded sufficient information to calculate Cohen’s d as the measure of effect size (ES) for each dependent variable, with d calculated so that a positive value indicated a favorable outcome for the treatment group (i.e., a reduction in substance use). Cohen’s d represents the differences between the means of the treatment group and the control group, divided by the pooled standard deviation, adjusted for sample size. For studies that did not use a control group, d represents the difference between the pre-treatment and post-treatment scores, divided by a pooled standard deviation. Effect size calculations were completed using Lipsey and Wilson’s (2001) web-based effect size calculator. Where possible, d was calculated directly using means and standard deviations, as this is the most precise method. If this was not possible, then d was calculated from other input data (e.g., t or F statistics, binary proportions, point-biserial correlations, etc.) according to formulas provided by Lipsey and Wilson (2001). Since the nature of the calculation may influence the size of the calculated effect, we coded the level of estimation required for all effect sizes, ranging from no estimation (coded as 1), where the ES was calculated based upon specific descriptive statistics (e.g., means and standard deviations), to a high degree of estimation (coded as 5), where the ES was based upon crude statistics (e.g., sample size and p value). Overall, we found that little estimation was required; the vast majority of studies were coded as “no estimation” (N = 303 of 650, or 46.6%) or “slight estimation” (coded as 2; N = 227 of 650, or 34.9%).
Reliability
To ensure reliable study and ES coding, graduate student coders completed intensive training over 4 weeks supervised by the second author. The training regimen called for double-coding of all studies until graduate student coders achieved sufficient reliability (exceeding 90% agreement). During the process of coding, coders met at least once a week to review coding issues, answer questions, and modify the coding system if necessary. Differences in coding decisions were resolved by consensus or adjudicated by the first and second authors, who also periodically reviewed coded studies to ensure a high degree of accuracy in coding and to uncover any unforeseen issues. For the variables coded in the meta-analysis, all intraclass correlation alphas (for continuous variables) were > .87 and Cohen’s kappas (for categorical variables) were > .90.
With regards to the program coding, the first and third authors double-coded a randomly selected subsample of programs (~10% of programs) and consistently achieved intraclass correlations (ICC’s) > .90. The complete program coding system can be obtained from the first author.
Analysis Plan
We used hierarchical linear modeling (Raudenbush & Bryk, 2002) to link program codes with ESs. Given our study design, we initially assumed a three-level model, with ESs nested within studies nested within programs. However, nearly half the programs (N = 18 out of 41) had only one study in our sample; since study-level estimates were not possible for these programs, we were forced to use a two-level model, with ESs nested within programs. The model was as follows (the effects of Level 1 covariates were initially assumed to be fixed at Level 2 and variance terms were explicitly tested as part of the model-building process):
(Level 1)
(Level 2)
(Level 2)
Our first step was to estimate an unconditional model (i.e., no predictors) to derive an average ES (i.e., program effect) across the sample. From these results, we also calculated the intraclass correlation (ICC), which indicated the amount of variance at Level 2 as compared to Level 1.
We then evaluated differences in ES by type of substance (i.e., tobacco use vs. alcohol use vs. marijuana/hard drugs/polysubstance use) using two dummy codes with tobacco use as the baseline for comparison. We then evaluated the effects of various covariates at both the ES level (Level 1) and the program level (Level 2) using a model-building approach; all covariates were tested individually, and only those that were significant were retained in the final model. At the ES level, we evaluated the nature of the control group (i.e., active vs. passive), the level of estimation required for the ES (from 1=none to 5=high), sample size and retention rate, average age, sex (i.e., percent female), ethnicity (i.e., percent non-White), adolescent risk status (i.e., selected vs. universal or mixed), and family risk status (i.e., socioeconomic status). Finally, we coded for the presence of a school-based program alongside the family-based program; this covariate had to be analyzed at Level 1 because the programs in our sample were sometimes evaluated with and without a school-based program, both within and across studies.
At the program level, we evaluated the program delivery method (i.e., traditional in-person/group-based vs. other models, such as Internet, DVD, in-home, etc.), the presence of role-playing exercises and booster sessions in the program design, and the presence of a community-based program alongside the family-based program (unlike above, where we analyzed the presence of school-based programs at Level 1, the presence of community-based programs did not vary across studies or ESs – in other words, the community-based programs were either part of the program model or they were not, and thus were analyzed at Level 2). Finally, we evaluated the effects of the program codes; as with the covariates, we took a model-building approach in which we initially evaluated each code individually and then created a combined model.
Several covariates were excluded from the analysis due to limited variance or a high degree of missing data (i.e., could not be coded based upon information provided in the manuscripts): study design (~95% randomized controlled trial vs. 5% quasi- or non-experimental); publication mode (only 1 manuscript was not a journal article); implementation fidelity (~50% missing); and, the presence of parental psychopathology (only ~10% affirmative). In addition, we did not analyze the following program codes, since more than 90% of the programs were coded as spending no (zero) time on these topics: Parental Monitoring and Behavior Management (delivered to youth without parents present); Fostering School Success (youth and whole family); and Ethnic Identity (parent, youth, and whole family). Finally, several program codes exhibited a severe degree of skewness (> 4.0) and were dichotomized, wherein any value greater than zero was re-coded as 1; these effects included Positive Family Relations (youth) and Problem Solving (youth and whole family). This approach addressed issues of severe skewness but reduced the amount of overall variance and thus was maximally conservative.
All modeling was conducted using Mplus 7.1 (Muthén & Muthén, 1998–2012) with Robust Maximum Likelihood (RML) estimation, which provides so-called “sandwich” or Huber-White standard errors. RML can provide unbiased estimates in the presence of missing and/or non-normal data. For each model, standard measures of fit are reported, including the comparative fit index (CFI), non-normed or Tucker-Lewis index (TLI), and root-mean squared error of approximation (RMSEA). CFI/TLI values greater than .95 and RMSEA values less than 0.5 indicate good fit (Bentler, 1990; Bentler & Bonett, 1980; Hu & Bentler, 1999).
Results
Descriptive data for the program codes are presented in Table 2 (intercorrelations can be obtained from the first author). Other program characteristics were as follows: 14.6% (6 programs) had a community-based component that was delivered in conjunction with the family-based content; 63.4% (26 programs) were delivered in-person in a group setting (the remainder were delivered on-line, via DVD, or in-person in the home); 29.3% (12 programs) contained some sort of optional booster session(s); and, 51.2% (21 programs) involved role playing.
Table 2.
Program Code | M | SD | Range | % Programs w/zero min. |
---|---|---|---|---|
1. Parental Monitoring and Behavior Management | ||||
Parent | 191.33 | 266.34 | .00–1080.00 | 26.8% |
Youth | 3.43 | 11.06 | .00–45.00 | 90.2% |
Whole family | 17.20 | 35.61 | .00–165.00 | 63.4% |
2. Fostering School Success | ||||
Parent | 47.51 | 104.83 | .00–420.00 | 68.3% |
Youth | 3.90 | 17.59 | .00–90.00 | 95.1% |
Whole family | 2.34 | 9.48 | .00–46.00 | 92.7% |
3. Positive Family Relationships | ||||
Parent | 73.24 | 130.15 | .00–480.00 | 39.0% |
Youth | 8.41 | 27.70 | .00–145.00 | 82.9% |
Whole family | 46.33 | 81.46 | .00–305.00 | 58.5% |
4. Substance Use Knowledge, Attitudes, and Values | ||||
Parent | 47.76 | 118.75 | .00–750.00 | 46.3% |
Youth | 6.83 | 25.64 | .00–155.00 | 85.4% |
Whole family | 11.56 | 23.65 | .00–99.00 | 70.7% |
5. Self-Regulation and Stress Management | ||||
Parent | 46.15 | 94.76 | .00–475.00 | 51.2% |
Youth | 39.12 | 120.67 | .00–560.00 | 82.9% |
Whole family | 9.57 | 32.30 | .00–185.00 | 80.5% |
6. Problem Solving | ||||
Parent | 124.62 | 192.55 | .00–780.00 | 39.0% |
Youth | 42.37 | 125.45 | .00–595.00 | 80.5% |
Whole family | 51.41 | 125.76 | .00–650.00 | 70.7% |
7. Resisting Peer Risk | ||||
Parent | 7.71 | 16.82 | .00–65.00 | 70.7% |
Youth | 15.20 | 42.99 | .00–190.00 | 82.9% |
Whole family | 18.00 | 32.33 | .00–110.00 | 61.0% |
8. Psycho-Education | ||||
Parent | 17.63 | 34.53 | .00–150.00 | 63.4% |
9. Ethnic Identity | ||||
Parent | 7.33 | 31.56 | .00–182.50 | 90.2% |
Youth | 4.09 | 19.98 | .00–120.00 | 95.1% |
Whole family | .00 | .00 | .00–.00 | 100.0% |
10. Future Orientation | ||||
Parent | 4.34 | 11.98 | .00–60.00 | 82.9% |
Youth | 9.61 | 27.22 | .00–135.00 | 78.0% |
Whole family | 10.85 | 41.28 | .00–255.00 | 85.4% |
11. Other | 225.41 | 307.24 | .00–1310.00 | 68.3% |
Note. “Parent” refers to intervention content delivered only to parents, “Youth” refers to intervention content that is delivered only to youth, “Whole family” refers to intervention content delivered to parents and youth simultaneously.
Descriptive data for effect sizes and sample sizes/retention rates are presented in Table 3 (intercorrelations can be obtained from the first author). Other ES covariates were as follows: 52.5% (335 of 638) had an active control group; 41.8% (272 of 650) had a school-based component alongside the family-based program; 26.8% (124 of 462) used low-SES families; and, 20.0% (127 of 636) used at-risk or Tier 2 adolescents.
Table 3.
Variable | M | SD | Range |
---|---|---|---|
Sample Size (N = 649) | 1568.43 | 2641.38 | 16.00–11024.00 |
Retention Rate (%; N = 621) | 78.71 | 11.90 | 46.00–100.00 |
Age (N = 378) | 12.64 | 2.60 | 5.70–18.50 |
Sex (% female; N = 510) | 53.95 | 16.62 | .00–100.00 |
Ethnicity (% non-White; N = 552) | 35.51 | 38.30 | .00–100.00 |
Overall effects
We fit an unconditional model and found that the ICC was .51, or that 51% of the variance was at Level 2 (the program level; Var = .21, SD = .45) as opposed to Level 1 (the ES level; Var = .20, SD = .45); the overall mean ES was .31. Model fit was good: CFI = 1.00; TLI = 1.00; RMSEA = .00 (confidence intervals for RMSEA were not provided by Mplus).
Effects by type of substance
Differences in ES by type of substance were not statistically significant, with tobacco ES = .25 (N = 103), alcohol ES = .31 (N = 290), and marijuana/hard drugs/polysubstance use ES = .35 (N = 257). In addition, the effects of the type of substance did not vary significantly across Level 2 units (i.e., prevention programs), suggesting that, although there were significant differences in overall program effectiveness, programs were equally effective across different types of substances. As above, model fit was good: CFI = 1.00; TLI = 1.00; RMSEA = .00.
Effects of covariates
The effects of Level 1 and Level 2 covariates are reported in Table 4; in each instance, model fit was good (as above). Several Level 1 covariates were significant and were retained for the final model, including age, family SES, sample size, and the presence of a school-based program. In no instance did the effect of a Level 1 covariate vary at Level 2, suggesting that the overall effect of the covariate was the same between programs.
Table 4.
Level 1 covariates | β | Var at Level 2 |
---|---|---|
Control group (active vs. passive) | .14 | .06 |
Supplementary school-based program (yes vs. no) | −.15* | .02 |
Estimation (1=none, 5 = high) | .03 | .04 |
Sample size (/1000) | −.18** | .00 |
Retention (%) | .03 | .00 |
At risk (Tier 2 vs. Tier 1 or mixed) | −.17 | .08 |
Age | .40** | .00 |
Socio-economic Status (low vs. middle or mixed) | .10* | .00 |
Sex (% female) | .18 | .00 |
Ethnicity (%) | .03 | .00 |
| ||
Level 2 covariates | β | |
| ||
Delivery mode (in-person/group-based vs. otherwise) | .06 | - |
Community-based program (yes vs. no) | −.14 | - |
Role-playing (yes vs. no) | .05 | - |
Booster session(s) (yes vs. no) | .05 | - |
Dosage (total minutes/1000) | .13 | - |
| ||
Level 2 program codes (minutes/60) | β | |
| ||
1. Parental Monitoring and Behavior Management | ||
Parent | .12 | - |
Youth | - | - |
Whole family | −.03 | - |
2. Fostering School Success | ||
Parent | .06 | - |
Youth | - | - |
Whole family | - | - |
3. Positive Family Relationships | ||
Parent | −.04 | - |
Youth | .49* | - |
Whole family | .26 | - |
4. Substance Use Knowledge, Attitudes, and Values | ||
Parent | −.05 | - |
Youth | .14 | - |
Whole family | −.11 | - |
5. Self-Regulation and Stress Management | ||
Parent | −.10 | - |
Youth | .16 | - |
Whole family | −.04 | - |
6. Problem Solving | ||
Parent | −.13 | - |
Youth | .41* | - |
Whole family | −.05 | - |
7. Resisting Peer Risk | ||
Parent | .29† | - |
Youth | .16 | - |
Whole family | −.15 | - |
8. Psycho-Education | ||
Parent | −.02 | - |
9. Ethnic Identity | ||
Parent | - | - |
Youth | - | - |
Whole family | - | - |
10. Future Orientation | ||
Parent | −.11 | - |
Youth | .64* | - |
Whole family | −.05 | - |
Note. Estimates were not derived for the following codes due to lack of variance (i.e., > 90% of programs had no/zero time): Parental Monitoring and Behavior Management (youth); Fostering School Success (youth and whole family); and Ethnic Identity (parent, youth, and whole family).
p < .10.
p < .05.
p < .01.
Effects of program components
The effects for the program components are also presented in Table 4; in each instance, model fit was good (as above). Several program codes were retained for the final model, including Positive Family Relations (youth; this was dichotomized, as discussed above), Problem Solving (youth; dichotomized), Resisting Peer Risk (parent; marginally significant), and Future Orientation (youth). All significant codes predicted larger effect sizes.
Final model
The final model is presented in Table 5; model fit was good (as above). Two program codes emerged as the strongest predictors: Positive Family Relations (youth); and, Future Orientation (youth). Sample size was the only covariate that remained significant in the final model, with larger samples predicting smaller effects. Overall, the final model explained 59.0% of the outcome variance between programs.
Table 5.
Level 1 | β | Var at Level 2 |
---|---|---|
School-based program (yes vs. no) | .14 | .04 |
Sample size (/1000) | −.22** | .00 |
Age | .29 | .00 |
Socio-economic Status (low vs. middle or mixed) | −.05 | .06 |
| ||
Level 2 | ||
| ||
Positive Family Relations (youth) | .44* | - |
Problem-Solving (youth) | −.11 | - |
Resisting Peer Risk (parent) | −.02 | - |
Future Orientation (youth) | .56* | - |
p < .05.
p < .01.
Discussion
This study reports on the results of a meta-analysis of family-based prevention programs for adolescent substance use. Overall, we find that these programs had significant, small-to-moderate effects sizes on adolescent substance use (mean ES = .31), and the effect sizes were not significantly different for tobacco use vs. alcohol use or tobacco use vs. marijuana/hard drugs/polysubstance use. Although there was significant variance between programs in terms of overall effectiveness, there were no differences in terms of their relative effectiveness for different types of substances. These results are similar, if somewhat more positive, than the findings from a previous meta-analysis of family-based programs that targeted adolescent substance use (i.e., mean ES = .25 for alcohol use; Smit et al., 2008).
Effects of covariates
Very few covariates were significant, suggesting that effect sizes were not influenced by factors such as the presence of an active vs. passive control group or the risk status (Tier 2 vs. Tier 1 or mixed) or sex/ethnicity ratio of the sample. We did find a few significant covariate effects at Level 1, suggesting that samples with older youth predicted larger effect sizes, as did low-SES samples. Large samples, and pairing the family-based program with a school program, predicted smaller effect sizes. However, only the effect for sample size remained significant in the final model. We hypothesize that larger samples can present unique problems related to the scale of implementation (e.g., staffing, fidelity) that are not present with smaller samples.
At Level 2, we found no significant differences for delivery mode (in-person/group-based vs. otherwise), the inclusion of a community-based program, the inclusion of role-playing, the presence of booster session(s), or even for overall dosage (total minutes). These findings support the notion that family-based programs can be delivered effectively by a variety of different mechanisms. The lack of findings for dosage mirror previous research on family-based programs in which the amount of time and number of sessions were not significantly correlated with child or parent outcomes (Lundahl et al., 2006).
Effects of program components
With regards to program components, we initially found effects for Positive Family Relations (youth; dichotomized), Problem Solving (youth; dichotomized), Resisting Peer Risk (parent), and Future Orientation (youth); however, only Positive Family Relations and Future Orientation were significant in the final model. The standardized coefficients suggest that these components may boost the overall mean effect size (ES = .31) with the addition of a Positive Family Relations component for youth (since this component was dichotomized to represent presence vs. absence) by .44 of a standard deviation (SD * .44 = .20). Similarly, an hour’s worth of youth-based programming related to Future Orientation (since this component was not dichotomized) predicts an even larger boost to effect sizes (SD * .56 = .25).
Traditional models of prevention have emphasized parent training, presuming that changes in parenting can elicit changes in family processes and, in turn, child behavior (Van Ryzin & Fosco, 2015). Our results suggest that including youth-focused components in family-based programs can enhance program efficacy, which reflects previous research suggesting that the integration of youth, parent, and family curricula can produce better outcomes than targeting youth or parents separately (Foxcroft, Ireland, Lister-Sharp, Lowe, & Breen, 2003; Foxcroft & Tsertsvadze, 2012). These results should not be taken as a repudiation of the basic tenants of family-based prevention, such as the centrality of parental monitoring and management of child behavior (as seen in Table 2, this component was large in terms of total time, and the vast majority of the programs contained this component). Instead, our results suggest that, within the universe of family-based prevention, where components such as parental monitoring and management of child behavior are nearly universal, there was simply not enough variance across programs to predict differences in effect sizes.
Interestingly, Table 2 suggests that many programs lack youth-focused components, but this does not indicate that youth are totally uninvolved in these programs. Rather, youth are involved in the “whole family” activities, which are far more common across programs, particularly the components focused on Parental Monitoring and Behavior Management, Positive Family Relations, Problem Solving, and Resisting Peer Risk.
Implications for family-based prevention research and policy
In addition to documenting the effectiveness of family-based programs in preventing adolescent substance use, our results also provide specific direction as to the type of curricula that can be incorporated into existing family-based programs in order to enhance program effects. The youth-focused components for Positive Family Relations and Future Orientation both represent situations in which youth are asked to spend time in reflection. In a typical youth-focused activity for Positive Family Relations, youth are asked to think about issues and conflicts from their parents’ point of view, or are asked to list ways in which their parents help and support them; these activities are meant to strengthen youths’ empathy for and appreciation of their parents. In a typical youth-focused activity for Future Orientation, youth are asked to envision their long-term goals and to list the steps that are required to attain those goals, which can help youth to gain insight into the ways in which their current behavior can either move them toward, or away from, their goals.
Although family systems theorists have long argued for a wholistic approach to family-based prevention (e.g., Minuchin, 1974), empirical guidance about the specific ways in which to most effectively include youth in family-based programs has been less readily available. Prior work focusing on family therapy has found that adolescent-focused content plays a key role in improving family relationship quality (Hogue, Dauber, Samuolis, & Liddle, 2006). These findings, and ours, provide important feedback for programs developers that heretofore have focused more on parent-only or whole-family content. Considering that only 17.1% of programs have youth-focused content aimed at Positive Family Relations, and only 22% of programs include any youth-focused Future Orientation content (see Table 2), there is clearly widespread room for improvement in family-based prevention programs.
Our findings also emphasize the importance of including accurate reports of key information in all published findings from prevention programs, including sample demographics (e.g., age, gender, SES), program fidelity, and a straightforward way in which program documentation can be obtained. In the current study, it was not possible to analyze program fidelity as a covariate because nearly 50% of studies did not report assessments of fidelity. We were also forced to remove 20 studies from our sample because of an inability to procure program manuals or related documentation. A greater emphasis on complete documentation (e.g., the CONSORT standards, http://www.consort-statement.org/), and a greater willingness to collaborate and share information, would better support future efforts at meta-analysis.
There also are several implications for policy. At a general level, this meta-analysis adds to the growing consensus that family-based programs can be an effective approach for reducing adolescent substance use, and the effects are robust across different substance use types. Given the negative implications of adolescent substance use for later physical and mental health (Brook et al., 2002; Kandel et al., 1986; Lennings et al., 2003; Soyka, 2000), these findings suggest that it would be wise to embed universal family-based programs into key settings that have a broad reach, such as educational or primary care settings. For example, training school counselors to provide family-based programming could have a population-level effect on adolescent substance use. Beyond these general implications, our component-based analyses suggest that further investment in existing programs to support the integration of youth-focused components targeting Positive Family Relationships and Future Orientation could enhance the effects of these programs. Thus, investing resources in this work could maximize the public health impact of family-based programming.
Strengths and limitations
This study had many strengths, including our comprehensive search of the literature, our extensive list of covariates, our innovative approach to capturing variance across programs, and our highly reliable coding mechanisms. These strengths add a degree of validity to our findings. However, there were also several limitations to this study that should temper interpretation of the results. First, we were forced to exclude a substantial portion of our initial sample of studies due to an inability to obtain program manuals or related program documentation; in some cases, such documentation was no longer accessible, and in other cases program authors simply did not respond to requests for this material. Second, since a substantial portion of our programs included only a single study, we were forced to collapse our analytical model from three to two levels, which may have created an unknown degree of bias. This lack of replication of findings should be a concern for the field of family-based prevention, given the emphasis on replication as an important aspect of program efficacy and effectiveness (Flay et al., 2005). Finally, we wished to explore cross-level interactions between key Level 1 covariates (e.g., type of substance, age, ethnicity) and Level 2 program codes, but were faced with (1) a lack of variance at Level 2 in the effects of these covariates, suggesting that the effects were similar among programs, and (2) a high degree of missing data in a few cases (for example, sample age was only available for N=378, or 58.2%, of the ES). This lack of documentation should be a concern for the field, given the limitations that it can present for meta-analyses and other summarization efforts.
Conclusion
In conclusion, we find that family-based interventions exhibit small-to-medium effects when targeting adolescent substance use, with non-significant differences in effect sizes across classes of substances, including tobacco, alcohol, and “other drugs” (i.e., marijuana, hard drugs, and polysubstance use). Programs themselves differed in a number of ways, with a variety of different delivery mechanisms, substantial differences in overall dosage, and the presence or absence of school-based or community-based components; however, in our final model, none of these differences predicted program efficacy. Importantly, we identified youth-focused components (Positive Family Relations and Future Orientation) that offered additive benefits to the overall program effect size. Our results suggest that delivering content directly to youth that is designed to encourage more positive family relationships and spur youth to think more concretely about their future may be able to add considerable impact to family-based programs. Future research should not only strive to explore and evaluate this hypothesis but should also extend our approach to different outcomes at different ages, providing additional opportunities for developers to strengthen family-based programs.
Highlights.
Little is known about why family-based prevention programs have been successful.
We conducted a components-centered meta-analysis of existing family-based programs.
We coded each program in terms of the amount of time spent on each component.
Using multi-level modeling, two youth components predicted larger effect sizes.
Enhancing youth components may increase the efficacy of family-based programs.
Acknowledgments
This project was supported by Grant DA032871 from the National Institute on Drug Abuse (NIDA) to Mark J. Van Ryzin. Gregory M. Fosco was supported by the Karl R. and Diane Wendle Fink Early Career Professorship for the Study of Families. The findings are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health. We also wish to acknowledge the many program authors who generously provided materials and answered questions to enable us to code their prevention programs. You represent the true collaborative spirit of science.
Appendix A – Study sample
(†=excluded due to inability to obtain program documentation)
- Bauman KE, Ennett ST, Foshee VA, Pemberton M, King TS, Koch GG. Influence of a family-directed program on adolescent cigarette and alcohol cessation. Prevention Science. 2000;1:227–237. doi: 10.1023/a:1026503313188. [DOI] [PubMed] [Google Scholar]
- Bauman KE, Ennett ST, Foshee VA, Pemberton M, King TS, Koch GG. Influence of a family program on adolescent smoking and drinking prevalence. Prevention Science. 2002;3:35–42. doi: 10.1023/a:1014619325968. [DOI] [PubMed] [Google Scholar]
- Bauman KE, Foshee VA, Ennett ST, Pemberton M, Hicks KA, King TS, Koch GG. The influence of a family program on adolescent tobacco and alcohol use. American Journal of Public Health. 2001;91:604–610. doi: 10.2105/ajph.91.4.604. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Bernat DH, August GJ, Hektner JM, Bloomquist ML. The Early Risers preventive intervention: Testing for six-year outcomes and mediational processes. Journal of Abnormal Child Psychology. 2007;35:605–617. doi: 10.1007/s10802-007-9116-5. [DOI] [PubMed] [Google Scholar]
- Biglan A, Ary DV, Smolkowski K, Duncan T, Black C. A randomised controlled trial of a community intervention to prevent adolescent tobacco use. Tobacco Control. 2000;9:24–32. doi: 10.1136/tc.9.1.24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Biglan A, Ary D, Yudelson H, Duncan TE, Hood D, James L, … Gaiser E. Experimental evaluation of a modular approach to mobilizing antitobacco influences of peers and parents. American Journal of Community Psychology. 1996;24:311–339. doi: 10.1007/BF02512025. [DOI] [PubMed] [Google Scholar]
- Bodin MC, Strandberg AK. The Örebro prevention programme revisited: a cluster-randomized effectiveness trial of programme effects on youth drinking. Addiction. 2011;106:2134–2143. doi: 10.1111/j.1360-0443.2011.03540.x. [DOI] [PubMed] [Google Scholar]
- Brody GH, Chen YF, Beach SR, Philibert RA, Kogan SM. Participation in a family-centered prevention program decreases genetic risk for adolescents’ risky behaviors. Pediatrics. 2009;124(3):911–917. doi: 10.1542/peds.2008-3464. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Brody GH, Chen YF, Kogan SM, Murry VM, Brown AC. Long-term effects of the Strong African American Families program on youths’ alcohol use. Journal of Consulting and Clinical Psychology. 2010;78:281–285. doi: 10.1037/a0018552. [DOI] [PMC free article] [PubMed] [Google Scholar]
- †.Brody GH, Chen YF, Kogan SM, Smith K, Brown AC. Buffering effects of a family-based intervention for African American emerging adults. Journal of Marriage and Family. 2010;72:1426–1435. doi: 10.1111/j.1741-3737.2010.00774.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Brody GH, Chen YF, Kogan SM, Yu T, Molgaard VK, DiClemente RJ, Wingood GM. Family-centered program deters substance use, conduct problems, and depressive symptoms in black adolescents. Pediatrics. 2012;129:108–115. doi: 10.1542/peds.2011-0623. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Brody GH, Murry VM, Gerrard M, Gibbons FX, McNair L, Brown AC, … Chen YF. The strong African American families program: prevention of youths’ high-risk behavior and a test of a model of change. Journal of Family Psychology. 2006;20:1–11. doi: 10.1037/0893-3200.20.1.1. [DOI] [PubMed] [Google Scholar]
- Brody GH, Murry VM, Kogan SM, Gerrard M, Gibbons FX, Molgaard V, … Wills TA. The Strong African American Families Program: a cluster-randomized prevention trial of long-term effects and a mediational model. Journal of Consulting and Clinical Psychology. 2006;74:356–366. doi: 10.1037/0022-006X.74.2.356. [DOI] [PubMed] [Google Scholar]
- †.Brody GH, Yu T, Chen YF, Kogan SM, Smith K. The Adults in the Making program: long-term protective stabilizing effects on alcohol use and substance use problems for rural African American emerging adults. Journal of Consulting and Clinical Psychology. 2012;80:17–28. doi: 10.1037/a0026592. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Brown EC, Catalano RF, Fleming CB, Haggerty KP, Abbott RD. Adolescent substance use outcomes in the Raising Healthy Children project: a two-part latent growth curve analysis. Journal of Consulting and Clinical Psychology. 2005;73:699–710. doi: 10.1037/0022-006X.73.4.699. [DOI] [PubMed] [Google Scholar]
- Catalano RF, Gainey RR, Fleming CB, Haggerty KP, Johnson NO. An experimental intervention with families of substance abusers: one-year follow-up of the Focus on Families project. Addiction. 1999;94:241–254. doi: 10.1046/j.1360-0443.1999.9422418.x. [DOI] [PubMed] [Google Scholar]
- Cervantes R, Goldbach J, Santos SM. Familia adelante: a multi-risk prevention intervention for Latino families. The Journal of Primary Prevention. 2011;32:225–234. doi: 10.1007/s10935-011-0251-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cheadle A, Pearson D, Wagner E, Psaty BM, Diehr P, Koepsell T. A community-based approach to preventing alcohol use among adolescents on an American Indian reservation. Public Health Reports. 1995;110:439–447. [PMC free article] [PubMed] [Google Scholar]
- †.Chou CP, Montgomery S, Pentz MA, Rohrbach LA, Johnson CA, Flay BR, MacKinnon DP. Effects of a community-based prevention program on decreasing drug use in high-risk adolescents. American Journal of Public Health. 1998;88:944–948. doi: 10.2105/ajph.88.6.944. [DOI] [PMC free article] [PubMed] [Google Scholar]
- †.Cohen DA, Rice JC. A parent-targeted intervention for adolescent substance use prevention lessons learned. Evaluation Review. 1995;19:159–180. doi: 10.1177/0193841X9501900203. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Coombes L, Allen D, Marsh M, Foxcroft D. The Strengthening Families Programme (SFP) 10–14 and substance misuse in Barnsley: the perspectives of facilitators and families. Child Abuse Review. 2009;18:41–59. [Google Scholar]
- Curry SJ, Hollis J, Bush T, Polen M, Ludman EJ, Grothaus L, McAfee T. A randomized trial of a family-based smoking prevention intervention in managed care. Preventive Medicine. 2003;37:617–626. doi: 10.1016/j.ypmed.2003.09.015. [DOI] [PubMed] [Google Scholar]
- Dawson-McClure SR, Sandler IN, Wolchik SA, Millsap RE. Risk as a moderator of the effects of prevention programs for children from divorced families: A six-year longitudinal study. Journal of Abnormal Child Psychology. 2004;32:175–190. doi: 10.1023/b:jacp.0000019769.75578.79. [DOI] [PubMed] [Google Scholar]
- DeGarmo DS, Eddy JM, Reid JB, Fetrow RA. Evaluating mediators of the impact of the Linking the Interests of Families and Teachers (LIFT) multimodal preventive intervention on substance use initiation and growth across adolescence. Prevention Science. 2009;10:208–220. doi: 10.1007/s11121-009-0126-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Dishion TJ, Andrews DW. Preventing escalation in problem behaviors with high-risk young adolescents: Immediate and 1-year outcomes. Journal of Consulting and Clinical Psychology. 1995;63:538–548. doi: 10.1037//0022-006x.63.4.538. [DOI] [PubMed] [Google Scholar]
- Donovan E, Wood M, Frayjo K, Black RA, Surette DA. A randomized, controlled trial to test the efficacy of an online, parent-based intervention for reducing the risks associated with college-student alcohol use. Addictive Behaviors. 2012;37:25–35. doi: 10.1016/j.addbeh.2011.09.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Eddy JM, Reid JB, Stoolmiller M, Fetrow RA. Outcomes during middle school for an elementary school-based preventive intervention for conduct problems: Follow-up results from a randomized trial. Behavior Therapy. 2003;34:535–552. [Google Scholar]
- †.Elder JP, Litrownik AJ, Slymen DJ, Campbell NR, Parra-Medina D, Choe S, … Ayala GX. Tobacco and alcohol use–prevention program for Hispanic migrant adolescents. American Journal of Preventive Medicine. 2002;23:269–275. doi: 10.1016/s0749-3797(02)00515-9. [DOI] [PubMed] [Google Scholar]
- Ennett ST, Bauman KE, Pemberton M, Foshee VA, Chuang YC, King TS, Koch GG. Mediation in a family-directed program for prevention of adolescent tobacco and alcohol use. Preventive Medicine. 2001;33:333–346. doi: 10.1006/pmed.2001.0892. [DOI] [PubMed] [Google Scholar]
- Fang L, Schinke SP. Two-year outcomes of a randomized, family-based substance use prevention trial for Asian American adolescent girls. Psychology of Addictive Behaviors. 2013;27:788–798. doi: 10.1037/a0030925. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Fang L, Schinke SP, Cole KC. Preventing substance use among early Asian–American adolescent girls: Initial evaluation of a Web-based, mother–daughter program. Journal of Adolescent Health. 2010;47:529–532. doi: 10.1016/j.jadohealth.2010.03.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- †.Flay BR, Graumlich S, Segawa E, Burns JL, Holliday MY. Effects of 2 prevention programs on high-risk behaviors among African American youth: a randomized trial. Archives of Pediatrics & Adolescent Medicine. 2004;158:377–384. doi: 10.1001/archpedi.158.4.377. [DOI] [PMC free article] [PubMed] [Google Scholar]
- †.Flay BR, Hansen WB, Johnson CA, Collins LM, Dent CW, Dwyer KM, … Ulene A. Implementation effectiveness trial of a social influences smoking prevention program using schools and television. Health Education Research. 1987;2:385–400. [Google Scholar]
- Forman SG, Linney JA, Brondino MJ. Effects of coping skills training on adolescents at risk for substance use. Psychology of Addictive Behaviors. 1990;4:67–76. [Google Scholar]
- †.Foster EM. Costs and effectiveness of the Fast Track intervention for antisocial behavior. The Journal of Mental Health Policy and Economics. 2010;13:101–119. [PMC free article] [PubMed] [Google Scholar]
- Furr-Holden CDM, Ialongo NS, Anthony JC, Petras H, Kellam SG. Developmentally inspired drug prevention: Middle school outcomes in a school-based randomized prevention trial. Drug and alcohol dependence. 2004;73:149–158. doi: 10.1016/j.drugalcdep.2003.10.002. [DOI] [PubMed] [Google Scholar]
- Gerrard M, Gibbons FX, Brody GH, Murry VM, Cleveland MJ, Wills TA. A theory-based dual-focus alcohol intervention for preadolescents: The Strong African American Families Program. Psychology of Addictive Behaviors. 2006;20:185–195. doi: 10.1037/0893-164X.20.2.185. [DOI] [PubMed] [Google Scholar]
- Gonzales NA, Dumka LE, Millsap RE, Gottschall A, McClain DB, Wong JJ, … Kim SY. Randomized trial of a broad preventive intervention for Mexican American adolescents. Journal of Consulting and Clinical Psychology. 2012;80:1–16. doi: 10.1037/a0026063. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Gordon J, Biglan A, Smolkowski K. The impact on tobacco use of branded youth anti-tobacco activities and family communications about tobacco. Prevention Science. 2008;9:73–87. doi: 10.1007/s11121-008-0089-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Guyll M, Spoth RL, Chao W, Wickrama KAS, Russell D. Family-focused preventive interventions: Evaluating parental risk moderation of substance use trajectories. Journal of Family Psychology. 2004;18:293–301. doi: 10.1037/0893-3200.18.2.293. [DOI] [PubMed] [Google Scholar]
- Haggerty KP, Skinner ML, Fleming CB, Gainey RR, Catalano RF. Long-term effects of the Focus on Families project on substance use disorders among children of parents in methadone treatment. Addiction. 2008;103:2008–2016. doi: 10.1111/j.1360-0443.2008.02360.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Haggerty KP, Skinner ML, MacKenzie EP, Catalano RF. A randomized trial of Parents Who Care: Effects on key outcomes at 24-month follow-up. Prevention Science. 2007;8:249–260. doi: 10.1007/s11121-007-0077-2. [DOI] [PubMed] [Google Scholar]
- Hawkins JD, Catalano RF, Kosterman R, Abbott R, Hill KG. Preventing adolescent health-risk behaviors by strengthening protection during childhood. Archives of Pediatrics & Adolescent Medicine. 1999;153:226–234. doi: 10.1001/archpedi.153.3.226. [DOI] [PubMed] [Google Scholar]
- Hawkins JD, Kosterman R, Catalano RF, Hill KG, Abbott RD. Promoting positive adult functioning through social development intervention in childhood: Long-term effects from the Seattle Social Development Project. Archives of Pediatrics & Adolescent Medicine. 2005;159:25–31. doi: 10.1001/archpedi.159.1.25. [DOI] [PubMed] [Google Scholar]
- †.Hostetler M, Fisher K. Project CARE substance abuse prevention program for high-risk youth: A longitudinal evaluation of program effectiveness. Journal of Community Psychology. 1997;25:397–419. [Google Scholar]
- Ichiyama MA, Fairlie AM, Wood MD, Turrisi R, Francis DP, Ray AE, Stanger LA. A randomized trial of a parent-based intervention on drinking behavior among incoming college freshmen. Journal of Studies on Alcohol and Drugs. 2009;16:67–76. doi: 10.15288/jsads.2009.s16.67. [DOI] [PMC free article] [PubMed] [Google Scholar]
- †.Johnson CA, Pentz MA, Weber MD, Dwyer JH, Baer N, MacKinnon DP, … Flay BR. Relative effectiveness of comprehensive community programming for drug abuse prevention with high-risk and low-risk adolescents. Journal of Consulting and Clinical Psychology. 1990;58:447–456. doi: 10.1037//0022-006x.58.4.447. [DOI] [PubMed] [Google Scholar]
- Johnson K, Bryant DD, Collins DA, Noe TD, Strader TN, Berbaum M. Preventing and reducing alcohol and other drug use among high-risk youths by increasing family resilience. Social Work. 1998;43:297–308. doi: 10.1093/sw/43.4.297. [DOI] [PubMed] [Google Scholar]
- Johnson K, Strader T, Berbaum M, Bryant D, Bucholtz G, Collins D, Noe T. Reducing alcohol and other drug use by strengthening community, family, and youth resiliency: An evaluation of the Creating Lasting Connections program. Journal of Adolescent Research. 1996;11:36–67. [Google Scholar]
- Jones DJ, Olson AL, Forehand R, Gaffney CA, Zens MS, Bau JJ. A family-focused randomized controlled trial to prevent adolescent alcohol and tobacco use: The moderating roles of positive parenting and adolescent gender. Behavior Therapy. 2005;36:347–355. [Google Scholar]
- Komro KA, Perry CL, Veblen-Mortenson S, Farbakhsh K, Kugler KC, Alfano KA, … Jones-Webb R. Cross-cultural adaptation and evaluation of a home-based program for alcohol use prevention among urban youth: The “Slick Tracy Home Team Program”. Journal of Primary Prevention. 2006;27:135–154. doi: 10.1007/s10935-005-0029-1. [DOI] [PubMed] [Google Scholar]
- Komro KA, Perry CL, Veblen-Mortenson S, Farbakhsh K, Toomey TL, Stigler MH, … Williams CL. Outcomes from a randomized controlled trial of a multi-component alcohol use preventive intervention for urban youth: Project Northland Chicago. Addiction. 2008;103:606–618. doi: 10.1111/j.1360-0443.2007.02110.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Komro KA, Perry CL, Williams CL, Stigler MH, Farbakhsh K, Veblen-Mortenson S. How did Project Northland reduce alcohol use among young adolescents? Analysis of mediating variables. Health Education Research. 2001;16:59–70. doi: 10.1093/her/16.1.59. [DOI] [PubMed] [Google Scholar]
- Koning IM, van den Eijnden RJ, Engels RC, Verdurmen JE, Vollebergh WA. Why target early adolescents and parents in alcohol prevention? The mediating effects of self-control, rules and attitudes about alcohol use. Addiction. 2011;106:538–546. doi: 10.1111/j.1360-0443.2010.03198.x. [DOI] [PubMed] [Google Scholar]
- Koning IM, Van den Eijnden RJ, Verdurmen JE, Engels RC, Vollebergh WA. Long-term effects of a parent and student intervention on alcohol use in adolescents: A cluster randomized controlled trial. American Journal of Preventive Medicine. 2011;40:541–547. doi: 10.1016/j.amepre.2010.12.030. [DOI] [PubMed] [Google Scholar]
- Koning IM, Verdurmen JE, Engels RC, van den Eijnden RJ, Vollebergh WA. Differential impact of a Dutch alcohol prevention program targeting adolescents and parents separately and simultaneously: Low self-control and lenient parenting at baseline predict effectiveness. Prevention Science. 2012;13:278–287. doi: 10.1007/s11121-011-0267-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Koning IM, Vollebergh WA, Smit F, Verdurmen JE, Van Den Eijnden RJ, Ter Bogt TF, … Engels RC. Preventing heavy alcohol use in adolescents (PAS): Cluster randomized trial of a parent and student intervention offered separately and simultaneously. Addiction. 2009;104:1669–1678. doi: 10.1111/j.1360-0443.2009.02677.x. [DOI] [PubMed] [Google Scholar]
- Koutakis N, Stattin H, Kerr M. Reducing youth alcohol drinking through a parent-targeted intervention: the Örebro Prevention Program. Addiction. 2008;103:1629–1637. doi: 10.1111/j.1360-0443.2008.02326.x. [DOI] [PubMed] [Google Scholar]
- Kumpfer KL, Xie J, O’Driscoll R. Effectiveness of a culturally adapted Strengthening Families Program 12–16 years for high-risk Irish families. Child & Youth Care Forum. 2012;41:173–195. [Google Scholar]
- Lochman JE, Wells KC. The Coping Power program at the middle-school transition: Universal and indicated prevention effects. Psychology of Addictive Behaviors. 2002a;16:S40–S54. doi: 10.1037/0893-164x.16.4s.s40. [DOI] [PubMed] [Google Scholar]
- Lochman JE, Wells KC. Contextual social–cognitive mediators and child outcome: A test of the theoretical model in the Coping Power program. Development and Psychopathology. 2002b;14:945–967. doi: 10.1017/s0954579402004157. [DOI] [PubMed] [Google Scholar]
- Lochman JE, Wells KC. Effectiveness of the Coping Power Program and of classroom intervention with aggressive children: Outcomes at a 1-year follow-up. Behavior Therapy. 2003;34:493–515. [Google Scholar]
- Lochman JE, Wells KC. The coping power program for preadolescent aggressive boys and their parents: Outcome effects at the 1-year follow-up. Journal of Consulting and Clinical Psychology. 2004;72:571–578. doi: 10.1037/0022-006X.72.4.571. [DOI] [PubMed] [Google Scholar]
- Loveland-Cherry CJ, Ross LT, Kaufman SR. Effects of a home-based family intervention on adolescent alcohol use and misuse. Journal of Studies on Alcohol and Drugs. 1999:94–102. doi: 10.15288/jsas.1999.s13.94. [DOI] [PubMed] [Google Scholar]
- Mallett KA, Turrisi R, Ray AE, Stapleton J, Abar C, Mastroleo NR, … Larimer ME. Do parents know best? Examining the relationship between parenting profiles, prevention efforts, and peak drinking in college students. Journal of Applied Social Psychology. 2011;41:2904–2927. doi: 10.1111/j.1559-1816.2011.00860.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Martinez CR, Jr, Eddy JM. Effects of culturally adapted parent management training on Latino youth behavioral health outcomes. Journal of Consulting and Clinical Psychology. 2005;73(5):841–851. doi: 10.1037/0022-006X.73.5.841. [DOI] [PubMed] [Google Scholar]
- Mason WA, Haggerty KP, Fleming AP, Casey-Goldstein M. Family intervention to prevent depression and substance use among adolescents of depressed parents. Journal of Child and Family Studies. 2012;21:891–905. doi: 10.1007/s10826-011-9549-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mason WA, Kosterman R, Hawkins JD, Haggerty KP, Spoth RL. Reducing adolescents’ growth in substance use and delinquency: Randomized trial effects of a parent-training prevention intervention. Prevention Science. 2003;4:203–212. doi: 10.1023/a:1024653923780. [DOI] [PubMed] [Google Scholar]
- Mason WA, Kosterman R, Hawkins JD, Haggerty KP, Spoth RL, Redmond C. Influence of a family-focused substance use preventive intervention on growth in adolescent depressive symptoms. Journal of Research on Adolescence. 2007;17:541–564. [Google Scholar]
- Milburn NG, Iribarren FJ, Rice E, Lightfoot M, Solorio R, Rotheram-Borus MJ, … Duan N. A family intervention to reduce sexual risk behavior, substance use, and delinquency among newly homeless youth. Journal of Adolescent Health. 2012;50:358–364. doi: 10.1016/j.jadohealth.2011.08.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Pantin H, Prado G, Lopez B, Huang S, Tapia MI, Schwartz SJ, … Branchini J. A randomized controlled trial of Familias Unidas for Hispanic adolescents with behavior problems. Psychosomatic Medicine. 2009;71(9):987–995. doi: 10.1097/PSY.0b013e3181bb2913. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Park J, Kosterman R, Hawkins JD, Haggerty KP, Duncan TE, Duncan SC, Spoth R. Effects of the “Preparing for the Drug Free Years” curriculum on growth in alcohol use and risk for alcohol use in early adolescence. Prevention Science. 2000;1:125–138. doi: 10.1023/a:1010021205638. [DOI] [PubMed] [Google Scholar]
- †.Pentz MA, Dwyer JH, MacKinnon DP, Flay BR, Hansen WB, Wang EYI, Johnson CA. A multicommunity trial for primary prevention of adolescent drug abuse: Effects on drug use prevalence. JAMA. 1989;261:3259–3266. [PubMed] [Google Scholar]
- †.Pentz MA, Johnson CA, Dwyer JH, Mackinnon DM, Hansen WB, Flay BR. A comprehensive community approach to adolescent drug abuse prevention: Effects on cardiovascular disease risk behaviors. Annals of Medicine. 1989;21:219–222. doi: 10.3109/07853898909149937. [DOI] [PubMed] [Google Scholar]
- †.Pentz MA, MacKinnon DP, Dwyer JH, Wang EY, Hansen WB, Flay BR, Anderson Johnson C. Longitudinal effects of the Midwestern Prevention Project on regular and experimental smoking in adolescents. Preventive Medicine. 1989;18:304–321. doi: 10.1016/0091-7435(89)90077-7. [DOI] [PubMed] [Google Scholar]
- †.Pentz MA, MacKinnon DP, Flay BR, Hansen WB, Johnson CA, Dwyer JH. Primary prevention of chronic diseases in adolescence: Effects of the Midwestern Prevention Project on tobacco use. American Journal of Epidemiology. 1989;130:713–724. doi: 10.1093/oxfordjournals.aje.a115393. [DOI] [PubMed] [Google Scholar]
- †.Pentz MA, Trebow EA, Hansen WB, MacKinnon DP, Dwyer JH, Johnson CA, … Cormack C. Effects of Program Implementation on Adolescent Drug Use Behavior The Midwestern Prevention Project (MPP) Evaluation Review. 1990;14:264–289. [Google Scholar]
- Pérez JME, Díaz SAH, Villa RS, Fernández-Hermida JR, Carballo JL, Garcia-Rodriguez O. Family-based drug use prevention: The “Familias que Funcionan” program. Psicothema. 2009;21:45–80. [PubMed] [Google Scholar]
- Perry CL, Lee S, Stigler MH, Farbakhsh K, Komro KA, Gewirtz AH, Williams CL. The impact of Project Northland on selected MMPI-A problem behavior scales. The Journal of Primary Prevention. 2007;28:449–465. doi: 10.1007/s10935-007-0105-9. [DOI] [PubMed] [Google Scholar]
- Perry CL, Williams CL, Komro KA, Veblen-Mortenson S, Stigler MH, Munson KA, … Forster JL. Project Northland: Long-term outcomes of community action to reduce adolescent alcohol use. Health Education Research. 2002;17:117–132. doi: 10.1093/her/17.1.117. [DOI] [PubMed] [Google Scholar]
- Perry CL, Williams CL, Veblen-Mortenson S, Toomey TL, Komro KA, Anstine PS, … Wolfson M. Project Northland: Outcomes of a communitywide alcohol use prevention program during early adolescence. American Journal of Public Health. 1996;86:956–965. doi: 10.2105/ajph.86.7.956. [DOI] [PMC free article] [PubMed] [Google Scholar]
- †.Pettersson C, Özdemir M, Eriksson C. Effects of a parental program for preventing underage drinking-The NGO program Strong and Clear. BMC Public Health. 2011;11:251–262. doi: 10.1186/1471-2458-11-251. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Prado G, Cordova D, Huang S, Estrada Y, Rosen A, Bacio GA, … McCollister K. The efficacy of Familias Unidas on drug and alcohol outcomes for Hispanic delinquent youth: Main effects and interaction effects by parental stress and social support. Drug and Alcohol Dependence. 2012;125:S18–S25. doi: 10.1016/j.drugalcdep.2012.06.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Prado G, Pantin H, Briones E, Schwartz SJ, Feaster D, Huang S, … Szapocznik J. A randomized controlled trial of a parent-centered intervention in preventing substance use and HIV risk behaviors in Hispanic adolescents. Journal of Consulting and Clinical Psychology. 2007;75:914–926. doi: 10.1037/0022-006X.75.6.914. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Redmond C, Spoth RL, Shin C, Schainker LM, Greenberg MT, Feinberg M. Long-term protective factor outcomes of evidence-based interventions implemented by community teams through a community–university partnership. The Journal of Primary Prevention. 2009;30:513–530. doi: 10.1007/s10935-009-0189-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Riesch SK, Brown RL, Anderson LS, Wang K, Canty-Mitchell J, Johnson DL. Strengthening Families Program (10–14) Effects on the Family Environment. Western Journal of Nursing Research. 2012;34:340–376. doi: 10.1177/0193945911399108. [DOI] [PMC free article] [PubMed] [Google Scholar]
- †.Rohrbach LA, Hodgson CS, Broder BI, Montgomery SB, Flay BR, Hansen WB, Pentz MA. Parental participation in drug abuse prevention: Results from the Midwestern Prevention Project. Journal of Research on Adolescence. 1994;4:295–317. [Google Scholar]
- Rotheram-Borus MJ, Stein JA, Lester P. Adolescent adjustment over six years in HIV-affected families. Journal of Adolescent Health. 2006;39:174–182. doi: 10.1016/j.jadohealth.2006.02.014. [DOI] [PubMed] [Google Scholar]
- Schinke SP, Cole KC, Fang L. Gender-specific intervention to reduce underage drinking among early adolescent girls: A test of a computer-mediated, mother-daughter program. Journal of Studies on Alcohol and Drugs. 2009;70:70. doi: 10.15288/jsad.2009.70.70. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Schinke SP, Fang L, Cole KC. Preventing substance use among adolescent girls: 1-year outcomes of a computerized, mother–daughter program. Addictive Behaviors. 2009a;34:1060–1064. doi: 10.1016/j.addbeh.2009.06.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Schinke SP, Fang L, Cole KC. Computer-delivered, parent-involvement intervention to prevent substance use among adolescent girls. Preventive medicine. 2009b;49:429–435. doi: 10.1016/j.ypmed.2009.08.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Schinke SP, Fang L, Cole KC, Cohen-Cutler S. Preventing substance use among Black and Hispanic adolescent girls: Results from a computer-delivered, mother-daughter intervention approach. Substance Use & Misuse. 2011;46:35–45. doi: 10.3109/10826084.2011.521074. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Schinke SP, Schwinn TM, Di Noia J, Cole KC. Reducing the risks of alcohol use among urban youth: three-year effects of a computer-based intervention with and without parent involvement. Journal of Studies on Alcohol. 2004;65:443–449. doi: 10.15288/jsa.2004.65.443. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Schwinn TM, Schinke SP. Preventing alcohol use among late adolescent urban youth: 6-year results from a computer-based intervention. Journal of Studies on Alcohol and Drugs. 2010;71:535–538. doi: 10.15288/jsad.2010.71.535. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Shortt AL, Shortt AL, Hutchinson DM, Shortt AL, Hutchinson DM, Chapman R, … Toumbourou JW. Family, school, peer and individual influences on early adolescent alcohol use: first-year impact of the Resilient Families programme. Drug and Alcohol Review. 2007;26:625–634. doi: 10.1080/09595230701613817. [DOI] [PubMed] [Google Scholar]
- Spoth RL, Clair S, Shin C, Redmond C. Long-term effects of universal preventive interventions on methamphetamine use among adolescents. Archives of Pediatrics & Adolescent Medicine. 2006;160:876–882. doi: 10.1001/archpedi.160.9.876. [DOI] [PubMed] [Google Scholar]
- Spoth RL, Guyll M, Day SX. Universal family-focused interventions in alcohol-use disorder prevention: Cost-effectiveness and cost-benefit analyses of two interventions. Journal of Studies on Alcohol and Drugs. 2002;63:219–228. doi: 10.15288/jsa.2002.63.219. [DOI] [PubMed] [Google Scholar]
- Spoth RL, Guyll M, Shin C. Universal intervention as a protective shield against exposure to substance use: Long-term outcomes and public health significance. American Journal of Public Health. 2009;99:2026–2033. doi: 10.2105/AJPH.2007.133298. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Spoth RL, Randall GK, Shin C. Increasing school success through partnership-based family competency training: Experimental study of long-term outcomes. School Psychology Quarterly. 2008;23:70–89. doi: 10.1037/1045-3830.23.1.70. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Spoth RL, Randall GK, Shin C, Redmond C. Randomized study of combined universal family and school preventive interventions: patterns of long-term effects on initiation, regular use, and weekly drunkenness. Psychology of Addictive Behaviors. 2005;19:372–381. doi: 10.1037/0893-164X.19.4.372. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Spoth RL, Randall GK, Trudeau L, Shin C, Redmond C. Substance use outcomes 5½ years past baseline for partnership-based, family-school preventive interventions. Drug and Alcohol Dependence. 2008;96:57–68. doi: 10.1016/j.drugalcdep.2008.01.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Spoth RL, Redmond C, Clair S, Shin C, Greenberg M, Feinberg M. Preventing substance misuse through community–university partnerships: Randomized controlled trial outcomes 4½ years past baseline. American Journal of Preventive Medicine. 2011;40:440–447. doi: 10.1016/j.amepre.2010.12.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Spoth RL, Redmond C, Lepper H. Alcohol initiation outcomes of universal family-focused preventive interventions: One-and two-year follow-ups of a controlled study. Journal of Studies on Alcohol and Drugs. 1999;13:103–111. doi: 10.15288/jsas.1999.s13.103. [DOI] [PubMed] [Google Scholar]
- Spoth RL, Redmond C, Shin C. Randomized trial of brief family interventions for general populations: adolescent substance use outcomes 4 years following baseline. Journal of Consulting and Clinical Psychology. 2001;69:627–642. doi: 10.1037//0022-006x.69.4.627. [DOI] [PubMed] [Google Scholar]
- Spoth R, Redmond C, Shin C, Azevedo K. Brief family intervention effects on adolescent substance initiation: school-level growth curve analyses 6 years following baseline. Journal of Consulting and Clinical Psychology. 2004;72:535–542. doi: 10.1037/0022-006X.72.3.535. [DOI] [PubMed] [Google Scholar]
- Spoth RL, Redmond C, Shin C, Greenberg M, Clair S, Feinberg M. Substance-use outcomes at 18 months past baseline: The PROSPER community–university partnership trial. American Journal of Preventive Medicine. 2007;32:395–402. doi: 10.1016/j.amepre.2007.01.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Spoth RL, Redmond C, Trudeau L, Shin C. Longitudinal substance initiation outcomes for a universal preventive intervention combining family and school programs. Psychology of Addictive Behaviors. 2002;16:129–134. [PubMed] [Google Scholar]
- Spoth RL, Reyes ML, Redmond C, Shin C. Assessing a public health approach to delay onset and progression of adolescent substance use: latent transition and log-linear analyses of longitudinal family preventive intervention outcomes. Journal of Consulting and Clinical Psychology. 1999;67:619–630. doi: 10.1037//0022-006x.67.5.619. [DOI] [PubMed] [Google Scholar]
- Spoth RL, Shin C, Guyll M, Redmond C, Azevedo K. Universality of effects: An examination of the comparability of long-term family intervention effects on substance use across risk-related subgroups. Prevention Science. 2006;7:209–224. doi: 10.1007/s11121-006-0036-3. [DOI] [PubMed] [Google Scholar]
- Spoth RL, Trudeau LS, Guyll M, Shin C. Benefits of universal intervention effects on a youth protective shield 10 years after baseline. Journal of Adolescent Health. 2012;50:414–417. doi: 10.1016/j.jadohealth.2011.06.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Spoth RL, Trudeau L, Guyll M, Shin C, Redmond C. Universal intervention effects on substance use among young adults mediated by delayed adolescent substance initiation. Journal of Consulting and Clinical Psychology. 2009;77:620–632. doi: 10.1037/a0016029. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Spoth RL, Trudeau L, Shin C, Redmond C. Long-term effects of universal preventive interventions on prescription drug misuse. Addiction. 2008;103:1160–1168. doi: 10.1111/j.1360-0443.2008.02160.x. [DOI] [PubMed] [Google Scholar]
- †.Springer JF, Wright LS, McCall GJ. Family interventions and adolescent resiliency: The Southwest Texas State High-Risk Youth Program. Journal of Community Psychology. 1997;25:435–452. [Google Scholar]
- Stanton B, Cole M, Galbraith J, Li X, Pendleton S, Cottrel L, … Kaljee L. Randomized trial of a parent intervention: parents can make a difference in long-term adolescent risk behaviors, perceptions, and knowledge. Archives of Pediatrics & Adolescent Medicine. 2004;158:947–955. doi: 10.1001/archpedi.158.10.947. [DOI] [PubMed] [Google Scholar]
- Stevens MM, Freeman DH, Mott LA, Youells FE, Linsey SC. Smokeless tobacco use among children: The New Hampshire study. American Journal of Preventive Medicine. 1993;9:160–167. [PubMed] [Google Scholar]
- Stevens MM, Mott LA, Youells F. Rural adolescent drinking behavior: Three year follow-up in the New Hampshire substance abuse prevention study. Adolescence. 1995;31:159–166. [PubMed] [Google Scholar]
- Stevens MM, Olson AL, Gaffney CA, Tosteson TD, Mott LA, Starr P. A pediatric, practice-based, randomized trial of drinking and smoking prevention and bicycle helmet, gun, and seatbelt safety promotion. Pediatrics. 2002;109:490–497. doi: 10.1542/peds.109.3.490. [DOI] [PubMed] [Google Scholar]
- †.Stevenson JF, McMillan B, Mitchell RE, Blanco M. Project HOPE: Altering risk and protective factors among high risk Hispanic youth and their families. Journal of Primary Prevention. 1998;18:287–317. [Google Scholar]
- Storr CL, Ialongo NS, Kellam SG, Anthony JC. A randomized controlled trial of two primary school intervention strategies to prevent early onset tobacco smoking. Drug and Alcohol Dependence. 2002;66:51–60. doi: 10.1016/s0376-8716(01)00184-3. [DOI] [PubMed] [Google Scholar]
- Testa M, Hoffman JH, Livingston JA, Turrisi R. Preventing college women’s sexual victimization through parent based intervention: A randomized controlled trial. Prevention Science. 2010;11:308–318. doi: 10.1007/s11121-010-0168-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Toomey TL, Williams CL, Perry CL, Murray DM, Dudovitz B, Veblen-Mortenson S. An alcohol primary prevention program for parents of 7th graders: The Amazing Alternatives! Home Program. Journal of Child & Adolescent Substance Abuse. 1997;5:35–54. [Google Scholar]
- Toumbourou JW, Gregg ME. Impact of an empowerment-based parent education program on the reduction of youth suicide risk factors. Journal of Adolescent Health. 2002;31:277–285. doi: 10.1016/s1054-139x(02)00384-1. [DOI] [PubMed] [Google Scholar]
- Tremblay RE, Masse LC, Pagani L, Vitaro F. From childhood physical aggression to adolescent maladjustment: The Montreal prevention experiment. In: Peters RD, McMahon RJ, editors. Preventing childhood disorders, substance abuse and delinquency. Thousand Oaks, CA: Sage; 1996. pp. 268–298. [Google Scholar]
- Trudeau L, Spoth R, Randall GK, Azevedo K. Longitudinal effects of a universal family-focused intervention on growth patterns of adolescent internalizing symptoms and polysubstance use: Gender comparisons. Journal of Youth and Adolescence. 2007;36:725–740. [Google Scholar]
- Turrisi R, Abar C, Mallett KA, Jaccard J. An examination of the mediational effects of cognitive and attitudinal factors of a parent intervention to reduce college drinking. Journal of Applied Social Psychology. 2010;40:2500–2526. doi: 10.1111/j.1559-1816.2010.00668.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Turrisi R, Jaccard J, Taki R, Dunnam H, Grimes J. Examination of the short-term efficacy of a parent intervention to reduce college student drinking tendencies. Psychology of Addictive Behaviors. 2001;15:366. doi: 10.1037//0893-164x.15.4.366. [DOI] [PubMed] [Google Scholar]
- Turrisi R, Larimer ME, Mallett KA, Kilmer JR, Ray AE, Mastroleo NR, … Montoya H. A randomized clinical trial evaluating a combined alcohol intervention for high-risk college students. Journal of Studies on Alcohol and Drugs. 2009;70:555–567. doi: 10.15288/jsad.2009.70.555. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wang Y, Browne DC, Petras H, Stuart EA, Wagner FA, Lambert SF, … Ialongo NS. Depressed mood and the effect of two universal first grade preventive interventions on survival to the first tobacco cigarette smoked among urban youth. Drug and Alcohol Dependence. 2009;100:194–203. doi: 10.1016/j.drugalcdep.2008.08.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wang Y, Storr CL, Green KM, Zhu S, Stuart EA, Lynne-Landsman SD, … Ialongo NS. The effect of two elementary school-based prevention interventions on being offered tobacco and the transition to smoking. Drug and Alcohol Dependence. 2012;120:202–208. doi: 10.1016/j.drugalcdep.2011.07.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- West B, Abatemarco D, Ohman-Strickland PA, Zec V, Russo A, Milic R. Project Northland in Croatia: results and lessons learned. Journal of Drug Education. 2008;38:55–70. doi: 10.2190/DE.38.1.e. [DOI] [PubMed] [Google Scholar]
- Williams CL, Perry CL, Farbakhsh K, Veblen-Mortenson S. Project Northland: Comprehensive alcohol use prevention for young adolescents, their parents, schools, peers and communities. Journal of Studies on Alcohol and Drugs. 1999;13:112–124. doi: 10.15288/jsas.1999.s13.112. [DOI] [PubMed] [Google Scholar]
- Wolchik SA, Sandler IN, Millsap RE, Plummer BA, Greene SM, Anderson ER, … Haine RA. Six-year follow-up of preventive interventions for children of divorce: A randomized controlled trial. JAMA. 2002;288:1874–1881. doi: 10.1001/jama.288.15.1874. [DOI] [PubMed] [Google Scholar]
- Wood MD, Fairlie AM, Fernandez AC, Borsari B, Capone C, Laforge R, Carmona-Barros R. Brief motivational and parent interventions for college students: a randomized factorial study. Journal of Consulting and Clinical Psychology. 2010;78:349–361. doi: 10.1037/a0019166. [DOI] [PMC free article] [PubMed] [Google Scholar]
- †.Worden JK, Flynn BS, Brisson SF, Secker-Walker RH, Mcauliffe TL, Jones RP. An adult communication skills program to prevent adolescent smoking. Journal of Drug Education. 1987;17:1–9. doi: 10.2190/4WCH-86HE-7B0T-RB4A. [DOI] [PubMed] [Google Scholar]
- Wu Y, Stanton BF, Galbraith J, Kaljee L, Cottrell L, Li X, … Burns JM. Sustaining and broadening intervention impact: A longitudinal randomized trial of 3 adolescent risk reduction approaches. Pediatrics. 2003;111:e32–e38. doi: 10.1542/peds.111.1.e32. [DOI] [PubMed] [Google Scholar]
- Zonnevylle-Bender MJ, Matthys W, Van De Wiel NM, Lochman JE. Preventive effects of treatment of disruptive behavior disorder in middle childhood on substance use and delinquent behavior. Journal of the American Academy of Child & Adolescent Psychiatry. 2007;46:33–39. doi: 10.1097/01.chi.0000246051.53297.57. [DOI] [PubMed] [Google Scholar]
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References
- Bentler PM. Comparative fit indexes in structural models. Psychological Bulletin. 1990;107:238–246. doi: 10.1037/0033-2909.107.2.238. [DOI] [PubMed] [Google Scholar]
- Bentler PM, Bonett DG. Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin. 1980;88:588–606. [Google Scholar]
- Brook DW, Brook JS, Zhang C, Cohen P, Whiteman M. Drug use and the risk of major depressive disorder, alcohol dependence, and substance use disorders. Archives of General Psychiatry. 2002;59:1039–1044. doi: 10.1001/archpsyc.59.11.1039. [DOI] [PubMed] [Google Scholar]
- Clark DB, Kirisci L, Tarter RE. Adolescent versus adult onset and the development of substance use disorders in males. Drug and Alcohol Dependence. 1998;49:115–121. doi: 10.1016/s0376-8716(97)00154-3. [DOI] [PubMed] [Google Scholar]
- Cooper HM, Hedges LV. The handbook of research synthesis. New York: Russell Sage Foundation; 1994. [Google Scholar]
- Dewit DJ, Adlaf EM, Offord DR, Ogborne AC. Age at first alcohol use: a risk factor for the development of alcohol disorders. American Journal of Psychiatry. 2000;157:745–750. doi: 10.1176/appi.ajp.157.5.745. [DOI] [PubMed] [Google Scholar]
- Dishion TJ, McMahon RJ. Parental monitoring and the prevention of child and adolescent problem behavior: A conceptual and empirical formulation. Clinical Child and Family Psychology Review. 1998;1:61–75. doi: 10.1023/a:1021800432380. [DOI] [PubMed] [Google Scholar]
- Dishion TJ, Nelson SE, Kavanagh K. The Family Check-Up with high-risk young adolescents: Preventing early-onset substance use by parent monitoring. Behavior Therapy. 2003;34:553–571. [Google Scholar]
- Duncan SC, Duncan TE, Biglan A, Ary D. Contributions of the social context to the development of adolescent substance use: A multivariate latent growth modeling approach. Drug and Alcohol Dependence. 1998;50:57–71. doi: 10.1016/s0376-8716(98)00006-4. [DOI] [PubMed] [Google Scholar]
- DuRant RH, Smith JA, Kreiter SR, Krowchuk DP. The relationship between early age of onset of initial substance use and engaging in multiple health risk behaviors among young adolescents. Archives of Pediatric and Adolescent Medicine. 1999;153:286–291. doi: 10.1001/archpedi.153.3.286. [DOI] [PubMed] [Google Scholar]
- Dusenbury L. Family-based drug abuse prevention programs: A review. Journal of Primary Prevention. 2000;20:337–352. [Google Scholar]
- Ellickson PL, Tucker JS, Klein DJ. High-risk behaviors associated with early smoking: Results from a 5-year follow-up. Journal of Adolescent Health. 2001;28:465–473. doi: 10.1016/s1054-139x(00)00202-0. [DOI] [PubMed] [Google Scholar]
- Farrell AD, White KS. Peer influences and drug use among urban adolescents: Family structure and parent-adolescent relationship as protective factors. Journal of Consulting and Clinical Psychology. 1998;66:248–258. doi: 10.1037//0022-006x.66.2.248. [DOI] [PubMed] [Google Scholar]
- Flay BR, Biglan A, Boruch RF, Castro FG, Gottfredson D, Kellam S, … Ji P. Standards of evidence: Criteria for efficacy, effectiveness and dissemination. Prevention Science. 2005;6:151–175. doi: 10.1007/s11121-005-5553-y. [DOI] [PubMed] [Google Scholar]
- Foxcroft DR, Ireland D, Lister-Sharp DJ, Lowe G, Breen R. Longer-term primary prevention for alcohol misuse in young people: A systematic review. Addiction. 2003;98:397–411. doi: 10.1046/j.1360-0443.2003.00355.x. [DOI] [PubMed] [Google Scholar]
- Foxcroft DR, Tsertsvadze A. Universal alcohol misuse prevention programmes for children and adolescents: Cochrane systematic reviews. Perspectives in Public Health. 2012;132:128–134. doi: 10.1177/1757913912443487. [DOI] [PubMed] [Google Scholar]
- Grant JD, Scherrer JF, Lynskey MT, Lyons MJ, Eisen SA, Tsuang MT, True WR, Bucholz KK. Adolescent alcohol use is a risk factor for adult alcohol and drug dependence: Evidence from a twin design. Psychological Medicine. 2006;36:109–118. doi: 10.1017/S0033291705006045. [DOI] [PubMed] [Google Scholar]
- Griffin KW, Botvin GJ, Scheier LM, Diaz T, Miller NL. Parenting practices as predictors of substance use, delinquency, and aggression among urban minority youth: Moderating effects of family structure and gender. Psychology of Addictive Behaviors. 2000;14:174–184. doi: 10.1037//0893-164x.14.2.174. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hogue A, Dauber S, Samuolis J, Liddle HA. Treatment techniques and outcomes in multidimensional family therapy for adolescent behavior problems. Journal of Family Psychology. 2006;20:535–543. doi: 10.1037/0893-3200.20.4.535. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling. 1999;6:1–55. [Google Scholar]
- Johnston LD, O’Malley PM, Bachman JG, Schulenberg JE. Monitoring the Future national results on adolescent drug use: Overview of key findings, 2009. Bethesda, MD: National Institute on Drug Abuse; 2010. NIH Publication No. 10–7583. [Google Scholar]
- Kaminski JW, Valle LA, Filene JH, Boyle CL. A meta-analytic review of components associated with parent training program effectiveness. Journal of Abnormal Child Psychology. 2008;36:567–589. doi: 10.1007/s10802-007-9201-9. [DOI] [PubMed] [Google Scholar]
- Kandel DB, Davies M, Karus D, Yamaguchi K. The consequences in young adulthood of adolescent drug involvement: An overview. Archives of General Psychiatry. 1986;43:746–754. doi: 10.1001/archpsyc.1986.01800080032005. [DOI] [PubMed] [Google Scholar]
- Krohn MD, Lizotte AJ, Perez CM. The interrelationship between substance use and precocious transitions to adult statuses. Journal of Health and Social Behavior. 1997;38:87–103. [PubMed] [Google Scholar]
- Kumpfer KL, Alvarado R, Whiteside HO. Family-based interventions for substance use and misuse prevention. Substance Use & Misuse. 2003;38:1759–1878. doi: 10.1081/ja-120024240. [DOI] [PubMed] [Google Scholar]
- Ledoux S, Miller P, Choquet M, Plant M. Family structure, parent-child relationships, and alcohol and other drug use among teenagers in France and the United Kingdom. Alcohol and Alcoholism. 2002;37:52–60. doi: 10.1093/alcalc/37.1.52. [DOI] [PubMed] [Google Scholar]
- Lennings CJ, Copeland J, Howard J. Substance use patterns of young offenders and violent crime. Aggressive Behavior. 2003;29:414–422. [Google Scholar]
- Liddle HA. Family-based therapies for adolescent alcohol and drug use: Research contributions and future research needs. Addiction. 2004;99:76–92. doi: 10.1111/j.1360-0443.2004.00856.x. [DOI] [PubMed] [Google Scholar]
- Lipsey MW, Wilson DB. Practical meta-analysis. Applied social research methods. Thousand Oaks, CA: Sage; 2001. [Google Scholar]
- Lochman JE, van den Steenhoven A. Family-based approaches to substance abuse prevention. Journal of Primary Prevention. 2002;23:49–114. [Google Scholar]
- Lundahl B, Risser HJ, Lovejoy MC. A meta-analysis of parent training: Moderators and follow-up effects. Clinical Psychology Review. 2006;26:86–104. doi: 10.1016/j.cpr.2005.07.004. [DOI] [PubMed] [Google Scholar]
- Lynsky MT, Heath AC, Bucholz KK, Slutske WS, Madden PAF, Nelson EC, Statham DJ, Martin NG. Escalation of drug use in early-onset cannabis users vs. co-twin controls. Journal of the American Medical Association. 2003;289:427–433. doi: 10.1001/jama.289.4.427. [DOI] [PubMed] [Google Scholar]
- Miller TR. The social costs of adolescent problem behavior. In: Biglan A, Brennan PA, Foster SL, Holder HD, editors. Helping adolescents at risk: Prevention of multiple problem behaviors. New York: Guilford; 2004. pp. 31–56. [Google Scholar]
- Minuchin S. Families and family therapy. Harvard University Press; 1974. [Google Scholar]
- Muthén LK, Muthén BO. Mplus: User’s Guide. 7. Los Angeles, CA: Muthén & Muthén; 1998–2012. [Google Scholar]
- National Institute of Drug Abuse. Understanding drug abuse and addiction. Washington DC: National Institute of Drug Abuse, U.S. Department of Health and Human Services; 2008. [Google Scholar]
- Sandler I, Schoenfelder E, Wolchik S, MacKinnon D. Long-term impact of prevention programs to promote effective parenting: Lasting effects but uncertain processes. Annual Review of Psychology. 2011;62:299–329. doi: 10.1146/annurev.psych.121208.131619. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Smit E, Verdurmen J, Monshouwer K, Smit F. Family interventions and their effect on adolescent alcohol use in general populations: A meta-analysis of randomized controlled trials. Drug and Alcohol Dependence. 2008;97:195–206. doi: 10.1016/j.drugalcdep.2008.03.032. [DOI] [PubMed] [Google Scholar]
- Soyka M. Substance misuse, psychiatric disorder and violent and disturbed behavior. British Journal of Psychiatry. 2000;176:345–350. doi: 10.1192/bjp.176.4.345. [DOI] [PubMed] [Google Scholar]
- Svensson R. Risk factors for different dimensions of adolescent drug use. Journal of Child and Adolescent Substance Abuse. 2000;9:67–90. [Google Scholar]
- Tapert SF, Aarons GA, Sedlar GR, Brown SA. Adolescent substance use and sexual risk-taking behavior. Journal of Adolescent Health. 2001;28:181–189. doi: 10.1016/s1054-139x(00)00169-5. [DOI] [PubMed] [Google Scholar]
- Van Ryzin MJ, Dishion TJ. Adolescent deviant peer clustering as an amplifying mechanism underlying the progression from early substance use to late adolescent dependence. Journal of Child Psychology and Psychiatry. 2014;55:1153–1161. doi: 10.1111/jcpp.12211. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Van Ryzin MJ, Fosco GM. Family-based approaches to prevention: The state of the field. In: Van Ryzin MJ, et al., editors. Family-based prevention programs for children and adolescents: Theory, research, and large-scale dissemination. New York, NY: Psychology Press; 2015. [Google Scholar]
- Van Ryzin MJ, Fosco GM, Dishion TJ. Family and peer predictors of substance use from early adolescence to early adulthood: An 11-year prospective analysis. Addictive Behaviors. 2012;37:1314–1324. doi: 10.1016/j.addbeh.2012.06.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Van Ryzin MJ, Kumpfer KL, Fosco GM, Greenberg MT, editors. Family-based prevention programs for children and adolescents: Theory, research, and large-scale dissemination. New York, NY: Psychology Press; 2015. [Google Scholar]
- Westen D, Novotny CM, Thompson-Brenner H. The empirical status of empirically supported psychotherapies: Assumptions, findings, and reporting in controlled clinical trials. Psychological Bulletin. 2004;130:631–663. doi: 10.1037/0033-2909.130.4.631. [DOI] [PubMed] [Google Scholar]