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Self-perceptions of Aging in the Context of Neighborhood and Their Interplay in Late-life Cognitive Health

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Self-perceptions of Aging in the Context of Neighborhood and
Their Interplay in Late-life Cognitive Health

By

Eunyoung Choi

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
(GERONTOLOGY)

August 2022

Copyright 2022 Eunyoung Choi
ii
Dedication
To my beloved grandmother, Ok Hee
In the midst of confusion and complexities of doctoral study and life,
she was my source of motivation, inspiration, and strength.

iii
Acknowledgements
I would like to express my deepest appreciation to my advisor and chair of my
committee, Dr. Elizabeth Zelinski, for her guidance and support, both professionally and
personally. I’m also deeply grateful to my defense committee, Drs. Jennifer Ailshire and Yuri
Jang, who have generously provided invaluable advice and feedback throughout my time at the
University of Southern California. I would also like to thank my mentor, Dr. Young Sun Kim
from Kyung Hee University, who introduced me to the field of Gerontology. Thank you, Dr. Hee
Yun Lee from the University of Alabama, for her mentorship and career guidance.
I would like to extend my thanks to my lab- and office- mates and cohort members for
their feedback sessions, editing help, and emotional support: Kristi Wisniewski, Carly Roman,
Gillian Fennell, Stephanie Rubinstein, Margarita Osuna, Olivia Wang, Qiao Wu, Mutian Zhang,
Yujin Franco, Valeria Cardenas, Shelby Bachman, Yujun Zhu, and Meki Singleton. Many thanks
should also go to my peer-mentor, Haley Gallo, and my peer-mentee, Erfei Zhao.
Lastly, I would like to mention my family for their support and encouragement. Special
thanks to my mother, Tae-suk Jin, my father, Ho-yong Choi, my younger sister, Jong-won Choi,
and my aunt In-suk Jin. Their infinite love and belief in me have kept my motivation and spirit
high during this doctoral journey. I would also like to thank my grandparents, Young-jo Choi,
Chan-myeong Jin, and Jung-ran Lee. They are the reason why I love growing older. And thank
you to Jong Woo Nam, for his remarkable patience and love for me and for reminding me that
life is full of happiness.

iv
Table of Contents
Dedication ....................................................................................................................................... ii
Acknowledgements ........................................................................................................................ iii
List of Tables ................................................................................................................................. vi
List of Figures ............................................................................................................................... vii
Abbreviations ............................................................................................................................... viii
Abstract .......................................................................................................................................... ix
Chapter 1: Introduction ................................................................................................................... 1
Self-perceptions of Aging Within the Broader Literature ....................................................... 1
Roots of Self-perceptions of Aging: Review of Theories ....................................................... 5
Predictors of Self-perceptions of Aging: Review of Empirical Evidence ............................. 10
Summary and Research Directions ........................................................................................ 21
Chapter 2: Neighborhood Social Environment and Self-perceptions of Aging ........................... 23
Abstract .................................................................................................................................. 23
Background ............................................................................................................................ 24
Methods ................................................................................................................................. 28
Results ................................................................................................................................... 33
Discussion .............................................................................................................................. 39
Chapter 3. Longitudinal Associations of Perceived Neighborhood Social Cohesion with Self-
perceptions of Aging and Loneliness ............................................................................................ 44
Abstract .................................................................................................................................. 44
Background ............................................................................................................................ 45
Methods ................................................................................................................................. 48
v
Results ................................................................................................................................... 53
Discussion .............................................................................................................................. 61
Chapter 4. Neighborhood Adversity and Cognitive Health: The Modifying Role of Self-
perceptions of Aging ..................................................................................................................... 67
Abstract .................................................................................................................................. 67
Background ............................................................................................................................ 68
Methods ................................................................................................................................. 71
Results ................................................................................................................................... 75
Discussion .............................................................................................................................. 84
Chapter 5: Conclusion ................................................................................................................... 88
Bibliography ................................................................................................................................. 91

vi
List of Tables
Table 1. Major Constructs of Subjective Aging ............................................................................. 3
Table 2. Predictors of Positive Self-perceptions of Aging at the Individual level ....................... 11
Table 3. Predictors of Positive Self-perceptions of Aging at the Environmental Level ............... 21
Table 4. Descriptive Sample Characteristics ................................................................................ 34
Table 5. Multilevel Linear Regression Models Estimating Self-perceptions of Aging ................ 36
Table 6. Multilevel Linear Regression Models Estimating Interaction Effects of Perceived
Neighborhood Social Cohesion and Age .......................................................................... 38
Table 7. Descriptive Sample Characteristics at Baseline .............................................................. 54
Table 8. Correlations among Neighborhood Social Cohesion, Self-perceptions of Aging, and
Loneliness ......................................................................................................................... 55
Table 9. Unconditional Latent Growth Modeling for Self-perceptions of Aging and Loneliness 57
Table 10. Effects Decomposition of Baseline Neighborhood Social Cohesion on Self-perceptions
of Aging and Loneliness at 8-year Follow up ................................................................... 61
Table 11. Descriptive Sample Characteristics at Baseline ............................................................ 76
Table 12. Three-level Variance-components Model for Cognitive Function ............................... 77
Table 13. Unconditional Growth Model for Cognitive Function over 8 years ............................. 78
Table 14. Mixed Effects Growth Curve Models for Cognitive Function over 8 years ................ 80
Table 15. Interaction Effects of Neighborhood Adversity Factors and Self-perceptions of Aging
at Baseline for Cognitive Function over 8 years ............................................................... 82

vii
List of Figures
Figure 1. Conceptual Framework of Antecedents and Outcomes of Self-perceptions of Aging . 11
Figure 2. Age Differences in the Effects of Social Cohesion on Self-perceptions of Aging ....... 39
Figure 3. Latent Growth Curve Models for Self-perceptions of Aging and Loneliness ............... 58
Figure 4. A Path Model Estimating Longitudinal Associations of Neighborhood Social Cohesion
with Loneliness and Self-perceptions of Aging ................................................................ 60
Figure 5. Modifying Effects of Self-perceptions of Aging in the Association of Neighborhood
Adversity with Cognitive Function ................................................................................... 83

viii
Abbreviations
ACS: American Community Survey
ADL: Activities of Daily Living
CES-D: Center for Epidemiological Studies Depression
HRS: Health and Retirement Study
NSC: Neighborhood Social Cohesion
SPA: Self-perceptions of Aging
ix
Abstract
A growing body of literature has shown that self-perception of aging is a particularly
robust and well-established predictor of health and well-being in later life (Diehl et al., 2021).
SPA is conceived as a fundamental process of adult development and constitutes an integral part
of the intentional self-development (Diehl et al., 2015). SPA is created, held, and reconstructed
through one’s personal aging experiences, laying the foundation upon which knowledge about
own aging process is based and retrieved (Diehl et al., 2014). This knowledge is then
incorporated into an overall self-concept in later life and becomes integral for understanding
individuals’ aging-related expectations, behaviors, and identity processes (Diehl et al., 2015).
Negative SPA is associated with older adults’ greater disease burden, poor physical and mental
health, impaired functional ability, and even mortality risk, as well as lower levels of well-being
and social isolation (Chang et al., 2020; Westerhof et al., 2014 for reviews).
Although previous studies on SPA have provided a solid foundation for understanding
how SPA is developed and its consequences on our later lives, the attempts to link it to broader
contextual-level factors have been limited. More attention should be paid to addressing the
impacts of a macro-level context, such as cultural, societal, and residential environment, to better
understand the formation of SPA (Diehl, 2021). Guided by two ecological frameworks,
Bronfenbrenner’s Ecology of Human Development theory (1977) and Lawton’s Ecological
Model of Aging (1973), this study focuses on neighborhoods as an integral context where
individuals develop SPA through the interaction between the residential environment and
psychological processes. Neighborhood environments may serve as a critical avenue for older
adults to remain healthy and socially active, which in turn contributes to their evaluations of how
they grow old. Consideration of the residential environment has greatly been overlooked in the
x
gerontological research (Wahl & Oswald, 2010) despite the early conceptualizations of later life
as a susceptible phase where the dynamic interactions of person and environment play an
essential role in the human development (Lawton & Nahemow, 1973).
This dissertation conducted three studies to fill the existing research gaps. Chapter 2
examines whether 1) neighborhood social environment is related to SPA and 2) age moderates
this relationship. Four social and economic aspects of neighborhoods were considered: (a)
neighborhood poverty; (b) percentage of older adults; (c) perceived social cohesion; and (d)
perceived disorder. Respondents who perceived their neighborhoods as more socially cohesive
reported more positive SPA after adjusting for individual-level covariates. It also found
significant interaction effects between neighborhood social cohesion and age: the effects of
neighborhood cohesion on shaping SPA were stronger at younger ages than older ages. These
findings provide insights into how neighborhood social context shapes subjective aging,
suggesting that a socially cohesive neighborhood may be important to promote more favorable
perceptions of aging, particularly for younger residents.
Chapter 3 examines 1) whether neighborhood social cohesion is related to the levels and
changes in SPA and loneliness and 2) the potential bidirectional associations between these two
factors for linking paths from neighborhood social cohesion to each other. It was hypothesized
that negative perceptions of the neighborhood would elicit feelings of loneliness, leading to more
negative evaluations of one’s own aging process and vice versa. Findings showed that more
limited neighborhood social cohesion predicted more negative SPA 8 years later but was not
significantly associated with the rate of change over time. Respondents living in neighborhoods
with more limited social cohesion at baseline were lonelier but had slower rates of increase over
time. In the path analyses, the effects of neighborhood social cohesion on SPA were primarily
xi
indirect via loneliness, whereas its effects on loneliness had both direct and indirect pathways.
These findings suggest that neighborhood social cohesion is longitudinally linked to one’s
perceptions of aging and loneliness through bidirectional mediation processes.
Chapter 4 conceptualized SPA as a potential moderator in the association between
neighborhood adversity and cognitive function as it reflects core beliefs about the self at older
ages. Guided by the ecology theory of aging and the diathesis-stress model, it is hypothesized
that more positive SPA would buffer the effects of neighborhood adversity on cognitive function.
The neighborhood indicators included 1) poverty rates, 2) perceived neighborhood social
cohesion, and 3) perceived neighborhood disorder. The findings showed that higher poverty
rates, more disorder, and less cohesion were associated with lower initial levels of cognitive
function but slower rates of cognitive decline. SPA partially moderated the linkage between
neighborhood adversity and the level of cognitive function. More positive SPA was associated
with reduced adverse effects of living in neighborhoods with higher poverty rates and more
physical disorders. These findings highlight the intersection of an individual-level psychological
factor and a contextual-level factor in shaping late-life cognitive health.
The findings from this study will not only contribute to the literature on SPA, but also
elucidate the psychological mechanisms through which neighborhoods influence older adults’
health, which is another area that has been underexplored (Yen et al., 2009). Knowledge about
how one’s reflections and evaluations of their own aging are embedded in their living
environment is of fundamental importance to the field of gerontology, sociology, and
environmental psychology. Furthermore, understanding untapped environmental resources that
can enhance or undermine positive SPA allows for developing novel interventions to promote
healthy aging within a community.
1
Chapter 1: Introduction
Self-perceptions of Aging Within the Broader Literature
Adults’ perceptions of and attitudes toward aging have been a long-standing research
topic across the field of gerontology, developmental psychology, and life-span sociology
(Bennett & Eckman, 1973; Diehl et al., 2015; Kastenbaum et al., 1972; Tuckman & Lorge,
1954). Aging is accompanied not only by decline but also significant growth throughout the life
course (Baltes, 1987); however, attitudes toward aging are predominantly negative. Globally,
half of the population is estimated to hold ageist attitudes (Officer et al., 2020), which can lead to
poor health, loss of opportunities for social engagement, and increased healthcare costs. The
World Health Organization (2021) called for urgent action to combat negative attitudes toward
aging and old persons so that people can flourish at any age. One develops beliefs about the
aging process through ongoing interactions between self and social environments as well as
one’s own experiences related to the aging (Boeder, 2021). How aging is framed within society
and culture (e.g., societal discourse and metaphors about age) plays a critical role in creating and
sustaining individuals’ perceptions of aging (Vincent, 2006).
Literature has identified two distinct targets of attitudes toward aging exerting their
influence (Ayalon & Tesch-Römer, 2017). One is directed at other people, which can be
understood as the concept of ageism. The term “ageism” was first coined by Robert N. Butler,
the founding director of the National Institute on Aging (1969). He described the phenomenon as
“Reflects a deep-seated uneasiness on the part of the young and middle-aged—a personal
revulsion to and distaste for growing old, disease, disability, and fear of powerlessness,
‘uselessness,’ and death” (1969, p. 243). After Butler’s seminal work on describing the features
of ageism, ageism has been further elaborated on the traditional tripartite division of attitudes:
2
affective (how we feel), cognitive (how we think), and behavioral (how we act) components
(Eagly & Chaiken, 1993). The affective category can be represented by individuals’ internal
feelings of good or bad towards older persons. The cognitive category refers to age-related
stereotypes about older adults (e.g., hard of hearing and decreasing intellect). Behavioral ageism
includes a willingness to help without being asked, recommending doctors’ evaluation after
memory failure, and not hiring older persons based on their chronological ages.
Negative attitudes toward aging can also be directed toward one’s own aging process if
aging stereotypes and stigma of older adults are internalized and further turned against oneself
(B. R. Levy, 2003). The Stereotype Embodiment Theory (B. R. Levy, 2009) suggests a process
of how the surrounding cultural discourse of aging can influence individuals’ perceptions of their
own aging in later life. As young as fourth grade, individuals start internalizing prevalent
negative societal messages about aging and older people (Seefeldt, 1984). This, in turn, shapes
their perceptions of growing older and reinforces the negative ideas of aging across the lifespan
(Kotter-Grühn & Hess, 2012). At older ages, age stereotypes become relevant to self and
translated into self-stereotyping, affecting health and well-being outcomes through multiple
pathways. A growing body of literature has shown that self-perception of aging is a particularly
robust and well-established predictor of health and well-being in later life (Diehl et al., 2021).
Negative SPA is associated with older adults’ greater disease burden, poor physical and mental
health, impaired functional ability, and even mortality risk, as well as lower levels of well-being
and social isolation (Chang et al., 2020; Westerhof et al., 2014 for reviews). The current study
focuses on individuals’ attitudes toward their own aging, or self-perceptions of aging (SPA).
The concept of SPA falls under the superordinate construct, or subjective aging. Research
on subjective aging has emerged based on the observation that we reflect on our own
3
development and interpret aging experiences across the lifespan (Diehl et al., 2015). For the past
several decades, various conceptualizations and operations have been proposed to understand
individuals’ subjective aging experiences, as shown in Table 1. As one of the most traditional
approaches, subjective age assesses one’s evaluations of their age (Kastenbaum et al., 1972).
Similarly, the term age identity refers to the simple approach to capturing which age group
individuals identify with (Kaufman & Elder, 2002). Attitudes toward aging have been
conceptualized as affective, cognitive, and evaluative components of behaviors toward older
adults and the aging process, which can be held at both societal and individual levels (Diehl et
al., 2014). The concept of awareness of age-related change (AARC) was developed most
recently to elucidate psychological processes and comprehensively cover the multidimensional
nature of one’s perceptions of age-related changes (Diehl & Wahl, 2010). AARC is “all those
experiences that make a person aware that his or her behavior, level of performance, or ways of
experiencing his or her life have changed as a consequence of having grown older” (p. 340).
Finally, SPA is anchored in one’s personal aging experience covering thoughts and beliefs about
whether aging is a time of growth or loss (Boeder, 2021).

Table 1. Major Constructs of Subjective Aging
Construct (interchangeable term) Definition
Subjective age (felt age) How old individuals feel or how old they view
themselves
Age identity (age identification) A person’s subjective sense of which age group they
identify with
Attitudes toward aging Societal and individual attitudes, including affective,
cognitive, and evaluative components of behavior
toward older adults as an age group and toward the
process of aging
Awareness of age-related change All experiences that make a person aware that his or
her behavior, level of performance, or ways of
4

Literature has increasingly paid attention to SPA because its multidimensionality and
representativeness as a form of self-knowledge provide a better understanding of individuals’
evaluations of their own aging process (Diehl et al., 2014). Critically, SPA is conceived as a
fundamental process of adult development and constitutes an important part of intentional self-
development (Diehl et al., 2015). To elaborate, SPA is created, held, and reconstructed through
one’s personal aging experiences, laying the foundation upon which knowledge about own aging
process is based and retrieved (Diehl et al., 2014). This knowledge is then incorporated into an
overall self-concept in later life and becomes integral for understanding individuals’ aging-
related expectations, behaviors, and identity processes (Diehl et al., 2015). For example, negative
age stereotypes (i.e., old age is a time of decline) can lead to having negative future self-views,
affecting individuals’ self-concepts as they grow older and experience age-related changes
(Kornadt & Rothermund, 2012). From a life span developmental perspective, perceptions of
aging-associated gains may motivate an awareness of developmental opportunities as well as
optimizing behaviors. In contrast, perceptions of aging-associated losses may result in
developmental constraints on one’s behaviors (M. Diehl & Wahl, 2010).
Diehl and colleagues (2015) explain the processes involved in the formation of SPA,
which can be triggered by (a) the aging-related feedback that one receives from the surrounding
social environment and (b) the changes that one perceives in functioning and behaviors caused
by growing older. Chronological age serves as a marker to ascribe social categorizations,
positions, and responsibilities in a given society, from which individuals develop an
experiencing his or her life have changed as a
consequence of having grown older
Self-perceptions of aging (self-
views of aging; attitudes toward
own aging)
The combination of one’s thoughts and beliefs about
their personal aging experience
5
understanding of their own aging. For example, social role transitions (e.g., becoming a
grandparent) foster an awareness of one's aging and position within the life course. In the
combination of the messages from interpersonal interactions and the mass media, this awareness
influences one's daily behaviors and experiences, contributing to how one appraises one's own
aging processes. Within the boundaries of socio-contextual conditions, one is also actively
involved in shaping SPA as a producer of their own development. Individuals build self-
knowledge from their perceptions of aging-related changes (e.g., wrinkles, the onset of diseases,
and reduced stamina), which becomes part of their self-concept with increasing age. The
following sections will further explore the roots of SPA, reviewing theoretical and empirical
evidence.
Roots of Self-perceptions of Aging: Review of Theories
Individual Level Theories
Several theories from social and developmental psychology have been developed to
explain the origins of negative perceptions of aging at the individual level. Earlier theoretical
approaches have focused on the internal state of mind (e.g., terror management theory) or
interactions among individuals (e.g., stereotype content model). In contrast, the recent theories
are based on the perspective of human development with emphasis on changes over the lifespan
(e.g., stereotype embodiment theory).
Terror management theory. Terror management theory proposes that the realization of
death can create terror, and accordingly, individuals attempt to resolve death-related anxiety by
relying on cultural beliefs (e.g., afterlife or religion) or symbolic immortality (e.g., national
identity) that can counter biological reality (Greenberg et al., 1986). From this perspective,
negative perceptions of aging might originate from individuals’ instinct efforts to manage the
6
anxiety and fear caused by death. Because the presence of older adults can serve as a reminder of
mortality, younger people may naturally want to distance themselves from older individuals by
adopting ageist thoughts and behaviors (Nelson, 2004). Some instances include the older person
displaying external indicators of aging. By doing so, younger persons can deny the fearful reality
that they will grow older and eventually be close to death.
Social identity theory. According to social identity theory (Tajfel, 1981), individuals
obtain a positive self-identity through having biases or strong preferences in favor of their
belonging group (in-group) over other groups (out-groups). Because social status and power are
not equally distributed among different social groups, in-group members might interact with
those from other groups in a competitive fashion to increase their in-group status or maintain a
positive social identity. In this sense, younger age groups might want to distinguish themselves
(us) from older age groups (them) and show age-biased attitudes toward them to enhance their
positive self-image.
The stereotype content model. The stereotype content model posits that people categorize
groups of others (e.g., the young vs. the old) based on different levels of warmth and competence
(Fiske et al., 2002). Older adults are commonly evaluated as having higher levels of warmth, but
lower levels of competence. These stereotyped perceptions can be understood as benevolent
ageism and make older persons be the target of pity and sympathy but not envy (Cuddy & Fiske,
2004). Empirical evidence has consistently supported this idea by suggesting that stereotypes
about older people are mixtures of high warmth and low competence across various cultures
(Cuddy et al., 2005).
Stereotype embodiment theory. Stereotype embodiment theory (B. R. Levy, 2009)
suggests that individuals are exposed to negative messages about growing older and older person
7
lifetime, which ultimately results in the internalization of ageism. According to the theory, a
three-step process operates: 1) age stereotypes are internalized from culture early in life through
interactions with older people and culturally shared images of aging (e.g., media or books), 2)
when individuals get older, age stereotypes are translated into self-stereotypes through self-
fulfilling prophecies, and 3) self-stereotyping impacts individuals’ late-life health and well-
being. Underlying this process, four main characteristics of age stereotypes have been identified:
age stereotypes 1) are internalized across the lifespan; 2) can operate in unconscious ways; 3)
become salient from self-relevance; and 4) run via physiological, psychological, and behavioral
pathways.
Lifespan development perspective. From a social developmental perspective, Montepare
and Zebrowitz (2002) proposed that ageist attitudes and behaviors are embedded in a broader
social context and individuals adopt and develop such negative perceptions toward aging
throughout the life course. The authors suggested that from a very early age, children acquire
knowledge of age prejudice. The main reason can be attributed to structural changes in society
with a shift from extended family units to nuclear family units. Under this circumstance, children
have less opportunity to contact grandparents. Additionally, children’s negative perceptions of
older people can be further promoted through the media, peer groups, and their parents.
Societal Level Theories
Understanding of ageism can be deepened by examining societal factors beyond the
individual level. One’s psychological process is closely linked with their surrounding social and
physical environment (Marques et al., 2017). Institutions, groups, and other social entities might
encourage age prejudice or discriminatory behaviors. Not only that, but negative aging attitudes
8
can also be located at a larger macro-level structure including the age profile of a country’s
population, its economic status, and political systems (Marques et al., 2017).
Age segregation. In most modernized societies, intergenerational contact among
different age groups is less likely to occur. Pre-structured life scripts, which are receiving
education, obtaining jobs, forming a family, and retiring, reduce opportunities for social
interactions across age groups and accordingly, create clear segregation between younger and
older people (Riley & Riley, 1994). This lack of intergenerational contact might lead to increased
ageism (Hagestad & Uhlenberg, 2005).
Intergroup threat theory. According to the intergroup threat theory, potential threats
caused by outgroups motivate individuals to behave in hostile manners toward outgroup
members (Stephan & Stephan, 2000). Threats can be realistic types that negatively impact on
resources and power of a certain group. Alternatively, they can be symbolic types that the
group’s belief system and values are undermined. Relative to this theory, ageist prejudice can be
triggered when young individuals perceive the presence of the older population as harmful.
Intergenerational conflict theory. Conceptually related to the intergroup threat theory,
the intergenerational conflict theory posits that intergenerational conflicts are exacerbated when
younger generations’ expectations of older adults are not met (North & Fiske, 2013). For
instance, they hold three expectations for the older population to inherit resources, minimally
utilized shared resources, and avoid symbolic identity belonging to the younger generation.
Supporting this idea, empirical research showed that the economic interests of one’s age group
elicited greater age discrimination (Garstka et al., 2005).
Modernization theory. Modernization theory suggests that a shift toward a modernized
society, featured with advanced industrialization and economic development, significantly
9
lowered the social status of older adults while increasing the power and status of the younger
generations (Cowgill & Holmes, 1972). In the advent of the industrial revolution, three major
changes were attributed to the source of ageism. First, the number of the older population
dramatically increased with the advancements in medicine and medical technology. As a result,
old age is more likely to be seen as frail or disabled, rather than as a “survival of the fittest.”
Second, innovations in technology led to the devaluing of the accumulated skills or knowledge
of older individuals. In a similar vein, much earlier, the invention of the printing press also
contributed to undermining the values of older adults’ knowledge and experiences (Branco &
Williamson, 1982). On top of that, the greater educational attainment of younger generations is
likely to be a key reason for the low status of older people in modern society. Urbanization is
another contributor to older adults’ marginalized position by disintegrating traditional extended
family units. Younger people moved to the city to find better jobs while older adults still
remained in their homes as they were not as mobile as the younger generation (Nelson, 2005).
Accordingly, the degree of international contact between age groups decreased and older family
members, once being in the center of production, lost their control and power (Cowgill &
Holmes, 1972).
Although modernization theory provides persuasive explanations for ageism, it has been
criticized for its oversimplification. Inglehart and Baker (2000) pointed out that culturally
different responses to advancements in socio-economic development, which originated from
unique values and belief systems of a certain culture, were not properly taken into consideration
in the theory. To address this concern, more recent study has begun to investigate the dynamic
interaction between the extent of modernization of a country and the proportion of the older
10
population actively involved in working activities and its impact on the degree of the social
status of older people (Vauclair et al., 2015).
Social representations theory. According to social representations theory, individuals’
perceptions of growing older and age discrimination are shaped by societal level views of aging
prevalent within a given culture (Moscovici, 1988). Society constitutes the foundation of its
members’ values, ideas, and beliefs on aging. Therefore, different from aging perceptions related
to physiological declines in functioning, perceptions of age-associated changes in societal roles
and status are expected to greatly vary across cultures. For example, if older people are
commonly viewed as incompetent in a given society, age discrimination is likely to occur. In line
with this idea, a recent growing body of literature has acknowledged potential cultural variations
in the experiences of subjective aging (North & Fiske, 2015).
Predictors of Self-perceptions of Aging: Review of Empirical Evidence
Individual Level Predictors
Researchers have proposed conceptual frameworks explaining a variety of predictors of
SPA (Diehl & Wahl, 2010; Kornadt et al., 2020), as shown in Figure 1. Supporting empirical
evidence, although still limited (Diehl et al., 2021), has identified several individual-level
determinants of SPA broadly divided into four categories: 1) demographic, 2) socioeconomic, 3)
psychosocial, and 4) health factors. Prior findings are summarized in Table 2. Overall, health-
related variables (e.g., depressive symptoms, chronic diseases, and self-rated health) have shown
the most consistent and strongest associations with SPA, such that poorer health predicted more
negative perceptions of aging (Diehl et al., 2021; Sabatini et al., 2021). The relationship of other
predictor categories with SPA has been no significant or mixed (Marques et al., 2019). Further
investigation is needed to better understand factors that create and shape SPA.
11
Figure 1. Conceptual Framework of Antecedents and Outcomes of Self-perceptions of Aging

Note. This figure was adapted from Diehl, M. K., & Wahl, H.-W. (2010). Awareness of Age-
Related Change: Examination of a (Mostly) Unexplored Concept. The Journals of Gerontology:
Series B, 65B(3), 340–350. https://doi.org/10.1093/geronb/gbp110

Table 2. Predictors of Positive Self-perceptions of Aging at the Individual level
First Author Year Age % Female Significant predictors (+)
Wurm 2014 40–85 49.5 Older age; female; higher income; fewer
number of and less severe chronic diseases
Lamont 2017 60–97 60.7 Younger age; good health status; satisfaction
with social support; positive self-perception
Bryant 2016 60+ 61.6 Younger age; lower financial status; higher
openness to experience, extraversion, and
agreeableness; lower neuroticism; good
physical/mental health; higher life satisfaction
Fullen 2016 56–97

84.0 Younger age, higher resilience, good total
wellness
Janečková

2013 59–102

84.0 Male; having children; good self-rated health;
no depression; good quality of life
Levy 2008 50+ 53.8 Less rigidity
Milligan 1985 65–85 0.0 Good health
12

Demographic factors. Prior studies examined whether there are age differences in SPA.
Although the findings are mixed (Wurm et al., 2014), most of the evidence suggests that older
people have more negative attitudes toward own aging than do younger people (Jung et al., 2021;
Lamont et al., 2017). At the intersection of sexism and ageism, older women have qualitatively
different experiences of ageism from men (McGann et al., 2016). Women are likely to have
greater concerns about growing older and hold more negative views of aging than their male
counterparts (Ayalon, 2014; Berger, 2017; Janečková et al., 2013). However, a few studies using
longitudinal design showed that women develop more favorable perceptions of aging over time,
Sargent-Cox 2012 65–103

44.4 Higher self-esteem, personal control, less
declines in activity of daily living
Trigg 2012 60–93 46.4 Not having dementia
Jung 2021 40+ 48.8 Younger age, female, higher education, fewer
illness, good subjective health, larger social
network, having spouse/partner
Sabatini 2021 61–64 49.5 Less declines in subjective physical health,
less increase in depressive symptoms
Diehl 2021 40–85 50.1 High education, less loneliness, larger
network size, higher positive affect, fewer
chronic diseases, good self-rated health
Miche 2014 40+ - Lower neuroticism, good health, fewer
depressive symptoms
Wolff 2018 40–85 48.7 Younger age, high education, better functional
health, fewer diseases
Wurm 2020 40–85 30.0 Not having a cardiovascular event
English 2019 60+ 49.7 Higher subjective socioeconomic status, good
health
Schwartz 2020 40+ - Younger age, more informal social activities,
better subjective health, fewer illness
Seidler 2017 40–93 49.0 Better processing speed
Chachamovich 2008 56.8 Less depressive symptoms
Kim 2021 65+ 46.0 Younger age, having racial/ethnic minority
background, high education, wealth, fewer
chronic conditions, less depressive symptoms
13
particularly for social and continuous growth aspects of aging (Jung et al., 2021; Wurm et al.,
2014). Most studies on SPA have come from the non-Hispanic White population, and little is
known about how other racial/ethnic groups perceive their aging. Earlier, researchers argued that
racial/ethnic minorities are more likely to report negative SPA because of the double jeopardy of
ageism and racism (Kasschau, 1977). Sarkisian and colleagues (2006) showed Hispanics had
more negative expectations than non-Hispanic Whites, and this was primarily explained by lower
educational level. Menkin and colleagues (2017) found that African American older adults had
more positive perceptions of aging than Hispanics and Asian Americans. Another study found
that Native Americans and Middle Easterners had the most favorable views on aging and older
adults because old age might be more valued in their cultures (Berger, 2017).
Socioeconomic factors. Literature suggests that besides demographic indicators, how we
perceive our own aging processes may be tied to socioeconomic characteristics. However, the
direction or the strength of such associations is yet to be determined. For example, some studies
showed that individuals with a higher level of education reported less negative SPA (Jung et al.,
2021; Y. K. Kim et al., 2021; Wolff et al., 2018), while others found no significant relationship
between education and SPA (Sabatini et al., 2021). Similarly, it is unclear whether financial
resources are associated with more positive or negative SPA. One line of research documented
the positive effects of high income and sufficient wealth on SPA in later life (Y. K. Kim et al.,
2021; Wurm et al., 2014), whereas another line of evidence suggested that low financial status
may lead to more positive perceptions of growing older (Bryant et al., 2016). A study by English
(2019) provides better insights into explaining the role of socioeconomic factors in shaping SPA.
Their findings showed that subjective socioeconomic status, not objective indicators,
14
significantly predicted SPA, highlighting the importance of one’s own evaluations of their social
standing compared to others in their reference group.
Psychosocial factors. Several studies identified the two-down effects of personal
characteristics on SPA, which remain relatively stable across a lifetime. One indicator is
personality, such that higher neuroticism, lower extraversion, agreeableness, and openness are
associated with negative perceptions of own aging process (Bryant et al., 2016; Miche, Elsässer,
et al., 2014). More rigidity also predicted more negative aging self-perceptions through its’ link
to endorsing more aging stereotypes (B. R. Levy, 2008). Traits to view the self and world more
negatively may make one more focus on losses with aging, detracting their attention from gains
from aging. Psychological factors such as positive self-perception, self-esteem, personal control,
and resilience serve as a protective buffer against challenges associated with the aging process
(e.g., functional limitations) (Fullen, 2016; Lamont et al., 2017; Sargent-Cox et al., 2012). Other
researchers focused on the bottom-up effects of psychological factors that fluctuate in response
to life events or concurrent situations. For example, positive affect and life satisfaction can
positively influence how one evaluates their own aging process (Bryant et al., 2016; Diehl,
Wettstein, et al., 2021; Janečková et al., 2013). Social factors are also closely related to SPA.
Individuals who had greater satisfaction with social support, larger social network size, and more
informal social activities perceived aging more positively (Janečková et al., 2013; Jung et al.,
2021; Lamont et al., 2017; Schwartz et al., 2021), whereas feelings of loneliness had adverse
effects on SPA (Diehl, Wettstein, et al., 2021). Maintaining a supportive social network may
attenuate the potential negative consequences of aging-related life events (e.g., retirement and
loss of a loved one).
15
Health factors. Mounting evidence has shown that adults’ health status makes significant
contributions to the formation of SPA. Functional declines (Sargent-Cox et al., 2012; Wolff et
al., 2018), the onset and severity of chronic illnesses (Jung et al., 2021; Y. K. Kim et al., 2021;
Wurm et al., 2020), and cognitive impairment (Sabatini et al., 2021; Seidler & Wolff, 2017;
Trigg et al., 2012), as well as subjective perceptions of such changes Field (M. Diehl et al., 2015;
Sabatini et al., 2021; Schwartz et al., 2021), may remind one of increased age and thus, affect
their interpretation of aging experiences. Older adults with poorer health may be more likely to
view themselves as the stereotyped “old person” compared to their healthier counterparts
(Milligan et al., 1985). Poor mental health also serves as a major determinant of how one
perceives own aging. Depression and even subclinical levels of depressive symptoms were found
to predict more unfavorable SPA (Chachamovich et al., 2008; Janečková et al., 2013; Miche,
Elsässer, et al., 2014). Greater depressive symptoms may undermine everyday functioning,
increase the susceptibility to ageism, and decrease the resilience against self-stereotyping,
consequently shaping SPA more negatively (Bodner et al., 2018).
Contextual Level Predictor
Our experience of aging is embedded in macrolevel cultural, societal, and environmental
contexts. Thus, individuals’ views on aging are influenced by where they grow up and what
historical and cultural backgrounds they have. Culturally shared expectations about life course
scripts and normative social roles in each developmental stage limit the opportunities individuals
have and prescribe behaviors at a given age (Kornadt et al., 2020). A fixed retirement age set in
each society (i.e., the age of 65) can serve as a salient marker of becoming “old”, triggering
aging stereotypes to be embodied among adults aged 65 and above. In line with this idea,
previous researchers found that contextual factors play an important role in creating and
16
intensifying the ageism (Ayalon & Tesch-Römer, 2018; Bugental & Hehman, 2007).
Furthermore, convincing evidence suggests that individuals integrate societal attitudes toward
aging and older people into their self-perceptions (Kotter-Grühn & Hess, 2012; Rothermund &
Brandtstädter, 2003). However, most prior studies focused on attitudes toward general aging and
older persons, rather than one’s own aging process. Therefore, this section will go over the
literature on the association between contextual level factors and ageism first.
Cross-national differences in ageism. National contexts have substantial impacts on
levels of age discrimination (Bratt et al., 2018). Löckenhoff and colleagues (2009) investigated
26 cultures in the world, including both Eastern and Western countries, and found considerable
differences in perceptions of aging across countries. Specifically, perceptions about aging-related
changes in socioemotional function and social roles showed wider variations across cultures
whereas age-related biological changes were least influenced by cultures. Among European
countries, the prevalence of age discrimination significantly differed across countries with a wide
range from 17% in Cyprus and Portugal to 54% in Czech Republic (Ayalon, 2014). Reports of
age discrimination among people aged 55 and above were especially more in Eastern or Post-
communist countries (Alvarez-Galvez & Salvador-Carulla, 2013; Vauclair et al., 2015). In
addition, individuals’ perceptions of how serious age discrimination is considered varied differed
across countries (Abrams et al., 2011). While greater than half (64%) of the UK respondents
perceived it as a quiet or very serious problem, the European average was less than half (44%).
Within western cultures, people in the UK showed significantly higher levels of perceptions of
age discrimination than in the United States: nearly one-third (35%) of individuals in England
reported age discrimination, but only 29% of the people in the U.S. did (Rippon et al., 2015).
These differences across countries indicate that ageism is not an inevitable outcome of biological
17
or developmental changes, but an amenable factor influenced by cultural, social, and political
contexts (Abrams et al., 2011).
Intra-national variation in ageism. Previous research suggested that beyond cross-
national differences, regional variation in aging stereotypes might exist within the same country.
Abrams and colleagues (2009) addressed this question by investigating 11 areas of the UK. They
found that people in the Southeast were more likely to report ageism than those in other regions.
Furthermore, regional differences in the prevalence of indirect prejudice were identified with the
least in Southeast and East of England. Age profiles partially explained the regional variation.
Regions with a higher proportion of older people were associated with less indirect prejudice.
For example, respondents in such regions reported more comfort with a boss aged 70 and above.
These findings suggest the importance of region-specific strategies to decrease age
discrimination.
Cultural influence. One of the most studied contextual factors is culture, which has been
often categorized as collectivism in the East and individualism in the West. Some researchers
pointed out the potential cultural bias because most of the previous findings on ageism were
from the European or American sample (Hummert, 2011; J. H. Liu et al., 2003). Asian countries
were presumed to have more positive views of aging than western societies with their Confucian
values related to filial piety and respect for the ancestor (Sung, 2000). However, empirical
evidence yielded inconsistent results (Löckenhoff et al., 2009) and a significant number of
studies suggested the opposite direction to the prediction: attitudes towards older adults were
more negative in Asian culture than in Western culture (Laidlaw et al., 2010). Cultural
individualism was also significantly associated with relatively more positive attitudes toward
older people (North & Fiske, 2015).
18
North and Fiske (2015) explained these unexpected findings by adopting a sociological
perspective. In the post-industrialization period, social tolerance, rational values, and respect for
the individual were more likely to be emphasized (Inglehart & Baker, 2000). As a result, older
adults are more appreciated for their experiences and insight. At the same time, a rapidly aging
population might make collectivist traditions in Eastern countries backfire due to conflict over
resources. However, cultural differences in this field have been based on rather simple
classifications into Eastern versus Western cultures, which might ignore variations across
individual countries. Therefore, more attention should be paid to investigating the specific
characteristics of cultural value systems related to forming perceptions of aging after adjusting
for confounding factors such as the socioeconomic status of each country (Löckenhoff et al.,
2009).
Social norms on intolerance of ageism. Vauclair and colleagues (2016) proposed that
social norms might regulate the expression of ageism. External social pressure to value tolerance
can be shaped by social norms regarding age-related prejudice and thus, individuals in a given
society are more or less motivated to be unprejudiced (Plant & Devine, 1998). Legislative efforts
to protect older adults’ rights or mandatory rules for politically proper speech might create
differences in the social norm against the expression of prejudice. Vauclair and colleagues found
that older adults reported fewer instances of discrimination based on age if their countries had
social norms of intolerance of ageism (e.g., it is important to be unprejudiced against people of
other age groups). Moreover, the results also showed that the statistical effect of social norms in
the prediction of age discrimination was greater than meta-perceptions at a societal level. The
authors concluded that social norms of intolerance of age discrimination would be an effective
source to improve older adults’ aging experience.
19
Population aging rate. Another source for contextual differences is the speed of aging. It
has been suggested that a dramatic increase in the aging population can result in relative
negativity toward aging and older adults (North & Fiske, 2015). Indeed, the authors found that
Eastern countries had much steeper rates of population aging compared to Western countries,
which in turn, led to an increased burden to take care of their older populations. Thus,
collectivistic cultural emphasis on respect for older adults might be devalued due to larger social
costs caused by the growth in population aging. Moreover, forced obligation to provide care for
older people could even spur resentment among younger people (North & Fiske, 2015).
Relatedly, Linder and colleagues (2006) found that a greater number of older populations
predicted stronger implicit preferences for young people across nations. The significant
relationship between the population aging rate and negative perceptions of older adults might be
partially explained by competition for resources among age groups (North & Fiske, 2015).
Perceptions of threat. Another important contributor to age prejudice would be people’s
perception of older adults as a threat to society (Ayalon, 2019). According to the intergroup
threat theory (Stephan et al., 2009), threats include realistic, symbolic, and economic types. The
realistic threats indicate a danger posed to the well-being of the ingroup; concerns about the
increased number of crimes committed by older persons. The symbolic threats are closely related
to a group’s beliefs, identity, and values and therefore, any forms of interference with such
customs by outgroups would be regarded as intimidating. Finally, economic threat refers to
worries that the older population will disproportionately consume social resources. Abrams and
colleagues (2011) found that people, regardless of which countries they belong to, view older
individuals as an economic threat with their little contribution to the national economy as well as
20
being a burden on health care services. These perceptions were especially stronger for younger
people compared to their older counterparts.
In a similar vein, research with a sample of Chinese adults also suggested that resentment
or negative attitudes toward older adults among younger individuals are associated with the
burden of providing care, legal responsibilities, and economic stress (Luo et al., 2013). On the
other side, the social participation or contribution of older people might attenuate the negative
perceptions, which is in line with the social role theory (Eagly & Chaiken, 1993). Bowen and
Skirbekk (2013) investigated 28 European countries and showed that older adults’ participation
in paid work and volunteer activities predicted more positive perceptions of older people in terms
of competence after adjusting for their actual competence.
While this body of literature suggests the critical role of contextual level factors in
shaping aging attitudes in general, only a handful of studies identified the environmental
predictors (i.e., regions and neighborhoods) of how one perceives their own aging process. As
shown in Table 3, only three studies focused on the association between immediate living
environments and SPA. To summarize, individuals living in areas with greater economic
resources (high GDP) and slower population aging rates perceive age-related changes more
positively (Wolff et al., 2018; Wurm et al., 2014). This could be because the availability of
neighborhood resources may determine whether one can age well, contributing to the evaluation
of one's own aging process (Diehl, Wettstein, et al., 2021). It is imperative to elucidate the role
of the environmental context we live in, which will provide a complete picture of how SPA
develops, changes, and operates across the lifespan.

21
Table 3. Predictors of Positive Self-perceptions of Aging at the Environmental Level

Summary and Research Directions
Although previous studies on SPA have provided a solid foundation for understanding
how SPA is developed and its consequences on our later lives, the attempts to link it to broader
contextual-level factors have been limited. More attention should be paid to addressing the
impacts of a macro-level context, such as cultural, societal, and residential environment, to better
understand the formation of SPA (Diehl, 2021). The existing literature on such surrounding
differences has primarily relied on cross-country comparisons. With contrasts across nations,
however, the specific contextual characteristics relevant to the origins of perceptions of aging
might be difficult to disentangle from individual country’s specific circumstances, including
aging population structure, levels of industrialization, and economic progress (Löckenhoff, et al.,
2009; North & Fiske, 2015). Currently, the literature lacks the focus on differences within
nations, particularly across different regions widely varying in age demographics, as well as
cultural norms, population characteristics of older people, and other factors shaping individuals’
evaluations of own aging. As speculated by some researchers (North & Fiske, 2015; Vauclair et
al., 2017), examining geographical location as a predictor or moderator would significantly
reveal a further layer of complexity in SPA.
Guided by two ecological frameworks, Bronfenbrenner’s Ecology of Human
Development theory (1977) and Lawton’s Ecological Model of Aging (1973), this study focuses
on neighborhoods as an integral context where individuals develop SPA through the interaction
First Author Year Age % Female Significant predictors (+)
Wurm 2014 40–85 49.5 High GDP per inhabitant
Diehl 2021 40–85 50.1 Living in West Germany vs. East Germany
Wolff 2018 40–85 48.7 Living in districts with slow population aging
22
between the residential environment and psychological processes. Consideration of the
immediate physical, spatial, and social environment has greatly been overlooked in
gerontological research (Wahl & Oswald, 2010) despite the early conceptualizations of later life
as a particularly sensitive phase where the dynamic interactions of person and environment play
an important role in human development (Lawton & Nahemow, 1973). To address these research
gaps, the aims of this study are three-fold: 1) exploring the neighborhood environment as a
source of SPA (Chapter 2), 2) investigating the potential pathway that connects the neighborhood
environment to SPA (Chapter 3), and 3) the examining how the neighborhood environment
interplays with SPA to influence cognitive health in later life (Chapter 4). The findings from this
study will not only contribute to the literature on SPA, but also elucidate the psychological
mechanisms through which neighborhoods influence older adults’ health, which is another area
that has been underexplored (Yen et al., 2009). Knowledge about how one’s reflections and
evaluations of their own aging are embedded in their living environment is of fundamental
importance to the field of gerontology, sociology, and environmental psychology. Furthermore,
understanding untapped environmental resources that can enhance or undermine positive SPA
allows for developing novel interventions to promote healthy aging within a community.

23
Chapter 2: Neighborhood Social Environment and Self-perceptions of Aging
Abstract
Self-perceptions of aging (SPA), one’s attitude toward their aging process, is associated with
health and well-being later in life. Though individual-level predictors of SPA have been
identified, much less is known about the role of neighborhood social context in SPA.
Neighborhood social environment may act as a critical avenue for older adults to remain healthy
and socially active, which in turn contributes to their evaluations of how they grow old. The
present study examines whether 1) neighborhood social environment is related to SPA and 2) age
moderates this relationship. Our analytic sample includes 11,145 adults aged 50+ from the 2014
and 2016 waves of the Health and Retirement Study (Mean Age=66, range 50-98). We included
four social and economic aspects of neighborhoods: (a) neighborhood poverty; (b) percentage of
older adults; (c) perceived social cohesion; and (d) perceived disorder. Multilevel linear
regression models showed that respondents in neighborhoods with higher percentages of the
older population and with perceptions of high neighborhood disorder reported more negative
SPA. On the other hand, those who perceived their neighborhoods as more socially cohesive
reported more positive SPA. After controlling for individual socioeconomic and health status,
only neighborhood social cohesion remained significant. We also found significant interaction
effects between neighborhood social cohesion and age: the effects of neighborhood cohesion on
shaping SPA were stronger at younger ages than older ages. Our findings provide insights into
how neighborhood social context shapes subjective aging, suggesting that a socially cohesive
neighborhood may be important to promote more favorable perceptions of aging, particularly for
younger residents.
Keywords: neighborhood social context, ecological model, perceptions of aging

24
Background
The term “self-perceptions of aging (SPA)” refers to individuals’ evaluations of their own
aging process (B. R. Levy, 2003), such as feeling more useless and thinking things get worse as
growing older. SPA has been closely related to health and well-being outcomes in later life
(Chang et al., 2020; Westerhof et al., 2014 for reviews). In recognition of its importance, a
growing body of literature has identified individual-level correlates of SPA. For example, older
age (Sun et al., 2017), lower education (Diehl, Wettstein, et al., 2021), poor physical and mental
health (Sabatini et al., 2021), and experience of age discrimination (Kornadt et al., 2021) were
significantly associated with more negative SPA. However, relatively little is known about the
role of contextual-level factors (Marques et al., 2020) and even less research has examined how
SPA may vary by one’s residential areas, or neighborhood environment.
Neighborhoods differ substantially in terms of demographic composition (e.g., age
distribution), socioeconomic characteristics (e.g., poverty), and interpersonal resources (e.g., age
segregation and community social capital), which block or facilitate essential opportunities for
older residents to interact with younger adults and overcome “us versus them” distinctions
(Hagestad & Uhlenerg, 2005). About 94% of Americans aged 65+ (corresponding to 33.4
million) live in the community, and the absolute number is projected to increase with population
aging. Moreover, older adults spend most of their daytime in their communities (Oswald &
Wahl, 2005) and have the greatest levels of socialization with neighbors than other age groups
(Cornwell et al., 2008). This work incorporates the Ecology of Human Development perspective
into the literature on aging attitudes by evaluating the role of neighborhood social contexts in
shaping older adults’ SPA. Our study uses data from a large, nationally representative survey of
older Americans along with the census tract-level information drawn from the U.S. Census.
25
The neighborhood social environment broadly refers to the sociodemographic
composition of the neighborhood, as well as the relationships and social processes among
residents living in a certain neighborhood (Carroll-Scott et al., 2013; Kepper et al., 2019).
Researchers have operationalized and measured neighborhood social environments in two ways:
(a) those using sociodemographic data for geographically defined areas such as census tracts; (b)
those using participants’ responses for their feelings about residential areas, or everywhere
within a 20-minute walk (Peterson et al., 2021; Yen et al., 2009). The first group uses indicators
of neighborhood socioeconomic status including poverty rates, income inequality, racial/ethnic
composition, and the density of older adults living alone (D. Kim et al., 2016; Kovalchik et al.,
2015; C.-C. Liu et al., 2019). The second group focuses on subjective perceptions of
neighborhood physical and social elements (Zaheed et al., 2019; Zhang et al., 2019). For
example, perceived neighborhood social cohesion refers to the trusting network of relationships,
the sense of belonging to a community, and shared norms among residents in a neighborhood
(Brisson, 2014). Perceived neighborhood disorder encapsulates features of neighborhoods that
may indicate the breakdown of social control and order, undermining the residents’ quality of
life (Gracia, 2014). Neighborhood disorder can be exemplified by vandalism, graffiti, abandoned
cars, and trash (Skogan, 1992).
Research supports that neighborhood social environment, either measured with objective
indicators or self-report, has been an important determinant of older adults’ health and well-
being (Yen et al., 2009). For example, neighborhood socioeconomic disadvantage is strongly and
consistently associated with a variety of poor health conditions, including poor self-rated health
(Subramanian et al., 2006), higher levels of depressive symptoms (Kubzansky et al., 2005), and
morbidity (Nordstrom et al., 2004). Low neighborhood social support is associated with the
26
incidence of heart disease (Chaix et al., 2007) and mortality (Wen & Christakis, 2005). These
associations have been explained by physical activity behavior or resources available in a
socially cohesive community (Saelens & Papadopoulos, 2008). The impacts of neighborhood-
level factors are likely to be accentuated among older adults because age-related functional
limitations, mobility decline, and reduction in social networks may lead to a greater reliance on
the immediate residential environment (Yen et al., 2009).
The Ecology of Human Development framework assumes that human development
occurs through being embedded within multiple layers of the external environments, from
immediate built and social settings to broader government, socio-cultural context
(Bronfenbrenner, 1977). In the field of gerontology, the Ecology Model of Aging (Lawton &
Nahemow, 1973) similarly orients attention to how physical and social aspects of the
neighborhood context influence individuals’ aging process over time, thereby contributing to
aging well (Wahl et al., 2012). Both perspectives suggest that one’s aging experience is
structurally embedded in the residential environment – where they live and what resources they
have within a community. The subsequent consequences of a disadvantaged neighborhood
environment can include 1) producing or reinforcing ageism, 2) increasing the risk for isolation
in later life, and 3) impeding the health and emotional well-being of older residents (Hagestad &
Uhlenerg, 2005). Likewise, individuals’ evaluations of their aging are likely to be influenced by
personal daily experiences and interactions with others in their residential communities.
The foundations of SPA are grounded within the self, incorporating aspects of one’s self-
concept (Boeder, 2021). We need a sense of self to be able to evaluate whether our aging process
is positive or negative. In this sense, the environmental psychology studies on the self provide
further theoretical support for the potential role of neighborhood social environments in shaping
27
SPA. As early as the 1900s, psychological theories viewed that the self is intertwined with the
outside world (James, 1890), conceptualizing the self as a product of one’s reflections from other
people and symbols existing in one’s social environment (Mead, 1934). As such, we develop
much of the sense of who we are from where we live. Places have been regarded as driving
sources for the development of self-identity as they embody social symbols and provide an
affective bond for residents (Giuliani, 2003; Proshansky, 1978). In addition, individuals establish
personal meaning by locating themselves within their residential communities throughout daily
routines and during exceptional circumstances, from which places constitute a symbolic
extension of the self (Pretty et al., 2003). Old age, in particular, has been seen as an important
period of the lifespan where local environments have increasing significance in relation to the
self-identity (Bonnes & Lee, 2003; Rubinstein & Parmelee, 1992). This body of literature
suggests that SPA may be constructed, developed, and changed by neighborhood social contexts.
There have been a handful of empirical studies relating neighborhood environment to
self-concepts. Boardman and Robert (2000) found that neighborhood unemployment and poverty
were associated with low levels of self-efficacy among residents even after adjusting for
individual-level socioeconomic status. Similarly, Fagg and colleagues (2013) showed that
neighborhood deprivation led to a lower sense of self-worth among adolescents. However, a
dearth of research has examined the neighborhood-level sources of older adults’ self-perceptions
of their own aging processes. To the best of our knowledge, only one study investigated the
relationship of regional characteristics with SPA (Wolff et al., 2018). Using longitudinal data
from the German Ageing Survey and information about population aging in districts, the authors
found that older adults living in districts with a faster population aging rate reported more
negative SPA. However, no empirical finding still exists as to the associations between a broad
28
range of neighborhood indicators and SPA. To bridge this gap, the present study aims to
determine whether the neighborhood sociodemographic environment and perceived
neighborhood characteristics are associated with SPA, controlling for individual-level
socioeconomic and health status. We hypothesized that poor neighborhood social conditions
would be associated with more negative SPA (Hypothesis 1).
Furthermore, the neighborhood social environment may not have equal effects across age
groups. According to the Socioemotional Selectivity Theory (Carstensen et al., 1999), we
develop better emotional regulation and a greater focus on intimate relations at older ages. As
our future time perspective becomes shorter, as it typically does with age, people tend to be
increasingly selective and invest more resources in emotionally meaningful goals and activities
than in knowledge acquisition. Distal social networks such as neighbors and a sense of belonging
to local areas, therefore, may have more powerful impacts on younger adults’ evaluations of their
aging experiences. In contrast, older adults are likely to put more value and meaning in their
interactions with family and close friends than their connections to neighbors. Given this, the
secondary aim of the present study is to investigate the age effects in the associations of the
neighborhood social environment with SPA. We hypothesized that the poor neighborhood social
environment will be more strongly related to negative perceptions of aging at younger ages than
at older ages (Hypothesis 2).
Methods
Data and Sample
This study uses data from the Health and Retirement Study (HRS). The HRS is a
longitudinal survey of a U.S. nationally representative sample of individuals aged 50 years and
older, and it has been conducted biannually since 1992. A wide range of information on socio-
29
demographic characteristics and health status has been collected through face-to-face or
telephone interviews. In 2006, a random half of the non-institutionalized sample was
administered the Psychosocial and Lifestyle Self-Administrated Questionnaire (SAQ), and the
other half received the SAQ in 2008, with the design repeated every four years. The current
study derived data from the 2014 and 2016 HRS survey, when questions on neighborhood social
capital (relatives and friends) were added. A total of 6,774 and 5,165 community-dwelling adults
aged 50 and above completed the questionnaire in 2014 and 2016, respectively. Cases with
missing study variables were excluded (n = 794), resulting in the final analytic sample of 11,145.
The neighborhood poverty and percentage of older adults living alone came from the U.S.
Census Bureau’s 2012-2016 American Community Survey (ACS) Five Year estimates. This
study linked the census tract-level neighborhood information to HRS respondents with census
tract identifiers. The use of the ACS five-year data allows statistically more reliable estimates for
areas with less population than one-year or three-year data.
Measures
Poverty rates and proportion of older adults. The percent of residents with income below
the federal poverty threshold was calculated for each census tract. Then, census tracts were
categorized into three groups based on Crane (1991): a) neighborhoods with low poverty rates:
0–19.99%; b) moderate poverty: 20–39.99%; c) high poverty: 40% or higher. The percent of
older residents aged 65 or above among the total population was calculated for each census tract.
Following the OECD and WHO guidelines (2020), census tracts were grouped into three
categories: a) non-aging neighborhoods: 7% or less; b) aging neighborhoods: 7– 13.99%; c) aged
neighborhoods: 14– 20.99%; and d) super-aged neighborhoods: 21% or higher.
30
Perceived neighborhood social cohesion and neighborhood disorder. Respondents were
asked to report their feelings about one’s local area, or everywhere within a 20-minute walk or
about a mile of one’s home. Responses were coded on a 7-point Likert scale (1 = most favorable
evaluation; 7 = worst evaluation). The measure (Cagney et al., 2009) included four items for
social cohesion: (1) I feel part of this area, (2) people in this area would help you, (3) people in
this area can be trusted, and (4) people in this area are friendly, and four items for physical
disorder: (1) vandalism/graffiti are a big problem in this area, (2) this area is full of rubbish, (3)
there are many vacant/deserted houses; and (4) people would be afraid to walk alone in this area.
After reverse-coding the items, the average scores were calculated (range: 1–7), with higher
scores indicating higher levels of cohesion (Cronbach’s α = 0.86 in 2014 and 0.87 in 2016) and
higher levels of disorder (Cronbach’s α = 0.84 in 2014 and 0.85 in 2016). Those with two or
more missing items were considered as missing on the final score for each index. This study
created three categories of low (mean values of 1–3), middle (mean values of 3–5), and high
(mean values of 5–7) for both cohesion and disorder indicators.
Self-perceptions of Aging. SPA was measured with 8 items. Five questions were based on
the subscale of the Philadelphia Geriatric Center Morale Scale (Lawton, 1975): (1) Things keep
getting worse as I get older, (2) I have as much pep as I did last year, (3) The older I get, the
more useless I feel, (4) I am as happy now as I was when I was younger, and (5) As I get older,
things are better than I thought they would be. Additional three items were derived from the
Berlin Aging Study (Kotter-Grühn et al., 2009): (6) So far, I am satisfied with the way I am
aging, (7) The older I get, the more I have had to stop doing things that I like, and (8) Getting
older has brought with it many things that I do not like. Participants rated each question on a six-
point Likert scale (1 = strongly disagree; 6 = strongly agree). After reverse coding items (1), (3),
31
(7), and (8), the scores across eight items were averaged, with a higher score indicating more
positive perceptions. The psychometrics of the scale had good internal consistency (Cronbach’s
α = 0.81 in 2014 and 0.81 in 2016).
Covariates. The current study used individual-level demographic factors, socioeconomic
status, and health conditions as control variables. Demographic factors included age, gender (1 =
female), race/ethnicity (1 = non-Hispanic White; 2 = non-Hispanic Black; 3 = Hispanic; and 4 =
others) and living arrangement (1 = married or partnered; 2 = single, living with others; 3 =
single, living alone). Education in years and total household income were used as indicators of
socioeconomic status. Education was assessed with years of formal schooling (range: 0–17), and
household income was measured by income from all possible sources such as earnings, pensions,
and social security. Due to high skewness, log-transformed values of income were used for the
multivariate analyses.
With regard to physical health conditions, this study considered the number of chronic
diseases and functional limitations at each wave. The respondents reported whether they had any
of the following of eight chronic diseases diagnosed by a physician (high blood pressure,
diabetes, cancer, lung disease, heart disease, stroke, psychiatric problems, and arthritis); then, the
sum value was calculated (range 0–8). For the functional limitations, the respondents indicated
whether they had any limitations in activities of daily living (ADLs), including bathing, dressing,
walking across a room, and getting in and out of bed. Summary scores were calculated (range: 0–
5) and a new binary variable (1 = having any functional limitations) was created for the
multivariate analyses. Finally, as a mental health indicator, depressive symptoms were assessed
by the 8-item Center for Epidemiological Studies Depression (CES-D) scale, a shortened version
of the original CES-D scale (Radloff, 1977). The respondents were asked to report whether they
32
experienced the following sentiments most or all of the time: (1) felt depressed, (2) everything is
an effort, (3) sleep is restless, (4) felt alone, (5) felt sad, (6) could not get going, (7) felt happy,
and (8) enjoyed life. Total scores were calculated by subtracting the two positive indicators from
the negative items, with a possible range of 0 to 8. Internal reliability was acceptable, with the
Kuder-Richardson 20 (KR-20) coefficient of .80.
We also controlled individuals’ neighborhood social capital. Respondents were asked to
report whether they had any good friends living in their neighborhood. A binary variable was
created for neighborhood friends (1 = yes). Respondents were asked to report whether they had
any relatives (besides people living with them) in their neighborhood. Another binary variable
was created for neighborhood relatives (1 = yes).
Analytical Strategy
The descriptive characteristics of the study variables were reviewed. Next, multilevel
linear regression models were estimated to explore the associations between neighborhood social
environment factors and SPA. After checking the intraclass correlation (ICC) in the
unconditional random intercept model, two nested models were examined: (1) census-tract level
and individual level neighborhood characteristics were included controlling for demographic
factors, and (2) socioeconomic and health status were further adjusted to test whether they
explain the observed neighborhood characteristics and SPA relationship. Sample weights were
applied to adjust for survey nonresponse and oversampling of African American and Hispanic
populations. All analyses were performed using Stata 17.0 (StataCorp. College Station, Texas).

Model Equations

a. Individual level (Level 1):
33
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neighborhood social cohesion and (b) perceived neighborhood disorder

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Results
Sample Characteristics
Table 4 presents the descriptive statistics of the sample. The mean age of the participants
was 66.4 years, about 54% were women, and 67% were married or partnered. The majority of
the sample consisted of non-Hispanic White (77.5%), followed by non-Hispanic Black (10.1%),
Hispanic (8.6%), and other racial/ethnic minorities (3.8%). About 67% were married or
34
partnered, and among singles, 11% lived with others (either family members or non-related
others), and 22% lived alone. On average, the participants had 13 years of education, 87K dollars
of annual household income, and about two chronic diseases. The scores of functional limitations
and depressive symptoms averaged at 0.3 and 1.3, with about 15% reporting one or more
functional limitations.
The overall mean score of the SPA measure was 3.9 on a 6-point scale, indicating the
slightly skewed distribution toward the positive perception. Regarding the neighborhood
sociodemographic environment, the average poverty rate was 14.7% and 16.4% for the
population aged 65+. The average perceived neighborhood social cohesion was moderately high,
about 5.4 on a 7-point scale, and the mean level of perceived neighborhood disorder was below
the midpoint (Mean = 2.5). On average, respondents’ perceptions of their neighborhood were
favorable. About 43% had good neighborhood friends, and 23% reported having relatives (not
co-living) in their neighborhood.

Table 4. Descriptive Sample Characteristics
Variables M ± SD %
Outcome
Self-perceptions of aging (range: 1–6) 3.91 ± 1.06

Neighborhood social environment
Sociodemographic condition
Poverty (%) 14.7 ± 0.11
Low poverty (range: 0–19.9) 75.5
Moderate poverty (range: 20–39.9) 20.2
High poverty (range: 40–75) 4.1
Older population aged 65+ (%) 16.4 ± 0.16
Non-aging neighborhood (range: 0–6.9) 5.5
Aging neighborhood (range: 7–13.9) 37.0
Aged neighborhood (range: 14–20.9) 40.4
Super-aged neighborhood (range: 21–87.2) 17.1
35

Note. Racial/ethnic identification of ‘other’ included American Indian, Alaskan Native, Asian,
and Pacific Islander.
Subjective perceptions measured by self-report
Perceived social cohesion (range: 1–7) 5.37 ± 1.37
Low cohesion (range: 1–3) 8.3
Middle cohesion (range: 3–5) 26.7
High cohesion (range: 5–7) 65.1
Perceived disorder (range: 1–7) 2.48 ± 1.41
Low disorder (range: 1–3) 73.5
Middle disorder (range: 3–5) 19.8
High disorder (range: 5–7) 6.7

Demographics
Age (range: 50–98) 66.4 ± 9.95
Gender
Male 46.0
Female 54.0
Race/ethnicity
Non-Hispanic White 77.5
Non-Hispanic Black 10.1
Hispanic 8.6
Other 3.8
Living arrangement
Married or partnered 66.6
Single, living with others 11.1
Single, living alone 22.3
Social capital in neighborhood
Having good friends 42.9
Having relatives (not co-living) 23.2

Socio-economic factors
Education (in years) 13.3 ± 2.93
Annual household income 86,849 ± 130,555

Health conditions
Chronic diseases (range: 0–8) 2.14 ± 1.51
Functional limitations (range: 0–5) 0.29 ± 0.82
Having any limitations 14.8
Depressive symptoms (range: 0–8) 1.32 ± 1.92
36
Neighborhood Social Environment and SPA
Table 5 presents the results of the multilevel linear regression models predicting self-
perceptions of aging. In the unconditional random intercept model without any predictors, we
found intra-class correlations of 0.16 at the census tract level. These numbers represent the
correlation in SPA between two older adults in the same census tract. We can also conclude that
16% of the variation in SPA can be attributed to the census tract. Model 1 tested the associations
of the neighborhood social environment indicators with SPA. Respondents who lived in the
neighborhoods with higher percentages of the older population reported more negative SPA. On
the other hand, those who perceived their neighborhoods as more socially cohesive and having
less disorder reported more positive SPA. After controlling for individual demographic,
socioeconomic, and health status (Model 2), only the perceived neighborhood social cohesion
remained significant. The significance of % the population aged 65+ disappeared when adjusting
for age and the significance of perceived disorder disappeared when adjusting for socioeconomic
or physical health status.

Table 5. Multilevel Linear Regression Models Estimating Self-perceptions of Aging
Model 1 Model 2
B SE B SE
Neighborhood Social Environment
Sociodemographic condition
Poverty (Ref=low poverty rates)
Moderate poverty -0.04 0.03 0.00 0.02
High poverty -0.01 0.05 0.01 0.05
% Population aged 65+ (Ref=non-aging)
Aging neighborhood -0.04 0.05 0.03 0.04
Aged neighborhood -0.11 * 0.05 0.01 0.04
Super-aged neighborhood -0.14 * 0.05 0.00 0.05
Subjective indicator
37
Perceived social cohesion (Ref=low)
Middle 0.02 0.05 -0.04 0.04
High 0.40 *** 0.05 0.24 *** 0.05
Perceived disorder (Ref=low)
Middle -0.08 * 0.03 -0.04 0.03
High -0.11 * 0.05 -0.03 0.05

Covariates
Age -0.01 *** 0.00
Female 0.10 *** 0.02
Race/ethnicity (Ref=NHW)
Non-Hispanic black 0.34 *** 0.03
Hispanic 0.23 *** 0.04
Other 0.02 0.05
Living arrangement (Ref=married/partnered)
Single, living with others 0.01 0.03
Single, living alone 0.04 0.02
Individuals’ social capital in neighborhood
Having good friends 0.06 *** 0.02
Having relatives (not co-living) 0.01 0.02

Years of education 0.02 *** 0.00
Annual household income (log-transformed) 0.04 *** 0.01
Having functional limitations -0.36 *** 0.03
Number of chronic diseases -0.11 *** 0.01
Depressive symptoms -0.17 *** 0.01
Intercept 3.74 *** 0.07 4.21 *** 0.13
Random Effects Variance SE Variance SE
Level 2 (census tract) 0.16*** 0.01 0.06 *** 0.01
Level 1 (individuals) 0.90*** 0.02 0.71 *** 0.12
* p < .05; ** p < .01; *** p < .001.
Note. Model 1 included neighborhood indicators; Model 2 controlled for individual-level
demographics, socioeconomic status, health conditions, and social capital in neighborhood.

Age Differences in the Effects of Neighborhood Social Environment on SPA
Using the same modeling approach, we next tested whether the strength of the
associations between neighborhood social environment indicators on SPA differed by age. Table
38
6 presents statically significant interaction effects between age and social cohesion after
controlling for all other covariates. The effects of neighborhood cohesion on shaping SPA were
stronger at younger ages than older ages. However, no significant age differences were observed
in the effects of other neighborhood indicators.

Table 6. Multilevel Linear Regression Models Estimating Interaction Effects of Perceived
Neighborhood Social Cohesion and Age
Variables B SE
Perceived neighborhood social cohesion (Ref=low) × Age
Middle × Age -0.01 0.004
High × Age -0.01 ** 0.003

Perceived neighborhood social cohesion (Ref=low)
Middle -0.05 0.04
High 0.22 *** 0.05

Age -0.003 0.003

* p < .05; ** p < .01; *** p < .001.
Note. All other covariates were included, but not shown for simplicity.

To further explore the significant interaction effects, we plotted the predicted SPA scores
and conducted post-hoc pairwise comparisons. As shown in Figure 2, younger respondents living
in the low cohesion neighborhoods reported comparable SPA scores to older respondents in
neighborhoods with high cohesion. The gaps between low and high neighborhood social
cohesion were larger at younger ages than older ages, indicating the stronger effects of
neighborhood social cohesion on SPA among younger respondents.

39
Figure 2. Age Differences in the Effects of Social Cohesion on Self-perceptions of Aging

Note. Younger age was about 56 years-old (-1 SD) and older age was 76 years-old (+1 SD).
Discussion
Despite the importance of SPA for health and well-being in later life, very little is known
about the role of contextual-level factors. Growing research documents the increased focus on
socially and emotionally meaningful goals in later life. Neighborhood social environment may
act as a critical avenue for older adults to remain socially active, which in turn, contributes to
their evaluations on how they grow older. Given this background, the current study examined the
role of neighborhood environment in molding one’s SPA. Specifically, three research questions
were explored: (1) whether the neighborhood sociodemographic environment and perceived
neighborhood characteristics are associated with SPA and (2) whether age moderates the
associations of significant neighborhood indicators with SPA. Four social and economic aspects
3.4
3.5
3.6
3.7
3.8
3.9
4
4.1
4.2
Low cohesion Middle cohesion High cohesion
Self-perceptions of aging
Younger Older
40
of neighborhoods were considered: (a) neighborhood poverty; (b) density of older adults; (c)
perceived social cohesion; and (d) perceived physical disorder.
The results provided partial positive support for the first question and strong support for
the second questions. Respondents in the neighborhood with more older populations reported
more negative SPA. On the other hand, those who perceived their neighborhoods as more
socially cohesive and having less disorder reported more positive SPA. Individual demographic,
socioeconomic and health status explained some of these associations (% older population and
perceived neighborhood disorder), but the perceived social cohesion still remained robust
predictors of SPA after the adjustment for covariates. There were significant age differences in
the effects of neighborhood social cohesion on SPA, such that the associations between
neighborhood cohesion and SPA were stronger at younger ages than older ages.
Our findings suggest that social connection aspects of neighborhood environment are
most closely associated with SPA among older adults, independent of individuals’ characteristics
and other socioeconomic conditions of neighborhoods. Notably, social cohesion was robust
factors of SPA even controlling for individuals’ neighborhood social network and living
arrangement. Neighborhood social cohesion characterizes social resources available within the
whole community and thus, is distinct from individual-level social capital and support (Cagney
et al., 2009). These findings highlight the strength of weak ties (Granovetter, 1973), consistent
with the prior evidence that social connections at the more peripheral levels are beneficial to our
well-being, often beyond strong ties such as with family and close friends (Sandstrom & Dunn,
2014). For older adults, socially cohesive neighborhoods can translate into better aging outcomes
through several psychosocial and instrumental pathways, including diffusion of health
information, provision of emotional and practical support among neighbors, maintenance of
41
healthy lifestyles through informal social control, and the enhancement of mutual respect and
self-efficacy (Cramm et al., 2013; Kawachi & Berkman, 2000).
Another finding deserving attention is the age difference, such that socially cohesive
neighborhoods promoted positive perceptions of aging more strongly for younger residents.
Socioemotional Selectivity Theory (Carstensen et al., 1999) provides a theoretical lens for
interpretation. According to the theory, people place greater emphasis on the social relationships
with close partners at older ages and develop better regulation and greater control over emotions,
whereas younger adults also focus on interacting with other from a broader social network such
as neighbors (Carstensen et al., 1999). Similarly, one recent study found that community
cohesion was a significant predictor of loneliness for younger people while family support
mattered more for older people (Park et al., 2021). Taken together, these findings point out the
importance of considering developmental stages and corresponding changes in social motives in
relation to adults’ perceptions of growing older.
The strength of this study includes the use of a diverse, nationally representative US
sample, which allows us to make a more reliable interpretation at the national level. Moreover,
we considered objective socioeconomic conditions of neighborhoods as well as subjectively
perceived neighborhood characteristics. This approach allows us to test the significant
associations between each neighborhood dimension and SPA, independently of one another.
Finally, the integration of perspectives from ecology, environmental psychology, and
gerontology contributes to understanding how the external environment relates to our evaluations
of age-related changes from a multidisciplinary point of view.
Our study can make a key contribution to the literature on attitudes toward aging. Prior
studies have primarily focused on identifying individual-level characteristics (e.g., age, health
42
status, and socioeconomic resources) as predictors of older adults’ SPA (Marques et al., 2020).
However, the current study went beyond by suggesting the significant role of a contextual-level
factor. Not only that, but our findings might also extend the existing conceptual framework for
subjective aging research. For example, Diehl and colleagues (M. Diehl, Brothers, et al., 2021;
M. K. Diehl & Wahl, 2010) proposed an interdisciplinary model of awareness of aging-related
change, which highlights that one’s perceptions related to own aging are influenced by a variety
of distal (e.g., sociodemographic and health status) and proximal (e.g., personal goals and felt
age) factors within a given cultural-societal context. Neighborhood social environment can serve
as such an important cultural-societal context where SPA develops, as well as potentially bridge
the broader context (e.g., cultural individualism or age-friendly society) to individuals’
evaluation processes of aging.
Limitations
There are a few limitations worth noting. First, the present study used cross-sectional
data, which does not allow claiming any causal argument. Future longitudinal research design
would provide better insights into the temporal ordering of the relationship between
neighborhood social environment and SPA scores while addressing concerns about selection or
omitted variable bias. Additionally, although this study used large, nationally representative data,
the sample largely consisted of non-Hispanic whites. Future studies should include more people
from diverse racial/ethnic backgrounds and explore whether the findings remain similar or vary.
Conclusion
These results provide some of the first insights into how neighborhood social context
shapes one’s perceptions of own aging, suggesting that a socially cohesive neighborhood may
promote more favorable perceptions of aging. The current study can inform community
43
intervention strategies to promote more positive perceptions of aging. In addition to the prior
focus on individual-level interventions, our study emphasizes the need for community
intervention strategies to bolster the cohesion of neighborhoods, which can translate into better
self-perceptions of aging among older residents.

44
Chapter 3. Longitudinal Associations of Perceived Neighborhood Social
Cohesion with Self-perceptions of Aging and Loneliness
Abstract
Perceived neighborhood social cohesion has been associated with physical health and well-being
outcomes in later life, independent of covariates. However, little attention has been paid to its
effects on one’s self-perceptions of aging (SPA) and feelings of loneliness, as well as their
underlying mechanisms. This study examines 1) whether perceived neighborhood social
cohesion is related to the levels and changes in SPA and loneliness and 2) the potential
bidirectional associations between these two factors for linking paths from neighborhood social
cohesion to each other. We hypothesized that more neighborhood social cohesion would reduce
feelings of loneliness, leading to more positive evaluations of one’s own aging process and vice
versa. Using 8-year data from the Health and Retirement Study (2008-2016), we conducted
latent growth curve modeling and path analyses to examine the longitudinal changes and
autoregressive cross-lagged mediation paths for SPA and loneliness over time. More
neighborhood social cohesion predicted more positive SPA 8 years later but was not significantly
associated with the rate of change over time. Respondents living in neighborhoods with more
social cohesion at baseline were less lonely but had faster rates of increase over time. In the path
analyses, the effects of neighborhood social cohesion on SPA were primarily indirect via
loneliness, whereas its effects on loneliness had both direct and indirect pathways. Overall, our
findings suggest that neighborhood social cohesion is longitudinally linked to both one’s
perceptions of aging and loneliness through bidirectional mediation processes.
Keywords: Self-perceptions of aging, loneliness, neighborhood social cohesion
45
Background
Growing research documents that neighborhood social cohesion has been associated with health
and well-being outcomes in later life (Choi & Matz-Costa, 2017; Cramm et al., 2013; E. S. Kim
et al., 2020). Socially cohesive neighborhoods are characterized by the close connections and
trust shared among neighbors, their willingness to intervene for the common good, and residents’
high degrees of belonging to the area (Kawachi & Subramanian, 2007). Previous studies have
suggested potential mechanisms of how neighborhood social cohesion may influence health
through resources (e.g., diffusion of health information), psychological factors (e.g., emotional
support in the face of distress), and behavioral (e.g., promoting health norms) mediators
(Kawachi & Berkman, 2000; Saelens & Papadopoulos, 2008). However, little attention has been
paid to the effects of neighborhood social cohesion on one’s perceptions of self and social
connections to others, which constitute another key aspect of well-being among older adults. The
goal of the current study is to examine the longitudinal associations of neighborhood social
cohesion with self-perceptions of aging (SPA) and loneliness. Further, we aim to identify the
underlying pathways in these associations by testing whether SPA and loneliness could act as a
mediator to one another.
Loneliness is often defined as the “unpleasant experience that occurs when a person’s
network of social support is deficient in some important way, either qualitatively or
quantitatively” (Peplau & Perlman, 1979). The Social Psychology Theory of Loneliness explains
that feelings of loneliness arise from a discrepancy between one’s desired and actual degrees of
social interactions (Peplau & Perlman, 1979). Neighborhood contexts, particularly social
cohesion, may serve as an important avenue for older adults to promote social connection to
other people in their daily lives as well as receive instrumental and emotional support. However,
46
research on loneliness has primarily focused on individual-level predictors, resulting in a limited
understanding of the role of neighborhood-level social factors. A handful number of recent
studies have shown that lower levels of neighborhood social cohesion lead to greater feelings of
loneliness (Bergefurt et al., 2019; Gutierrez-Kapheim & Mitchell, 2020; Park et al., 2021; R. Yu
et al., 2021; X. Yu et al., 2021). While these findings support the adverse effects of low
neighborhood social cohesion on loneliness, they were primarily based on the cross-sectional
research design and convenience samples. Further investigation with a large, longitudinal,
nationally representative sample is required to clarify whether a lack of social cohesion at the
neighborhood level is associated with an increased risk of loneliness.
As shown in Chapter 2, neighborhood social cohesion is also significantly associated
with individuals’ evaluations of their own aging process (SPA). From the perspective of the
strength of weak ties (Granovetter, 1973), social connections at the more peripheral levels, such
as neighborhoods, can be beneficial to health and well-being in later life for their contributions to
one’s social support as well as the promotion of self-efficacy and mutual respect among
neighbors (Cramm et al., 2013; Kawachi & Berkman, 2000). People living in a socially cohesive
neighborhood may develop more positive perceptions of aging because of their better health
status, more available resources, and neighbors’ friendly attitudes toward them. Building on this
cross-sectional finding, the current study will examine whether neighborhood social cohesion is
longitudinally associated with the levels and changes of SPA.
Both SPA and late-life loneliness are ingrained in individuals’ experiences of growing
older (Bodner, 2022). Another consideration, therefore, should be given to the potential
bidirectional associations between SPA and loneliness, such that negative SPA leads to more
loneliness and vice versa. A growing literature has increasingly shown that negative perceptions
47
of aging are predictive of higher loneliness (Losada-Baltar et al., 2020; Pikhartova et al., 2016;
Segel-Karpas et al., 2021; Spitzer et al., 2019). Losada-Baltar and colleagues (2021) found that
negative SPA, particularly perceiving oneself as a burden, was significantly associated with
increased loneliness during the COVID-19 related lockdown period. Several underlying
mechanisms have been suggested to explain these associations (Shiovitz-Ezra et al., 2018). First,
based on the self-fulfilling prophecies, unfavorable perceptions of the normal aging process (e.g.,
old age is a time of loneliness) may increase one’s rejection sensitivity and anxiety, leading to
less willingness to actively engage in social interactions such as making new friends (Menkin et
al., 2016; Pikhartova et al., 2016). Another perspective in line with the stereotype embodiment
theory (B. R. Levy, 2009) posits that older adults who internalize stereotypes about old age, such
as boring and lonely, are inclined to report feeling lonelier. Negative SPA may also increase
one’s expectation of social support from others (e.g., I become weak as aging, and thus need
more help), which can predispose to dissatisfaction with social relationships, thereby increasing
levels of loneliness (Cheng, 2016).
On the other hand, loneliness can also shape negative perceptions of one’s own aging
process. To address this question, one recent study examined bidirectional associations between
perceptions of aging and social connections (Schwartz et al., 2021). Their findings showed that
positive perceptions of one’s aging led to high levels of informal social involvement over time
and, reciprocally, maintaining active informal social interactions promoted more positive
evaluations of own aging process. However, the concurrent processes between SPA and
loneliness have not been thoroughly examined yet. It is plausible that limited neighborhood
social cohesion increases loneliness via more negative SPA. Likewise, the effects of
neighborhood social cohesion on SPA may be indirect through loneliness. Thus, we aim to
48
simultaneously examine SPA and loneliness as two outcome variables while accounting for the
previous levels of each factor.
Using 8-year longitudinal data (3 waves) of a nationally representative sample of U.S.
older adults, we conducted latent growth curve modeling and path analyses to better understand
the association of neighborhood social cohesion with SPA and loneliness. Specifically, we
sought to answer 1) whether baseline levels of neighborhood social cohesion are associated with
trajectories of SPA and loneliness and 2) whether loneliness (or SPA) mediates the longitudinal
association between neighborhood social cohesion and SPA (or loneliness). For the first
question, we hypothesized that older adults with higher neighborhood social cohesion would
have more positive SPA and less loneliness and slower rates of increase over time. As for the
second question, we expected that higher neighborhood social cohesion would be associated with
subsequent less loneliness (or positive SPA), and in turn, would lead to positive SPA (or less
loneliness). The study findings would elucidate the pathways of neighborhood social cohesion
influencing two important aspects of subjective perceptions at older ages – SPA and loneliness.
Methods
Data and Sample
This study analyzed data from the Health and Retirement Study (HRS), a biennial
longitudinal survey of a U.S. nationally representative sample of individuals aged 50 years and
older. A wide range of information on socio-demographic characteristics and health status has
been collected through face-to-face or telephone interviews. In 2006, the HRS started collecting
data on psychosocial and lifestyle factors using a self-administered questionnaire (SAQ). The
SAQ was administered to a random half of the HRS sample first, and the other half received it in
2008, with the design repeated every four years (Smith et al., 2017). The current study selected
49
the sample from the 2008 survey when questions on self-perceptions of aging were added. A
total of 6,540 community-dwelling respondents completed the questionnaire in 2008. We
excluded 378 cases with missing study variables, resulting in the final analytic sample of 6,162.
In 2012, 5,119 individuals from the initial sample participated the survey, and 3,954 respondents
continued in 2016.
Measures
Perceived neighborhood social cohesion. Respondents were asked to report their feelings
about one’s local area or anywhere within a 20-minute walk or about a mile from home.
Responses were coded on a 7-point Likert scale (1 = most favorable evaluation; 7 = worst
evaluation). The measure (Cagney et al., 2009) included four items for social cohesion: (1) I
really feel part of this area/I feel that I don’t belong in this area, (2) most people in this area can
be trusted/most people in this area can’t be trusted, (3) most people in this area are friendly/most
people in this area are unfriendly, and (4) if you were in trouble, there are lots of people in this
area who would help you/if you were in trouble, there is nobody in this area who would help
you. The responses were summed to calculate the average scores after reversing coding (range:
1–7), with higher scores indicating higher levels of social cohesion (Cronbach’s α = 0.86 in
2008). Those with more than two missing items were considered missing on the final score.
Self-perceptions of aging. SPA was measured with eight items. Five questions were based
on the subscale of the Philadelphia Geriatric Center Morale Scale (Lawton, 1975): (1) Things
keep getting worse as I get older, (2) I have as much pep as I did last year, (3) The older I get, the
more useless I feel, (4) I am as happy now as I was when I was younger, and (5) As I get older,
things are better than I thought they would be. Additional three items were derived from the
Berlin Aging Study (Kotter-Grühn et al., 2009): (6) So far, I am satisfied with the way I am
50
aging, (7) The older I get, the more I have had to stop doing things that I like, and (8) Getting
older has brought with it many things that I do not like. Participants rated each question on a six-
point Likert scale (1 = strongly disagree; 6 = strongly agree). The scores across eight items were
averaged after reverse coding four negatively worded items (1), (3), (7), and (8). Higher scores
indicated more positive perceptions, and those with more than five missing items were
considered missing on the final score. The scale had good internal consistency (Cronbach’s α =
0.81 in 2008).
Loneliness. This study used the 11-item scale of loneliness derived from the 20-item
Revised UCLA Loneliness Scale (Russell, 1996). Respondents were asked how often they feel
the following: (1) lack companionship; (2) left out; (3) isolated from others; (4) in tune with
others around them; (5) alone; (6) there are people they can talk to; (7) that there are people they
can turn to; (8) that there are people who really understand them; (9) there are people they feel
close to; (10) part of a group of friends; and (11) that they have a lot in common with the people
around them. Responses were coded on a three-point Likert scale (1 = often, 2 = some of the
time, 3 = hardly ever or never). A summary index of loneliness was created by averaging the
scores across eleven items after reverse-coding four negatively worded items (1), (2), (3), and
(5). The final score was set to missing for cases with more than five items of missing values. The
internal consistency for the loneliness scale was 0.88 in 2008.
Covariates. This study included covariates of demographic factors, socioeconomic status,
and health conditions measured at the baseline. Demographic factors included age, gender (1 =
female), race/ethnicity (1 = non-Hispanic White; 2 = non-Hispanic Black; 3 = Hispanic; and 4 =
others) and marital status (0 = separated, divorced, widowed, or never married; 1 = married or
partnered). Education in years and total household income were used as indicators of
51
socioeconomic status. Education was assessed with years of formal schooling (range: 0–17), and
household income was measured by income from all possible sources such as earnings, pensions,
and social security. Due to high skewness, log-transformed values of income were used for the
multivariate analyses.
With regard to physical health conditions, this study considered the number of chronic
diseases and functional limitations. The respondents reported whether a physician had told them
that they had any of the following eight chronic diseases (high blood pressure, diabetes, cancer,
lung disease, heart disease, stroke, psychiatric problems, and arthritis); then, the sum value was
calculated (range 0–8). For the functional limitations, the respondents indicated whether they had
any limitations in activities of daily living (ADLs), including bathing, dressing, walking across a
room, and getting in and out of bed. Summary scores were calculated (range: 0–5), and a new
binary variable (1 = having any functional limitations) was created for the multivariate analyses.
Finally, as a mental health indicator, depressive symptoms were assessed by the 8-item CES-D
scale, a shortened version of the original CES-D scale (Radloff, 1977). The respondents were
asked to report whether they experienced the following most or all of the time: (1) felt depressed,
(2) everything was an effort, (3) sleep was restless, (4) felt alone, (5) felt sad, (6) could not get
going, (7) felt happy, and (8) enjoyed life. Total scores were calculated by subtracting the two
positive indicators from the negative items, with a possible range of 0 to 8. Internal consistency
was acceptable, with the Kuder-Richardson 20 (KR-20) coefficient of .80.
Analytic Strategies
We first examined whether attrition was explained by other observed variables (e.g., age
and gender) to test the assumption about the missing at random (MAR) assumption. Logistic
regression was conducted to identify predictive variables of the missing status (Jeličić et al.,
52
2009). The number of respondents with complete data was 3,895 among the final analytic
sample, and the remaining 2,267 people had missing data for at least one of the follow-up waves.
Those who participated in all waves were significantly younger, more likely to be female,
married or partnered, have higher levels of education, have fewer chronic diseases, and less
likely to have functional limitations than those with incomplete data. These missing patterns are
consistent with general patterns observed in other longitudinal studies of older adults (Kapteyn et
al., 2006). Under the MAR assumption, we used full information maximum likelihood (FIML)
estimation of parameters to maximize statistical power as well as prevent potential biases due to
selective attrition (Graham, 2009).
Univariate statistics and bivariate correlations were examined to describe the sample
characteristics and the longitudinal associations among the main study variables (neighborhood
social cohesion, SPA, and loneliness). The latent growth curve models were implemented to test
the associations of neighborhood social cohesion at baseline with slopes of SPA and loneliness.
To estimate unconditional growth models of each outcome, latent variables of intercept and slope
were constructed using three manifest variables of observations at each time point (2008, 2012,
and 2016). The factor loadings of the slope were fixed to -2, -1, and 0, so the intercept
represented the status in 2016. Then neighborhood social cohesion and other covariates were
added as predictors of the intercept and slope. To estimate the goodness-of-fit of the models, chi-
square statistics and four additional fit indices were used: (1) the Root Mean Square Error of
Approximation (RMSEA), (2) the Comparative Fit Index (CFI), (3) Tucker-Lewis Index (TLI),
and (4) the Standardized Root Mean Square Residual (SRMR). Values of RMSEA less than
0.05, CFI and TLI over 0.95, and SRMR less than 0.08 indicate a good fit (L. Hu & Bentler,
53
1999; Tucker & Lewis, 1973). Respondent-level sample weights were applied to adjust for
oversampling of African Americans and Hispanics as well as survey nonresponse.
A path analysis allowing for autoregressive and cross-lagged relations among variables
(Cole & Maxwell, 2003) was then conducted to examine longitudinal mediation mechanisms
liking neighborhood social cohesion with SPA and loneliness. Indirect effects were defined as
the paths starting with baseline neighborhood social cohesion, passing through mediators in
2012, and ending with two outcomes in 2016 (SPA or loneliness), independent of all covariates.
Direct effects were defined as the paths of baseline neighborhood social cohesion with two
outcomes in 2016 (SPA or loneliness) that did not pass through mediators, independent of other
covariates. After examining a fully saturated model, we examined more parsimonious structural
models that restricted statistically non-significant paths to zero. Based on the final model, the
total, direct, and indirect effects were calculated for baseline neighborhood social cohesion on
2016 SPA and loneliness outcomes. The estimates of mediated effect and standard errors were
calculated based on a bootstrap analysis (n = 1,000), and effect sizes were measured using the
proportion mediated approach (MacKinnon et al., 2007). Descriptive analyses were performed
using Stata 17.0 (StataCorp. College Station, Texas), and structural equation modeling was
computed with Mplus 8.0 (Muthén & Muthén 1998-2017). The statistical significance level was
set at a two-tailed 0.05. Mplus MLR estimator was specified to use maximum likelihood
estimation with robust standard errors for non-normally distributed data.
Results
Sample Characteristics
Table 7 presents the descriptive sample characteristics at baseline (2008). The overall
mean score of the SPA measure was 3.90 on a 6-point scale (higher values reflecting more
54
positive), indicating a slightly skewed distribution toward the positive perception (close to 1 than
6). The mean level of loneliness was 1.52 on a 3-point scale, suggesting that respondents felt
lonely some of the time on average. The average perceived neighborhood social cohesion was
moderately high, about 5.51 on a 7-point scale. The mean age of the participants was 67.0 years,
about 55% were women, and 65% were married or partnered. The majority of the sample
consisted of non-Hispanic White (81.1%), followed by non-Hispanic Black (9.1%), Hispanic
(7.3%), and other racial/ethnic minorities (2.5%). On average, the participants had 12.9 years of
education, 73K dollars of annual household income, and about two chronic diseases. The scores
of functional limitations and depressive symptoms averaged 0.29 and 1.42, with about 15.2%
reporting one or more functional limitations. The depressed cases were 19.6%, classified using
the cutoff of 3 or more scores (Courtin et al., 2015).

Table 7. Descriptive Sample Characteristics at Baseline
Variables M ± SD %
Main variables
Self-perceptions of aging (range: 1–6) 3.90 ± 1.07
Feelings of loneliness (range: 1–3) 1.52 ± 0.43
Neighborhood social cohesion (range: 1–7) 5.51 ± 1.37

Demographic factors
Age (range: 54–100) 67.0 ± 9.77
Gender
Male 45.2
Female 54.8
Race/ethnicity
Non-Hispanic White 81.1
Non-Hispanic Black 9.13
Hispanic 7.28
Other 2.51
Marital status
Separated, divorced, widowed, never married 34.9
55

Note. Racial/ethnic identification of ‘other’ included American Indian, Alaskan Native, Asian,
and Pacific Islander. Estimates were weighted.

Table 8 shows pair-wise correlations between the main study variables. Neighborhood
social cohesion was correlated with both SPA and loneliness across all three time points, with
stronger associations with loneliness (r = -0.24, -0.31) compared to SPA (r = 0.15, 0.23).
Correlations between SPA and loneliness were also significant cross-sectionally (r = -0.44, -
0.45) and longitudinally (r = -0.38, -0.40).

Table 8. Correlations among Neighborhood Social Cohesion, Self-perceptions of Aging, and
Loneliness
Married or partnered 65.1

Socio-economic status
Education (in years) 12.9 ± 3.05
Annual household income $73,052 ± $113,225

Health conditions
Chronic diseases (range: 0–8) 2.03 ± 1.45
Functional limitations (range: 0–5) 0.29 ± 0.82
Having any limitations 15.2
Depressive symptoms (range: 0–8) 1.42 ± 1.98

2008 2012 2016
NSC SPA Loneliness SPA Loneliness SPA Loneliness
2008
NSC – 0.23 -0.31 0.18 -0.25 0.15 -0.24
SPA – -0.45 0.62 -0.39 0.58 -0.40
Loneliness – -0.35 0.65 -0.29 0.59
2012
SPA – -0.44 0.66 -0.38
56
Note. NSC = neighborhood social cohesion; SPA = self-perceptions of aging. Estimates were
weighted. All correlations were significant at p < .001.

Baseline Neighborhood Social Cohesion and Trajectories of SPA
As presented in Table 9, the unconditional growth model for SPA showed a statistically
significant negative linear slope (β = -0.42, SE = 0.07, p < .001), indicating an average decrease
of 0.42 standardized units every 4 years (𝜒
&
= 2.11 (1), RMSEA = 0.013, CFI = 0.999, TLI =
0.998, SRMR = 0.004). This means that respondents’ SPA became more negative as time
increased. As shown in Figure 1, the next model added baseline neighborhood social cohesion
and other covariates (𝜒
&
= 20.01 (11), RMSEA = 0.012, CFI = 0.998, TLI = 0.994,
SRMR = 0.009). Baseline neighborhood social cohesion was significantly and positively
associated with the intercept, showing that respondents with higher neighborhood social
cohesion were more likely to have positive SPA in 2016. However, the association between
baseline neighborhood social cohesion and the slope of SPA was not significant.

Baseline Neighborhood Social Cohesion and Trajectories of Loneliness
As presented in Table 9, the unconditional growth model for loneliness showed a
statistically significant positive linear slope (β = 0.11, SE = 0.05, p < .05), indicating an average
increase of 0.11 standardized units every 4 years (𝜒
&
= 3.11 (1), RMSEA = 0.019, CFI = 0.999,
TLI = 0.996, SRMR = 0.006). This means that respondents reported higher levels of loneliness
as time increased. As shown in Figure 3, the next model added baseline neighborhood social
Loneliness – -0.34 0.63
2016
SPA – -0.44
Loneliness –
57
cohesion and other covariates (𝜒
&
= 22.11 (11), RMSEA = 0.013, CFI = 0.997, TLI = 0.991,
SRMR = 0.008). Baseline neighborhood social cohesion was negatively associated with the
intercept, showing that more neighborhood social cohesion at baseline was associated with less
loneliness in 2016. The association between baseline neighborhood social cohesion and the slope
was positively significant, indicating that respondents with higher neighborhood social cohesion
had faster rates of increase in loneliness over time.

Table 9. Unconditional Latent Growth Modeling for Self-perceptions of Aging and Loneliness
Note. Estimates were standardized; SE = standard error; RMSEA = root-mean-square error of
approximation; CFI = comparative fit index; TLI = Tucker-Lewis index; SRMR = standardized
root-mean-square residual.
* p < .05. *** p < .001.

Self-perceptions of Aging
Loneliness

β SE β SE
Means
Intercept 3.95*** 0.02 4.21*** 0.11
Slope -0.42*** 0.07 0.11* 0.05
Covariance
Intercept – Slope 0.38*** 0.08 0.28*** 0.07
Model Fit Index 90% CI 90% CI
𝜒
&
(df) 2.109 (1) 3.114 (1)
RMSEA 0.013 0.000–0.039 0.019 0.000 – 0.043
CFI 0.999 0.999
TLI 0.998 0.996
SRMR 0.004 0.006
58
Figure 3. Latent Growth Curve Models for Self-perceptions of Aging and Loneliness

Note. Estimates were standardized. SPA = Self-perceptions of aging. Higher scores on
neighborhood social cohesion and SPA represent higher levels of cohesion and more positive
Neighborhood
Social Cohesion
(2008)
Intercept Slope
SPA
(2008)
SPA
(2012)
SPA
(2016)
0.11*** -0.06
-0.50 -0.25 0.00
0.57***
Neighborhood
Social Cohesion
(2008)
Intercept Slope
Loneliness
(2008)
Loneliness
(2012)
Loneliness
(2016)
-0.20*** 0.14*
0.44***
-0.44 -0.22 0.00
59
perceptions, respectively. The models controlled for age, gender, race, marital status, household
income, chronic diseases, functional limitations, and depressive symptoms.
* p < .05. *** p < .001.

Direct, Indirect, and Total Effects of NSC on SPA and Loneliness
The findings from path analyses are presented in Figure 4. All paths except the one from
neighborhood social cohesion (2008) to SPA (2016) were statistically significant. Subsequently,
we tested whether a more parsimonious model where the nonsignificant path from baseline
neighborhood social cohesion to SPA in 2016 was constrained to zero fitted the data well
(𝜒
&
= 2.72 (1), RMSEA = 0.019, CFI = 1.000, TLI = 0.981, SRMR = 0.004). The total effects
were decomposed based on this model, as shown in Table 10. For the association between 2008
neighborhood social cohesion and 2016 SPA, the total effects were 0.07 (p < .001) and 14.3%
were the indirect effects through 2012 loneliness (β = 0.01, p < .01). Higher neighborhood social
cohesion at baseline led to less loneliness four years later, which in turn contributed to more
positive SPA in 2016. The indirect effects through 2012 SPA (autoregressive path) were also
significant (β = 0.06, p < .001), accounting for 85.7% of the total effects.
As for the association between 2008 neighborhood social cohesion and 2016 loneliness,
the standardized total effects were -0.17 (p < .001), about 35.3% of which were the direct effects
(β = 0.06, p < .001). The indirect effects passing through 2012 SPA were significant (β = 0.02, p
< .001), suggesting that more neighborhood social cohesion led to more positive SPA in the
following wave, subsequently decreasing levels of loneliness in 2016. This mediating path
accounted for 5.8% of the total effects. The indirect effects through 2012 loneliness
(autoregressive path) were also significant (β = 0.10, p < .001), accounting for 58.9% of the total
60
effects. The mediating effects of loneliness in the path between 2008 NSC and 2016 SPA were
larger than the mediating effects of SPA in the path between 2008 NSC and 2016 loneliness. The
total effects of 2008 NSC on 2016 loneliness (β = -0.17) were more than twice the total effects of
2008 NSC on 2016 SPA (β = 0.07).

Figure 4. A Path Model Estimating Longitudinal Associations of Neighborhood Social Cohesion
with Loneliness and Self-perceptions of Aging

Note. SPA = self-perceptions of aging. Higher scores on neighborhood social cohesion and SPA
represent lower and more negative perceptions, respectively. The figures shown are standardized
path coefficients. Paths from T1 covariates toT2 and T3 social disconnectedness and loneliness
are not shown. Solid blue lines show the path from initial neighborhood social cohesion to
loneliness via SPA, while solid red lines show the path to SPA via loneliness. Dotted lines show
a non-significant association.
** p < .01. *** p < .001.

Neighborhood
Social Cohesion
(2008)
SPA
(2012)
SPA
(2016)
Loneliness
(2012)
Loneliness
(2016)
-0.19***
0.11***
-0.07**
0.04
-0.37*** -0.25***
0.57***
0.58***
-0.07**
-0.07**
61
Table 10. Effects Decomposition of Baseline Neighborhood Social Cohesion on Self-perceptions
of Aging and Loneliness at 8-year Follow up
Note. Estimates were standardized. The bootstrap resampling from 1000 samples was conducted
for the 95% confidence interval (CI). NSC = neighborhood social cohesion; SPA = self-
perceptions of aging.
** p < .01. *** p < .001.

Discussion
Given Americans’ preferences to age in place and the projected growing number of
community-dwelling older adults, it is important to understand neighborhood factors of older
adults’ perceptions of aging and loneliness. The findings of this study showed that more
neighborhood social cohesion is associated with lower levels but faster rates of increase in
loneliness over the 8-year study period. In addition, those with higher neighborhood social
cohesion had more positive SPA but were not associated with changes in SPA over time. We
also found that the path from neighborhood social cohesion to SPA was greatly explained by
loneliness. In contrast, the effects of SPA were small in linking the association between
neighborhood social cohesion and loneliness. The major strengths of our study include the use of
Final Model β 95% CI Effect size
2008 Cohesion to 2016 SPA
Total effects 0.07*** 0.05, 0.09
Indirect effects
2008 Cohesion → 2012 Loneliness → 2016 SPA 0.01** 0.01, 0.02 14.3%
2008 Cohesion → 2012 SPA → 2016 SPA 0.06*** 0.05, 0.08 85.7%
2008 Cohesion to 2016 Loneliness
Total effects -0.17*** -0.14, -0.21
Direct effects -0.06*** -0.03, -0.09 35.3%
Indirect effects
2008 Cohesion → 2012 SPA → 2016 Loneliness -0.01*** -0.01, -0.02 5.8%
2008 Cohesion → 2012 Loneliness → 2016 Loneliness -0.10*** -0.08, -0.12 58.9%
62
large, nationally representative survey data with longitudinal repeated measures of outcome
variables to provide reliable results. In addition, we conducted the simultaneous evaluation of the
reciprocal relationship between SPA and loneliness across time using the longitudinal path
model that allows possible autoregressive and cross-lagged relations. In this way, our findings
can provide a more stringent and reasonable test of possible bidirectional mediating associations
between SPA and loneliness.
We found that living in more socially cohesive neighborhoods at baseline predicted more
positive SPA 8 years later. This finding is in line with the strength of weak ties (Granovetter,
1973) and corroborates previous findings that social connections at a neighborhood level
significantly contribute to late-life health and well-being outcomes through enhanced social
support, self-efficacy, and mutual respect among neighbors (Cramm et al., 2013; Kawachi &
Berkman, 2000). Considering that SPA is based on one’s age-related experiences in various life
domains as well as age-related feedback from their social environment (Miche, Wahl, et al.,
2014), a residential context where older adults live in their daily lives may be a critical
determinant of their evaluation of aging processes (Diehl, Wettstein, et al., 2021; Wolff et al.,
2018). Our findings support the growing attention on societal and macrolevel factors for shaping
adults’ SPA above and beyond the personal-level characteristic (M. Diehl, Brothers, et al., 2021;
Gerstorf et al., 2020). However, neighborhood social cohesion was not associated with the rate of
change in SPA over time. Changes in one’s situation, rather than the distal contextual situation,
may precede significant changes in how one evaluates their own aging processes. For example,
an increase in spousal strain and a decrease in spousal support were associated with a more
negative SPA (Y. K. Kim et al., 2021). It is also plausible that the subjective assessment of social
connectedness is more important than the structure of one’s surrounding social environment.
63
Diehl and colleagues (2021) found that loneliness was consistently and more strongly related to
SPA changes than social network size. The results from our path analyses showed that loneliness
primarily explained (fully mediated) the association between neighborhood social cohesion and
SPA.
Older adults with more neighborhood social cohesion had lower levels of loneliness at a
later time. This finding supports a few prior studies documenting the significant role of
neighborhood social cohesion in shaping feelings of loneliness (Bergefurt et al., 2019; Gutierrez-
Kapheim & Mitchell, 2020; Park et al., 2021; R. Yu et al., 2021; X. Yu et al., 2021) by providing
consistent and longitudinal evidence with representative data of older adults. More socially
cohesive neighborhoods can directly enhance one’s social connectedness (e.g., neighborhood
friends) but also indirectly prevent loneliness by the benefits of collective efficacy in maintaining
social order and buffering stressful conditions (H. H. Kim & Jung, 2022). Unexpectedly, more
neighborhood social cohesion was associated with the faster rates of increase in loneliness,
which conversely suggests that low neighborhood social cohesion was related to the slower rates
of loneliness increase. This is similar to Hu and Li’s recent report (2022) that more negative SPA
was linked to slower increases in loneliness. The authors explained their findings from the
perspective of the evolutionary theory of loneliness (Cacioppo & Cacioppo, 2018). According to
the theory, the feeling of loneliness serves as an evolutionary warning system signaling any
threats to human social well-being. Thus, older adults who felt lonelier due to the initial low
neighborhood social cohesion may be motivated to pursue alternative ways to alleviate their
feelings of loneliness, slowing down its further increase. Overall, our findings can be
encapsulated within the Ecological Model of Aging (Lawton & Nahemow, 1973) framework that
64
highlights that one’s aging experience is structurally influenced by social aspects of the
residential environment.
Our path analyses disentangled the direct and indirect effects of neighborhood social
cohesion on SPA and loneliness. First, we found that the association between high neighborhood
social cohesion and positive SPA was primarily indirect through decreased loneliness as well as
that neighborhood social cohesion did not directly influence SPA. As briefly discussed earlier,
being embedded into a socially cohesive neighborhood may prevent feelings of loneliness (Holt-
Lunstad et al., 2015). This can, in turn, attenuate the consequences of age-related challenges
(e.g., functional limitations, loss of a loved one, and retirement) on shaping the perceptions of
own aging more favorably. Relatedly, Schwartz and colleagues (2021) showed that less active
informal social interactions led to more negative evaluations of their own aging process.
On the other hand, low neighborhood social cohesion increased the risk of loneliness
directly and indirectly through negative SPA. The attributional approach raised by the Social
Psychology Theory of Loneliness (Peplau & Perlman, 1979) provides insights for interpreting
the mediating role of SPA. Having negative perceptions of old age as being frail and receiving
more support from others may heighten the expectations from social network members, thus
contributing to larger gaps between the desired and actual degrees of social interactions (Cheng,
2016). This is in line with an increasing number of recent findings on the significant effects of
negative perceptions of aging on loneliness (R. X. Hu & Li, 2022; Losada-Baltar et al., 2020,
2021; Pikhartova et al., 2016; Segel-Karpas et al., 2021).
This study has a few limitations worth noting. First, our measure of SPA was
conceptualized as a unidimensional concept (either negative or positive) and thus, could not
address its multidimensional nature in terms of different domains (e.g., physical, psychological,
65
and social aspects of aging) and valences (e.g., gains and losses). Future studies can benefit from
using multidimensional measures of SPA such as awareness of age-related change (AARC)
(Diehl & Wahl, 2010) to provide a domain-specific pathway as documented in previous studies
(Diehl, Wettstein, et al., 2021; Jung et al., 2021). For example, the effects of neighborhood social
cohesion may be only pronounced in the social aspects of SPA. Second, we focused on a
baseline assessment of neighborhood social cohesion to clarify the temporal order in estimating
its effects on trajectories of SPA and loneliness. Therefore, the following research direction
should investigate the dynamics of neighborhood social cohesion, such as its within-person
changes or the rates of such changes over time. Third, our main variables were measured based
on self-report. Unobserved variables that particularly can affect one’s reporting styles (e.g.,
personality) constitute threats to the validity of our findings. Future research should include a
broad, comprehensive range of covariates in the models.
Conclusion
To conclude, this study provides some of the first insights into the longitudinal effects of
neighborhood social cohesion on SPA and loneliness among older adults. In the extension of
neighborhood studies focusing on health and well-being outcomes, we showed that the
neighborhood social environment might also shape one’s subjective perceptions of the aging
process and social connections. Our findings also contribute to the literature on loneliness by
documenting a significant contextual-level factor beyond the individual-level predictors. A
neighborhood environment can be a promising target for interventions to improve subjective
perspectives of aging and social isolation (Fong et al., 2021). Furthermore, we suggest that
loneliness is a potential underlying mechanism through which neighborhood social cohesion may
feed into and shape SPA and vice versa (SPA for the path from social cohesion to loneliness).
66
Previous scholarly attention has been mostly paid to the individual-level health status and
socioeconomic factors as determinants of the SPA (Diehl, Wettstein, et al., 2021). The current
findings elucidate neighborhood conditions and subsequent changes in one’s perceptions of
social connectedness as developmental correlates of SPA.

67
Chapter 4. Neighborhood Adversity and Cognitive Health:
The Modifying Role of Self-perceptions of Aging

Abstract
Research has documented the increased cognitive impairment risk among older adults in
socioeconomically disadvantaged neighborhoods. Much less is known about the factors that
moderate this risk. We conceptualized self-perceptions of aging (SPA) as a potential moderator
because it reflects core beliefs about the self at older ages but is also closely linked to late-life
health. Guided by the ecology theory of aging and the diathesis-stress model that postulates the
interactive roles of individual-level cognitive styles and contextual-level stressors in shaping
health outcomes, we hypothesized that more positive SPA would buffer the effects of
neighborhood adversity on cognitive function. Using data from the Health and Retirement Study
(2008–2016), the analytic sample consisted of adults aged 54 and older (N=5,904). Cognitive
function was assessed by the Telephone Interview for Cognitive Status. The neighborhood
indicators included 1) poverty rates, 2) perceived neighborhood social cohesion, and 3) perceived
neighborhood disorder. Three-level growth curve models were separately estimated for each
neighborhood indicator’s effect as well as its interaction with SPA on the 8-year cognitive
function trajectories. Findings showed that higher poverty rates, more disorder, and less cohesion
were associated with lower initial levels of cognitive function but slower rates of cognitive
decline. SPA partially moderated the linkage between neighborhood adversity and the level of
cognitive function. More positive SPA was associated with reduced adverse effects of living in
neighborhoods with higher poverty rates and more physical disorders. These findings highlight
the intersection of an individual-level psychological factor and a contextual-level factor in
shaping late-life cognition.

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Background
A growing body of research has shown the adverse effects of poor neighborhood
environments on cognitive aging. Most study found that living in neighborhoods with less safe,
lower levels of trust, and lower socioeconomic status led to cognitive impairments and increased
dementia risk in later life (Peterson et al., 2021). For example, objective indicators of
neighborhood conditions such as crime rates and sociodemographic structure have been
consistently associated with cognitive health outcomes (Clarke et al., 2012; Friedman et al.,
2017). While limited compared to research on objective neighborhood environments, a handful
of studies documented that subjectively perceived neighborhood environments such as physical
disorder and cohesion also contributed to late-life cognitive function (Estrella et al., 2020;
Thierry et al., 2021). In a cross-sectional study, Zaheed et al. (2019) showed that greater
neighborhood physical disorder was related to worse episodic memory, and lower neighborhood
social cohesion was related to worse semantic verbal fluency. In another cross-sectional study,
Zhang et al. (2019) reported that higher perceived social cohesion predicted better global
cognition, episodic memory, and executive functioning in older adults. Further, some evidence
showed that subjective perceptions of neighborhood quality were linked to cognitive health
beyond the effects of objective indicators (Lee & Waite, 2018).
As one of few longitudinal studies, Sharifian et al. (2020) examined the associations of
perceived neighborhood social environment and cognition changes over a 4-year study period.
They found that perceived physical disorder is linked to worse cognition via higher anxiety and
that perceived social cohesion is linked to better cognition via lower anxiety and depression.
While these studies laid the foundation for the role of neighborhood environments in influencing
cognitive health, most studies relied on cross-sectional observations with a few exceptions. In
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research with a cross-sectional design, the temporal order of cognitive outcomes and perceptions
of neighborhoods is unclear, leading to difficulties in making any causal inferences. In addition,
further investigation is needed to assess whether early and middle-stage neighborhood social
environment exposures have long-term cumulative effects on cognitive trajectories in later life
(Peng et al., 2022). In addition, much less is known about potential modifiers through which the
effects of neighborhood characteristics on cognitive function are attenuated or intensified. To
this end, this study examined the longitudinal association of neighborhood adversity with
cognitive trajectories over 8 years, with self-perceptions of aging (SPA) tested as a potential
moderator.
SPA may serve as a significant factor in maintaining and promoting cognitive health.
Much of the prior work on the association between SPA and cognition is focused on the memory
(B. R. Levy, 2003). Work conducted by Levy and colleagues has found that more negative views
of aging and age stereotypes are associated with worse recall memory performance (B. Levy,
1996; B. Levy & Langer, 1994) and a greater decline in memory performance 38 years later (B.
R. Levy et al., 2012). In contrast, positive perceptions of aging are related to less
neuropathological burden 10 years later (B. R. Levy et al., 2016), protecting against developing
dementia (B. R. Levy et al., 2018). More recent research has demonstrated the effects of SPA on
cognitive ability beyond memory function. Seidler and Wolff (2017) documented a 3-year
reciprocal association between SPA and processing speed performance in older adults. Brown et
al. (2021) showed that only positive SPA, but not negative, predicted mental status, short-delay
memory, processing speed, and executive function. Robertson et al. (2016) found the association
of positive and negative SPA with verbal fluency, memory, and self-rated memory. Increasing
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evidence indicates that how one evaluates their aging process plays an essential role in shaping
late-life cognitive health.
According to the ecology theory of aging (Lawton & Nahemow, 1973) and the cognitive
diathesis-stress model (Abramson et al., 1989), we conceptualized SPA as a moderator of the
associations between adverse neighborhood environments and cognitive health in later life. With
an emphasis on the person-environment interactions, the ecology theory of aging suggests that
varying types and levels of personal competence manifest differently in environments. Their
unique combinations lead to different aging-related outcomes across individuals. Conceived as a
form of self-knowledge, SPA is one of the most important personal factors involved in a
fundamental process of self-development in later life (M. Diehl et al., 2015). Thus, it is plausible
that SPA may modify the neighborhoods–cognitive health connection. Furthermore, the
cognitive diathesis-stress model postulates that negative cognitive styles interact with stressful
life events, contributing to the onset and maintenance of depression. Expanding this perspective,
we hypothesize that those with more negative views about their aging process are more likely to
experience cognitive health impairments when exposed to adverse neighborhood conditions.
The current study addresses two research questions about the role of neighborhood
adversity and self-perceptions of aging in shaping cognitive function in later life. Specifically,
the first aim is to extend past research by examining the longitudinal association of three
indicators of neighborhood adversity, both objectively and subjectively measured, with cognitive
function in older adults. We used multilevel growth curve modeling to account for data
clustering and provide estimates of both the level and changes in cognitive function over the 8-
year study period. The second aim is to test whether self-perceptions of aging moderate the
associations between neighborhood adversity and cognitive function. We expected that the
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negative effects of living in poor neighborhoods on late-life cognitive function would be stronger
for those with negative SPA.
Methods
Data and Sample
Data for this study were drawn from the Health and Retirement Study (HRS). The HRS is
a longitudinal panel survey of a nationally representative sample of Americans over 50, and it
has been conducted biannually since 1992. A wide range of information on physical and mental
health profiles, financial status, and family support has been collected through face-to-face or
telephone interviews. In 2006, a random half of the non-institutionalized sample was invited to
complete the Psychosocial and Lifestyle Self-Administrated Questionnaire (SAQ). The other half
received the SAQ in 2008, with the administration rotated every four years. The current study
derived data from 2008, 2012, and 2016 waves of the HRS survey as questions on SPA were first
added in 2008. A total of 6,540 community-dwelling older adults aged 54 and above completed
the SAQ in 2008. Of 6,281 respondents, this study excluded 259 individuals categorized as
‘having dementia’ based on the Langa-Weir classification (Crimmins et al., 2011). Those who
did not have any data on the study variables were further excluded (n= 377). The final analytic
sample consisted of 5,904 individuals with a respondent-wave observation of 23,792. Among our
final sample, 5,858 had data on the cognitive measure in 2008 (0.8%), falling to 3,666 in 2016
(62%). This study linked the census tract-level neighborhood information to HRS respondents
with census tract identifiers. Data for neighborhood poverty rates came from the U.S. Census
Bureau’s 2008-2012 American Community Survey (ACS) Five-Year estimates.
Measures
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Cognitive function. Cognitive function was measured by a 27-point scale that indicates a
summary score on three domains of cognitive performance, with greater scores indicating a
better cognitive function (Ofstedal & Fisher, 2005). First, (episodic) memory was assessed by
immediate and delayed 10-noun free recall tests. The participants were given a list of 10 words
and asked to recall the words immediately. After an approximately five-minute delay, they were
again asked to recall as many words as possible from the previous list. Immediate and delayed
recall scores were constructed respectively by using the number of correctly recalled words, with
a possible range of 0 to 20 points. The Serial 7’s subtraction test was administered to assess
working memory. The respondents were asked to subtract 7 from 100 for five trials serially. The
points were given for the correct answers, with a composite score ranging from 0 to 5. Lastly, the
speed of mental processing was evaluated on a 2-point scale by asking the respondents to count
backward for 10 serial numbers from 20.
Neighborhood poverty rates. The percent of residents with income below the federal
poverty threshold was calculated for each census tract. Then, census tracts were categorized into
quartiles, with upper quartiles indicating neighborhoods with higher poverty rates.
Lack of neighborhood social cohesion. Respondents were asked to report their feelings
about one’s local area or anywhere within a 20-minute walk or about a mile from home.
Responses were coded on a 7-point Likert scale (1 = most favorable evaluation; 7 = worst
evaluation). The measure (Cagney et al., 2009) included four items for social cohesion: (1) I feel
part of this area, (2) people in this area would help you, (3) people in this area can be trusted, and
(4) people in this area are friendly. The average scores were calculated (range: 1–7), with higher
scores representing more lack of social cohesion.
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Neighborhood physical disorder. The same measure also included four items for physical
disorder: (1) vandalism/graffiti are a big problem in this area, (2) this area is full of rubbish, (3)
there are many vacant/deserted houses; and (4) people would be afraid to walk alone in this area.
The average scores were calculated (range: 1–7), with higher scores indicating higher levels of
disorder. Those with two or more missing items were considered as missing on the final score for
each index. This study created quartiles based on the distribution of perceived neighborhood
scores to examine threshold effects.
Self-perceptions of Aging. SPA was measured with 8 items. Five questions were based on
the subscale of the Philadelphia Geriatric Center Morale Scale (Lawton, 1975): (1) Things keep
getting worse as I get older, (2) I have as much pep as I did last year, (3) The older I get, the
more useless I feel, (4) I am as happy now as I was when I was younger, and (5) As I get older,
things are better than I thought they would be. Additional three items were derived from the
Berlin Aging Study (Kotter-Grühn et al., 2009): (6) So far, I am satisfied with the way I am
aging, (7) The older I get, the more I have had to stop doing things that I like, and (8) Getting
older has brought with it many things that I do not like. Participants rated each question on a six-
point Likert scale (1 = strongly disagree; 6 = strongly agree). After reverse coding items (1), (3),
(7), and (8), the scores across eight items were averaged, with a higher score indicating more
positive perceptions.
Covariates. The current study used individual-level demographic factors, socioeconomic
status, and health conditions as control variables. Demographic factors included age, gender (1 =
female), race/ethnicity (1 = non-Hispanic White; 2 = non-Hispanic Black; 3 = Hispanic; and 4 =
others) and marital status (0 = separated, divorced, widowed, or never married; 1 = married or
partnered). Education in years and total annual household income were used as indicators of
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socioeconomic status. Education was assessed with years of formal schooling (range: 0–17), and
annual household income was measured by income from all possible sources such as earnings,
pensions, and social security. Due to high skewness, log-transformed values of income were used
for the multivariate analyses.
With regard to health conditions, this study considered the number of chronic diseases
and depressive symptoms at each wave. The respondents reported whether they had any of the
following eight chronic diseases diagnosed by a physician (high blood pressure, diabetes, cancer,
lung disease, heart disease, stroke, psychiatric problems, and arthritis); then, the sum value was
calculated (range 0–8). Finally, depressive symptoms were assessed by the 8-item CES-D scale,
a shortened version of the original CES-D scale (Radloff, 1977). The respondents were asked to
report whether they experienced the following sentiments most or all of the time: (1) felt
depressed, (2) everything was an effort, (3) sleep was restless, (4) felt alone, (5) felt sad, (6)
could not get going, (7) felt happy, and (8) enjoyed life. Total scores were calculated by
subtracting the two positive indicators from the negative items, with a possible range of 0 to 8.
Internal reliability was acceptable, with the Kuder-Richardson 20 (KR-20) coefficient of .80.
Analytical Strategy
First, the descriptive characteristics of the study variables at baseline were reviewed.
Next, multiple multilevel growth curve models were estimated to examine the research
questions. For each model, data was structured with observations (level 1) nested within
respondents (level 2) nested within census tracts (level 3). This allows accounting for the
clustered structure of the analytic sample, providing more precise standard errors compared to
pooled regression approaches. The time measure was age, which was centered on the age of 54.
We examined the models systematically, beginning with an unconditional random intercept
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model for cognitive function to estimate the intraclass correlation (ICC). Next, we examined the
unconditional growth model wherein a random slope at the respondent level was added for
cognitive function. Separate models were then analyzed for each neighborhood adversity
indicator, adjusting for SPA and other covariates. Furthermore, the moderating effect of SPA
was then examined by including interaction terms of SPA and indicators of neighborhood
adversity in the models. Missing data were handled using the maximum likelihood estimator. All
analyses were performed using Stata 17.0 (StataCorp. College Station, Texas).
Results
Sample Characteristics
Table 11 presents the descriptive sample characteristics at baseline (2008). The poverty
rate was averaged at 15.2% across census tracts. The mean lack of neighborhood social cohesion
was moderately low, about 2.5 on a 7-point scale. The fourth quartile included respondents with
scores of 3.3 to 7, indicating the most lack of social cohesion. The mean level of perceived
neighborhood disorder was below the midpoint (M = 2.5). The fourth quartile included 22% of
respondents with the worst neighborhood physical disorder. The average score of the SPA
measure was 3.9 on a 6-point scale, with higher values reflecting more positive, which indicated
a slightly skewed distribution toward the positive perception. The mean age of the participants
was 69.6 years, about 59% were women, and 65% were married or partnered. Most of the sample
consisted of non-Hispanic White (78.2%), followed by non-Hispanic Black (11.9%), Hispanic
(7.7%), and other racial/ethnic minorities (2.1%). On average, the participants had 12.8 years of
education, 66K dollars of annual household income, and about two chronic diseases. The scores
of depressive symptoms averaged 1.32.

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Table 11. Descriptive Sample Characteristics at Baseline
Variables M ± SD %
Neighborhood adversity
Poverty % 15.15 ± 0.11
1
st
Quartile (range: 0–6.7%) 24.5
2
nd
Quartile (range: 6.7–12.7%) 26.4
3
rd
Quartile (range: 12.7–22.3%) 27.9
4
th
Quartile (range: 22.3–90.3%) 21.2
Lack of social cohesion (range: 1–7) 2.46 ± 1.37
1
st
Quartile (range: 1–1.3) 25.2
2
nd
Quartile (range: 1.3–2) 25.1
3
rd
Quartile (range: 2.3–3.3) 27.6
4
th
Quartile (range: 3.3–7) 22.1
Physical disorder (range: 1–7) 2.46 ± 1.42
1
st
Quartile (range: 1–1.25) 26.6
2
nd
Quartile (range: 1.3–2) 26.2
3
rd
Quartile (range: 2.3–3.3) 25.0
4
th
Quartile (range: 3.3–7) 22.2

Self-perceptions of aging (range: 1–6) 3.90 ± 1.05

Demographic factors
Age (range: 54–99) 69.6 ± 9.21
Gender
Male 41.1
Female 58.9
Race/ethnicity
Non-Hispanic White 78.2
Non-Hispanic Black 11.9
Hispanic 7.74
Other 2.08
Marital status
Separated, divorced, widowed, or never married 35.4
Married or partnered 64.6

Socio-economic and health status
Education (in years) 12.8 ± 2.96
Annual household income 66,210 ± 120,232
Chronic diseases (range: 0–8) 2.14 ± 1.41
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Note. Racial/ethnic identification of ‘other’ included American Indian, Alaskan Native, Asian,
and Pacific Islander.

Multilevel Variance Components
There were 5,904 respondents with 4 waves each on average (range: 1–5), where 9.5%
had 1 wave, 8.8% had 2 waves, 10.1% had 3 waves, 12.7% had 4 waves, and 59% had 5 waves
of cognitive function data. There were 2,899 census tracts with 3.9 respondents each on average
(range: 1–34), where 51.2% had 1 respondent, 29.8% had 2 respondents, 7.7% had 3
respondents, 4.3% had 4 respondents, and 7% had 5 or more respondents. The respondent-wave
observation was 23,792. As shown in Table 12, the mean cognitive function score was estimated
as 14.74 on a 27-score scale. In the random part of the model, the within-individual variance was
estimated as 7.18 (accounting for 38% of the total variance). And the variance between people
was estimated as 8.92 (48% of the total variance). Lastly, the variance across census tracts was
estimated as 2.69 (14% of the total variance), We found the estimated intra-class correlations of
0.14 at the census tract level and 0.62 at the individual level. These numbers represent the
correlation in cognitive function between two older adults in the same census tract, and between
two observations of the same respondent (living in the same census tract). We can also conclude
that 14% of the variation in cognitive function can be attributed to the census tract and 62% to
the older adult (which includes the census tract).

Table 12. Three-level Variance-components Model for Cognitive Function
Depressive symptoms (range: 0–8) 1.32 ± 1.89
Estimate SE
Fixed effects
Intercept 14.74 *** 0.06
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*** p < .001.

Unconditional Growth Model for Cognitive Function
Table 13 shows the unconditional growth models for cognitive function over 8 years. The
fixed-effect estimates yielded an intercept of 18.42 and a slope of -.20. These values can be
interpreted as the average scores at the age of 54 (centered), and the average rate of change per
year. On average, cognitive function decreases significantly over time. The random part of the
model remained similar to the variance-components model.

Table 13. Unconditional Growth Model for Cognitive Function over 8 years

*** p < .001.

Random effects
Level 3 intercept variance (between-census tracts) 2.69 *** 0.27
Level 2 intercept variance (between-people) 8.92 *** 0.27
Level 1 residual variance (within-individual) 7.18 *** 0.08

Intraclass correlation
Level 3: census tracts 0.14 0.01
Level 2: individual 0.62 0.01
Estimate SE
Fixed effects
Intercept 18.42 *** 0.09
Age (centered at the age of 54) -0.20 *** 0.004

Random effects
Level 3 intercept variance (between-tracts) 2.31 *** 0.24
Level 2 slope variance (between-person) 0.002 *** 0.00
Level 2 intercept variance (between-person) 6.62 *** 0.31
Level 1 residual variance (within-person) 6.64 *** 0.07
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Mixed Effects Growth Curve Models for Cognitive Function: Main Effects
Table 14 presents the findings for the multilevel model predicting cognitive function that
was separately estimated for each neighborhood adversity factor: poverty, lack of social
cohesion, and disorder. Overall, a growth curve model had 11.3 points of cognitive function by
the age of 54 and decreased by -0.18 points per year, adjusting for other covariates of 0 values.
Across three models, SPA was not significantly associated with the levels of cognitive function
but related to slower rates of decrease in cognitive function.
Poverty. Controlling for covariates, neighborhoods with the highest poverty rates (Q4)
were associated with a 54-years-old mean that was -0.99 lower levels of cognitive function and
decreased 0.02 points less per year changes in cognitive function than neighborhoods with the
lowest poverty rates (Q1). This suggests that a neighborhood with the highest poverty rates
would have a 54-years-old mean of 10.4 and a decrease of 0.16 points per year.
Lack of cohesion. Neighborhoods with the greatest lack of social cohesion (Q4) were
associated with a 54-years-old mean that was -1.20 lower levels of cognitive function and
decreased by 0.05 points less per year changes in cognitive function compared to neighborhoods
with the lowest poverty rates (Q1).
Disorder. Respondents who lived in neighborhoods with the most physical disorder (Q4)
reported -0.96 points lower cognitive function than those living in the least physical disorder
neighborhoods (Q1). Similar to the other two factors of neighborhood adversity, neighborhoods
with the highest physical disorder were associated with slower rates of decrease in cognitive
function over time (b = 0.03, p < .01).

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Table 14. Mixed Effects Growth Curve Models for Cognitive Function over 8 years
Note. NHW = non-Hispanic white; L3 = level 3 FIPS code; L2 = level 2 respondent.
a
Racial/ethnic ‘other’ included American Indian, Alaskan Native, Asian, and Pacific Islander.
b
Annual household income was log-transformed.
* p < .05. ** p < .01. *** p < .001.
Variables
Neighborhood adversity (NA) factors at baseline
Poverty Lack of cohesion Physical disorder
B SE B SE B SE
Fixed effects
Intercept 11.37 *** 0.33 11.26 *** 0.33 11.27 *** 0.32
Age -0.18 *** 0.01 -0.19 *** 0.01 -0.18 *** 0.01
Neighborhood Adversity factors
(Ref=Q1, best)

Q2 -0.39 0.21 -0.23 0.21 -0.22 0.2
Q3 -0.43 * 0.21 -0.38 0.21 -0.51 * 0.21
Q4 -0.99 *** 0.22 -1.20 *** 0.23 -0.96 *** 0.22
Neighborhood Adversity factors
× Age (Ref=Q1)

Q2 × Age 0.01 0.01 0.02 0.01 0.02 0.01
Q3 × Age 0.00 0.01 0.02 0.01 0.02 * 0.01
Q4 × Age 0.02 * 0.01 0.05 *** 0.01 0.03 ** 0.01
Self-perceptions of aging 0.07 0.07 0.00 0.07 0.03 0.07
Self-perceptions of aging × Age 0.01 ** 0.00 0.01 *** 0.00 0.01 ** 0
Covariates
Female 1.03 *** 0.08 1.03 *** 0.08 1.03 *** 0.08
Race/ethnicity (Ref=NHW)
Non-Hispanic black -1.95 *** 0.12 -2.02 *** 0.12 -1.99 *** 0.12
Hispanic -0.63 *** 0.15 -0.69 *** 0.15 -0.70 *** 0.15
Other
a
-1.59 *** 0.25 -1.59 *** 0.25 -1.59 *** 0.25
Married/partnered 0.03 0.07 0.03 0.07 0.03 0.07
Years of education 0.41 *** 0.01 0.42 *** 0.01 0.42 *** 0.01
Annual household income
b
0.18 *** 0.02 0.18 *** 0.02 0.18 *** 0.02
Number of chronic diseases -0.12 *** 0.02 -0.13 *** 0.02 -0.13 *** 0.02
Depressive symptoms -0.13 *** 0.01 -0.13 *** 0.01 -0.13 *** 0.01
Random effects
L3 Intercept variance 0.36 ** 0.12 0.41 ** 0.13 0.40 ** 0.13
L2 Slope variance 0.003 *** 0 0.003 *** 0.00 0.003 *** 0.00
L2 Intercept variance 4.60 *** 0.23 4.55 *** 0.22 4.58 *** 0.22
Residual variance 6.64 *** 0.07 6.64 *** 0.07 6.64 *** 0.07
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The Moderating Effects of Self-perceptions of Aging
We tested interactions between SPA and indicators of neighborhood adversity on
cognitive function. As shown in Table 15, significant moderating effects of SPA were found for
neighborhood poverty and disorder. The negative effects of higher neighborhood poverty rates
(Q2 and Q3) on cognitive function, compared to neighborhoods with the least poverty rate (Q1),
were attenuated by having more positive SPA (b = 0.60, p < .01 and b = 0.42, p < .05 for each
quartile). However, the interaction effects were non-significant for neighborhoods with the
highest poverty rates (Q4). Similarly, we found a significant interaction between the second
quartile of physical disorder and SPA (b = 0.47, p < .05), while the interactions were not
significant for Q3 and Q4. No moderating effects of SPA were found for neighborhood adversity
and the rates of changes in cognitive function. Figure 5 shows the further investigation of the
significant interaction terms. Respondents with more positive SPA had reduced negative effects
of neighborhood adversity on the levels of cognitive function compared to those with more
negative SPA. These attenuating effects of positive SPA, however, were limited only to the
moderately adverse neighborhood environment (i.e., Q2 and Q3 for neighborhood poverty and
Q2 for neighborhood disorder).

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Table 15. Interaction Effects of Neighborhood Adversity Factors and Self-perceptions of Aging
at Baseline for Cognitive Function over 8 years
Note. NA = neighborhood adversity; SPA = self-perceptions of aging; L3 = level 3 FIPS code;
L2 = level 2 respondent.
* p < .05. ** p < .01. *** p < .001.

Variables
Neighborhood adversity (NA) factors
Poverty Lack of cohesion Disorder
B SE B SE B SE
Fixed effects
NA × SPA (Ref= Q1)

Q2 × SPA 0.60 ** 0.20 0.00 0.21 0.47 * 0.19
Q3 × SPA 0.42 * 0.19 -0.16 0.20 0.13 0.19
Q4 × SPA 0.25 0.21 -0.27 0.21 0.09 0.2
NA × SPA × Age (Ref= Q1)

Q2 × SPA × Age -0.02 0.01 0.01 0.01 -0.01 0.01
Q3 × SPA × Age -0.01 0.01 0.01 0.01 0.00 0.01
Q4 × SPA × Age -0.01 0.01 0.01 0.01 0.00 0.01
Random effects

L3 Intercept variance 0.35 ** 0.12 0.42 ** 0.13 0.40 ** 0.13
L2 Slope variance 0.003 *** 0 0.003 *** 0.00 0.003 *** 0.00
L2 Intercept variance 4.58 *** 0.22 4.54 *** 0.22 4.54 *** 0.22
Residual variance 6.64 *** 0.07 6.64 *** 0.07 6.64 *** 0.07
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Figure 5. Modifying Effects of Self-perceptions of Aging in the Association of Neighborhood
Adversity with Cognitive Function

Note. Negative SPA was defined by below 1 standard deviation of the mean SPA score, whereas
positive SPA was defined by above 1 standard deviation.
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Discussion
The current study aimed to advance the understanding of the association of neighborhood
adversity – high poverty, perceptions of high disorder, and lack of social cohesion – with
cognitive trajectories among older Americans. Although the effects of neighborhood
characteristics and cognitive health are quite established, the factors modifying the connection
between neighborhood adversity to late-life cognitive function are largely underexplored.
Further, few studies focused on how neighborhood conditions lead to longitudinal changes in
cognitive function. Neighborhood contexts are particularly important for older adults as this
population is more likely to rely more on their immediate residential environment due to age-
related functional limitations, mobility decline, and reduction in social networks (Yen et al.,
2009). This study examined the extent to which indicators of neighborhood adversity were
associated with the levels and rates of changes in cognitive function using 8-year data.
Additionally, we texted the moderating effects of self-perceptions of aging (SPA) on the
relationship between neighborhood adversity and cognitive function.
The findings from the three-level growth curve models indicated that neighborhoods with
higher poverty rates, more physical disorder, and less cohesion at baseline were all significantly
associated with lower initial levels of cognitive function but slower rates of cognitive decline.
The observed association between living in disadvantaged neighborhoods and decreased levels
of cognitive function coincides with prior findings (Clarke et al., 2012; Friedman et al., 2017;
Lee & Waite, 2018). Neighborhood adversity (e.g., social fragment and disorder) can accelerate
cognitive declines by increasing psychological distress (Peterson et al., 2021; Scott et al., 2018),
whereas neighborhood resources (e.g., community centers, accessible public transit, and good
public spaces) may promote cognitive reserve for individuals aging in place by increasing
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opportunities for social engagement, physical activities, and cognitive stimulation (Clarke et al.,
2015).
Our findings on the role of neighborhood adversity in slowing down cognitive decline
contradict some previous studies showing that residents in socioeconomically advantaged areas,
not disadvantaged neighborhoods, had slower rates of cognitive decline (Clarke et al., 2015;
Sheffield & Peek, 2009). According to the cognitive reserve theory (Stern, 2009), individuals
with better protective capacities against cognitive impairments (high cognitive reserve) show
slower rates of cognitive decline than those with low reserve until the brain enters advanced
neuro-aging. However, this pattern may reverse at later stages of cognitive aging, such that those
with high reserve undergo more rapid cognitive decline due to more severe pathology at their
initial manifestation of cognitive deficit. Consistent with this idea, a few prior studies showed
that disadvantaged characteristics such as having less mentally challenging occupations (Hyun et
al., 2021), being Blacks compared to Whites (Jang et al., 2021), and being widowed compared to
being married (Monserud, 2019) were associated significantly slower rates of cognitive decline.
Our analyses of interaction effects showed that SPA moderated the association of
neighborhood poverty and disorder with cognitive function. More positive SPA was associated
with reduced adverse effects of living in neighborhoods with higher poverty rates and more
physical disorders on levels of cognitive function. Indeed, when SPA was positive, the cognitive
function scores among those in more disadvantaged neighborhoods were similar to those in the
most advantaged areas (i.e., Q1). However, these attenuating effects of positive SPA were
limited only to the moderately adverse neighborhood environment (i.e., Q2 and Q3 for
neighborhood poverty and Q2 for neighborhood disorder). The interaction effects were non-
significant for neighborhoods with the highest poverty rates (Q4) and greater levels of physical
86
disorder (Q3 and Q4). In addition, no moderating effects of SPA were found for neighborhood
adversity and the rates of changes in cognitive function. Overall, our findings support the
ecology theory of aging and the cognitive diathesis-stress model, which suggests that the
combination of individuals’ cognitive styles and stressors shapes health outcomes across the
lifespan. Individuals with more positive evaluations of their aging may be more resilient to the
adverse effects of stressful neighborhood conditions. Those with more positive SPA may
perceive their neighborhood environments differently (e.g., more favorably) than those with
more negative SPA. These findings are in accordance with evidence suggesting that higher levels
of optimism and hopefulness mitigated the negative influence of neighborhood physical
disadvantage and low social cohesion on inflammation levels (Nguyen et al., 2022). Our study
highlights that a psychological construct such as SPA may result in individual differences in the
consequences of environmental stimuli on cognitive health later in life.
It is important to note several limitations in interpreting the study findings. First, we used
a summary score measuring three domains of cognitive function: episodic memory, working
memory, and mental processing speed. Thus, this overall assessment may not have fully
considered the multidimensional nature of the cognitive function. In addition, we cannot exclude
the possibility that the effects of neighborhood adversity and SPA may differ across subdomains
of cognitive function. Future studies would benefit from using more comprehensive
measurements designed for assessing subdomains to better understand the domain-specific
associations of neighborhood environments with cognitive function. Second, this study used a
unidimensional measure of SPA (either negative or positive) and thus, could not adequately
address its multidimensional nature across different domains (e.g., physical, psychological, and
social aspects of aging) and valences (e.g., gains and losses). Furthermore, we focused on the
87
longitudinal effects of neighborhood adversity and SPA at baseline in shaping cognitive health
trajectories over time. Future research should be directed at examining whether their changes
(e.g., transitions to a less adverse local community) may prevent or facilitate cognitive decline
and development of dementia by using longitudinal repeated measures.
Conclusion
In conclusion, living in adverse neighborhood environments may lead to lower levels of
cognitive function but slower rates of decrease in later life. SPA partially served as a moderating
factor in the association of neighborhood poverty and perceived disorder with levels of cognitive
function. These findings can contribute to a better understanding of how the contextual-level
residential environment interacts with an individual-level psychological factor to shape late-life
cognitive health. The significant mitigating role of positive SPA may provide preliminary
evidence for developing interventions to promote more positive evaluations of own aging
process, particularly for older adults who reside in neighborhoods with high levels of poverty
and physical disorder.

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Chapter 5: Conclusion
This dissertation aimed to examine the role of neighborhood contexts in shaping SPA
among older adults and investigate their interplay on late-life cognitive function. Informed by
theories and prior findings across the field of gerontology, sociology, and psychology, this
dissertation provides the first insights into how individuals’ evaluations of their own aging
process are created, changed, and influenced by a broader residential context. In addition, this
dissertation expands the neighborhood study by suggesting SPA as a potential psychological
pathway or moderator through which neighborhood environments influence older adults’ health
and well-being.
Overview
Chapter 2 is grounded in the ecology of human development framework and the ecology
model of aging to identify significant and robust indicators of neighborhood social environment
in predicting SPA among a nationally representative sample of adults aged 50 and above. It
focused on four social and economic aspects of neighborhoods: (a) neighborhood poverty; (b)
density of older adults living alone; (c) perceived social cohesion; and (d) perceived disorder.
The results showed that living in socially cohesive neighborhoods was most consistently
associated with more positive SPA. Furthermore, the effects of neighborhood cohesion on SPA
were stronger at younger than older ages. These findings were discussed in relation to the
strength of weak ties, highlighting the potential benefits from peripheral social networks in
individuals’ aging experiences and evaluations. It also connected the findings to the
Socioemotional Selectivity Theory, providing further evidence that our social motives moved
towards more intimate relationships at older ages. The key contribution of Chapter 2 is to the
89
literature on subjective aging by proposing neighborhood social environment, particularly social
cohesion, as an essential societal context where one develops SPA.
Chapter 3 focused on the longitudinal association between neighborhood social cohesion
and SPA and how this association further contributes to feelings of loneliness. The findings
showed that more limited neighborhood social cohesion at baseline was associated with more
negative levels of SPA and loneliness 8 years later. Moreover, we found that the effects of
neighborhood social cohesion on SPA were primarily indirect via loneliness, whereas its effects
on loneliness had both direct and indirect pathways. These findings suggest that the well-
established effects of neighborhood social environments on health and well-being outcomes may
extend to one’s subjective perceptions of social connections. Furthermore, it contributes to the
literature on loneliness and SPA by elucidating potential bidirectional mechanisms through
which neighborhood social cohesion may feed into each other (loneliness to SPA and vice
versa). Overall, this study can serve as a preliminary foundation to inform community-based
interventions to promote more positive perceptions of aging as well as decrease feelings of
loneliness.
Chapter 4 examined SPA as a moderating factor in the association between neighborhood
adversity and cognitive function among older adults. The three indicators of neighborhood
adversity were: 1) high poverty rates, 2) lack of social cohesion, and 3) high physical disorder.
Guided by the ecology theory of aging postulating that the unique combinations of personal
competence and environmental characteristics determine aging outcomes, it hypothesized that
those with more positive views about their aging process are less likely to experience cognitive
health impairments when exposed to adverse neighborhood conditions. The findings indicated
that neighborhoods with higher poverty rates, more physical disorder, and less cohesion at
90
baseline were all significantly associated with lower initial levels of cognitive function but
slower rates of cognitive decline. Furthermore, the moderation analyses showed that having
positive SPA reduced the effects of living in poor neighborhoods on late-life cognitive function,
but only in moderately adverse neighborhoods. The findings highlight that a psychological
construct such as SPA may result in individual differences in the consequences of environmental
stimuli on cognitive health later in life. It also advances our understanding of the health
implications of neighborhood adversity by identifying a previously underexplored modifying
factor.

91
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Asset Metadata

Creator Choi, Eunyoung (author)

Core Title Self-perceptions of Aging in the Context of Neighborhood and Their Interplay in Late-life Cognitive Health

School Leonard Davis School of Gerontology

Degree Doctor of Philosophy

Degree Program Gerontology

Degree Conferral Date 2022-08

Publication Date 07/22/2022

Defense Date 05/02/2022

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag cognitive health,Loneliness,neighborhood,OAI-PMH Harvest,self-perceptions of aging

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Zelinski, Elizabeth (committee chair), Ailshire, Jennifer (committee member), Jang, Yuri (committee member)

Creator Email choieuny@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC111373885

Unique identifier UC111373885

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Document Type Dissertation

Format application/pdf (imt)

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Type texts

Source 20220722-usctheses-batch-960 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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