Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
Prevention Science
Published in: Prevention Science 2/2006

01-06-2006 | Original Paper

Universality of Effects: An Examination of the Comparability of Long-Term Family Intervention Effects on Substance Use Across Risk-Related Subgroups

Authors: Richard Spoth, Chungyeol Shin, Max Guyll, Cleve Redmond, Kari Azevedo

Published in: Prevention Science | Issue 2/2006

Login to get access

Abstract

This study extends earlier investigation of family risk-related moderation of two brief, family-focused preventive interventions. It examines effects on the trajectories of substance initiation over a period of six years after a pretest assessment, evaluating whether effects were comparable across higher- and lower-risk subgroups. The two interventions, designed for general-population families of adolescents, were the seven-session Iowa Strengthening Families Program (ISFP) and the five-session Preparing for the Drug Free Years program (PDFY). Thirty-three rural public schools were randomly assigned to either the ISFP, the PDFY, or a minimal contact control condition. Curvilinear growth curve analyses were used to evaluate the universality of intervention effectiveness by testing for risk moderation of intervention effects on school-level substance use trajectories of initiation of alcohol and illicit substance use. Results were most consistent with the interpretation that both interventions provided comparable benefits for both outcome measures, regardless of family risk status. Findings are discussed in terms of their implications for implementing universal preventive interventions in general populations.
Appendix
Available only for authorised users
Footnotes
1
For purposes of this study, analyses examining differential attrition across experimental conditions were extended to the 12th-grade follow-up assessment. Two 2 (Condition: Intervention vs. Control) × 2 (Attrition status: Attritted vs. Retained) analyses of variance (ANOVA) were conducted for each outcome measure, with one ANOVA focusing on PDFY-control comparisons and the other ANOVA focusing on ISFP-control comparisons. No significant Condition × Attrition status interaction effects were found for either of the outcome variables between the 6th grade pretest and the 12th-grade follow-up. At each of the six waves of data collection, the numbers of participating families who had also completed all possible preceding assessments from posttest through the final assessment were 551, 458, 419, 374, and 344, respectively, which corresponds to an average wave-to-wave retention rate of 87.7%.
 
2
Over the six year course of the study, 28% of all respondents (23% ISFP, 35% PDFY, 27% control) made an inconsistent report on at least one of the three lifetime alcohol use items; 4% of all respondents (4% ISFP, 5% PDFY, 3% control) made an inconsistent report on at least one of the two lifetime illicit substance use items.
 
3
Although inconsistent with assumptions for the specific statistical model used to analyze the reported results, parallel, exploratory analyses were conducted in which individual initiation scores were allowed to freely change across data collection waves. These analyses were performed to help gauge the robustness of the findings reported. The pattern of statistically significant results remained unchanged (see also Spoth, Redmond, & Shin, 2001).
 
4
For the purpose of our power analyses for both measures we used a minimal difference of 12-months of delay in initiation between the higher- and lower-risk subgroups as our criterion. We selected this criterion because there is a case to be made that a difference of less than 12 months would be of questionable practical significance. This conclusion is based on consideration of prevalence data from Grant and Dawson (1997) that links age of initiation during adolescence to the development of alcohol use disorders in adulthood. A 12-month difference between higher- and lower-risk groups would correspond to only about a 3% difference in the risk associated with developing an alcohol use disorder in the future for the current sample. It should also be noted that a 12 month difference represents only about half of the intervention effect when ignoring risk grouping on most initiation measures. We calculated the results of the school-level power analyses on the alcohol initiation measure for the range of 12–18 months of initiation delay. In the one-tailed case for ISFP they range from .75 to .95 and, for PDFY, they range from .68 to .93. Power was markedly lower for analyses focusing on illicit substance initiation, ranging from .16 to .25 for ISFP, and from .18 to .29 for PDFY.
We considered the possibility of conducting analyses at the individual-level in order to attain greater power. However, conducting individual-level analyses was not desirable in this case, for two reasons. First, individual-level responses on the outcome variables were quite discontinuous in shape across time. That is, individual trajectories of use would often “jump” abruptly from one assessment to the next. Thus, the individual-level outcome data are not well described by growth curve trajectories in general, nor by the logistic growth curve used in the current analysis. As a result, the model provided a poor fit to the individual-level data. By contrast, school-level responses are formed by averaging the individual-level responses, a procedure that produces trajectories that are more continuous across time and better described by growth curve modeling. Second, the individual-level analyses actually possessed less statistical power due to the fact that there was much greater variation among individual-level responses than among school-level responses. That is, even though the individual-level analyses did tend to benefit power insomuch as they increased degrees of freedom, this gain was more than offset by the greater imprecision of the parameter estimates.
 
5
More general results associated with the fitting of logistic growth curves to initiation outcome measures through six years following pretest—regardless of family risk status—are provided by Spoth, Redmond, Shin, and Azevedo (2004). To avoid undue lengthiness and to maintain focus on evaluating intervention-control differences in the context of lower- and higher-risk families, we do not reproduce those more general results, and instead refer the interested reader to the cited report.
 
6
In comparison to linear trajectory models, greater care is required to correctly interpret the meaning of the parameters of S-shaped nonlinear trajectory models. For example, the lack of statistically significant intervention-control differences on parameters associated with maximum values (as captured by the parameters, α P and α S ) should not be interpreted as meaning that there were no intervention-control differences in the levels of substance use at the 12th grade (the final wave of data assessment). Rather, null findings for should be interpreted as meaning that the estimated growth trajectories of the two conditions will ultimately–at some later point in time–approach maximum limits that are not statistically different from each other. If one wishes to test for intervention and risk status effects on a particular outcome, it is necessary to appropriately adapt the traditional growth curve model, as described in the Appendix.
 
Literature
Achenbach, T. M., & Edelbrock, C. (1983). Manual for the child behavior checklist and revised child behavior profile. Burlington, VT: Author.
Akers, R. L. (1977). Deviant behavior: A social learning approach (2nd ed.). Belmont, CA: Wadsworth Press.
August, G. J., Realmuto, G. M., Hektner, J. M., & Bloomquist, M. L. (2001). An integrated components preventive intervention for aggressive elementary school children: The Early Risers program. Journal of Consulting and Clinical Psychology, 69(4), 614–626.PubMedCrossRef
Azevedo, K., Redmond, C., Lillehoj, C., & Spoth, R. L. (2003). Contradictions across in-school and in-home reports of adolescent substance use initiation. Poster presented at the Society for Prevention Research 11th Annual Meeting, Washington, DC.
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182.PubMedCrossRef
Biglan, A. (1995). Changing cultural practices: A contextual framework for intervention research. Reno, NV: Context Press.
Bradley, R. H., Burchinal, M. R., & Casey, P. H. (2001). Early intervention: The moderating role of the home environment. Applied Developmental Science, 5(1), 2–8.CrossRef
Brown, C. H., & Liao, J. (1999). Principles for designing randomized preventive trials in mental health: An emerging developmental epidemiology paradigm. American Journal of Community Psychology, 27, 673–710.PubMedCrossRef
Catalano, R. F., & Hawkins, J. D. (1996). The social development model: A theory of antisocial behavior. In J. D. Hawkins (Ed.), Delinquency and crime: Current theories (pp. 149–197), Cambridge University Press.
Center for Substance Abuse Prevention. (1998). Family-centered approaches to prevent alcohol, tobacco, and other drug use among children and adolescents. DHHS Publication No. (SMA) 3223-FY98.
Conduct Problems Prevention Research Group. (2002). Predictor variables associated with positive Fast Track outcomes at the end of third grade. Journal of Abnormal Child Psychology, 30(1), 37–52.CrossRef
Dawson-McClure, S. R., Sandler, I. N., Wolchik, S. A., & Millsap, R. E. (2004). 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, 32(2), 175–190.PubMedCrossRef
DeMarsh, J., & Kumpfer, K. L. (1986). Family-oriented interventions for the prevention of chemical dependency in children and adolescents. Prevention, 18, 117–151.
Derogatis, L. R. (1977). SCL-90-R: Administration scoring and procedures manual. J. Baltimore: Clinical Psychometric Research.
Durlak, J. A. (1997). School-based prevention programs for children and adolescents. Thousand Oaks, CA: Sage.
Elliott, D. S., Ageton, S. S., Huizinga, D., Knowles, B. A., & Canter, R. J. (1983). The prevalence and incidence of delinquent behavior: 1976–1980. Boulder, CO: Behavioral Research Institute.
Grant, B. F., & Dawson, D. A. (1997). Age at onset of alcohol use and its association with DSM-IV alcohol abuse and dependence: Results from the national longitudinal alcohol epidemiologic survey. Journal of Substance Abuse, 9, 103–110.PubMedCrossRef
Guyll, M., Spoth, R. L., Chao, W., Wickrama, K. A. S., & Russell, D. (2004). Family-focused preventive interventions: evaluating parental risk moderation of substance use trajectories. Journal of Family Psychology, 18(2), 293–301.PubMedCrossRef
Hansen, W. B., Tobler, N. S., & Graham, J. W. (1990). Attrition in substance abuse prevention research: A meta-analysis of 85 longitudinally followed cohorts. Evaluation Review, 14(6), 677–685.CrossRef
Hawkins, J. D., & Weis, J. G. (1985). The social development model: An integrated approach to delinquency prevention. Journal of Primary Prevention, 7, 73–97.CrossRef
Hirschi, T. (1969). Causes of delinquency. Berkeley, CA: University of California Press.
Johnston, L. D., O’Malley, P. M., & Bachman, J. G. (2002). The Monitoring the Future national survey results on adolescent drug use: Overview of key findings, 2001 (NIH Publication No. 02-5105). Bethesda, MD: National Institute on Drug Abuse.
Kaestner, R. (1991). The effect of illicit drug use on the wages of young adults. Journal of Labor Economics, 9, 381–412.CrossRef
Kandel, D. B., & Logan, J. A. (1984). Patterns of drug use from adolescence to young adulthood: I. Periods of risk for initiation, continued use, and discontinuation. American Journal of Public Health, 74, 660–666.PubMedCrossRef
Kellam, S. G., Ling, X., Merisca, R., Brown, C. H., & Ialongo, N. (1998). The effect of the level of aggression in the first grade classroom on the course and malleability of aggressive behavior into middle school. Development and Psychopathology, 10(2), 165–185.PubMedCrossRef
Kellam, S. G., & Rebok, G. W. (1992). Building developmental and etiological theory through epidemiologically based preventive intervention trials. In J. McCord, & R. E. Tremblay (Eds.), Preventing antisocial behavior: Interventions from birth through adolescence (pp. 162–195). New York: Guilford Press.
Kosterman, R., Hawkins, J. D., Spoth, R., Haggerty, K. P., & Zhu, K. (1997). Effects of a preventive parent-training intervention on observed family interactions: Proximal outcomes from Preparing for the Drug Free Years. Journal of Community Psychology, 25(4), 337–352.CrossRef
Kumpfer, K. L., Molgaard, V., & Spoth, R. (1996). The Strengthening Families Program for the prevention of delinquency and drug use. In R. D. Peters, & R. J. McMahon (Eds.), Preventing childhood disorders, substance abuse, and delinquency (pp. 241–267). Thousand Oaks, CA: Sage.
Lindstrom, M. J., & Bates, D. M. (1990). Nonlinear mixed effects models for repeated measures data. Biometrics, 46, 673–687.PubMedCrossRef
Lipsey, M. W. (1990). Theory as method: Small theories of treatments. In R. Sechrest, J. Bunker, & E. Perrin (Eds.), Health services research methodology: Strengthening causal inference from nonexperimental research. Washington, DC: U.S. Public Health Service, National Center for Health Services Research and Health Care Technology Research.
MacKinnon, D. P., Weber, M. D., & Pentz, M. A. (1988). How do school-based drug prevention programs work and for whom? Drugs and Society, 3, 125–143.CrossRef
Mrazek, P. J., & Haggerty, R. J. (Eds.) (1994). Reducing risks for mental disorders: Frontiers for preventive intervention research. Washington, DC: National Academy Press.
Myers, R. H. (1989). Classical and modern regression with applications (2nd ed.). Boston, MA: PSW-Kent.
Nathan, P. E., & Gorman, J. M. (Eds.) (2000). A guide to treatments that work (2nd ed.). New York: Oxford University Press.
National Center on Addiction and Substance Abuse. (2000). No place to hide: Substance abuse in midsize cities and rural America. New York, NY: National Center on Addiction and Substance Abuse (CASA) at Columbia University.
Newcomb, M. D., & Bentler, P. M. (1988). The impact of family context, deviant attitudes, and emotional distress on adolescent drug use: Longitudinal latent-variable analyses of mothers and their children. Journal of Research in Personality, 22, 154–176.CrossRef
Offord, D. R., Kraemer, H. C., Kazdin, A. E., Jensen, P. S., & Harrington, R. (1998). Lowering the burden of suffering from child psychiatric disorder: Trade-offs among clinical, targeted, and universal interventions. Journal of the American Academy of Child and Adolescent Psychiatry, 37, 686–694.PubMedCrossRef
Olds, D. L., Eckenrode, J., Henderson, C. R., Kitzman, H., Powers, J., Cole, R., et al. (1997). Long-term effects of home visitation on maternal life course and child abuse and neglect. Journal of the American Medical Association, 278(8), 637–643.PubMedCrossRef
Pacific Institute for Research and Evaluation. (1999). Costs of underage drinking. Presented at the Office of Juvenile Justice and Delinquency Prevention National Leadership Conference, Washington, DC. Available [On-line]: www.pire.org/udetc/documents/costs.pdf
Price, R. H., van Ryn, M., & Vinokur, A. D. (1992). Impact of a preventive job search intervention on the likelihood of depression among the employed. Journal of Health and Social Behavior, 33, 158–167.PubMedCrossRef
Redmond, C., Shin, C., Eickhoff, J., & Spoth, R. (2003). Risk moderation of parent and child preventive intervention outcomes. Unpublished document, Institute for Social and Behavioral Research, Iowa State University, Ames.
Romer, D. (Ed.) (2002). Reducing adolescent risk: Toward an integrated approach (Preface). Thousand Oaks, CA: Sage.
Smith, G. T., McCarthy, D. M., & Goldman, M. S. (1995). Self-reported drinking and alcohol-related problems among early adolescents: Dimensionality and validity over 24 months. Journal of Studies on Alcohol, 36, 383–394.
Society for Prevention Research. (2004). Standards of evidence: Criteria for efficacy, effectiveness and dissemination. Falls Church, VA: Author.
Spoth, R., Goldberg, C., Neppl, T., Trudeau, L., & Ramisetty-Mikler, S. (2001). Rural-urban differences in the distribution of parent-reported risk factors for substance use among young adolescents. Journal of Substance Abuse, 13, 609–623.PubMedCrossRef
Spoth, R., Goldberg, C., & Redmond, C. (1999). Engaging families in longitudinal preventive intervention research: Discrete-time survival analysis of socioeconomic and social-emotional risk factors. Journal of Consulting and Clinical Psychology, 67, 157–163.PubMedCrossRef
Spoth, R., Guyll, M., & Day, S. X. (2002). Universal family-focused interventions in alcohol-use disorder prevention: Cost-effectiveness and cost-benefit analyses of two interventions. Journal of Studies on Alcohol, 63, 219–228.PubMed
Spoth, R. L., Kavanagh, K., & Dishion, T. (2002). Family-centered preventive intervention science: Toward benefits to larger populations of children, youth, and families. Prevention Science, 3, 145–152.PubMedCrossRef
Spoth, R., & Molgaard, V. (1999). Project Family: A partnership integrating research with the practice of promoting family and youth competencies. In T. R. Chibucos, & R. Lerner (Eds.), Serving children and families through community-university partnerships: Success stories (pp. 127–137). Boston: Kluwer Academic.
Spoth, R., & Redmond, C. (1994). Effective recruitment of parents into family-focused prevention research: A comparison of two strategies. Psychology and Health: An International Journal, 9, 353–370.CrossRef
Spoth, R., & Redmond, C. (1996). Illustrating a framework for rural prevention research: Project Family studies of rural family participation and outcomes. In R. D. Peters, & R. J. McMahon (Eds.), Preventing childhood disorders, substance abuse, and delinquency (pp. 299–328). Thousand Oaks, CA: Sage.
Spoth, R., & Redmond, C. (2002). Project Family prevention trials based in community-university partnerships: Toward scaled-up preventive interventions. Prevention Science, 3(3), 203–221.PubMedCrossRef
Spoth, R., Redmond, C., & Shin, C. (1998). Direct and indirect latent-variable parenting outcomes of two universal family-focused preventive interventions: Extending a public health-oriented research base. Journal of Consulting and Clinical Psychology, 66, 385–399.PubMedCrossRef
Spoth, R., Redmond, C., & Shin, C. (2001). Randomized trial of brief family interventions for general populations: Adolescent substance use outcomes four years following baseline. Journal of Consulting and Clinical Psychology, 69, 627–642.PubMedCrossRef
Spoth, R., Redmond, C., Shin, C., & Azevedo, K. (2004). Brief family intervention effects on adolescent substance initiation: School-level curvilinear growth curve analyses six years following baseline. Journal of Consulting and Clinical Psychology, 72(3), 535–542.PubMedCrossRef
Spoth, R., Redmond, C., Shin, C., Lepper, H., Haggerty, K., & Wall, M. (1998). Risk moderation of parent and child outcomes in a preventive intervention: A test and replication. American Journal of Orthopsychiatry, 68, 565–579 (invited article for special section on preventive intervention research).PubMedCrossRef
Stoolmiller, M., Eddy, J. M., & Reid, J. B. (2000). Detecting and describing preventive intervention effects in a universal school-based randomized trial targeting delinquent and violent behavior. Journal of Consulting and Clinical Psychology, 68, 296–306.PubMedCrossRef
Tulchinsky, T. H., & Varavikova, E. A. (2000). The new public health: An introduction for the 21st Century. San Diego, CA: Academic Press.
Williams, C. L., Perry, C. L., Dudovitz, B., Veblen-Mortenson, S., Anstine, P. S., Komro, K. A., & Toomey, T. L. (1995). A home-based prevention program for sixth-grade alcohol use: Results from Project Northland. Journal of Primary Prevention, 16, 125–147.CrossRef
Wolchik, S. A., Sandler, I. N., Millsap, R. E., Plummer, B. A., Greene, S. M., Anderson, E. R. et al. (2002). Six year follow-up of a randomized, controlled trial of preventive interventions for children of divorce. Journal of the American Medical Association, 288, 1874–1881.PubMedCrossRef
Wolchik, S. A., West, S. G., Sandler, I. N., Tein, J. Y., Coatsworth, D., Lengua, L. et al. (2000). An experimental evaluation of theory-based mother and mother-child programs for children of divorce. Journal of Consulting & Clinical Psychology, 68(5), 843–856.CrossRef
Metadata
Title
Universality of Effects: An Examination of the Comparability of Long-Term Family Intervention Effects on Substance Use Across Risk-Related Subgroups
Authors
Richard Spoth
Chungyeol Shin
Max Guyll
Cleve Redmond
Kari Azevedo
Publication date
01-06-2006
Publisher
Springer US
Published in
Prevention Science / Issue 2/2006
Print ISSN: 1389-4986
Electronic ISSN: 1573-6695
DOI
https://doi.org/10.1007/s11121-006-0036-3

Other articles of this Issue 2/2006

Prevention Science 2/2006 Go to the issue

OriginalPaper

Can Evidence-Based Prevention Programs be Sustained in Community Practice Settings? The Early Risers’ Advanced-Stage Effectiveness Trial

ReviewPaper

Clustering of Health-Related Behaviors and Their Determinants: Possible Consequences for School Health Interventions

Original Paper

Adolescents’ Reactions to Universal and Indicated Prevention Programs for Depression: Perceived Stigma and Consumer Satisfaction

Original Paper

Mediation of the Effects of the Family Bereavement Program on Mental Health Problems of Bereaved Children and Adolescents

Original Paper

Bridge-It: A System for Predicting Implementation Fidelity for School-Based Tobacco Prevention Programs

Original Article

School Predictors of the Intensity of Implementation of School-Based Prevention Programs: Results from a National Study