PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Adolesc Health. Author manuscript; available in PMC Dec 18, 2013.
Published in final edited form as:
PMCID: PMC3867289
NIHMSID: NIHMS327673
Early Effects of Communities That Care on Targeted Risks and Initiation of Delinquent Behavior and Substance Use
J. David Hawkins, Ph.D.,a* Eric C. Brown, Ph.D.,a Sabrina Oesterle, Ph.D.,a Michael W. Arthur, Ph.D.,a Robert D. Abbott, Ph.D.,b and Richard F. Catalano, Ph.D.a
aSocial Development Research Group, University of Washington, Seattle, Washington
bCollege of Education, University of Washington, Seattle, Washington
*Address correspondence to: J. David Hawkins, Social Development Research Group, 9725 3rd Avenue NE, Suite 401, Seattle, WA 98115., jdh/at/u.washington.edu
Purpose
Communities That Care (CTC) is a prevention system designed to reduce levels of adolescent delinquency and substance use through the selection and use of effective preventive interventions tailored to a community’s specific profile of risk and protection. This article describes early findings from the first group-randomized trial of CTC.
Methods
A panel of 4407 fifth-grade students was surveyed annually through seventh grade. Analyses were conducted to assess the effects of CTC on reducing levels of targeted risk factors and reducing initiation of delinquent behavior and substance use in seventh grade, 1.67 years after implementing preventive interventions selected through the CTC process.
Results
Mean levels of targeted risks for students in seventh grade were significantly lower in CTC communities compared with controls. Significantly fewer students in CTC communities than in control communities initiated delinquent behavior between grades 5 and 7. No significant intervention effect on substance use initiation by spring of seventh grade was observed.
Conclusions
CTC’s theory of change hypothesizes that it takes from 2 to 5 years to observe community-level effects on risk factors and 5 or more years to observe effects on adolescent delinquency or substance use. The early findings indicating hypothesized effects of CTC on targeted risk factors and initiation of delinquent behavior are promising.
Keywords: Delinquency, Substance use, Adolescents, Intervention, Prevention science
Preventing alcohol, tobacco, and other drug use, delinquency, violence, and risky sexual behavior among adolescents is a national priority [1,2]. Although advances in prevention science over the past 2 decades have produced a growing list of tested and effective programs and policies for preventing these behaviors [36], widespread dissemination and high-quality implementation of these effective programs and policies in communities has not been achieved [710]. The development and testing of approaches for translating prevention research findings into effective community prevention service systems is important to achieve reductions in the prevalence of adolescent health and behavior problems [11,12].
Communities That Care (CTC) [13,14] is a prevention system that empowers communities to address adolescent health and behavior problems through a focus on empirically identified risk and protective factors. The CTC system is manualized, and includes training events and guides for community leaders and board members. CTC is designed to mobilize community leaders and a community prevention coalition (called “community prevention board” in CTC) to identify elevated risk factors and depressed protective factors in the community, and to select and implement a set of tested preventive interventions to reduce elevated risk factors and promote protective factors. Repeated assessments of community risk and protective factors are used for on-going evaluation of CTC communities’ prevention systems and to guide future prevention planning.
CTC is installed in communities through a series of six training events delivered over the course of 6 to 12 months by certified CTC trainers. All CTC training materials are available on the Internet [15]. Through the series of CTC training events and community board actions prescribed in CTC, the CTC system is expected to produce community-level changes in prevention service system characteristics, including greater adoption of science-based prevention, increased collaboration among service providers, and increased use of tested and effective preventive interventions that address risk and protective factors prioritized by the community. These changes in prevention service systems are expected to produce changes in the risk factors targeted by the preventive interventions chosen by the community. These reductions in risk factors in the community are expected, in turn, to reduce adolescent delinquent behaviors and substance use among young people in the community. According to CTC’s theory of change, it should take from 2 to 5 years to observe community-level changes in targeted risk factors in CTC communities, and from 5 to 10 years to observe community-level changes in substance use and delinquency outcomes [16].
The Community Youth Development Study (CYDS) [17] is the first community-randomized trial of CTC. The initial 5-year experimental study is currently being conducted in 24 communities across seven states nationally. To test the effects of CTC in achieving observable reductions in targeted risk factors, delinquent behavior, and substance use within the 5 years of this study as hypothesized by CTC’s theory of change, the intervention communities in CYDS were asked to focus their prevention plans on interventions for youths aged 10 to 14 years (grades 5–9) and their families. It was hypothesized that, if widely implemented during this period of developmental transition, tested and effective preventive interventions chosen by communities through the CTC system would produce measurable communitywide effects on targeted risk and protective factors and on the prevalence of delinquent behavior and substance use. CTC boards selected policies and programs from a menu of tested preventive interventions found to be effective with this age group included in “Communities That Care Prevention Strategies Guide” [18]. Each intervention included in the menu (1) has demonstrated positive effects in reducing one or more risk factors and in reducing delinquent behavior or substance use in an adequately controlled experimental or quasi-experimental study; (2) has training, technical assistance, and manuals available to guide the implementation of the policy or program; and (3) has been found to have effects on youths aged 10 through 14.
In the CYDS, CTC training and implementation began in the summer of 2003. Intervention communities received six CTC trainings from certified CTC trainers. Community leaders were oriented to the CTC system and identified or created a coalition of diverse stakeholders to implement CTC. Coalition members were trained to use data from surveys of community students collected in 1998, 2000, and 2002 in a prior study [19] to prioritize risk factors to target with preventive actions, to choose tested prevention policies and programs that address the community’s targeted risk factors, to implement these interventions with fidelity, and to monitor implementation and outcomes of newly installed preventive interventions. In addition, CYDS implementation staff provided technical assistance through weekly phone calls, written e-mails and reports, and site visits to intervention communities at least once per year. By June of 2004, intervention communities had selected preventive interventions to address their prioritized risks and had created strategic plans to implement these interventions. The 12 intervention communities selected 13 different tested and effective prevention programs to implement during the 2004 –2005 school year and 16 programs to implement during the 2005–2006 school year. Implemented programs included school-based programs (All-Stars, Life Skills Training, Lion’s Quest Skills for Adolescence, and Program Development Evaluation Training), community-based youth-focused programs (Participate and Learn Skills, Big Brothers/Big Sisters, Stay Smart, and academic tutoring), and family-focused programs (Strengthening Families 10–14, Guiding Good Choices, Parents Who Care, and Family Matters) [20]. Communities contracted with the developers of these interventions or their designated training organizations for the specific trainings required to implement their selected interventions. Once training was completed, the new programs were implemented by local providers, including teachers, human services workers, and community volunteers. About half the programs were chosen by multiple communities, and many programs were delivered more than once during the year. For example, Guiding Good Choices was provided 38 times (i.e., 38 cycles) across six communities. In total, 13 programs were delivered in 95 cycles in 2004 –2005, and 16 programs were delivered in 156 cycles during 2005–2006 [21].
Previous analyses have found that, by 18 months after initial training began, the CTC system had been successfully implemented with fidelity in intervention communities [20], and that tested and effective preventive programs were selected and well implemented in the intervention communities during the 2004 –2005 school year [21]. Further, analyses have found significant between-condition differences favoring the CTC communities in levels of adoption of science-based prevention and in levels of community collaboration 1.5 years after introducing CTC in intervention communities [22]. Given these findings, it is appropriate to ask whether CTC has affected levels of risk and delinquency and substance use outcomes among adolescents in these communities. The current study investigates the effects of CTC on average levels of targeted risk factors and on the initiation of delinquent behavior and substance use in a panel of students followed from grade 5 through grade 7 in CTC communities and control communities, after approximately 1.67 years of implementation of new prevention programs in CTC communities.
CYDS
Communities were selected from a larger pool of matched pairs of communities in seven states (Colorado, Illinois, Kansas, Maine, Oregon, Utah, and Washington) that participated in a naturalistic study of prevention [19]. Communities were matched within state by total population, poverty, racial/ethnic diversity, and unemployment and crime indices. Data from the prior study indicated that in 13 pairs of communities, neither community was using tested and effective prevention programs to address prioritized community risk factors. Twelve of these pairs of matched communities were recruited for the CYDS. One community from within each matched pair was randomly assigned by coin toss to either intervention (CTC) or control condition. These communities have populations ranging from 1500 to 50,000 residents with clear community identities and boundaries. They are small- to moderate-sized towns with their own governmental, educational, and law enforcement structures.
The design of the CYDS includes multiple assessments of student outcomes, mediators, and prevention service system functioning [17]. In addition to the panel of students analyzed in this paper, the CYDS includes a nested cross-sectional design assessing adolescent substance use, delinquency, risk, and protection using repeated anonymous biennial population-based surveys of 6th-, 8th-, 10th-, and 12th-grade students in CTC and control communities. Data from these cross-sectional CTC Youth Surveys [23] were used by the CTC communities to prioritize risks to target with preventive interventions.
Prioritization of risk factors
In the fall of 2003, members of the CTC community prevention boards attended the CTC Community Assessment Training to review their community’s data on risks from the CTC Youth Surveys from 1998, 2000, and 2002. During the training, board members learned to assess data trends and identify risk and protective factors that were consistently elevated or depressed over time. They identified the behaviors and elevated risk factors that their community would prioritize for preventive action, based primarily on data from sixth- and eighth-grade students.
Each CTC community prevention board prioritized between two and seven risk factors to target with preventive interventions. Some risk factors were targeted across multiple communities. Other factors were targeted less frequently. These targeted risk factors are shown in Table 1.
Table 1
Table 1
Targeted risk factors in CYDS intervention communities
Data collection
The data analyzed here are from annual repeated measurements of a panel of students who were in the fifth grade during the 2003–2004 school year. The first wave of data was collected in the spring of 2004, and represents a pre-intervention baseline assessment. Tested prevention programs were implemented in CTC communities beginning in the summer and fall of 2004. Grade 6 data collection was conducted in the spring of 2005 and included an effort to recruit and survey students in the cohort who were not recruited in grade 5. The third annual wave of data was collected in the spring of 2006 when students in the panel who were progressing normally were in grade 7—about 1.67 years after the prevention programs chosen by CTC communities were first implemented.
Students in the panel who remained in CTC or control communities for at least one semester in grade 6 have been tracked and surveyed subsequently, even if they left the community. To ensure confidentiality, no names or other identifying information were included on the surveys. Parents of panel students provided written informed consent for their children’s participation in the study. Students read and signed assent statements and agreed to participate in the study. Upon completion of the survey, students received small incentive gifts worth approximately $5 to $8. The University of Washington’s Human Subjects Review Committee has approved this protocol.
Recruitment and baseline equivalence between intervention and control conditions for the panel are described in Brown et al [24]. During grades 5 and 6, parents of 4420 students (76.4% of the overall eligible population) consented to their participation in the study. Thirteen of these students were absent during scheduled dates of data collection and were not available for surveying. Three additional students who reported being honest only “some of the time” or having used a fictitious drug included in the survey as a validity screen were excluded from the analysis. The resulting sample of 4404 students was split evenly between male and female students. Seventy percent of participants were white or Caucasian, 9% were Native American, 4% were African American, and 20% were of Hispanic origin. At grade 5, students were an average of 11.1 years of age (SD = 0.4). The mean number of students per community was 184 (SD = 122). Fifty-five percent of the analysis sample was in CTC communities and 45% was in control communities.
Measures
Measures of risk factors, substance use, delinquency, and demographic characteristics were obtained from the Youth Development Survey [25], a self-administered, paper-and-pencil questionnaire designed to be administered in a 50-minute classroom period. In the current study, we examined three outcome measures: (1) risk factors targeted by CTC communities, (2) onset of delinquent behavior, and (3) onset of substance use.
Targeted risk factors
Risk factor scales consisted of composites of multiple items. Scoring of risk factors entailed standardizing scale items across all three waves of data and taking the mean value of standardized items within a scale for each separate wave of data. Scales missing one or more items were coded as missing data with scale scores imputed using multiple imputation analyses. Because each CTC community targeted a specific set of risk factors, each CTC community’s specific set of targeted risk factors was compared to the same set of risk factors in its matched control community. Risk factors were averaged within each wave of data collection.
Onset of delinquent behavior
Onset of delinquent behavior was the first occurrence of any one of up to nine delinquent behaviors students reported committing in the past year. Items measuring delinquent behaviors varied by wave of data collection because questions relating to more severe forms of delinquency were added to later waves as they became developmentally appropriate (Table 2).
Table 2
Table 2
Delinquent behavior items by grade (wave)
Onset of substance use
Items measuring onset of substance use consisted of the first reported lifetime use of any of four types of drugs: alcohol, marijuana, cigarettes, or other illicit drugs (e.g., inhalants, cocaine, barbiturates, ecstasy, prescription drugs), between grades 5 and 7.
Student and community characteristics
Variables measuring student characteristics included: age at time of the grade 6 survey; gender (coded 0 = male, 1 = female); race/ ethnicity (coded 1 = white or Caucasian, 0 = other); whether the student was Hispanic (coded 1 = yes, 0 = no); parental education level (ranging from 1 = grade school or less to 6 = graduate or professional degree); attendance at religious services during grade 5 (coded 0 = never to 4 = about once a week or more); and rebelliousness, which consisted of the mean of three items: I like to see how much I can get away with; I ignore rules that get in my way; and I do the opposite of what people tell me, just to get them mad (coded from 1 = very false to 4 = very true). Variables measuring community demographic characteristics included the total population of students in the community, percentage increase in the student population of the community between 2001 and 2004, and the percentage of students who were eligible for free or reduced-price school lunch. Intervention condition was treated as a community-level variable and was coded 0 for CTC communities and 1 for control communities.
Missing data
Among the 4404 students comprising the analysis sample, 26.5% did not have Wave 1 (grade 5) data because they were recruited in Wave 2 (grade 6); 3.9% and 3.8% of students missed Wave 2 and Wave 3 data collection, respectively, because they were not available for a follow-up interview.
Beginning in grade 7, a planned missing-data three-form design [26] was initiated to accommodate the growing number of items in the survey. A subset of items was distributed evenly across two of the three versions of the survey, with each form administered randomly to one-third of the active panel sample. All but two of the targeted risk factor items and all delinquent behaviors, substance use measures, and demographic characteristics reported in this paper were asked of the entire sample.
Across the set of targeted risk factors, 4.8%, 6.9%, and 10.3% of students were missing two or more risk factors in Waves 1, 2, and 3, respectively. Across the set of delinquent behavior items, 0.2%, 1.4%, and 1.6% of students were missing data, respectively. Across the set of substance use items, 8.9%, 9.9%, and 9.1% of students were missing data, respectively.
Missing data were dealt with via multiple imputation [27]. Using NORM version 2.03 [28], 10 separate data sets [26], including data from all three waves, were imputed separately by intervention condition. Imputation models included student and community characteristics, risk and protective factors, substance use and delinquent behavior outcomes, and dummy-coded indicators of community membership. Imputed data sets were combined to include intervention and control groups for analysis.
Data analyses
Pretest–posttest analysis of covariance (ANCOVA)
Pretest–posttest ANCOVA was implemented using the general linear mixed model [29] to test for differences in average levels of targeted risk factors between CTC and control communities. In this analysis, the Gaussian-distributed outcome measure consisted of the community–pair-specific targeted risk factors obtained from the grade 7 administration of the Youth Development Survey, with regression adjustment for fifth-grade baseline levels of targeted risk factors, student characteristics, and community characteristics. All student characteristics were grand-mean centered. The analysis modeled all student characteristics as nonrandomly varying effects (i.e., varying only as a function of community-level covariates) and all community characteristics (not including intervention condition) as fixed effects. To account for the hierarchical data structure, random effects were included to model (1) the correlation of students within communities, (2) the correlation of communities within matched pairs of communities, (3) the variability of intervention effects across matched pairs of communities, and (4) residual error. The intervention effect was estimated as the adjusted within-matched pair difference between CTC and control community means in targeted risk factors, and was tested against the average variation within matched pairs among the CTC versus control community means. The pretest–posttest ANCOVA was conducted using HLM version 6.0 [30], with results averaged across imputed data sets using Rubin’s method [31].
Multilevel discrete-time survival analyses
Multilevel discrete-time survival analysis (ML-DTSA) [32,33] was used to assess the effects of the CTC intervention on preventing the initiation of delinquent behavior and substance use between grades 5 and 7. To assess effects on students who had not yet initiated these behaviors, students who had already initiated delinquent behavior (22.2%) or substance use (27.5%) prior to the intervention were not included in the analyses.
The ML-DTSA was implemented using the generalized linear mixed model [34,35] with logit link for the dichotomous outcomes. Students who did not initiate delinquent behavior or substance use, respectively, during sixth or seventh grades were treated as right-censored observations [36]. Student- and community-level variables were included in the model as covariates to control for possible community differences; intervention condition was included in the model as a community-level variable; and random effects were included to account for variation among students within communities, communities within matched pairs of communities, intervention effects across matched pairs of communities, and residual error. The effect of the intervention was estimated as the adjusted within-matched pair difference in community-level hazard of onset between CTC and control communities, assuming proportional hazards over time, and was tested against the average variation in hazard of onset among the matched pairs of CTC and control communities. Analyses were conducted using ML-wiN version 2.02 [37], with results averaged across imputed data sets using Rubin’s rules [31].
Targeted risk factors
To determine if baseline levels of targeted risk factors were significantly different between CTC and control communities, an ANCOVA was conducted using levels of targeted risk factors at grade 5 as the dependent variable, and including intervention condition and all background variables as predictors in the model. Mean levels of targeted risks observed at grade 5 were not significantly different by intervention condition, t (11) = 0.61, p > .05, indicating that CTC and control groups had equivalent baseline levels of targeted risk factors prior to the intervention.
Results of the pretest–posttest ANCOVA of targeted risk factors are shown in Table 3. Controlling for grade 5 levels of risk and student and community characteristics, grade 7 risk levels were significantly higher for students in control communities compared with students from CTC communities. The between-group difference in grade 7 corresponded to a standardized intervention effect size of δ = .15 (variance σ2δ = 0.08) [38]. Additionally, grade 5 levels of risk, students’ age, and parental education were associated with grade 7 levels of risk (Table 3). No other background variables were significantly associated with levels of targeted risks in grade 7.
Table 3
Table 3
Intervention effect on targeted risk factors in grade 7 using pretest–posttest ANCOVA
Onset of delinquent behavior and substance use
Results of the ML-DTSA of delinquent behavior and substance use initiation are shown in Table 4. These analyses found a significant intervention effect on the initiation of delinquent behavior but no significant effect on substance use initiation. The adjusted odds ratio for the effects of the intervention on delinquent behavior onset was 1.27, suggesting that students from control communities were 27% more likely to initiate delinquent behavior during grades 6 and 7 than were students from CTC communities. Figure 1 shows the observed cumulative initiation probabilities of delinquent behavior for those who had not yet initiated delinquent behavior in grade 5. Among the student characteristics, age, gender, race/ethnicity, parental education, and rebelliousness were associated significantly with onset of delinquent behavior. Students’ race/ethnicity, parental education, religious attendance, and rebelliousness were associated significantly with onset of substance use. The community demographic characteristics included in analyses as covariates were not associated significantly with either outcome.
Table 4
Table 4
Intervention effect on delinquent behavior and substance use initiation using multilevel discrete-time survival analysis
Figure 1
Figure 1
Observed cumulative initiation probabilities of delinquent behavior.
The CTC system seeks to activate community coalitions of stakeholders to use data to prioritize risk factors that will be targeted by preventive actions, to choose tested and effective preventive interventions that address the community’s targeted risk factors, to implement these interventions with fidelity, and to monitor implementation and outcomes of newly installed preventive interventions. It is hypothesized that these changes in preventive services will produce reductions in the risk factors addressed by the preventive interventions chosen by a community. These community-level reductions in risk factors are expected, in turn, to reduce delinquent behaviors and substance use among young people. The CYDS seeks to determine whether CTC’s trainings and technical assistance result in changes in prevention service systems that affect communitywide levels of risk, delinquency, and substance use.
The CTC system has been implemented with fidelity in the CYDS [20]. Tested and effective preventive programs have been selected and well implemented [21], and levels of adoption of science-based prevention and levels of community collaboration were significantly higher in CTC than control communities 2 years after CTC training began [22]. The present study shows that 1.67 years after preventive interventions selected through the CTC process were implemented, the levels of risk factors targeted by CTC communities were significantly lower among panel students in grade 7 in intervention communities than in control communities. At fifth-grade baseline, there were no significant differences between CTC and control panel students in average levels of targeted risk factors. Thus, it is unlikely that the observed differences in these factors by grade 7 reflect selection bias. Although the standardized intervention effect of δ = .15 is small [39], this effect has been found early in the CTC process and may grow as implementation progresses.
This study found that students in control communities were significantly more likely to initiate delinquent behavior between fifth and seventh grades than were students in CTC communities. Although no significant intervention condition effects on substance use initiation between grades 5 and 7 were observed, evidence of an early effect on delinquent behavior after the introduction of CTC in intervention communities is noteworthy given findings from the longitudinal National Youth Survey, which showed that initiation of delinquent behavior typically precedes and predicts initiation of substance use [40]. It is encouraging to see evidence of an effect of CTC on delinquency initiation after 1.67 years of implementation of new prevention programs in CTC communities.
Limitations of the present study should be noted. First, the study relies on self-reports of young people regarding risk exposure and behavior. Second, the study includes only small- to moderate-sized towns in seven states. Although regional variation increases the generalizability of findings, the present study does not provide data on the efficacy of CTC in larger cities.
These early findings from the first community-randomized trial of CTC are promising but not conclusive. Longer follow-ups will be needed to determine whether CTC has significant enduring effects on delinquency and drug use as hypothesized. Panel students are surveyed again in 2007 and 2008. These data will allow tests of CTC’s effects on delinquency and substance use through the spring of grade 9, almost 5 years after CTC was introduced in intervention communities and approximately 3.67 years after communities began implementing tested and effective prevention programs chosen through the CTC system.
Acknowledgments
This work was supported by a research grant from the National Institute on Drug Abuse (R01 DA015183-03) with cofunding from the National Cancer Institute, the National Institute of Child Health and Human Development, the National Institute of Mental Health, and the Center for Substance Abuse Prevention. The authors wish to acknowledge the contributions of the communities participating in the Community Youth Development Study.
1. Centers for Disease Control and Prevention. [Accessed January 30, 2007];Surgeon General’s Report: The Health Consequences of Smoking. 2004 Available at: http://www.cdc.gov/tobacco/sgr/sgr_2004/index.htm.
2. Substance Abuse and Mental Health Services Administration—U.S. Department of Health and Human Services. [Accessed September 5, 2007];Report to Congress: A Comprehensive Plan for Preventing and Reducing Underage Drinking. Available at: http://www.stopalcoholabuse.gov/media/underagedrinking/pdf/underagerpttocongress.pdf.
3. Aos S, Lieb R, Mayfield J, et al. Benefits and Costs of Prevention and Early Intervention Programs for Youth. Olympia, WA: Washington State Institute for Public Policy; 2004.
4. Mihalic S, Fagan A, Irwin K, et al. Blueprints for Violence Prevention (NCJ 204274) Washington, DC: Office of Juvenile and Delinquency Prevention; 2004.
5. Substance Abuse and Mental Health Services Administration. Science-based Prevention Programs and Principles. Effective Substance Abuse and Mental Health Programs for Every Community. U.S. Department of Health and Human Services, Substance Abuse and Mental Health Services Administration, Center for Substance Abuse Prevention. [Accessed September 5, 2007]; Available at: http://download.ncadi.samhsa.gov/prevline/pdfs/BKD479.pdf.
6. Welsh BC, Farrington DP. Evidence-based crime prevention. In: Welsh BC, Farrington DP, editors. Preventing Crime. What Works for Children, Offenders, Victims, and Places. Dordrecht, The Netherlands: Springer; 2006. pp. 1–17.
7. Ennett ST, Ringwalt CL, Thorne J, et al. A comparison of current practice in school-based substance use prevention programs with meta-analysis findings. Prev Sci. 2003;4:1–14. [PubMed]
8. Gottfredson DC, Gottfredson GD. Quality of school-based prevention programs: results from a national survey. J Res Crime Delinq. 2002;39:3–35.
9. Hallfors D, Godette D. Will the “Principles of Effectiveness” improve prevention practice? Early findings from a diffusion study. Health Educ Res. 2002;17:461–70. [PubMed]
10. Wandersman A, Florin P. Community interventions and effective prevention. Am Psychol. 2003;58:441– 8. [PubMed]
11. Wandersman A. Community science: bridging the gap between science and practice with community-centered models. Am J Community Psychol. 2003;31:227– 42. [PubMed]
12. Spoth RL, Greenberg MT. Toward a comprehensive strategy for effective practitioner-scientist partnerships and larger-scale community health and well-being. Am J Community Psychol. 2005;35:107–26. [PMC free article] [PubMed]
13. Hawkins JD, Catalano RF. Investing in Your Community’s Youth: An Introduction to the Communities That Care System. South Deer-field, MA: Channing Bete; 2002.
14. Hawkins JD, Catalano RF, Arthur MW. Promoting science-based prevention in communities. Addict Behav. 2002;27:951–76. [PubMed]
15. Substance Abuse and Mental Health Services Administration. [Accessed October 31, 2007];Communities That Care Planning System. Available at: http://ncadi.samhsa.gov/features/ctc/resources.aspx.
16. Hawkins JD, Catalano RF. [Accessed November 26, 2007];Communities That Care Community Board Orientation: Participant’s Guide. Available at: http://ncadi.samhsa.gov/features/ctc/resources.aspx.
17. Hawkins JD, Catalano RF, Arthur MW, et al. Testing Communities That Care: the rationale, design and behavioral baseline equivalence of the Community Youth Development Study. Prev Sci. (in press) [PMC free article] [PubMed]
18. Hawkins JD, Catalano RF. [Accessed November 27, 2007];Communities That Care: Prevention Strategies Guide. Available at: http://ncadi.samhsa.gov/features/ctc/resources.aspx.
19. Arthur MW, Glaser RR, Hawkins JD. Steps towards community-level resilience: community adoption of science-based prevention programming. In: Peters RD, Leadbeater B, McMahon RJ, editors. Resilience in Children, Families, and Communities: Linking Context to Practice and Policy. New York: Kluwer Academic/Plenum Publishers; 2005. pp. 177–94.
20. Quinby RK, Fagan AA, Hanson K, et al. Installing the Communities That Care prevention system: implementation progress and fidelity in a randomized controlled trial. J Community Psychol. (in press)
21. Fagan AA, Hanson K, Hawkins JD, et al. Bridging science to practice: achieving prevention program implementation fidelity in the Community Youth Development Study. Am J Community Psychol. (in press) [PubMed]
22. Brown EC, Hawkins JD, Arthur MW, et al. Effects of Communities That Care on prevention services systems: outcomes from the Community Youth Development Study at 1. 5 years. Prev Sci. 2007;8:180–91. [PubMed]
23. Arthur MW, Hawkins JD, Pollard JA, et al. Measuring risk and protective factors for substance use, delinquency, and other adolescent problem behaviors: The Communities That Care Youth Survey. Eval Rev. 2002;26:575– 601. [PubMed]
24. Brown EC, Graham JW, Hawkins JD, et al. Design and analysis of the Community Youth Development Study extended nested cohort sample. Manuscript under review. [PMC free article] [PubMed]
25. Social Development Research Group. Community Youth Development Study, Youth Development Survey [Grades 5–7] Seattle, WA: University of Washington, Social Development Research Group, School of Social Work; 2005–2007.
26. Graham JW, Taylor BJ, Olchowski AE, et al. Planned missing data designs in psychological research. Psychol Methods. 2006;11:323– 43. [PubMed]
27. Schafer JL, Graham JW. Missing data: our view of the state of the art. Psychol Methods. 2002;7:147–77. [PubMed]
28. NORM for Windows 95/98/NT [computer program]. Version 2.03. University Park, PA: Center for the Study and Prevention through Innovative Methodology at Pennsylvania State University; 2000.
29. Murray DM. Design and Analysis of Group-Randomized Trials. New York: Oxford University Press; 1998.
30. Raudenbush SW, Bryk AS, Cheong YF, et al. HLM 6: Hierarchical Linear and Nonlinear Modeling. Lincolnwood, IL: Scientific Software International, Inc; 2004.
31. Rubin DB. Multiple Imputation for Nonresponse in Surveys. New York: Wiley; 1987.
32. Barber JS, Murphy S, Axinn WG, et al. Discrete-time multilevel hazard analysis. Sociol Method. 2000;30:201–35.
33. Reardon SF, Brennan RT, Buka SL. Estimating multi-level discrete-time hazard models using cross-sectional data: neighborhood effects on the onset of adolescent cigarette use. Multivariate Behav Res. 2002;37:297–330.
34. Breslow N, Clayton DG. Approximate inference in generalized linear mixed models. J Am Stat Assoc. 1993;88:9–25.
35. Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika. 1986;73:13–22.
36. Singer JD, Willett JB. Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. New York: Oxford University Press; 2003.
37. MLwiN [computer program]. Version 2.02. Bristol, UK: University of Bristol, Multilevel Models Project, Institute of Education; 2004.
38. Raudenbush SW, Liu X. Statistical power and optimal design for multisite randomized trials. Psychol Methods. 2000;5:199–213. [PubMed]
39. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2. Hillsdale, NJ: Lawrence Erlbaum; 1988.
40. Elliott DS. Serious violent offenders: onset, developmental course, and termination. The American Society of Criminology 1993 Presidential Address Criminology. 1994;32:1–22.