, Volume 35, Issue 1, pp 109-120

Socioeconomic Context, Social Support, and Adolescent Mental Health: A Multilevel Investigation

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This study examined whether the impact of contextual-level socioeconomic disadvantage on adolescent mental health is contingent upon individual-level perceptions of social support. Data are from the National Longitudinal Study of Adolescent Health (Add Health), a panel survey of a nationally representative United States sample (analytic N=18,417) of students in 7th through 12th grade. Effects of social support and social context on both internalizing problems (depressive symptoms) and externalizing problems (minor delinquency and violent behavior) are analyzed. Contextual-level socioeconomic disadvantage is positively associated with depressive symptoms, negatively associated with minor delinquency, and not directly associated with violent behavior. High perceived support from family, friends, and other adults offsets poor mental health, but is most protective in areas of low socioeconomic disadvantage. The mental health benefits of perceived social support are dampened in socioeconomically disadvantaged areas, compared to advantaged areas. Results suggest that interventions targeting only individual- or family-level processes within disadvantaged contexts may be inadequate at stemming psychological distress among adolescents.

Richard G. Wight, Assistant Research Sociologist, conducts life course mental health research in the UCLA Department of Community Health Sciences. His work emphasizes the intersection of individual- and contextual-level factors that impact health within dyads, families, and neighborhoods.
Amanda L. Botticello is a doctoral student in the UCLA Department of Community Health Sciences, where her work addresses the reciprocal relationships between depressive symptoms and problem drinking among adolescents.
Carol S. Aneshensel is a Professor of Community Health Sciences at UCLA, where she applies principals of social stratification and life course theory to the analysis of quantitative data to better understand disparities in mental health risks.