Substance use and comorbid health-related behaviors among youth and young adults have a high cost to society. Although considerable progress has been made in developing effective intervention programs for prevention, their effectiveness depends to a large extent on a clear understanding of multiple risks as they coexist. Prevention scientists everywhere have access to rich sources of data on early and concurrent risk factors for developmental outcomes. Many of these data sets have been previously analyzed with the objective of examining risk factors, and in fact much progress has been made in the identification of developmental models that describe how individual and multiple risks contribute to disorders such as substance abuse. However, it is now possible to move beyond the traditional approaches to modeling risk that have been used previously in order to examine the complex interplay among risk factors at multiple levels. Intervention scientists stand to gain a powerful new understanding of risk by moving from a traditional 'risk factors' approach to a novel 'risk profiles' approach. The proposed research employs a relatively new and underutilized person-centered statistical technique, latent class analysis with covariates, to (a) identify nuanced multilevel risk profiles in existing empirical data and (b) establish the ability of the risk profiles to predict future problem behavior. The data sets to be analyzed are from two community-based and two national longitudinal studies and contain rich data on a variety of risk factors as well as substance use and comorbid behaviors. Gender and ethnic group differences will be explored in the prevalence of risk profiles and the link between risk profile membership and later health-related outcomes. The proposed research will fill an important gap in current knowledge about the interplay of multiple risks at multiple levels, thereby helping intervention scientists to develop more effective programs and to target those programs more effectively. A series of articles will be submitted to peer-review journals, and a project Web site will be added to the Methodology Center Web site at Penn State, where free SAS software for latent class modeling currently is available for download. The project site will provide information for intervention scientists on how to use latent class analysis to uncover risk profiles in their own data. Substance use and comorbid health-related behaviors among youth and young adults have a high cost to society. Although considerable progress has been made in developing effective intervention programs for prevention, their effectiveness depends to a large extent on a clear understanding of multiple risks as they coexist. The proposed research will fill an important gap in current knowledge about the interplay of multiple risks at multiple levels, thereby helping intervention scientists to develop more effective programs and to target those programs more effectively. ? ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute on Drug Abuse (NIDA)
Type
Small Research Grants (R03)
Project #
1R03DA023032-01
Application #
7234649
Study Section
Behavioral Genetics and Epidemiology Study Section (BGES)
Program Officer
Lopez, Marsha
Project Start
2007-09-30
Project End
2009-08-31
Budget Start
2007-09-30
Budget End
2008-08-31
Support Year
1
Fiscal Year
2007
Total Cost
$63,500
Indirect Cost
Name
Pennsylvania State University
Department
Miscellaneous
Type
Schools of Allied Health Profes
DUNS #
003403953
City
University Park
State
PA
Country
United States
Zip Code
16802
Lanza, Stephanie T; Rhoades, Brittany L (2013) Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment. Prev Sci 14:157-68
Lanza, Stephanie T; Rhoades, Brittany L; Greenberg, Mark T et al. (2011) Modeling multiple risks during infancy to predict quality of the caregiving environment: contributions of a person-centered approach. Infant Behav Dev 34:390-406
Rhoades, Brittany L; Greenberg, Mark T; Lanza, Stephanie T et al. (2011) Demographic and familial predictors of early executive function development: contribution of a person-centered perspective. J Exp Child Psychol 108:638-62
Lanza, Stephanie T; Patrick, Megan E; Maggs, Jennifer L (2010) Latent Transition Analysis: Benefits of a Latent Variable Approach to Modeling Transitions in Substance Use. J Drug Issues 40:93-120
Lanza, Stephanie T; Rhoades, Brittany L; Nix, Robert L et al. (2010) Modeling the interplay of multilevel risk factors for future academic and behavior problems: a person-centered approach. Dev Psychopathol 22:313-35
Maldonado-Molina, Mildred M; Lanza, Stephanie T (2010) A FRAMEWORK TO EXAMINE GATEWAY RELATIONS IN DRUG USE: A N APPLICATION OF LATENT TRANSITION ANALYSIS. J Drug Issues 40:901-924
Bray, Bethany C; Lanza, Stephanie T; Collins, Linda M (2010) Modeling Relations Among Discrete Developmental Processes: A General Approach to Associative Latent Transition Analysis. Struct Equ Modeling 17:541-569
Lanza, Stephanie T; Savage, Jennifer S; Birch, Leann L (2010) Identification and prediction of latent classes of weight-loss strategies among women. Obesity (Silver Spring) 18:833-40
BeLue, Rhonda; Lanza, Stephanie T; Figaro, M Kathleen (2009) Lifestyle therapy changes and hypercholesterolemia: identifying risk groups in a community sample of Blacks and Whites. Ethn Dis 19:142-7