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Neighborhood context and adolescent mental health: development and mechanisms
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Neighborhood context and adolescent mental health: development and mechanisms
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Content
NEIGHBORHOOD CONTEXT AND ADOLESCENT MENTAL HEALTH:
DEVELOPMENT AND MECHANISMS
by
Woo Jung Lee, MA
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(SOCIAL WORK)
August 2023
Copyright 2023 Woo Jung Lee
ii
Acknowledgements
First and foremost, my journey as a researcher would not have been possible without the
dedication and generosity of my mentors, Jungeun Olivia Lee and Daniel Hackman. I am so
fortunate to have not one but two brilliant advisors and their unwavering support from Day 1 to
the completion of this dissertation. I am so grateful to have been supported by their enthusiasm
and insight for my growth whether it be personal or professional.
I also thank my committee members, David Takeuchi, Santiago Morales, and Lawrence
Palinkas for their time and dedication to helping me think deeply and cautiously about my
research questions.
This research would not have been possible without the contributions of the participants
as well as all involved in the ABCD study. I am also thankful for the participants who shared
their experiences with me.
There were many mentors, colleagues, friends, and family who have been integral long
before and during my academic journey. I would not be where I am today without all of their
genuine interest in my past stories and future goals, and I am only a product of all of their
encouragement, guidance, and support.
Last but not least, my family is the reason I was, I am, and I will be on this path. I thank
my parents for being my rock through all ups and downs, my sister Angie for being the closest
confidant for anything and everything, and my furry companion Maru for providing warmth
without words.
iii
Table of Contents
Acknowledgements ......................................................................................................................... ii
List of Tables ................................................................................................................................. iv
List of Figures ................................................................................................................................. v
Abstract .......................................................................................................................................... vi
Chapter 1: Introduction ................................................................................................................... 1
Chapter 2: Neighborhood Disadvantage and Patterns of Mental Health Trajectories .................... 9
Introduction ............................................................................................................. 9
Methods................................................................................................................. 16
Results ................................................................................................................... 21
Discussion ............................................................................................................. 24
Chapter 3: Emotion Regulation: Mechanisms of Neighborhood Effects on Mental Health ........ 45
Introduction ........................................................................................................... 45
Methods................................................................................................................. 52
Results ................................................................................................................... 56
Discussion ............................................................................................................. 58
Chapter 4: Neighborhood and Asian Youth: A Qualitative Study on the Intersection of
Race/Ethnicity and Neighborhood in Understanding Mental Health among Asian Youth .......... 70
Introduction ........................................................................................................... 70
Methods................................................................................................................. 73
Results ................................................................................................................... 76
Discussion ............................................................................................................. 84
Chapter 5: Conclusion and Implications ....................................................................................... 96
References ................................................................................................................................... 101
iv
List of Tables
Table 2.1: Descriptive Statistics for Sample Characteristics ........................................................ 31
Table 2.2: Neighborhood Factor Score ......................................................................................... 34
Table 2.3: Correlations Among Study Variables .......................................................................... 35
Table 2.4: Fit indices for Growth Mixture Model of Internalizing Behaviors ............................. 36
Table 2.5: Intercept and Slope for Internalizing Trajectory Classes ............................................ 37
Table 2.6: Internalizing Symptoms: Raw and T-Scores Across ABCD Study Waves ................. 38
Table 2.7: Logistic Regression Results for Internalizing Trajectory Groups ............................... 39
Table 2.8: Fit indices for Growth Mixture Model of Externalizing Behaviors ............................ 40
Table 2.9: Intercept and Slope for Externalizing Trajectory Classes ........................................... 41
Table 2.10: Externalizing Symptoms: Raw and T-Scores Across ABCD Study Waves .............. 42
Table 2.11: Logistic Regression Results for Externalizing Trajectory Groups ............................ 43
Table 3.1: Descriptive Statistics for Sample Characteristics ........................................................ 63
Table 3.2: Correlations Among Study Variables .......................................................................... 66
Table 3.3: Mediating Role of AER on Internalizing and Externalizing Behaviors ...................... 67
Table 4.1: Characteristics of Study Sample .................................................................................. 92
Table 4.2: Objective Measures of Neighborhood Disadvantage .................................................. 93
Table 4.3: Warm-Up Questions .................................................................................................... 93
Table 4.4: Interview Questions ..................................................................................................... 94
v
List of Figures
Figure 2.1: GMM Trajectories for Internalizing and Externalizing Behaviors ............................ 44
Figure 3.1: Emotional N-Back Task ............................................................................................. 68
Figure 3.2: Path Model for Internalizing and Externalizing Behaviors ........................................ 69
vi
Abstract
The neighborhood environment has been identified as a key context for youth’s mental
health. Although there is growing evidence of the association between neighborhood
disadvantage and adolescent internalizing and externalizing behaviors, much remains to be
explored in what, how, and for whom neighborhood context matters for adolescent mental
health. To examine these gaps in the literature, three distinct but related studies were conducted
using mixed methodology with data from the Adolescent Brain Cognitive Development (ABCD)
Study and semi-structured interviews with 12 Asian American adolescents.
Study 1: Neighborhood Disadvantage and Patterns of Mental Health Trajectories
Relatively little is known about how neighborhood disadvantage is associated with the
development of internalizing and externalizing behaviors across time, indicative of when and in
what manner neighborhood-related risk begins to emerge. Moreover, neighborhood disadvantage
may be associated with distinct, group-based patterns of elevation or change in symptoms that
may not be captured by examining linear developmental trajectories. Using data from the
Adolescent Brain and Cognitive Development (ABCD) study (n = 9,823), including baseline,
year 1, and year 2 follow-up assessments (ages 9-13), the first study aimed to identify subgroups
with distinct trajectories of internalizing and externalizing behaviors and examine how
neighborhood disadvantage is associated with the trajectory group membership. For internalizing
behavior, there were two trajectory classes such that most participants demonstrated low initial
levels of internalizing behavior which decreased slightly over time, while a relatively small
sample showed clinically elevated initial levels of internalizing behavior which increased over
time. Similar results were found for externalizing behavior. A higher proportion of participants
living in disadvantaged neighborhoods were more likely to be in the “high-increasing” trajectory
vii
group for internalizing behavior but not for externalizing behavior. Neighborhood disadvantage
is thus an important risk factor for clinically elevated levels of internalizing behavior by age 9
that persist or increase throughout early adolescence, indicating that the association may begin
earlier in childhood. Risk screening and interventions addressing internalizing symptoms in
children who are living in disadvantaged neighborhoods may be warranted prior to age 9. From a
public health standpoint, community and policy-level programs targeted at enhancing the
neighborhood context and reducing neighborhood-level risk exposures may improve adolescent
mental health.
Study 2: Emotion Regulation: Mechanisms of Neighborhood Effects on Mental Health
Less is known about the underlying process through which neighborhood disadvantage
confers risk on adolescent mental health. Specifically, limited research has explored the role of
automatic emotion regulation (AER) as a mechanism linking neighborhood disadvantage to
adolescent mental health. AER is the higher-level regulatory process during which modulation of
emotional stimuli occur without deliberate or effortful intention. While AER is a foundational
capacity that continues to develop throughout adolescence, there has been little investigation into
its association with neighborhood disadvantage and adolescent mental health. Using a sample (n
= 7,862) of youth aged 9-13 from the Adolescent Brain Cognitive Development (ABCD) study,
mediation analysis was conducted to examine whether AER mediated the path between
neighborhood disadvantage and mental health. Neighborhood disadvantage was associated with
lower AER capacity (β = -.23, p < .001), and AER was associated with lower levels of
internalizing (β = -.05, p = .004) and externalizing (β = -.08, p < .001) symptoms. AER fully
mediated the association between neighborhood disadvantage and mental health outcomes.
Neighborhood disadvantage and AER may potentially serve as important markers of
viii
vulnerability for pre-adolescents. Future work exploring the longitudinal mechanism of
socioemotional consequences of altered emotion regulation on mental health among youth living
in disadvantaged neighborhoods is warranted. More research in targeted prevention or
intervention programs that address underlying automatic emotion regulation processes may be
important.
Study 3: Neighborhood and Asian Youth: A Qualitative Study on the Intersection of
Race/Ethnicity and Neighborhood in Understanding Mental Health among Asian Youth
Neighborhood studies have heavily focused on socioeconomic features of neighborhoods,
but socioeconomic factors may not fully capture the neighborhood experiences of racial/ethnic
minority youth including Asian youth. The recent surge of racial discrimination against the Asian
American community as a result of the Coronavirus (COVID-19) pandemic also calls for a better
understanding of how non-socioeconomic features of neighborhoods, such as racial
discrimination in neighborhoods and co-ethnic density affect youth mental health. Semi-
structured interviews were conducted with 12 Asian American adolescents (ages 15-17) to
explore how various neighborhood factors intersect with each other to influence mental health
among Asian American youth. Results suggested that pan-Asian co-ethnic density and specific
Asian group co-ethnic density conferred distinct benefits as well as risk for youth mental health.
Co-ethnic density, whether in the form of direct interaction or by indirectly fostering a sense of
safety, was perceived as a protective factor against potential harmful effects of discrimination on
Asian youth’s mental health. Asian youth perceived likeness of residents to be an important
feature of feeling social connectedness, and thus assessments of social aspects of neighborhoods
neighborhood measures such as social cohesion may incorporate such conditions. Racial
discrimination in the neighborhood was prevalent and heightened Asian youth’s awareness of
ix
their surroundings, thus serving as a barrier to normal activity in the neighborhood. Asian
youth’s racial discrimination experiences in the neighborhood may be a complex process and
capturing their experiences and perception may need more nuanced approaches in future
research.
1
CHAPTER 1: Introduction
A substantial number of adolescents experience mental health problems (Twenge et al.,
2019). As many as 40% of adolescents develop a mental health disorder in a given year (Kessler
et al., 2012). Mental health disorders comprise a leading cause of health-related disability for
young people. Internalizing and externalizing behaviors, which describe a continuum of
problematic emotional and behavioral functioning, are especially prevalent during adolescence
(Krueger, 1999). Internalizing behaviors describe a propensity to express distress inwards and
can include mood and anxiety disorders and obsessive-compulsive disorder, while externalizing
behaviors demonstrate the tendency to express distress outwards and are implicated in antisocial
personality disorder, attention-deficit/hyperactivity disorder, conduct disorder, oppositional
defiant disorder, and substance use disorders. Internalizing and externalizing symptoms during
childhood and adolescence are associated with a host of negative outcomes that persist into
adulthood (Miettunen et al., 2014; Simonoff et al., 2004; Wong et al., 2021). They have been
observed across many societies, diverse geographic regions, and racial/ethnic and cultural groups
(Achenbach et al., 2016). It is a public health issue with far reaching consequences, but gaps
remain between needs and resources, contributing to mental health disparities (Kieling et al.,
2011).
The social determinants framework for mental health provides that mental health
disorders can develop in adverse social environments where inequalities are associated with
differential social, economic, and physical characteristics that have distinctive impact on specific
life stages (World Health Organization, Commission on Social Determinants of Health, 2008).
Neighborhood disadvantage, in particular, is one of the social determinants of health that has far-
reaching consequences on adolescent mental health as it deprives youth of socioeconomic and
2
demographic resources critical for development (Ross & Mirowsky, 2001). Ecological theory
(Bronfenbrenner & Morris, 2006) provides a useful framework in conceptualizing the role of
neighborhood context as a social determinant of mental health for adolescents in a dynamic
perspective. Ecological theory provides that the developing child is embedded in a series of
interrelated contexts that interact with the child’s own biological and psychological development
over the life course (Bronfenbrenner & Morris, 2006). Further, especially with urbanization,
multilevel perspectives highlight understanding the role of the neighborhood environment
including socioeconomic and non-socioeconomic features in contributing to mental health
disparities (Reardon & Bischoff, 2011; Vlahov et al., 2007). Taken together, to better understand
whether and how neighborhood context influences adolescent mental health, it is critical to take
a life course approach that integrates the operation of interrelated and dynamic influences
between multiple levels over time (Leventhal & Dupéré, 2019).
The neighborhood environment has also been identified as a key context for youth’s
mental health (Leventhal, 2018). Research has found that the neighborhood context is associated
with a wide range of mental health outcomes for adolescents including both internalizing and
externalizing behaviors. Neighborhood disadvantage exposure in early adolescence has been
found to be linked to adolescent depressive symptoms (Barr, 2018; Solmi et al., 2017), anxiety
symptoms (King et al., 2022), and externalizing behavior (Li et al., 2017). While prior works
have shed light on the role of neighborhood in shaping mental health among youth, there remain
critical gaps. First, the developmental nature of mental health trajectories has been overlooked.
Secondly, the role of individual-level mechanism in the link between neighborhood disadvantage
and adolescent mental health has not been adequately investigated. Lastly, a rapidly growing
racial/ethnic minority group, Asian youth, has been overlooked. It is even more important to
3
understand this group given the recent surge of racial discrimination against the Asian American
community as a result of the Coronavirus (COVID-19) pandemic.
Accordingly, taking a dynamic perspective on the interaction between neighborhood and
children’s development requires recognition of the unique characteristics of a given
developmental period (Leventhal, 2018). Research documenting the trajectories of internalizing
and externalizing behaviors during adolescence reveals important within-individual patterns of
change over time. The overall developmental trajectory for internalizing behavior generally
demonstrated an increase throughout adolescence (Bongers et al., 2003; J. R. Cohen et al., 2018)
while externalizing trajectory showed a general decline throughout adolescence (Bongers et al.,
2004). Taken together, the period of late childhood and adolescence may be a time of meaningful
changes in internalizing and externalizing behaviors despite granular differences found among
different studies. However, such developmental nature of mental health has not been
incorporated in the literature examining the relationship between neighborhood disadvantage and
mental health.
Importantly, there is heterogeneity in the levels of mental health symptoms experienced
(Cicchetti & Rogosch, 2002) and the trajectories of mental health problems throughout
adolescence (Bongers et al., 2004; Wang et al., 2014). It is important to distinguish such
subgroup differences, as they may have implications for treatment programs specifically
targeting youth at risk. However, empirical evidence of the longitudinal link between
neighborhood and subgroup differences in the trajectories of mental health problems across
adolescence is limited. Neighborhood disadvantage may be associated with distinct, group-based
patterns of elevation or change in symptoms that may not be captured by examining linear
developmental trajectories. To address this gap, the first study of this dissertation project
4
(Chapter 2) identifies subgroups with distinct trajectories of internalizing and externalizing
behaviors and examine how neighborhood disadvantage is longitudinally associated with the
distinct group-based patterns of trajectories during late childhood and early adolescence.
Secondly, one area that has been less explored but may have profound effects for youth is
the role of individual-level regulatory processes in the association between neighborhood
disadvantage and youth mental health. Neighborhood disadvantage can affect mental health
through varying potential pathways (P. A. Braveman et al., 2005). Emotion processing may be
one of those key pathways. The challenges associated with neighborhood disadvantage are likely
to be perceived as stressful by its residents including youth (Hyde et al., 2020; Tomlinson et al.,
2020), and stress has been known to compromise emotion regulation (P. Kim et al., 2013),
including the automatic aspects of emotion regulation (AER). AER is the “higher-level”
regulatory process during which modification to one’s emotions are made without the deliberate
or conscious attempts to do so (Braunstein et al., 2017; Mauss, Bunge, et al., 2007) and is likely
more pervasive in daily life (Hyde et al., 2020). The failure to regulate the emotional responses
to stressors present in the neighborhood environment may have implications for their mental
health. AER has been linked to mental health (Vargas & Mittal, 2021), strengthening the
possibility that AER may function as a key individual-level regulatory process linking
neighborhood to mental health. Although it is highly feasible, no identified studies have
evaluated AER as a mechanism linking neighborhood disadvantage and youth mental health,
which undermines the understanding of whether emotion regulation is a key intervention target
to mitigate the negative impacts of living in disadvantaged neighborhoods on youth mental
health. To address this gap, the second study (Chapter 3) of the dissertation examines AER as a
mechanism underlying the link between disadvantaged neighborhood context and mental health.
5
Third, research on the effects of culturally salient neighborhood-level domains such as
co-ethnic density is limited with mixed findings, particularly among Asian youth, the fastest
growing racial/ethnic minority group in the U.S. (Dodds & Hess, 2021). The neighborhood
context is multifaceted in nature and one size may not fit all. The relevant studies have heavily
focused on socioeconomic features of neighborhoods, but socioeconomic factors may not fully
capture the neighborhood experiences of racial/ethnic minority youth (P. Braveman & Barclay,
2009) including Asian youth. The recent surge of racial discrimination against the Asian
American community as a result of the Coronavirus (COVID-19) pandemic (Tessler et al., 2020)
further calls for a better understanding of how non-socioeconomic features of neighborhoods,
such as racial discrimination in neighborhoods, affects youth mental health. The relevant
literature about Asian youth is sparse and mixed (E. H. Lee et al., 2014; W. J. Lee et al., 2022;
Wei et al., 2020), which necessitates a more nuanced approach that considers the neighborhood
experiences unique to Asian American youth, such as discrimination experiences in
neighborhoods. Given that a nuanced approach is critical in understanding in what unique ways
neighborhood factors unfold and intersect with each other to influence mental health among
Asian youth, Chapter 4 employs a qualitative approach to examine the lived experiences of
neighborhood-level risk and protective factors among Asian American youth.
Taken together, this dissertation aims to address the three gaps identified above and move
the literature forward by making the explicit connection between neighborhood factors and
individual-level processes using a developmental framework and mixed methodology. Findings
can inform targeted prevention and intervention strategies to improve mental health inequities
attributable to neighborhood. Specifically, the current study investigated how neighborhood
disadvantage is linked to the distinct, group-based patterns of elevation or change of internalizing
6
and externalizing behaviors across the period of late childhood and early adolescence, which
have implications for understanding the emergence of neighborhood-related risk on youth mental
health. A clearer understanding of individual-level functions that may be influenced by
neighborhood disadvantage during this developmental period may have important implications
for informing targeted prevention and intervention approaches. Neighborhoods characterized by
socioeconomic disadvantage tend to have features of chaotic and degenerating physical
environments. These features may be perceived as stressful and affect youth’s sense of safety
and security, invoking disruptions in emotional processing and regulation. Thus, the role of
emotion regulation as a mechanism underlying the link between disadvantaged neighborhood
context and mental health was examined. Finally, while socioeconomic deprivation, the focal
neighborhood feature in the first two studies of the dissertation, is an important neighborhood-
level risk factor for youth mental health, other features of neighborhood may also be important
for a racial/ethnic minority group, such as co-ethnic density and racial discrimination. Given that
Asian youth, a rapidly increasing racial/ethnic minority group, has been relatively overlooked
compared to Black and Latinx youth (Kiang et al., 2017), the current dissertation focused on
Asian youth. Particularly, racial discrimination, which has been recognized as a key risk factor
contributing to racial/ethnic minority youth’s mental health, has been intensified for Asian youth
during the pandemic. As such, focusing on Asian youth will complement the existing literature
by providing empirical evidence about Asian youth’s experience which have been relatively
limited in the current science base. Further, by understanding Asian youth who have recently
experienced an intensified neighborhood level-risk factor (i.e., racial discrimination in
neighborhood), the study will advance the existing literature on in what ways the impacts of a
neighborhood risk factor that gets intensified during a historical event may unfold and intersect
7
with other neighborhood features to influence racial/ethnic minority youth’s mental health.
Collectively, this dissertation aimed to investigate the complex and dynamic nature of the
association between neighborhood context and mental health specifically during adolescence,
when rapid changes take place physically, emotionally, socially, and culturally. Through a
consideration of the interplay between the individual and ecology and utilization of mixed
methodology that addresses the gaps in the literature, this research aimed to investigate whether
and how neighborhood shapes adolescent mental health to inform prevention and intervention
strategies that can reduce mental health burdens and inequities in youth.
Specific Aims
Aim 1. Neighborhood disadvantage and patterns of mental health trajectories. To
elucidate the nature of the link between neighborhood disadvantage and the patterns of onset and
development of mental health problems and examine how neighborhood disadvantage is
associated with the group-based patterns of trajectories.
Aim 2. Emotion regulation: Mechanisms of neighborhood effects on mental health. To
understand the underlying mechanism of automatic emotion regulation that accounts for the
association between neighborhood disadvantage and mental health outcomes.
Aim 3. Neighborhood and Asian youth: A Qualitative Study on the Intersection of
race/ethnicity and neighborhood in understanding mental health among Asian youth. To explore
how neighborhoods are experienced by Asian American adolescents, including the intersection
of cultural context, racial discrimination experiences, and non-socioeconomic features of
neighborhood.
8
Organization of the Study
The overall aim of this dissertation research was to examine the way in which
neighborhood context influences adolescent mental health and the mechanisms involved in the
process. This dissertation is organized into five chapters. The current chapter (Chapter 1)
provides the background of neighborhood context and the association with adolescent mental
health. This chapter highlights the gaps in the current literature on the nature of the association
between neighborhood disadvantage and the trajectory of youth mental health, the individual-
level mechanism through which neighborhood disadvantage is linked to adolescent mental
health, and unique ways in which non-socioeconomic features of the neighborhood unfold and
intersect with each other to influence mental health among Asian youth. Chapter 2 identifies the
trajectory classes of internalizing and externalizing behaviors and the prospective link between
earlier neighborhood socioeconomic disadvantage and subsequent trajectory groups of
internalizing and externalizing psychopathology using a growth mixture modeling approach.
Chapter 3 presents the second paper which tested the mediation effect of automatic emotion
regulation, a potential key individual-level mechanism, in the association between neighborhood
disadvantage and internalizing and externalizing behaviors. Chapter 4 presents the third paper
which explored the significance of the neighborhood context in Asian American adolescents.
Consideration of the unique experiences of Asian American youth in light of sociocultural
history and current events was given to understand neighborhood-level racial discrimination
experiences and potential buffering effects of co-ethnic density using qualitative approaches.
Lastly, Chapter 5 presents a discussion on the key findings from the dissertation, implications for
research and practice, strengths and limitations of the studies, and the conclusion.
9
CHAPTER 2: Neighborhood Disadvantage and Patterns of Mental Health Trajectories
Introduction
Mental health problems impose a significant contribution to epidemiological burden of
disease (Bor et al., 2014). Importantly, mental health problems in adolescence (ages 10-19;
WHO) have been increasing in prevalence, with 1 in 4 American high school students
experiencing poor mental health (Clayton et al., 2023) and approximately one in seven
adolescents living with a mental disorder worldwide (World Mental Health Report, 2022).
Internalizing problems, characterized by mood and anxiety disorders, are one of the most
common forms of psychopathology experienced by youth that tend to increase in prevalence
throughout adolescence (WHO, 2021). As many as 25% of adolescents experience anxiety
disorders (Kessler et al., 2012) while adolescent depression rates increased from 8% in 2010 to
14.4% in 2018 (Substance Abuse and Mental Health Services Administration, 2019).
Externalizing problems, such as conduct disorder and oppositional-defiant disorder, show rates
as high as 16.3% (Kessler et al., 2012) with a general downward trend from age 6 to 14 (Givens
& Reid, 2019). Additionally, adolescent depression has been shown to predict a greater
likelihood of experiencing depression in adulthood (Jonsson et al., 2011) and externalizing
behavior in adolescences, especially attention and conduct problems, is associated with adult
offending (Fergusson et al., 2012) as well as depression (Loth et al., 2014). Thus, adolescence is
a critical time when mental health symptoms tend to emerge and increase in prevalence, and an
important time to understand the contributing factors to unique patterns of internalizing and
externalizing behaviors.
10
Developmental Trajectories of Mental Health
A few studies documenting the trajectories of internalizing and externalizing behaviors
within individuals during adolescence reveal important patterns of change. At least three studies
using large community samples show dynamic patterns in internalizing behavior, although with
some variation. In one study, a population-average developmental trajectory showed a
curvilinear increase in internalizing symptoms from age 4 to 18 in which symptoms increase
until about mid-adolescence and stabilize (Bongers et al., 2003). Another study showed that
anxiety and depressive symptoms decreased from age 7 to age 12, at which point both symptoms
start to increase again throughout early adolescence (J. R. Cohen et al., 2018). Another study that
investigated depressive and anxiety symptoms in ages 11 to 17 found that symptoms are at
higher levels in early adolescence but start to decrease towards late adolescence, although trends
varied slightly for girls whose prevalence increased in later adolescence (Burstein et al., 2010).
For externalizing behaviors, trajectory showed a general decline from ages 4 to 18 (Bongers et
al., 2003). Overall, general population trends suggest heightened levels of internalizing and
externalizing behaviors in early to mid-adolescence.
Subgroup Differences in Mental Health Trajectories
Despite the presence of average developmental change in internalizing and externalizing
behavior, individual differences in these trends exist, indicating that there is variability in the
trajectories of mental health problems throughout adolescence (Cohen et al., 2018). However, it
is important to distinguish subgroups of individuals who are at risk for developing elevated and
chronic symptoms and those whose symptoms are transient. Average trajectories may not
represent smaller subgroups, because in studies with large community samples, evidence shows
that the large majority demonstrates few symptoms. Thus, subgroups’ specific levels and patterns
11
may be difficult to detect in the aggregate level. For example, study of a Dutch population
sample of young children (ages 1.5-10), found four distinct trajectories for anxiety and
depressive symptoms in which a majority (82.4%) of the sample showed low levels of symptoms
while one small group showed an increasing trajectory, although all trajectories were below
clinical range (De Lijster et al., 2019). A large U.S. representative study of depressed mood in
adolescence to young adulthood aged 12 to 25 also demonstrated four trajectories, showing that a
small group (around 3%) continued to increase symptom levels beyond adolescence into young
adulthood and showed clinical levels of symptoms (Costello et al., 2008). A smaller sample on
internalizing behaviors with 238 children ages 3 to 10 also yielded four trajectories: low, low-
moderate, rising, and severe-decreasing (Ip et al., 2019). In this study, the rising trajectory class
showed clinically elevated symptoms towards the end of the study period although it consisted of
a small sample of approximately 35 children. Thus, the studies suggest that clinical differences
may emerge towards the late childhood and early adolescence, but the patterns specifically
during this period at this point are unclear.
Less evidence exists with respect to group-based trajectories for externalizing behavior.
A study using a community sample (ages 12-22) investigated patterns of externalizing problems
and found three trajectories, stable high, decreasing moderate, and decreasing low, where the
decreasing low was the majority and the stable high group a small minority (8%) (Latendresse et
al., 2011). Other studies with large samples exist (Bongers et al., 2008; Givens & Reid, 2019;
Wang et al., 2014), but they examined subtypes of externalizing behaviors and found three to six
trajectories depending on the subtype, indicating more variability when specific subtypes of
externalizing behaviors are examined. Taken together, the few extant studies are limited in
understanding the emergence of heterogeneity of internalizing and externalizing behaviors,
12
particularly the emergence of clinical differences in the period of late childhood to early
adolescence, using large diverse samples.
Neighborhood Environment and Adolescent Mental Health
The social determinants framework for mental health posits that mental health disorders
can develop in unfavorable social environments where inequalities are associated with
differential social, economic, and physical characteristics that have a distinctive impact on
specific life stages (World Health Organization, Commission on Social Determinants of Health,
2008). The neighborhood environment has been identified as a key context for youth’s mental
health (Leventhal, 2018). A neighborhood is defined as a cluster of individuals living in
proximity in a particular geographical area (Aneshensel, 2009). Ecological theory
(Bronfenbrenner & Morris, 2006) provides that the neighborhood is a key level of influence for
adolescent development and that individuals, neighborhoods, and their associations are not static
and evolve over time. Neighborhood disadvantage, in particular, is a structural dimension that
comprises the aggregate socioeconomic and demographic characteristics of the individuals
residing in the geographical area. It is typically measured by aggregating factors such as poverty
level, unemployment, percentage of individuals with education level lower than high school
diploma, or prevalence of female-headed households at the census tract level (Elliott et al.,
1996). Disadvantaged neighborhoods may predict youth’s adverse behavioral and emotional
health by limiting access to critical resources important for development such as institutional
resources, quality relationships and social network, and collective efficacy particularly around
supervision of youth and monitoring physical danger (Leventhal & Brooks-Gunn, 2000).
The neighborhood may play a particularly important role in shaping mental health in the
formative years of childhood and adolescence. Early adolescence is an important time when
13
rapid biological, cognitive, and social changes can make adolescents more vulnerable to the
environmental context (Sawyer et al., 2018). While infants and younger children primarily spend
their time in the home with caregivers, adolescents exercise more autonomy and begin to interact
more directly with the environment outside of the immediate surrounding (Leventhal, 2018;
Sharkey & Faber, 2014). Additionally, a dangerous neighborhood may provide more
opportunities to engage in risky behaviors (Lansford et al., 2006). Indeed, research has found that
the neighborhood context is associated with a wide range of mental health outcomes including
both internalizing and externalizing behaviors throughout childhood and adolescence.
Neighborhood disadvantage in early adolescence has been associated with increased depressive
symptoms during mid-adolescence (Barr, 2018) as well as anxiety (King et al., 2022). Another
study found a longitudinal association between prolonged exposure to disadvantaged
neighborhoods throughout childhood and increased depressive symptoms at age 13 and 18
(Solmi et al., 2017). Neighborhood disadvantage has also been found to be associated with
various behavioral problems and externalizing problems including aggression, delinquency, and
conduct problems, with the strongest effects during early childhood and late adolescence, rather
than mid-adolescence (Murry et al., 2011). Similarly, early neighborhood disadvantage was
longitudinally associated with externalizing behavior for late childhood to early adolescence
(ages 7-12), but not for children 6 years or younger (Li et al., 2017).
Despite prior studies documenting the link between neighborhood disadvantage and
adolescent mental health, relatively little is known about how the neighborhood environment
may be linked to the unique patterns of trajectories of mental health problems among youth. The
stress processing theory (Pearlin et al., 2005) may explain how neighborhood disadvantage
influences the course of mental health. Youth in disadvantaged environments early in life may
14
exhibit escalation of mental health problems that further accumulate and contribute to emergence
of differences and patterns of change in mental health symptoms (Wickrama et al., 2008). Thus,
early exposure to neighborhood disadvantage may put those who are experiencing adverse
mental health in a persisting trajectory thus taking a long-term toll on their mental health. A few
longitudinal studies that examined trajectories of mental health show evidence for this
possibility. A study from mid-adolescence to adulthood found that neighborhood environment is
associated with greater initial levels of depressive symptoms during mid-adolescence which
persists throughout adulthood (Barr, 2018). A similar pattern was found for externalizing
problems such that neighborhood disadvantage was associated with higher initial levels of
physical aggression but not the slopes for adolescents aged 11 to 18, indicating that early
neighborhood context is linked to physical aggression during early adolescence (Karriker-Jaffe et
al., 2013).
Neighborhood Disadvantage and Group-Based Patterns of Mental Health Trajectory
The extant research raises questions about the nature of the association between
neighborhood disadvantage and trajectories of mental health: whether there are early differences
that narrow, widen, or persist, or whether differences emerge later in adolescence. A few studies
using group-based trajectory modeling provided emerging evidence for differences that persist or
widen. A study using group-based trajectory modeling found that living in high-risk
neighborhoods in grade 6 predicted group membership of moderate-risk and high-risk increasing
trajectory groups of risky behaviors for Bahamian adolescents from grade 6 to 9 (Wang et al.,
2014). In another study that investigated adolescence to young adulthood specifically in the
Latinx groups (Estrada-Martínez et al., 2019), neighborhood risk was associated with in an
increasing risk involvement trajectory. None of these studies, however, address internalizing
15
behavior nor do they examine externalizing behavior across subtypes. Additionally, in both
studies, family-level socioeconomic status was not accounted for, which is critical given that
individual-level SES tends to correlate with neighborhood-level SES. Thus, it is important to
examine both levels in order to distinguish whether neighborhood disadvantage independently
explains variation in mental health symptoms. Further, the above studies do not use large diverse
community samples, and it is important to understand the patterns in a larger population during
late childhood to early adolescence, when clinically meaningful subgroup differences in mental
health may emerge. Taken together, missing from the current literature is whether neighborhood
disadvantage is associated with unique patterns of trajectories of internalizing and externalizing
behavior during early adolescence beyond family-level socioeconomic status investigated in a
large community sample. Examination of these associations in a group-based approach will
capture subgroups of divergent internalizing and externalizing trajectories and elucidate whether
neighborhood disadvantage may be associated with distinct, group-based patterns of elevation or
change in symptoms that may not be captured by examining linear developmental trajectories.
Study Aims
The current study employs longitudinal data from the Adolescent Brain and Cognitive
Development (ABCD) from late childhood to early adolescence, in order to address two key
gaps in the literature regarding neighborhood disadvantage and the patterns of onset and
development of mental health problems. First, this study aims to identify subgroups with distinct
trajectories of internalizing and externalizing behaviors. It is hypothesized that a large majority
would exhibit lower levels of symptoms while smaller subgroups demonstrate elevated levels of
symptoms that persist. Second, the association between neighborhood disadvantage and distinct,
group-based patterns of elevation or change in symptoms was examined. It is hypothesized that
16
higher neighborhood disadvantage would be associated with trajectory groups with higher levels
of internalizing and externalizing behaviors.
Methods
Participants and Procedures
The Adolescent Brain and Cognitive Development (ABCD) study is a nation-wide multi-
site study on biological, social, and environmental factors that influence adolescent brain and
neurocognitive development (https://ABCDStudy.org/). The study recruited 11,876 children
from ages 9-10 who will be followed annually for ten years from 21 sites in large metropolitan
areas (Children’s Hospital Los Angeles, Los Angeles, California; Florida International
University, Miami, Florida; Laureate Institute for Brain Research, Tulsa, Oklahoma; Medical
University of South Carolina, Charleston, South Carolina; Oregon Health and Science
University, Portland, Oregon; SRI International, Menlo Park, California; University of California
San Diego, San Diego, California; UCLA [University of California, Los Angeles, California];
University of Colorado Boulder, Boulder, Colorado; University of Florida, Gainesville, Florida;
University of Maryland at Baltimore, Baltimore, Maryland; University of Michigan, Ann Arbor,
Michigan; University of Minnesota, Minneapolis, Minnesota; University of Pittsburgh,
Pittsburgh, Pennsylvania; University of Rochester, Rochester, New York; University of Utah,
Salt Lake City, Utah; University of Vermont, Burlington, Vermont; University of Wisconsin-
Milwaukee, Milwaukee, Wisconsin; Virginia Commonwealth University, Richmond, Virginia;
Washington University in St. Louis, St. Louis, Missouri; and Yale University, New Haven,
Connecticut) (Volkow et al., 2018). The study includes extensive demographic information for
youth and parents, behavioral and mental health reported by parents, proximal and distal
environmental factors, and neurocognitive tasks performance and neuroimaging data. Written
17
informed consent was provided by each parent or caregiver, and each child provided written
assent. Centralized institutional review board (IRB) approval was obtained from the University
of California, San Diego, and each site obtained IRB approval from local institutions. The
ABCD data are publicly available with a data use agreement, and this secondary analysis was
approved by the University of Southern California IRB.
A stratified probability sampling method was used to recruit the participants from each
site at the school level (Feldstein Ewing et al., 2018; Garavan et al., 2018). The study also
includes a twin study for which recruitment involved direct contact through birth registries
(Iacono et al., 2018). The sample is not representative of the U.S. population, though it was
designed to resemble the diversity of regions and demography of the U.S. population (Compton
et al., 2019). Children were 9.00 to 10.99 years old at the time of enrollment, and they and their
parent or caregiver completed a baseline visit between October 1, 2016 and October 13, 2018, at
which time clinical interviews, surveys, neuroimaging, and neurocognitive tests were conducted.
The current study used data from ABCD release 4.0 which includes baseline data (ages 9-
11) and subsequent waves at 1-year follow-up (10-12 years) and at 2-year follow-up (ages 11-13)
(Garavan et al., 2018). The current analysis limited the sample to collection dates prior to the
onset of the COVID-19 pandemic to account for potential confounding effects of the pandemic
on adolescent mental health (Feldstein Ewing et al., 2022). At baseline, the sample included
9,854 families - 7,898 families with one participant and 1,956 families with two or more
participants in the study. One child per family was randomly selected to reduce nonindependence
due to the presence of twins and siblings (n = 9,854) (Hackman et al., 2021, 2022; Marshall et
al., 2022). 31 participants were further removed from one site that ceased data collection. This
resulted in a final analytic sample of 9,823 participants, for the first analysis step of modeling
18
trajectories of internalizing and externalizing problems. The second analysis step involved
additional variables which had missingness (detailed below), and thus resulted in an analytic
sample of 8,337 participants. The descriptive statistics for the analytic sample at each analysis
step and the full ABCD cohort at baseline are in Table 2.1.
Measures
Neighborhood Disadvantage. Each participant’s primary residential address was
collected at baseline and geocoded using vector geospatial data by the Data Analysis, Informatics
and Resource Center of the ABCD Study. Variables from the American Community Survey
(2011-2015 estimates) were linked to the individuals’ U.S. census tract. Following the methods
in a previous ABCD study (Fan et al., 2021), five frequently used constructs that are non-
redundant and not dependent on real estate markets (Hackman et al., 2021; Hannan et al., 2023)
were selected to measure disadvantage: percentage of residents with at least a high school
diploma, median family income, unemployment rate, percentage of families living below the
federal poverty level, and percentage of single-parent households. Non-valid addresses were also
removed from the study (n = 47). A factor score was created using a maximum likelihood
exploratory factor analysis. A single factor of neighborhood disadvantage explained 74.1% of the
variance in the measures. Factor loadings, presented in Table 2.2, were used to create a
regression-weighted factor score for neighborhood disadvantage for each participant. Because
census tracts are not available in the ABCD dataset, data were treated at the individual level with
no nesting/clustering for neighborhood level.
Child Behavior Checklist (CBCL). Adolescent mental health was assessed by
symptoms reported by parents using the Child Behavior Checklist (CBCL) collected at Baseline
(ages 9-11), Year 1 (ages 10-12), and Year 2 (ages 11-13). The CBCL contains 112 items that
19
measure behavioral and emotional problems in adolescents (Achenbach & Rescorla, 2001), with
symptoms scored on a three-point Likert scale (0 = absent, 1 = occurs sometimes, 2 = occurs
often). The subscales for internalizing (i.e., anxiety, depression, somatic complaints, withdrawn
behavior) and externalizing (i.e., rule-breaking and aggressive behavior) were used. Reliability
and validity of the CBCL have been demonstrated in diverse samples (Barch et al., 2018;
Ivanova et al., 2007). Given the focus on the overall mental health of young adolescents during a
developmental period of emergence of symptomatology, total raw scores of internalizing and
externalizing symptoms were used. Data were drawn from baseline, 1-year follow-up, and 2-year
follow-up which cover the ages 9-14. The current analysis limited the sample to collection dates
prior to the onset of the COVID-19 pandemic to account for potential confounding effects of the
pandemic on adolescent mental health. No Baseline and Year-1 follow-up data were removed as
data collection finished pre-COVID. For Year-2 follow-up CBCL data, 468 of 6,644 datapoints
were removed. The internalizing (33 items; α = .86 at all timepoints) and externalizing (35 items;
α = .91 at all timepoints) problems exhibited good reliability.
Covariates. Covariates included age, sex assigned at birth (male = 0, female = 1), child
race/ethnicity as reported by caregiver (Non-Hispanic White, Hispanic, Non-Hispanic Black,
Non-Hispanic Native American, Non-Hispanic Asian, and Other), and parental education level
(highest educational achievement by the parent or caregiver: < High school diploma, High school
diploma or GED, Some college, Bachelor degree, Postgraduate degree). 11 participants did not
provide data for educational background, thus resulting in 9,821 participants with valid data on
parental educational level. Total family income was measured by all sources of income including
wages, benefits, child support payments, and others, collected in ordinal ranges (< $5,000;
$5,000 - $11,999; $12,000 - $15,999; $16,000 - $24,999; $25,000 - $34,999; $35,000 - $49,999;
20
$50,000 - $74,999; $75,000 - $99,999; $100,000 - $199,999; > $200,000) (Hackman et al.,
2022). To create a per capita income, the midpoint of each category was taken to create a
continuous variable, which was divided by the caregiver-reported household size to account for
varied levels of resources needed depending on the household size (Lam, 1997). Household sizes
reported as lower than 2 (n = 68) were treated as missing, with per capita income then treated as
missing, as it was an implausible size given that the minimum household size included caregiver
and child. Household size reported as greater than 20 (n = 2) was also treated as missing. Of the
analytic sample (n = 9,823), 424 participants reported they did not know the income, 435
participants did not provide the income, and 308 participants did not report household size.
When all missingness was taken into account, there were 8,762 participants with valid income
data.
Data Analyses
A growth mixture model (GMM) was used to identify latent subgroups with distinct
trajectories of internalizing and externalizing problems from Baseline to Year 2, using mean,
variance, and covariance patterns of repeated measurements of latent intercepts, linear slopes,
and quadratic slopes (Muthen & Muthen, 2000). A series of GMMs with progressively
increasing number of trajectory classes were estimated. The optimal number of classes was
selected based on multiple fit indices (Bayesian Information Criterion (BIC), entropy values, and
Lo-Mendell-Rubin (LMR) likelihood ratio tests (Nylund et al., 2007), conceptual interpretation,
and prevalence of subgroups (Muthen & Muthen, 2000). Analyses were conducted in Mplus
version 8 (Muthén & Muthén, 1998-2017). To account for the nested structure of the sampling
design by site, complex analysis was used (Type = Complex). Data were managed using full
information maximum likelihood estimation (FIML), a method recommended to handle missing
21
data (Buhi, 2008; Schlomer et al., 2010). After selecting the optimal model that represents
internalizing and externalizing behaviors, logistic regression analyses were run in Stata
Statistical Software Package 16.1 to evaluate whether neighborhood disadvantage predicts
membership in the different trajectory groups controlling for age, sex, race/ethnicity, per capita
income, and parental education level. Given the reduced sample size resulting from a large
degree of missingness especially in family-level income, descriptives of the excluded sample
were explored in a post-hoc analysis of descriptive statistics and bivariate correlations which
indicated that the exclusion is unlikely to have an influence on the results.
Results
Correlations among study variables are presented in Table 2.3. Although the current
sample is not representative of the overall U.S. population, and generally reflects a higher SES
sample, a sizable number of individuals live in highly disadvantaged neighborhoods in which
more than 25% of families are below the poverty line (n = 1,202; 12.9% of the participants).
Mean levels of internalizing behavior ranged from 5.08 to 5.14 (SD = 5.60 – 5.76) indicating
minimal changes at the aggregate level across study waves. Similarly, mean levels of
externalizing behavior ranged from 3.97 to 4.36 (SD = 5.42 – 5.90) also indicating minimal
changes at the aggregate level across the study.
Internalizing symptomatology.
Fit statistics of GMMs on 1 to 4- class solutions suggested a 2-class solution best fit the
data for internalizing behavior (Table 2.4). The model demonstrated an entropy value of .92,
which represents high entropy (Clark & Muthen, 2009), substantial class size, and a significantly
better fit than the 1-class model (LMR p-value = .004). The 3-class model, although it also had a
high entropy value of .92, it was not significantly different from the 2-class model (LMR p-value
22
= .050). Similarly, the 4-class model (entropy = .91) did not differ significantly from the 3-class
model (LMR p-value = .084). Additionally, in the 3-class model, the two high-symptom groups,
one group with an increasing slope (6%) and the other with a decreasing slope (7%), were
substantively similar to the single high-increasing group in the 2-class model. Considering the
substantive interpretation, parsimony, and substantial class sizes, the 2-class model was chosen
as the best fitting model. Figure 2.1 shows the 2-class trajectory groups. Class 1 (“low-
decreasing” trajectory; 91%) was characterized by low initial levels of internalizing behavior
(raw score of 4.24) at age 9 which slightly decreased over time (linear slope = -.19, p = .002).
Class 2 (“high-increasing” trajectory; 9%) consisted of a relatively small sample demonstrating
high initial levels of internalizing behavior (raw score of 14.01) at age 9 which increased over
time (linear slope = 2.83, p < .001). The equivalent T-scores of internalizing behavior indicated
that the low-decreasing group remained in the normal range while the high-increasing group
demonstrated clinically elevated symptoms (Achenbach et al., 2016). Table 2.5 present the
intercept and slope values for the trajectory classes and Table 2.6 presents the raw and T-scores
of internalizing behavior at each timepoint.
Neighborhood disadvantage as a predictor. Logistic regression analyses examined
neighborhood disadvantage as a predictor of the internalizing group membership using Class 1
(“low-decreasing”) as the reference group. A bivariate-level analysis showed that living in a
disadvantaged neighborhood is associated with increased odds of being in the “high-increasing”
trajectory group for internalizing behavior (1.10:1 odds; 95% CI [1.01, 1.21]). When all
covariates were included, neighborhood disadvantage remained a significant predictor and
associated with increased odds of being in the “high-increasing” trajectory group for
23
internalizing behavior (1.16:1 odds; 95% CI [1.02, 1.32]), controlling for age, sex, race/ethnicity,
per capita family income, and parental education (Table 2.7).
Externalizing symptomatology.
Similar to the results of internalizing symptomatology, fit statistics of GMMs on 1 to 4-
class solutions suggested a 2-class solution best fit the data for externalizing behavior (Table
2.8). The model demonstrated an entropy value of .96, which represents high entropy (Clark &
Muthen, 2009), substantial class size, and a significantly better fit than the 1-class model (LMR
p-value < .001). The 3-class model, although it also had a high entropy value of .96 it was not
significantly different from the 2-class model (LMR p-value = .239). Similarly, the 4-class model
(entropy = .94) did not differ significantly from the 3-class model (LMR p-value = .289). Similar
to the results of internalizing symptomatology, in the 3-class model, the two high-symptom
groups, one group with an increasing slope (5%) and another with a decreasing slope (5%), were
substantively similar to the single high-increasing group in the 2-class model. Thus, the 2-class
model was chosen as the best fitting model. Figure 2.1 shows the 2-class trajectory groups. Class
1 (“low-decreasing” trajectory; 92%) was characterized by low initial levels of externalizing
behavior (raw score of 3.20) at age 9 which slightly decreased over time (linear slope = -.26, p
< .001). Class 2 (“high-increasing” trajectory; 8%) consisted of a relatively small sample
demonstrating high initial levels of externalizing behavior (raw score of 16.37) at age 9 which
increased over time (linear slope = 1.04, p < .001). The equivalent T-scores of externalizing
behaviors indicated that the low-decreasing group remained in the normal range while the high-
increasing group demonstrated clinically elevated symptoms. Table 2.9 present the intercept and
slope values for the trajectory classes and Table 2.10 presents the raw and T-scores of
externalizing behaviors at each timepoint.
24
Neighborhood disadvantage as a predictor. The same steps were taken to examine
neighborhood disadvantage as a predictor of the externalizing group membership using Class 1
(“low-decreasing”) as the reference group. A bivariate-level analysis showed that living in a
disadvantaged neighborhood is associated with increased odds of being in the “high-increasing”
trajectory group for externalizing behavior (1.25:1 odds; 95% CI [1.15, 1.36]). When all
covariates were included, results suggested that living in a disadvantaged neighborhood is not
associated with odds of being in the “high-increasing” trajectory group (1.04:1 odds; 95% CI
[.92, 1.19]), controlling for age, sex, race/ethnicity, per capita income, and parental education
(Table 2.11).
Discussion
The current study leveraged a large nation-wide dataset to examine the heterogeneity in
trajectories of internalizing and externalizing behavior and neighborhood disadvantage as a
correlate of the unique patterns during the transition from late childhood to early adolescence.
Two distinct trajectories were found in each of the models for internalizing and externalizing
behaviors: a majority of the sample demonstrated low initial levels and a slight decrease over
time, while a small group displayed high initial symptom levels which then increased
significantly over time. Higher levels of neighborhood disadvantage were associated with greater
likelihood of being in the high-increasing internalizing group, and this association remained
significant while controlling for household income and parental education levels, which indicate
that there are unique contributions that the neighborhood environment makes beyond family-
level socioeconomic status. For externalizing behaviors, neighborhood disadvantage did not
predict distinct patterns of trajectories.
25
For both internalizing and externalizing behaviors, two distinct trajectories were found:
low-decreasing and high-increasing trajectory groups. The levels of internalizing and
externalizing psychopathology were continually low for the large majority of participants
belonging to a low-decreasing group, which is consistent with previous findings of group-based
trajectory studies (Bongers et al., 2004; Ip et al., 2019). However, the findings revealed a
meaningful variability in trajectories of internalizing and externalizing behaviors which may
have been hidden in an aggregate level analysis of latent growth curves. In particular, and
contrast to previous studies that reported three or more different trajectories (Bongers et al.,
2008; De Lijster et al., 2019; Ip et al., 2019), the current study found only two classes. There
may be two possibilities in the differences. First, the sample is a large, community sample with
generally high SES. Consequently, the sample may under-represent the high-risk population, and
the potential varied patterns within higher-risk samples. Secondly, the current analysis had three
timepoints, ages 9 to 13, which is a smaller age range than previously examined (Costello et al.,
2008; Latendresse et al., 2011). It is possible that in a longer range of time, there will be greater
variability across time that drives the class pattern detection. Nevertheless, the two classes
demonstrated meaningful differences. For both internalizing and externalizing behaviors,
average levels of psychopathology in the high-increasing group demonstrated clinically
significant elevations. Distinct, group-based patterns of elevation were present at age 9,
indicating that a subgroup of the youth was already demonstrating clinically elevated
internalizing and externalizing symptoms at the inception of the study. From a preventive
perspective, the findings indicate that it may be informative to investigate the developmental
trajectory of internalizing and externalizing symptoms prior to this time point to understand the
point of emergence of a distinct at-risk subgroup.
26
Neighborhood disadvantage was associated with a greater likelihood of being in stably,
clinically elevated levels of internalizing behavior by nine years of age, rather than small
symptom differences across the population. These findings are in line with previous research
showing neighborhood disadvantage’s association with greater initial levels of the depressive
symptom trajectory during mid-adolescence (Barr, 2018). The findings also indicate that the
association may begin earlier in childhood, which has implications for models of sensitive
periods (Leventhal, 2018). The early childhood model (Duncan et al., 2010) posits that exposure
to adverse neighborhood during childhood is most salient while the adolescence model
(Leventhal et al., 2009; Steinberg & Morris, 2001) stipulates that neighborhood effects are most
important during adolescence. The cumulative exposure model of neighborhood context
(Crowder & South, 2011) on the other hand suggests that children’s cumulative experiences may
be more important than the exposures during a specific developmental period. Although the
current study did not explicitly test which model is best supported, the findings provide that
childhood exposure does have meaningful implications for mental health. In other words,
findings may be able to challenge models that argue childhood exposures are not salient and
adolescence as the only meaningful developmental period for exposure (Leventhal et al., 2009).
The association remained significant while controlling for household income and parental
education levels, which indicate that there are unique contributions that the neighborhood
environment makes beyond family-level socioeconomic status. A few previous studies have
examined the association between neighborhood disadvantage and trajectory group patterns but
did not control for family-level socioeconomic status (Estrada-Martínez et al., 2019; Wang et al.,
2014). The current study provides important evidence for neighborhood-level social
27
determinants of mental health as well as potential mechanisms operating at levels beyond the
family context.
A number of possible mechanisms may account for the association between
neighborhood disadvantage and trajectories of internalizing behavior. Drawing on the lens of
social determinants of health and the stress processing theory (Pearlin, 2005), disadvantaged
neighborhoods may limit access to critical resources important for development such as
institutional resources, quality relationships and social network, and collective efficacy
particularly around supervision of youth thereby predicting youth’s adverse behavioral and
emotional health by (Leventhal & Brooks-Gunn, 2000). Further, structural dimensions of
neighborhood disadvantage that aggregate socioeconomic and demographic risk may increase
the likelihood that youth residing in disadvantaged neighborhoods may experience greater
threats, such as possibilities of crime and violence (Sampson et al., 1997). Ambient hazards in
disorderly or dangerous neighborhoods may be linked to stressful experiences which may cause
greater levels of anxiety and depression (Aneshensel, 2009). Future research should incorporate
these possible mechanisms through which neighborhood level risk factors confers influence on
mental health. Addressing the structural origins of these issues may be beneficial in improving
mental health overall and reducing disparities.
The association between neighborhood disadvantage and the experience of stable,
clinically elevated internalizing problems also has important implications for prevention and
intervention. Under-resourced neighborhoods may need stronger investment in screening,
intervention services, or prevention programs, especially for internalizing behavior. From a
public health standpoint, community and policy-level programs targeted at enhancing the
28
neighborhood context and reducing neighborhood-level risk exposures may improve adolescent
mental health development.
Contrary to hypotheses, neighborhood disadvantage did not predict distinct patterns of
trajectories of externalizing behavior. Prior research found that living in high-risk neighborhoods
in grade 6 predicted group membership of high-risk, increasing trajectories throughout early and
mid-adolescence (Wang et al., 2014). Given that such an association was not found in the current
study that covered the range of late childhood to early adolescence, results may suggest that the
link between neighborhood disadvantage and externalizing behaviors are more likely to emerge
in mid- or late adolescence (Murry et al., 2011). Another potential explanation is that the
previous literature focused on risky behaviors, a subtype of externalizing behavior that may have
more specific patterns of elevation and change which may not be detected in the overall
externalizing symptomatology. Additionally, this association was significant when individual
level socioeconomic status was not taken into account, suggesting that at this developmental
stage, family-level socioeconomic status may be more salient than neighborhood-level context.
The findings have implications for future research directions. As the longitudinal data
collection continues with the sample in ABCD, it will be possible to examine whether the
patterns of group-based trajectories emerge in later adolescence for externalizing behavior. It
will also allow for investigation of whether neighborhood disadvantage predicts trajectories that
include later timepoints. Additionally, given the gender differences in levels of internalizing and
externalizing symptomatology, there may also be observable gender differences in the
trajectories, especially given that internalizing symptoms tend to increase for females in mid-
adolescence (Gutman & Codiroli McMaster, 2020). Future studies may be able to investigate the
gender differences in the association between neighborhood disadvantage and trajectory groups.
29
Further, future analyses will be able to examine the mechanisms by which neighborhood
disadvantage predicts mental health symptoms that are specific to mid- and late adolescence.
Additionally, future models can examine family and neighborhood-level factors that can mitigate
the association between neighborhood disadvantage and internalizing trajectories, such as
parental monitoring and supervision and school climate (Murry et al., 2011).
Limitations and Strengths
The current results should be interpreted in light of limitations. The current analysis used
pre-COVID-19 data which contained three time points of observation. The limited time points
did not allow for adequate modeling of non-linear patterns of trajectory groups that may be
present. The current study did not have a neighborhood disadvantage variable that varied over
time and thus it was not possible to directly test the developmental timing and cumulative
exposures to risky conditions. Missingness in key variables may lead to bias in the results that
need further investigation. Additionally, given the correlational nature between neighborhood
and baseline mental health measures, results do not indicate causality. However, previous
empirical evidence (Ludwig et al., 2008) for the potential causal effect of neighborhood provides
support for the precedence and contribution of neighborhood disadvantage exposure to mental
health outcomes. For measures of internalizing and externalizing behaviors, parent reports were
used which may not accurately capture symptomatology of pre-adolescents. The study also used
a community sample with relatively low levels of internalizing and externalizing
symptomatology as well as socioeconomic disadvantage. Nevertheless, the results revealed
existence of variability, suggesting important heterogeneity.
Despite the limitations, several strengths should be considered. The current study used a
large diverse longitudinal dataset from 21 sites across the U.S. that resemble the regional and
30
demographic diversity of the U.S. population. Although it consisted of a generally high-SES
sample, significant levels of neighborhood disadvantage were nevertheless captured allowing
investigation of more typical and pervasive patterns in a relatively low-risk, community sample.
Conclusion
The findings provide empirical description of the developmental course of internalizing
and externalizing behaviors in late childhood to early adolescence. Results highlight
neighborhood disadvantage as an important risk factor for clinically elevated levels of
internalizing behavior by late childhood that persist or increase throughout early adolescence,
indicating that the association may begin earlier in childhood. Prevention and intervention efforts
addressing internalizing symptoms for children who are living in disadvantaged neighborhoods
may be warranted prior to age 9. Community and policy-level programs may target enhancing
the neighborhood context and reducing neighborhood-level risk exposures to improve adolescent
mental health development. Future work may investigate the mechanisms and protective factors
in the link to inform further prevention and intervention efforts.
31
Table 2.1
Descriptive Statistics for Sample Characteristics
Analytic Sample for GMM
(n = 9,823)
Analytic Sample for Logistic
Regression Analyses
(n = 8,337)
ABCD Full Sample before
Exclusion
(n = 11,876)
Variable N (%) M (SD) N (%) M (SD) N (%) M (SD)
Sex (Baseline)
Female
4,651 (47.3%)
3,959 (47.5%)
5,680 (47.8%)
Age (years) (Baseline) 9.90 (.62) 9.90 (.62) 9.92 (.62)
Race
Hispanic
Non-Hispanic White
Non-Hispanic Black
Non-Hispanic Asian
Other
2,101 (21.4%)
4,904 (49.9%)
1,477 (15.0%)
207 (2.1%)
1,134 (11.5%)
1,648 (19.8%)
4,477 (53.7%)
1,076 (12.9%)
169 (2.0%)
967 (11.6%)
2,411 (20.3%)
6,094 (51.3%)
1,765 (14.9%)
239 (2.0%)
1,367 (11.5%)
32
Parental Education
< High school diploma
High school diploma or GED
Some college
Bachelor’s degree
Postgraduate degree
509 (5.2%)
969 (9.9%)
2,568 (26.1%)
2,432 (24.8%)
3,334 (33.9%)
308 (3.7%)
692 (8.3%)
2,124 (25.5%)
2,179 (26.1%)
3,034 (36.4%)
593 (5.0%)
1,132 (9.5%)
3,079 (26.0%)
3,015 (25.4%)
4,043 (34.1%)
Income, $ (per capita) 24,235.03
(18,432.96)
24,302.50
(18,409.73)
23,985.44
(18,101.08)
Internalizing
Baseline
Year 1
Year 2
5.16 (5.60)
5.29 (5.65)
5.08 (5.67)
5.18 (5.55)
5.32 (5.60)
5.10 (5.60)
5.05 (5.53)
5.12 (5.56)
4.94 (5.62)
Externalizing
Baseline
4.52 (5.90)
4.47 (5.83)
4.45 (5.86)
33
Year 1
Year 2
4.26 (5.66)
3.97 (5.42)
4.22 (5.58)
3.95 (5.36)
4.18 (5.66)
3.93 (5.52)
Neighborhood Disadvantage
Factor Score (Baseline)
0.00 (.97) -0.07 (.91) .00 (.97)
High School Diploma, % 87.92 (11.75) 88.70 (11.08) 88.19 (11.57)
Median Family Income, $ 75,801.50
(36,162.95)
77,542.02
(35,837.45)
76,551.99
(36,186.56)
Unemployment Rate, % 9.23 (6.14) 8.88 (5.79) 9.07 (6.05)
Families Below Poverty
Level, %
11.88 (12.45) 11.05 (11.61) 11.59 (12.25)
Single-Parent Families, % 18.38 (13.10) 17.57 (12.40) 18.08 (12.91)
34
Table 2.2
Neighborhood factor score
Neighborhood measure Factor loadings
High School Diploma, % -0.74
Median Family Income, $ -0.76
Unemployment Rate, % 0.79
Families Below Poverty Level, % 0.95
Single-Parent Families, % 0.88
35
Table 2.3
Correlations Among Study Variables
1 2 3 4 5 6 7 8 9
1. Neighborhood Disadvantage
(Factor Score) (Baseline)
2. Internalizing (Baseline) .04***
3. Internalizing (Year 1) .01 .69***
4. Internalizing (Year 2) .01 .64*** .69***
5. Externalizing (Baseline) .10*** .58*** .43*** .39***
6. Externalizing (Year 1) .09*** .44*** .57*** .40*** .75***
7. Externalizing (Year 2) .07*** .41*** .42*** .55*** .69*** .73***
8. Sex (Baseline)
(male = 0; female = 1)
.00 -.01 .02 .04** -.12*** -.10*** -.09***
9. Age (Baseline) -.03* .01 .01 .01 -.02* -.01 -.003 -.02*
10. Household Income -.47*** -.09*** -.07*** -.07*** -.14*** -.14*** -.13*** -.001 .02*
Note. * p < .05. ** p < .01. *** p < .001.
36
Table 2.4
Fit indices for Growth Mixture Model of Internalizing Behaviors
No. of
Classes
BIC SSABIC Entropy No. of
Parameters
LMRT Class Size (%, n)
1 119269.16 119243.74 8
2 116834.79 116799.84 .92 11 .004 Class 1: 91%; 8,937
Class 2: 9%; 844
3 115484.81 115440.33 .92 14 .050 Class 1: 87%; 8,601
Class 2: 7%; 644
Class 3: 6%; 576
4 114652.62 114598.60 .91 17 .084 Class 1: 82%; 8,053
Class 2: 2%; 197
Class 3: 6%; 599
Class 4: 10%; 972
37
Table 2.5
Intercept and Slope for Internalizing Trajectory Classes
Intercept Slope
Class 1 (n = 8,937)
(91%; low-decreasing)
4.24*** -.19**
Class 2 (n = 884)
(9%; high-increasing)
14.01*** 2.83***
Note. ** p < .01. *** p < .001.
38
Table 2.6
Internalizing Symptoms: Raw and T-Scores Across ABCD Study Waves
Baseline
Raw (T-score)
Year 1 Follow-up
Raw (T-score)
Year 2 Follow-up
Raw (T-score)
Class 1 (n = 8,937)
(91%; low-decreasing)
4.24 (47) 4.18 (47) 3.86 (46)
Class 2 (n = 884)
(9%; high-increasing)
14.01 (63) 17.50 (67) 19.59 (69)
39
Table 2.7
Logistic Regression Results for Internalizing Trajectory Groups
High-increasing
(n = 699)
OR (95% CI)
Neighborhood disadvantage 1.16 (1.02, 1.32)
a
Age 1.00 (.99, 1.01)
Gender (reference = male)
Female 1.16 (.97, 1.39)
Race/ethnicity (reference = White)
Hispanic .84 (.65, 1.09)
Black .57 (.40, .82)
a
Asian .32 (.12, .88)
a
Other 1.14 (.88, 1.49)
Income .992 (.985, .998)
a
Parental education (reference = HS graduate/GED)
Less than HS .87 (.46, 1.63)
Some college 1.34 (.92, 1.96)
Bachelor’s degree 1.05 (.70, 1.59)
Postgraduate 1.08 (.71, 1.64)
a
Odds ratio for trajectory group significantly different from the “low-decreasing” group (p < .05).
40
Table 2.8
Fit indices for Growth Mixture Model of Externalizing Behaviors
No. of
Classes
BIC SSABIC Entropy No. of
Parameters
LMRT Class Size (%, n)
1 116385.93 116360.51 8
2 113113.46 113078.50 .96 11 <.001 Class 1: 92%; 9,035
Class 2: 8%; 786
3 111350.89 111306.41 .96 14 .239 Class 1: 90%; 8,868
Class 2: 5%; 452
Class 3: 5%; 501
4 110352.82 110298.8 .94 17 .289 Class 1: 85%; 8,328
Class 2: 2%; 177
Class 3: 4%; 422
Class 4: 9%; 894
41
Table 2.9
Intercept and Slope for Externalizing Trajectory Classes
Intercept Slope
Class 1 (n = 9,035)
(92%; low-decreasing)
3.20*** -.26**
Class 2 (n = 786)
(8%; high-increasing)
16.37*** 1.04*
Note. * p < .05. ** p < .01. *** p < .001.
42
Table 2.10
Externalizing Symptoms: Raw and T-Scores Across ABCD Study Waves
Baseline
Raw (T-score)
Year 1 Follow-up
Raw (T-score)
Year 2 Follow-up
Raw (T-score)
Class 1 (n = 9,035)
(92%; low-decreasing)
3.20 (44) 2.92 (43) 2.71 (42)
Class 2 (n = 786)
(8%; high-increasing)
16.37 (64) 17.60 (65) 18.03 (65)
43
Table 2.11
Logistic Regression Results for Externalizing Trajectory Groups
High-increasing
(n = 628)
OR (95% CI)
Neighborhood disadvantage 1.04 (.92, 1.19)
Age .99 (.98, 1.01)
Gender (reference = male)
Female .59 (.49, .71)
a
Race/ethnicity (reference = White)
Hispanic .63 (.48, .84)
a
Black 1.02 (.75, 1.41)
Asian .41 (.15, 1.11)
Other 1.23 (.84, 1.62)
Income .98 (.97, .99)
a
Parental education (reference = HS graduate/GED)
Less than HS .49 (.25, .95)
a
Some college .92 (.66, 1.28)
Bachelor’s degree .68 (.47, .99)
a
Postgraduate .63 (.43, .93)
a
a
Odds ratio for trajectory group significantly different from the “low-decreasing” group (p < .05).
44
Figure 2.1
GMM trajectories for internalizing and externalizing behaviors
45
CHAPTER 3: Emotion Regulation: Mechanisms of Neighborhood Effects on Mental
Health
Introduction
There is growing evidence that living in a disadvantaged neighborhood is associated with
youth mental health (Leventhal & Dupéré, 2019). Prior literature has documented the
longitudinal association between neighborhood disadvantage and increased depressive symptoms
(Barr, 2018; Solmi et al., 2017), anxiety symptoms (King et al., 2022), and externalizing
symptoms (Li et al., 2017; McBride Murry et al., 2011). Empirical evidence from randomized
experimental studies also suggests neighborhood effects on mental health may be causal, at least
in part (Kessler et al., 2016; Leventhal & Brooks-Gunn, 2003; Ludwig et al., 2008). However,
less is known about the underlying process through which neighborhood disadvantage may be
associated with adolescent mental health (Sharkey & Faber, 2014). Mechanisms are complex and
may operate at the exposure level, such as through lack of institutional or social-interactive
resources, as well as individual levels, including quality of the family environment (Minh et al.,
2017). Importantly, there are key underlying aspects of socioemotional and cognitive
development at the individual level that may shape the risk for mental health outcomes. Emotion
regulation is one of the key self-regulatory functions critical for development that may be
associated with the neighborhood context (McCoy et al., 2016), and early challenges with
emotion regulation may increase the likelihood for mental health problems.
Theoretical Framework: Contextual Stressors and Emotion Regulation
Stress may be a primary pathway through which neighborhood context may be related to
emotional development and regulation. Stress is a central construct that has been persistently
linked to poor mental health (Pearlin, 1989), and neighborhood disadvantage may undermine
46
youth mental health through the exposure to stressful conditions (Aneshensel, 2009). It has been
proposed that disadvantage experienced early in life and the associated stressors may
fundamentally alter the way in which emotional information is prioritized and processed
(McEwen, 2006; McLaughlin et al., 2019). Relatedly, the concept of allostatic load and the
alterations in stress response have been proposed as a central mechanism underlying
socioeconomic disparities. Allostatic load is the consequence of allodynamic wear-and-tear on
the body and brain that promotes adverse health as a result of stressful experiences (McEwen,
2006; McEwen & Gianaros, 2010). The stressors can influence how brain regions such as the
hippocampus, amygdala, and prefrontal cortex respond to stress through structural remodeling,
which alters physiological and behavioral responses. Children living in poverty are likely to
confront physical and psychological stressors, and accumulating exposures to stressors can
overwhelm physiological responses designed to handle occasional acute environmental demands,
thus disrupting the self-regulatory processes that help children cope with external demands
(Evans & Kim, 2013).
Neighborhood Context as a Stressor
Neighborhood-level disadvantage can be associated with adolescents’ self-regulatory and
emotional development through the stress pathway. Neighborhood socioeconomic disadvantage
is characterized by the simultaneous absence of economic, social, and family resources (Ross &
Mirowsky, 2001), and neighborhoods characterized by poverty tend to have features of chaotic
environments such as degenerating physical infrastructure, visible signs of unsafe surroundings,
and possible danger of crime and violence (Aneshensel & Sucoff, 1996; Ross & Mirowsky,
2001). These “ambient hazards” in the neighborhood in addition to the absence of resources may
be perceived as stressful and affect the sense of safety and security (Aneshensel & Sucoff, 1996).
47
Indeed, early exposure to poverty has been associated with reduced inhibitory control in
adulthood, a process partially explained by elevated allostatic load (Evans et al., 2021). At the
neighborhood level, living in a high-crime neighborhood has been found to be associated with
children’s maladaptive emotion regulation, such as greater use of emotional suppression (McCoy
et al., 2016) and dysfunctional emotion regulation (Sun et al., 2020). Thus, neighborhood
disadvantage exposure in early life may have consequences for how emotion is processed and
regulated through alterations in the stress pathway.
Emotion Regulation
Emotion regulation is an important child-level transdiagnostic factor that has implications
for mental health. Emotions arise in response to internal or external stimuli as an individual
experiences and appraises a context (Lazarus, 1991). Emotion regulation is a deliberate or
automatic attempt to modify the quality, intensity, and duration of emotions (Gross &
Thompson, 2007). The model of emotion regulation (Gross & Thompson, 2007) provides that
emotion regulation process consists of multiple steps, including 1) engagement in an emotionally
arousing situation, 2) selective attention toward the source of emotional arousal, 3) appraisal of
the valence, value, and familiarity of the emotion, and implementation of behavioral, cognitive,
or physiological response to the emotion. The ability to successfully deploy regulatory strategies
to modify the magnitude of the emotional experience is important in everyday social functioning
and long-term mental health (Aldao et al., 2010).
Emotion regulation develops throughout the life course within a complex interplay of
social, cognitive, and biological domains. As children enter adolescence, the ability to regulate
emotions increases, through the interaction of physiological development at the biological level
and increased socialization at the environmental level (Zeman et al., 2006). The development of
48
emotion regulation is especially important during adolescence for two reasons: (1) adolescence is
a time of rapid changes in biological, cognitive, and social systems and one of the central tasks
during this period is to learn adaptive strategies in regulating affect independently (Steinberg &
Avenevoli, 2000) and (2) emotionally challenging situations become more intense and frequent
during this developmental period as adolescents begin to engage in new situations and challenges
independently (A. O. Cohen et al., 2016), amplifying the instability of emotional states during
early adolescence (Larson et al., 2002). Thus, adolescence marks an important time for
development of emotion regulation, and disruption in normative emotional development may
have consequences for psychological functioning.
Emotion Regulation and Psychopathology
Emotion dysregulation has been found to contribute to many types of psychopathology in
adolescents including depression (McLaughlin et al., 2011; Silk et al., 2003) anxiety and
aggressive behaviors (McLaughlin et al., 2014), and externalizing behaviors (Heleniak et al.,
2016). Recent reviews and meta-analyses reiterate these findings (Beauchaine, 2015; Compas et
al., 2017), supporting the role that emotion regulation plays longitudinally in predicting co-
occurrence of internalizing and externalizing problems (Weissman et al., 2019). Thus, emotion
regulation is a central component of socioemotional development, related to psychopathology
across diagnostic domains, that can potentially contribute to the association between
neighborhood disadvantage and mental health across multiple outcomes.
Automatic Emotion Regulation
Emotion regulation incorporates both deliberate conscious emotion regulation, which is
more controlled and effortful, and automatic emotion regulation, a nonconscious, habitual
process (Mauss, Bunge, et al., 2007). When faced with an emotional challenge, it is not always
49
possible or efficient to operate regulatory functions in a conscious, effortful manner. Automatic
emotion regulation (AER) is the ability to carry out the everyday tasks and meet the demands
that are invoked spontaneously in the face of emotional interference or stimuli, and this capacity
may be a key functioning that shapes mental health (Gyurak et al., 2011; Taylor et al., 2018).
While conscious, effortful emotion regulation highlights individuals’ agency in deliberately
controlling emotional responses (Gross, 2007), AER refers to the “higher-level” regulatory
process during which modification to one’s emotions are made without the deliberate or
conscious attempts to do so (Braunstein et al., 2017; Mauss, Bunge, et al., 2007) and is likely to
be pervasive in daily life.
However, less is known about the role of the automatic and nonconscious aspect of
emotion regulation in shaping youth mental health, as prior research on emotion regulation has
primarily focused on the conscious aspect of emotion regulation strategies that are effortful such
as cognitive reappraisal, rumination, or suppression (Braunstein et al., 2017). Emerging literature
on adults has documented that decreased performance on AER tasks is cross-sectionally linked
to adult depressive symptoms (Hopp et al., 2011) and increased anger provocation (Mauss,
Cook, et al., 2007). Less research has investigated AER in youth. AER may be particularly
important for youth’s mental health because it is argued that AER reflects overlearned habits,
sociocultural norms, implicit hedonic goals, and regulatory strategies learned early in life
(Mauss, Bunge, et al., 2007). Very few studies (i.e., Crum et al., 2021a; S. G. Kim et al., 2021a;
Vargas & Mittal, 2021) have examined the role of AER in youth mental health with mixed
findings—some studies have reported that AER was linked to adolescent depressive symptoms
(Vargas & Mittal, 2021), although such link in high-risk youth was not confirmed in another
study (Kim et al., 2021), potentially due to differences in measurement and sample
50
characteristics. The emerging body of evidence points to the important role AER may play in
predicting adolescent mental health.
Underlying Neural Bases of AER
It is important to consider the underlying process of AER at the neural and behavioral
levels to understand the process of AER and how it may be impacted by stressors. A few recent
studies have found evidence that regions in the prefrontal cortex (PFC), areas related to cognitive
control and decision making, are implicated in AER. Functional MRI (fMRI) studies with
children demonstrate that AER relies on the process of inhibiting emotional distracters through
top-down modulatory control of amygdala responsivity by pregenual cingulate cortex (PgACC),
dorsolateral prefrontal cortex (DLPFC) (Marusak et al., 2015) and the medial orbitofrontal
regions (Urbain et al., 2017), suggesting involvement of attention regulation. Another study of
adults differentiated the neural circuitry between conscious and automatic emotion regulation
while participants performed a task (Hallam et al., 2015). In conscious emotion regulation, the
study found activation of the dorsolateral prefrontal cortex (DLPFC), an area related with
cognitive control, while in AER, increased modulation of the amygdala and limbic system by
medial orbitofrontal cortex (mOFC) was observed. Results suggest that conscious emotion
regulation was associated with less effective modulation of left amygdala, while AER was
associated with increased efficacy of emotion regulation, providing support for AER’s
importance in that it may involve less effort and more efficiency (Hallam et al., 2015). Evidence
highlights the importance of development of AER during early life as the capacity for cognitive
control develops rapidly during childhood (Zelazo et al., 2003) and the brain regions continue to
develop throughout adolescence (Luna, 2009). Understanding the neural bases of AER helps
51
elucidate how contextual stressors may be associated with the capacity to perform everyday tasks
in the face of different types of stimuli at the behavioral level.
AER as a Mechanism
Thus, the development of regulatory mechanisms and the neural bases may be one of the
underlying mechanisms for neighborhood disadvantage - adolescent mental health link.
Alterations to stress response systems, neural structures and circuitry, and behavioral responses
may impede the development of emotion regulation such as that needed for healthy development
(Blair, 2010). Evidence suggests that chronic stress during adolescence may underlie the
association between childhood poverty and disruptions in brain regions implicated in conscious
reappraisal, such as the DLPFC. (P. Kim et al., 2013). Functional MRI studies showed that
neighborhood poverty is associated with increased activities in the emotion processing areas such
as the amygdala, hippocampus, ventral medial prefrontal cortex, and medial orbitofrontal cortex
(Hyde et al., 2020). A study using behavioral task to measure response inhibition found that
neighborhood poverty predicts reduced inhibition performance via lower activation of the
inferior frontal gyrus, indicating that disadvantaged context may undermine self-control through
effects on the brain (Tomlinson et al., 2020). Thus, emerging evidence in both neural and task-
based studies points to the fact that neighborhood disadvantage may disrupt the development of
neural mechanisms of AER and thus may in turn be implicated in adolescent psychopathology.
However, no identified studies have evaluated AER at the behavioral level as a mechanism
linking neighborhood disadvantage and youth mental health. By examining AER as a mechanism
underlying the association between disadvantaged neighborhood context and mental health, the
field can further clarify how ecological stressors and emerging patterns of emotion regulation
may relate to maladaptive outcomes over time.
52
Study Aims
The current analysis uses data from the Adolescent Brain Cognitive Development
(ABCD) Study to investigate AER’s role as a mediator in the association between neighborhood
disadvantage and internalizing and externalizing symptoms. The current study employs a
subsample of the data using the same methodology as in Chapter 2. Trajectories for internalizing
and externalizing behaviors remain generally stable during late childhood to early adolescence
(Bongers et al., 2003; J. R. Cohen et al., 2018) and findings from Chapter 2 indicate minimal
overall changes in internalizing and externalizing symptomatology from baseline (ages 9-11) to
year 2-follow-up (ages 11-13). Thus, AER as a mediator may not be well-suited to explain the
change in symptoms from baseline to year 2. Given the limitations, the current study investigated
cross-sectional associations among neighborhood disadvantage, AER, and internalizing and
externalizing behaviors at Baseline, which were then modeled to predict internalizing and
externalizing behaviors at Year 2 to test for continuity 2 years later as a first novel step. It is
hypothesized that AER will mediate the association between neighborhood disadvantage and
AER, such that higher neighborhood disadvantage is associated with lower levels AER, and that
lower AER is associated with higher internalizing and externalizing problems at baseline, which
then predict year 2 mental health symptoms.
Methods
Participants and Procedures
The Adolescent Brain and Cognitive Development (ABCD) study, using the same
analytic sample (n = 9,823) from the previous study created after random selection of one child
per family (Hackman et al., 2021) as a starting point (See Chapter 2 for detailed descriptions on
procedures, participants, measures, and missingness). From this subsample, the current analysis
53
excluded participants with missing data on the Emotional N-Back task (n = 317). Then,
participants with performance accuracy below 60% (n = 1,568) were excluded to remove very
low performance that may indicate not understanding the task or task non-compliance. Lastly,
those who did not complete the full task were excluded (n = 76), resulting in a final analytic
sample of 7,862 participants. For neighborhood disadvantage, Emotional N-Back task, and
covariates, baseline data were used. For internalizing and externalizing behaviors, both baseline
and year 2 data were used.
Measures
Neighborhood Disadvantage. Neighborhood disadvantage was operationalized using the
same methodology as in Chapter 2 (See Chapter 2 for detailed descriptions on the creation of the
variable). A factor score was created with five constructs, percentage of residents with at least a
high school diploma, median family income, unemployment rate, percentage of families living
below the federal poverty level, and percentage of single-parent households.
Emotional N-Back. The Emotional N-Back, completed at the baseline visit while
participants were undergoing functional magnetic resonance imaging (fMRI), was used to
measure AER, the capacity to engage in a challenging neurocognitive process in the face of
interference from different types of emotional stimuli (Casey et al., 2018). The task consisted of
160 total trials, with two memory load conditions of 8 blocks of 10 trials each: 2-back (80 trials)
and 0-back (80 trials), presented in alternating blocks (see Figure 3.1). On each trial, participants
were asked to indicate whether the picture is a “match” or “no match.” For the 0-back task,
participants must retain in memory one stimulus for the entire sequence and indicate the match
every time it is presented throughout the sequence of stimuli. In the 2-back condition, the task
was to correctly indicate whether the new stimulus in the sequence of faces matched the old
54
stimulus presented “2” times before, which engages working memory (Chaarani et al., 2021).
Participants were presented with a sequence of stimuli on a computer screen, which included 8
blocks of 10 trials of positive (happy) faces, negative (fearful) faces, neutral (no expression)
faces, and places, under both memory load conditions. The Emotional N-Back task requires the
ability to inhibit the emotional valence of the stimuli and perform the task with accuracy. The
primary measure of emotion regulation is the total correct responses, including correct hits and
omissions, for all items in both the 2-back and 0-back conditions. Given the high correlation
between the total score that includes all types of stimuli and the face-trial only score (r = .97, p
< .001), the total overall score was used to provide a summary measure of cognitive performance
accuracy in the face of a variety of emotional and non-emotional stimuli. Additionally, the
overall performance score rather than d prime was used to capture the overall regulatory
performance following similar steps in a previous ABCD study (Vargas & Mittal, 2021). A
performance threshold of 60% accuracy and completion of all 160 items was used to remove
very low performance that may indicate not understanding the task or task non-compliance as
flagged by the ABCD team. It is a threshold based on the idea that if a participant simply repeats
one answer (match vs. no match), the accuracy would presumably be 50% and thus does not
represent task compliance (Vargas & Mittal, 2021). The Emotional N-Back task is
developmentally appropriate for children (Barch et al., 2013) and has been used widely (Owen et
al., 2005).
Child Behavior Checklist (CBCL). Adolescent mental health was assessed by mental
health symptoms reported by parents using the Child Behavior Checklist (CBCL), created using
the same methodology as in Chapter 2 (See chapter 2 for detailed descriptions on the assessment
information and inclusion criteria). The Baseline and Year 2 measures of internalizing and
55
externalizing behaviors were used. Both internalizing (33 items; α = .91 at both timepoints) and
externalizing (35 items; α = .86 at both timepoints) behaviors indicated good reliability.
Covariates. Covariates included age, sex assigned at birth (male = 0, female = 1),
race/ethnicity as reported by caregiver (Non-Hispanic White, Hispanic, Non-Hispanic Black,
Non-Hispanic Native American, Non-Hispanic Asian, and Other), and parental education level
(highest educational achievement by the parent or caregiver: < High school diploma, High school
diploma or GED, Some college, Bachelor degree, Postgraduate degree). Total family income was
measured by all sources of income including wages, benefits, child support payments, and
others. It was collected in ordinal ranges (< $5,000; $5,000 - $11,999; $12,000 - $15,999;
$16,000 - $24,999; $25,000 - $34,999; $35,000 - $49,999; $50,000 - $74,999; $75,000 -
$99,999; $100,000 - $199,999; > $200,000). To create per capita income, midpoints of each
category were taken to create a continuous variable, which was divided by the caregiver-reported
household size. Household sizes reported as lower than 2 (n = 68) were treated as missing as it
was an implausible size given that the minimum household size included caregiver and child.
Household size reported as greater than 20 (n = 2) was also treated as missing.
In addition, the NIH Toolbox List Sorting Working Memory Test (TLSWMT)
(Weintraub et al., 2013) was used to account for the variance that is generally due to working
memory which allows for parsing out the emotion regulation component of the Emotional N-
Back task. The List Sorting Working Memory Test requires participants to sequence task stimuli
based on category membership and perceptual characteristics and thus measures information
processing, categorization, and working memory.
56
Data Analyses
Structural Equation Modeling (SEM) was used to investigate the hypothesized mediation.
In the model, neighborhood disadvantage was specified to have a main effect on the mediator
(performance on the N-Back task), which in turn had main effects on two outcomes
(internalizing and externalizing behaviors at baseline). Internalizing behavior at baseline was
specified have a main effect on internalizing behavior at year 2. Similarly, externalizing behavior
at baseline was specified have a main effect on externalizing behavior at year 2. Models included
the paths from covariates - age, sex assigned at birth, race/ethnicity, parental education level,
family income, and working memory - to both internalizing and externalizing symptomatology.
Multiple fit indices including comparative fix index (CFI > .95), the root mean square error of
approximation (RMSEA < .06), and standardized root mean squared residual (SRMR < .08)
were used to estimate model fit to the data (Hu & Bentler, 1999). All analyses were conducted in
Mplus version 8 (Muthén & Muthén, 1998-2017). The Model Indirect command was used to
calculate a standardized indirect effect parameter. The option Type: Complex in Mplus was used
to address potential non-independence due to clustering at the site level. Data were managed
using full information maximum likelihood estimation (FIML), a method recommended to
handle missing data (Schlomer et al., 2010).
Results
Descriptive statistics for the analytic sample (n = 7,862) and the full ABCD sample for
comparison (n = 11,876) are presented in Table 3.1. Correlations among study variables are
presented in Table 3.2. The average performance for the Emotional N-Back task was 132.08 (SD
= 12.40) out of the highest possible score of 160. Bivariate analyses indicated that higher levels
of neighborhood disadvantage were correlated with lower Emotional N-Back task performance
57
(β = -.24, p < .001), increased internalizing behavior (β = .04, p < .001), and increased
externalizing behavior (β = .09, p < .001) all measured at baseline. Better Emotional N-Back task
performance was associated with internalizing (β = -.05, p < .001) and externalizing (β = -.12, p
< .001) behaviors measured at baseline.
The results indicated that the model provided an adequate fit to the data (CFI = .912,
RMSEA = .055, SRMR = .030). The main study variables and their associations are depicted in
Table 3.3 and Figure 3.2. Baseline neighborhood disadvantage was negatively associated with
the Emotional N-back task performance measured at baseline (β = -.23, p < .001). Next, task
performance at baseline was negatively associated with baseline internalizing behavior (β = -.05,
p = .004). Internalizing at baseline predicted internalizing behavior at year 2 (β = .63, p < .001).
There was a significant and positive total effect of neighborhood disadvantage on baseline
internalizing behaviors (β = .04, p = .005). A non-significant direct effect (β = .03, p = .082) and
a significant and positive indirect effect (β = .01, p = .008) of neighborhood disadvantage on
baseline internalizing behavior indicated a full mediation by Emotional N-Back task
performance.
Similar results were found for externalizing behavior. N-Back task performance was
negatively associated with baseline externalizing behavior (β = -.08, p < .001). Externalizing
behavior at baseline predicted externalizing behavior at year 2 (β = .70, p < .001). There was a
significant and positive total effect of neighborhood disadvantage on baseline externalizing
behavior (β = .05, p = .012). A non-significant direct effect (β = .03, p = .107) and a significant
and positive indirect effect (β = .02, p < .001) of neighborhood disadvantage on baseline
externalizing behavior indicated a full mediation by Emotional N-Back task performance.
58
Discussion
The current study leveraged a diverse, national dataset to test whether automatic emotion
regulation (AER) accounts for the association between neighborhood disadvantage and
adolescent mental health. To date, no study has tested this hypothesized mechanism. The current
study found significant cross-sectional associations between neighborhood disadvantage and
AER as well as the association between AER and internalizing and externalizing symptoms for
youth. Support for the role of AER as a possible mediating mechanism for the association
between neighborhood disadvantage and mental health was also found.
The findings suggest that living in a disadvantaged neighborhood is associated with lower
levels of performance on AER, adding support to a few recent studies that investigated the role
of neighborhood context in neural and behavioral correlates of AER in youth (Hyde et al., 2020;
Tomlinson et al., 2020). Although the current findings are cross-sectional in nature, results add to
an emerging area of research on how neighborhood disadvantage may be related to maladaptive
regulatory strategies that may be implicated in adolescent psychopathology. Findings also extend
the literature beyond the association with deliberate emotion regulation (McCoy et al., 2016).
The results are also consistent with the theories that suggest potential sensitivity to
environmental stressors during this developmental period (Blair, 2010).
Much of the research on emotion regulation has focused on deliberate, effortful aspect of
regulation. The Emotional N-Back Task is designed to index the ability to inhibit the emotional
valence of the stimuli and perform the task with accuracy, representing the ability to carry out
everyday tasks the meeting demands that are invoked spontaneously in the face of emotional
interference (Gyurak et al., 2011). The current study found that AER fully mediated the
association between neighborhood disadvantage and internalizing and externalizing behaviors
59
extending the broader literature on emotion regulation and the associations with mental health.
Results suggest that such capacities may be important for mental health during late childhood to
early adolescence when demands increase in intensity and scope. Findings corroborate results
from a previous study on adolescent depressive and psychotic-like experiences (Vargas & Mittal,
2021) in youth. Of note, the findings were from the same ABCD study as the current analysis but
focused on depressive symptoms and psychotic-like experiences, rather than internalizing and
externalizing symptoms. This association, however, was not found in a study on anxiety and
depressive symptoms and PTSD symptoms cross-sectionally or prospectively for high-risk
adolescents (S. G. Kim et al., 2021), nor in anxiety symptoms in another study of adolescents
(Crum et al., 2021). One potential reason for the discrepant findings is the different measures of
AER used in the studies. The two studies used the Affective Stroop Task. Using either the
behavioral task (S. G. Kim et al., 2021) or neural correlates of performance (Crum et al., 2021),
these studies did not find the associations, suggesting that the tasks may capture potentially
divergent aspects of regulatory strategies or related neural regions. Additionally, the studies did
not account for working memory, which may be an additional component beyond emotion
regulation measured through the task. The samples in the studies were also older, covering late
adolescence. As evidence is emerging, more studies will be needed to understand the nature of
the association between AER and adolescent mental health in various developmental periods.
Current findings add to the literature by highlighting the role of AER in the association
with ecological context as well as mental health symptomatology. However, it is important to
highlight that the current study examined a cross-sectional path model and thus all hypothesized
paths were theoretical in nature. A longitudinal analysis that shows temporal order of the
disadvantage exposure, AER, and internalizing and externalizing behaviors will be important in
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further investigating the mediational pathway especially as additional timepoints become
available in the ABCD study. Although future research is needed to corroborate the mechanism,
significant concurrent associations among neighborhood disadvantage, AER, and mental health
symptoms at age 9-11 suggest that the mechanistic process may have occurred prior to this
period. Thus, an investigation prior to early adolescence may reveal when and how ecological
context influences biological and psychological correlates of the brain in the emergence of
psychopathology, elucidating potential sensitive periods for AER. Additionally, the current study
used the performance measure of AER as a first novel step as it captures the observable capacity
to perform everyday tasks in the face of different types of stimuli. To supplement the current
finding, future research is needed to examine the role of the neural processes and differences in
PFC-related activity that may underlie the mechanisms, consistent with the theory. Use of
multiple levels of analysis may also reveal further etiological and mechanistic understandings of
AER.
There are prevention and intervention implications for youth who are exposed to
environmental stressors such as neighborhood disadvantage. Programs that focus on developing
better emotional regulatory skills may be a practical point of intervention for improving mental
health outcomes. Theory and research suggest that AER develops from an early point in life,
through overlearned habits and strategies learned in early childhood (Mauss, Cook, et al., 2007).
Extant programs generally address the explicit emotion regulation system through helping youth
modify dysfunctional thinking and behavioral patterns. Addressing the automatic processes may
involve supporting healthy emotional development through programs that address the nature of
maladaptive defense mechanisms, building resilience and supporting normative socioemotional
development, especially targeting youth who are experiencing disadvantage. For example,
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psychotherapy programs may be targeted at identifying underlying habits from childhood that
shape maladaptive automatic emotion regulation (Prout et al., 2019).
Limitations and Strengths
The above results should be interpreted considering the following limitations. Given the
observational and correlational data examined, all hypothesized paths were theoretical in nature
and causality cannot be established. Missingness in key variables may also introduce potential
bias, particularly regarding demographic differences in the excluded sample, which may require
further investigation. For measures of internalizing and externalizing behaviors, parent reports
were used which may not accurately capture symptomatology of pre-adolescents which may
show discrepancies with self-reported mental health. However, the strong correlation between
parent-reported internalizing and externalizing behaviors and a performance-based measure of
AER may lend credibility to parent-reports of psychopathology.
The current study also includes important strengths. First, use of a behavioral task to
measure AER allows for representing the unconscious aspects of regulatory process, which may
be a more accurate representation of how adolescents attend to and process day-to-day emotional
stimuli than self-reports. Second, using a large national dataset allowed for the examination of
key variables in a diverse sample. Lastly, the current study tested for the mediational model
while controlling for important covariates including family income and parental education,
parsing out the independent contribution of neighborhood disadvantage on mental health
outcomes.
Conclusion
The sparse findings that link neighborhood disadvantage, AER, and psychopathology
among adolescents have led to gaps in understanding the underlying mechanisms that account
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for how the environment is associated with development of maladaptive outcomes.
Neighborhood disadvantage is a salient ecological stressor during early adolescence. The impact
of this stressor on youth internalizing and externalizing behaviors may be mediated by AER,
even when controlling for family-level income. The results point to future research that can
elucidate the developmental nature of AER and the neural underpinnings that may be critical for
understanding the etiology of internalizing and externalizing psychopathology. The findings also
indicate a potential point of prevention and intervention efforts that may mitigate the link
between ecological stressors and maladaptive outcomes for youth.
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Table 3.1
Descriptive Statistics for Sample Characteristics
Analytic Sample for Current Analysis
(n = 7,862)
ABCD Full Sample before Exclusion
(n = 11,876)
Variable N (%) M (SD) N (%)
Sex (Baseline)
Female
4150 (52.8%)
5,680 (47.8%)
Age (years) (Baseline) 9.18 (.62) 9.92 (.62)
Race
Hispanic
Non-Hispanic White
Non-Hispanic Black
Non-Hispanic Asian
Other
1611 (20.5%)
4207 (53.5%)
954 (12.1%)
181 (2.3%)
909 (11.6%)
2,411 (20.3%)
6,094 (51.3%)
1,765 (14.9%)
239 (2.0%)
1,367 (11.5%)
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Parental Education
< High school diploma
High school diploma or GED
Some college
Bachelor’s degree
Postgraduate degree
310 (3.9%)
639 (8.1%)
1954 (24.9%)
2039 (25.9%)
2913 (37.1%)
593 (5.0%)
1,132 (9.5%)
3,079 (26.0%)
3,015 (25.4%)
4,043 (34.1%)
Income, $ (per capita) 25446.90
(18,358.62)
23,985.44
(18,101.08)
Emotional N-Back* 132.08 (12.40) 132.01 (12.41)
NIH Toolbox List Sorting Working
Memory
98.26 (11.33) 96.64 (12.11)
Internalizing
Baseline
Year 2
5.04 (5.43)
5.08 (5.66)
5.05 (5.53)
4.94 (5.62)
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Externalizing
Baseline
Year 2
4.23 (5.63)
3.75 (5.18)
4.45 (5.86)
3.93 (5.52)
Neighborhood Disadvantage Factor
Score
-0.09 (.89)
.00 (.97)
High School Diploma, % 88.45 (11.24) 88.19 (11.57)
Median Family Income, $ 77299.01 (36945.03) 76,551.99
(36,186.56)
Unemployment Rate, % 9.29 (6.40) 9.07 (6.05)
Families Below Poverty Level, % 11.73 (12.71) 11.59 (12.25)
Single-Parent Families, % 18.29 (13.64) 18.08 (12.91)
*Note. Highest possible score for Emotional N-Back Task = 160
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Table 3.2
Correlations Among Study Variables
1 2 3 4 5 6 7 8 9
1. Neighborhood Disadvantage
(Factor Score) (Baseline)
2. AER -.24***
3. Working Memory -.24*** .36***
4. Internalizing (Baseline) .04*** -.05*** -.03*
5. Internalizing (Year 2) .03* -.03* -.01 .64***
6. Externalizing (Baseline) .09*** -.12*** -.10*** .58*** .40***
7. Externalizing (Year 2) .07*** -.09*** -.07*** .42*** .55*** .69***
8. Sex (Baseline)
(male = 0; female = 1)
-.001 -.07*** -.03** -.01 .05*** -.13*** -.10***
9. Age (Baseline) -.01 .19*** .12*** .01 .02 -.01 -.003 -.02*
10. Household Income -.46*** .19*** .21*** -.09*** -.06*** -.13*** -.11*** .01 .01
Note. * p < .05. ** p < .01. *** p < .001.
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Table 3.3
Mediating Role of AER on Internalizing and Externalizing Behaviors
Coefficient, p
Total Effect
Neighborhood à Internalizing (Baseline) β = .04, p = .005
Neighborhood à Externalizing (Baseline) β = .05, p = .012
Direct Effect
Neighborhood à AER β = .03, p = .082
AER à Internalizing (Baseline) β = -.05, p = .004
Internalizing (Baseline) à Internalizing (Year 2) β = .64, p < .001
AER à Externalizing (Baseline) β = -.08, p < .001
Externalizing (Baseline) à Externalizing (Year 2) β = .70, p < .001
Indirect Effect
Neighborhood à Internalizing (Baseline) β = .01, p = .008
Neighborhood à Externalizing (Baseline) β = .02, p < .001
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Figure 3.1
Emotional N-Back Task (Adapted from Casey et al., 2018)
69
Figure 3.2
Path Model for Internalizing and Externalizing Behaviors
Note. * p < .05. ** p < .01. *** p < .001.
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CHAPTER 4: Neighborhood and Asian Youth: A Qualitative Study on the Intersection of
Race/Ethnicity and Neighborhood in Understanding Mental Health among Asian Youth
Introduction
Neighborhood is a key aspect of the ecological context with links to adolescent mental
health (Leventhal, 2018). Current research demonstrates a consistent link between neighborhood
risk and adolescent mental health (Barr, 2018), including the significance of the role of culturally
salient neighborhood-level domains such as co-ethnic density for mental health in racial/ethnic
minority adolescents (Gonzales et al., 2011). However, research on these effects in Asian
American adolescents is limited and the few extant studies show mixed findings. The recent
increase in racial discrimination against the Asian American community during the Coronavirus
(COVID-19) pandemic also calls for a better understanding of how neighborhood characteristics
including racial discrimination in neighborhoods that recently surged affect youth. The sparse
and mixed literature on neighborhood influences points to a more nuanced approach that
considers the neighborhood experiences unique to Asian American youth, such as discrimination
experiences in neighborhoods, and how various neighborhood factors intersect with each other to
influence mental health among Asian American youth. The limited understanding of
neighborhood effects on Asian youth mental health is concerning considering that Asian
American youth, the fastest growing racial/ethnic minority group in the U.S. (Colby & Ortman,
2015), experience substantial mental health problems (National Institute of Mental Health,
2019). To address these gaps in the extant research, the current study took a qualitative approach
to investigate unique neighborhood experiences of East Asian American adolescents in depth.
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Neighborhood and Mental Health among Asian American Youth
The influences of neighborhoods risk and protective factors on adolescent mental health
may differ for racial/ethnic groups (Osypuk et al., 2012), and a few prior studies empirically
supported this possibility (Assari, 2021; Barajas-Gonzalez & Brooks-Gunn, 2014). Most studies,
however, have focused largely on African American and Latinx youth and the influence of
neighborhoods on Asian American youth’s mental health has received little attention with a very
few exceptions (E. H. Lee et al., 2014b; W. Lee et al., 2022; Wei et al., 2020) that produced
mixed results. On the one hand, neighborhood disadvantage was associated with internalizing
problems for Asian American adolescents (W. Lee et al., 2022) and externalizing problems for
Chinese children aged 6-9 (E. H. Lee at al., 2014). On the other hand, neighborhood
disadvantage was not associated with internalizing problems (E. H. Lee et al., 2014) and
depressive symptoms among Asian youth (Wei et al., 2020). Similarly, although co-ethnic
density (i.e., the percentage of residents who are of the same race/ethnicity) (Shaw et al., 2012;
Zhou, 2013), a key dimension of neighborhood for racial/ethnic minority youth, has been
identified as a key protective factor for African American (Martin et al., 2011) and Latinx (M. J.
Lee & Liechty, 2015) youth, it has shown mixed effects for Asian youth’s mental health (White
et al., 2020). Co-ethnic density was found to be associated with increased internalizing and
externalizing behavior among Chinese children (E. H. Lee et al., 2014). However, in another
study of Asian American adolescents, co-ethnic density was not found to be a significant
predictor of internalizing problems (W. Lee et al., 2022). Mixed findings in the existing few
studies, along with the paucity of research with a keen focus on Asian youth, demonstrate that
further examination of the impacts of neighborhood features, particularly non-economic
neighborhood features, on mental health is needed for Asian American youth. One of the reasons
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for mixed findings in the current literature might be that there may be other non-socioeconomic
features of the neighborhood that are salient for Asian American youth’s experience of
neighborhood embedded in their unique contexts more broadly. Further, because the Asian
American population is heterogeneous, with many ethnic groups, divergent cultures, and
languages (Chu & Sue, 2011), it is possible that the effects of ethnic resources differ across
Asian ethnic groups. However, this proposition has not been explored for Asian youth.
COVID-19 Pandemic and Racial Discrimination in the Neighborhood Context
The Coronavirus (COVID-19) pandemic has fueled a recent surge in anti-Asian
sentiments and discriminatory crimes against Asians in the U.S. (Tessler et al., 2020). Asian
Americans have reported feeling unsafe walking in their own neighborhoods with the news of
hate crimes and vandalization of Asian businesses and neighborhoods (Ahrens, 2020). It raises
yet another question regarding what neighborhood-level factors such co-ethnic density, which
were traditionally cited as protective factors, mean to Asian American youth. Research on other
racial/ethnic minority groups has found that racial discrimination intersects with neighborhood
context. For instance, discrimination against black adolescents was pronounced in majority white
neighborhoods (Stewart et al., 2009). In another study, it was found that African youth’s
expectations of racial discrimination were higher in economically disadvantaged neighborhoods
(Witherspoon et al., 2016). In Asian American youth, the tendency to minimize or downplay the
racial discrimination experience has been observed and thus may impact their perception of
discrimination (Yoon et al., 2017). Given that anti-Asian sentiments have recently surged during
the pandemic, it is unknown how such surge in neighborhoods intersects with other key
neighborhood characteristics, such as co-ethnic density to influence mental health.
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Current Study
The present study investigated neighborhood context and its impact on Asian American
adolescent mental health in the cultural nuances and the COVID-19 pandemic. Given that a
nuanced approach is critical in understanding in what unique ways neighborhood factors unfold
and intersect with each other to influence mental health among Asian youth, a qualitative study
was conducted to examine the lived experiences of neighborhood-level risk and protective
factors among Asian Americans. Qualitative studies investigating Asian Americans are sparse
(Kiang et al., 2016). Qualitative approaches, however, can provide an in-depth exploration of
small groups of individuals whose perspectives are often overlooked (Hoyt & Bhati, 2007).
Further, they are valuable in examining phenomena that may not fit easily into preselected
variables, which are critical to adequately represent the unique experiences of ethnocultural
minority groups (Nagata et al., 2017).
The study aimed 1) to explore how co-ethnic density and the presence of ethnic
community centers in neighborhoods, traditionally viewed as protective factors for racial/ethnic
minority youth’s adjustment, play a role in Asian American youth’s mental health; 2) to
understand youth’s view on rising discrimination toward Asians under the pandemic and how it
intersects with other key neighborhood characteristics to shape Asian American youth’s mental
health; and 3) to understand Asian American youth’s views on other non-socioeconomic aspects
of neighborhood more broadly.
Methods
Recruitment
Eligibility criteria included individuals who are 14-17 years old and East Asian (e.g.,
Chinese, Japanese, Korean, Taiwanese). All respondents resided in urban areas in southern
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California. The study focuses on East Asian youth given that East Asians have been the main
target of rising discrimination in the COVID-19 pandemic era (Ahn et al., 2022). Age range to
reflect high school age was chosen considering that this age starts to exercise more autonomy
and navigate neighborhoods more independently (Leventhal, 2018). Additionally, it is when a
sense of membership in a particular racial/ethnic group and implications of this awareness begin
to crystallize (Brown & Bigler, 2005; Peterson-Sweeney, 2005). The participants were recruited
using snowball and convenience sampling through social media posts and community centers.
Participants
The analyses draw from interviews conducted with 12 participants between August 2022
and January 2023. All participants resided in relatively affluent ethnic neighborhoods. There was
an overrepresentation of Korean American participants. The sample size and composition are
presented in Table 4.1.
Procedures
Participants filled out an eligibility questionnaire, and if they fit eligibility criteria and
were interested in participating, they e-mailed informed parental consent and participant assent.
The individual interviews lasted about 45 minutes and were conducted online through video calls
(Dodds & Hess, 2021). Participants were compensated $15 for their time. All interviews were
recorded and transcribed verbatim, which were uploaded to the qualitative analysis software,
NVivo. Data collection was approved by the University of Southern California Institutional
Review Board.
Interview guide
In order to prepare the participants for discussion about their neighborhood experience,
the participants were given definitions of the terms “racial discrimination,” “neighborhood
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disadvantage” (Table 4.2), and “co-ethnic density.” Rather than using the term co-ethnic density
throughout the interview, however, they were asked about the presence of the wider Asian
residents in general as well as the presence of residents of specific ethnic group that the
participant identified with. The interview was preceded by a warm-up activity that asked
participants to reflect on their own physical and social dimensions of neighborhood (Table 4.3).
Following the warm-up, a semi-structured interview was conducted inviting participants to
reflect on their responses to the warm-up and with specific interview questions (Table 4.4).
Data Analyses
Data were analyzed using thematic analysis with the goal of extracting meaningful
themes and patterns (Braun & Clarke, 2006). Thematic analysis allows for exploration of in-
depth perspectives and nuanced interpretation of the data through descriptive and interpretive
approaches. Data were analyzed by a doctoral student researcher following the phases of 1)
familiarization with the data, 2) generation of initial codes, 3) search for themes, 4) review of
themes, and 5) defining and naming themes, and 6) production of the report. After the initial
cleaning, the researcher read and reread the interview transcripts, documenting initial notes and
reflections. Then, codes were developed by merging and refining codes until the codes
represented conceptually distinct aspects of neighborhood experiences. The codes were then
sorted into themes and subthemes which were reviewed and refined. Lastly, the themes and
subthemes were named and extracts from the data were carefully selected to illustrate each
theme. NVivo 20 was used to facilitate the analytical process.
The researcher is an Asian American female doctoral student. The researcher was
mindful of the influences she brings to the research process, including potential bias from her
own ethnic and gender identity and residential history as well as power relationships between the
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researcher and the research participants which could potentially shape the data collection process
(reflexivity) (Kuper et al., 2008). The themes were discussed and reflected with a senior
researcher to retain a reflexive stance and achieve minimal bias.
Results
1. Pan-Asian co-ethnic density and ethnic/cultural centers are a salient part of the
neighborhood experience.
From the perspective of Asian American adolescents, it was found that participants felt
the importance of pan-Asian co-ethnic density (described as the wider presence of Asian
neighbors whether they personally know them or generally see their presence) in their day-to-day
lives. The respondents expressed feelings of belongingness and comfort regardless of whether
they knew their neighbors personally as well as connection and relatability based on shared
customs and traditions:
I would just feel more, I don’t want to say happy, I would just feel more comfortable in
my environment and like I would feel like I belong more because there’s more people
like me that look like me. They would go to the same places as me (female, 16).
It would make a difference, I think. Just the connections of the same kind of
race/ethnicity. I think it helps with more bonding time with each other because we can
kind of have the same interest when it comes to customs and traditions. And that way we
can connect more with our neighborhood (male, 15).
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Relatedly, participants also reported frequenting ethnic community centers, including restaurants,
convenience stores, and medical offices. Importantly, they noted that these locations offered
cultural connection and promoted ethnic heritage especially when with others who are not Asian:
Our neighborhood is surrounded by Asian people, so I think it just makes everyone in
already more comfortable because everything is so close by. I feel more comfortable too
because I’m more familiar with Asian culture and food… Maybe an example that could
be like when I’m out with my friends, and they’re maybe not Asian, I feel like.. I could
like show them around and be able to teach them about culture like mine…. I’m pretty
proud of my heritage, and when I’m ordering food at a Korean restaurant, I can talk to the
people in Korean and stuff because I’m proud of like who I am (male, 15).
However, the high pan-Asian co-ethnic density also functions as a source of stress that may
compromise youth’s mental health. Participants noted general academic pressures and the
resulting stressful experiences. One participant noted that the competition for academic standard
was high among Asian peers although there was not a particular pressure from parents:
It’s kind of unnerving…. I felt like the standard was only so high, especially for um
Asian people, and I always felt like I was kind of like here compared to what all these
people were like. So, I was kind of pressured… I felt like I have like a standard to live up
to. Even though I wasn't like raised that way with my dad. He never like compared to
other people, and you know, but like it's just kind of like a self kind of thing where I was
like, oh, I need to get like good grades … Look at her or his GPA…. Because I feel like
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there's a lot of Asian people in my school, and a majority of them were pretty smart, like
APs and everything (female, 17).
2. Asian ethnic group specific co-ethnic density (i.e., neighbors of the same specific ethnic
background) is different from pan-Asian co-ethnic density (having Asian neighbors
regardless of specific ethnicity).
Participants also noted that having neighbors of the same ethnicity, beyond having Asian
neighbors in general, is helpful in feeling connected and building bonds because they share the
same language and customs especially given that families are often a part of the neighborhood
experience.
Yeah, it would make a difference honestly because the whole reason that our families are
friends with our neighbors’ family is because we speak the same language and we're from
the same place. So, if they were like a different type of Asian, we would not be as close
as we are to them right now (female, 16).
A participant who was born in South Korea and immigrated to the U.S. also noted that living in a
neighborhood with other Asians present reminded her of her life back in Korea and stated,
It’s more inclusive. Because I used to live in Korea, and I used to be just surrounded by
Koreans, and like people who speak Korean. So, I definitely get like a nostalgic feeling,
almost if because like there’s a lot of Koreans living here. So sometimes it feels like I’m
still living in Korea…. It makes me feel very safe (female, 16).
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Another participant noted that there is a functional aspect to having Asian neighbors, implying
the value of instrumental support, stating,
Since we have Korean neighbors, we tend to share food or ask for help or something.
They just kind of help each other. So, I think that’s a really big part of my experience
living in this neighborhood (female, 16).
Another participant, however, noted that that there also exists exclusivity that has a negative
impact on well-being and feeling included.
Even in the Asian community, I feel like Koreans and then like Chinese, or like other
Asian groups, they would tend to like gather themselves with each other. So, a lot of
Koreans would gather themselves with Koreans, and then like Chinese people would
gather themselves with the Chinese people so they could talk in their language. And then
so that's one division. … I actually wanted to be friends with one of the groups of the
Korean girls, but they’ve known each other since at a young age since elementary… not
only had they known each other since, like elementary, but, like their parents, also got
along because they were all Korean, and then they come from the same like cultural
background, too (female, 15).
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3. Racial discrimination experience can vary in frequency and scope, but COVID-19
pandemic has changed the landscape.
Racial discrimination experiences varied among the participants. Some reported that they
have experienced overt racial discrimination in their neighborhood while some were more subtle.
Others did not recall experiencing discrimination prior to and during the pandemic.
One participant encountered a direct form of discrimination, to which she also reported a strong
emotional response:
Sometimes we’d walk around, and people would kind of look at me like, “Do you guys
like have COVID, or some stuff?” That kind of bothered me… that upset me a lot.
Because obviously, just because you’re Asian doesn’t mean you’re sick or stuff like that
(female, 16).
Other experiences were more subtle but demonstrated increased awareness of heightened risks
and avoidance of engagement to prevent dangerous situations:
I don’t think it really impacted how I interact with people but… just when I go out, and
just more aware of what could happen. So, I try to either avoid certain things or just be
aware…. If someone were to say something to me, it’s more avoid engaging in that like
topic. If it’s like controversial… I mean for like safety concerns (female, 16).
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Participants also reported that they became more aware of the speaking their native language in
public during and beyond the pandemic and how others might perceive them:
It definitely makes me more aware…. After, or like during COVID, I didn’t really like
think about it much, but like thinking about it now, I guess people could have seen it as
like, oh, they’re Asian, and they speak different languages (female, 16).
I’ve gotten more aware, not just because of COVID-19, but because I got older, and I’ve
listened to just people around me, and they’re just talking to like other people. Other kids
in my age are speaking English, just full English to their parents… Some … speak
English to their Asian parents, so I’ve definitely become more aware that I’m speaking
Korean to them. I don’t really care if I speak a different language from like other people,
but I don’t know how others would perceive us as (female, 16).
Family was also a significant part of racial discrimination experiences. Participants often worried
for their elderly family members. One participant mentioned experiences stemming from the
pandemic:
When it was the stop Asian hate movement. We were scared for my grandma, who likes
walking a lot to the groceries nearby (female, 15).
There were also many instances when participants displayed ambivalent attitudes or minimized
the effects of discrimination:
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It makes me feel like not only excluded but makes me feel bad of them. …they are
stereotyping me, even though they don’t really know me…. It was pretty stressful. I was
upset about it, but it didn’t really affect me as much as I thought because I just thought it
was stupid… why is she telling me this? (female, 16).
Participants reported that co-ethnic density was helpful in dealing with discrimination
experiences that happened to them or hearing about it from others or seeing it on the news citing
feelings of safety in numbers. Importantly, participants mentioned that discussions with co-
ethnic peers in neighborhoods were helpful in validating the experience:
Because if there’s more of us, it’s more safety in numbers… from any discrimination that
goes against Asians and hateful doings (female, 16).
At first, like since we like played it off like we didn’t really care, or we didn’t really talk
about it first. But then, after like experiencing like the great impact that it actually had, it
became like a kind of a more serious thing that we should have taken more seriously.
And we did talk about it. But we thought it was just really strange and it shouldn’t have
been said… [It was helpful to talk about it with friends] because it just confirmed me. It
wasn’t just me who thought that was weird. (female, 16).
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4. Other Key Non-Economic Neighborhood Features.
Participants had a clear definition of positive and negative features of the neighborhood,
noting cleanliness, safety, green space, security, and secluded neighborhoods, especially in
concern for the family’s well-being:
I think because I don't love to worry so much about like my surroundings in my
neighborhood. It takes a lot off my mind, so I feel very safe, I can like focus on my
studies or focus on what I need to do without worrying just like the safety of me and my
family (female, 16).
A majority of the participants noted that parks were often utilized to help relieve stress from
school and home, and walkability was important especially during the pandemic:
Especially during Covid and stuff, all we could do was walk around. We don’t stay at
other people’s houses because yeah… I think that helped really getting out of the house
and stuff… and we couldn’t if it was a safety issue (female, 17).
Some participants noted that despite generally feeling safe, they avoided being out in certain
conditions:
There are some kind of like bad areas in my neighborhood, but they’re mostly avoided
for me and my sister like near the park. I think the area … has some drug smell … and I
think there’s a lot of drug use going on there….It’s mostly my own awareness because I
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know that I shouldn’t be in a place like that, if I was alone or like in the dark or
something (male, 15).
Discussion
Findings suggest that pan-Asian co-ethnic density is an important feature of the
neighborhood for Asian American adolescents. A majority of the participants reported positive
aspects of having Asian neighbors, such as feelings of connection and belongingness.
Importantly, personally knowing the neighbors was not a necessity for a sense of connection.
Specifically, having others “who look like me” was a feature that was highlighted as helpful even
if they do not personally know the other residents. Having others who resembled them in
physical features, interests, or lifestyles was helpful in the participants’ feelings of
belongingness, which has been shown to be related to lower stress and depression (Stebleton et
al., 2014). Potentially, it may be an important feature that should be reflected in measures of
assessing social aspects of neighborhoods, especially for racial/ethnic minorities. Both pan-Asian
and Asian ethnic group specific co-ethnic infrastructure in the community, including community
centers, restaurants, convenience stores, and medical offices were also noted as significant and
salient parts of the neighborhood for American adolescents. The benefits extended beyond
convenience or accessibility of familiar goods to foster cultural connection and promote ethnic
heritage. The findings are in line with previous research that suggests that cultural exposure can
nurture children’s ethnic identity that may otherwise be rejected due to the pressure to assimilate
(Zhou & Kim, 2006). Given the empirical evidence for the association between ethnic identity
and mental health for Asian American youth (Stein et al., 2014), features of the neighborhood
that may foster ethnic identity may be important for Asian youth mental health. Taken together,
specific aspects of co-ethnic density, such as presence of residents who share similar features and
85
cultural centers that specifically serve Asian ethnic groups, were important features of the
neighborhood that need to be further explored as potentially protective factors for Asian youth
mental health.
Findings may have implications for future research directions. First, the benefits of co-
ethnic density may depend on specific conditions. Literature shows that because recency of
immigration and concentration of ethnic population tend to correlate with neighborhood
disadvantage (Osypuk et al., 2009)—often referred as ethnic enclaves, co-ethnic density may be
a proxy for neighborhood disadvantage and thus the potentially positive effects of co-ethnic
density may be obscured by correlates of socioeconomic disadvantage that tend to co-occur with
high levels of ethnic concentration (Mair et al., 2010). The study participants in the current study
live in ethnoburbs rather than ethnic enclaves. Ethnoburbs, unlike ethnic enclaves, are
characterized by affluent residents who have attained higher than average education and
ethnic/cultural centers and businesses (Zhou et al., 2008). As such, the perceptions on co-ethnic
density may be especially highlighted among the study participants. Overall, the relative
independence of co-ethnic density from neighborhood disadvantage in the current study offered
an opportunity to isolate the role of cultural factors in the neighborhood setting from
socioeconomic factors. However, it should be noted that disadvantaged neighborhoods may be
more segregated and less diverse (Reardon & Bischoff, 2011), potentially undermining the
benefits of co-ethnic density. As the current sample is homogeneous in that all participants reside
in affluent neighborhoods and thus did not have variance in socioeconomic status, more research
is needed to investigate the potential interactive effects of co-ethnic density and socioeconomic
factors in diverse samples.
86
On the other hand, a few of the participants reported that co-ethnic density was a
reminder for academic competition and high standards for performance, which may be deemed
stressful. Academic competition and the resulting pressure have been reported in many studies
about Asian communities. The presence of ethnic population gathered around ethnic and cultural
centers, particularly academic institutions, higher than average parental education backgrounds,
authoritarian parenting practices associated with academic expectations, and financial affluence
may enable high levels of academic competition (Zhou, 2009). Indeed, Asian American
adolescents have reported academic pressures as one of the main stressors for their mental health
including depressive symptoms (Liu et al., 2022). Especially in an affluent environment,
resources such as time and economic means may amplify parenting practices associated with
high levels of academic competition. Previous literature has found that higher affluence was
associated with higher anxiety for Asian youth (Coley et al., 2018). Thus, findings may suggest
the dual nature of co-ethnic density particularly in affluent neighborhoods for Asian youth’s
mental health, and it may potentially explain the mixed findings in previous quantitative studies,
one of which found that co-ethnic density was negatively associated with mental health (E. H.
Lee et al., 2014). Further research is needed to understand the correlates of co-ethnic density and
its potential adverse and protective aspects for Asian youth mental health.
There were specific differences between having neighbors of pan-ethnic Asian race and
having neighbors of the same ethnic background. In these reflections, the experience of the
family as a unit was a significant part of feeling connected and close to the community. Primary
interactions with neighbors took place in the form of helping other co-ethnic families and sharing
meals in and outside of the home, and because the participants’ parents tended to speak in their
native language, having neighbors of the same ethnic background was essential in forming close
87
bonds. Mutual understanding of cultural similarities and differences with the mainstream culture
may nurture family relationships and alleviate cultural conflicts that may be present in immigrant
families (Zhou & Kim, 2006). Literature has also highlighted the importance of shared language
in exchange of information and social capital among co-ethnic (vs. pan-ethnic) residents
especially in the context of other socioeconomic disadvantages related to immigration (Khoir et
al., 2015; Zhou, 2009). Asian American adolescents who are of 1.5
th
or 2
nd
generation may not
be limited by their native language, but the interactions between the families as a unit may also
translate to form the social network of adolescents. Previous research has found that higher
levels of family connectedness is associated with less emotional distress in Asian immigrant
youth (Hilario et al., 2014), suggesting that a neighborhood environment that fosters family
connectedness is potentially an important factor for Asian youth mental health. Thus, findings
suggest a meaningful distinction between race and ethnicity that may need to be reflected in
future studies of the effects of co-ethnic density for Asian adolescents. The distinction between
pan-Asian co-ethnic density and Asian ethnic group specific co-ethnic density and its
implications for Asian youth’s experience in the neighborhood may also be a potential area for
further investigation.
Regarding racial discrimination experiences, participants conveyed mixed reflections.
While some did not recount any discriminatory experiences, others experienced both overt and
subtle cases of racial discrimination during and beyond the COVID-19 pandemic. Interestingly,
for those who reported experiencing discrimination, participants’ reflections were dualistic and
ambivalent. A majority of the participants who experienced racial discrimination disapproved of
such actions and admitted that discrimination has become a source of stress. However, they also
reported “playing it off” or “not really caring,” minimizing the experiences. Previous literature
88
documents the tendency for Asians to minimize or even deny the negative impact of racism
(Yoon et al., 2017), providing further support. Racial discrimination for Asian youth
demonstrates complex processes in which subjective perception may not accurately reflect the
actual account of discrimination.
Racial discrimination experiences also intersected with other key neighborhood
characteristics to shape Asian American youth’s overall perception and experience. As a result of
the COVID-19 pandemic, participants were more aware of their surroundings, and the
heightened awareness served as a barrier to normal activities in the neighborhood such as
engaging with other neighbors. Participants also worried about how their appearance may be
perceived in the neighborhood setting and reflected on speaking Asian languages in public.
Previous research suggests that high levels of co-ethnic density may heighten residents’
awareness of racial/ethnic discrimination which may in turn have adverse effects on mental
health (Juang & Alvarez, 2011). On the other hand, respondents noted that the presence of other
Asian residents was helpful in mitigating the adverse effects of discrimination, providing support
for benefits of co-ethnic density in the presence of structural risk factors. Whether in the form of
direct interaction with Asian peers to process discriminatory acts or indirect perception of feeling
“safety in numbers,” co-ethnic density was perceived as helpful factor. While it is possible that
co-ethnic density may heighten awareness, having co-ethnic residents may be a protective factor
against potential harmful effects of discrimination on Asian youth’s mental health.
The participants conveyed a clear definition of the positive features of the neighborhood
broadly. Safety measures such as presence of gates, cameras, or secluded corners away from the
streets helped respondents feel safe and comfortable, which in turn helped them focus and not
have to worry about their or their family’s well-being. Physical dimensions such as walkability
89
and availability of green space provided opportunities for relief especially in the context of
COVID-19 pandemic when other activities were relatively limited. Results are in line with
research that suggest the importance of green space exposure on adolescent mental health across
demographic and socioeconomic groups (Vanaken & Danckaerts, 2018). Overall, the presence of
safety measures in the neighborhood, walkability, and green space emerged as especially salient
features of the neighborhood for Asian youth and thus may be considered in the
operationalization of neighborhood factors for future work with this population.
Findings inform measurements of Asian experiences in the neighborhood. First, co-ethnic
density may be separated into domains of pan-Asian co-ethnic density and Asian ethnic group
specific co-ethnic density. It is possible that measuring specificities my shed light on how ethnic
density influences Asian youth’s well-being. Secondly, Asian youth perceived likeness of
residents to be an important feature of feeling social connectedness. Thus, assessments of social
aspects of neighborhoods may incorporate such conditions. Thirdly, Asian youth’s racial
discrimination experiences in the neighborhood may be a complex process and capturing their
experiences and perception may need more nuanced approaches. Lastly, salient features of the
neighborhood, such as security devices, walkability, and green space should be reflected in the
operationalization of neighborhood context for Asian youth.
Limitations
Findings are not without limitations. The results were based on an independent coder’s
decisions and thus may benefit from future double coding for reliability. Given the sampling
strategy and the small sample size, the current study had no representation of youth from
economically deprived neighborhoods who may experience more adverse health outcomes
related to the neighborhood experience. Many Asian American youth live in neighborhood with
90
economic deprivation such as Chinese immigrants in New York City and other Asian population
in Koreatown in Los Angeles, who are more likely to be immigrant, low-skilled working class
(Walton, 2015). Additionally, those who are of South and Southeastern Asian descents are also
more represented in disadvantaged areas (Tam et al., 2015). Given the limitations of snowball
sampling, this study did not reach these populations, and future research is critical in filling this
gap. Relatedly, current sample may overrepresent youth who report the importance of others who
resemble them in their social connections. The findings are based on East Asian adolescents
from a few geographically concentrated areas based in southern California and may not extend to
other types of communities in the larger geographical boundaries. Given that the current study
had a small sample size and less representation of male participants, findings may serve as initial
query into how neighborhood and other non-socioeconomic neighborhood features interact for
Asian American youth.
Conclusion
The study extends the research on ethnic neighborhoods and how they confer benefits to
Asian American adolescents using in-depth interviews to explore the features of the
neighborhood that Asian American adolescents perceive as important. Both pan-Asian and ethnic
group-specific co-ethnic density offered salient benefits for Asian adolescents with some caveats.
As the benefits of co-ethnic density may operate differently in disadvantaged neighborhoods,
more research is needed to explore potential interactive effects of co-ethnic density and
socioeconomic status on Asian youth’s mental health. Racial discrimination in the neighborhood
setting is a prevalent risk factor for Asian American adolescent mental health during and beyond
the COVID-19 pandemic, and more research is critical in understanding the long-term effects of
discrimination experiences in the neighborhood setting. The findings also have implications for
91
adequately characterizing both socioeconomic and non-socioeconomic neighborhood features to
understand the potential risk and protective factors for Asian youth’s mental health.
92
Table 4.1
Characteristics of Study Sample
Characteristics (n = 12)
Gender
Male 4 (33%)
Female 8 (67%)
Age, mean (SD) 15.8 (.72)
Ethnicity
Korean 11 (92%)
Chinese 1 (8%)
Country of birth
Korea 3 (25%)
U.S. 8 (67%)
Russia 1 (8%)
Neighborhood Socioeconomic Status
>$60,000 median household income/>25% college graduates 12 (100%)
<$60,000 median household income/<25% college graduates. 0 (0%)
93
Table 4.2
Objective Measures of Neighborhood Disadvantage
Percent aged ≥ 25 years with greater than or equal to a high school diploma
Median family income
Percent of civilian labor force population ≥ 16 years of age unemployed
Percent of families below the poverty level
Percent of single-parent households with children < 18 years of age
Table 4.3
Warm-Up Questions
a. What is your identified gender?
b. How old are you?
c. Where were you born?
d. What language do you speak at home?
e. If you were to walk out from your residence, what are some features of your neighborhood
you see? (Ex. houses, buildings, parks, schools, playground, etc.)
f. Do you know the people who live in your neighborhood? Who are they? (Ex. friends from
school, relatives, friends of your parents/guardian, etc.)
g. What locations in your neighborhood do you visit the most?
94
Table 4.4
Interview Questions
Domain Question
Application of objective definitions of
neighborhood disadvantage
• What would be an example of
neighborhood disadvantage and how
does it affect your mental health?
• What else about your neighborhood
impacts your mental health?
Co-ethnic density and other protective factors • What are some positive aspects of
your neighborhood and how do they
help with your well-being?
• Are there other residents who are
Asian and/or belong to the ethnic
group you identify with?
• How does this influence your
experience with your neighborhood?
• Are there community centers for
Asians that you know of? How do you
think they shape your experience with
your neighborhood?
• Where do you go – if not?
95
COVID-19-related racial discrimination
experience as it pertains to the context of
neighborhood
• Have you or anyone you know
experienced discriminatory
experiences?
• Has your interaction with the
neighborhood changed due to the
rising discrimination?
• Language you speak with
family/friends
• Awareness of your surroundings
96
CHAPTER 5: Conclusion and Implications
The neighborhood environment has been identified as a key context for youth’s mental
health (Leventhal, 2018). Previous research has found that neighborhood’s socioeconomic
disadvantage exposure in early adolescence is linked to a wide range of mental health problems
including depressive symptoms (Barr, 2018; Solmi et al., 2017), anxiety symptoms (King et al.,
2022), and externalizing behavior (Li et al., 2017) during adolescence. Non-socioeconomic
aspects of neighborhood disadvantage, such as co-ethnic density, has also been identified as a
contributing factor in the mental health of racial/ethnic minority youth (Juang & Alvarez, 2010).
Despite the advances in understanding the role neighborhood disadvantage plays in adolescent
mental health, three key gaps remain. First, the developmental nature of mental health
trajectories has been overlooked. Secondly, the role of individual-level mechanism in the link
between neighborhood disadvantage and adolescent mental health has not been adequately
investigated. Lastly, a rapidly growing racial/ethnic minority group, Asian youth, has been
overlooked although it is important to understand this group given the recent surge of racial
discrimination against the Asian American community during the Coronavirus (COVID-19)
pandemic. The current dissertation took a mixed methods approach to incorporate three studies
with the goal of addressing the key gaps in the extant literature.
Chapter 2 of the current study sought to identify subgroups with distinct trajectories of
internalizing and externalizing behaviors and examine how neighborhood disadvantage is
associated with the group-based patterns of trajectories. Results showed that for both
internalizing and externalizing behaviors, there were two trajectory classes. A majority of the
participants demonstrated generally stable-low levels of internalizing behavior, while a relatively
small group showed clinically elevated initial levels of internalizing behavior which persisted
97
over time. Similar results were found for externalizing behavior. A higher proportion of
participants living in disadvantaged neighborhoods were more likely to be in the “high-
increasing” trajectory group for internalizing behavior but not for externalizing behavior. This
association remained significant while controlling for household income and parental education
levels, which suggest the unique contributions of the neighborhood socioeconomic environment
beyond family-level socioeconomic status. The strength of this study draws from the use of a
large, diverse sample using growth mixture modeling strategies to identify subgroups that may
not be detected in an aggregate level of analysis. Expanding previous empirical evidence
(Leventhal & Brooks-Gunn, 2003; Ludwig et al., 2008), the current study provides support for
neighborhood disadvantage exposure as an important risk factor for clinically elevated levels of
internalizing symptoms.
Despite empirical evidence demonstrating the longitudinal association between
neighborhood disadvantage and adolescent mental health, there is less known about the
underlying process through which neighborhood disadvantage confers risk on adolescent mental
health (Sharkey and Faber, 2014). Thus, Chapter 3 examined the mediating pathway of
automatic emotion regulation (AER) in the association between neighborhood disadvantage and
internalizing and externalizing behaviors. Results suggest that neighborhood disadvantage was
associated with lower AER capacity, and AER was associated with lower levels of internalizing
and externalizing symptoms. AER fully mediated the association between neighborhood
disadvantage and mental health outcomes. The findings suggest that diminished AER capacity
may potentially serve as an important marker of vulnerability, thus serving as a potential target
for prevention and intervention strategies. Extant programs generally address the explicit
emotion regulation system by helping youth modify dysfunctional thinking and behavioral
98
patterns after the occurrence and thus there is less consideration of the key role of enhancing the
automatic aspects of responding to emotionally demanding situations. Addressing the automatic
processes may involve supporting healthy emotion development through psychotherapy
programs that address the nature of maladaptive defense mechanisms, such as by identifying the
underlying habits from childhood (Mauss, Cook, et al., 2007; Prout et al., 2019). This study
contributes to the knowledgebase of how neighborhood-level of disadvantage may be linked to
mental health symptoms through an individual-level mechanism of AER. Nonetheless, the
associations are cross-sectional in nature thus implications on directionality are limited. Future
work exploring the longitudinal mechanism of socioemotional consequences of altered emotion
regulation on mental health among youth living in disadvantaged neighborhoods is warranted.
While socioeconomic features of neighborhood disadvantage are nevertheless important
for adolescent mental health, other non-socioeconomic and social features of the neighborhood
may be salient particularly for racial/ethnic minority youth. To address this gap, Chapter 4
focused on Asian American adolescents, who have not received adequate attention in the
neighborhood literature. A qualitative approach was taken to explore the neighborhood
experiences unique to Asian American youth, such as discrimination experiences in
neighborhoods, and how various neighborhood factors intersect with each other to influence
mental health among Asian American youth. Results suggested that youth had clear perception
and implication of safety and disorderliness in their neighborhoods for their well-being, such as
the importance of green space and walkability, which may need to be reflected in the measures
of neighborhood disadvantage for future research with Asian youth. Pan-Asian co-ethnic density
and specific Asian group co-ethnic density conferred distinct benefits for youth mental health,
and thus the distinction may need to be reflected in future studies on the effects of co-ethnic
99
density for Asian adolescents’ mental health. Additionally, benefits of co-ethnic density included
both social and institutional support, pointing to potential areas of research in mechanisms for
Asian youth. Racial discrimination in the neighborhood was prevalent and heightened Asian
youth’s awareness of their surroundings thus serving as a barrier to normal activity in the
neighborhood, a potential source of stress. Connecting the pieces together, while co-ethnic
density may also play a role in increasing awareness of racial discrimination (Juang & Alvarez,
2011), having co-ethnic residents, whether in the form of direct interaction or by indirectly
fostering a sense of safety, may be a protective factor against potential harmful effects of
discrimination on Asian youth’s mental health, and thus an important area for future research.
The findings, however, were limited in sample size and generalizability and thus call for further
exploration of the salience of socioeconomic and non-socioeconomic features of the
neighborhood and their link to Asian American youth mental health.
This dissertation project is not without limitations. As analyses were cross-sectional in
nature, causal implications cannot be inferred. Secondly, the samples are not representative of
the population and thus caution must be employed in generalizing the findings. Specifically, the
sample of Asian youth did not include individuals from economically deprived areas, which
limited in-depth investigations of the effects of neighborhood disadvantage. Nevertheless, this
dissertation project generated knowledge on the neighborhood literature by considering the
connection between neighborhood factors and individual-level processes using a developmental
framework and mixed methodology. A number of implications emerged from the results. First,
the findings indicate that it may be informative to investigate the developmental trajectory of
internalizing and externalizing symptoms prior to early adolescence to understand the point of
emergence of a distinct at-risk subgroup. Accordingly, prevention programs beginning as early
100
as elementary school age may be helpful. At the individual level, programs that focus on
developing better emotional regulatory skills may be a practical point of intervention for
improving mental health outcomes especially for at-risk youth who are exposed to environmental
stressors such as neighborhood disadvantage. The findings also highlight that childhood
exposure to neighborhood disadvantage has meaningful implications for adolescent mental
health, providing evidence for the importance of childhood neighborhood in shaping youth
mental health. Thus, at the structural level, community and policy-level programs targeted at
enhancing the neighborhood context and reducing neighborhood-level risk exposures especially
during childhood may improve adolescent mental health development. Non-socioeconomic
factors and social features of the neighborhood should also be incorporated into prevention and
intervention strategies. For example, at the structural level, investing in cultural institutions in
neighborhoods may be foster belongingness in racial/ethnic minority adolescents, thus
potentially promoting their mental health. Given the added stressors related to neighborhood-
level racial discrimination for racial/ethnic minority youth, fully understanding the impacts of the
neighborhood context on mental health among racial/ethnic minority youth may require a keen
focus on neighborhood risk factors that surge at a given time and their intersection with other
neighborhood factors. The findings from the three studies collectively serve as evidence to
advance the understanding of what, how, and for whom neighborhood context matters for
adolescent mental health.
101
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Abstract (if available)
Abstract
The neighborhood environment has been identified as a key context for youth’s mental health. Although there is growing evidence of the association between neighborhood disadvantage and adolescent internalizing and externalizing behaviors, much remains to be explored in what, how, and for whom neighborhood context matters for adolescent mental health. To examine these gaps in the literature, three distinct but related studies were conducted using mixed methodology with data from the Adolescent Brain Cognitive Development (ABCD) Study and semi-structured interviews with 12 Asian American adolescents.
Study 1: Neighborhood Disadvantage and Patterns of Mental Health Trajectories
Relatively little is known about how neighborhood disadvantage is associated with the development of internalizing and externalizing behaviors across time, indicative of when and in what manner neighborhood-related risk begins to emerge. Moreover, neighborhood disadvantage may be associated with distinct, group-based patterns of elevation or change in symptoms that may not be captured by examining linear developmental trajectories. Using data from the Adolescent Brain and Cognitive Development (ABCD) study (n = 9,823), including baseline, year 1, and year 2 follow-up assessments (ages 9-13), the first study aimed to identify subgroups with distinct trajectories of internalizing and externalizing behaviors and examine how neighborhood disadvantage is associated with the trajectory group membership. For internalizing behavior, there were two trajectory classes such that most participants demonstrated low initial levels of internalizing behavior which decreased slightly over time, while a relatively small sample showed clinically elevated initial levels of internalizing behavior which increased over time. Similar results were found for externalizing behavior. A higher proportion of participants living in disadvantaged neighborhoods were more likely to be in the “high-increasing” trajectory group for internalizing behavior but not for externalizing behavior. Neighborhood disadvantage is thus an important risk factor for clinically elevated levels of internalizing behavior by age 9 that persist or increase throughout early adolescence, indicating that the association may begin earlier in childhood. Risk screening and interventions addressing internalizing symptoms in children who are living in disadvantaged neighborhoods may be warranted prior to age 9. From a public health standpoint, community and policy-level programs targeted at enhancing the neighborhood context and reducing neighborhood-level risk exposures may improve adolescent mental health.
Study 2: Emotion Regulation: Mechanisms of Neighborhood Effects on Mental Health
Less is known about the underlying process through which neighborhood disadvantage confers risk on adolescent mental health. Specifically, limited research has explored the role of automatic emotion regulation (AER) as a mechanism linking neighborhood disadvantage to adolescent mental health. AER is the higher-level regulatory process during which modulation of emotional stimuli occur without deliberate or effortful intention. While AER is a foundational capacity that continues to develop throughout adolescence, there has been little investigation into its association with neighborhood disadvantage and adolescent mental health. Using a sample (n = 7,862) of youth aged 9-13 from the Adolescent Brain Cognitive Development (ABCD) study, mediation analysis was conducted to examine whether AER mediated the path between neighborhood disadvantage and mental health. Neighborhood disadvantage was associated with lower AER capacity (β = -.23, p < .001), and AER was associated with lower levels of internalizing (β = -.05, p = .004) and externalizing (β = -.08, p < .001) symptoms. AER fully mediated the association between neighborhood disadvantage and mental health outcomes. Neighborhood disadvantage and AER may potentially serve as important markers of vulnerability for pre-adolescents. Future work exploring the longitudinal mechanism of socioemotional consequences of altered emotion regulation on mental health among youth living in disadvantaged neighborhoods is warranted. More research in targeted prevention or intervention programs that address underlying automatic emotion regulation processes may be important.
Study 3: Neighborhood and Asian Youth: A Qualitative Study on the Intersection of Race/Ethnicity and Neighborhood in Understanding Mental Health among Asian Youth
Neighborhood studies have heavily focused on socioeconomic features of neighborhoods, but socioeconomic factors may not fully capture the neighborhood experiences of racial/ethnic minority youth including Asian youth. The recent surge of racial discrimination against the Asian American community as a result of the Coronavirus (COVID-19) pandemic also calls for a better understanding of how non-socioeconomic features of neighborhoods, such as racial discrimination in neighborhoods and co-ethnic density affect youth mental health. Semi-structured interviews were conducted with 12 Asian American adolescents (ages 15-17) to explore how various neighborhood factors intersect with each other to influence mental health among Asian American youth. Results suggested that pan-Asian co-ethnic density and specific Asian group co-ethnic density conferred distinct benefits as well as risk for youth mental health. Co-ethnic density, whether in the form of direct interaction or by indirectly fostering a sense of safety, was perceived as a protective factor against potential harmful effects of discrimination on Asian youth’s mental health. Asian youth perceived likeness of residents to be an important feature of feeling social connectedness, and thus assessments of social aspects of neighborhoods neighborhood measures such as social cohesion may incorporate such conditions. Racial discrimination in the neighborhood was prevalent and heightened Asian youth’s awareness of their surroundings, thus serving as a barrier to normal activity in the neighborhood. Asian youth’s racial discrimination experiences in the neighborhood may be a complex process and capturing their experiences and perception may need more nuanced approaches in future research.
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Lee, Woo Jung
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Neighborhood context and adolescent mental health: development and mechanisms
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Suzanne Dworak-Peck School of Social Work
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2023-08
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