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Background: The rate of adolescent overweight and obesity has more than quadrupled over the past few decades, and has become a major public health problem [1]. In 2011, 55% of 12-19 year olds in the United States (U.S.) were overweight or obese [2]. Adolescence is a pivotal time in which many health risk behaviors such as tobacco, alcohol, and drug use are initiated. Such health risk behaviors have been significantly associated with overweight and obesity among adolescents.

Objective: The purpose of this study is to evaluate the relationship between obesity and the health risk behaviors most commonly associated with premature morbidity and mortality among adolescents with a novel micro area estimate approach that uses weighted hierarchical logistic regression to nest individuals in classes, classes in schools, and schools in districts.

Methods: This study is a secondary analysis of a state-wide representative sample of middle school students that participated in the 2010 Tennessee Middle School Youth Risk Behavior Survey (YRBS). Data was collected from 119 (85.6%) of Tennessee’s local education agencies (LEAs), 456 (95.2%) schools, and 64,790 of 78,441 (82.6%) students. The outcome variable was adolescent obesity (≥ 95th BMI percentile). Explanatory variables were divided into four levels [1] district level: use seatbelt/helmet, asked to show ID for tobacco purchase; [2] school level: ever tried smoking, received HIV education in school; [3] class level: average number of days smoked, having ever exercised to lose weight; [4] individual level: having ever been in fight, early onset of substance use, physical activity, and thought about, planed, or attempted suicide. Weighted hierarchical logistic regression analysis was performed to assess the association between risk factors or protective factors and obesity using effect size (ES) and odds ratio (OR) estimates.

Results: The study sample included 64,790 middle school students in the state of Tennessee with a mean age of 12.8 years, of which (49.42%) were females and (50.58%) were males. Nearly one-fourth of the students had a BMI at or above the 95th percentile (22.30%). Weighted hierarchical logistic regression analysis shows that seatbelt and helmet use [ES: -2.161 OR: 0.020, 95% CI: (0.006, 0.070)], and weight misperception [ES: 1.256 OR: 9.720, 95% CI: (9.216, 10.251)], having ever exercised to lose weight [ES: -0.340 OR: 0.540, 95% CI: (0.446, 0.654)], having ever tried smoking [ES: 0.705 OR: 3.581, 95% CI: (2.637, 4.863)] and gender (male vs female) [ES: 0.327 OR: 1.810, 95% CI: (1.740, 1.880)] were strongly associated with adolescent obesity. Results from this study also showed that Black, Hispanic or Latino adolescents were more likely to be obese than Whites, Indian, and Asian adolescent [ES: 0.129 OR: 1.260, 95% CI: (1.200, 1.330)], students with grades of mostly C, D and F were more likely to be obese than those with grades of mostly A and B [ES: 0.189 OR: 1.409, 95% CI: (1.303, 1.523)], and that students having an eating disorder [ES: 0.251 OR: 1.576, 95% CI: (1.508, 1.648)] and/or engagement in sports teams [ES: -0.197 OR: 0.700, 95% CI: (0.674, 0.728)] had small or medium ES association with adolescent obesity.

Conclusion:This study uses small area estimates in weighted hierarchical logistic regression models to describe the prevalence and distribution of health risk behaviors associated with adolescent obesity among middle school student subpopulations in Tennessee. The value of small area estimates has been demonstrated previously in a variety of other contexts, and again here offers important insights for intervention design and resource allocation at different micro-levels within small and large areas (i.e., district, school, and class). This work adds to the growing body of research that supports community-driven school-based lifestyle interventions targeting early-onset chronic disease and, more specifically, enhances the geographic resolution with which adolescent obesity can be addressed in middle school populations across Tennessee.

Publisher Statement

©2016 Zheng et al. This document was originally published in Biometrics & Biostatistics International Journal.

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Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.