Clare R. Evans

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

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 cross-classified multilevel modeling is needed to understand the relative effects of schools and neighborhoods.

Can intersectionality theory enrich population health research

Originating in black feminist scholarship (Collins, 1990, Crenshaw, 1989), intersectionality theory is emerging as a cornerstone of sociological thought. It encourages us to consider the ways in which upstream social determinants such as racism, sexism and classism form interlocking systems of oppression that shape the experiences and life chances of individuals as a consequence of their multi-dimensional social identities. Contextual forces such as sexism or racism do not operate in isolation but interact with each other in the production of health inequalities. Intersectionality is being increasingly adopted in social epidemiology because it dovetails with the domains focus on the underlying power structures that produce inequalities (rather than inequalities simply resulting from the accumulation of independent risk factors). The most common way in which intersectionality is operationalised by health inequalities researchers has been “inter-categorical intersectionality” (McCall, 2005), which calls for considering numerous interactions between dimensions of social identity (and the social forces they are proxies for). In this commentary, we examine the potential contributions of intersectionality to applications in epidemiology and health-related fields. We identify challenges and opportunities for research, and outline future directions.

Contextual Effects of Neighborhoods and Schools on Adolescent and Young Adult Marijuana Use in the United States

Little is known about the unique contribution of schools vs neighborhoods in driving adolescent marijuana use. This study examined the relative contribution of each setting and the influence of school and neighborhood socioeconomic status on use. We performed a series of cross-classified multilevel logistic models predicting past 30-day adolescent (N = 18 329) and young adult (N = 13 908) marijuana use using data from Add Health. Marijuana use differed by age, sex, race/ethnicity, and public assistance in adjusted models. Variance parameters indicated a high degree of clustering by school (σ2 = 0.30) and less pronounced clustering by neighborhood (σ2 = 0.06) in adolescence when accounting for both levels simultaneously in a cross-classified multilevel model. Clustering by school persisted into young adulthood (σ2 = 0.08). Parental receipt of public assistance increased the likelihood of use during adolescence (odds ratio = 1.39; 95% confidence interval: 1.19-1.59), and higher parental education was associated with increased likelihood of use in young adulthood. These findings indicate that both contexts may be promising locations for intervention.

Disentangling overlapping influences of neighborhoods and schools on adolescent body mass index.

To compare the simultaneous influence of schools and neighborhoods on adolescent body mass index (BMI).

The persistent clustering of adult body mass index by school attended in adolescence.

Background It is well known that adolescent body mass index (BMI) shows school-level clustering. We explore whether school-level clustering of BMI persists into adulthood. Methods Multilevel models nesting young adults in schools they attended as adolescents are fit for 3 outcomes: adolescent BMI, self-report adult BMI and measured adult BMI. Sex-stratified and race/ethnicity-stratified (black, Hispanic, white, other) analyses were also conducted. Results School-level clustering (wave 1 intraclass correlation coefficient (ICC)=1.3%) persists over time (wave 4 ICC=2%), and results are comparable across stratified analyses of both sexes and all racial/ethnic groups (except for Hispanics when measured BMIs are used). Controlling for BMI in adolescence partially attenuates this effect. Conclusions School-level clustering of BMI persists into young adulthood. Possible explanations include the salience of school environments in establishing behaviours and trajectories, the selection of adult social networks that resemble adolescent networks and reinforce previous behaviours, and characteristics of school catchment areas associated with BMI.

Are Physical Function and Subjective Well-Being Linked in Older Adults From Low- and Middle-Income Countries? Results From the Study on Global AGEing and Adult Health (SAGE)

BACKGROUND Physical function is positively associated with subjective well-being in older adults from high-income nations. This study tests whether this association is evident in low- and middle-income countries. METHODS Data were drawn from the study on global AGEing and adult health, using nationally representative samples of individuals over 50 years old from China, Ghana, India, Mexico, Russia, and South Africa. Participant interviews measured well-being (quality of life, mood, and happiness) and physical function (grip strength, usual and rapid gait speed). Logistic regressions tested relations between physical function and well-being variables within each country. RESULTS Higher physical function measures exhibited moderate, yet significant, associations with increased odds of highly rated well-being (p < .05). However, higher gait speeds were unexpectedly associated with decreased odds of highly rated well-being (p < .05) in South Africa and Russia. CONCLUSION These results suggest that physical function is generally positively associated with perceived well-being in older individuals from lower income nations.

Does an uneven sample size distribution across settings matter in cross-classified multilevel modeling? Results of a simulation study

Background: Recent advances in multilevel modeling allow for modeling non‐hierarchical levels (e.g., youth in non‐nested schools and neighborhoods) using cross‐classified multilevel models (CCMM). Current practice is to cluster samples from one context (e.g., schools) and utilize the observations however they are distributed from the second context (e.g., neighborhoods). However, it is unknown whether an uneven distribution of sample size across these contexts leads to incorrect estimates of random effects in CCMMs. Methods: Using the school and neighborhood data structure in Add Health, we examined the effect of neighborhood sample size imbalance on the estimation of variance parameters in models predicting BMI. We differentially assigned students from a given school to neighborhoods within that schools catchment area using three scenarios of (im)balance. 1000 random datasets were simulated for each of five combinations of school‐ and neighborhood‐level variance and imbalance scenarios, for a total of 15,000 simulated data sets. For each simulation, we calculated 95% CIs for the variance parameters to determine whether the true simulated variance fell within the interval. Results: Across all simulations, the “true” school and neighborhood variance parameters were estimated 93–96% of the time. Only 5% of models failed to capture neighborhood variance; 6% failed to capture school variance. Conclusions: These results suggest that there is no systematic bias in the ability of CCMM to capture the true variance parameters regardless of the distribution of students across neighborhoods. Ongoing efforts to use CCMM are warranted and can proceed without concern for the sample imbalance across contexts. HighlightsRecent advances in multilevel modeling allow modeling non‐hierarchical contexts.Sample size considerations are unknown for these models.A series of simulations were performed to assess small sample impact on random effect estimation.True random effect estimates were captured in 93–96% of simulations.Uneven sample size distribution does not bias random effect estimation.