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APHA Scientific Session and Event Listing

Mark A. Schuster, MD, PhD¹, Michael Windle, PhD², Jo Anne Grunbaum, EdD³, Marc N. Elliott, PhD¹, Susan Tortolero, PhD⁴, Paula Cuccaro, PhD⁴, M. Janice Gilliland, PhD², and Frank Franklin, MD MPH PhD⁵. (1) RAND, 1776 Main Street, P.O. Box 2138, Santa Monica, CA 90407, (2) Director of the Center for the Advancement of Youth Health, University of Alabama at Birmingham, 912 Building, 1530 3rd Avenue S., Birmingham, AL 35294, (3) Prevention Research Center Program, Centers for Disease Control and Prevention, 4770 Buford Highway, Mailstop K45, Atlanta, GA 30341, (4) Center for Health Promotion and Prevention Research, University of Texas Health Science Center-Houston, 7000 Fannin, 26th floor, Houston, TX 77030, (5) Maternal and Child Health, University of Alabama at Birmingham School of Public Health, 4831 Bridgewater Rd., Birmingham, AL 35243, 205-934-7161, frankln@uab.edu

We provide an overview of a community-based, long-term study of protective and risk factors (e.g., personal, parental, peer, and school influences), health behaviors (e.g., dietary practices, physical activity, substance use, violent activity, sexual behavior), and health outcomes (e.g., obesity, injuries, mental health, STDs) for a fifth-grade cohort to be followed biennially to 20 years old. We employ a two-stage probability sampling procedure (children within schools) to select 5,250 fifth-grade students (2004-2006) from schools in Birmingham, Houston, and Los Angeles to ensure sufficient sample sizes of African-Americans, Hispanics, and Whites (non-Hispanic) to support precise statistical inferences. Computer-assisted interviews are conducted with children and their primary caregivers (usually a parent); anthropometric measures are also being collected. Teachers and other school personnel complete questionnaires, and observational data are collected in schools and neighborhoods. To fully utilize the multilevel, multimethod structure of the data, statistical models include latent-growth mixture modeling, multilevel modeling, time-series analysis, survival analysis, latent transition analysis, and structural equation modeling will be used for data analysis. Analyses will focus on both the co-occurrence and predictors of growth trajectories for different health behaviors across time. An initial cross-sectional study (spring 2003) aided the development of study measures and the neighborhood observation scales, as well as the development of the data weighting, imputation, and transformation strategies. Healthy Passages could provide an empirical basis for effective social and educational policies and intervention programs that foster positive health and well-being during both adolescence and adulthood.

Learning Objectives:

• 1. Describe the structure and content of a community-based longitudinal study of adolescent protective and risk factors, health behaviors, and health outcomes.
• 2. Identify methodological improvements revealed by the Phase I cross-sectional project.

Presenting author's disclosure statement:

Any relevant financial relationships? No