Using cross-classified multilevel models to disentangle school and neighborhood effects: An example focusing on smoking behaviors among adolescents in the United States

Dunn, Erin C.; Richmond, Tracy K.; Milliren, Carly E.; Subramanian, S. V.

doi:10.1016/j.healthplace.2014.12.001

Cited by 59 publications

(59 citation statements)

References 27 publications

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“…Therefore, schools may be an important context for reducing the population-level burden of depression by targeting school-level predictors, including school-based interventions. These findings also underscore the need to use CCMM, as these findings would have been missed had we used traditional MLM (34, 52). …”

Section: Discussionmentioning

confidence: 81%

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

et al. 2015

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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.

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Section: Discussionmentioning

confidence: 81%

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

et al. 2015

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“…These models assume a two-level structure where adolescents are only nested in neighbourhoods (ignoring schools). Second, a two-level model was run with schools at level-2 (ignoring neighbourhoods) (Dunn et al 2015). Third, in a cross-classified model, individual adolescents were grouped simultaneously into two non-nested contexts (neighbourhood and school).…”

Section: Methodsmentioning

confidence: 99%

“…Results from neighbourhood studies that only assess neighbourhood variation may be misleading if variation from other contexts, such as school, are ignored (De Clercq et al 2014; Dunn et al 2015). Studies that examined adolescent smoking, using cross-classified multilevel models to account for the influence of non-nested contexts (where individuals are nested in schools and neighbourhoods, but schools are not necessarily nested within neighbourhoods or vice versa), found that neighbourhood effects are overestimated when ignoring school-level variation (De Clercq et al 2014; Dunn et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Studies that examined adolescent smoking, using cross-classified multilevel models to account for the influence of non-nested contexts (where individuals are nested in schools and neighbourhoods, but schools are not necessarily nested within neighbourhoods or vice versa), found that neighbourhood effects are overestimated when ignoring school-level variation (De Clercq et al 2014; Dunn et al 2015). …”

Section: Introductionmentioning

confidence: 99%

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The neighbourhood social environment and alcohol use among urban and rural Scottish adolescents

Martin

Inchley

Marshall

et al. 2018

Int J Public Health

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ObjectivesThis research examined the relationship between neighbourhood social environmental characteristics and drinking outcomes among a sample of urban and rural adolescents.MethodsFrom a sample of 1558 Scottish secondary schoolchildren, surveyed as part of the 2010 Health Behaviour in School-aged Children study, we modelled three drinking outcomes on a variety of neighbourhood conditions, including social cohesion, disorder, alcohol outlet density, deprivation, and urban/rurality. Nested and cross-classified multilevel logistic regressions were specified.ResultsAn urban-to-rural gradient was found with non-urban adolescents exhibiting higher odds of having ever drank. Neighbourhood social cohesion related to having ever drank. Among drinkers, those living in accessible small towns had higher odds of weekly drinking and drunkenness compared to urban areas. Higher odds of drunkenness were also found in remote rural areas. Those residing in the least deprived areas had lower odds of weekly drinking.ConclusionsIn Scotland, inequalities exist in adolescent alcohol use by urban/rurality and neighbourhood social conditions. Findings support regional targeting of public health efforts to address inequalities. Future work is needed to develop and evaluate intervention and prevention approaches for neighbourhoods at risk.Electronic supplementary materialThe online version of this article (10.1007/s00038-018-1181-8) contains supplementary material, which is available to authorized users.

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“…A new fruitful area of LCHD epidemiologic research will focus on better characterizing the role and impact of the places where children and families live, grow, learn, work, and play (Pearce 2014;Dunn et al 2015). With the expanding number of place-based interventions that attempt to improve the organization and functioning of entire community ecosystems, there is a mounting interest in how place as an organizing force influences health development at both the individual and population levels.…”

Section: Epidemiological Researchmentioning

confidence: 99%

Life Course Research Agenda (LCRA), Version 1.0

Halfon¹,

Forrest

Lerner

et al. 2017

Handbook of Life Course Health Development

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Research Network (LCRN) and charged it with creating a new infrastructure for catalyzing progress and enhancing funding to support basic, theoretical, applied, and translational life course health development research of relevance to practice and policy. In addition to undertaking a variety of other activities and projects aimed at advancing the emerging field, the LCRN initiated an inclusive agendasetting process that resulted in the Handbook of Life Course Health Development, including this chapter and the research agenda it contains. For a more detailed description of the development and history of the LCRN, the Handbook, and the research agenda, please see the Preface and Introduction to this volume.

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Using cross-classified multilevel models to disentangle school and neighborhood effects: An example focusing on smoking behaviors among adolescents in the United States

Cited by 59 publications

References 27 publications

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

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

The neighbourhood social environment and alcohol use among urban and rural Scottish adolescents

Life Course Research Agenda (LCRA), Version 1.0

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