Title of article :
Stratified Multilevel Logistic Regression Modeling for Risk Factors of Adolescent Obesity in Tennessee
Author/Authors :
Zheng, Shimin Department of Biostatistics and Epidemiology- East Tennessee State University- Johnson City, United States , Strasser, Sheryl School of Public Health- Department of Health Promotion & Behavior- Georgia State University, Atlanta, United States , Holt, Nicole Department of Biostatistics and Epidemiology- East Tennessee State University- Johnson City, United States , Quinn, Megan Department of Biostatistics and Epidemiology- East Tennessee State University- Johnson City, United States , Liu, Ying Department of Biostatistics and Epidemiology- East Tennessee State University- Johnson City, United States , Morrell, Casey Department of Biostatistics and Epidemiology- East Tennessee State University- Johnson City, United States
Abstract :
Background: US adolescent obesity rates have quadrupled over the past 3 decades. Research examining complex factors associated
with obesity is limited.
Objectives: The purpose of this study was to utilize a representative sample of students (grades 6 - 8) in Tennessee to determine the
co-occurrence of risk behaviors with adolescent obesity prevalence and to analyze variations by strata.
Patients and Methods: The 2010 youth risk behavior survey dataset was used to examine associations of obesity with variables
related to sample demographics, risk and protective behaviors, and region. Hierarchical logistic regression analyses stratified by
demographics and region were conducted to evaluate variation in obesity risk occurring on three hierarchical levels: class, school
and district.
Results: The sample consisted of 60715 subjects. The overall obesity rate was 22%. High prevalence of obesity existed in males, nonwhite
race, those ever smoked and was positively correlated with age. Across three state regions, race, gender, and specific behaviors
(smoking, weight misperception, disordered eating, +3 hours TV viewing, and no sports team participation) persisted as significant
predictors of adolescent obesity, although variations by region and demographics were observed. Multilevel analyses indicate that
< 1%, 0- 1.97% and 4.03 - 13.06% of the variation in obesity was associated with district, school and class differences, respectively, when
stratifying the sample by demographic characteristics or region.
Conclusions: Uniform school-based prevention efforts targeting adolescent obesity risk may have limited impact if they fail to
respond to geographical and demographic nuances that hierarchal modeling can detect. Study results reveal that stratified hierarchical
analytic approaches to examine adolescent obesity risk have tremendous potential to elucidate significant prevention
insights.
Keywords :
Adolescents , Obesity , Health Risk Behaviors , Hierarchical , Logistic Models , Regression Analysis
Journal title :
Astroparticle Physics