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Disentangling the relative influence of schools and neighborhoods on adolescents' risk for depressive symptoms

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  • Dunn, E.C.
  • Milliren, C.E.
  • Evans, C.R.
  • Subramanian, S.V.
  • Richmond, T.K.

Abstract

Objectives. Although schools and neighborhoods influence health, little is known about their relative importance, or the influence of one context after the influence of the other has been taken into account. We simultaneously examined the influence of each setting on depression among adolescents. Methods. Analyzing data from wave 1 (1994-1995) of the National Longitudinal Study of Adolescent Health, we used cross-classified multilevel modeling to examine between-level variation and individual-, school-, and neighborhood-level predictors of adolescent depressive symptoms. Also, we compared the results of our cross-classified multilevel models (CCMMs) with those of a multilevel model wherein either school or neighborhood was excluded. Results. In CCMMs, the school-level random effect was significant and more than 3 times the neighborhood-level random effect, even after individual-level characteristics had been taken into account. Individual-level indicators (e.g., race/ethnicity, socioeconomic status) were associated with depressive symptoms, but there was no association with either school- or neighborhood-level fixed effects. The between-level variance in depressive symptoms was driven largely by schools as opposed to neighborhoods. Conclusions. Schools appear to be more salient than neighborhoods in explaining variation in depressive symptoms. Future work incorporating crossclassified multilevel modeling is needed to understand the relative effects of schools and neighborhoods. © 2015, American Public Health Association Inc. All rights reserved.

Suggested Citation

  • Dunn, E.C. & Milliren, C.E. & Evans, C.R. & Subramanian, S.V. & Richmond, T.K., 2015. "Disentangling the relative influence of schools and neighborhoods on adolescents' risk for depressive symptoms," American Journal of Public Health, American Public Health Association, vol. 105(4), pages 732-740.
    • Handle: RePEc:aph:ajpbhl:10.2105/ajph.2014.302374_5
    DOI: 10.2105/AJPH.2014.302374
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    Cited by:

    1. A.-M. Esnard & B. S. Lai & C. Wyczalkowski & N. Malmin & H. J. Shah, 2018. "School vulnerability to disaster: examination of school closure, demographic, and exposure factors in Hurricane Ike’s wind swath," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 90(2), pages 513-535, January.
    2. Simon Grund & Oliver Lüdtke & Alexander Robitzsch, 2023. "Handling Missing Data in Cross-Classified Multilevel Analyses: An Evaluation of Different Multiple Imputation Approaches," Journal of Educational and Behavioral Statistics, , vol. 48(4), pages 454-489, August.
    3. Evans, Clare R. & Onnela, Jukka-Pekka & Williams, David R. & Subramanian, S.V., 2016. "Multiple contexts and adolescent body mass index: Schools, neighborhoods, and social networks," Social Science & Medicine, Elsevier, vol. 162(C), pages 21-31.
    4. Visser, Kirsten & Bolt, Gideon & Finkenauer, Catrin & Jonker, Merel & Weinberg, Dominic & Stevens, Gonneke W.J.M., 2021. "Neighbourhood deprivation effects on young people's mental health and well-being: A systematic review of the literature," Social Science & Medicine, Elsevier, vol. 270(C).

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