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Life course implications of adverse childhood experiences: impacts on elder mistreatment, subjective cognitive decline, and caregivers' health

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Life course implications of adverse childhood experiences: impacts on elder mistreatment, subjective cognitive decline, and caregivers' health

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2023 Elizabeth S. Avent

LIFE-COURSE IMPLICATIONS OF ADVERSE CHILDHOOD EXPERIENCES: IMPACTS
ON ELDER MISTREATMENT, SUBJECTIVE COGNITIVE DECLINE, AND CAREGIVERS’
HEALTH

by

Elizabeth S. Avent

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 2023

ii
DEDICATION
To my grandparents, Mary and Authur Freeman.
Grandma, you have done one of the most difficult and selfless jobs, being a caregiver. Your
strong will and faith to keep going has been an inspiration to me to follow through with my
aspirations.
Granddaddy, you were always an example of someone who lived their life as their most genuine
self, and I strive to live by that each day.
Rest well.

iii
ACKNOWLEDGEMENTS
First, I would like to thank my family for their continued, love, support, encouragement,
and patience during this journey. To the Graham, Freeman, and Avent families, thank you for
keeping me grounded and inspired. During the times I quietly considered giving up, many of you
were right on time with words of wisdom, appreciation, and encouragement. I would especially
like to thank my grandmother, Mary, for her insights on caregiving and dementia. Also, I would
like to my great aunt and namesake, Liz, and my great uncle, Alonzo, for their insights on
caregiving and aging, as well. Those conversations have been more helpful that you may ever
know.
I would also especially like to thank my parents, Dee, Dwight, and Darnell over a
thousand times. There are too many things to name that I can thank you for, so I can just say that
I am forever grateful for you all, and that I hope to be able to repay you all for the tangible and
intangible things you have provided and done for me. To my siblings (Nana, Dwight, Derrick,
Dion, Pacman, and Quan), you all have been some of the best cheerleaders; thank you for the
check-ins (even when I couldn’t answer the phone or responded way too late), jokes, photos of
my niece and nephews, and being an inspiration to keep going. To my niece, Rayne, thank you
for the weekend FaceTime calls and making my day a bit brighter each time.
I would like to thank my mentors and committee members at the University of Southern
California, Drs. Kate Wilber, Zach Gassoumis, and Donna Benton. I am extremely grateful for
the mentorship and guidance I have received from the three of you. You all have been great
examples of committed and kind mentors with integrity. There are so many things that I cannot
put into words that can express my appreciation for you all, and you all have contributed to
making me a more well-rounded scholar and person.

iv
Kate, thank you for your compassion, flexibility, and wisdom over the years. Nearly
every conversation (both professionally and personally) we have had over the years has left a
positive impact. Thank you for empowering me and pushing me out of my comfort zone many
times because it always ended up being beneficial to my professional and personal growth.
Zach, I am forever indebted to you for everything you have done over the years. Thank
you so much for being a consistent and positive presence in both my professional and personal
life. Thank you for introducing me to quantitative methods; I would have never thought I would
have done an entirely quantitative dissertation without your guidance, support, and commitment.
I am grateful for the numerous all-day and late-night work sessions and talks in suite 208, over
the phone, and on Zoom.
Donna, I have enjoyed getting to know you and working with you over the years. I
admire your enthusiasm and openness. Thank you for the many holiday dinners and allowing me
to spend time with you and your family. You, Dana, and Bapa are wonderful and inspiring
people. Thank you for giving me an occasional escape from the academic environment and
making Los Angeles feel more like home to me.
I would like to thank Drs. Laura Mosqueda and Sherry Hamby for their mentorship, as
well. Dr. Mosqueda, thank you for the consistent encouragement, support, and advice you have
given me. I am honored to work with you the Better Together Study. Sherry, thank you for the
uplifting 1-on-1s, and thank you for inviting me to your weekly writing groups and
ResilienceCon. All of these helped reignited my passion for my research and writing during my
moments of burnout.
Thank you to my past and present lab members in Secure Old Age Lab for being a
pleasure to work alongside: Jeanine Yonashiro-Cho, Haley Gallo, Gerson Galdamez, Kylie

v
Meyer, Laura Rath, Julia Martinez, Kelly Marnfeldt, Suzy Mage, Sheila Salinas Navarro,
Mengzhao Yan, Eleanor Batista-Malat, and Lilly Estenson.
Also, thank you to the Better Together Study Team for being some of the best people to
work with: Ro Peterson, Maria Madrigal, and Rose Trujillo. Along with Zach and Jeanine, thank
you for truly being a support system. I always enjoyed our interviews, writing sessions, team
meetings and debriefs, and lunch meetups, and I hope to be lucky enough to continue to work
with people who are as caring, efficient, and pleasant as you all have been.
Additionally, I would like to acknowledge the staff at the USC Leonard Davis School of
Gerontology for making this entire process go smoothly. To Senior Associate Dean Maria
Henke, thank you for your support over last six years; running into you on those late nights in the
Gero building were more encouraging than you may know. To Sara Robinson, I am grateful for
your encouragement and support from the very beginning, and I enjoyed our small talks in your
office. To Leon Watts, you have been one of the biggest supports during this journey. Thank you
for your advice and generosity.
My friends, near and far, have played a major role in this journey. Brook, I’m blessed to
have you as both an aunt and friend. Tiffany, my bestie, I am beyond blessed for such a long and
stable friendship with you. Thank you both for the phone calls, FaceTime calls, advice, the
Thursday evening Knowledge Kickbacks, and proofreading my writing and presentations, among
numerous other things. The appreciation I have for the both of you is beyond words I can
describe. Whitney, thank you for the random hilarious texts that send us down memory lane,
sending pics of my precious nephew, and being an example of resilience and perseverance.
These have been more uplifting and inspiring than you realize.

vi
To my LA friends, I don’t know what I would do without you all; I couldn’t have met
better people. Shelby, Amber, and Andreshe’a- my first friends I made in LA and at USC- thank
you x1000. Andreshe’a, being your practice patient during your nurse protocol sessions
definitely gave me a break in between my work, and I’m glad we were able to get acclimated to
LA together while you were here. Shelby and Amber, thank you for being here every step of the
way- through all the super late nights working in the Gero student lounge, the all-nighters on
Zoom and Discord, and the decompression and rant calls. And it wasn’t always work; I have
truly enjoyed our outings, girls’ trips, lunches, shopping trips, movie nights, and so many other
activities that gave me balance outside of research and writing. I’m so blessed to have you two as
lifelong besties.
Drs. Lauren Brown and Kai Mathews, my sisses (whatever the plural form of sis is), you
both have been amazing and inspirational. I can’t imagine how this journey would have been
without you two. Having you as friends and role models kept me motivated and made me believe
I could successfully complete this dissertation. I’m grateful for every minute that we spent
together whether it was work or play. Both of your energies balanced me out. Thank you for
your guidance and generosity, as well as being safe spaces. I am truly grateful for your
friendship.
Mercedes, I’m so glad that we became roommates when I started at USC because it
blossomed into such a beautiful friendship. Thank you for providing me with a home away from
home and accepting me into your wonderful family. To Mrs. Maria Amezcua, thank you for
opening up your home and treating me as one of your own. You both will always be family to
me.

vii
Deborah and Meki, suite 208 and this PhD program would not have been nowhere as
lively without you two. Thank you for making the office a brighter place to be. Deborah, thank
you for being the absolute best peer mentor and co-teacher. The things I learned from you have
been priceless. Meki, thank you for letting me distract you with our random talks in the office,
the phone calls, and the rides home. I’m grateful to have you both as friends.
Olivia, my friend and peer mentee, you have given me just as much advice and guidance
as I have given you. I’m glad that our peer mentorship became a wonderful friendship. I’m so
grateful to have completed this dissertation and program alongside you.
To my Atlanta besties: Lauren-Ashley, Brittani, Amber, and Te’, thank you for being so
uplifting and being a constant presence even after I left Atlanta and even through my late text
responses or non-responses (sorry!). Thank you, Te,’ for the LA visits with your beautiful family
and FaceTime calls. Thank you, Lauren-Ashley, Brittani, and Amber, for always coming
together when I visit Atlanta. Those times spent together were a light at points when everything
seemed dark and heavy. You all are such bright and awesome women.
Sally, you have been my rock over the last six years. I’m so lucky to have you as a
therapist, and I believe it has been one of the best decisions I made. Thank you for being a source
of guidance and rationality. I am truly grateful for you.
I could never forget my mentors at Georgia State University in the Gerontology Institute,
my first academic home: Drs. Candace Kemp, Chivon Mingo, Jennifer Craft Morgan, and
Elisabeth Burgess. Thank you for taking me under your wings and being a constant presence of
encouragement throughout this entire journey; I could write so many more pages on the
guidance, patience, and compassion you have provided over the years. Thank you to my peers
and former colleagues at Georgia State, as well: Victoria Helmly, Joy Dillard Appel, Christina

viii
Barmon, Debby Yoder, and Eugenie Stephenson. Each of you at GSU planted and watered the
seed for my passion for gerontology and research, and you all have been sources of inspiration
and support.
Finally, I would like to thank my fur babies, Zoro and Sapphire, for being the best
emotional support dogs while writing this dissertation.
To everyone I named in these acknowledgements (and those who are not), I hope I have
reciprocated as much as you all have given me. My brain is at its bandwidth at this point, so to
anyone that I have excluded, please charge it to my head and not my heart.

ix
TABLE OF CONTENTS
DEDICATION ................................................................................................................................ ii
ACKNOWLEDGEMENTS ........................................................................................................... iii
LIST OF TABLES ........................................................................................................................ xii
LIST OF FIGURES ..................................................................................................................... xiv
ABBREVIATIONS ..................................................................................................................... xiv
ABSTRACT .................................................................................................................................. xv
CHAPTER 1: BACKGROUND AND OVERVIEW OF THE DISSERTATION ........................ 1
Physical Health Outcomes..................................................................................................... 2
Mental Health Outcomes ....................................................................................................... 3
Adverse and Risky Health Behaviors .................................................................................... 3
Abuse Victimization and Perpetration .................................................................................. 4
Mechanisms of ACEs Influencing Health, Behavioral and Social Outcomes ...................... 4
ACE Pyramid Model ................................................................................................... 4
Toxic Stress ................................................................................................................. 5
Gaps in ACEs Research ........................................................................................................ 6
Late-life Intimate Partner Violence ............................................................................. 6
Physical IPV in Older Victims .................................................................................... 8
Dementia, Cognitive Impairment, and Subjective Cognitive Decline ........................ 9
Caregiver Well-Being ................................................................................................ 12
Conceptual Framework ....................................................................................................... 13
Overview of Chapters .......................................................................................................... 14
CHAPTER 2: WHAT’S AGE GOT TO DO WITH IT? EARLY-LIFE RISK
FACTORS FOR LATE-LIFE PHYSICAL INTIMATE PARTNER VIOLENCE
USING ADVERSE CHILDHOOD EXPERIENCES .................................................................. 16
Overview ............................................................................................................................. 16

x
Introduction ......................................................................................................................... 16
Risk Factors of IPV Victimization in Later Life ....................................................... 18
Childhood Adversity and Elder Mistreatment ........................................................... 20
Physical IPV in Older Victims .................................................................................. 20
The Web of Violence ................................................................................................. 21
ACEs & IPV .............................................................................................................. 22
Methods ............................................................................................................................... 23
Dataset ....................................................................................................................... 24
Analytic Strategy ....................................................................................................... 26
Results ................................................................................................................................. 27
Discussion ........................................................................................................................... 28
Limitations ................................................................................................................. 29
Implications ............................................................................................................... 30
CHAPTER 3: THE CONTINUING IMPACT OF CHILDHOOD ADVERSITY
ACROSS THE LIFESPAN: ASSESSING THE RELATIONSHIP BETWEEN
ACES AND SUBJECTIVE COGNITIVE DECLINE ................................................................. 37
Overview ............................................................................................................................. 37
Introduction ......................................................................................................................... 38
Methods ............................................................................................................................... 42
Measures .................................................................................................................... 42
Analysis ..................................................................................................................... 43
Results ................................................................................................................................. 44
Discussion ........................................................................................................................... 46
CHAPTER 4: FOUR ACES ISN’T ALWAYS A WINNING HAND: THE
IMPACT OF ADVERSE CHILDHOOD EXPERIENCES ON CAREGIVERS’
HEALTH....................................................................................................................................... 58
Overview ............................................................................................................................. 58

xi
Introduction ......................................................................................................................... 59
Adverse Childhood Experiences ................................................................................ 60
Methods ............................................................................................................................... 62
Dataset ....................................................................................................................... 62
Measures .................................................................................................................... 63
Analysis ..................................................................................................................... 64
Results ................................................................................................................................. 65
Descriptives ............................................................................................................... 65
Discussion ........................................................................................................................... 71
Future Research ......................................................................................................... 75
CHAPTER 5: DISCUSSION AND CONCLUSION ................................................................... 95
Summary of Results ............................................................................................................ 95
Limitations ............................................................................................................... 100
ACE Research Limitations ...................................................................................... 101
Future Research ................................................................................................................. 102
REFERENCES ........................................................................................................................... 104

xii
LIST OF TABLES
Table 2.1. Sample Characteristics ................................................................................................. 32
Table 2.2. Logistic Regression of Physical IPV (ACE Scores)
a
................................................... 33
Table 2.3. Logistic Regression of Physical IPV (Individual ACEs)a ........................................... 34
Supplemental Table 2.1. WLS Questions Used for Variables ...................................................... 35
Table 3.1. Sample Characteristics (Demographics) ...................................................................... 48
Table 3.2. Sample Characteristics (ACE Score and ACEs).......................................................... 49
Table 3.3. Logistic Regression of SCD, ACE Score
a
................................................................... 50
Supplemental Table 3.1. Full Ordered Logistic Regression of SCD, ACE Score
a
...................... 52
Supplemental Table 3.2. Full Logistic Regression of SCD and Individual ACEs
a
...................... 55
Table 4.1. Sample Characteristics (Demographics) ...................................................................... 76
Table 4.2. Sample Characteristics (Health Conditions and Behaviors) ........................................ 78
Table 4.3. Sample Characteristics (ACE Score and ACEs).......................................................... 79
Table 4.4. Zero-Inflated Negative Binomial Regression of Caregivers' “Not good”
Mental Health Days, ACE Score
a
................................................................................................. 80
Table 4.5. Zero-Inflated Negative Binomial Regression of Caregivers' “Not good”
Mental Health Days, Individual ACEs
a
........................................................................................ 81
Table 4.6. Zero-Inflated Negative Binomial Regression of Caregivers' “Not good”
Physical Health Days, ACE Score
a
............................................................................................... 82
Table 4.7. Zero-Inflated Negative Binomial Regression of Caregivers' “Not good”
Physical Health Days, Individual ACEs
a
...................................................................................... 83
Supplemental Table 4.1. Full Zero-Inflated Negative Binomial Regression of
Caregivers' “Not good” Mental Health Days, ACE Score
a
.......................................................... 84
Supplemental Table 4.2. Full Zero-Inflated Negative Binomial Regression of
Caregivers' “Not good” Mental Health Days, Individual ACEs
a
................................................. 86
Supplemental Table 4.3. Full Zero-Inflated Negative Binomial Regression of
Caregivers' “Not good” Physical Health Days, ACE Score ......................................................... 89

xiii
Supplemental Table 4.4. Full Zero-Inflated Negative Binomial Regression of
Caregivers' “Not good” Physical Health Days, Individual ACEs................................................. 92

xiv
ABBREVIATIONS
ACE/ACEs Adverse Childhood Experiences
ADRD Alzheimer’s Disease and Related Dementias
BRFSS Behavioral Risk Factor Surveillance Survey
CDC Centers for Disease Control
EM Elder Mistreatment
IPV Intimate Partner Violence
SCD Subjective Cognitive Decline
WLS Wisconsin Longitudinal Study

xv
ABSTRACT
Adverse childhood experiences (ACEs), defined as potentially traumatic events that
occur up until age 18, have a lifespan impact on health, health behaviors and abuse perpetration
and victimization. Research has linked ACEs to several negative physical and mental health
outcomes, harmful health behaviors, and victimization in adulthood. Although there has been
extensive and consistent evidence of the impact of ACEs over the life course, some relationships
have not been explored or have been underexplored. This dissertation explores the impact of
ACEs through a life-course perspective, aiming to examine associations between ACEs and three
outcomes: 1) physical intimate partner violence (IPV) in later life, 2) subjective cognitive decline
(SCD) in later life, and 3) poor mental and physical health among caregivers. Chapter 1
discusses research on ACEs, identifies the gaps in research, and introduces the study chapters.
Chapter 2 investigates the relationship between ACEs and physical IPV victimization at ages 60
and older using data from the Wisconsin Longitudinal Survey (WLS). Logistic regression was
conducted to determine whether higher ACE scores and which of the individual ACEs are
significantly associated with higher risk of victimization of physical IPV at ages 60 and older.
Chapter 3 explores the relationship between ACEs and reports of subjective cognitive decline
(SCD) at ages 55 and older using data from the 2011 Behavioral Risk Surveillance Survey
(BRFSS). Bivariate analyses and ordered logistic regressions using nested models were run to
determine whether there is a significant association between higher ACE scores and SCD, as
well as whether there are specific ACEs that are the drivers of this relationship. Chapter 4
explores the relationship between ACEs and caregivers’ physical and mental health. Data from
the 2019 and 2020 BRFSS were used to run bivariate analyses and zero-inflated negative
binomial regressions to examine associations between ACE scores and reported number of poor

xvi
mental and physical health days among caregivers, and to determine which, if any individual
ACEs are the drivers of this relationship. Chapter 5 summarizes findings from the dissertation
and discusses implications for research, prevention, and intervention. Limitations and potential
areas for future research are also addressed.

1
CHAPTER 1: BACKGROUND AND OVERVIEW OF THE DISSERTATION
Adverse childhood experiences (ACEs) are potentially traumatic events that occur up
until age 18. The original ACE study was developed and implemented by Kaiser Permanente and
the Centers for Disease Control and Prevention (CDC). Collecting two waves of data from over
17,000 members of Kaiser Permanente’s Southern California Health Maintenance Organization
(HMO) from 1995 to 1997 (CDC, 2022; Felitti et al., 1998b), the study investigated the impact
of childhood adversity on health and well-being in adulthood. A ten-item questionnaire asked
about events of physical, sexual, and psychological abuse, physical and emotional neglect,
witnessing intimate partner violence (IPV), parental divorce or separation, substance abuse in the
household, mental illness of household member, and incarceration of a household member. From
these categories, an ACE score was developed. The seminal ACE study provided the
groundwork to measuring childhood adversity, and the ACE questionnaire and modified versions
of it have been widely used in research and practice.
Study results showed that over 60% of participants experienced at least one ACE, and
12.5% experienced four or more ACEs. Substance abuse in the household was the most prevalent
ACE; nearly 26% reported living with someone who was an alcoholic or with someone who used
street drugs. (CDC, 2022; Felitti et al., 1998b). Higher ACEs scores, especially 4 or more ACEs,
strongly increased the risk of negative health behaviors such as smoking, alcoholism, illicit drug
use, sexual promiscuity, obesity, physical inactivity, and suicide attempt. Similarly, higher ACE
scores increased the risk of adverse health conditions, including heart attack, stroke, chronic
bronchitis, diabetes, and cancer (Felitti et al., 1998b). Building on the original ACE study, data
collected from 34 U.S. states in the 2011-2017 Behavioral Risk Factor Surveillance Surveys
(BRFSS) (N=211,376), indicated that nearly 60% of individuals had experienced at least 1 ACE,

2
and over 21% has experienced 3 or more ACEs. Emotional abuse was the most common ACE
(33.5%), followed by parental separation/divorce (28.2%) and substance abuse in the household
(26.8%). Over 17% experienced physical abuse, and 11% experienced sexual abuse (Giano et al.,
2020).
ACEs have lifespan impact on health, health behaviors, and abuse perpetration and
victimization. Subsequent studies have linked ACEs to several negative physical and mental
health outcomes, harmful health behaviors, and victimization in adulthood. Usually, ACE studies
conceptualize ACEs using cumulative ACE score. An ACE score of 4 or more is commonly
characterized as high ACEs though some studies may go as high as 5 ACEs or as low as 3 ACEs.
Physical Health Outcomes
Individuals with high ACE scores are more likely than those who have an ACE score of 0
to report poor or fair health and comorbidities (Amemiya et al., 2019; Cannon et al., 2010; Dube
et al., 2010; Gilbert et al., 2015; Greenfield & Marks, 2009; K. Hughes et al., 2017). Having 4 or
more ACEs has consistently been found to increase the risk of a variety of health and behavioral
outcomes. Findings from experiencing 4 or more ACEs have ranged from having twice the risk
to more than 3.6 times the risk of obesity, disturbed and insufficient sleep, and impaired memory
of childhood, (Anda et al., 1999; Anda, Felitti, Bremner, et al., 2006a; Cannon et al., 2010;
Chapman et al., 2011, 2013; Dube et al., 2010).
Having 4 or more ACEs also increased the likelihood of both ischemic and coronary
heart disease, cancer diagnosis, chronic obstructive pulmonary disorder (COPD), asthma, and
diabetes (Anda et al., 2008; Bellis et al., 2014; Campbell et al., 2016; Danese & Tan, 2014; M.
Dong, Giles, et al., 2004; Dube et al., 2010; Gilbert et al., 2015; Holman et al., 2016; K. Hughes
et al., 2017; Iniguez & Stankowski, 2016; Ports et al., 2019). Those having 3 or more ACEs,
were at higher risk of being hospitalized due to various autoimmune diseases, including

3
rheumatoid arthritis, findings that were more pronounced in women (Dube et al., 2009). High
ACEs have also been linked to increased likelihood of cardiovascular disease (Su et al., 2015).
Ultimately, ACEs increased the likelihood of early mortality. Individuals with 6 or more ACEs
died 20 years earlier on average compared to those with no ACEs, and they were more than
twice as likely to die before age 65 (D. W. Brown et al., 2009).
Mental Health Outcomes
Furthermore, individuals with ACEs were more likely to have poor mental well-being
and more mental health distress (Bellis et al., 2014; Gilbert et al., 2015; K. Hughes et al., 2017;
Strine et al., 2012), with eight times the odds of reporting psychological distress for those who
have 6 or more ACEs (Manyema et al., 2018). High ACEs increases the risk of mental health
issues, including major depressive disorder, geriatric depression, generalized anxiety disorder,
and chronic stress (Anda, Felitti, Bremner, et al., 2006b; Cannon et al., 2010; Manyema et al.,
2018; Rhee et al., 2019; Waite & Shewokis, 2012). Having 4 or more ACEs also increased the
odds of depressed affect, panic reactions, and hallucinations (Anda, Felitti, Bremner, et al.,
2006b) Other mental health conditions related to ACEs include post-traumatic stress disorder
(PTSD), lower life-satisfaction, suicidality and suicide attempts, and other forms of self-harm
(Becker et al., 2009; Campbell et al., 2016; Chapman et al., 2004; De Ravello et al., 2008;
Edwards et al., 2003; Iniguez & Stankowski, 2016).
Adverse and Risky Health Behaviors
Adverse and risky health behaviors are linked to most of the health outcomes previously
listed, and individuals who have experienced ACEs are more likely to engage in these behaviors.
Experiencing 4 or more ACEs has been linked to higher likelihood of smoking and illicit and
injected drug use (Anda et al., 1999; Anda, Felitti, Bremner, et al., 2006b; Bellis et al., 2014;
Campbell et al., 2016; Dube et al., 2003, 2010; Ford et al., 2011). People who have experienced

4
ACEs are also more likely to engage in binge drinking and develop alcoholism in adulthood
(Baiden et al., 2022; Bellis et al., 2014; Campbell et al., 2016; Dube et al., 2002; K. Hughes et
al., 2017; Strine et al., 2012). ACEs have also been linked to sexual intercourse at an earlier age,
promiscuity, and sexual dissatisfaction (Anda, Felitti, Bremner, et al., 2006a).
Abuse Victimization and Perpetration
Additionally, ACEs have been linked to abuse victimization and perpetration in
adulthood, especially witnessing IPV and experiencing physical and sexual abuse in childhood.
Women who experienced childhood sexual abuse by family or household members were twice as
likely to experience subsequent sexual and physical abuse (J. E. Barnes et al., 2009). Physical
abuse, sexual abuse, and witnessing IPV during childhood can lead to a cycle of IPV
victimization or perpetration in adulthood (Anda, Felitti, Bremner, et al., 2006a; Anda, Felitti,
Brown, et al., 2006; De Ravello et al., 2008; Whitfield et al., 2003). Having 4 or more ACEs
increased the risk of perpetrating IPV, and physical, sexual, and emotional abuse, neglect and
witnessing IPV were significantly associated with perpetrating IPV (Anda, Felitti, Brown, et al.,
2006; Fonseka et al., 2015).
Mechanisms of ACEs Influencing Health, Behavioral and Social Outcomes
ACE Pyramid Model
The ACE Pyramid model (Figure 1.1.) is a theoretical model that explains how ACEs
influence development and health, illustrating the possible life trajectory of someone who has
experienced ACEs (CDC, 2022; Felitti et al., 1998b). The ACE Pyramid indicates that ACEs
lead to biopsychosocial impairments, which increase the chances of engaging in risky health
behaviors that contribute to the onset of chronic illnesses, and ultimately lead to early death
(Boullier & Blair, 2018; Felitti et al., 1998b; P. Hughes & Ostrout, 2020).

5

Figure 1.1. ACE Pyramid (CDC, 2022)

Toxic Stress
Another mechanism of ACEs and health, behavioral, and social outcomes is toxic stress.
Toxic stress is chronic stress that occurs from intense and persistent adversity and abuse, such as
ACEs (Shonkoff et al., 2012). Although stress responses are a natural physiological reaction and
can be beneficial, for example, in increasing alertness and focus, supporting quick decision-
making, and/or boosting the immune system to fight off infection, prolonged stress continuously
releases stress hormones, such as cortisol. This can lead to brain structural and functional
changes, as well as changes in the immune system. Toxic stress is especially harmful during
childhood, as the body and brain are still developing and are critically sensitive to environmental
influences. These physiological and metabolic changes lead to various health and social
problems, such as heart disease, diabetes, emotional dysregulation, inability social relationships,

6
and learning difficulties. The presence of caring adults and supportive relationships can serve as
a buffer to the effects of toxic stress in children, preventing long-lasting health problems and
promoting longevity. (Bucci et al., 2016; Shonkoff et al., 2012).
Gaps in ACEs Research
Although there has been extensive and consistent evidence of the impact of ACEs over
the life course, there are some relationships that have not yet been explored or that have been
underexplored. We know that ACEs can induce a cycle of abuse into adulthood, but studies have
not investigated this relationship extending into later life, particularly focusing on IPV.
Concurrent with the aging of the population, there has been increases in the numbers, people
experiencing Alzheimer’s disease and related dementias (ADRD). Therefore, evidence linking
ACEs and cognitive health in later life has been growing, covering ADRD, cognitive decline,
and various areas of cognitive functioning. However, there is a need for more of this research
using consistent methods and variables with large population data. With increasing number of
people living with dementia (PLWD) and other chronic health conditions comes the increasing
need for caregivers. Research has shown that caregiving stressors can negatively impact
caregivers’ health, but no research has comprehensively investigated whether life course factors,
such as ACEs, worsens health outcomes for caregivers. This dissertation explores these
relationships aiming to examine associations between ACEs and three outcomes: 1) physical IPV
in later life, 2) subjective cognitive decline in later life, and 3) poor mental and physical health
among caregivers.
Late-life Intimate Partner Violence
Intimate partner violence (IPV), defined as the “physical violence, sexual violence,
stalking and psychological aggression by a current or former intimate partner” (Breiding, Basile,
Smith, Black, & Mahendra, 2015), is widely recognized as a public health issue (CDC, 2016).

7
Late-life IPV focus specifically on older victims who are abused by their intimate partners,
though the age used to identify at what age someone is categorized as older varies (e.g., ages
45+, 50+, 60+). Late-life IPV can begin in older age as a new phenomenon that occurs for the
first time in a relationship, or it can be ongoing or intermittent IPV that has occurred over time
prior to older adulthood. Some studies suggest that much of late-life IPV occurrences are
patterns of abuse grown old, whether the abuse exists in one relationship over time or in a series
of relationships. These include experiences of physical abuse, verbal aggression, economic
abuse, isolation and coercive control in marriages and romantic relationships spanning from 10
to 35 years (Band-Winterstein, 2015; Cheung et al., 2015; Harris, 1996; Podnieks, 1993).
Although one of the most prominent risk factors of IPV is a history of family violence
(Ports et al., 2019; Whitfield et al., 2003), there is relatively little research linking ACEs to IPV
in older age. Of the nearly 80 peer-reviewed articles that have been published using ACEs (CDC,
2016), the few that have focused on IPV have shown that witnessing IPV in childhood or
experiencing physical or sexual abuse in childhood increased the risk of IPV victimization by a
factor of 3.5 for women (Anda, Felitti, Bremner, et al., 2006b; Anda, Felitti, Brown, et al., 2006;
Dube et al., 2002; Whitfield et al., 2003). However, most of these studies focus on IPV in early-
to mid-adulthood rather than IPV extending into later life. Other studies focusing on links
between abuse and adversity in early life and later life suggest that experiencing early-life
adversity and childhood sexual abuse increase the risk of experiencing physical abuse in later life
(X. Dong & Wang, 2019a; Kong & Easton, 2018; Policastro & Finn, 2015).
We are beginning to learn about the mechanisms through which childhood maltreatment
and adversity increase the risk of elder mistreatment (EM), with self-reported health in midlife,
depressive symptoms, and cognitive functioning serving as pathways to vulnerability to EM

8
(Easton & Kong, 2021). EM is “a single or repeated act, or lack of appropriate action, occurring
within any relationship where there is an expectation of trust, which causes harm or distress to an
older person” (World Health Organization, 2023). This includes five main forms of
mistreatment: physical, psychological, sexual, neglect, and financial exploitation. Although EM
can inform how we can think about this, some research suggests that late-life IPV is a distinctive
phenomenon that differs from EM by spouses and partners (Crockett et al., 2015; Harris, 1996;
Vinton, 1991, 1999). While cases of partner and spousal abuse at older ages may be categorized
as EM rather than IPV or domestic violence (Vinton, 1999; LeBlanc & Weeks, 2013),
categorizing late-life IPV as a form of EM focuses primarily on frailty, impaired capacity, and
vulnerability related to cognitive and physical functioning. Using an EM framework may,
therefore, disregard the distinctive experiences of older victims of IPV, as Adult Protective
Services (APS) resources may not completely resolve or address the needs of older adults who
experience abuse by their intimate partners and spouses where impaired capacity is not the
central issue. EM may also tend to emphasize abuse perpetrated in a caregiving context. The EM
paradigm may also ignore gendered violence and older women who are victims of IPV who may
not have low physical and cognitive capacity (Straka & Montminy, 2006; Weeks & LeBlanc,
2011; Wilke & Vinton, 2005).
Physical IPV in Older Victims
Due to the intersection of gender and age, the impact of IPV, primarily physical IPV
(slapping, shoving, kicking, punching, or hitting with an object), may be more detrimental to
older victims than younger victims. Although both younger and older women experience adverse
health outcomes due to IPV victimization, older women are at greater risk for exacerbating
health problems and physical injury due to pre-existing age-related health conditions and
increased frailty, such as arthritis, chronic pain, digestive problems, respiratory issues,

9
hypertension, osteoporosis, and type 2 diabetes (Fisher & Regan, 2006; Loxton et al., 2006;
Stöckl & Penhale, 2015). These health conditions may make it more difficult for the victim to
leave the abusive relationship, access and utilize domestic violence resources, and even when
access and utilization are attempted, older adults may face additional barriers. Many domestic
violence resources are not designed with consideration to age-inclusivity, including physical
mobility and healthcare supports (Beaulaurier et al., 2007; Lazenbatt et al., 2013; LeBlanc &
Weeks, 2013; Weeks & LeBlanc, 2011; Shiel, 2016).
Much like IPV in younger women, prevalence varies across IPV types; some specific
types of abuse are reported more often. Psychological abuse has been found to be more common
than physical abuse in both younger and older women who report experiencing IPV (Band-
Winterstein & Eisikovits, 2009; Roberto, 2016). However, while evidence suggests that
specifically among older women, physical IPV seems to be less common (Vinton, 1991; Wilke
& Vinton, 2005), the extent to which physical IPV is truly less common in later life versus more
likely to be underreported is unclear. It is possible that physical IPV decreases and other forms of
abuse, particularly psychological abuse, increase in later life. However, a recent longitudinal
study found that physical abuse actually does not decrease with age; in fact, physical abuse may
worsen if the perpetrator becomes ill, whether terminally or temporarily (Wydall, 2021).
Dementia, Cognitive Impairment, and Subjective Cognitive Decline
Nearly 5.7 million older adults in the U.S. have ADRD. As the Baby Boomer generation
ages, ADRD numbers will increase as ADRD is strong linked to advanced age; it is estimated
that the number of ADRD cases will be 13.8 million by 2050 (Hebert et al., 2013). ADRD is the
sixth leading cause of death in the U.S., and deaths from Alzheimer’s disease increased by 123%
between 2000 and 2013 (“2020 Alzheimer’s Disease Facts and Figures,” 2020) Subjective
cognitive decline (SCD), self-perceived increasing confusion or memory loss (“2020

10
Alzheimer’s Disease Facts and Figures,” 2020; CDC, 2019) is often the earliest sign of
Alzheimer’s disease and can be used to identify those who may be at risk for ADRD. SCD is
often used as a criterion in distinguishing normal cognitive aging from pathological cognitive
aging (e.g., dementia) (Parfenov et al., 2020; Reid & MacLullich, 2006; Wang et al., 2004). Over
11% of adults aged 45 and older report SCD (“2020 Alzheimer’s Disease Facts and Figures,”
2020; CDC, 2019).
SCD is associated with several adverse health conditions and outcomes (Anderson, 2015;
Cosentino et al., 2018). People aged 65 and older with SCD are more likely to have two or more
chronic health conditions compared those who are aged 65 and older without SCD (Taylor et al.,
2020). Compared to individuals who do not report SCD, those with SCD have a higher
prevalence of heart attack, coronary heart disease, stroke, diabetes, and higher mortality risk.
Those with SCD also have poorer mental and self-rated health than those who did not report
SCD (Gupta, 2021; Hao et al., 2017; Luck et al., 2015; Taylor et al., 2018, 2020). Though the
risk factors of SCD have been widely discussed, one risk factor that is increasingly being
highlighted in recent literature is childhood adversity. Many of these studies have used the ACE
questionnaire or modifications of the questionnaire to measure and collect information on past
adversity and abuse.
Literature linking childhood adversity and cognitive decline and impairment in later life
is growing. Although many studies show associations between childhood adversity and cognitive
functioning, variables to measure childhood adversity and cognitive status differ by study.
Consequently, the findings vary regarding the extent to which individual ACEs and the
composite number of ACEs are associated with cognitive functioning. Earlier studies found
significant associations between childhood adversity and cognitive functioning in later-life using

11
different measures of childhood adversity (e.g., family socioeconomic status, family
environment, death of parents, frequent relocation) and more objective assessments of cognitive
functioning. (L. L. Barnes et al., 2012; Everson-Rose et al., 2003; Fors et al., 2009; Kaplan et al.,
2001; Ritchie et al., 2011). Other studies have linked childhood adversity and cognitive
functioning using the Childhood Trauma Questionnaire (Grainger et al., 2020; Radford et al.,
2017) and other adverse experiences such as experiencing war or the death of a parent or
important person (Korten et al., 2014). Although many recent studies have specifically used
items from the ACE questionnaire to measure childhood adversity, findings show variations in
cognitive outcome measures, including SCD, cognitive impairment, dementia, and Alzheimer’s
disease. One study that focused on Japanese older adults showed that experiencing three or more
ACEs increases the risk of developing dementia (Tani et al., 2020). A key finding came from a
report by the Center for Youth Wellness (2020), showing that those with four or more ACEs
were over 11 times as likely to be diagnosed with Alzheimer’s disease in later life, compared to
those with no ACEs. The most recent studies have found associations between ACEs and SCD
using large national datasets, such as the Behavioral Risk Surveillance Survey (BRFSS). These
findings indicate that having ACE scores ranging from 3 to 4 made the odds of reporting SCD 2-
3 times as likely compared to having an ACE score of 0 (Baiden et al., 2021; M. J. Brown et al.,
2022; Terry et al., 2023). Using longitudinal datasets, higher ACE scores were significantly
associated with worse cognitive outcomes in later life (Halpin et al., 2022; O’Shea et al., 2021).
Conversely, Gold et al. (2021) found that ACEs score had no impact on cognitive functioning in
later life. However, in this study, a modified version of ACEs was used and SCD was not used as
a cognitive outcome; rather three other aspects of cognition were used: verbal episodic memory,
semantic memory, and executive functioning.

12
Caregiver Well-Being
Caregiving can be a highly stressful responsibility. Due to its potential intensity and
persistency, caregiving is considered a major stressor; consequently, caregiving has been
associated with negative physical and mental health outcomes (Schulz et al., 2020). Often,
persistent and intensive caregiving tasks bring about caregiver stress, especially when social
support, social engagement and appropriate resources are unavailable and inaccessible, leading
to negative physical and mental health consequences for the caregiver (AARP and National
Alliance for Caregiving, 2020; Hoffmann & Mitchell, 1998; Liu et al., 2020; Scommegna, 2016).
Compared to non- caregivers, caregivers report having poor or fair health overall (Edwards et al.,
2020; Richardson et al., 2013) and have higher mortality, up to 63% higher (Beach et al., 2000;
Bom et al., 2019; Perkins et al., 2013; Richardson et al., 2013; Schulz & Beach, 1999). The
stressors of caregiving predisposes caregivers to stress hormones and inflammatory markers,
such as cortisol, C-reactive protein, and interlukin-6 that can heighten the risk of several
cardiovascular conditions (Richardson et al., 2013; von Känel et al., 2012). Compared to non-
caregivers, caregivers are at higher risk for chronic health conditions, including coronary heart
disease, cardiovascular disease, stroke, diabetes, hypertension, arthritis, and cancer (Ahn et al.,
2022; Mortensen et al., 2018; Sambasivam et al., 2019; Vitaliano et al., 2003; von Känel et al.,
2008). They are also more likely to be diagnosed with depression and anxiety and exhibit more
psychological distress and vulnerability compared to non-caregivers (Corà et al., 2012; del-Pino-
Casado et al., 2021; H. S. Lee et al., 2001; MacNeil et al., 2010; Penning & Wu, 2016;
Sambasivam et al., 2019; Song et al., 2011). Moreover, health and coping behaviors due to
caregiving stressors may further increase the risk of the development of these health conditions,
such as substance use (Acton, 2002; Rospenda et al., 2010).

13
It is apparent that the health consequences of ACEs are nearly identical to the health
consequences of intensive and prolonged caregiving. As more time is being devoted to providing
care, caregivers are likely less invested in their own health and self-care as a result. Caregivers
who are already in poor health or have one or more chronic health conditions risk further
complications and decline due to intensive caregiving and caregiver stress (AARP and National
Alliance for Caregiving, 2020). These outcomes can be even more adverse for caregivers who
have reported high ACEs. The additive effect of early life stressors and caregiving stressors may
exacerbate adverse health outcomes for caregivers (Kiecolt-Glaser et al., 2011), potentially
adding stress and burden to their caregiving situation.
Conceptual Framework
This dissertation will explore the impact of ACEs through a life-course perspective.
According to this perspective, individuals’ lives are shaped by a range of experiences and events
occurring throughout their lives, including childhood experiences, family dynamics, and health
issues, in particular (Elder & Rockwell, 1979). When considering the life course perspective,
circumstances in childhood, in this case, ACEs, have long-term effects on lifespan patterns of
abuse and later-life health. The additive effect of early-life stressors and caregiving stressors will
also be explored through a life course perspective.
This dissertation is also based on based on concept of the web of violence, which
describes abuse and trauma as co-occurring in different patterns across the lifespan and
recognizes the “interconnections among different forms of interpersonal violence” (Hamby &
Grych, 2013). The web of violence suggests that the outcome of experiencing or perpetrating a
specific type of violence is contingent on which other types of violence have been experienced.
According to this concept, children who are victims of one or more types of abuse and exposed
to violence are more likely to be victims of other types of abuse over the life course. However,

14
the associations between these forms of abuse may vary depending on the combinations,
features, and severity of the abuse. The web of violence offers a valuable framework for
investigating family violence by explaining abuse victimization mechanisms over the life course.
Overview of Chapters
This work will contribute to the literature by adding to the lifespan implications of ACEs
and adding new evidence of the long-term impacts of ACEs in three underexplored domains:
late-life IPV, subjective cognitive decline, and caregivers’ mental and physical health.
Chapter 2 investigates the relationship between ACEs and physical IPV in later-life using
data from the Wisconsin Longitudinal Survey (WLS), a cohort study of 10,317 randomly
selected 1957 graduates of Wisconsin high schools, along with their siblings and spouses. For
this study, I use logistic regression to determine whether or not higher ACE scores impact IPV
in later life and identify which, if any, of the individual ACEs are significantly associated with
higher risk of victimization of physical IPV at ages 60 and older.
Chapter 3 explores the relationship between ACEs and reports of subjective cognitive
decline (SCD) at ages 55 and older using data from the 2011 Behavioral Risk Surveillance
Survey (BRFSS). The BFRSS phone survey is conducted across the U.S every year, providing a
relatively representative and diverse sample. Bivariate analyses and ordered logistic regression
using nested models were run to examine associations between higher ACE scores and SCD, as
well as whether there are specific ACEs that are drivers of this relationship.
Chapter 4 explores the relationship between ACEs and caregivers’ physical and mental
health. Using BRFSS (2019 and 2020), I conducted bivariate analyses and zero-inflated negative
binomial regressions: 1) to examine associations between ACE scores and reported number of
mental and physical health days that were considered “not good” among caregivers, and 2) to
explore which of the individual ACEs are the drivers of this relationship.

15
Chapter 5 summarizes findings from Chapters 2-4 and discusses implications for
research, prevention, and intervention. Limitations and potential areas for future research are also
addressed.

16
CHAPTER 2: WHAT’S AGE GOT TO DO WITH IT? EARLY-LIFE RISK FACTORS
FOR LATE-LIFE PHYSICAL INTIMATE PARTNER VIOLENCE USING ADVERSE
CHILDHOOD EXPERIENCES
Overview
Research linking adverse childhood experiences (ACEs) and intimate partner violence
(IPV) in adulthood has focused primarily on early adulthood IPV. To add to the research on IPV
in older adults, we used the Wisconsin Longitudinal Survey (WLS) to explore whether ACEs are
associated with late-life physical IPV (age 60+). We used two logistic regression models to
determine whether higher ACE scores are associated with experiencing late-life physical IPV
and which individual ACE items were significantly associated. Individuals with an ACE score of
2 or higher had greater odds of experiencing late-life physical IPV. Two individual ACEs,
experiencing sexual abuse and witnessing IPV, increased the risk of experiencing late-life
physical IPV. Findings suggest that there is a life-long impact of being exposed to early-life
abuse and trauma. Further research can help inform the mechanisms of lifespan victimization,
which is important for formulating effective prevention and intervention strategies for older
victims.
Introduction
Intimate partner violence (IPV), defined as the “physical violence, sexual violence,
stalking and psychological aggression by a current or former intimate partner” (Breiding, Basile,
Smith, Black, & Mahendra, 2015), is widely recognized as a public health issue (CDC, 2016).
However, the focus of much of the literature and public health efforts on IPV has been on
adolescents and young adults for whom IPV is more prevalent (Harris, 1996; Rennison, 2001;
O’Donnell et al., 2002; Crockett et al., 2015). Although there is an increasing focus on IPV
among adults aged 60 and older, gaps remain, including variations in reported prevalence of IPV

17
in later life. Differences may occur because measurements to determine prevalence, age cutoffs
and time of occurrence (e.g., within the last year, since turning 60) tend to vary across studies.
For example, in the literature, although past-year occurrence is more commonly used, six-month
and 24-month IPV and lifetime IPV are also used. One of the most recent and key narrative
reviews on IPV demonstrated the variations in late-life IPV prevalence and measurements. In
this narrative review of 48 studies that included older adults who experience IPV, prevalence
ranged from 2%-28.5% within 6-24 months among those aged 45-65 and older, though some
studies included participants as young as age 18 (Pathak et al., 2019).
The lack of focus on older victims of IPV has been discussed in early literature, and
researchers have recognized theoretical and conceptual differences between IPV in later life and
elder mistreatment (EM) by a partner or spouse (Crockett et al., 2015; Harris, 1996; Phillips,
2000; Vinton, 1991, 1999). This foundational literature underscored how the intersection of
sexism and ageism further marginalizes older victims of IPV, leading to gaps in services for this
population from domestic violence services and Adult Protective Services (APS). A paternalistic
approach to older victims of abuse has also been criticized (Wydall, 2021).
Building on and adding to these studies, researchers have sought to understand the lived
experiences of older IPV victims. A few late-life IPV studies have been qualitative as well, using
focus groups, case studies, and semi-structured interviews to better understand older victims’
experiences. Studies (Band-Winterstein, 2012; LeBlanc and Weeks, 2011; Zink, Regan,
Jacobson, & Pabst, 2003) focused on internal and external barriers to seeking help, engagement
with community partners and healthcare providers, health impacts and consequences, risk
factors, and reasons for remaining in abusive relationships. Findings indicate that older women
who are victims of IPV and who remain in long-term abusive relationships identified

18
commitment to and care of family members; fear of loss of financial resources; societal
expectations; health challenges; fear of loneliness; and ageism from healthcare professionals, the
justice system, and family violence services.
Late-life IPV can be a new phenomenon that occurs in a relationship, or it can be ongoing
or intermittent IPV that has occurred over time. Some studies suggest that much of late-life IPV
occurrences are patterns of abuse grown old, whether the abuse exists in one relationship over
time or in a series of relationships. Podnieks (1993) found that victims, aged 65 and older who
reported physical violence and /or verbal aggression by their spouses described it as occurring
within troubled marriages. A study by Harris (1996) indicated that couples aged 60 and over who
reported violence in their relationships had experienced IPV, which spanned from 10 to 25 years.
In a more recent study, older women described experiences of IPV perpetuated by their husbands
of over 30 years (Band-Winterstein, 2015). A case study of an older Chinese woman described
over 35 years of verbal, physical, and economic abuse, coercive control, and isolation from her
husband, with increasing frequency and severity of physical abuse, including being attacked
with a knife. Despite various attempts to leave the relationship, she still ultimately returned to
her partner, as she had no financial resources or social network to support herself and her
children (Cheung et al., 2015).
Risk Factors of IPV Victimization in Later Life
Many of the risk factors of IPV in early adulthood are the same for late-life IPV, such as
low income, early exposure to family violence, social isolation, partner’s substance abuse, being
a part of a culture with strict gender roles, disability, poor health, and a wide age range between
partners (Umubyeyi et al., 2014; Yon et al., 2014; Abramsky et al., 2019; Hoppe, 2020).
However, various factors associated with later life, such as spousal retirement, social isolation,
and caregiving, may increase risks for IPV for older women. Retirement is a unique risk factor

19
for older women in that it is a transition that largely occurs in midlife or later life. Abuse may
begin or intensify shortly after retirement of the perpetrator, due to more time being spent
together and the partner being able to exert more control (Montminy, 2005; Wydall, 2021). The
retirement of a perpetrator may lead to a loss of identity tied to a career, and a reduction in
income may contribute to a perceived loss of control, which may exacerbate ongoing abusive
behavior. Work may have served as a protective factor or buffer from IPV. The loss of social
networks and financial flexibility that comes with retirement may isolate the victim even more
and causes more isolation and economic dependency on the perpetrator (Livingston et al., 2021).
Caregiving can also be a distinctive risk factor for older women due to feelings of obligation to
remain in the relationship as their partner's health declines. Research by Wydall (2021) suggests
that abuse may become worse if the perpetrating partner becomes ill, is preparing for surgery, or
has been diagnosed with a terminal illness, often due to the partner’s perceived loss of control.
One of the most prominent risk factors of IPV is a history of family violence and
victimization of childhood abuse. Research indicates that late-life IPV is a multigenerational
occurrence (Finfgeld-Connett, 2014), and many older women who are victims of IPV have
revealed coming from families in which abuse was common, recalling events of physical and
sexual by multiple family members during childhood and teenage years, as well as these
incidents happening to their own children (Finfgeld-Connett, 2014; Grunfeld et al., 1996;
Lazenbatt et al., 2013). Women who experienced childhood sexual abuse by family or household
members were twice as likely to experience subsequent sexual and physical abuse in adulthood
(J. E. Barnes et al., 2009) Physical abuse, sexual abuse, and witnessing IPV during childhood can
lead to a cycle of IPV victimization or perpetration in adulthood (Anda, Felitti, Brown, et al.,
2006; Whitfield et al., 2003).

20
Childhood Adversity and Elder Mistreatment
We know that childhood maltreatment and adversity have long-term impacts on health
and increases abuse victimization over the life course; in particular, childhood adversity has been
linked to EM victimization (Fulmer et al., 2005; Policastro & Finn, 2015). One of the earliest
studies applying a life course perspective to EM found a history of childhood abuse among
participants who reported experiencing abuse in since age 55 (McDonald & Thomas, 2013).
Kong and Easton (2018) found that particularly childhood sexual abuse and emotional neglect
increases the risk of EM victimization.
We are beginning to learn about the mechanisms through which childhood maltreatment
and adversity increase the risk of EM, with self-reported health in midlife, depressive symptoms
and cognitive functioning serving as pathways to vulnerability to EM (Easton & Kong, 2021).
Although EM can inform how we can think about this, some research suggests that late-life IPV
is a distinctive phenomenon that differs from EM by spouses and partners. While cases of partner
and spousal abuse at older ages may be categorized as EM rather than IPV or domestic violence
(Vinton, 1999; LeBlanc & Weeks, 2013), categorizing late-life IPV as a form of EM focuses
primarily on frailty, capacity, and vulnerability related to cognitive and physical functioning.
Using an EM framework may, therefore, disregard the distinctive experiences of older women
who are victims of IPV. EM may also tend to emphasize abuse perpetrated in a caregiving
context. The EM paradigm may also ignore gendered violence and older women who are victims
of IPV who may not have low physical and cognitive capacity (Straka & Montminy, 2006;
Weeks & LeBlanc, 2011; Wilke & Vinton, 2005).
Physical IPV in Older Victims
Due to the intersection of gender and age, the impact of physical IPV, may be more
detrimental to older victims than younger victims. Although both younger and older women

21
experience adverse health outcomes due to IPV victimization, older women are at greater risk for
exacerbating health problems and physical injury due to pre-existing age-related health
conditions and increased frailty, such as arthritis, chronic pain, digestive problems, respiratory
issues, hypertension, osteoporosis, and type 2 diabetes (Fisher & Regan, 2006; Loxton et al.,
2006; Stöckl & Penhale, 2015). These health conditions may make it more difficult for the
victim to leave the abusive relationship, access and utilize domestic violence resources, and even
when access and utilization are attempted, older adults may face additional barriers. Many
domestic violence resources are not designed with consideration to age-inclusivity, including
physical mobility and healthcare supports (Beaulaurier et al., 2007; Lazenbatt et al., 2013;
LeBlanc & Weeks, 2013; Weeks & LeBlanc, 2011; Shiel, 2016).
Much like IPV in younger women, prevalence varies across IPV types; some specific
types of abuse are reported more often. Psychological abuse may be more common than physical
abuse in both younger and older women who report experiencing IPV (Band-Winterstein &
Eisikovits, 2009; Roberto, 2016). However, among older women, physical IPV seems to be less
common (Vinton, 1991; Wilke & Vinton, 2005). The extent to which physical IPV is truly less
common in later life versus more likely to be underreported is unclear. It is possible that physical
IPV decreases and other forms of abuse, particularly psychological abuse, increase in later life
(Sawin & Parker, 2011). However, a recent longitudinal study found that physical abuse actually
does not decrease with age; in fact, physical abuse may worsen if the perpetrator becomes ill,
whether terminally or temporarily (Wydall, 2021).
The Web of Violence
Our conceptual framework for this study is based on the web of violence, which
describes abuse and trauma as co-occurring in different patterns across the lifespan and
recognizes the “interconnections among different forms of interpersonal violence” (Hamby &

22
Grych, 2013; Hamby and Grych, 2013, p.67). Additionally, the concept suggests the outcome of
experiencing or perpetrating a specific type of violence is contingent on which other types of
violence have been experienced. According to this concept, youth who are victims of one or
more types of abuse and exposed to violence are more likely to be victims of other types of abuse
over the life course. However, the associations between these forms of abuse may vary
depending on the combinations, features, and severity of the abuse. The web of violence offers a
valuable framework for investigating family violence by explaining abuse victimization
mechanisms over the life course. In particular, we explored the associations between particular
types of violence and traumatic events, either occurring separately or co-occurring in early life,
and the occurrence of physical IPV after age 60. Based on the web of violence framework, we
expect that those exposed to abuse and traumatic events in early life have a higher risk of
physical IPV in later life.
ACEs & IPV
To better understand the possible impact of child abuse and trauma on late-life IPV, we
drew from items in the Adverse Childhood Experiences (ACEs) Study (Felitti et al., 1998a).
Developed by Kaiser Permanente in partnership with the Centers for Disease Control (CDC) to
investigate the impact of childhood trauma on health and well-being in adulthood, ACE scores
measure the number of traumatic events, from one to ten, that individuals experienced before age
18. The original study found that over 60% of participants experienced at least one ACE, and
12.5% experienced four or more ACEs. Substance abuse in the household was the most prevalent
ACE; more than 25% of respondents reported living with someone who was an alcoholic or with
someone who used street drugs before age 18 (CDC, 2022; Felitti et al., 1998a). One major
subsequent finding revealed that exposure to physical and sexual abuse and witnessing IPV in
childhood increases the risk of IPV victimization in adulthood (Anda et al., 2006).

23
Although there is extensive literature on the relationship of ACEs to health outcomes in
later life, there is little research linking ACEs to IPV in older age. Of the nearly 80 peer-
reviewed articles that have been published using ACEs (CDC, 2016), the few that have focused
on IPV have shown that witnessing IPV in childhood or experiencing physical or sexual abuse in
childhood increased the risk of IPV victimization by a factor of 3.5 for women (Anda et al. 2006;
Dube, 2002; Whitfield et al., 2003). Most of these studies focus on IPV in early adulthood rather
than IPV extending into later life. Among the few studies focusing on links between abuse in
early life and later life, findings suggest that experiencing early-life trauma and childhood sexual
abuse increase the risk of experiencing physical abuse in late life (Kong & Easton, 2018;
Policastro and Finn, 2015). Though these findings suggest that early-life abuse is linked to abuse
in late life, we are not aware of any studies exploring the relationship between ACEs and late-life
IPV. In this study, we aim to investigate the extent to which ACE scores and specific ACE items
are associated with being a victim of IPV at ages 60 and older.
Methods
We used data from the Wisconsin Longitudinal Study (WLS) to explore the extent to
which ACE scores and individual ACEs are related to late-life physical IPV. Based on previous
literature in early-adulthood IPV, we hypothesized that 1) higher ACE scores would be
significantly associated with late-life physical IPV, 2) specific individual items--physical and
sexual abuse and witnessing IPV—would be significantly associated with late-life physical IPV,
and 3) women would have higher rates of victimization compared to men, as statistics show that
women are more likely than men to be victims of IPV (Devries et al., 2013; Umubyeyi et al.,
2014; Centers for Disease Control, 2021b; World Health Organization, 2021).

24
Dataset
The WLS is a longitudinal cohort study of 10,317 randomly selected Wisconsin high
school graduates in 1957, along with a random sample of their siblings and spouses through
phone, mail, and in-person surveys. The study has collected information about the life course
from late adolescence through age 73. It is one of the few, if not only, studies that asks about
early-life adversity and late-life IPV longitudinally. The sample was majority non-Hispanic
White and had at least a high school education. Data were collected from the graduates at six
different time points and from siblings at four different time points between 1957 and 2011.
Spouses of both graduates and siblings were interviewed in 2004 & 2005. These data were
collected to examine the influence of early life experiences, such as family background, income,
and adolescent characteristics on mid- and late-life outcomes including, but not limited to, abuse,
psychological well-being, cognition, physical health, and mortality.
Variables
We used composite ACE scores and individual ACE items as independent variables in
the analysis. Similar to the methods of previous research (Kong & Easton, 2018; Easton & Kong,
2020), we used the WLS to find questions comparable to the ten questions in the most current
version of the ACE Questionnaire (see Supplemental Table 2.1. for selected questions). In the
2003-2005 wave of WLS, questions in the “Social Relationships”, “Dealing with Problems”, and
“Alcohol” modules covered early traumatic experiences until age 18. For parental separation,
respondents were asked whether they lived with both parents up until age 18 in the 1975 wave in
the “Childhood: Parent Information and Job Aspirations” module. WLS items on physical,
psychological, and sexual abuse, neglect, and witnessing IPV were used to measure occurrence
and extent of abuse.

25
Using the original ACE study’s approach (Felitti, et al., 1998) in developing the ACE
questionnaire, we recoded these WLS questions into dichotomous variables to create one
variable for eight out of a possible ten ACE items. The remaining two ACE questions,
incarceration of a household member and physical neglect, were not available in the WLS for
analysis. Because the vast majority of the WLS sample’s ACE scores fell in the 0-2 range, scores
were categorized into three groups: those who had an ACE score of 0, those with an ACE score
of 1, and those who had an ACE score of 2 or more.
Experiencing late-life physical IPV, the dependent variable, included anyone who had
experienced IPV at aged 60 and older. Though some studies use earlier ages, such as 45 and 50,
to define late-life IPV, we are using age 60 as a cutoff from a gerontological perspective, as
gerontological literature typically begin age cutoffs at ages 60 or 65. To capture late-life physical
IPV victimization, we used the questions “How old were you the first or only time your spouse
or partner treated you in a way that some would think of as physical abuse?” and “How old were
you the last time your spouse or partner treated you in a way that some would think of as
physical abuse?” from the 2003-2005 and 2011 waves of the “Dealing with Problems” module.
We combined these questions and recoded them as one dichotomous variable for whether
physical IPV had occurred since age 60. Yoon et al. (2020) has been the only study so far that we
are aware of that has used physical IPV variables from WLS, however; the question “Has your
spouse or partner ever treated you in a way that some would think of as physical abuse?” was
used as a dependent variable for physical IPV instead. Because this study focuses particularly on
abuse that has occurred at ages 60 and older, we used age at first and most recent occurrence as
opposed to any occurrence ever to capture only those who have experienced physical IPV since
age 60 rather than older adults who have experienced physical IPV at any point over the life

26
course. Our selection of questions is more appropriate in capturing physical IPV that occurs
specifically in later life. Referring to Pathak et al. (2019), many studies on IPV among older
women, use IPV occurrence in the past 6, 12, or 24 months to measure prevalence, and most
WLS respondents turned 65 in 2004, so using first and last occurrence may be more accurate in
determining physical IPV that occurs in later life.
Gender, education, marital status between 2004 and 2012 and self-reported general health
in 2004 were control variables. Race data were not publicly accessible and, therefore, were not
used in this analysis; however, nearly the entire sample of respondents in the WLS were non-
Hispanic white.
Analytic Strategy
Descriptive statistics were calculated for demographics, ACE scores, each of the
individual ACEs, and late-life physical IPV. In the first of our two logistic regressions, we used
the three categories of ACE scores (0, 1, and 2) to determine whether experiencing ACEs was
associated with experiencing physical IPV in late life. The second model identified which ACEs
were associated with late-life physical IPV occurrence. After accounting for respondents who
died before 2003, when the abuse and household dysfunction questions in the WLS were asked,
the original dataset had varying amounts of missing data (28-33%) for each independent variable
Problematic missing patterns were investigated by running crosstabs of each independent
variables’ missingness with the ACE variables. Based on the absence of significant associations,
we did not have cause to reject the assumption that the data were missing at random. Therefore,
we used full maximum information likelihood (FIML) using Mplus to run all analyses in a way
that accounted for the high levels of missing data. FIML estimates regression parameters using
all available, non-missing data without imposing case-wise deletion, generating unbiased
parameter estimates and standard errors (Enders & Bandalos, 2001).

27
Results
Descriptive statistics are presented in Table 2.1. Women made up over 53% of the
sample. Based on the sampling method of the WLS, all respondents had at least a high school
diploma. Over 87% were either married or partnered between 2004 and 2011. Most of the
respondents reported that they were in good health in 2004;nearly 38% reported that they were in
good health, 28% reported that they were in very good health, and over 23% reported that they
were in excellent health. Fair health was reported by 8% and poor health by 2.3% of the sample.
The majority (63.4%) reported no ACEs; 24.0% experienced 1 ACE, and 12.7 % had an ACE
score of 2 or higher. Substance abuse in the household was the most reported ACE at 20.9%.
Emotional neglect and parental separation followed, reported by 12.2% and 10.1%, respectively.
Roughly 5% reported experiencing late-life physical IPV.
The logistic regression of physical IPV and ACE scores is presented in Table 2.2.
Compared to those with an ACE score of 0, those with an ACE score of 2 or more had higher
odds of experiencing physical IPV in later life (odds ratio, [OR]= 1.98, p <0.001). Experiencing
1 ACE was not significantly different from an ACE score of 0 in this analysis. As shown in
Table 2.3, two individual ACEs, sexual abuse and witnessing IPV, were strongly associated with
IPV in later life. Among those who reported childhood sexual abuse compared to those who did
not, the odds of experiencing late-life physical IPV increased by 2.06 (p <0.001). Similarly,
witnessing IPV in childhood was associated with increased odds of experiencing physical IPV in
later life compared to those who did not witness IPV (OR= 2.09, p <0.001). Compared to men,
women had a greater risk of experiencing physical IPV at age 60 and older compared to men in
both models (Model 1: OR= 2.48, p <0.001; Model 2: OR= 2.37, p <0.001). The remaining
ACEs were not found to be statistically significant in this analysis.

28
Because patterns of domestic violence are often gendered and women are more likely
than men to be victims of IPV, certain types of abuse (e.g., sexual abuse), and repeated abuse
over the life course (Black, et al., 2011; Walby & Towers, 2017, Walby & Towers, 2018), we
tested for interaction effects between gender and the composite ACE scores, as well as gender
and the individual ACEs. In both models, we found that gender had no impact of the gender by
ACE interaction term on the occurrence of late-life physical IPV.
Discussion
This study investigated the relationship between ACEs and physical late-life IPV using
data from the WLS. Based on previous research, we hypothesized that 1) higher ACE scores
were significantly associated with late-life physical IPV, 2) individual ACEs--physical abuse,
sexual abuse, and witnessing IPV were significantly associated with late-life physical IPV, and
3) women had higher victimization rates than men.
Using logistic regression models to associate ACEs with physical IPV in later life,
experiencing two or more ACEs was associated with a greater risk of experiencing late-life
physical IPV compared to experiencing no ACEs. Exploring the individual ACEs, sexual abuse,
and witnessing IPV in childhood were strongly related to physical IPV occurring in later life.
Although women had higher risk of experiencing of IPV in later life, gender did not have a
significant moderating effect on experiencing physical IPV at ages 60 or older. However, this
should not conclude that late-life physical IPV isn’t gendered, but that further research outside
the scope of this analysis may be needed. Though findings supported our other hypotheses, we
did not find a significant relationship between physical abuse in childhood and late-life physical
IPV. Over 5% of respondents aged 60 and over reported physical IPV victimization, which is
higher than the 2% that has been previously reported (Rennison & Rand, 2003). This possibly
indicates that prevalence of IPV in late life is more common than we assume; the low percentage

29
of IPV occurrence among older adults may be due to underreporting rather than IPV not
occurring in old age (Harris, 1996).
These findings support previous research that show long-term impact of childhood
experiences on subsequent family violence. This study adds new information by indicating that
the impact persists throughout the life course (e.g., being exposed to trauma or a victim of abuse
in early life is associated with a higher risk of being a victim of abuse in late life).
For the ACEs not statistically significant, especially physical abuse, it is possible that
they are associated with other types of abuse that are outside the scope of this study. It is relevant
to note that forms of IPV can change over the life course (Sawin & Parker, 2011). Roberto and
McCann (2018) found that most of the older women in their sample reported experiencing
psychological abuse after experiencing other types of abuse in early life or in previous marriages.
This approach builds on a life course perspective, which holds that events that occurred
in early life influence future behaviors, decisions, and outcomes in mid and late life (Gisele &
Elder, 1998). According to Gisele and Elder (1998, p.22), life course is defined as "a sequence of
socially defined events and roles that the individual enacts over time." This perspective coupled
with the web of violence framework suggest that those working in programs designed to address
and prevent EM should consider risk factors throughout the life course.
Limitations
Although this study uses data collected over multiple waves to report on an area that has
received very little attention, it is important to note that the accounts of abuse and household
dysfunction were retrospective self-reports; that is, they were not measured during the early
waves of the WLS. Moreover, they are proxies for direct ACEs measures and represent 8 of the
10 ACEs. Despite this limitation, retrospective and self-reporting of abuse are standard in
collecting data about historical life events. Optimally, longitudinal data or retrospective data can

30
be used to capture the negative long-term and cumulative effects of violence and abuse in the
lives of older adults (Roberto et al., 2013).
Comparing our results to the original CDC/Kaiser Permanente study, fewer ACEs were
identified in our sample compared. Whereas 12.7% of our sample reported two or more ACEs;
40% of the CDC/Kaiser Permanente sample reported two or more ACEs (Felitti et al., 1998a). It
should be noted that the WLS respondents are fairly homogeneous non-Hispanic white, middle-
class sample and older adults, and non-Hispanic Whites tend to report fewer ACEs than younger
people and racial and ethnic minorities (Merrick et al., 2018)..
Implications
Despite the limitations, this study is one of the few that we are aware of to use large
population data to examine early-life associations of IPV in later life. The WLS, which has over
10,000 respondents, is one of the few datasets that has collected longitudinal data on adolescents
through old age. The original ACE study and subsequent studies using ACEs and ACE scores
have linked early-life abuse and trauma to chronic health conditions, behavioral risks, and risk of
IPV in adulthood. To our knowledge, this study is one of the few to examine the links of ACEs
with the extension of IPV into later-life and builds on Kong and Easton’s (2018) study linking
ACEs to EM.
Further research should focus on examining how ACE scores and ACEs influence the
likelihood of experiencing other types of IPV in later life. Replicating this study with different
datasets would be beneficial in informing reliability, generalizability, and causality. Using data
with racial and ethnic diversity would allow for investigating possible emerging racial and ethnic
differences in ACE frequencies, as well as differences in the risk of experiencing physical IPV in
late life. Future research should also investigate the progression of early-life abuse leading to
IPV victimization and to identify pathways of early-life abuse to late-life IPV to inform

31
prevention. Additional studies on the relationship of ACEs and late-life IPV can help discern the
paths and inform the mechanisms of victimization over the life course, which is important for
creating effective prevention and intervention strategies for older victims. Though many
practitioners utilize the ACE questionnaire for assessing and flagging family violence and
adversity, it can be valuable for them to also view ACEs in a life course context; we will have a
substantial older population going forward that may have ACEs, so there may be an increased
need for risk assessment for later-life abuse.

32
Table 2.1. Sample Characteristics
(N= 10,317)

n Percent
Control Variables
Female 4852 53.1
Education
a

Some College 1165 16.0
Bachelor’s Degree 982 13.5
Graduate/Professional Degree 999 10.9
Married/Partnered 6490 87.2
Self-Reported Health (2004)

Poor 176 2.28
Fair 609 7.89
Good 2161 28.0
Very Good 2929 38.0
Excellent 1843 23.9
Independent Variables
ACE Score
0 5524 63.4
1 2092 24.0
2+ 1103 12.7
Adverse Childhood Experiences (ACEs)

Psychological Abuse 249 3.7
Physical Abuse 233 3.7
Sexual Abuse 376 5.6
Neglect 814 12.2
Parental Separation/Divorce 868 10.0
Witness IPV 514 7.7
Alcohol/Drug Use in Household 1580 20.9
Mental Illness in Household 456 6.8
Dependent Variable
Physical IPV 380 5.4
a. All respondents had at least a high school degree

33
Table 2.2. Logistic Regression of Physical IPV (ACE Scores)
a

Odds Ratio (95% CI)
Female 2.48 (1.94, 3.17)***
Education
a

Some College 1.24 (0.92, 1.67)
Bachelor’s Degree 1.14 (0.82, 1.60)
Graduate/Professional Degree 1.31 (0.95, 1.83)
Married/Partnered 0.52 (0.41, 0.68)***
Self-reported Health

0.88 (0.79, 0.98)*
ACE Score 1 1.21 (0.94, 1.56)
ACE Score 2+ 1.98 (1.52, 2.56)***
a. Reference Group: ACE Score of 0; High school diploma
*p<.05, **p<0.01, ***p<0.001

34
Table 2.3. Logistic Regression of Physical IPV (Individual ACEs)
a

Odds Ratio (95% CI)
Female 2.37 (1.85, 3.03)***
Education

Some College 1.23 (0.91, 1.65)
Bachelor’s Degree 1.09 (0.78, 1.53)
Graduate/Professional Degree 1.27 (0.91, 1.78)
Married/Partnered 0.53 (0.41, 0.68)***
Self-reported Health 0.88 (0.79, 0.98)*
Adverse Childhood Experiences (ACEs)

Psychological Abuse 1.46 (0.84, 2.55)
Physical Abuse 1.23 (0.62, 2.05)
Sexual Abuse 2.06 (1.46, 2.89)***
Neglect 1.02 (0.72, 1.45)
Parental Separation/Divorce 0.83 (0.58, 1.20)
Witness IPV 2.09 (1.45, 3.01)***
Substance Abuse in Household 1.07 (0.82, 1.39)
Mental Illness in Household 1.44 (0.98, 2.11)
a. Reference Group: High school diploma
*p<.05, **p<0.01, ***p<0.001

35
Supplemental Table 2.1. WLS Questions Used for Variables

Variables WLS Question WLS Module
Physical abuse
Up-until you were 18, to what extent did your father slap, shove
or throw things at you?
2003-2005
Graduates
"Social
Relationships"

Up-until you were 18, to what extent did your father treat you in
a way that you would now consider physical abuse?
2003-2005
Graduates
"Social
Relationships"

Up-until you were 18, to what extent did your mother slap,
shove or throw things at you?
2003-2005
Graduates
"Social
Relationships"

Up-until you were 18, to what extent did your mother treat you
in a way that you would now consider physical abuse?
2003-2005
Graduates
"Social
Relationships"
Psychological/Emotional
abuse Up-until you were 18, to what extent did your father insult or
swear at you?
2003-2005
Graduates
"Social
Relationships"

Up-until you were 18, to what extent did your mother insult or
swear at you?
2003-2005
Graduates
"Social
Relationships"
Sexual abuse
Up-until you were 18, to what extent did your father have oral,
anal, or vaginal sex with you against your wishes?
2003-2005
Graduates
"Social
Relationships"

Up-until you were 18, to what extent did your father use
physical violence during an unwanted sexual act with you?
2003-2005
Graduates
"Social
Relationships"

Up-until you were 18, to what extent did your father treat you in
a way that you would now consider sexual abuse?
2003-2005
Graduates
"Social
Relationships"

Up-until you were 18, to what extent did any other person have
oral, anal, or vaginal sex with you against your wishes?
2003-2005
Graduates
"Social
Relationships"

Up-until you were 18, to what extent did any other person use
physical violence during an unwanted sexual act with you?
2003-2005
Graduates
"Social
Relationships"

Up-until you were 18, to what extent did any other person treat
you in a way that you would now consider sexual abuse?
2003-2005
Graduates
"Social
Relationships"
Emotional neglect
Up-until you were 18, how often did you know that there was
someone to take care of you and protect you?
2003-2005
Graduates

36
"Social
Relationships"
Parental
separation/divorce
Did you live with both parents most of time up until 1957?
1975 Graduates
"Parent
Information and
Job Aspirations"
Witnessed IPV
Up-until you were 18, how often did you see a parent or one of
your brothers or sisters get beaten at home?
2003-2005
Graduates
"Social
Relationships"
Substance abuse in
household
Have your parents ever drank or used drugs so much or so
regularly it caused problems for the family?
2003-2005
Graduates
"Dealing with
Problems"

When you were growing up, that is during your first 18 years,
did you live with anyone who was a problem drinker or
alcoholic?
2003-2005
Graduates
"Alcohol"
Mental illness in the
household
Did you grow up with any brothers or sisters who had a
disability or mental illness?
2003-2005
Graduates
"Siblings"
Spousal physical abuse
How old were you the first or only time your spouse, or
romantic partner, treated you in a way that some would think of
as physical abuse?
2003-2005/2011
Graduates
"Dealing with
Problems"

How old were you the last time your spouse, or romantic
partner, treated you in a way that some would think of as
physical abuse?
2003-2005/2011
Graduates
"Dealing with
Problems"

37
CHAPTER 3: THE CONTINUING IMPACT OF CHILDHOOD ADVERSITY ACROSS
THE LIFESPAN: ASSESSING THE RELATIONSHIP BETWEEN ACES AND
SUBJECTIVE COGNITIVE DECLINE
Overview
Subjective cognitive decline (SCD) can be an indicator of future diagnosis of
Alzheimer’s disease and other related dementias (ADRD). Existing research found that
childhood adversity is associated with increased risk of cognitive decline and impairment. This
study investigates the Adverse Childhood Experience (ACE) -SCD relationship, building on
growing literature in this area.
Data from the 2011 Behavioral Risk Factor Surveillance System (BRFSS) were analyzed
using logistic regressions of ACEs and ACE score vs. SCD. Seven ACE questions were asked of
respondents in three states (n=5,898 aged 55 and older), including physical abuse, emotional
abuse, sexual abuse, parental separation/divorce, witnessed intimate partner violence, substance
abuse in the household, and mental illness in the household. Among the total sample, 50.6%
reported 0 ACEs, 24.8% reported 1 ACE, and 5.7% reported 4+ ACEs. Psychological abuse was
the most frequently reported (23.8%), followed by substance abuse in the household (22.8%).
SCD was reported by 14.5% of respondents. Among those with SCD, 34.6 reported 0 ACEs,
28.7 reported 1 ACE, and 10.8% reported 4 or more ACEs, with psychological abuse (34.9%)
and substance abuse in the household (30.5%) also being the most frequently reported ACEs.
Logistic regression results showed gradual increases with each additional ACE reported, up to
4.29 times the odds of SCD for 4+ ACEs compared to 0 ACEs. In a separate model of individual
ACEs, those reporting physical abuse and sexual abuse had the greatest odds of SCD.
Findings demonstrate the association between childhood adversity and SCD, with the
experience of child maltreatment placing individuals at higher risk. Although survey responses

38
for individuals with cognitive impairment must be treated with caution, the large increases in
odds of SCD with higher ACEs underscores the impact of these experiences across the lifespan.
These results indicate the importance of considering a lifespan perspective for work on
childhood adversity and family violence, as well as the importance of considering ACEs and
early-life experiences when considering risk for cognitive impairment.
Introduction
Nearly 5.7 million older adults in the U.S. have Alzheimer’s disease or related dementias
(ADRD). Due to the aging of the Baby Boomer generation, ADRD prevalence has been rapidly
increasing; it is estimated that the number of ADRD cases will be 13.8 million by 2050
(Herbert, Wueve, Scherr, & Evans, 2013). ADRD is the sixth leading cause of death in the U.S.,
and deaths from Alzheimer’s disease increased by 123% between 2000 and 2013 (Alzheimer's
Association, 2020). Subjective cognitive decline (SCD) is self-perceived increasing confusion or
memory loss (Alzheimer’s Association, 2020; Center for Disease Control, 2019). SCD is often
the earliest sign of Alzheimer’s disease and can be used to identify those who may be at risk for
ADRD. SCD is often used as a criterion in distinguishing normal cognitive aging from
pathological cognitive aging (e.g., dementia) (Parfenov et al., 2020; Reid & MacLullich, 2006;
Wang et al., 2004). Over 11% of adults aged 45 and older report SCD (Alzheimer’s Association,
2020; Centers for Disease Control, 2019). In the 2015 and 2016 BRFSS samples, of those who
reported SCD in the three states included in this study, over 11% of respondents reported SCD in
California; 11% reported SCD in Washington, and 10% reported SCD in Wisconsin (Taylor et
al., 2018).
SCD is associated with several adverse health conditions and outcomes (Anderson, 2015;
Cosentino et al., 2018). People aged 65 and older with SCD are more likely to have two or more
chronic health conditions compared those who are aged 65 and older without SCD (Taylor et al.,

39
2020). Compared to individuals who do not report SCD, those with SCD have a higher
prevalence of heart attack, coronary heart disease, stroke, diabetes, and higher mortality risk.
Those with SCD also have poorer mental and self-rated health than those who did not report
SCD (Gupta, 2021; Hao et al., 2017; Luck et al., 2015; Taylor et al., 2018, 2020).
Though the risk factors of SCD have been widely discussed (J. Lee et al., 2020; Schliep
et al., 2022; Wen et al., 2021), one risk factor that is increasingly being highlighted in recent
literature is childhood adversity. Often these studies have used the ACE questionnaire or
modifications of the questionnaire to measure and collect information on past adversity and
abuse. ACEs are traumatic events experienced before age 18. The original ACE study was
developed and implemented by Kaiser Permanente and the Centers for Disease Control and
Prevention (CDC), collecting two waves of over 17,000 members of the of Kaiser Permanente’s
Southern California Health Maintenance Organization (HMO) from 1995 to 1997. The entire
sample was mostly white, older (age 50 and older), and had a college degree. The study
investigated the impact of childhood adversity on health and well-being in adulthood (Felitti, et
al., 1998). A ten-item questionnaire asked about events of physical, sexual, and psychological
abuse, neglect, witnessing intimate partner violence, parental divorce or separation, substance
abuse, mental illness, and imprisonment (of a household member). From these categories, an
ACE score was developed, measuring the number of traumatic events that respondents
experienced before age 18 (Felitti, et al., 1998).
Results showed that 52% of participants experienced at least one ACE, and 6.2%
experienced four or more ACEs. Substance abuse in the household was the most prevalent ACE;
more than 25% reported living with someone who was an alcoholic or with someone who used
street drugs (Felitti et al., 1998). In 2009, the CDC added an ACE module to the Behavioral Risk

40
Factor Surveillance System (BRFSS) and has continued to collect ACE data from U.S. states.
From the 2011 to 2014 BRFSS data, more than 23% of study participants reported one ACE, and
more than 15% reported four or more ACEs. Psychological abuse was the most reported ACE, at
34%. More than 60% reported experiencing at least one ACE (Merrick et al., 2018).
Exposure to ACEs have been linked to more than 40 negative health conditions,
including poor mental health, substance use disorder, adverse health behaviors, chronic physical
disease, insufficient sleep, and shortened life span (Campbell et al., 2016; Chapman et al., 2013;
Danese et al., 2009; Vig et al., 2020). A meta-analysis of 37 studies that examined 23 health
outcomes found that individuals who reported more than 4 ACEs had higher odds of cancer,
heart disease, respiratory disease, mental illness, and poor self-rated health overall (Hughes et al.,
2017).
Though the literature exploring the links between childhood adversity and cognitive
decline and impairment in later life is growing, and most show associations between childhood
adversity and cognitive functioning; however, variables to measure childhood adversity and
cognitive status differ by study. Consequently, the findings vary regarding the extent to which
individual ACEs and the composite number of ACEs are associated with cognitive functioning.
Earlier studies found significant associations between childhood adversity and cognitive
functioning in later-life using different measures of childhood adversity (e.g., family
socioeconomic status, family environment, death of parents, frequent relocation) and more
objective assessments of cognitive functioning. (L. L. Barnes et al., 2012; Everson-Rose et al.,
2003; Fors et al., 2009; Kaplan et al., 2001; Ritchie et al., 2011). Other studies have linked
childhood adversity and cognitive functioning using the Childhood Trauma Questionnaire
(Grainger et al., 2020; Radford et al., 2017) and other adverse experiences such as experiencing

41
war or the death of a parent or important person (Korten et al., 2014). The most recent studies
began to specifically use items from the ACE questionnaire to measure childhood adversity but
still varied in cognitive outcome measures, including SCD, cognitive impairment, dementia, and
Alzheimer’s disease. One study that focused on Japanese older adults showed that experiencing
three or more ACEs increases the risk of developing dementia (Tani et al., 2020). A key finding
came from a report by the Center for Youth Wellness (2020), showing that those with four or
more ACEs were over 11 times as likely to be diagnosed with Alzheimer’s disease in later life.
The most recent studies have found associations between ACEs and SCD using large national
datasets, such as the Behavioral Risk Surveillance Survey (BRFSS). Findings indicate that
having ACE scores from 3 to 4 resulted in odds of reporting SCD that were 2-3 as likely as
having an ACE score of 0 (Baiden et al., 2021; M. J. Brown et al., 2022; Terry et al., 2023).
Using longitudinal datasets, higher ACE scores were significantly associated with worse
cognitive outcomes in later life (Halpin et al., 2022; O’Shea et al., 2021). Conversely, Gold et al.
(2021) found that ACEs score had no impact on cognitive functioning in later life; however, a
modified version of ACEs was used and SCD was not used as a cognitive outcome, rather three
other aspects of cognition were used: verbal episodic memory, semantic memory, and executive
functioning.
This study is based on life course theory, a framework for understanding how various
factors, such as social, psychological, and historical contexts, influence individuals’ life courses
(Elder, 1998). It emphasizes the importance of studying life trajectories and how they affect
individuals' experiences, opportunities, and outcomes. According to this theory, individuals are
shaped by a range of experiences, particularly in this case, early childhood experiences. In turn,
these experiences influence individuals' development and future trajectories. Therefore, it is

42
probable that adverse experiences that occurred in early life impacts health and development in
later life.
The purpose of this study is to investigate the association of individual ACEs and ACE
scores with subjective cognitive decline. Our hypotheses are 1) those who report multiple ACEs
(4+) are significantly more likely to report SCD, and 2) that occurrences of psychological,
physical, and sexual abuse significantly drive the association between ACEs and SCD.
Methods
Landline and cellphone data from the cognitive decline and ACEs modules in the 2011
Behavioral Risk Factor Surveillance Survey (BRFSS) were used for the analyses. BRFSS is a
collaborative project between the Centers for Disease Control and individual U.S states and
territories. Data collection, which began in 1984,collects ongoing demographic, chronic health,
and preventative health data from adults aged 18 and older from participating states each year.
Our sample included adults aged 55 and older from California, Washington, and Wisconsin (N=
5,898), as these were the states that reported data for both modules.
Measures
We used subjective cognitive decline (SCD) as the dependent variable for the analysis.
The cognitive decline module in BRFSS measured SCD by whether one has experienced
memory loss that has been increasing or getting worse within the last 12 months. We used the
individual ACEs and ACE score as primary independent variables of interest. The seven
individual ACEs included in the ACEs module were physical abuse, emotional abuse, sexual
abuse, separation or divorce of parents, witnessing intimate partner violence (IPV), substance
abuse in the household, and mental illness of a household member.
Based on previous literature (Baiden et al., 2021; M. J. Brown et al., 2022; Felitti et al.,
1998b; Terry et al., 2023) and our sample distribution, we coded ACE scores into 5 categories:

43
0, 1, 2, 3, and 4 or more ACEs. The 2011 BRFSS module asked whether substance abuse in the
household, mental illness of a household member, and separation or divorce of parents occurred.
The BRFSS module measured how often physical abuse, sexual abuse, emotional abuse, and
witnessing IPV occurred. We recoded physical abuse, sexual abuse, emotional abuse, and
witnessing IPV into dichotomous variables (0= “No”, 1= “Yes”) (Baiden et al., 2021; M. J.
Brown et al., 2022; Terry et al., 2023). For those who reported experiencing any of the sexual
abuse questions at least once in the BRFSS module, the response was recoded as a “Yes”; an
occurrence of physical abuse, emotional abuse, and witnessing IPV was recoded as a “Yes” from
those who reported experiencing these particular ACEs more than once in the BRFSS module.
Covariates included race, age, gender, education, and state; self-reported health, chronic health
conditions such as coronary heart disease, myocardial infarction (heart attack), diabetes, and
stroke, and mental health distress were each included in additional models.
Analysis
Stratum weights were applied to improve the representativeness of the sample and to
avoid sampling error (Bethlehem, 2008; Centers for Disease Control, 2021a). Chi-square tests
were run to determine whether there were differences between those who report SCD and those
who did not report SCD in ACE scores and the individual ACEs reported. An ordered logistic
regression model was run for cumulative ACE scores. Logistic regression models were run for
each of the individual ACEs. Nested models were used to test the parameters of multiple
predictors. Variables used in the nested model were self-reported health, chronic health
conditions such as coronary heart disease, myocardial infarction (heart attack), diabetes, and
stroke, and mental health distress, which can be risk factors of SCD and ADRD. Nested models
were used to build the models. Model 1, the original model, included the covariates and ACEs
or ACE score vs. SCD. Model 2 added self-reported health (poor, fair, good, excellent), and

44
Model 3 added chronic health conditions (coronary heart disease, stroke, diabetes, and
myocardial infarction). Model 4 added number of bad mental health days in the past 30 days (0-
1, 2-6, 7-13, 14+).
Results
Table 3.1 shows the demographics of the sample. The mean age of the sample was 67.1
years. Over half of the sample was female, and over 70% of the sample was non-Hispanic white.
The majority of the sample (57.5%) had at least some college education or a technical degree,
and fourteen percent had less than a high school education. Most reported good or excellent
health, and the majority reported having 0-1 bad mental health days within the last 30 days. More
than fifty percent of the respondents lived in California.
Table 3.2 shows the ACE scores, individual ACEs among those with SCD and those
without SCD, as well as the chi-square results of the sample. The mean ACE score was 1.09; half
(50.6%) of the respondents reported no ACEs. Nearly one in four (24.8%) reported 1 ACE, and
5.71% percent reported 4 or more ACEs. Among those who reported SCD, more than ten percent
reported 4 or more ACEs. Psychological abuse was the most reported ACE at 23.8%, followed
by substance abuse in the household at 22.8%. Over 14% reported SCD.
The ACE scores of those who reported memory loss in the last 12 months differed from
those who reported no memory loss in the last 12 months (χ2= 117.40, 4, p<.0001). Chi-square
results for the individual ACEs show that there are significant differences between those with
SCD and those without SCD: physical abuse (χ2= 134.37, 1, p<.0001); emotional abuse (χ2=
60.97, 1, p<.0001); Sexual abuse (χ2= 86.05, 1, p<.0001); parental divorce/separation (χ2=
14.08, 1, p= .0002); witnessed IPV (χ2= 26.89, 1, p<.0001); substance abuse in the household
(χ2= 30.70, 1, p<.0001); mental illness of a household member (χ2= 41.37, 1, p<.0001).

45
Table 3.3 shows the results of the ordered logistic regression of ACE scores vs. SCD and
the logistic regression results of the individual ACEs vs. SCD. In particular, childhood physical
and sexual abuse remained strongly significant in all the models. Compared to respondents who
reported no ACEs, those with one ACE have odds almost two times the odds (odds ratio
[OR]=1.93, p<.0001) of reporting SCD; respondents who reported two ACEs have over twice
the odds (OR=2.51, p<.0001) of reporting SCD. Compared to respondents with no ACEs, those
with 3 ACEs also have over twice the odds of reporting SCD (OR=2.46, p<.0001) , and those
with four ACEs have more than four times the odds (OR=4.29, p<.0001) of reporting SCD.
Significant associations between SCD and four of the seven ACEs were also found in Model 1.
Compared to respondents who did not experience childhood physical abuse, those who did have
odds of reporting SCD that are twice as high (OR=2.10, p<.0001). Those who experienced
childhood sexual abuse had nearly twice the odds of reporting SCD (OR= 1.84, p<.0001).
Respondents who reported substance abuse in their household during childhood had greater odds
of reporting SCD (OR =1.275, p=0.017). Respondents who reported living with someone who
had a mental illness during childhood had greater odds of reporting SCD (OR= 1.438, p=0.005).
In Model 2, where self-reported health was included as a covariate, those with 4 ACEs
had nearly four times the odds (OR=3.72, p<.0001) of reporting SCD compared to those who
had 0 ACEs. Experiencing childhood physical abuse (OR=1.88, p<.0001) and sexual abuse
(OR=1.74, p<.0001)had greater odds of having SCD than not experiencing childhood physical
abuse or sexual abuse. Living with someone who had a mental illness during childhood also had
greater odds of reporting SCD (OR= 1.76, p=0.0061).
As the chronic health conditions variables were added in Model 3, the odds of reporting
SCD decreased but remained strongly significant at almost all ACE scores except 4 or more,

46
which had a minimal increase from Model 2. Having 4 or more ACEs had greater odds of
reporting SCD (OR=3.78, p<.0001) compared to those who had no ACEs.
When controlling for all demographic and health variables, including mental health
distress, in Model 4, the odds of reporting SCD decreased at all ACE scores but remained
significant. Those with an ACE score of 4 or more had nearly three times the odds (OR=2.91,
p<.0001) of reporting SCD, compared to those with no ACEs. Also, out of all seven ACEs,
physical (OR=1.75, p<.0001) and sexual abuse (OR=1.70, p<.0001) were most associated with
SCD, with nearly two times the odds of reporting SCD.
Discussion
We hypothesized that 1) those who report multiple ACEs (4+) would be significantly
more likely to report SCD, and 2) that occurrences of psychological, physical, and sexual abuse
would drive the association between ACEs and SCD. Logistic regression, with multiple nested
models, was used to examine associations between ACE scores and SCD and individual ACEs
and SCD. Results show that higher numbers of ACEs increased the odds of reporting SCD, with
childhood sexual and physical abuse associated with the greatest risk of SCD. Findings show a
significant association between ACEs and SCD, supporting previous studies (Baiden et al., 2021;
M. J. Brown et al., 2022; Terry et al., 2023). The association between ACEs and SCD remained
strongly significant even when controlling for health factors that increase the risk of SCD. The
increases in odds of SCD with higher ACEs underscores the impact of these experiences across
the lifespan. This study is yet another example how family violence and early life adversity has
implications for later-life health.
Psychological abuse was not associated with SCD in this study; however, it should not be
discounted as a factor impacting cognitive health. Psychological abuse is often part of
polyvictimization, victimization of multiple forms of abuse and violence that can occur

47
subsequently or concurrently (Finkelhor, 2008). It is common for different types of abuse to
overlap with one another. Therefore, more than likely, psychological abuse is occurring
concurrently with the other ACEs, particularly physical and sexual abuse, and it is very likely
that those who are experiencing physical and sexual abuse are also experiencing psychological
abuse (Anda, Felitti, Brown, et al., 2006; Higgins & McCabe, 2001).
Although this study advances our understanding of links between ACE and later life
health and mental health problems, several limitations should be noted. One issue is that using
ACEs to understand problems in older age typically requires recalling early-life abuse as an adult
can involve false reporting and biases based on mood and timing (Hardt & Rutter, 2004).
Surveying those with SCD, could increase the chance for inaccurate reporting of early-life abuse.
However, false reports of abuse are rare, and respondents tend to underestimate occurrences of
abuse (Hardt & Rutter, 2004). A second limitation is that some BRFSS modules are optional, and
many states do not report data consistently. To complete the analyses, we had to use data from a
year in which both ACEs and cognitive impairment data had been collected, which resulted in
using data from three states. Due to limited data availability, these results may not be
generalizable beyond these states. Additionally, people with serious cognitive impairment or
advanced stage dementia may not have had the capacity to respond to the survey.
These results show that ACE scores can be impactful in practice as it is a widely used
tool in measuring childhood adversity. Additionally, this study adds to the evidence in how
family violence and childhood adversity impacts health in later life. The study shows the
importance of considering a lifespan perspective for work on childhood adversity, family
violence, and EM. It also identifies areas to explore for targeting prevention efforts.

48
Table 3.1. Sample Characteristics (Demographics)

(N= 5,898)
a

Total
Percent
SCD Non-SCD χ
2
p
Age (55+)
b
67.1 (6.9) 67.9 (6.9) 66.8 (6.6) -4.25 <.0001***
Gender

0.76 0.382
Female 53.09 54.10 52.41

Male 46.90 45.90 47.59

Education

16.89 0.0007**
Less than High School Diploma 14.02 17.56 12.31

High School Diploma/GED 23.78 24.10 24.18

Some College/Technical Degree 33.74 31.71 34.19

Bachelor’s Degree or higher 28.46 26.63 29.32

Race/Ethnicity

28.07 <.0001***
White (Non-Hispanic) 72.14 68.82 75.34

Black (Non-Hispanic) 5.44 4.71 5.16

Asian/Pacific Islander 6.75 6.11 6.03

Hispanic (of any race) 12.76 16.92 10.56

Other 2.91 3.44 2.91

Self-reported Health

235.27 <.0001***
Poor 6.88 14.61 5.43

Fair 17.20 29.10 15.06

Good 31.21 30.26 30.78

Excellent 44.72 26.04 48.73

Chronic Health Conditions

Coronary Heart Disease 9.46 14.99 8.69 30.01 <.0001***
Stroke 5.52 12.15 4.25 81.13 <.0001***
Heart Attack 8.82 13.79 8.05 26.90 <.0001***
Diabetes 17.62 20.23 17.43 3.55 0.060
Mental Health Distress

0-1 days 73.26 48.07 77.60 336.24 <.0001***
2-6 days 11.86 17.54 10.88

7-13 days 4.45 9.71 3.61

14+ days 10.43 24.69 7.90

State

20.09 <.0001***
California 53.82 54.96 49.09

Washington 21.20 24.30 22.46

Wisconsin 24.97 20.74 28.45
a
weighted sample
b
mean, standard deviation, & T-test

*p<0.05, **p<0.01, ***p<0.001

49
Table 3.2. Sample Characteristics (ACE Score and ACEs)

(N= 5,898)
a

Total
Percent
SCD
Non-
SCD
χ
2
p
Independent Variables
ACE Score 1.09

117.40 <.0001***
0 50.57 34.60 53.35

1 24.80 28.70 24.14

2 12.10 16.66 11.29

3 6.82 9.25 6.40

4+ 5.71 10.79 4.82

Adverse Childhood Experiences (ACEs)
Physical Abuse 12.71 25.51 10.52 134.37 <.0001***
Psychological Abuse 23.83 34.88 21.93 60.96 <.0001***
Sexual Abuse 12.15 22.18 10.41 86.05 <.0001***
Parental Separation/Divorce 15.96 20.47 15.13 14.08 0.0002**
Witness IPV 10.54 15.81 9.60 26.89 <.0001***
Substance Abuse in Household 22.84 30.51 21.48 30.70 <.0001***
Mental Illness in Household 10.76 17.37 9.61 41.37 <.0001***
Dependent Variable
Subjective Cognitive Decline (SCD) 14.53
a
weighted sample

*p<0.05, **p<0.01, ***p<0.001

50
Table 3.3. Logistic Regression of SCD, ACE Score
a

MODEL 1 MODEL 2 MODEL 3 MODEL 4

Odds Ratio
(95% CI)
p
Odds Ratio
(95% CI)
p
Odds Ratio
(95% CI)
p
Odds Ratio
(95% CI)
p
ACE Score

1
1.925
(1.585, 2.338)
<.0001***
1.811
(1.486, 2.207)
<.0001***
1.803
(1.472, 2.207)
<.0001***
1.665
(1.352, 2.05)
<.0001***
2
2.514
(1.982, 3.189)
<.0001***
2.314
(1.815, 2.95)
<.0001***
2.43
(1.9, 3.109)
<.0001***
2.268
(1.759, 2.924)
<.0001***
3
2.463
(1.832, 3.31)
<.0001***
2.21
(1.632, 2.994)
<.0001***
2.179
(1.596, 2.974)
<.0001***
1.819
(1.316, 2.515)
0.0003**
4+
4.29
(3.188, 5.773)
<.0001***
3.722
(2.744, 5.049)
<.0001***
3.782
(2.77, 5.164)
<.0001***
2.908
(2.103, 4.021)
<.0001***
Logic Regression of SCD and Individual ACES
a

Physical Abuse
2.104
(1.659, 2.669)
<.0001***
1.878
(1.47, 2.39)
<.0001***
1.863
(1.455, 2.386)
<.0001***
1.754
(1.359, 2.264)
<.0001***
Psychological
Abuse
1.216
(0.979, 1.510)
0.077
1.198
(0.96, 1.49)
0.106
1.226
(0.981, 1.533)
0.074
1.127
(0.896, 1.418)
0.308
Sexual Abuse
1.841
(1.470, 2.306)
<.0001***
1.74
(1.38, 2.19)
<.0001***
1.762
(1.392, 2.228)
<.0001***
1.697
(1.334, 2.16)
<.0001***
Parental
Separation or
Divorce
1.036
(0.830, 1.292)
0.757
1.044
(0.83, 1.31)
0.709
1.044
(0.83, 1.314)
0.711
1.008
(0.796, 1.276)
0.947
Witnessed IPV
0.947
(0.724, 1.238)
0.689
0.954
(0.73, 1.26)
0.739
0.928
(0.7, 1.23)
0.602
0.928
(0.694, 1.241)
0.614

51
Household
Substance
Abuse`
1.275
(1.045, 1.557)
0.017*
1.223
(0.99, 1.50)
0.052
1.216
(0.989, 1.496)
0.064
1.214
(0.982, 1.501)
0.074
Household
Mental Illness
1.438
(1.119, 1.846)
0.0045**
1.757
(1.11, 1.85)
0.0061*
1.492
(1.15, 1.934)
0.0025*
1.284
(0.98, 1.68)
0.069
a Reference Groups: Non-Hispanic White, Less than HS, California, Poor health, 0-1 Bad Mental Health Days, ACE Score of 0
Note: Model 1 controls for age, sex, race/ethnicity, & education. Model 2 controls for all variables included in Model 1, plus self-rated health.
Model 3 includes chronic health conditions. Model 4 includes mental distress.
*p<0.05, **p<0.01, ***p<0.001

52
Supplemental Table 3.1. Full Ordered Logistic Regression of SCD, ACE Score
a

MODEL 1 MODEL 2 MODEL 3 MODEL 4

Odds Ratio
(95% CI)
p
Odds Ratio
(95% CI)
p
Odds Ratio
(95% CI)
p
Odds Ratio
(95% CI)
p
Covariates/Control Variables
Age
1.025
(1.016, 1.034) <.0001***
1.023
(1.014, 1.032) <.0001***
1.02
(1.011, 1.03) <.0001***
1.03
(1.02, 1.04)
<.0001**
*
Female
0.952.
(0.812, 1.116) 0.544
1.001
(0.852, 1.177) 0.987
0.979
(0.829, 1.158) 0.806
0.917
(0.772, 1.09) 0.327
Race/Ethnicity

Black (Non-Hispanic) 0.981
(0.676, 1.423) 0.920
0.81
(0.554, 1.185) 0.277
0.809
(0.543, 1.204) 0.296
0.805
(0.535, 1.211) 0.298
Asian/Pacific Islander
1.512.
(1.071, 2.135) 0.019
1.328
(0.932,
1.892) 0.116
1.358
(0.949, 1.944) 0.094
1.28
(0.887, 1.848) 0.187
Hispanic (of any race) 1.637
(1.257, 2.131) 0.0003**
1.343
(1.021, 1.768) 0.0351*
1.389
(1.049, 1.839) 0.0218*
1.348
(1.01, 1.801) 0.043*
Other 1.153
(0.745, 1.783) 0.524
1.05
(0.672, 1.642) 0.830
1.044
(0.66, 1.651) 0.853
0.988
(0.608, 1.604) 0.960
Education

High School Diploma/GED 0.891
(0.681, 1.167) 0.402
1.193
(0.899, 1.581) 0.221
1.083
(0.811, 1.446) 0.590
1.173
(0.868, 1.585) 0.300
Some College/Technical Degree 0.775
(0.596, 1.008) 0.058
1.131
(0.857, 1.493) 0.383
1.086
(0.818, 1.442) 0.569
1.189
(0.886, 1.596) 0.249
Bachelor's Degree or Higher 0.798
(0.604, 1.054) 0.112
1.279
(0.95, 1.721) 0.105
1.229
(0.908, 1.664) 0.181
1.376
(1.005, 1.883) 0.047
State

53
Washington 1.034
(0.848, 1.261) 0.742
0.998
(0.815, 1.223) 0.988
1.005
(0.816, 1.237) 0.965
1.074
(0.867, 1.33) 0.516
Wisconsin
0.779
(0.629, 0.966) 0.0227*
0.756
(0.608, 0.941) 0.0123*
0.739
(0.591, 0.924) 0.0081*
0.764
(0.607, 0.961) 0.0213*
Self-reported Health

Poor

4.537
(3.412, 6.034) <.0001***
4.307
(3.162, 5.866) <.0001***
2.596
(1.862, 3.62)
<.0001**
*
Fair

3.416
(2.741, 4.258) <.0001***
3.335
(2.644, 4.206) <.0001***
2.364
(1.853, 3.016)
<.0001**
*
Good

1.759
(1.433, 2.159) <.0001***
1.785
(1.449, 2.199) <.0001***
1.541
(1.243, 1.909)
<.0001**
*
Chronic Health Conditions

Coronary Heart Disease

1.112
(0.84, 1.471) 0.458
1.198
(0.899, 1.597) 0.217
Stroke

2.278
(1.713, 3.03) <.0001***
2.317
(1.725, 3.112)
<.0001**
*
Myocardial Infarction (Heart
Attack)

1.095
(0.814, 1.473) 0.549
1.095
(0.808, 1.484) 0.557
Diabetes

0.702
(0.565, 0.873) 0.0014**
0.747
(0.598, 0.934) 0.0103*
# of Bad Mental Health Days
c

2-6 days

2.331
(1.841, 2.95)
<.0001**
*
7-13 days

3.414
(2.475, 4.71)
<.0001**
*
14+ days

3.649
(2.873, 4.634)
<.0001**
*
ACE Score

54
1
1.925
(1.585, 2.338) <.0001***
1.811
(1.486, 2.207) <.0001***
1.803
(1.472, 2.207) <.0001***
1.665
(1.352, 2.05)
<.0001**
*
2 2.514.
(1.982, 3.189) <.0001***
2.314
(1.815, 2.95) <.0001***
2.43
(1.9, 3.109) <.0001***
2.268
(1.759, 2.924)
<.0001**
*
3
2.463 (1.832,
3.31) <.0001***
2.21
(1.632, 2.994) <.0001***
2.179
(1.596, 2.974) <.0001***
1.819
(1.316, 2.515) 0.0003**
4+
4.29
(3.188, 5.773) <.0001***
3.722
(2.744, 5.049) <.0001***
3.782
(2.77, 5.164) <.0001***
2.908
(2.103, 4.021)
<.0001**
*
a Reference Groups: Non-Hispanic White, Less than HS, California, Poor health, 0-1 Bad Mental Health Days, ACE Score of 0

Note: Model 1 controls for age, sex, race/ethnicity, & education. . Model 2 controls for all variables included in Model 1, plus self-rated health.

*p<0.05, **p<0.01, ***p<0.0001

55
Supplemental Table 3.2. Full Logistic Regression of SCD and Individual ACEs
a

MODEL 1 MODEL 2 MODEL 3 MODEL 4

Odds Ratio
(95% CI)
p
Odds Ratio
(95% CI)
p
Odds Ratio
(95% CI)
p
Odds Ratio
(95% CI)
p
Age
1.027
(1.018, 1.036)
<.0001**
*
1.024
(1.015, 1.034)
<.0001**
*
1.021
(1.012, 1.031)
<.0001**
*
1.031
(1.021, 1.041) <.0001***
Female
0.935
(0.792, 1.104) 0.428
0.983
(0.83, 1.165) 0.844
0.961
(0.806, 1.145) 0.657
0.897
(0.749, 1.075) 0.2399
Race/Ethnicity

Black (Non-Hispanic)
1.184
(0.808, 1.736) 0.386
0.961
(0.649, 1.424) 0.506
0.943
(0.626, 1.419) 0.778
0.914
(0.602, 1.388) 0.674
Asian/Pacific Islander 1.379
(0.96, 1.98) 0.082
1.191
(0.821, 1.729) 0.512
1.214
(0.832, 1.772) 0.315
1.101
(0.747, 1.622) 0.6271
Hispanic (of any race)
1.496
(1.135, 1.972) 0.0043**
1.227
(0.921, 1.635) 0.285
1.245
(0.928, 1.67) 0.144
1.225
(0.906, 1.656) 0.1878
Other 1.07
(0.674, 1.699) 0.774
1.012
(0.631, 1.621) 0.767
1.012
(0.624, 1.642) 0.96
1.015
(0.617, 1.671) 0.9529
Education

High School Diploma/GED 0.876
(0.661, 1.162) 0.360
1.157
(0.861, 1.554) 0.802
1.042
(0.77, 1.411) 0.788
1.149
(0.839, 1.572) 0.3867
Some College/Technical Degree
0.784
(0.595, 1.032) 0.083
1.126
(0.843, 1.506) 0.915
1.066
(0.793, 1.435) 0.671
1.189
(0.875, 1.617) 0.2683
Bachelor's Degree or Higher 0.812
(0.605, 1.088) 0.163
1.273
(0.932, 1.738) 0.149
1.207
(0.878, 1.657) 0.246
1.39
(1.001, 1.931) 0.0494
State

56
Washington
1.059
(0.861, 1.302) 0.586
1.025
(0.83, 1.266) 0.098
1.024
(0.825, 1.27) 0.831
1.082
(0.866, 1.35) 0.4887
Wisconsin
0.776
(0.621, 0.97) 0.026*
0.748
(0.596, 0.939) 0.0056*
0.727
(0.576, 0.917) 0.0072*
0.746
(0.589, 0.947) 0.0159*
Self-reported Health

Poor

4.285
(3.186, 5.764)
<.0001**
*
3.935
(2.848, 5.437)
<.0001**
*
2.461
(1.741, 3.477) <.0001***
Fair

3.424
(2.729, 4.295)
<.0001**
*
3.289
(2.591, 4.177)
<.0001**
*
2.365
(1.841, 3.038) <.0001***
Good

1.757
(1.421, 2.171) 0.0004**
1.776
(1.431, 2.204)
<.0001**
*
1.537
(1.232, 1.918) 0.0001**
Chronic Health Conditions

Coronary Heart Disease

1.145
(0.86, 1.523) 0.355
1.231
(0.918, 1.651) 0.1645
Stroke

2.369
(1.767, 3.175)
<.0001**
*
2.379
(1.755, 3.225) <.0001***
Myocardial Infarction (Heart
Attack)

1.072
(0.792, 1.45) 0.652
1.062
(0.779, 1.448) 0.7039
Diabetes

0.738
(0.591, 0.923) 0.0077*
0.785
(0.625, 0.987) 0.0379*
# of Bad Mental Health Days (in
the past 30 days)

2-6 days

2.355
(1.848, 3.001) <.0001***
7-13 days

3.315
(2.362, 4.652) <.0001***
14+ days

3.628
(2.831, 4.649) <.0001***
ACEs

57
Physical Abuse
2.104
(1.659, 2.669)
<.0001**
*
1.878
(1.474, 2.392)
<.0001**
*
1.863
(1.455, 2.386)
<.0001**
*
1.754
(1.359, 2.264) <.0001***
Psychological Abuse 1.216
(0.979, 1.51) 0.0770
1.198
(0.962, 1.491) 0.1060
1.226
(0.981, 1.533) 0.074
1.127
(0.896, 1.418) 0.308
Sexual Abuse 1.841 (1.47,
2.306)
<.0001**
*
1.74
(1.382, 2.191)
<.0001**
*
1.762
(1.392, 2.228)
<.0001**
*
1.697
(1.334, 2.16) <.0001***
Parental Separation or Divorce
1.036
(0.83, 1.292) 0.7569
1.044
(0.833, 1.307) 0.7090
1.044
(0.83, 1.314) 0.711
1.008
(0.796, 1.276) 0.947
Witnessed IPV 0.947
(0.724, 1.238) 0.6891
0.954
(0.725, 1.257) 0.7392
0.928
(0.7, 1.23) 0.602
0.928
(0.694, 1.241) 0.614
Household Substance Abuse`
1.275
(1.045, 1.557) 0.017*
1.223
(0.998, 1.498) 0.0520
1.216
(0.989, 1.496) 0.064
1.214
(0.982, 1.501) 0.074
Household Mental Illness
1.438
(1.119, 1.846) 0.0045**
1.431
(1.108, 1.85) 0.0061*
1.492
(1.15, 1.934) 0.0025*
1.284
(0.98, 1.68) 0.069
a Reference Groups: Non-Hispanic White, Less than HS, California, Poor health, 0-1 Bad Mental Health Days, ACE Score of 0

Note: Model 1 controls for age, sex, race/ethnicity, & education. . Model 2 controls for all variables included in Model 1, plus self-rated health.

*p<0.05, **p<0.01, ***p<0.0001

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CHAPTER 4: FOUR ACES ISN’T ALWAYS A WINNING HAND: THE IMPACT OF
ADVERSE CHILDHOOD EXPERIENCES ON CAREGIVERS’ HEALTH
Overview
As the population ages, demands for more family caregiving increases. At the same time,
caregiving tasks for many individual caregivers are becoming more frequent, persistent, and
intensive, increasing the risk of caregiver stress. Consequently, caregivers may be more at risk
for adverse health outcomes, and they tend to report fair or poor health compared to non-
caregivers. Adverse childhood experiences (ACEs), which are traumatic events experienced
before age 18, have also been associated with several health conditions and overall poor health in
adulthood. People who have ACEs may enter later life with complex care needs, and those who
are most likely to be caring for them, family caregivers, may also have experienced ACEs, as a
result of intergenerational transmission of abuse and continuation of family dysfunction. The
additive effect of early life stressors and caregiving stressors may have a compounded impact on
the health of caregivers, contributing additional stress and burden to their caregiving situation.
Data from the 2019 and 2020 Behavioral Risk Factor Surveillance System (BRFSS) were used to
analyze the number of poor mental and physical health days in the last 30 days in the context of
caregiving and ACEs, based on responses from Florida, Georgia, Tennessee, Utah, and Virginia
(N= 33,139). Among the caregivers (20.3% of the total sample), nearly 23% reported 4 or more
ACEs, compared with 13% of non-caregivers. Caregivers were more likely to report more
mental health days that were “not good” (M=5.53, SD=9.61) compared to non-caregivers
(M=3.95, SD=8.08); t=6.65, p<.001; caregivers were also more likely to report more “not good”
physical health days (M=4.29, SD=8.31) compared to non-caregivers (M=3.76, SD=8.76);
t=2.54, p=.011. Zero-inflated negative binomial regression models showed that, after including
the combination of ACEs and being a caregiver, even having 1 ACE (IRR= 1.35, p=.007, 95%

59
CI [1.09, 1.68]) or 2 ACEs (IRR= 1.49, p<.001, 95% CI [1.16, 1.92]) increased the number of
“not good”mental health days. However, the effect of ACE score alone on both mental and
physical health persisted, with the strongest effect for individuals with 4 or more ACEs being
more likely to report more mental and physical health days “not good”. Emotional abuse, sexual
abuse, and living with someone with mental illness were the ACEs most strongly associated with
increased number of mental and physical health days that were “not good”. These findings
highlight the importance of a lifespan approach when considering caregiver health and,
potentially, burden. Practice implications, suggest that screening caregivers on their history of
ACEs may be a valuable tool to identify those at higher risk for poor health outcomes who may
benefit from additional resources to support their health and well-being.
Introduction
The number of friends and family members who are caregivers in the U.S is nearly 44
million. The need is expected to grow as more adults live longer with chronic health conditions
(Schulz et al., 2020; Scommegna, 2016). One in five adults has reported providing substantial
care to someone in the past 30 days, at an average of 24 hours per week (AARP and National
Alliance for Caregiving, 2020; Edwards et al., 2020). For many, caregiving can be a highly
stressful responsibility that has been associated with negative physical and mental health
outcomes (Schulz et al., 2020). Persistent and intensive caregiving tasks can lead to caregiver
stress, especially when social support, social engagement and appropriate resources are
unavailable or inaccessible, (A, 2020; Hoffmann & Mitchell, 1998; Liu et al., 2020;
Scommegna, 2016). Over 19% of caregivers in the U.S. report having poor or fair health overall
(Edwards et al., 2020; Richardson et al., 2013). Studies have also shown that caregivers have
increased mortality (Beach et al., 2000; Bom et al., 2019; Perkins et al., 2013; Richardson et al.,
2013). The stressors of caregiving predisposes caregivers to stress hormones and inflammatory

60
markers, such as cortisol, C-reactive protein, and interlukin-6 that can heighten the risk of poor
health outcomes (Richardson et al., 2013; von Känel et al., 2012). Compared to non-caregivers,
caregivers are at higher risk for chronic health conditions, including coronary heart disease,
cardiovascular disease, stroke, diabetes, hypertension, arthritis, and cancer (Ahn et al., 2022;
Mortensen et al., 2018; Sambasivam et al., 2019; Vitaliano et al., 2003; von Känel et al., 2008).
They are also more likely to be diagnosed with depression and anxiety and exhibit more
psychological distress and vulnerability compared to non-caregivers (Corà et al., 2012; del-Pino-
Casado et al., 2021; H. S. Lee et al., 2001; MacNeil et al., 2010; Penning & Wu, 2016;
Sambasivam et al., 2019; Song et al., 2011). Moreover, negative coping behaviors, such as
substance use, used to address caregiving stressors may further increase the risk of the
development of these health conditions (Acton, 2002; Rospenda et al., 2010).
Adverse Childhood Experiences
In addition to current caregiving related stress, adverse childhood experiences (ACEs),
which are events of abuse and household dysfunction experienced before age 18, have been
associated with several health conditions and overall poor health in adulthood (Felitti et al.,
1998b). ACEs have consistently been linked to numerous negative health outcomes and
conditions, including heart disease, heart attack stroke, diabetes, arthritis, cancer, chronic
obstructive pulmonary disease (COPD), higher risk of mortality (Amemiya et al., 2019; Anda,
Felitti, Bremner, et al., 2006a; Anda et al., 2008; Bellis et al., 2014; M. J. Brown et al., 2013;
Campbell et al., 2016; M. Dong, Giles, et al., 2004, 2004; Gilbert et al., 2015; Lanius et al.,
2009; Merrick et al., 2018; Strine et al., 2012; Vig et al., 2020), and anxiety and depression
(Danese et al., 2009; Edwards et al., 2003; Ege et al., 2015; Mersky et al., 2013). Those with
high ACEs (usually 4 or more) are also more likely to engage in negative health behaviors, such
as alcohol and tobacco use (Baiden et al., 2022; Bellis et al., 2014; Campbell et al., 2016; Lanius

61
et al., 2009; Strine et al., 2012; Umberson et al., 2014). Those who experience high ACEs may
enter later life with complex health care needs. In addition, family members who are most likely
to be caring for them may also have experienced ACEs, as a result of intergenerational
transmission of abuse and continuation of family dysfunction.
In many ways, the health consequences of ACEs are similar to the health consequences
of intensive and prolonged caregiving. As more time is devoted to providing care, caregivers
may be less likely to invest in their own health and self-care. Caregivers who are already in poor
health or have one or more chronic health conditions may risk further complications and decline
due to caregiver stress (Acton, 2002; AARP and National Alliance for Caregiving, 2020;
Richardson et al., 2013). These outcomes can be even more adverse for caregivers who have
reported high ACEs if the additive effect of early life stressors and caregiving stressors
exacerbate adverse health outcomes (Kiecolt-Glaser et al., 2011)..
This study examines the intersectionality between early life stressors and caregiver stress
using a life course perspective. According to this perspective, individuals’ lives are shaped by a
range of experiences and events occurring throughout their lives, including childhood
experiences, family dynamics, and health issues (Elder & Rockwell, 1979). When considering
the life course perspective, circumstances in childhood, in this case, ACEs, have long-term
effects on health and can also shape caregivers’ experiences in their role, ultimately impacting
their mental and physical health.
Although current studies (Acton, 2002; Ahn et al., 2022; Bom et al., 2019) connect
caregiving and health outcomes, these studies generally consider current psychosocial factors
and physical health, such as caregiver stress and social support. However, physical and mental
health conditions prior to becoming a caregiver also contribute to poor physical and mental

62
health among caregivers. As part of this picture, because childhood adversity has long-term
impacts on health, we should also explore whether caregivers who have experienced ACEs are
more likely to have poorer health outcomes than those who have not experienced ACEs.
We were able to identify only one study that examine how childhood adversity impacted
caregiving stress, among caregivers of older adults. This study, which focused on telomere
length and inflammation (Kiecolt-Glaser et al., 2011) found that experiencing childhood abuse
and adversity and being a caregiver increased depressive symptoms among ADRD caregivers
compared to non-caregivers.
We use a life course perspective, described in Chapter 1, to investigate the extent to
which ACEs impact family caregivers’ mental and physical health. The research questions are:
1) Do caregivers with ACEs have worse mental and physical health than caregivers without
ACEs? 2) If ACEs are associated with poorer mental and physical health, which ACEs are the
drivers of this relationship? It is hypothesized that: 1) based on existing literature focused on
caregiver stress and health, caregivers will report higher ACEs and more mental and physical
health days that were “not good” in the past 30 days compared to non-caregivers, and 2) being a
caregiver with 4 or more ACEs will be associated with more mental and physical health days that
were “not good” in the past 30 days compared to non-caregivers as well as those with no ACEs.
Methods
Dataset
Data from the 2019 and 2020 Behavioral Risk Factor Surveillance System (BRFSS) were
used to investigate the impact of ACEs on caregivers’ mental and physical health. BRFSS is a
collaborative project between the Centers for Disease Control and United States’ states and
territories that collects ongoing demographic and health data from adults aged 18 and older from
participating states each year. Questions were from the ACEs, Caregiving, and Healthy Days

63
modules to develop independent and outcome variables. Results were based on responses from
the five states that included data on both ACEs and caregiving: Florida, Georgia, Tennessee,
Virginia, and Utah (N=41,340 representing 15.48% of the total sample weighted and 11.46%
unweighted). Inclusion criteria included participants who responded to each of the modules.
Those who responded, “Don’t know”, “Not sure”, or had missing data in the Caregiving module
were excluded from the sample.
Measures
The outcome variable, mental and physical health, were, measured using the Healthy
Days module in BRFSS. Each was measured by the number of days in the past 30 days (0-30)
that one’s physical or mental health was “not good” (“...how many days during the last 30 days
was your physical/mental health not good?”). Caregiver status (cared for someone in the past 30
days), total ACE score (0-8), and the individual ACEs were used as the primary independent
variables. The individual ACEs included in the ACEs module were physical abuse,
emotional/verbal abuse, sexual abuse, separation or divorce of parents, witnessing intimate
partner violence (IPV), substance abuse in the household, incarceration of a household member,
and mental illness of a household member. The highest possible ACE score in this study was 8
because only 8 out of 10 ACEs are asked in BRFSS; the two neglect variables (physical and
emotional were not included in the ACEs module.
ACE scores were coded into five categories: 0, 1, 2, 3, and 4 or more ACEs (Baiden et
al., 2021; Felitti et al., 1998b). Being a caregiver (caregiver status) was determined by whether
one has provided regular care to a family or friend with a disability or health problem within the
past 30 days. Covariates included demographics, including employment, marital status, and
household size (number of children and number of adults); chronic health conditions consisting
of coronary heart disease, heart attack, diabetes, stroke, arthritis, and cancer; and health

64
behaviors, measured by the occurrence of exercise within the last 30 days, tobacco use, and
alcohol use.
Analysis
Stratum weights were applied to ensure accurate representativeness of the sample, as well
as to reduce potential non-response bias (Bethlehem, 2008; Centers for Disease Control, 2021a;
Lavallée & Beaumont, 2015). Chi-square tests of independence were run to compare the
characteristics between caregivers and non-caregivers, as well as whether caregivers were more
likely to have more ACEs compared to non-caregivers. Two-sample t-tests were run to determine
whether there were significant differences in the amount of poor mental and physical health days
between caregivers and non-caregivers as well as between caregivers with 4 or more ACEs and
those with no ACEs. Given that the data set included zeros, several methods were considered. To
decide whether to run Poisson regressions or negative binomial regressions, we performed a
goodness of fit test, which determined that negative binomial regression was the better fit for
both models. We performed a Vuong’s test to determine whether to use regular negative
binomial regressions or zero-inflated negative binomial regressions (ZINB) for each model,
which determined that ZINB was more appropriate. Additionally, because histograms showed
that both outcome variables have an over-dispersion of zeros, we used ZINB for our analyses,
which runs 2 types of regressions: 1) a logit analysis (dichotomous: 0/1) to determine the odds
of reporting 0 mental or physical health days that were “not good” in the past 30 days, and 2) a
count analysis (0-30 days) for the number of mental or physical health days that were “not good”
in the past 30 days.
Two ZINB regressions models were run: one for mental health and one for physical
health. The mental health model examined: 1) whether there was a significant association
between the ACE score (0-4+) and the likelihood of reporting 0 mental health days that were not

65
good within the past 30 days, and 2) which of the individual ACEs were significantly associated
with the number of mental health days that were not good in the past 30 days. The physical
health model examined: 1) whether there was a significant association between the ACE score
(0-4+) and the likelihood of reporting 0 physical health days that were “not good” within the past
30 days (“not good” physical health days), and 2) which of the individual ACEs were
significantly associated with the number of “not good” physical health days in the past 30 days.
Additionally, because the additive effect of early-life stressors and caregiving stressors can
further lead to adverse health outcomes, interaction effects were run for each regression to
determine whether being a caregiver with ACEs (1-4+) was associated with the likelihood of
reporting 0 mental and physical health days that were not good in past 30 days and the number of
mental and physical health days that were not good in the past 30 days.
Results
Descriptives
Table 4.1 shows the analytic sample’s (N=33,139) caregiving status, demographics, and
chi-square and t-tests results. Caregivers made up over 20% of the total sample. The mean age of
the sample was 49.7 years (SD=18.24), and women made up over 53%. The sample was
relatively well-educated, with nearly 30% having a high school diploma or GED and a little over
31% at least attending college or having a technical degree. Non-Hispanic whites made up the
majority of the sample at 61%. Over half of the overall sample was married or partnered
(54.5%), followed by never married (22.3%), divorced or separated (14.6%), and widowed
(8.1%). Over half was employed (53.8%). The mean number of adults in the household was 2.3
(SD=1.11), and 0.7 (SD=1.13) was the mean number of children in the household. The mean age
of caregivers was slightly higher than non-caregivers at 51.5 years vs. 49.2 years. There was a
large difference in employment status between caregivers and non-caregivers: nearly 10% vs.

66
43%, respectively. Also in Table 4.1, t-test results showed significant differences in age between
caregivers (M=51.50, SD=17.31) and non-caregivers (M=49.22, SD=18.43); t=4.42, p<.001. The
t-test results showed significant differences in the number of adults in the household between
caregivers (M=2.40, SD=1.17) and non-caregivers (M=2.26, SD=1.10); t=3.88, p<.001. Chi-
square test results showed differences between caregivers and non-caregivers in gender (χ2=
111.47, p<.001), race (χ2= 166.20, p<.001), education, (χ2= 103.37, p<.001), and marital status
(χ2= 84.24, p=.001).
Table 4.2 shows the sample’s characteristics for health conditions and health behaviors,
including the dependent variables, number of poor mental and physical health days in the last 30
days. The mean number of “not good” mental health days was 4.27 (SD=8.24), and the mean
number of “not good” physical health days was 3.87 (SD=8.40). The most reported health
condition was arthritis, at over 27%, followed by cancer (13.6%) and diabetes (12.7%). Five
percent of the sample reported having had a heart attack, and 4.6% reported having coronary
heart disease. A little over four percent of the sample reported having had a stroke. A large
majority (71.8%) reported exercising in the last 30 days. Most of the sample reported not using
tobacco in any form (81.2%); over 6% reported using tobacco some days, and almost 13%
reported using tobacco every day. The mean number of alcoholic drinks consumed in the last 30
days was 4.47 (SD=7.98). T-test results in Table 4.2 showed significant differences in the
number of “not good” mental health days between caregivers (M=5.53, SD=9.61) and non-
caregivers (M=3.95, SD=8.08); t=6.65, p<.001, as well as differences in the number of “not
good” physical health days between caregivers (M=4.29, SD=8.31) and non-caregivers (M=3.76,
SD=8.76); t=2.54, p=.011. Chi-square results showed differences between caregivers and non-

67
caregivers in having arthritis (χ2= 367.07, p<.001) and cancer (χ2= 105.25, p<.001), exercising
in the last 30 days (χ2= 22.51, p=0.032), and using tobacco (χ2=194.94, p<.001).
Table 4.3 shows the sample’s total ACE scores and frequency of individual ACEs. The
mean ACE score of the total sample was 1.53 (SD=1.84), with nearly 40% with 0 ACEs; over
23% with 1 ACE; 13.2% with 2 ACEs; 8.2% with 3 ACEs; and over 15% with 4 or more ACEs.
The most reported ACE was parental separation/divorce (31.3%), followed by substance abuse in
the household (27.2%); emotional/verbal abuse (26.9%); physical abuse (17.4%); living with
someone with mental illness (15.8%); sexual abuse (13.4%); witnessing IPV (12.2%); and
incarceration of a household member (9.5%). A higher percentage of caregivers had 4 or more
ACEs compared to non-caregivers (22.8% vs. 13.5%), as well as a higher percentage of 2 ACEs
(14.9% vs 12.8%) and 3 ACEs (9.9% vs. 7.8%). Additionally, a higher percentage of caregivers
(4.5%) reported living with someone with mental illness in childhood compared to non-
caregivers (1.1%). The mean ACE scores of caregivers (M=1.99, SD=2.14) significantly
differed from non-caregivers (M=1.42, SD=1.75); t=8.61, p=<.001; caregivers were more likely
to have higher ACE scores compared to non-caregivers (χ2= 508.05, 4, p<.0001).
Table 4.4 shows the results of the ZINB regressions for the mental health model (“not
good” mental health days in the last 30 days) and ACE score, first without the interaction effect
of being a caregiver with ACEs and then with interaction effect added. The logit analysis of the
mental health model without the interaction effect shows that being a caregiver decreased the
odds of reporting 0 mental health days that were “not good” (OR=0.75, p<.001,) compared to not
being a caregiver, (i.e., caregivers were more likely to have mental health days that were “not
good” in the past 30 days compared to non-caregivers). The results also show that the odds of
reporting 0 “not good” mental health days decreased with higher ACE scores. Compared to those

68
with 0 ACEs, respondents with 1 ACE had lower odds of reporting 0 “not good” mental health
days (odds ratio OR=0.74, p<.001); 2 ACEs had even lower odds of reporting 0 “not good”
mental health days (OR=0.51, p<.001); for 3 ACEs the odds of reporting 0 “not good” mental
health days were 41% (p<.001); and those with 4 or more ACEs the odds of reporting 0 “not
good” mental health days were 31% ( p<.001).
In the count analysis of the same model, being a caregiver did not have a significant
impact on the number of “not good” mental health days in the last 30 days, only the odds of
reporting 0 “not good” mental health days. Additionally, those who were on the higher end of the
ACE score scale (3 & 4+ ACEs) reported more “not good” mental health days. Compared to
those with 0 ACEs, those with 3 ACEs report 17% (p=.009) more “not good” mental health days
on average, and those with 4 or more ACEs report 36% (p<.001) more “not good” mental health
days on average.
In the mental health model with the interaction effect, the logit analysis showed that
being a caregiver alone remained significantly associated with of the odds of reporting 0 “not
good” mental health days (OR=0.75, p=.013). Also, the ACE score alone remained significantly
associated with reporting 0 “not good” mental health days in the last 30 days: 1 ACE (OR=0.75,
p<.001); 2 ACEs (OR=0.48, p<.001); 3 ACEs (OR=0.42, p<.001); 4+ ACEs (OR=0.32, p<.001).
However, the interaction effect was not significant for any of the ACE scores. In contrast, in the
count analysis of the model, the significance remained for those with 3 ACEs: incidence rate
ratio, (IRR)= 1.16, p=.021) and 4+ ACEs (IRR= 1.33, p=<.001). The interaction effect was
significant at 1 ACE (IRR= 1.35, p=.007) and 2 ACEs (IRR= 1.49, p<.001).
Table 4.5 shows the results of the ZINB regressions for the mental health model and the
individual ACEs, also without the interaction effect of being a caregiver with ACEs and then

69
with interaction effect added. The logit analysis of the model shows that caregivers had lower
odds of reporting 0 “not good” mental health days compared to non-caregivers (OR=0.73,
p<.001). Among the individual ACEs, those who reported emotional abuse (OR=0.58, p<.001,);
sexual abuse (OR=0.61, p<.001); living with someone who abused alcohol and drugs (OR=0.76,
p<.001); and living with someone with mental illness (OR=0.67, p<.001) had a lower odds of
reporting 0 “not good” mental health days in the past 30 days. In the count analysis of the model,
being a caregiver was not significant, only physical abuse and having a household member with
mental illness were associated with having more mental health days that were “not good” on
average. Compared to those who did not report physical abuse, those who did report childhood
physical abuse had more “not good” mental health days on average (IRR=1.12, p<.001).
Respondents who reported living with someone with mental illness during childhood reported
1.23 (p<.001) more “not good” mental health days on average, compared to those who did not
have a household member with mental illness during childhood.
The interaction effect between ACEs and being a caregiver was not significant for either
the logit or the count analyses of the mental health model for any of the individual ACEs.
However, in the logit analysis, being a caregiver alone remained significant (OR= 0.76, p=.006).
Most of the individual ACEs were significant as well in the logit analysis: emotional abuse (OR=
0.56, p<.001); sexual abuse (OR= 0.60, p<.001); living with someone with substance abuse
(OR= 0.74, p=.001); and living with someone with mental illness during childhood (OR= 0.70,
p<.001). In the count analysis of the model, only physical abuse (IRR= 1.08, p=.003) and having
a household member with mental illness (IRR= 1.22, p<.001) during childhood remained
significant.

70
Table 4.6 displays the results of the ZINB regressions for the physical health model (“not
good” physical health days in the last 30 days) and ACE score, first, without the interaction
effect of being a caregiver with ACEs and then with the interaction effect added. The logit
analysis of the model shows decreased odds of reporting 0 “not good” physical health days with
increased ACE scores: 1 ACE (OR= 0.74, p<.001); 2 ACEs (OR= 0.64, p<.001); 3 ACEs (OR=
0.55, p<.001); 4+ ACEs (OR= 0.43, p<.001). Being a caregiver was significant in the count
model, reporting fewer “not good” physical health days on average (IRR= 0.90, p=.033)
compared to non-caregivers. Only having 4 or more ACEs was significantly associated with
reporting more “not good” physical health days on average compared to having 0 ACEs (IRR=
1.14, p=.036). Similar to the mental health model, the interaction effect was not significant for
either the logit or the count analyses in the physical health model. However, when the interaction
effect was added, the decreased odds of reporting 0 “not good” physical health days with
increased ACE score remained significant in the logit analysis, but only having 4 or more ACEs
remained significant in the count model (IRR= 1.19, p=.014).
Table 4.7 displays the results of the ZINB regressions for the physical health model and
the individual ACEs, also without the interaction effect of being a caregiver with ACEs and then
with the interaction effect added. In the logit analysis, those who experienced emotional abuse
(OR=0.71, p<.001); sexual abuse (OR=0.63, p<.001); substance abuse in the household
(OR=0.81, p=.009); and mental illness of a household member (OR=0.69, p<.001) during
childhood were less likely to report 0 “not good” physical health days compared to those who did
not have these experiences. The count analysis shows significant associations with the number of
“not good” physical health days and respondents who lived with someone who was incarcerated
during childhood (IRR=0.81 p=.024) and those who lived with someone with mental illness

71
during childhood (IRR= 1.16, p=.025). The interaction effect did not have a significant effect on
either the odds of reporting 0 “not good” physical health days or on the number of “not good”
physical health days in the last 30 days. Noticeably, in the logit analysis with the interaction
added, the significance for substance abuse in the household disappeared, and witnessing IPV
became significant (OR=1.29, p=.028).
Discussion
This study investigated the impact of ACEs on caregivers’ mental and physical health.
We aimed to answer two research questions: 1) Do caregivers with ACEs report worse mental
and physical health than caregivers without ACEs? and 2) Which ACEs, if any, are drivers of the
relationship between ACEs and caregivers’ health? We hypothesized that that 1) caregivers will
report higher ACEs and more “not good” mental and physical health days compared to non-
caregivers, and 2) being a caregiver with 4 or more ACEs will be associated with a higher
number of “not good” mental and physical health days compared to non-caregivers as well as
those with no ACEs. Using bivariate analyses (chi-square and t-tests) and zero-inflated negative
binomial regression models (ZINB), we found associations between ACE score and the number
of “not good” mental and physical health days reported in the last 30 days, as well as
associations between particular ACEs and the number of poor mental and physical health days
among caregivers.
Findings supported the first hypothesis; descriptive statistics and bivariate analyses
showed that caregivers reported both more “not good” mental and physical health days within
the last 30 days than non-caregivers. Chi-square results also showed that caregivers were more
likely to report 4 or more ACEs compared to non-caregivers. Conversely, findings did not
support the second hypotheses, as the interaction effect was not significant for 4 or more ACEs
in either the mental health model or the physical health model. However, the interaction effect

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for the ZINB for “not good” mental health days was significant for caregivers with 1 and 2
ACEs, indicating that on average, caregivers with 1 and 2 ACEs reported more “not good”
mental health days compared to caregivers with 0 ACEs.
Living in a household with someone with mental illness, emotional abuse, sexual abuse,
and/or substance abuse were the key drivers of the relationship between ACEs and caregivers’
health. These particular ACEs consistently were associated with lower odds of reporting 0 “not
good” mental health days, or a higher number of “not good” days on average in either the mental
health model, physical health model, or both, compared to respondents who did not experience
these ACEs. Mental illness in the household remained strongly significantly associated with the
likelihood in both reporting 0 “not good” mental health days and the number of “not good”
mental health days, even with the interaction effect in the model. However, once the interaction
effect was added to the physical health model, mental health in the household was not
significantly related to the number of “not good” physical heath days, though it was associated
with the likelihood of reporting 0 “not good” physical health days. Childhood sexual and
emotional abuse decreased the likelihood of reporting 0 “not good” mental health days, and
increased the number of “not good” mental health among those who reported emotional abuse
For the physical health model, both ACEs were only significant in determining the likelihood of
reporting 0 “not good” physical health days. Living in a household with someone who abused
alcohol and/or drugs significantly impacted the likelihood of reporting 0 “not good” mental and
physical health days. After the interaction effect was added, the relationship was no longer
significant.
Based on the lack of significance of the interaction effects in the analyses and lack of
support for hypothesis #2, the present analyses do not show evidence of ACEs’ impact on

73
caregivers’ health that is distinctive from non-caregivers. ACEs, both compositely and
individually, still had an impact on mental and physical health regardless of being a caregiver,
but it is important to highlight our finding that caregivers who had 1 or 2 ACEs had poorer
mental health compared to caregivers with 0 ACEs. This finding suggests that there may be a
lower threshold at which health outcomes can become adverse for caregivers with ACEs,
indicating the possibility of an additive effect of early-life stressors and caregiving stressors. This
particular effect is only observed in our mental health model, but even being a caregiver alone
has more of an impact on mental health than physical health, as shown Tables 4.4 and 4.5.
Among the leading individual ACEs that strongly impact both mental and physical
health, living with someone with mental illness during childhood persisted regardless of
caregiver status, especially in the mental health model. However, it is important to note that
caregivers in this sample were more likely to report living with someone with mental illness
during childhood, as indicated by the chi-square results in Table 4.3. This observation suggests
an intergenerational effect, whether hereditary or environmental, of health outcomes for
caregivers. Being a caregiver with ACEs does have adverse health impacts, which is even more
for mental health than physical health. When considering the results comprehensively, it is
apparent that caregivers with ACEs in this sample have worse mental and physical health.
Although this study advances our understanding of caregiver stress in several important
way, limitations should be noted. One limitation is that ACEs data were collected years later
about experiences that occurred in childhood. Some researchers believe such retrospective
reports of abuse and adversity can be unreliable and that longitudinal data would increase the
accuracy and validity of data collection of abuse (Breton et al., 2022; Hardt & Rutter, 2004;
Widom et al., 2004; Wierson & Forehand, 1994). There is a higher probability of false reporting,

74
inconsistencies, and biases when recalling early-life abuse and trauma as an adult due to several
factors, such as current mental health status and psychological symptoms (e.g., depression,
chronic stress), age at which the adverse event occurred, and the perceived severity and
circumstances of the adverse event (Colman et al., 2016; Widom & Morris, 1997; Widom &
Shepard, 1996). However, false reports of abuse are rare, and respondents tend to underestimate
or repress occurrences of abuse and trauma (Briere & Conte, 1993; Hardt & Rutter, 2004). In
fact, Dube et al. (2004) proved with test-retest reliability that reports of abuse and household
dysfunction in the original study were consistent over time. Because the analyses in this study
are not longitudinal, the results should be taken with caution. Additionally, some BRFSS
modules are optional, and many states do not report data on these questions consistently, which
resulted in using data from fewer states, impacting the generalizability of the results. Due to this
limited data availability, certain variables, related to caregivers mental and physical health, such
as hours of sleep and marijuana use could not be used in this analysis.
Despite the limitations, this is the first study that we are aware of that examines the
relationship between ACEs and the mental and physical health of family caregivers, specifically.
These findings highlight the importance of a lifespan perspective when researching and
addressing caregiver physical and mental health. Findings have implications for practice,
suggesting that screening caregivers on their history of ACEs may be a valuable tool to identify
caregivers at higher risk for poor health outcomes who may benefit from additional resources to
support and well-being. For instance, incorporating the ACE questionnaire would be beneficial
for caregiver support programs, as it can be a step in providing trauma-informed approaches to
support caregivers. In healthcare, there have been steps taken to putting this into practice,
particularly in California. ACEs Aware, a policy initiative implemented by the Office of the

75
California Surgeon General and the California Department of Health Care Services, trains health
care providers to screen both children and adults for ACEs, as well as effectively treat the effects
of ACEs (e.g., toxic stress).
Future Research
To capture a more comprehensive relationship between ACEs and caregivers’ health,
future research should explore differences by caregiving relationship, especially since the person
receiving care could potentially be a parent or guardian who played a role in the caregivers
ACEs. Furthermore, health condition of the care receiver (e.g., dementia) and intensity of
caregiving (e.g., hours of caregiving per week) should also be considered in future research
efforts, as these details can also influence the extent to which the additive effect of early-life
stressors and caregiving stressors impact caregivers’ mental and physical health. In sum , even
though interaction effects were only found for mental and not physical health, the contextual
findings indicate that the ACEs are related to caregiver health and well-being. There are potential
reasons why for the lack of significance of the interaction effect (being a caregiver with ACEs).

76
Table 4.1. Sample Characteristics (Demographics)
(N= 33,139)
a

Total Percent or
Mean (SD)
Caregiver Non-Caregiver χ
2
(t) p
Caregiver 20.25

Age
b
49.68 (18.24) 51.50 (17.31) 49.22 (18.43) 4.42 <.001***
Gender

111.47 <.001***
Female 52.57 58.32 51.11

Male 47.43 41.68 48.89

Education

103.37 <.001***
Less than High School Diploma 12.62 10.25 13.23

High School Diploma/GED 29.17 29.26 29.15

Some College/Technical Degree 31.06 35.31 29.93

Bachelor’s Degree or higher 2.69 24.91 27.40

Race/Ethnicity

166.20 0.001**
White (Non-Hispanic) 61.17 66.70 59.77

Black (Non-Hispanic) 17.82 17 18.02

Asian/Pacific Islander 2.56 1.24 2.89

Hispanic (of any race) 14.40 11.09 15.24

Other 4.05 3.96 4.07

Marital Status

84.24 0.001**
Married/Partnered 54.47 58.86 53.86

Divorced/Separated 14.56 16.36 14.10

Widowed 8.05 6.23 8.52

Never Married 22.34 20.14 22.89

Employment Status

19.57 0.068
Employed 53.78 51.96 54.25

Not Employed 45.39 47.47 44.87

Household Composition
b

Number of Adults in Household 2.28 (1.11) 2.40 (1.17) 2.26 (1.10) 3.88 <.001***
Number of Children in Household 0.67 (1.13) 0.67 (1.24) 0.67 (1.10) 0.01 0.889

77
State

86.21 <.001***
Florida 38.36 38.58 38.30

Georgia 2.25 19.66 23.22

Tennessee 15.69 18.86 14.88

Utah 2.70 2.49 2.76

Virginia 20.75 20.41 20.84

a
weighted sample
b
mean, t-statistic
*p<0.05, **p<0.01, ***p<0.001

78
Table 4.2. Sample Characteristics (Health Conditions and Behaviors)

(N= 33,139)
a

Total Percent or
Mean (SD)
Caregiver
Non-
Caregiver
χ
2
(t) p
Number of “Not good” Mental Health Days (in the last 30 days)
b
4.27 (8.24) 5.53 (9.61) 3.95 (8.08) 6.65 <.001***
Number of “Not good” Physical Health Days (in the last 30 days)
b
3.87 (8.40) 4.29 (8.31) 3.76 (8.76) 2.54 0.01*
Heart Attack 5 1.09 3.92 5.34 0.38
Coronary Heart Disease 4.58 0.97 3.61 0.72 0.82
Stroke 4.04 0.83 3.21 0.08 0.98
Diabetes 12.68 2.75 9.92 10.87 0.12
Arthritis 27.37 7.43 19.94 367.07 <.001***
Cancer 13.57 3.52 10.06 105.25 <.001***
Exercise (in the last 30 days) 71.82 15.01 56.82 22.51 0.03*
Tobacco Use

194.94 <.001***
Not at all 81.24 75.36 82.73

Some days 6.20 7.84 5.79

Every day 12.52 16.78 11.44

Alcohol Consumption (number of drinks in the past 30 days) 4.47 (7.98) 4.45 (8.21) 4.47 (7.92) -0.12 0.91
a
weighted sample
b
mean, t-statistic

*p<0.05, **p<0.01, ***p<0.001

79
Table 4.3. Sample Characteristics (ACE Score and ACEs)

(N= 33,144)
a

Total Percent
or Mean
(SD)
Caregiver Non-Caregiver χ
2
(t) p
ACE Score
b
1.53 (1.84) 1.99 (2.14) 1.42 (1.75) 8.61 <.001***
0 39.73 32.79 41.49 508.05 <.001***
1 23.48 19.59 24.46

2 13.22 14.95 12.78

3 8.22 9.85 7.81

4+ 15.35 22.82 13.45

Individual ACEs

Physical Abuse 17.37 4.71 12.66 207.56 <.001***
Emotional/Verbal Abuse 26.89 7.15 19.74 303.60 <.001***
Sexual Abuse 13.42 3.93 9.48 261.93 <.001***
Parental Separation/Divorce 31.32 6.74 24.58 20.07 0.17
Witness IPV 12.17 3.42 8.75 117.19 <.001***
Substance Abuse in Household 27.18 7.21 19.97 362.38 <.001***
Household Member Incarcerated 9.48 2.69 6.79 143.14 <.001***
Household Member with Mental Illness 15.77 4.46 1.13 270.14 <.001***
a
weighted sample
b
mean, t-statistic

*p<0.05, **p<0.01, ***p<0.001

80
Table 4.4. Zero-Inflated Negative Binomial Regression of Caregivers' “Not good” Mental Health Days, ACE Score
a

Without Interaction With Interaction

Logit Count Logit Count

OR (95% CI) p IRR (95% CI) p OR (95% CI) p IRR (95% CI) p
Caregiver
0.75 (0.65-0.86) <.001*** 1.06 (0.98-1.14) 0.13 0.75 (0.6-0.94) 0.01* 0.89 (0.76-1.04) 0.14
ACE Score

1 ACE 0.74 (0.64-0.86) <.001*** 1.09 (0.98-1.21) 0.10 0.75 (0.64-0.89) <.001*** 1.03 (0.91-1.15) 0.66
2 ACEs 0.51 (0.42-0.61) <.001*** 1.11 (0.99-1.25) 0.07 0.48 (0.38-0.59) <.001*** 1.02 (0.89-1.16) 0.78
3 ACEs 0.41 (0.34-0.51) <.001*** 1.17 (1.04-1.31) 0.01* 0.42 (0.33-0.53) <.001*** 1.16 (1.02-1.32) 0.02*
4+ ACEs 0.31 (0.26-0.37) <.001*** 1.36 (1.23-1.49) <.001*** 0.32 (0.26-0.39) <.001*** 1.33 (1.19-1.48) <.001***
Interaction

1 ACE x Caregiver

0.9 (0.63-1.29) 0.57 1.35 (1.09-1.68) 0.01*
2 ACEs x Caregiver

1.28 (0.83-1.99) 0.26 1.49 (1.16-1.92) <.001***
3 ACEs x Caregiver

0.97 (0.62-1.54) 0.91 1.06 (0.8-1.4) 0.69
4+ ACEs x Caregiver

0.9 (0.6-1.34) 0.60 1.14 (0.94-1.39) 0.17
a
Controlling for all covariates in Tables 1 and 2

Note: OR, Odds Ratio; IRR, Incidence Rate Ratio; CI, confidence interval

*p<0.05, **p<0.01, ***p<0.001

81
Table 4.5. Zero-Inflated Negative Binomial Regression of Caregivers' “Not good” Mental Health Days, Individual ACEs
a

Without Interaction With Interaction

Logit Count Logit Count

OR (95% CI) p IRR (95% CI) p OR (95% CI) p IRR (95% CI) p
Caregiver
0.73 (0.64-0.85) <.001*** 1.06 (0.98-1.15) 0.13 0.76 (0.63-0.92) 0.01* 1.07 (0.94-1.22) 0.30
ACEs

Physical Abuse 0.88 (0.74-1.05) 0.17 1.12 (1.02-1.22) 0.02* 0.95 (0.77-1.17) 0.61 1.18 (1.06-1.31) 0.003**
Emotional/Verbal Abuse 0.58 (0.49-0.67) <.001*** 1.04 (0.95-1.13) 0.42 0.56 (0.48-0.67) <.001*** 1.04 (0.94-1.15) 0.42
Sexual Abuse 0.61 (0.5-0.75) <.001*** 1.07 (0.98-1.17) 0.15 0.6 (0.47-0.77) <.001*** 1.06 (0.95-1.19) 0.27
Parental Divorce/Separation 0.98 (0.85-1.13) 0.78 0.97 (0.89-1.05) 0.47 0.96 (0.82-1.13) 0.65 0.97 (0.88-1.06) 0.49
Witnessed IPV 1.16 (0.94-1.43) 0.16 1.01 (0.91-1.12) 0.85 1.19 (0.94-1.51) 0.16 1 (0.89-1.12) 0.99
Substance Abuse in the
Household
0.76 (0.66-0.89) <.001*** 0.98 (0.9-1.07) 0.69 0.74 (0.62-0.88) 0.001** 0.97 (0.87-1.08) 0.56
Household Member
Incarcerated
0.98 (0.77-1.25) 0.87 1.05 (0.94-1.17) 0.36 1.05 (0.8-1.39) 0.73 1.02 (0.9-1.17) 0.73
Household Member with
Mental Illness
0.67 (0.57-0.8) <.001*** 1.23 (1.14-1.33) <.001*** 0.7 (0.58-0.85) <.001*** 1.22 (1.11-1.33) <.001***
Interaction

Physical Abuse x Caregiver

0.72 (0.48-1.1) 0.13 0.84 (0.69-1.02) 0.08
Emotional/Verbal Abuse x
Caregiver

1.11 (0.76-1.62) 0.59 0.98 (0.8-1.2) 0.85
Sexual Abuse x Caregiver

1.02 (0.67-1.57) 0.92 1.01 (0.85-1.2) 0.93
Parental Divorce/Separation
x Caregiver

1.08 (0.77-1.51) 0.65 1.02 (0.86-1.21) 0.84
Witnessed IPV x Caregiver

0.94 (0.58-1.52) 0.80 1.03 (0.82-1.3) 0.80
Substance Abuse in the Household x Caregiver

1.17 (0.83-1.65) 0.38 1.03 (0.86-1.24) 0.71
Household Member Incarcerated x Caregiver

0.74 (0.42-1.29) 0.29 1.08 (0.87-1.35) 0.48
Household Member with Mental Illness x
Caregiver
0.81 (0.53-1.24) 0.34 1.04 (0.88-1.22) 0.80
a
Controlling for all covariates in Tables 1 and 2

Note: OR, Odds Ratio; IRR, Incidence Rate Ratio; CI, confidence interval

*p<0.05, **p<0.01, ***p<0.001

82
Table 4.6. Zero-Inflated Negative Binomial Regression of Caregivers' “Not good” Physical Health Days, ACE Score
a

Without Interaction With Interaction

Logit Count Logit Count

OR (95% CI) p IRR (95% CI) p OR (95% CI) p IRR (95% CI) p
Caregiver
0.86 (0.74-1.01) 0.10 0.9 (0.82-0.99) 0.03* 0.88 (0.7-1.1) 0.26 0.92 (0.79-1.08) 0.33
ACE Score

1 ACE 0.74 (0.63-0.87)
<.001***
0.91 (0.82-1.02)
0.10 0.73 (0.6-0.88)
0.001** 0.89 (0.79-1) 0.05
2 ACEs 0.64 (0.52-0.77)
<.001***
1.14 (0.95-1.36)
0.16 0.63 (0.51-0.78)
<.001*** 1.15 (0.92-1.43) 0.22
3 ACEs 0.55 (0.44-0.69)
<.001***
1.01 (0.88-1.16)
0.90 0.59 (0.46-0.75)
<.001*** 1.03 (0.88-1.2) 0.73
4+ ACEs 0.43 (0.36-0.52)
<.001***
1.14 (1.01-1.28)
0.04* 0.44 (0.36-0.53)
<.001*** 1.19 (1.04-1.36) 0.01*
Interaction

1 ACE x Caregiver

1.08 (0.75-1.54) 0.69 1.15 (0.91-1.45) 0.23
2 ACEs x Caregiver

1.04 (0.65-1.65) 0.88 0.95 (0.68-1.33) 0.77
3 ACEs x Caregiver

0.75 (0.45-1.27) 0.29 0.92 (0.66-1.27) 0.60
4+ ACEs x Caregiver

0.95 (0.58-1.55) 0.83 0.86 (0.67-1.11) 0.25
a
Controlling for all covariates in Tables 4.1 and 4.2

Note: OR, Odds Ratio; IRR, Incidence Rate Ratio; CI, confidence interval

*p<0.05, **p<0.01, ***p<0.001

83
Table 4.7. Zero-Inflated Negative Binomial Regression of Caregivers' “Not good” Physical Health Days, Individual ACEs
a

Without Interaction With Interaction

Logit Count Logit Count

OR (95% CI) p IRR (95% CI) p OR (95% CI) p IRR (95% CI) p
Caregiver
0.87 (0.74-1.03) 0.10 0.92 (0.83-1.01) 0.08 0.9 (0.74-1.09) 0.28 1.02 (0.9-1.16) 0.77
ACEs

Physical Abuse 0.94 (0.78-1.13) 0.51 1.02 (0.91-1.14) 0.72
1 (0.82-1.23)
0.99
1.09 (0.95-1.25)
0.21
Emotional/Verbal Abuse 0.71 (0.6-0.84) <.001*** 1.07 (0.94-1.23) 0.31 0.67 (0.56-0.82) <.001*** 1.09 (0.92-1.29) 0.30
Sexual Abuse 0.63 (0.5-0.78) <.001*** 1.03 (0.91-1.17) 0.64 0.66 (0.51-0.85) <.001*** 1.01 (0.87-1.18) 0.87
Parental Divorce/Separation 0.98 (0.83-1.16) 0.83 0.94 (0.84-1.06) 0.30 0.96 (0.79-1.16) 0.65 0.96 (0.84-1.1) 0.54
Witnessed IPV 1.24 (1-1.52) 0.05 1.13 (0.98-1.29) 0.10 1.29 (1.03-1.61) 0.03* 1.14 (0.97-1.35) 0.11
Substance Abuse in the
Household
0.81 (0.69-0.95) 0.01* 1.05 (0.91-1.21) 0.50
0.84 (0.7-1.01)
0.06
1.06 (0.89-1.26)
0.51
Household Member Incarcerated 1.02 (0.77-1.35) 0.88 0.81 (0.68-0.97) 0.02* 1.22 (0.92-1.62) 0.16 0.84 (0.68-1.04) 0.12
Household Member with Mental
Illness
0.69 (0.57-0.84) <.001*** 1.16 (1.02-1.32) 0.03*
0.61 (0.5-0.74)
<.001***
1.11 (0.96-1.29)
0.15
Interaction

Physical Abuse x Caregiver

0.78 (0.5-1.2) 0.26 0.8 (0.62-1.02) 0.07
Emotional/Verbal Abuse x
Caregiver

1.22 (0.81-1.86)
0.34 0.92 (0.71-1.19) 0.52
Sexual Abuse x Caregiver

0.81 (0.5-1.33) 0.41 1.05 (0.82-1.35) 0.68
Parental Divorce/Separation x
Caregiver

1.15 (0.79-1.68)
0.47 0.93 (0.74-1.17) 0.52
Witnessed IPV x Caregiver

0.86 (0.52-1.43) 0.57 0.94 (0.71-1.26) 0.69
Substance Abuse in the Household x Caregiver

0.88 (0.62-1.26) 0.49 0.94 (0.74-1.21) 0.64
Household Member Incarcerated x Caregiver

0.53 (0.25-1.11)
0.09 0.94 (0.66-1.33) 0.71
Household Member with Mental Illness x Caregiver 1.54 (0.98-2.4) 0.06 1.15 (0.9-1.48) 0.26
a
Controlling for all covariates in Tables 4.1 and 4.2

Note: OR, Odds Ratio; IRR, Incidence Rate Ratio; CI, confidence interval

*p<0.05, **p<0.01, ***p<0.001

84
Supplemental Table 4.1. Full Zero-Inflated Negative Binomial Regression of Caregivers' “Not good” Mental Health Days, ACE
Score
a

Without Interaction With Interaction

Logit Count Logit Count

OR (95% CI) p IRR (95% CI) p OR (95% CI) p IRR (95% CI) p
Age
1.04 (1.04-1.05) <.001*** 0.99 (0.99-1) <.001*** 1.04 (1.04-1.05) <.001** 0.99 (0.99-1) <.001***
Female
0.51 (0.45-0.58) <.001*** 1.06 (0.98-1.13) 0.14 0.51 (0.46-0.58) <.001** 1.06 (0.99-1.13) 0.12
Race/Ethnicity

Black (Non-Hispanic) 1.4 (1.2-1.65) <.001*** 0.99 (0.89-1.09) 0.80 1.4 (1.2-1.65) <.001*** 0.98 (0.89-1.09) 0.76
Asian/Pacific Islander 1.35 (0.9-2.03) 0.15 0.98 (0.75-1.28) 0.88 1.35 (0.89-2.03) 0.16 0.98 (0.75-1.27) 0.87
Hispanic (of any race) 1.69 (1.31-2.18) <.001*** 1.02 (0.89-1.17) 0.75 1.69 (1.32-2.18) <.001** 1.02 (0.9-1.17) 0.72
Other 1.09 (0.88-1.34) 0.44 1.09 (0.95-1.25) 0.23 1.09 (0.88-1.34) 0.44 1.09 (0.95-1.25) 0.22
Education

High School Diploma/
GED
1.21 (0.95-1.53) 0.12 0.93 (0.82-1.04) 0.20 1.2 (0.95-1.52) 0.13 0.92 (0.81-1.03) 0.14
Some College/ Technical
Degree 0.96 (0.76-1.21)
0.73
0.91 (0.81-1.03)
0.13 0.96 (0.76-1.2) 0.70 0.91 (0.81-1.02) 0.11
Bachelor's Degree or
Higher
0.85 (0.68-1.08) 0.18 0.79 (0.69-0.89) <.001*** 0.85 (0.68-1.07) 0.16 0.77 (0.68-0.88) <.001***
Marital Status

Divorced/Separated 0.7 (0.59-0.84) <.001*** 1.17 (1.06-1.29) <.001*** 0.7 (0.59-0.84) <.001*** 1.17 (1.06-1.28) <.001**
Widowed 0.85 (0.71-1.01) 0.07 1.17 (1.05-1.3) <.001*** 0.85 (0.71-1.01) 0.07 1.17 (1.05-1.31) <.001**
Never Married 0.68 (0.57-0.81) <.001*** 1.05 (0.96-1.16) 0.28 0.68 (0.57-0.81) <.001*** 1.05 (0.96-1.16) 0.27
Employed
1.34 (1.18-1.53) <.001*** 0.82 (0.76-0.89) <.001** 1.35 (1.18-1.54) <.001*** 0.83 (0.77-0.9) <.001***
Household Composition

Number of Adults in
Household
0.94 (0.88-1.01) 0.08 0.98 (0.95-1.01) 0.72 0.94 (0.88-1.01) 0.08 1 (0.96-1.03) 0.77
Number of Children in
Household
1.04 (0.98-1.09) 0.21 0.99 (0.96-1.03) 0.17 1.04 (0.98-1.1) 0.19 0.98 (0.95-1.01) 0.18
Caregiver
0.75 (0.65-0.86) <.001*** 1.06 (0.98-1.14) 0.13 0.75 (0.6-0.94) 0.01* 0.89 (0.76-1.04) 0.14
Chronic Health Conditions

85
Heart Attack 0.95 (0.75-1.2) 0.64 1.15 (1.01-1.31) 0.03* 0.94 (0.75-1.19) 0.63
1.14 (1.01-1.3)
0.04*
Coronary Heart Disease 0.69 (0.55-0.87) <.001*** 1.17 (1-1.36) 0.06 0.69 (0.55-0.87) <.001*** 1.18 (1.01-1.38) 0.14
Diabetes 0.75 (0.65-0.88) <.001*** 1.07 (0.98-1.17) 0.11 0.75 (0.64-0.88) <.001***
1.07 (0.98-1.16)
<.001***
Stroke 0.67 (0.53-0.84) <.001***
0.98 (0.88-1.09)
0.74 0.67 (0.53-0.84) <.001*** 0.97 (0.88-1.08) 0.64
Arthritis 0.62 (0.54-0.7) <.001*** 1.14 (1.06-1.23) <.001*** 0.62 (0.54-0.7) <.001*** 1.14 (1.06-1.23) <.001***
Cancer 0.83 (0.72-0.97) 0.02* 1.03 (0.94-1.12) 0.56 0.84 (0.72-0.97) 0.02* 1.04 (0.95-1.13) 0.44
Health Behaviors

Exercise (in the last 30
days)
1.16 (1.01-1.34) 0.04* 0.75 (0.7-0.81) <.001*** 1.16 (1.01-1.34) 0.03 0.76 (0.7-0.81) <.001***
Tobacco Use

Some Days 0.87 (0.61-1.22) 0.42 1.11 (1-1.23) 0.05 0.87 (0.62-1.22) 0.41 1.11 (1-1.24) 0.05
Every Day 0.86 (0.73-1) 0.05 1.23 (1.14-1.33) <.001*** 0.86 (0.73-1) 0.05 1.24 (1.14-1.34) <.001***
Alcohol Consumption
(number of drinks in the
past 30 days)
0.99 (0.98-1) 0.02* 1 (1-1.01) 0.04* 0.99 (0.98-1) 0.02 1 (1-1.01) 0.05
State

Georgia 0.9 (0.77-1.05) 0.16 0.96 (0.87-1.06) 0.43 0.9 (0.77-1.05) 0.18 0.97 (0.88-1.07) 0.54
Tennessee 0.83 (0.72-0.97) 0.02* 0.9 (0.82-0.99) 0.03* 0.84 (0.72-0.98) 0.03* 0.91 (0.83-1) 0.06
Utah 0.57 (0.49-0.66) <.001*** 0.9 (0.82-0.98) 0.02* 0.57 (0.49-0.66) <.001** 0.9 (0.82-0.99) 0.04*
Virginia 0.92 (0.8-1.05) 0.21 0.93 (0.85-1.01) 0.10 0.92 (0.8-1.06) 0.23 0.94 (0.86-1.03) 0.16
ACE Score

1 ACE 0.74 (0.64-0.86) <.001*** 1.09 (0.98-1.21) 0.10 0.75 (0.64-0.89) <.001*** 1.03 (0.91-1.15) 0.66
2 ACEs 0.51 (0.42-0.61) <.001*** 1.11 (0.99-1.25) 0.07 0.48 (0.38-0.59) <.001*** 1.02 (0.89-1.16) 0.78
3 ACEs 0.41 (0.34-0.51) <.001*** 1.17 (1.04-1.31) 0.01* 0.42 (0.33-0.53) <.001*** 1.16 (1.02-1.32) 0.02*
4+ ACEs 0.31 (0.26-0.37) <.001*** 1.36 (1.23-1.49) <.001*** 0.32 (0.26-0.39) <.001*** 1.33 (1.19-1.48) <.001***
Interaction

1 ACE x Caregiver

0.9 (0.63-1.29) 0.57 1.35 (1.09-1.68) 0.01*
2 ACEs x Caregiver

1.28 (0.83-1.99) 0.26 1.49 (1.16-1.92) <.001***
3 ACEs x Caregiver

0.97 (0.62-1.54) 0.91 1.06 (0.8-1.4) 0.69
4+ ACEs x Caregiver 0.9 (0.6-1.34) 0.60 1.14 (0.94-1.39) 0.17
a
OR, Odds Ratio; IRR, Incidence Rate Ratio; CI, confidence interval

*p<0.05, **p<0.01, ***p<0.001

86
Supplemental Table 4.2. Full Zero-Inflated Negative Binomial Regression of Caregivers' “Not good” Mental Health Days, Individual
ACEs
a

Without Interaction With Interaction

Logit Count Logit Count

OR (95% CI) p IRR (95% CI) p OR (95% CI) p IRR (95% CI) p
Age
1.04 (1.04-1.05) <.001*** 1 (0.99-1) 0.01* 1.04 (1.03-1.05) <.001*** 1 (0.99-1) 0.004**
Female
0.53 (0.47-0.6) <.001*** 1.04 (0.97-1.12) 0.28 0.53 (0.47-0.6) <.001*** 1.04 (0.97-1.12) 0.26
Race/Ethnicity

Black (Non-Hispanic) 1.26 (1.06-1.5) 0.01* 1.03 (0.93-1.16) 0.55 1.26 (1.06-1.5) 0.01* 1.03 (0.93-1.15) 0.55
Asian/Pacific Islander 1.3 (0.85-1.99) 0.23 0.99 (0.76-1.31) 0.97 1.3 (0.85-1.99) 0.23 0.99 (0.75-1.3) 0.94
Hispanic (of any race) 1.57 (1.21-2.05) <.001*** 1.02 (0.89-1.18) 0.75 1.57 (1.21-2.04) <.001*** 1.02 (0.89-1.18) 0.74
Other 1.01 (0.8-1.27) 0.97 1.1 (0.95-1.28) 0.20 1.01 (0.8-1.27) 0.96 1.1 (0.95-1.28) 0.21
Education

High School Diploma/
GED
1.15 (0.89-1.48)
0.30
0.99 (0.87-1.12) 0.83 1.14 (0.89-1.47) 0.31 0.99 (0.87-1.12) 0.85
Some College/
Technical Degree
0.94 (0.74-1.21) 0.64 0.93 (0.82-1.05) 0.24 0.94 (0.74-1.2) 0.63 0.93 (0.83-1.05) 0.25
Bachelor's Degree or
Higher
0.87 (0.68-1.11)
0.27
0.81 (0.71-0.92)
<.001**
*
0.87 (0.68-1.11) 0.25 0.81 (0.71-0.92) 0.002**
Marital Status

Divorced/Separated 0.68 (0.56-0.82) <.001*** 1.16 (1.05-1.29) 0.01* 0.68 (0.56-0.82) <.001*** 1.16 (1.05-1.29) <.001***
Widowed 0.82 (0.68-0.99) 0.04* 1.17 (1.04-1.32) 0.01* 0.82 (0.68-0.99) 0.04* 1.17 (1.04-1.32) 0.01*
Never Married 0.68 (0.57-0.82) <.001*** 1.07 (0.97-1.18) 0.19 0.68 (0.57-0.82) <.001*** 1.06 (0.97-1.17) 0.21
Employed
1.31 (1.14-1.51) <.001*** 0.83 (0.77-0.9)
<.001**
*
1.31 (1.14-1.51) 0.001** 0.83 (0.76-0.9) <.001***
Household Composition

Number of Adults in
Household
0.97 (0.91-1.03) 0.34 0.99 (0.96-1.03) 0.77 0.97 (0.91-1.03) 0.35 1 (0.96-1.03) 0.79
Number of Children in
Household
1.06 (1.01-1.12) 0.03* 0.99 (0.96-1.02) 0.47 1.07 (1.01-1.13) 0.02* 0.99 (0.96-1.02) 0.45
Caregiver
0.73 (0.64-0.85) <.001*** 1.06 (0.98-1.15) 0.13 0.76 (0.63-0.92) 0.01* 1.07 (0.94-1.22) 0.30

87
Chronic Health Conditions

Heart Attack 0.92 (0.71-1.18) 0.51 1.16 (1-1.33) 0.04* 0.92 (0.72-1.19) 0.53 1.15 (1-1.33) 0.05
Coronary Heart Disease 0.67 (0.53-0.85) <.001*** 1.18 (0.98-1.41) 0.08 0.67 (0.52-0.85) <.001*** 1.18 (0.98-1.41) 0.08
Diabetes 0.82 (0.7-0.95) 0.01* 1.09 (0.99-1.19) 0.07 0.81 (0.7-0.95) 0.01* 1.09 (0.99-1.19) 0.07
Stroke 0.63 (0.49-0.81) <.001*** 0.95 (0.84-1.07) 0.41 0.63 (0.49-0.81) <.001*** 0.95 (0.84-1.07) 0.42
Arthritis 0.64 (0.56-0.73) <.001*** 1.15 (1.06-1.24)
<.001**
*
0.63 (0.56-0.72) <.001*** 1.14 (1.06-1.24) 0.001**
Cancer 0.86 (0.74-1.01) 0.06 1.02 (0.92-1.12) 0.73 0.86 (0.74-1.01) 0.06 1.01 (0.92-1.11) 0.77
Health Behaviors

Exercise (in the last 30
days)
1.13 (0.98-1.3) 0.10 0.75 (0.69-0.81)
<.001**
*
1.13 (0.98-1.3) 0.09 0.75 (0.69-0.81) <.001***
Tobacco Use

Some Days 0.88 (0.6-1.29) 0.51 1.15 (1.02-1.28) 0.02* 0.88 (0.61-1.29) 0.52 1.15 (1.02-1.29) 0.02*
Every Day 0.82 (0.7-0.97) 0.02* 1.27 (1.16-1.38)
<.001**
*
0.83 (0.7-0.97) 0.02* 1.27 (1.17-1.38) <.001***
Alcohol Consumption
(number of drinks in the
past 30 days)
0.99 (0.99-1) 0.03* 1.01 (1-1.01) 0.01* 0.99 (0.99-1) 0.03* 1.01 (1-1.01) 0.02*
State

Georgia 0.83 (0.71-0.97) 0.02* 0.96 (0.87-1.07) 0.46 0.83 (0.7-0.97) 0.02* 0.96 (0.87-1.07) 0.49
Tennessee 0.77 (0.66-0.91) <.001*** 0.89 (0.81-0.98) 0.02* 0.77 (0.66-0.91) <.001*** 0.89 (0.81-0.99) 0.03*
Utah 0.55 (0.46-0.64) <.001***
0.89 (0.81-0.99)
0.03* 0.54 (0.46-0.64) <.001*** 0.89 (0.81-0.99) 0.03*
Virginia 0.87 (0.75-1.01) 0.06
0.93 (0.85-1.03)
0.17 0.87 (0.75-1) 0.06 0.94 (0.85-1.03) 0.17
ACEs

Physical Abuse 0.88 (0.74-1.05) 0.17 1.12 (1.02-1.22) 0.02* 0.95 (0.77-1.17) 0.61
1.18 (1.06-1.31)
0.003**
Emotional/Verbal Abuse 0.58 (0.49-0.67) <.001*** 1.04 (0.95-1.13) 0.42 0.56 (0.48-0.67) <.001***
1.04 (0.94-1.15)
0.42
Sexual Abuse 0.61 (0.5-0.75) <.001*** 1.07 (0.98-1.17) 0.15 0.6 (0.47-0.77) <.001***
1.06 (0.95-1.19)
0.27
Parental
Divorce/Separation
0.98 (0.85-1.13) 0.78 0.97 (0.89-1.05) 0.47 0.96 (0.82-1.13) 0.65
0.97 (0.88-1.06)
0.49
Witnessed IPV 1.16 (0.94-1.43) 0.16 1.01 (0.91-1.12) 0.85 1.19 (0.94-1.51) 0.16
1 (0.89-1.12)
0.99
Substance Abuse in the
Household
0.76 (0.66-0.89) <.001*** 0.98 (0.9-1.07) 0.69 0.74 (0.62-0.88) 0.001**
0.97 (0.87-1.08)
0.56
Household Member
Incarcerated
0.98 (0.77-1.25) 0.87 1.05 (0.94-1.17) 0.36 1.05 (0.8-1.39) 0.73
1.02 (0.9-1.17)
0.73

88
Household Member
with Mental Illness
0.67 (0.57-0.8) <.001*** 1.23 (1.14-1.33)
<.001**
*
0.7 (0.58-0.85) <.001*** 1.22 (1.11-1.34) <.001***
Interaction

Physical Abuse x
Caregiver

0.72 (0.48-1.1) 0.13 0.84 (0.69-1.02) 0.08
Emotional/Verbal Abuse
x Caregiver

1.11 (0.76-1.62) 0.59 0.98 (0.8-1.2) 0.85
Sexual Abuse x
Caregiver

1.02 (0.67-1.57) 0.92 1.01 (0.85-1.2) 0.93
Parental Divorce/
Separation x Caregiver

1.08 (0.77-1.51) 0.65 1.02 (0.86-1.21) 0.84
Witnessed IPV x
Caregiver

0.94 (0.58-1.52) 0.80 1.03 (0.82-1.3) 0.80
Substance Abuse in the Household x
Caregiver

1.17 (0.83-1.65) 0.38 1.04 (0.86-1.25) 0.71
Household Member Incarcerated x
Caregiver

0.74 (0.42-1.29) 0.29 1.08 (0.87-1.34) 0.48
Household Member with Mental Illness x
Caregiver

0.81 (0.53-1.24) 0.34 1.02 (0.87-1.21) 0.8
Note: OR, Odds Ratio; IRR, Incidence Rate Ratio; CI, confidence interval

*p<0.05, **p<0.01, ***p<0.001

89
Supplemental Table 4.3. Full Zero-Inflated Negative Binomial Regression of Caregivers' “Not good” Physical Health Days, ACE
Score

Without Interaction With Interaction

Logit Count Logit Count

OR (95%
CI)
p IRR (95% CI) p OR (95% CI) p IRR (95% CI) p
Age
1.02 (1.01-1.02)
<.001**
*
1 (1-1.01) 0.05 1.02 (1.01-1.02) <.001*** 1 (1-1.01) 0.05
Female
0.83 (0.74-0.94) 0.003** 0.99 (0.9-1.08) 0.80 0.83 (0.74-0.94) 0.004** 0.99 (0.91-1.08) 0.81
Race/Ethnicity

Black (Non-Hispanic) 1.14 (0.94-1.37) 0.18 1.06 (0.91-1.23) 0.44 1.14 (0.94-1.37) 0.18 1.06 (0.92-1.22) 0.45
Asian/Pacific Islander 0.99 (0.64-1.54) 0.97 0.92 (0.68-1.25) 0.60 0.99 (0.64-1.54) 0.98 0.93 (0.69-1.25) 0.62
Hispanic (of any race) 1.41 (1.11-1.8) 0.01* 1.06 (0.88-1.27) 0.53 1.42 (1.12-1.8) 0.004** 1.07 (0.89-1.28) 0.49
Other 0.84 (0.63-1.11) 0.21 0.98 (0.86-1.11) 0.72 0.84 (0.63-1.1) 0.21 0.97 (0.85-1.11) 0.67
Education

High School
Diploma/GED
1.16 (0.91-1.46)
0.23
0.97 (0.85-1.11) 0.68 1.16 (0.92-1.46) 0.22 0.97 (0.85-1.12) 0.71
Some College/Technical
Degree
1.26 (1.01-1.58) 0.04* 0.87 (0.77-0.99) 0.03* 1.27 (1.01-1.58) 0.04* 0.88 (0.78-0.99) 0.03*
Bachelor's Degree or
Higher
1.12 (0.89-1.41)
0.33
0.77 (0.67-0.88) <.001*** 1.12 (0.89-1.41) 0.32 0.77 (0.68-0.88) <.001***
Marital Status

Divorced/Separated 0.78 (0.65-0.94) 0.01* 1.16 (1.05-1.27) 0.003** 0.78 (0.65-0.94) 0.01* 1.16 (1.05-1.28) 0.003**
Widowed 0.85 (0.71-1.03) 0.11 0.98 (0.88-1.1) 0.78 0.86 (0.71-1.04) 0.11 0.99 (0.89-1.1) 0.84
Never Married 0.81 (0.66-0.99) 0.04* 0.93 (0.81-1.07) 0.30 0.81 (0.66-0.99) 0.04* 0.93 (0.81-1.07) 0.31
Employed
1.45 (1.26-1.66)
<.001**
*
0.75 (0.67-0.83) <.001*** 1.45 (1.26-1.66) <.001*** 0.74 (0.67-0.82) <.001***
Household Composition

Number of Adults in
Household
0.96 (0.9-1.03) 0.23 1.02 (0.97-1.08) 0.39 0.96 (0.9-1.03) 0.24 1.02 (0.97-1.08) 0.36
Number of Children in
Household
1.02 (0.95-1.09) 0.66 0.97 (0.93-1.02) 0.26 1.02 (0.95-1.09) 0.64 0.97 (0.93-1.02) 0.31
Caregiver
0.86 (0.74-1.01) 0.06 0.9 (0.82-0.99) 0.03* 0.88 (0.7-1.1) 0.26 0.92 (0.79-1.08) 0.33

90
Chronic Health Conditions

Heart Attack 0.72 (0.54-0.94) 0.02* 0.99 (0.88-1.11) 0.85 0.71 (0.54-0.94) 0.02* 0.99 (0.88-1.11) 0.83
Coronary Heart Disease 0.57 (0.44-0.73)
<.001
***
1.23 (1.1-1.36) <.001** 0.57 (0.44-0.74) <.001*** 1.23 (1.11-1.37) <.001***
Diabetes 0.61 (0.52-0.72)
<.001
***
1.11 (1.03-1.2) 0.01* 0.61 (0.52-0.72) <.001*** 1.11 (1.03-1.2) 0.01*
Stroke 0.48 (0.36-0.64)
<.001
***
1.26 (1.13-1.4) <.001** 0.48 (0.36-0.64) <.001*** 1.26 (1.13-1.4) <.001***
Arthritis 0.39 (0.35-0.45)
<.001
***
1.36 (1.26-1.46) <.001** 0.39 (0.35-0.45) <.001*** 1.35 (1.25-1.46) <.001***
Cancer 0.74 (0.63-0.86)
<.001
***
1.17 (1.07-1.28) <.001** 0.74 (0.63-0.86) <.001*** 1.17 (1.07-1.28) <.001***
Health Behaviors

Exercise (in the last 30
days)
1.42 (1.24-1.63)
<.001**
*
0.64 (0.59-0.7) <.001*** 1.42 (1.25-1.63) <.001*** 0.64 (0.59-0.7) <.001***
Tobacco Use

Some Days 0.79 (0.52-1.19) 0.26 0.88 (0.73-1.05) 0.16 0.79 (0.52-1.19) 0.26 0.88 (0.74-1.05) 0.16
Every Day 0.95 (0.8-1.13) 0.56 1.18 (1.06-1.32) 0.002** 0.95 (0.8-1.13) 0.56 1.19 (1.07-1.32) 0.001**
Alcohol Consumption
(number of drinks in the
past 30 days)
1.01 (1-1.01) 0.23 0.99 (0.99-1) <.001 1.01 (1-1.01) 0.23 0.99 (0.99-1) <.001***
State

Georgia 0.79 (0.67-0.95) 0.01* 0.83 (0.73-0.95)
0.01*
0.79 (0.67-0.95) 0.01 0.83 (0.73-0.95) 0.01*
Tennessee 0.63 (0.53-0.75)
<.001**
*
0.96 (0.84-1.09)
0.49
0.63 (0.53-0.75) <.001*** 0.96 (0.84-1.09) 0.52
Utah 0.79 (0.66-0.93)
0.01* 0.93 (0.81-1.07)
0.33 0.79 (0.66-0.93) 0.01 0.93 (0.81-1.07) 0.33
Virginia
0.61 (0.52-0.71)
<.001**
* 0.93 (0.82-1.06)
0.28 0.61 (0.52-0.71) <.001*** 0.93 (0.82-1.06) 0.28
ACE Score

1 ACE 0.74 (0.63-0.87)
<.001**
*
0.91 (0.82-1.02) 0.10 0.73 (0.6-0.88) 0.001** 0.89 (0.79-1) 0.05
2 ACEs 0.64 (0.52-0.77)
<.001**
*
1.14 (0.95-1.36) 0.16 0.63 (0.51-0.78) <.001*** 1.15 (0.92-1.43) 0.22
3 ACEs 0.55 (0.44-0.69)
<.001**
*
1.01 (0.88-1.16) 0.90
0.59 (0.46-0.75) <.001***
1.03 (0.88-1.2) 0.73
4+ ACEs 0.43 (0.36-0.52)
<.001**
*
1.14 (1.01-1.28) 0.04* 0.44 (0.36-0.53) <.001*** 1.19 (1.04-1.36) 0.01*

91
Interaction

1 ACE x Caregiver

1.08 (0.75-1.54) 0.69 1.15 (0.91-1.45) 0.23
2 ACEs x Caregiver

1.04 (0.65-1.65) 0.88 0.95 (0.68-1.33) 0.77
3 ACEs x Caregiver

0.75 (0.45-1.27) 0.29 0.92 (0.66-1.27) 0.60
4+ ACEs x Caregiver

0.95 (0.58-1.55) 0.83 0.86 (0.67-1.11) 0.25
Note: OR, Odds Ratio; IRR, Incidence Rate Ratio; CI, confidence interval

*p<0.05, **p<0.01, ***p<0.001

92
Supplemental Table 4.4. Full Zero-Inflated Negative Binomial Regression of Caregivers' “Not good” Physical Health Days, Individual
ACEs

Without Interaction With Interaction

Logit Count Logit Count

OR (95% CI) p IRR (95% CI) p OR (95% CI) p IRR (95% CI) p
Age
1.02 (1.01-1.02) <.001*** 1 (1-1.01) 0.01* 1.02 (1.01-1.02) <.001*** 1 (1-1.01) 0.01*
Female
0.91 (0.79-1.03) 0.14 1.01 (0.92-1.12) 0.82 0.91 (0.8-1.04) 0.16 1.02 (0.93-1.12) 0.69
Race/Ethnicity

Black (Non-Hispanic) 0.96 (0.78-1.19) 0.72 1.06 (0.89-1.26) 0.50 0.96 (0.77-1.18) 0.67 1.04 (0.88-1.24) 0.61
Asian/Pacific Islander 0.93 (0.58-1.49) 0.76 0.82 (0.59-1.15) 0.26 0.93 (0.58-1.49) 0.75 0.81 (0.58-1.11) 0.19
Hispanic (of any race) 1.35 (1.05-1.74) 0.02* 1.09 (0.9-1.32) 0.37 1.37 (1.07-1.74) 0.01* 1.11 (0.93-1.33) 0.26
Other 0.79 (0.57-1.08) 0.13 0.98 (0.85-1.12) 0.76 0.78 (0.57-1.07) 0.13 0.98 (0.85-1.12) 0.73
Education

High School
Diploma/GED
1.16 (0.9-1.49)
0.24
0.95 (0.82-1.1) 0.47 1.17 (0.92-1.5) 0.21 0.96 (0.83-1.1) 0.54
Some College/Technical
Degree
1.35 (1.07-1.72) 0.01* 0.82 (0.71-0.94) 0.004** 1.36 (1.07-1.72) 0.01* 0.82 (0.72-0.94) 0.01*
Bachelor's Degree or
Higher
1.21 (0.95-1.55)
0.13
0.73 (0.63-0.85) <.001*** 1.22 (0.96-1.56) 0.11 0.73 (0.63-0.85) <.001***
Marital Status

Divorced/Separated 0.78 (0.64-0.95) 0.01* 1.17 (1.05-1.31) 0.004* 0.78 (0.64-0.95) 0.01* 1.17 (1.05-1.3) 0.004**
Widowed 0.77 (0.63-0.94) 0.01* 0.96 (0.86-1.08) 0.52 0.76 (0.62-0.94) 0.01* 0.96 (0.86-1.08) 0.53
Never Married 0.76 (0.61-0.95) 0.02* 0.95 (0.82-1.1) 0.51 0.77 (0.62-0.95) 0.01* 0.96 (0.84-1.1) 0.56
Employed
1.4 (1.21-1.62) <.001*** 0.74 (0.66-0.82) <.001*** 1.41 (1.21-1.63) <.001*** 0.74 (0.66-0.82) <.001***
Household Composition

0.74 (0.66-0.82) <.001***
Number of Adults in
Household
0.97 (0.9-1.04) 0.35 1.03 (0.98-1.09) 0.23 0.97 (0.9-1.04) 0.36 1.04 (0.98-1.09) 0.20
Number of Children in
Household
0.99 (0.92-1.06) 0.70 0.96 (0.92-1.01) 0.15 0.99 (0.93-1.06) 0.85 0.97 (0.92-1.02) 0.18
Caregiver
0.87 (0.74-1.03) 0.10 0.92 (0.83-1.01) 0.08 0.9 (0.74-1.09) 0.28 1.02 (0.9-1.16) 0.77
Chronic Health Conditions

93
Heart Attack
0.72 (0.54-
0.97)
0.03* 0.99 (0.87-1.13) 0.91 0.72 (0.53-0.97) 0.03 0.99 (0.87-1.12) 0.88
Coronary Heart Disease
0.57 (0.43-
0.76)
<.001**
*
1.22 (1.09-1.37) <.001*** 0.58 (0.43-0.77) <.001*** 1.22 (1.09-1.37) 0.001**
Diabetes
0.65 (0.55-
0.76)
<.001**
*
1.09 (1.01-1.19) 0.03* 0.65 (0.55-0.76) <.001*** 1.1 (1.01-1.19) 0.03*
Stroke
0.47 (0.34-
0.65)
<.001**
*
1.21 (1.08-1.36) 0.001** 0.47 (0.34-0.65) <.001*** 1.21 (1.08-1.36) 0.001**
Arthritis
0.39 (0.34-
0.45)
<.001**
*
1.34 (1.23-1.45) <.001*** 0.39 (0.34-0.45) <.001*** 1.34 (1.24-1.45) <.001***
Cancer
0.74 (0.63-
0.87)
<.001**
*
1.15 (1.05-1.26) 0.003** 0.74 (0.63-0.87) <.001*** 1.15 (1.05-1.26) 0.002**
Health Behaviors

Exercise (in the last 30
days)
1.38 (1.2-1.59) <.001*** 0.65 (0.6-0.71) <.001*** 1.4 (1.22-1.6) <.001*** 0.66 (0.61-0.72) <.001***
Tobacco Use

Some Days 0.75 (0.47-1.2) 0.23 0.85 (0.7-1.04) 0.12 0.75 (0.47-1.19) 0.22 0.86 (0.71-1.05) 0.14
Every Day 0.95 (0.79-1.13) 0.56 1.17 (1.04-1.31) 0.01* 0.94 (0.79-1.13) 0.53 1.17 (1.04-1.31) 0.01*
Alcohol Consumption
(number of drinks in the
past 30 days)
1 (1-1.01) 0.29 0.99 (0.99-1) 0.003** 1 (1-1.01) 0.30 0.99 (0.99-1) 0.004**
State

Georgia 0.76 (0.63-0.91) 0.004* 0.82 (0.71-0.95)
0.01*
0.75 (0.62-0.9) 0.003** 0.81 (0.7-0.94) 0.01*
Tennessee 0.61 (0.51-0.73) <.001*** 0.95 (0.82-1.09) 0.43 0.61 (0.51-0.73) <.001*** 0.95 (0.82-1.09) 0.43
Utah 0.77 (0.65-0.92) 0.004* 0.89 (0.77-1.03) 0.12 0.77 (0.64-0.91) 0.002** 0.89 (0.77-1.03) 0.12
Virginia 0.57 (0.49-0.68) <.001*** 0.9 (0.79-1.03) 0.12 0.57 (0.49-0.67) <.001*** 0.9 (0.79-1.03) 0.12
ACEs

Physical Abuse 0.94 (0.78-1.13) 0.51 1.02 (0.91-1.14) 0.72 1 (0.82-1.23) 0.99 1.09 (0.95-1.25) 0.21
Emotional/Verbal Abuse 0.71 (0.6-0.84) <.001*** 1.07 (0.94-1.23) 0.31 0.67 (0.56-0.82) <.001*** 1.09 (0.92-1.29) 0.30
Sexual Abuse 0.63 (0.5-0.78) <.001*** 1.03 (0.91-1.17) 0.64 0.66 (0.51-0.85) 0.001** 1.01 (0.87-1.18) 0.87
Parental Divorce/
Separation
0.98 (0.83-1.16) 0.83 0.94 (0.84-1.06) 0.30 0.96 (0.79-1.16) 0.65 0.96 (0.84-1.1) 0.54
Witnessed IPV 1.24 (1-1.52) 0.05 1.13 (0.98-1.29) 0.10 1.29 (1.03-1.61) 0.03* 1.14 (0.97-1.35) 0.11
Substance Abuse in the
Household
0.81 (0.69-0.95) 0.01* 1.05 (0.91-1.21) 0.50 0.84 (0.7-1.01) 0.06 1.06 (0.89-1.26) 0.51
Household Member 1.02 (0.77-1.35) 0.88 0.81 (0.68-0.97) 0.02* 1.22 (0.92-1.62) 0.16 0.84 (0.68-1.04) 0.12

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Incarcerated
Household Member with
Mental Illness
0.69 (0.57-0.84) <.001*** 1.16 (1.02-1.32) 0.03* 0.61 (0.5-0.74) <.001*** 1.11 (0.96-1.29) 0.15
Interaction

Physical Abuse x Caregiver
Emotional/Verbal Abuse x Caregiver
Sexual Abuse x Caregiver

0.78 (0.5-1.2) 0.26
0.8 (0.62-1.02)
0.07

1.22 (0.81-1.86) 0.34
0.92 (0.71-1.19)
0.52

0.81 (0.5-1.33) 0.41 1.05 (0.82-1.35) 0.68
Parental Divorce/Separation x Caregiver

1.15 (0.79-1.68) 0.47
0.93 (0.74-1.17)
0.52
Witnessed IPV x
Caregiver

0.86 (0.52-1.43) 0.57
0.94 (0.71-1.26)
0.69
Substance Abuse in the Household x
Caregiver

0.88 (0.62-1.26) 0.49
0.94 (0.74-1.21)
0.64
Household Member Incarcerated x Caregiver

0.53 (0.25-1.11) 0.09
0.94 (0.66-1.33)
0.71
Household Member with Mental Illness x
Caregiver
1.54 (0.98-2.4) 0.06
1.15 (0.9-1.48)
0.26
Note: OR, Odds Ratio; IRR, Incidence Rate Ratio; CI,
confidence interval

*p<0.05, **p<0.01,
***p<0.001

95
CHAPTER 5: DISCUSSION AND CONCLUSION
Summary of Results
This dissertation aimed to examine relationships among ACEs and three underexplored
outcomes: 1) physical IPV at ages 60 and older (Chapter 2), 2) subjective cognitive decline at
ages 55 and older (Chapter 3), and 3) caregivers’ mental and physical health (Chapter 4).
Chapter 2 investigated the relationship between ACEs and physical late-life IPV using data from
the WLS. Hypotheses were that 1) higher ACE scores will be significantly associated with late-
life physical IPV, 2) Three types of ACEs: physical abuse, sexual abuse, and witnessing IPV will
be significantly associated with late-life physical IPV, and 3) women will have higher
victimization rates than men. For hypothesis 1, experiencing 2 or more ACEs was associated
with a greater risk of experiencing late-life physical IPV compared to experiencing no ACEs. For
hypothesis 2 sexual abuse, and exposure to IPV in childhood were strongly related to physical
IPV occurring in later life. For hypothesis 3, gender did not have a significant moderating effect
on experiencing physical IPV at ages 60 and older.
Although hypothesis 3 could indicate that late-life physical IPV is not gendered, further
research outside the scope of this analysis is needed. Though findings supported our other
hypotheses, increased risk of late-life physical IPV victimization with 2 or more ACEs and
increased risk of late-life physical IPV victimization with childhood sexual abuse and witnessing
IPV in childhood), we did not find a significant relationship between physical abuse in childhood
and late-life physical IPV. It is possible that physical abuse is significantly correlated with other
types of abuse (e.g., psychological) that are outside the scope of this study (M. Dong, Anda, et
al., 2004; Felitti et al., 1998b; Finkelhor, 2008). It should be noted that physical abuse may be
treated differently in different studies, such as including spanking. BRFSS explicitly states “not

96
including spanking” for the physical abuse question in the ACEs module (“Not including
spanking, (before age 18), how often did a parent or adult in your home ever hit, beat, kick, or
physically hurt you in any way?”).
It is also relevant to note that forms of IPV can change over the life course (Sawin &
Parker, 2011). Roberto and McCann (2018) found that most of the older women in their sample
reported experiencing psychological abuse after experiencing other types of abuse in early life or
in previous marriages. These findings support similar studies that shows early-life abuse
increases the risk of later life abuse (X. Dong & Wang, 2019b; Kong & Easton, 2018; Policastro
& Finn, 2015). Also, the findings add to the evidence that sexual abuse and witnessing IPV in
childhood increases the risk of abuse victimization in adulthood, illustrating lifespan patterns of
abuse (Anda, Felitti, Bremner, et al., 2006a, 2006a; Dube et al., 2002; Whitfield et al., 2003).
The original ACE study and subsequent studies using ACEs and ACE scores have linked early-
life abuse and trauma to chronic health conditions, behavioral risks, and risk of IPV in
adulthood; this study is one of the few to examine the links of ACEs with the extension of IPV
into later life.
Chapter 3 investigated the relationship between ACEs and SCD using the 2011 BRFSS.
Hypotheses were 1) those who report multiple ACEs (4+) would significantly be more likely to
report SCD, and 2) that occurrences of psychological, physical, and sexual abuse would drive the
association between ACEs and SCD. Logistic regression with multiple nested models was used
to examine associations between ACE scores and SCD and individual ACEs and SCD. The
results show that higher ACEs increases the odds of reporting SCD , with childhood sexual and
physical abuse accounting for the greatest risk of SCD.

97
Psychological abuse was not associated with SCD in this study; however, it should not be
discounted from being a factor in impacting cognitive health. Psychological abuse is often part of
polyvictimization, victimization of multiple forms of abuse and violence that can often occur
subsequently or concurrently (Finkelhor, 2008). It is common for different types of abuse to
overlap with one another (M. Dong, Anda, et al., 2004). Therefore, more than likely,
psychological abuse is occurring concurrently with the other ACEs, particularly physical and
sexual abuse, and it is likely for those who are experiencing physical and sexual abuse are also
experiencing psychological abuse (Anda, Felitti, Brown, et al., 2006; M. Dong, Anda, et al.,
2004; Felitti et al., 1998b; Higgins & McCabe, 2001). Correlation analyses in Chapter 3 showed
a moderate correlation between physical abuse and psychological abuse, r (10,414)= .46,
p<.0001, and a moderate correlation between sexual abuse and psychological abuse, r (10,365)=
.24, p<.0001. The findings in Chapter 3 supports the current literature showing that ACEs
increase the risk of SCD (Baiden et al., 2021; M. J. Brown et al., 2022; Terry et al., 2023).
Chapter 4 investigated the impact of ACEs on caregivers’ mental and physical health.
Hypotheses were that 1) caregivers will report higher ACEs and more “not good” mental and
physical health days compared to non-caregivers, and 2) being a caregiver with 4 or more ACEs
will be associated with more “not good” mental and physical health days compared to non-
caregivers as well as those with no ACEs. Using bivariate analyses (chi-square and t-tests) and
zero-inflated negative binomial regression models (ZINB), we found associations between ACE
score and the number of “not good” mental and physical health days reported in the last 30 days,
as well as associations between particular ACEs and the number of “not good” mental and
physical health days among caregivers.

98
Findings in Chapter 4 supported the first hypothesis: descriptive statistics and bivariate
analyses showed that caregivers reported both a higher number of mental and physical health
days that were “not good” within the last 30 days than non-caregivers as well as a lower odds of
reporting 0 poor mental and physical health days in the last 30 days. Chi-square results also
showed that caregivers were more likely to report 4 or more ACEs compared to non-caregivers.
Conversely, findings did not support the second hypotheses, as the interaction effect was not
significant for 4 or more ACEs in either the mental health model or the physical health model.
However, the interaction effect for the ZINB for “not good” mental health days was significant
for caregivers with 1 and 2 ACEs, indicating that on average, caregivers with 1 and 2 ACEs
reported more mental health days that were “not good” compared to caregivers with 0 ACEs.
Living in a household with someone with mental illness, emotional abuse, sexual abuse,
and living in a household with substance abuse were the key drivers of the relationship between
ACEs and caregivers’ health. These particular ACEs were consistently associated with lower
odds of reporting 0 “not good” days, or a higher number of “not good” days on average in either
the mental health model or physical model, or both, compared to respondents who did not
experience these ACEs. Mental illness in the household remained strongly associated with the
likelihood in both reporting 0 “not good” mental health days and the number of “not good”
mental health days, even with the interaction effect. However, once the interaction effect was
added to the physical health model, this ACE did not significantly impact the number of poor
physical heath days, though it still was associated with the likelihood of reporting 0 physical
health days. Childhood sexual and emotional abuse strongly impacted the likelihood of reporting
0 “not good” mental health days, as well as the number of “not good” mental health days, but for
the physical health model, both ACEs were only significant in determining the odds of reporting

99
0 “not good” physical health days. Living in a household with someone who abuses alcohol
and/or drugs significantly impacted the likelihood of reporting 0 “not good” mental and physical
health days, until the interaction effect was added.
Based on the nonsignificant interaction effects, the present analyses do not show
evidence of ACEs’ impact on caregivers’ health that is distinctive from non-caregivers. ACEs,
both compositely and individually, still had an impact on mental and physical health regardless
of being a caregiver, but it is important to highlight our finding that caregivers who had 1 or 2
ACEs had poorer mental health compared to caregivers with 0 ACEs. This shows that there may
be a lower threshold at which health outcomes can become adverse for caregivers with ACEs,
which shows some evidence of an additive effect of early-life stressors and caregiving stressors.
This particular effect is only observed in our mental health model, but even being a caregiver
alone has more of an impact on mental health than physical health, as shown Tables 4.4 and 4.5.
Among the leading individual ACEs that strongly impact both mental and physical
health, living with someone with mental illness during childhood persisted regardless of
caregiver status, especially in the mental health model. However, it is important to note that
caregivers were more likely to report living with someone with mental illness during childhood,
as indicated by the chi-square results in Table 4.3. This observation suggests an intergenerational
effect, whether hereditary or environmental, of health outcomes for caregivers. Being a caregiver
with ACEs does have adverse health impacts, even more so for mental health than physical
health. When considering the results comprehensively, it is apparent that the caregivers with
ACEs in this sample have worse mental and physical health. These findings underscore the
importance of a lifespan approach when researching and addressing caregiver health and stress.

100
The findings in this dissertation increases the understanding of the complex and
extensive relationship that childhood adversity has on health, social, and behavioral outcomes
over the life course. It emphasizes the importance of prevention and intervention strategies and
adopting a trauma-informed approach when aiding people with ACEs. Although existing
prevention and intervention efforts tend to focus on children, it is important to include adults,
especially older adults when developing trauma-informed care programs and other community-
based interventions Prevention and intervention efforts must also address social determinants of
health, as more macro factors such as poverty and discrimination increase the risk of childhood
adversity. As described in Chapter 4, incorporation of ACEs into caregiver support programs can
be beneficial in addressing caregiver well-being. Also noted in Chapter 4, California’s ACEs
Aware Initiative is an example of implementation of prevention and intervention strategies, in
which health care providers are trained to screen and treat children and adults for ACEs.
Limitations
Limitations on the studies in this dissertation include data availability for a limited
number of states, the use of retrospective self-reports of ACEs, and potential exclusion of racial
and ethnic minorities and those with severe cognitive impairment. The dataset used in Chapter 2,
WLS, had no race and ethnicity data publicly available as nearly 100% of the sample was non-
Hispanic white, which means racial and ethnic differences could not be accounted for in the
analyses. To better understand the impact of ACES, future research will need to capture
information from key populations, racial and ethnic minorities, who may be more at risk for
ACEs due to discrimination are not in the current data. Many of the modules in the BRFSS
dataset are optional, and states choose whether to administer that those modules to residents and
whether to send back data to the CDC if it has been collected. To complete the analyses for
Chapters 3 and 4 we had to use data from years in which both the primary independent variable

101
(ACEs) and the dependent variables (SCD and poor mental and physical health days) had been
collected, which resulted in using data from fewer states. In Chapter 3, it is likely that people
with severe cognitive impairment or advanced stage dementia were not adequately represented
because they lacked the capacity to respond to the survey. Those with serious cognitive
impairment may have experienced more ACEs and may not have the capacity to accurately
report past events.
The validity of retrospective reports of abuse may be questioned. Some researchers
believe retrospective reports of abuse can be unreliable and biased, and that longitudinal data
may be more accurate (Breton et al., 2022; Hardt & Rutter, 2004; Widom et al., 2004; Wierson
& Forehand, 1994), although the time window to do this would be extremely be long. Recalling
early-life abuse as an adult can involve false reporting and biases based on mood and timing
(Hardt & Rutter, 2004). (Colman et al., 2016; Widom & Morris, 1997; Widom & Shepard,
1996). Yet, false reports of abuse are rare, and respondents tend to underestimate or repress
rather than overstate occurrences of abuse and trauma (Briere & Conte, 1993; Hardt & Rutter,
2004)Particularly, when surveying those with SCD, there could be a higher chance for inaccurate
reporting of early-life abuse. However, false reports of abuse are rare, and respondents tend to
underestimate and repress occurrences of abuse (Briere & Conte, 1993; Hardt & Rutter, 2004).
ACE Research Limitations
The original ACE questionnaire has limitations that should be noted. It has been
criticized as being overly simplistic (Finkelhor et al., 2013; Kalmakis & Chandler, 2015).
Though it captures whether or not someone recalls experiencing each of the events, the severity
or duration of the events are not asked in the questionnaire and therefore, the degree, intensity,
and duration are not captured Moreover, it does not consider the weight of the some of the items
compared to others (e.g., sexual abuse vs. parents separating/divorcing) (Bateson et al., 2020). It

102
has been noted that data to create the original ACE questionnaire was based on non-Hispanic
white middle- to upper-class participants (Cronholm et al., 2015). It also doesn’t include
influences outside the home and more macro level factors, such as culture, discrimination, and
geography. In response to these limitations, several modifications of the ACE questionnaire have
been developed by practitioners and organizations to capture factors, such as neighborhood and
peer violence, financial status, war, and genocide.
Despite the limitations, this study is one of the few to use large population data to
examine early-life associations of IPV in later life. The WLS, which has over 10,000
respondents, is one of the few datasets that has collected longitudinal data on adolescents
through old age.
Future Research
Moving forward, more comprehensive versions of the ACE questionnaire should be used
to accurately measure childhood adversity and its impact on later-life outcomes.
Though the worst outcome of ACEs is early mortality, there are people who survive well
into later life who have experienced childhood adversity. We must also focus on protective
factors that have enabled them to survive into late adulthood. There numerous studies showing
negative outcomes, however; what makes these older adults resilient, even if they are living with
chronic physical and mental health conditions? Future research should focus on resilience from
childhood abuse and adversity among older adults to inform the development and
implementation of appropriate intervention strategies.
Additional studies on the relationship of ACEs and later-life outcomes using longitudinal
data can help discern the paths and inform the mechanisms over the life course and identify
resilience factors. However, there is a lack of data sources that simultaneously collect

103
information on ACEs, health outcomes, and abuse trajectories over the life course. Analyses that
include these elements will be important for creating effective prevention and intervention
strategies for those who have experienced ACEs. Supporting children and adults who have
experienced ACEs requires a comprehensive and integrated approach that addresses the
underlying social determinants of health, promotes resilience, and provides access to appropriate
care and support.

104
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Abstract (if available)

Abstract Adverse childhood experiences (ACEs), defined as potentially traumatic events that occur up until age 18, have a lifespan impact on health, health behaviors and abuse perpetration and victimization. Research has linked ACEs to several negative physical and mental health outcomes, harmful health behaviors, and victimization in adulthood. Although there has been extensive and consistent evidence of the impact of ACEs over the life course, some relationships have not been explored or have been underexplored. This dissertation explores the impact of ACEs through a life-course perspective, aiming to examine associations between ACEs and three outcomes: 1) physical intimate partner violence (IPV) in later life, 2) subjective cognitive decline (SCD) in later life, and 3) poor mental and physical health among caregivers. Chapter 1 discusses research on ACEs, identifies the gaps in research, and introduces the study chapters. Chapter 2 investigates the relationship between ACEs and physical IPV victimization at ages 60 and older using data from the Wisconsin Longitudinal Survey (WLS). Logistic regression was conducted to determine whether higher ACE scores and which of the individual ACEs are significantly associated with higher risk of victimization of physical IPV at ages 60 and older. Chapter 3 explores the relationship between ACEs and reports of subjective cognitive decline (SCD) at ages 55 and older using data from the 2011 Behavioral Risk Surveillance Survey (BRFSS). Bivariate analyses and ordered logistic regressions using nested models were run to determine whether there is a significant association between higher ACE scores and SCD, as well as whether there are specific ACEs that are the drivers of this relationship. Chapter 4 explores the relationship between ACEs and caregivers? physical and mental health. Data from the 2019 and 2020 BRFSS were used to run bivariate analyses and zero-inflated negative binomial regressions to examine associations between ACE scores and reported number of poor mental and physical health days among caregivers, and to determine which, if any individual ACEs are the drivers of this relationship. Chapter 5 summarizes findings from the dissertation and discusses implications for research, prevention, and intervention. Limitations and potential areas for future research are also addressed.

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Asset Metadata

Creator Avent, Elizabeth Shimere (author)

Core Title Life course implications of adverse childhood experiences: impacts on elder mistreatment, subjective cognitive decline, and caregivers' health

School Leonard Davis School of Gerontology

Degree Doctor of Philosophy

Degree Program Gerontology

Degree Conferral Date 2023-08

Publication Date 08/02/2024

Defense Date 05/01/2023

Publisher University of Southern California. Libraries (digital)

Tag adverse childhood experiences,caregiver mental health,caregiver physical health,cognitive decline,elder mistreatment,late-life intimate partner violence,life course,OAI-PMH Harvest

Language English

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Advisor Wilber, Kathleen (committee chair), Benton, Donna (committee member), Gassoumis, Zachary (committee member)

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