Disentangling the Relative Influence of Schools and Neighborhoods on Adolescents’ Risk for Depressive Symptoms

Abstract

Objectives Although schools and neighborhoods influence health outcomes, little is known about their relative importance, or the influence of one context after accounting for the other. Our objective was to simultaneously examine the influence of each setting on levels of depressive symptoms among adolescents. Methods Analyzing cross-sectional data from the National Longitudinal Study of Adolescent Health (Add Health), we used cross-classified multilevel modeling (CCMM) to examine between-level variation (random effects) and individual-, school-, and neighborhood-level predictors of adolescent depressive symptoms (fixed effects). We also compared CCMM results to results from a multilevel model (MLM) where either school or neighborhood was ignored. Results In CCMMs examining each context simultaneously, the school-level random effect was statistically significant and more than three times the neighborhood-level random effect, even after accounting for individual-level characteristics. While individual-level indicators (e.g., race/ethnicity, gender, socioeconomic status) were significantly associated with depressive symptoms, neither school- nor neighborhood-level fixed effects were. CCMM results showed that the between-level variance in depressive symptoms was driven largely by the school (ICC=3.0%) and not by the neighborhood (ICC=0.8%), as suggested by the school- (ICC=3.6%) and neighborhood-only (ICC=3.2%) MLM. Conclusions Schools appear more salient than neighborhoods in explaining variation in depressive symptoms. However, the school-level demographic variables examined were not determinants of youth depression. Future work using CCMM is needed to better understand the relative effect of schools and neighborhoods on youth mental health. These findings also underscore the need for CCMM over MLM when youth are nested in more than one context.

The Impact of Positive School Experiences and School Ses on Depressive Symptoms in Chinese Children: A Multilevel Investigation

“…However, in another study examining longitudinal changes in depressive symptoms, school-level aggregated family poverty was not found to have a significant effect on the trajectories of depressive symptoms from adolescence to young adulthood (Wickrama & Vazsonyi, 2011). Similarly, Dunn, Milliren, Evans, Subramanian, and Richmond (2015) found that school-level SES, calculated as the mean of standardized measures of school-level poverty, school-level parental occupation, and school-level percentage of Whites, was not significantly related to youth depressive symptoms. These mixed findings might be attributed to different operationalizations of school SES.…”

Risk factors for disordered weight control behaviors among Korean adolescents: Multilevel analysis of the Korea Youth Risk Behavior Survey

“…In order to understand the extent to which variables at each level contribute to between‐level variations in DWCB, we started from four‐level random intercept null models, and added each set of covariates in the fixed part in sequence, with Model 1 (added age, BMI status, and pubertal maturity), Model 2 (added perceived household economic status, parental education, living structure), Model 3 (added school type, sex‐composition, and percentage of students who received school nutrition education), and Model 4 (added urbanicity) added (Austin et al, ). All multilevel models were performed based on MLwiN 2.28 (Center for Multilevel Modeling, UK) by employing Markov Chain Monte Carlo methods (Dunn et al, ). All fixed and random parts parameters were evaluated at the significance level of 0.05 (two‐sided), and the final model was determined based on the deviance information criterion (DIC) statistic (Dunn et al, ).…”

“…All multilevel models were performed based on MLwiN 2.28 (Center for Multilevel Modeling, UK) by employing Markov Chain Monte Carlo methods (Dunn et al, ). All fixed and random parts parameters were evaluated at the significance level of 0.05 (two‐sided), and the final model was determined based on the deviance information criterion (DIC) statistic (Dunn et al, ).…”

“…Understanding risk and protective factors is important to identify individuals at greater risk and to develop effective prevention strategies. The significance of social contextual influences such as schools and neighborhoods on diverse health outcomes among adolescents have been well‐established (Dunn, Milliren, Evans, Subramanian, & Richmond, ). The Social Ecological Model developed by Bronfenbrenner (1979) postulates that an individual's behavior is a function of factors at multiple levels, including intra‐individual at the inner most circle, interpersonal, family, school, neighborhood, and larger socio‐political systems at the outer‐most circle, with emphasis on interacting influences across levels (Bronfenbrenner, ).…”

Exploring the relation between school pushout and future outlook for black youth.

“…In an analysis of cross-sectional data from the National Longitudinal Study of Adolescent Health, researchers reported the significance of school impact (i.e., adolescents' relationship to school) regarding between-level variance of depressive symptoms in adolescents (N = 16,172;Dunn, Milliren, Evans, Subramanian, & Richmond, 2015). The same finding was not significant for the impact of neighborhoods.…”