Prevalence of Depression Symptoms among Adolescents Ages 12-17 years in California and the Role of Overweight as a Risk Factor.

Griselda Alvarez, MD (alvar639@ucla.edu)
Department of Pediatrics, Charles Drew University and College of Medicine UCLA, David Geffen School of Medicine
May, 2008
 

Abstract

Background: Literature documentation of the health consequences of obesity among adolescents continues to grow including the psychosocial consequences of obesity on this population.
Objective: Specific aim of this study was to identify prevalence of depression in adolescents age 12 to 17 and identify the role of overweight as a risk factor for depression.
Methods: Secondary data analysis of the adolescent version of the 2005 California Health Interview Survey. Symptoms of depression were measured with a reduced version of the Center for Epidemiologic Studies Depression Scale. Weight status was determined using the Centers for Disease Control definitions and those recommended by the American Academy of Pediatrics. Results: The sample was nearly half male (50.6%) and half female (49.4%). The majority of the adolescents in the sample were White (47.2%) followed by Latino (33.5%). Just about 10% of the adolescents reported depression symptoms >10. Based on BMI, 16.5% of the sample was at-risk of being overweight, and 14.7% were overweight. However, 24.4% of sample thought they were ‘slightly overweight or very overweight’. We did not find any statistically significant association between weight status and symptoms of depression, but at the bivariate level we did find a statistically significant association between perception of one’s weight p < 0.001 and depression. We also found that gender (OR 3.10; CI 2.07-4.51), perceived health (OR 2.25; CI 1.53-3.31), smoking (OR 1.8; CI 1.30-2.69), and alcohol use (OR 2.06; CI 1.44-2.95) were independently associated with depression symptoms.
Conclusion: Even thought we were unable to prove the proposed association, but our findings still is of noteworthy given that the association between these variables are still less clear in the literature. Future studies which attempt to study the relationship between these two variables may benefit from longitudinal design, inclusion of multi-item risk and protective predictors; inclusion of social-context related variables; perceived weight, and family history of obesity.



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