79097664717J Epidemiol Community HealthJ Epidemiol Community HealthJournal of epidemiology and community health0143-005X1470-273827103664547315010.1136/jech-2015-206613NIHMS865196ArticleIncome inequality within urban settings and depressive symptoms among adolescentsPabayoRomanPhD12DunnErin C.ScD, MPH345GilmanStephen E.ScD26KawachiIchiroMD, PhD2MolnarBeth E.ScD7University of Nevada, Reno, School of Community Health SciencesHarvard School of Public Health, Department of Social and Behavioral SciencesPsychiatric and Neurodevelopmental Genetics Unit, Massachusetts General HospitalDepartment of Psychiatry, Harvard Medical SchoolStanley Center for Psychiatric Research, The Broad Institute of Harvard and MITHealth Behavior Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentBouvé College of Health Sciences, Northeastern UniversityAddress correspondence to: Roman Pabayo, School of Community Health Sciences, University of Nevada, Reno. 1664 North Virginia Street, Reno Nevada, 89557, rpabayo@hsph.harvard.edu. Phone: +1-775-682-7048134201721420161020160110201770109971003Background
Although recent evidence has shown that area-level income inequality is related to increased risk for depression among adults, few studies have tested this association among adolescents.
Methods
We analyzed cross-sectional data from a sample of 1,878 adolescents living in 38 neighborhoods participating in the 2008 Boston Youth Survey. Using multilevel linear regression modeling, we: (1) estimated the association between neighborhood income inequality and depressive symptoms.; (2) tested for cross-level interactions between sex and neighborhood income inequality; and (3) examined neighborhood social cohesion as a mediator of the relationship between income inequality and depressive symptoms.
Results
The association between neighborhood income inequality and depressive symptoms varied significantly by sex, with girls in higher income inequality neighborhood reporting higher depressive symptom scores, but not boys. Among girls, a unit increase in Gini Z-score was associated with more depression symptoms (β=0.38, 95% CI=0.28, 0.47, p=0.01) adjusting for nativity, neighborhood income, social cohesion, crime, and social disorder. There was no evidence that the association between income inequality and depressive symptoms was due to neighborhood-level differences in social cohesion.
Conclusion
The distribution of incomes within an urban area adversely affects adolescent girls’ mental health; future work is needed to understand why, as well as to examine in greater depth the potential consequences of inequality for males, which may have been difficult to detect here.
Income inequalitydepressionmultilevel analysissocioeconomic factorsneighborhood characteristics1. Introduction
Depression among teens is a major public health concern in the United States. Population-based studies have found that 11.7% of adolescents meet diagnostic criteria for a depressive disorder at some point in their lives,1 with girls twice as likely than boys to experience depression. School-based studies have shown that 28.5% of adolescents report experiencing symptoms of depression, such as feeling sad or hopeless in the previous 12 months.4 Also, in comparison to white students, Hispanic students are more likely to report depressive symptoms, while Black students were less likely.5 Teens from lower socioeconomic status (SES) backgrounds are more likely to experience depression.6 For example, the Population Attributable Risk (PAR) for income and education on depression was 26% and 40%, respectively in one study.7 The population health impacts of depression include suicide, which is the second leading cause of death among adolescents aged 12–17 years in 2010, shortened educational careers, disruption of social networks,2 and increased risk in participation in adverse behaviors such as smoking, alcohol consumption, and drug use.2
Neighborhood characteristics - or the social and physical characteristics of the places where teens live and often go to school – have been associated with depression among children and adolescents.38 In particular, neighborhoods with poor quality housing, few resources, and unsafe conditions have been linked to the risk of depression.9 Neighborhood disorder, low social cohesion, and lack of safety have also been identified as predictors of adolescent depression.10–12
An important feature of the neighborhood that has not been studied extensively, but may be relevant for understanding risk for depression is the distribution of incomes in the neighborhood – or the level of neighborhood income equality/inequality.13 Income inequality within society can have negative consequences on health because when the gap widens between the incomes of the poor and the rest of society, feelings of insecurity, shame and misery intensify among those who are left behind.14 The resulting feelings of shame and failure are likely to be especially pronounced in American society where the majority still believe that it is possible for everyone to achieve the “American Dream,” even though evidence indicates that social mobility in the US is more constrained than in most European countries.15 Income inequality also erodes social cohesion,16 which can accentuate the sense of social exclusion and isolation for those left behind. Since a decline in social cohesion can be reflected as an erosion of trust in other members in the community,16 a potential consequence could be an increase in depressive symptoms.
To date, 11 studies have investigated the role of income inequality on depression among adults, with all but two1718 indicating that higher income inequality associates with more depressive symptoms.19–27 Two studies were longitudinal1927 while the rest were either ecological or cross-sectional.20–26 Overall, there is strong evidence that income inequality exerts a contextual influence on depressive symptoms and major depressive disorder.
However, to our knowledge, no prior studies have examined the association between income inequality and depressive symptoms among adolescents, a sensitive developmental period for the emergence of depression.28 Of the few adult studies that have analyzed individual-level data, only one examined the relationship between neighborhood level income inequality and depression;24 instead the vast majority of published studies have focused on US State or county level income inequality. From the point of view of generating stressful social comparisons (and hence feelings of shame and exclusion), income inequality measured at the neighborhood level may be more relevant for depression risk than income inequality at a larger scale, such as by states. Thus, further research is needed to determine if an association exists between neighborhood-level income inequality and depression among youth.
The current study addresses these gaps by investigating income inequality measured at the neighborhood level and in a sample of adolescents. We hypothesized that youth residing in neighborhoods with higher income inequality would be at greater risk for depression compared with those living in neighborhoods with lower income inequality. Furthermore, we hypothesized that neighborhood income inequality would have a stronger association with depression among girls than boys, based on previous research showing differential sex effects of area-level socioeconomic characteristics on health.2930. Lastly, we sought to test whether the association between income inequality and depressive symptoms can be explained by neighborhood-level variation in social cohesion.
2. METHODS
Data came from the 2008 Boston Youth Survey (BYS), a biennial cross-sectional survey of high school students in grades 9–12 in Boston Public Schools (n=31).3132 All 32 public high schools in Boston serving traditional daytime students (i.e. not those schools serving adults in evening programs) were invited and 22 agreed to participate (69%). The final sample of schools was representative of all schools in the Boston area in terms of race/ethnicity of the students, school drop-out rates and other socio-demographic variables.33
A unique list of classrooms was obtained from each school, and classrooms stratified by grade were randomly selected for participation in the BYS until 100–120 students were identified per school. All students in randomly selected classrooms were invited to participate.33 We used passive consent, and students were free to decline to participate at any time before or during the survey administration. The response rate was 69%, which yielded a sample size of 1878 youth. Since complete data were available for 1246/1878=66.3% students, we used multiple imputation to address missing socio-demographic and behavioral data.34 Students who did not provide the location of their residence were excluded from the current analysis. As a result, complete socio-demographic and individual-level social cohesion data, within the imputed data set were available for 1,614/1878 (85.9.%) students. We created five multiply imputed datasets. The covariates used in the imputation models included sex, age, race, nativity, depressive symptoms, social cohesion, and neighborhood factors. Using SAS version 9.4, we then used multilevel regression analyses to fit the model of interest to each of the imputed data sets. Next, we averaged the estimates to obtain estimated associations.34 Those with missing data were more likely to be male, black and older in age and to have immigrated to the USA within the last 4 years.
Data collection
The BYS team of investigators developed a survey questionnaire using established scales that demonstrated good reliability and validity to measure behaviors and experiences in the neighborhood. During the spring of 2008, a paper-and-pencil survey was administered in classrooms by trained staff. The Office of Human Research Administration at the Harvard School of Public Health approved all data collection procedures for the BYS.
Study variablesArea-level covariates
Students were asked the nearest cross-street of their residence for geocoding to U.S. Census tracts, resulting in useable residential information for geocoding for 85.9% of the total sample (n=1,614). Key informants from Boston neighborhoods helped the research team aggregate the 157 Boston Census tracts (each with a population of approximately 4,000) into 38 socially meaningful neighborhood clusters of tracts.35 The details of this process are described elsewhere.33 Neighborhood-level characteristics that might act as confounders were included in this investigation.
Census Tract (CT) level income inequality was the main exposure of interest, which was measured using the Gini coefficient. The Gini coefficient ranges from 0 (perfect equality, every household in the CT has the exact same income) to 1.0 (perfect inequality, where households in the CT earn a wide range of incomes). The calculation of the Gini coefficient has been provided elsewhere.36 The Gini coefficient is based on the Lorenz curve, a cumulative frequency curve that compares the distribution of a specific variable with the uniform distribution that represents equality.36 In this investigation, the Gini coefficient was calculated for each census tract by the Boston Indicators Project (http://www.bostonindicators.org/). We standardized the Gini coefficient using the z-transformation.
Economic deprivation, which is a socioeconomic composite score, was created for each of the 38 neighborhoods using principal components analysis. U.S. Census indicators included for this score included proportion of residents living below the poverty level, proportion of households receiving public assistance, and proportion of families with a female head of household (Cronbach α = .84). A higher score was indicative of greater economic deprivation. Tertiles of the neighborhood economic deprivation were used to categorize economic deprivation into low, moderate, and high values.
For each of the 38 neighborhoods, disorder scores were determined using data collected from the biennial, random-digit dial telephone survey, the Boston Neighborhood Survey (BNS) to assess neighborhood disorder, which has been described elsewhere.3337 Adult residents (≥18 years) were randomly selected from a list-assisted sampling frame, stratified proportional to population size of the 16 large neighborhoods defined by the Boston Redevelopment Authority, resulting in a sample size of 1,710 adults in 2008. Information from the BNS was used to enrich the BYS data with contextual information about neighborhood-level conditions and social processes perceived by adult residents.3337 Neighborhood disorder is comprised of both social (ie., presence or absence of drinking alcohol in public) and physical disorders (i.e., abandoned cars). A combined score was created using these two indicators, with higher scores indicating greater neighborhood disorder. Tertiles were used to categorize neighborhoods into low, moderate, and high neighborhood disorder.
We also used the BNS to measure neighborhood social cohesion at the neighborhood level by adapting a previously used questionnaire with ascertained reliability and validity in adults.38 Respondents were asked if they strongly agreed, agreed, disagreed, or strongly disagreed with five statements. For example, People in my neighborhood can be trusted; People in my neighborhood are willing to help their neighbors; and I live in a neighborhood where people know and like each other. A combined score was created and a greater score indicated higher social cohesion.
We used data from the Boston Police Department to measure neighborhood danger in each of the 38 neighborhoods. Counts of criminal homicide, robbery, aggravated assault, burglary, larceny theft, vehicle theft, and arson were matched to the U.S. Census tracts. The higher the score, the greater the danger was within the neighborhood. Tertiles were used to categorize danger within the neighborhood into low, moderate, and high.
Individual level covariates in the study were students’ age, nativity, (U.S. born, foreign born arrived ≤4 years, and foreign born arrived >4 years), and race or ethnicity (white, black, Asian, Hispanic, and other).
Assessment of depressive symptoms
Depressive symptoms were measured using a brief adapted version of the Modified Depression Scale (MDS), which has been described elsewhere.39 Students were asked to report the frequency of five symptoms in the past month: (a) very sad; (b) grouchy or irritable, or in a bad mood; (c) feel hopeless about the future; (d) sleep a lot more or less than usual; and (e) have difficulty concentrating on your school work. Response options included: (1) never, (2) rarely, (3) sometimes, (4) often, and (5) always. Total scores were calculated by summing items among participants for all five items (range: 5–25). Scores were standardized using the z-transformation to facilitate interpretation. A prior study using BYS data found the MDS has good psychometrics (Cronbach’s α=0.79).39 The adapted MDS also significantly differentiated youth who had engaged in risk behavior or who had been victimized from those who had not (OR’s ranged from 1.07–1.31, p<0.001).39
On top of social cohesion measured at the neighborhood level, we measured social cohesion at the individual level. We asked students to assess their perception of neighborhood social cohesion using five statements. These statements included: I live in a neighborhood where people know and like each other; People in my neighborhood are willing to help their neighbors; People in my neighborhood generally get along with each other; People in my neighborhood generally share the same beliefs about what is right and wrong; People in my neighborhood can be trusted. Response options included (1) strongly disagree; (2) disagree; (3) agree; and (4) strongly agree. The average social cohesion score was 12.0 (standard deviation [SD]=2.9) and the range was 5–20. The items showed high internal consistency (Cronbach α=0.80). Tertile cutoffs were used to categorize social cohesion into low, moderate, and high values.
Statistical Analysis
Since students were nested within neighborhoods, we used multilevel modeling to investigate the relationship between neighborhood income inequality and depression, while controlling for both individual and neighborhood level characteristics. Since students were nested within CTs, which were nested within neighborhoods, a three-level multi-level model was initially considered-i.e., linear mixed models with a random effect specified for each CT and each neighborhood.40 However, because a negligible amount of variation in depressive symptoms was explained at the CT-level (data not shown), we treated income inequality as an individual-level exposure resulting in a two-level model (with neighborhood as the level-two unit).
We fitted the following sequence of models to investigate the association between neighborhood income inequality and depression. First, we fitted an intercept-only model, which allowed us to calculate the Intraclass Correlation Coefficient (ICC), quantifying the proportion of variance in depressive symptoms explained at the neighborhood and individual levels. Second, we fitted models adding individual and neighborhood characteristics (model 1). Third, we added the sex*income inequality interaction term to determine if the association between income inequality and depression differed between boys and girls (model 2). Finally, we added students’ perceptions of neighborhood social cohesion to determine if perceptions mediated the relationship between neighborhood income inequality and depression.41 Mediation was evaluated using the Baron and Kenny method by testing and comparing results from three different models, among males and females separately: (1) income inequality and depression, (2) income inequality and social cohesion; and (3) social cohesion and depression.
3. RESULTS
Characteristics of the 1,641 students attending public secondary schools in the Boston area are found in Table 1. Overall, the sample had more females (54.3%), almost half were black (41.6%), and a majority was born in the United States (69.5%). The average depressive symptom score was 13.6 (SD=4.3; range 5–25). The average depressive symptom score was 14.4 (SD=4.2) among females and 12.8 (SD=4.4) among males. The average Gini score across the census tracts was 0.45 (SD=0.06; range=0.33–0.65). The Gini score of Boston is similar to the overall value for the US, which has a score of 0.47.42
A summary of the neighborhood characteristics can also be found in Table 1. The average economic deprivation score was 0.02 (SD=1.01; range=−1.79 to 2.42). The average proportion of the neighborhood that was black was 37.8% (SD=28.1; rage=1.8 to 92.5).
The ICC derived from the null model was 0.05, which indicates that 5% of the variance in depressive symptoms was explained by neighborhood level characteristics. The results of models for the relationship between neighborhood income inequality and depression are presented in table 2. In the adjusted model (table 2, model 1), there was no significant relationship between income inequality and depression (β=0.03, 95% CI=−0.01,0.08). When a sex-income inequality interaction was included, females had significantly higher depression scores (β=0.39, 95% CI=0.30,0.48) and a Sex x Gini coefficient interaction term was significant (β=0.11, 95% CI=0.02,0.20, p=0.01) (table 2, model 2). Therefore, girls living in higher Gini-coefficient areas had significantly higher depressive scores than those living in more equal areas. An average depression score for girls from the most equal neighborhoods is 0.39 while the average score for girls in the most equal neighborhoods is 0.50.
Unexpectedly, we found a significant inverse relationship between neighborhood economic deprivation and depressive symptoms. For example, in comparison to those who lived in the least economically deprived neighborhoods, those living in moderate (β= −0.13, 95% CI=−0.27,0.01) and high economically deprived neighborhoods (β= −0.18, 95% CI=−0.33, −0.03) had lower depressive symptom scores.
When individual-level social cohesion was tested as a possible mediator between income inequality and depression, the risk estimates did not change (table 2, model 3). Therefore, there was no evidence for the involvement of social cohesion in the association between neighborhood income inequality and depression. This finding was also confirmed with the Baron and Kenny method to test for mediation. Income inequality was associated with a decreased likelihood of reporting high social cohesion among boys only and was not significantly associated with depressive symptoms. Individual level perceptions of neighborhood social cohesion were associated with depression among both boys and girls. Results of testing possible mediation relationships are shown in Table 3. The association between social cohesion and depressive symptoms was mainly due to social cohesion at the individual level since the association between area-level social cohesion and depressive symptoms was somewhat attenuated when individual level social cohesion was added to the model.
4. DISCUSSION
We investigated the association between neighborhood-level income inequality and depressive symptoms among Boston-area adolescents. Our results suggest that neighborhood level income inequality is associated with higher levels of depressive symptoms among adolescent girls but not boys. These findings are consistent with previous research showing US State-level income inequality predicts an increased odds of experiencing a depressive episode among women and not men.19
Income inequality, or the unequal distribution of income in a society, has been theorized to be associated with depression. Several mechanisms have been proposed, which may be adapted to the residential neighborhood setting. Neighborhoods with high levels of income inequality might lead to stressful comparisons and a growing sense of relative deprivation.43 These invidious social comparisons might intensify feelings of competition, making one’s status more important compared with societies with more equitable income distributions.14 Feelings of anger and frustration might amplify because of lack of access to resources and status.14 Adolescents might be aware of their status,44 which might foster a social environment that leads to social isolation and therefore depression.45
Another mechanism is that income inequality erodes social cohesion, which could affect all members of a neighborhood, including individuals from both high and low socioeconomic backgrounds. The erosion of social cohesion and trust in turn lead to feelings of fear and insecurity, which are associated with depression.43 However, our results suggest that student self-reported social cohesion does not explain the association between income inequality and depression.
These results indicate that girls and boys react differently to their residential environment. While girls living in more unequal areas had higher levels of depressive symptoms than their male counterparts, findings from a recently published article indicate that boys living in high inequality neighborhoods were more likely to be involved in acts of violence as well as to be victims of aggression, in comparison to girls in similar residential neighborhoods.46 Thus boys and girls may react or cope differently with exposure to a highly unequal environment. For example, when income inequality is eroded, stress levels might increase. Females have been shown to cope with stress differently in comparison to males. Generally speaking, boys react to stress through externalizing behaviors (acting out, or engaging in aggression), while girls are more likely to internalize their problems (i.e. becoming withdrawn and depressed).47
These findings should be interpreted in light of the following limitations. We used cross-sectional data to investigate the relationship between income inequality and depression and therefore temporality could not be established. Also, since individual-level socioeconomic covariates, such as household income, parental education or occupation were not collected, residual confounding might be an issue. Also, the Baron and Kenny method of mediation assessment might lead to biased results because of unmeasured confounding that may exist between mediator and outcome.4849 Furthermore, characteristics of the classroom or school settings that were not available to this study have been shown to be important in shaping the health of adolescents50, since these contexts are more proximal to the student and therefore more likely to be more influential on the health of the individual. Taking into account school, classroom and neighborhood factors in a cross-classified analysis would be helpful in disentangling the relationship between contextual income inequality and depression. Finally, generalizability is a limitation; while we might be able to generalize the results to urban centers that have similar distributions of income and population sizes to Boston, results may not be applicable to other populations.
In conclusion, findings from this study suggest that income inequality is associated with depressive symptoms among adolescent girls living in an urban setting. Income inequality is potentially harmful to the development and mental health of adolescents living within a residential neighborhood with high income inequality. Further investigation should include longitudinal analysis to determine whether neighborhood income inequality is a determinant of depression among adolescents and to identify potential mediators that can explain this association and for the sex differences observed.
Funding Statement: The Boston Youth Survey (BYS) was conducted in collaboration with the Boston Public Health Commission (Barbara Ferrer, Executive Director), Boston’s Office of Human Services (Larry Mayes, Chief), Boston Public Schools (Carol Johnson, Superintendent), and the Office of The Honorable Mayor Thomas M. Menino. The survey would not have been possible without the participation of the faculty, staff, administrators, and students of Boston Public Schools. This work was supported by a grant from the Center for Disease Control and Prevention (CDC) National Center for Injury Prevention and Control (NCIPC) (U49CE00740) to the Harvard Youth Violence Prevention Center (David Hemenway, Principal Investigator). Roman Pabayo is a Canadian Institutes of Health Research postdoctoral fellowship recipient. Research reported in this publication was also supported, in part, by the National Institute Of Mental Health of the National Institutes of Health under Award Number K01MH102403 (Dunn) and by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Conflict of Interest: No financial disclosures were reported by the authors of this paper.
Licence for Publication: “The Corresponding Author has the right to grant on behalf of all authors and does grant on behalf of all authors, an exclusive licence (or non exclusive for government employees) on a worldwide basis to the BMJ Publishing Group Ltd and its Licensees to permit this article (if accepted) to be published in JECH editions and any other BMJPGL products to exploit all subsidiary rights, as set out in our licence (http://group.bmj.com/products/journals/instructions-for-authors/licence-forms/).”
Competing Interest: None to declare
Contribution Statement: R. Pabayo developed the research question and conceptual model, conducted the main analyses, and was lead author. E.C. Dunn evaluated the psychometric properties of the modified depression scale and helped write the manuscript. S.E. Gilman contributed to the development of the conceptual framework, interpretation of the results, and critical revision of the manuscript. I. Kawachi provided theoretical support and assisted with the writing of the paper. B.E. Molnar helped develop the research question, provided assistance with the writing of the manuscript, and provided guidance throughout the whole manuscript process.
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Sociodemographic characteristics of adolescents (n=1,614) and neighborhoods (n=38) participating in the Boston Youth Study
n
proportion (%)
Sex
Male
738
45.7
Female
876
54.3
Race
Black
672
41.6
White
150
9.3
Asian/South Asian
145
9.0
Hispanic
553
33.0
Other
114
7.1
Nativity
New Immigrant
161
10.0
Settled immigrant
331
20.5
Born in USA
1122
69.5
Age, years
13 or 14
133
8.2
15
313
19.4
16
433
26.8
17
421
26.1
18
225
13.9
19
77
4.8
Mean (SD)
Range
Social Cohesion
12.2(2.9)
5–20
Gini Coefficient (Tract)
0.45
0.33–0.65
Neighborhood features (n=38)
Mean(SD)
Range
Economic Deprivation
0.02(1.01)
−1.79 to 2.42
Danger
0.01(1.10)
−1.17 to 3.46
Disorder
2.87(0.49)
2.06 to 3.98
Proportion Black (%)
37.8(28.1)
1.8 to 92.5
Social Cohesion
3.65(0.49)
2.1–4.0
The relationship between neighborhood and individual characteristics with depression among adolescents participating in the 2008 BYS.
Model 1
Model 2
Model 3
β
95% CI
β
95% CI
β
95% CI
Intercept
−0.37
(−0.65, −0.09)
−0.39
(−0.66, −0.11)
−0.19
(−0.50,0.12)
Neighborhood Characteristics
Economic Deprivation (ref: low)
Moderate
−0.13
(−0.27, 0.01)
−0.13
(−0.27,0.01)
−0.13
(−0.27,0.00)
High
−0.18
(−0.32, −0.04)
−0.18
(−0.33, −0.03)
−0.19
(−0.34, −0.05)
Danger (ref: low)
Moderate
0.18
(0.04,0.32)
0.18
(0.04,0.32)
0.15
(0.01,0.30)
High
−0.03
(−0.14,0.09)
−0.03
(−0.14,0.08)
−0.05
(−0.17,0.06)
Disorder (ref: low)
Moderate
−0.04
(−0.16,0.09)
−0.03
(−0.16,0.10)
−0.03
(−0.16,0.10)
High
−0.09
(−0.28,0.11)
−0.08
(−0.28,0.12)
−0.08
(−0.28,0.12)
Proportion Black (ref:low)
Moderate
0.05
(−0.05,0.14)
0.05
(−0.05,0.14)
0.04
(−0.06,0.13)
High
0.15
(0.01,0.29)
0.15
(0.01,0.28)
0.14
(0.00,0.27)
Social Cohesion (ref: low)
Moderate
0.12
(−0.02,0.26)
0.12
(−0.02,0.26)
0.13
(0.00,0.26)
High
−0.06
(−0.24,0.12)
−0.06
(−0.23,0.12)
−0.03
(−0.20,0.13)
Individual-level Characteristics
Sex
Male
Female
0.38
(0.28,0.47)
0.39
(0.30,0.48)
0.39
(0.30,0.47)
Gini (Z-transformed)
−0.03
(−0.11,0.05)
−0.03
(−0.11,0.05)
Female * Gini
0.11
(0.02,0.20)
0.11
(0.01,0.20)
Race
White (ref)
Black
0.04
(−0.14,0.21)
0.04
(−0.13,0.22)
0.01
(−0.19,0.20)
Asian
0.05
(−0.21,0.32)
0.05
(−0.21,0.32)
0.04
(−0.23,0.31)
Hispanic
0.05
(−0.12,0.22)
0.06
(−0.11,0.23)
0.01
(−0.17,0.19)
Other
0.19
(−0.09,0.47)
0.20
(−0.08,0.48)
0.16
(−0.13,0.45)
Immigrant Status
US born (ref)
Immigrant (< 5 years)
−0.07
(−0.24,0.11)
0.02
(−0.08,0.11)
0.01
(−0.09,0.11)
Immigrant (≥ 5 years)
0.01
(−0.08,0.11)
−0.06
(−0.24,0.13)
−0.08
(−0.25,0.10)
Age
14 years
15 years
0.08
(−0.08,0.24)
0.08
(−0.09,0.24)
0.07
(−0.10,0.24)
16 years
0.16
(−0.01,0.34)
0.16
(−0.02,0.34)
0.17
(−0.01,0.34)
17 years
0.10
(−0.05,0.25)
0.10
(−0.06,0.25)
0.10
(−0.05,0.26)
18 years
0.22
(0.04,0.39)
0.21
(0.03,0.39)
0.21
(0.03,0.39)
19 years
0.28
(−0.13,0.70)
0.28
(−0.14,0.69)
0.25
(−0.15,0.66)
Social Cohesion
Low (ref)
Moderate
−0.16
(−0.28, −0.05)
High
−0.26
(−0.40, −0.13)
Bivariate analysis of the relationships between social cohesion, as a potential mediator, and income inequality and depression among boys and girls: 2008 Boston Youth Survey.
High Social CohesionOR (95% CI)
Depression Z-Scoreβ (95% CI)
Boys
Income Inequality
Gini Z-Score
0.79(0.65,0.96)
−0.06(−0.15, 0.02)
Social Cohesion Score (ref: low)
Moderate
−0.10(−0.27,0.06)
High
−0.22(−0.38, −0.07)
Girls
Income Inequality (ref: Gini <75th percentile)
Gini Z-Score
0.89(0.76,1.06)
0.04(−0.03,0.12)
Social Cohesion Score (ref: low/moderate)
Moderate
−0.22(−0.36, −0.07)
High
−0.27(−0.41, −0.13)
Summary Box
What is already known on this subject?
Contextual income inequality has shown to be related to depressive symptoms among women.
However, most studies that have investigated the relationship between contextual income inequality have been conducted among adults and have not looked at income inequality within neighborhoods.
What does this study add?
Neighborhood income inequality is associated with depressive symptoms among adolescent girls but not boys.