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Exploring the social determinants of health in a population with similar access to healthcare: experiences from United States active-duty army wives

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Exploring the social determinants of health in a population with similar access to healthcare: experiences from United States active-duty army wives

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Content Copyright 2021 Jessica Rampton Dodge
Exploring the Social Determinants of Health in a Population with Similar Access to
Healthcare:
Experiences from United States Active-Duty Army Wives
by
Jessica Dodge, M.P.H, M.S.W
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHER CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(SOCIAL WORK)
August 2021
ii
Dedication
For all the sacrifices made by service members and their families.
To all my teachers, friends, and family. I would not be here without your continued support and
guidance.
iii
Acknowledgements
I could write another dissertation to acknowledge all the love, support, and guidance I
received in pursuit of my dream to get a PhD. This section seeks to recognize everyone who
helped me along the way.
I would first like to acknowledge my mentor and dissertation chair, Dr. Carl Castro for
your dedication to better the care and treatment of service members and their families. I am
forever grateful for your generous sharing of time and resources and I look forward to continuing
our collaborations. To the rest of my dissertation committee. To Dr. Peer Fiss, for your
commitment to Configurational Comparative Methodologies. I feel so fortunate to have been
able to learn from you about this methodology to understand intersectional issues impacting
health. To Dr. Ben Henwood, for your passion for alternative ways to tell and document stories
to paint a full picture of any research question.
To the USC Suzanne Dworak-Peck School of Social Work PhD Program, staff and
faculty. To Dr. Michael Hurlburt, your commitment to the wellbeing of students is an example of
integrity I will forever carry with me. To Malinda Sampson, for always being there and
genuinely caring about my journey. To Dr. Jungeun Olivia Lee and Dr. Jordan Davis, for
teaching and sharing your love for methods and statistics. To Dr. Larry Palinkas, for opening the
door to the world of medical sociology. To Dr. Julie Cederbaum, for your commitment to
understand and provide the best support for high-risk families. To Dr. Monica Perez Jolles, for
your passion for larger systems research. To Dr. David Bringhurst and Professor Rafael Angulo,
your example of teaching is one I hope to emulate. To Dr. Gayla Margolin, for your knowledge
of the National Institute of Health system and commitment to family research.
iv
To everyone at the USC Center for Innovation and Research on Veterans and Military
Families (CIR) and its collaborators. To Dr. Sara Kintzle, your commitment to military research
is contagious and I will never forget your friendship. To Dr. Hazel Atuel, your kind heart got me
through one of my toughest times. To Eva Alday and Gisele Corletto, for your support and
ability to create community. To Dr. Nicola Fear, for your collaboration and commitment to
global military research.
To Karla Alvarez, for your friendship and showing me what it means to be a social
worker in my first field placement. To everyone at the UCLA Veteran Family Wellness Center
for your commitment to providing the best care possible to veterans and their families: Tess
Banko, Tom Babayan, Dr. Patricia Lester, and Dr. Melissa Wasserman.
To my student colleagues and friends. To Sara Sembroski, your friendship has been a
rock for me throughout my time, thank you for always being there. To Adriane Clomax, your
friendship and determination have inspired me and kept me going. To Eunhye Ahn, for your kind
heart and curiosity to think about data in a different way. To Graham Diguiseppi, for your
example of kindness, hard work, and knowledge of statistics. To Erika Salinas, for your passion
on Native American equity and sharing your knowledge. To Leslie Schnyder, for your listening
ear and commitment to self-care. To Abigail Molina, for your curiosity about Configurational
Comparative Methods. To Dr. Cary Klemmer, for your courage and lessons on balance. To Dr.
Chyna Hill for your example of getting two degrees in four years. To Dr. Taylor Harris, for your
love of preventing veteran’s experiencing homelessness. To Dr. Katie McNamara, for your
friendship, guidance, and perspective. To Dr. Sapna Mendon, and your love for Qualitative
Comparative Analysis. To the All Things Configured student work group for your motivation
v
and support in the final leg of this journey, especially Kelly Coates & Marie-Claire Gwayi-
Chore.
To Dr. Kate Sullivan, for your generosity of support throughout my entire graduate
school process. I think it is safe to say I would not be here today without your guidance and
friendship—I am forever grateful. To Caroline Kale, for your energy and eagerness to do
research.
To Dr. Edward Miech, for your mission to spread the knowledge of Configurational
Comparative Methodologies and for always offering support and wisdom.
To my friends and family. You have been my rock throughout this journey. To Mom,
Dad, Jim, Lindsey, and Julia for loving me and always taking my call when I needed
encouragement and advice (especially Mom). To my partner in life, Sam, your supply of hugs
and willingness to provide me sweet things never faltered. To Tallen, for your belief in me and
reminding me how far I have come. To Kasia, for your loving heart. To my cousin Sarah, for
your words of motivation and validation. To Naomi, for your continued emotional support and
never saying no to reading last minute edits. To Becky for your encouragement and grammatical
eye. To Kate and Emma, for your walks and talks.
To my spiritual teachers, you taught me how to sustain my mind, body, and soul through
this crazy process. And to one, that shall remain nameless as that is her style, your commitment
to social equity never seems to falter and continues to inspire me.

vi

Table of Contents
Dedication ...................................................................................................................................... ii
Acknowledgements ........................................................................................................................ iii
List of Tables ................................................................................................................................. ix
List of Figures ..................................................................................................................................x
Abbreviations ................................................................................................................................. xi
Abstract ......................................................................................................................................... xii
Chapter 1. Introduction ....................................................................................................................1
Goals of Dissertation............................................................................................................6
Conceptual Model and Empirical Framework .....................................................................8
Social Determinants of Health (SDoH) ...............................................................................8
WHO SDoH Conceptual Framework ..................................................................................9
Structural Determinants .......................................................................................................9
Social Cohesion and Capital Determinants................................................11
Intermediary Determinants ........................................................................12
Material Circumstances .................................................................12
Physical Factors .............................................................................12
Psychosocial Factors ......................................................................13
Health System ................................................................................13

Chapter 2 (Study 1). Army Wives: Exploring the Social Determinants of Health in a Military
Population with Universal Healthcare ...........................................................................................15
Highlights ...........................................................................................................................15
Abstract ..............................................................................................................................16
Introduction ........................................................................................................................18
The Unique Military Environment.....................................................................................19
Adapting the World Health Organization’s Social Determinants of Health
Framework .............................................................................................................20
Structural Determinants .............................................................................20
Social Cohesion and Capital Determinants................................................22
Intermediary Determinants ........................................................................22
The Current Study ..............................................................................................................23
Materials & Methods .........................................................................................................24
Data & Participants ................................................................................................24
Measures ....................................................................................................25
Structural Determinants .................................................................25
Cultural & Socio-Demographic Position ...........................25
Social Cohesion & Capital Determinants ......................................25
Intermediary Determinants ............................................................27
vii
Material Circumstance .......................................................27
Physical Factors .................................................................27
Psychosocial Factors ..........................................................27
Health Outcomes ............................................................................28
Physical Health ..................................................................28
Mental Health.....................................................................28
Analytic Plan ..........................................................................................................28
Results ................................................................................................................................29
Discussion ..........................................................................................................................33
Limitations .............................................................................................................37
Implications............................................................................................................37
Chapter 3. (Study 2): Exploring the Social Determinants of Mental Health by Race and Ethnicity
in Army Wives ...............................................................................................................................44
Abstract ..............................................................................................................................44
Introduction ........................................................................................................................45
The Unique Military Context .............................................................................................45
Military Social Determinants of Mental Health Conceptual Framework ..........................46
Structural Determinants .............................................................................46
Social Cohesion and Capital Determinants................................................48
Intermediary Determinants ........................................................................48
The Current Study ..................................................................................................50
Methods..............................................................................................................................52
Data & Participants ................................................................................................52
Measures ................................................................................................................53
Mental Health Outcome .............................................................................53
Analytic Plan ..........................................................................................................54
Step 1 .........................................................................................................55
Step 2 .........................................................................................................56
Step 3 .........................................................................................................58
Results ................................................................................................................................59
Clinically Significant Depression Symptoms Pathways ........................................62
Non-Clinically Significant Depression Symptoms Pathways................................64
Discussion ..........................................................................................................................65
Limitations .............................................................................................................69
Implications............................................................................................................70
Supplemental Materials .....................................................................................................83
Chapter 4. (Study 3): Army Wives’ Mental Health Treatment Engagement: Logistical and
Psychological Barriers to Care.......................................................................................................88
Abstract ..............................................................................................................................88
Introduction ........................................................................................................................90
Logistical Barriers to Mental Health Care .............................................................91
Psychological Barriers to Mental Health Care.......................................................91
Socio-demographic Factors and Military Spouses’ Mental Health Care ...............93
Methods..............................................................................................................................94
viii
Data & Participants ................................................................................................94
Measures ................................................................................................................95
Logistical Barriers ......................................................................................95
Psychological Barriers ...............................................................................96
Socio-demographic Variables ....................................................................96
Mental Health.............................................................................................97
Mental Health Treatment ...........................................................................97
Data Analysis .........................................................................................................97
Results ................................................................................................................................98
Discussion ..........................................................................................................................99
Limitations & Future Research ............................................................................101
Conclusion ...........................................................................................................103
Supplemental Materials ...................................................................................................107
Chapter 5. Conclusion. .................................................................................................................109
Introduction ......................................................................................................................109
Review of Major Findings and Integration with Existing Research ................................109
Army Wives: Exploring the Social Determinants of Health in a Military
Population with Universal Healthcare: Paper 1 ...................................................110
Exploring the Social Determinants of Mental Health by Race and Ethnicity in
Army Wives: Paper 2 ...........................................................................................113
Army Wives’ Mental Health Treatment Engagement: Logistical and
Psychological Barriers to Care: Paper 3 ..............................................................116
Clinical Implications ........................................................................................................117
References ....................................................................................................................................119
ix
List of Tables
Table 2.1. Demographics of Analytic Sample of Active-Duty Army Wives ................................39
Table 2.2. Pairwise Correlation Coefficients among Social Determinants of Mental Health and
Health Outcomes among Army Wives ..........................................................................................40
Table 2.3. Social Determinants of Mental Health Hierarchical Linear Regression Results ..........41
Table 2.4. Social Determinants of Physical Health Hierarchical Linear Regression Results........42
Table 3.1. Demographic Characteristics of Whole Sample and Each Race/Ethnicity Subsample ....
........................................................................................................................................................71
Table 3.2. All Measures Considered for Final Analysis ................................................................73
Table 3.3. Calibration of Conditions Considering for Final Analysis ...........................................75
Table 3.4. Relevant Conditions for Clinically Significant Depression Symptoms for Army Wives
........................................................................................................................................................77
Table 3.5. Pathways for Clinically Significant Depression Symptoms by Race/Ethnicity ...........78
Table 3.6. Pathways for Not Clinically Significant Depression Symptoms by Race/Ethnicity ....80
Supplemental Table 3.1. Truth Table Characteristics ....................................................................83
Table 4.1. Demographic Characteristics ......................................................................................104
Table 4.2. Barriers to Care Among Army Wives with Unmet Mental Health Care Needs (n=85)
......................................................................................................................................................105
Table 4.3. Logistical Regression Predicting Current Mental Health Services Use .....................106
Supplemental Table 4.1. Pairwise Correlation Coefficients Among Barriers to Mental Health
Care and Treatment Usage among Army Wives .........................................................................107
Supplemental Table 4.2. Proportions of Cases Classified in Logistic Regression ......................108
x
List of Figures
Figure 1.1 Dissertation Conceptual Framework ..............................................................................8
Figure 2.1 Military Spouses Social Determinants of Health Conceptual Framework ...................43
Figure 3.1 Social Determinants of Mental Health Conceptual Framework for Military Spouses .....
........................................................................................................................................................82
xi
Abbreviations
SDoH Social Determinants of Health
SDoMH Social Determinants of Mental Health
CCMs Configurational Comparative Methodologies
QCA Qualitative Comparative Analysis
CNA Coincidence Analysis
HLR Hierarchical Linear Regression
MDD Major Depressive Disorder
WRAIR Walter Reed Army Institute of Research

xii

Abstract
Lack of access to healthcare perpetuates health inequalities in the United States (US) and
is a key social determinant of health. Military spouses are a unique population within the U.S. in
that they have universal healthcare. With guidance from the World Health Organization’s
(WHO) Social Determinants of Health (SDoH) conceptual framework, the purpose of this
dissertation was to explore various aspects of the SDoH in a sample with universal healthcare.
Through three different studies, outlined through three different papers, this dissertation
highlights how even in an environment with equal access to universal healthcare coverage, there
are still social determinant factors that negatively impact health as well as engagement with
universal care. Paper 1 documents how the social determinants of health still exist within a
population with equal access to universal healthcare coverage, however, the impact of the
structural social determinants on health appear to be attenuated. Paper 2 documents that the
different groups of racial/ethnic Army wives have different determinant pathways that lead to
clinically significant poor mental health symptoms. While preliminary, these findings
demonstrate that even within a group with similar exposure to unique stressors (in this case,
military-specific stressors), causal conditions combine in ways that create different pathways to
affect and protect mental health. Paper 3 documents that Army wives with increased depression
symptoms are likely to be in mental health care treatment and that Army wives with higher
psychological barriers to mental health care, including stigma around mental health, are less
likely to seek care. Collectively, this dissertation supports the use of the adapted WHO SDoH
conceptual framework to understand various aspects of health for Army wives.
1
Chapter 1. Introduction
Lack of access to healthcare perpetuates health inequalities in the United States (US) and is
a key social determinant of health (Center for Disease Control and Prevention, 2020). The Social
Determinants of Health (SDoH) are globally defined as, “Conditions in which people are born,
grow, work, live and age and the wider set of forces and systems shaping conditions of daily
life” (“WHO | Social Determinants of Health,” 2020; p.1). The military, including military
dependents, is a unique population within the US for many reasons, including not having the
barrier of access to healthcare. Close to one million military spouses receive universal healthcare
benefits through TRICARE (Department of Defense, 2019). The benefits afforded to military
personnel and their families are in place to minimize the stressors of being in the US Armed
Forces and to support the service member in being mission-ready. Stressors for military spouses
include frequent relocations that uproot them from their community, employment, prolonged
separations from their military spouse through deployments and training, as well as worrying
about the life and wellbeing of their military spouse during those deployments (Lester et al.,
2016; Marini et al., 2018). In order to be mission-ready, the military healthcare aims to eliminate
any possible distractions that could impact a service members’ focus on the task or operation at
hand.
Despite the healthcare provided to military families, there are still some socio-economic
disparities in health outcomes (Donoho et al., 2018; Lundquist, Xu, et al., 2014; Lundquist, Elo,
et al., 2014; Trail et al., 2019). While some of these disparities could be explained by the social
and financial hierarchy within the military rank system (i.e., junior vs. senior rank; Ziff &
Garland-Jackson, 2019), there remain questions: What socio-economic factors beyond military
rank significantly contribute to health disparities among military families (Study 1)? How do
2
health-related socio-economic factors differ by race and ethnicity in an environment with equal
access to healthcare (Study 2)? What barriers still exist in a population with equal access to
healthcare (Study 3)? In order to answer all of these questions, there remains the issue of how
can socio-economic differences in health outcomes be conceptualized in an environment with
equal healthcare coverage to all its members? Therefore, this dissertation has adapted the holistic
World Health Organization (WHO) SDoH framework to the military (see Figure 1).
The WHO SDoH framework's primary constructs are designed to be adapted to all contexts
around the globe. This dissertation's adapted framework was created by linking its key constructs
with known factors that impact military spouses’ health. The three primary SDoH categories are
(1) structural determinants, (2) social cohesion & capital, and (3) intermediary determinants
(“WHO | Social Determinants of Health,” 2020). The framework highlights how determinants
interact with each other to create health inequalities (Solar & Irwin, 2010; see Figure 1 for
adapted framework). For example, the framework shows how structural determinants of socio-
economic position and cultural and societal values pave the road to intermediary determinants of
stressors or supports, including housing, stigma around mental health, and family health. Then
the WHO SDoH framework considers the profound impact social cohesion and capital can have
on both the structural and intermediate determinant trajectories. For example, if the structural
determinants are the road to health, then the intermediary determinants are the vehicles that
travel down that road. Furthermore, social cohesion and capital are the friend that shows an
individual a different path or gives an individual a ride; in other words, interrupt the health
trajectory. For a full explanation of how this framework was adapted to the military context skip
to Empirical Framework and Conceptual Model section (pg. 8).

3

This dissertation explores how the structural, social cohesion and capital, and intermediary
determinants impact health within a population of Army wives. This dissertation focuses on
Army wives’ experiences because more than nine out of 10 (91.3%) military spouses are self-
identified women. Furthermore, research documents how self-identified men and women can
experience the structural determinant of obtaining and maintaining employment differently as
well as the intermediary determinant of work-family conflict than self-identified men (Barnett &
Hyde, 2001a; Department of Defense, 2019; Mary Clare Lennon & Sarah Rosenfield, 1992).
Building upon the documented socio-economic disparities in military wives’ mental health
and the limited research that documents socio-economic disparities in military wives’ physical
health, Study 1 tests the adapted SDoH framework to assess and predict both physical and mental
health outcomes for Army wives. The documented mental health research shows spouses
married to an enlisted service member to have high-stress levels and more likely to screen
positive for Major Depressive Disorder (MDD) than spouses married to officers (Donoho et al.,
2018; Trail et al., 2019). The pay differences across ranks could explain this socio-economic
difference (i.e., junior vs. senior) and the social hierarchy and segregation that follows (i.e.,
separate, often inferior, quarters for junior ranks vs. senior ranks) (Soeters et al., 2006; Ziff &
Garland-Jackson, 2019). These same studies, however, found that MDD and increased stress
levels were associated with military spouses who were less educated, unemployed, had a large
family (four or more children), or had previously served in the military (Donoho et al., 2018;
Trail et al., 2019). While research documents some socio-economic disparities in mental health
outcomes for military spouses, there is less research on what factors contribute to military
spouses’ physical health.
4
Limited research has been done on what specific socio-economic factors could be
impacting physical health, leaving a critical gap in how to support a diversity of military
spouses’ physical health. Racial and rank disparities have been reported in maternal health
outcomes of women in military care (spouse or service member) (Barfield et al., 1996;
Lundquist, Elo, et al., 2014; Lundquist, Xu, et al., 2014; Rawlings & Weir, 1992). Additional
research on military wives’ physical health centers on how specific aspects of health relate to
mental health and have not included demographic variables such as race/ethnicity, rank,
employment, or education, as predictors (Fields et al., 2012; Holliday et al., 2016). This leaves
the question: How do the other social factors such as family size, educational attainment, prior
military service, or the economic element of employment impact Army wives’ physical health?
Study 2 takes a closer examination of the Social Determinants of Mental Health (SDoMH)
by race/ethnicity. While the majority of military wives cope well with the unique stressors of
being partnered to someone in the military, there is conflicting evidence on whether racial/ethnic
minorities are at greater risk for adverse mental health issues (Donoho et al., 2018; Sinclair et al.,
2019; Sullivan et al., 2020). This is in large part because of the minimal research focused on the
mental health outcomes of racial and ethnic minority military spouses (National Academies of
Sciences Engineering and Medicine, 2019). One of the reasons for the lack of research could be
the lack of a framework to guide an understanding for how socio-demographic factors, such as
race/ethnicity, interact with the surrounding environment to impact mental health. Using
Configurational Comparative Methods (CCMs), the second study will utilize a military specific
SDoMH framework to understand the different mental health pathways by specific racial/ethnic
groups of Army wives. This question can prove difficult to explore through traditional
correlational approaches to quantitative data, since they are designed to detect net effects using
5
probabilistic methods. CCMs identify how multiple conditions work together in configurations
that operate jointly as a whole and allow for assessing for equifinality (when multiple paths lead
to an outcome) as well as complex causality (how a condition may only be relevant to an
outcome if it is paired with another condition) (Furnari et al., 2020; Ragin, 2014). In other words,
there is more than one path to an outcome and paths typically consist of specific conditions that
must appear together to yield the outcome.
The ability of CCMs to detect causal complexity and equifinality make it ideal in
understanding the interplay of Army wives’ structural, social cohesion and capital, and
intermediary determinants in the mental health of Army wives. Furthermore, CCMs are useful in
exploring potential inequalities that entail the intersection of causal conditions such as the
structural, social cohesion and capital, and intermediary determinants of mental health (Ragin &
Fiss, 2017; Rich et al., 2020; Spangaro et al., 2016). This study is one of the first to specifically
use Coincidence Analysis (CNA) as an exploratory method for factor selection prior to modeling
with Qualitative Comparative Analysis (QCA). The CCM approach is a good fit for this study
due to the extensive prior literature on the military wives’ mental health and factors that
contribute to it or exacerbate it and the adapted SDoMH framework to guide factor selection and
interpretation of findings.
Study 3 examines the specific aspect of the health system construct in the WHO SDoMH
conceptual framework by understanding the impact of logistical and psychological barriers to
mental health care on Army wives’ treatment usage, controlling for several socio-demographic
variables and mental health symptoms. Supporting military spouses in receiving mental health
care is important not only for their health but for the health of the military family as well as
retention of the service member within the Military (Green et al., 2013; Rosen & Durand, 1995).
6
Previous research has explored logistical and psychological barriers military spouses experience
when accessing mental health care treatment (Eaton et al., 2008; Schvey et al., 2021). These
prior studies, however, have not examined how these barriers could affect treatment usage
among spouses; instead, they focus on predicting perceived barriers (i.e., gender, military status,
age, race, and provisional psychiatric disorder) (Schvey et al., 2021). Furthermore, prior work
has not controlled for a variety of socio-demographic variables, including, for example, spouse
employment. Understanding how different barriers might be impacting mental health care
engagement while accounting for socio-demographic characteristics will offer a more nuanced
understanding of mental healthcare usage for a diversity of military spouses. Understanding
these nuances will help paint a more complete picture of factors, including typical barriers, that
could be contributing to treatment engagement for military spouses.
Goal of Dissertation
This dissertation's main objective is to test the WHO SDoH framework in a military
population of spouses. Specifically, this dissertation will do this through three studies, each with
a specific aim. Study 1 will explore the main socio-economic factors that impact Army wives’
physical and mental health through the WHO SDoH framework (Aim 1). Study 2 will use the
adapted SDoMH framework and CCMs to examine what SDoMH factors work together to lead
to and prevent adverse mental health for specific racial/ethnic groups of Army wives (Aim 2).
Lastly, Study 3 will explore specific barriers that are impacting mental health treatment usage
among Army wives (Aim 3). The adapted framework is a conceptual guide for factor selection
for Study 1 and Study 2 and illustrates how social determinants, including specific barriers to
care, still exist in the military’s unique context of universal healthcare coverage. This dissertation
will be one of the first bodies of work to adapt and then test the WHO SDoH and WHO SDoMH
7
conceptual frameworks in a US military context. It will also contribute to the limited health
research using CCMs. Additionally, this dissertation will be one of the first bodies of work to
look at the socio-economic factors contributing to military wives' overall physical health.
Finally, this will be one of the first studies that accesses whether or not specific barriers
significantly prevent treatment usage among military spouses.
8
Conceptual Model and Empirical Framework
Figure 1.1 Dissertation Conceptual Framework.
Social Determinants of Health (SDoH)
Health, broadly defined, includes both physical and mental wellbeing. There are many
definitions of the SDoH. Shokouh et al. (2017) reviews 21 different social determinants of health
frameworks and breaks down all the socio-economic indicators measured across these models
into three interacting categories: (1) Common or classical indicators, which include education,
income, and occupation (2) Fixed and demographic indicators, which include sex/gender,
9
ethnicity/race, age, marital status, and religion (3) Proxy and complementary indicators, which
include household size, social and family support, utilization and access to healthcare services,
health behavior, housing, culture or cultural factors, place of residence, and social and family
safety. The Shokouh et al. (2017) article concludes and emphasizes that a framework must be
“context-specific” to understand the social mechanisms and causal pathways where the socio-
economic inequalities in health persist.
WHO SDoH Conceptual Framework
This dissertation uses the WHO’s SDoH conceptual framework and adapts it to the
military context. The WHO SDoH framework was selected for three reasons: (1) The military
has universal access to healthcare, therefore making it more aligned with a global framework
than a domestic one, (2) how it incorporates the important role social connections have on health
trajectories, and (3) it is a comprehensive model that can be tailored to any context and accounts
for modifiable and non-modifiable determinants. This framework is designed to be tailored to
assess socio-economic environments and their impact on health unique to the population/context
of interest (Solar & Irwin, 2010). The framework considers how socio-economic positions shape
specific determinants of health status that reflect people’s place within social hierarchies. The
WHO framework breaks the SDoH into three distinct categories (1) structural determinants, (2)
social cohesion and capital, and (3) intermediary determinants (Solar & Irwin, 2010).
Structural Determinants
Structural determinants include factors that “generate stratification and social class divisions
in the society and define the individual socio-economic position within hierarchies of power,
prestige, and access to resources” (Solar & Irwin, 2010, p.5). In the military context, structural
10
determinants of socio-economic position include race/ethnicity, sex, rank, employment, and
adverse childhood experiences (Borah & Fina, 2017; Donoho et al., 2018; Trail et al., 2019).
In the military, sex, education, rank, and race/ethnicity are socio-demographic factors that
could impact Army wives’ health. As nine out of ten military spouses are self-identified as
women, this study will eliminate sex as a socio-demographic determinant by focusing on self-
identified women (Department of Defense, 2019). Prior mental health research documents socio-
demographic disparities in military spouse’s mental wellbeing. Specifically, large scale studies
have found military spouses with a junior rank status, less educational attainment, large families
(four or more children), experiencing unemployment, or who have previously served in the
military to negatively affect mental health (Donoho et al., 2018; Trail et al., 2019).
There are conflicting findings when examining racial/ethnic disparities in military wives'
health outcomes. When examined in large sample sizes, there appears not to be an effect on
mental health (Donoho et al., 2018; Trail et al., 2019). When used as a control variable in
empirical studies with smaller sample sizes, however, it has been a risk factor for mental health
issues (Sinclair et al., 2019; Sullivan et al., 2020). Prior literature documents smaller maternal-
child health disparities by race/ethnicity in the military than in the civilian community (Barfield
et al., 1996; Lundquist, Xu, et al., 2014; Lundquist, Elo, et al., 2014). Specifically, studies found
Black women in military hospitals (spouses and service members) to have more equal birth
weight for baby, prenatal care use for mom, and breastfeeding outcomes over time than Black
civilian women living in the same area and national datasets (Barfield et al., 1996; Lundquist,
Xu, et al., 2014; Lundquist, Elo, et al., 2014). The conflicting racial/ethnic disparity findings
demonstrate a need for a framework to conceptualize how social and economic environmental
factors could be contributing to their overall health.
11
Another social factor that is an important structural determinant is an exposure to Adverse
Childhood Experiences (ACES). ACES encompasses various aspects of an adverse childhood
home environment including the psychological, physical, sexual, and four different types of
household dysfunction (Felitti et al., 1998). ACES have been shown to have a negative mental
health trajectory for the general population as well as military spouses ability to cope with
stressors (Felitti et al., 1998; Sinclair et al., 2019).
Cultural and societal values that could impact a military spouses’ mental health are
personal history with the military. Prior research has found that spouses who have previously
served in the military are more at risk for MDD (Donoho et al., 2018). These circumstances
could have possibly exposed them to greater military stressors than spouses without these
previous experiences, therefore it is important to know if the spouse grew up in a military family
or previously dated a service member as well as if they previously served in the military (Marini
et al., 2018).
Social Cohesion and Capital Determinants
One of the unique aspects of the WHO SDoH framework is how it incorporates social
cohesion and capital. This social connection construct cuts across structural and intermediary
dimensions, with features that link both aspects together. In the context of the military, this
includes the presence of social supports and community as well as the absence of a safe social
environment. For example, military spouses with access to formal and informal (religious group,
community, or friends and family) social support during military separations had lower anxiety
rates (Bowen & Martin, 2011; Burrell et al., 2003). Additionally, a higher sense of community
connection to the Army protected against mental health symptoms in a sample of Army wives
12
(Donoho et al., 2017). Research also shows how higher social support can positively impact
overall physical health for military spouses (Fields et al., 2012). The absence of a positive social
environment, such as a history of violence or current intimate partner violence (IPV), is a known
risk factor inside and outside the military for adverse mental and physical health (see Jones, 2012
for review of literature on IPV in the military; Satyanarayana et al., 2015).
Intermediary Determinants
The intermediary determinants are the direct determinants of an individual’s health status.
The intermediary determinants, “…determine differences in exposure and vulnerability to health-
compromising conditions” (Solar & Irwin, 2010, p. 36). In the context of the military, this
includes (1) material circumstances, (2) physical factors, (3) psychosocial factors, and (4) the
health system.
Material Circumstances. Material circumstances include if a spouse lives on or off a
base, post, or installation. Services such as healthcare as well as subsidized food, gas, and
financial assistance are all on-base/post/installation. Therefore, being far away could impact
access to these services.
Physical Factors. Known physical factors that can impact military wives' health include
recent childbirth and history of an illness or injury (Godier-Mcbard et al., 2019). Research has
shown that military spouses with a deployed partner are at greater risk for poor perinatal mental
health (Godier-Mcbard et al., 2019). Research outside of the military documents how the
prevalence of health issues significantly corresponds to evaluation of both mental and physical
health symptoms (O’Donnell et al., 2013).
13
Psychosocial Factors. Psychosocial factors that could impact spouse health are the
health of the service member, family size and work-family conflict (Sinclair et al., 2019;
Sullivan et al., 2020; Trail et al., 2019). For example, a service members’ adverse health can
create additional caregiving burden for their spouse, which can lead to spouse mental health
symptoms (Lambert et al., 2012). Prior military spouse literature found larger family sizes to be
significantly related to MDD in spouses (Donoho et al., 2018). Furthermore, the military and
their family have been referred to as “greedy institutions” for the demands they both place on
service members, indicating a high potential for work-family conflict (Segal, 1986). Work-
family conflict may also be more likely in military families because of stressors associated with
relocating, prolonged separations, and heavy workloads (Segal, 1986; Wadsworth & Southwell,
2011). Work-family conflict has been shown to impact parents' health outcomes as well as the
family as a whole (Frone et al., 1997; Wadsworth & Southwell, 2011).
The Health System. While every service member and their family have access to
universal healthcare coverage, there are still barriers to treatment within the military healthcare
system (Eaton et al., 2008; Lewy et al., 2014; Schvey et al., 2021). For example, a spouse may
simply choose to not receive health treatment, not know the free supports available to them, or
not have the time to engage in such supports (Eaton et al., 2008; Lewy et al., 2014). These are all
potential logistical barriers to healthcare, but there could also be psychological barriers to
healthcare such as stigma associated with seeking or receiving care that prevent a spouse from
utilizing universal coverage.
Mental health stigma is high in the military and could be a barrier to seeking treatment
for military spouses (Kim et al., 2011; Sharp et al., 2015). Spouses report concerns that seeking
mental health treatment will adversely affect others’ opinions of and confidence in them (Eaton
14
et al., 2008; Hoge et al., 2004), a concern which carries significant consequences in the military
context where personnel and their families are expected to be “mission ready” (Schvey et al.,
2021; Sharp et al., 2015). A mission-ready mentality means that any ailment that could get in the
way of being ready to embark on or complete the mission is seen as a weakness and could
negatively impact a service member’s career (Sharp et al., 2015). Further, spouses express
concerns that seeking treatment could limit their partners’ opportunities for promotion,
advancement, security clearance and could lead to separation or removal (Sharp et al., 2015).
Lewy et al. (2014), compared some psychological barriers for military wives with a national
sample of non-military affiliated women and found military wives to have significantly more
psychological barriers to mental health treatment such as, community negative opinion and
information might not be kept confidential than the national sample.
For a visualization of the adapted SDoH framework and how each study will address
certain aspects of it please reference Figure 1.
15
Chapter 2 (Study 1). Army Wives: Exploring the Social Determinants of Health in a
Population with Universal Healthcare
Highlights
• Applied the World Health Organizations Social Determinants of Health Conceptual
Framework to the military.
• The presence of social connections, a safe environment, and physical and psychosocial
factors were significantly related to both physical and mental health for Army wives.
• Economic or demographic characteristics were not significantly related to either mental
or physical health for Army wives.
• Findings suggest when universal access to both coverage and care level out the effects of
structural inequalities that prevent individuals from seeking care in other populations.
16
Abstract
Introduction: Lack of access to healthcare perpetuates health inequalities in the United States
(U.S.) and is a key social determinant of health. Military spouses are a unique population within
the U.S. in that they have universal healthcare. With guidance from the World Health
Organization’s (WHO) Social Determinants of Health (SDoH) conceptual framework, the
purpose of this study was to explore the SDoH in a sample with universal healthcare: Methods:
The present study is a secondary analysis of survey data collected in 2012 from 327 US Army
wives from a deployed Army unit. Bivariate pairwise correlations and hierarchical linear
regressions (HLR) were used to explore two research questions: 1) what socio-economic factors
at the structural, social cohesion/capital, or intermediary determinant SDoH categories are
significantly associated with Army wives’ health, and (2) what SDoH category of socio-
economic factors most explain Army wives' health? Results: Results suggest significant
bivariate associations at all determinant categories outlined in the WHO SDoH framework
(structural, social cohesion/capital, intermediary) for physical health symptoms and within the
social cohesion/capital and intermediary categories for mental health symptoms. The best-fitting
HLR models were those with all determinant categories: Mental health (MH) (F [16, 261] =
6.30, p < .001, R
2
= 0.279); Physical health (PH) (F [16, 262]= 6.08, p < .001, R
2
= 0.271). The
significant socio-economic factors for both mental and physical health were: (1) sense of Army
community (MH: EXP(β)= -1.47, p=.010; PH: EXP(β)= -.80, p=.031), (2) recent childbirth (MH:
EXP(β)= -7.90, p=.048; PH: EXP(β)= -8.49, p=.003) (3) mental health of service member (MH:
EXP(β)= 16.76, p=.001; PH: EXP(β)= 7.43, p=.036) (4), and work-family conflict (MH:
EXP(β)= .537, p=.019; PH: EXP(β)= .490, p=.003). Implications: While preliminary, results
highlight several significant SDoH factors from the WHO framework that impact overall health
in a context with universal healthcare. Implications explored in the discussion.
17
Key words: Military, Spouse, Social Determinants of Health, Socio-economic Factors, Mental
Health, Physical Health
18
Introduction
Lack of access to healthcare perpetuates health inequalities in the United States (U.S.)
and is a key social determinant of health (Center for Disease Control and Prevention [CDC],
2020). The Social Determinants of Health (SDoH) are globally defined as, “Conditions in which
people are born, grow, work, live and age and the wider set of forces and systems shaping
conditions of daily life” (“WHO | Social Determinants of Health,” 2020; p.1). Military families
are a unique population within the U.S. for several reasons including not having the barrier of
access to healthcare (Military.com, 2021). Military spouses and children receive the benefits of
universal healthcare and do not have health requirements to obtain or maintain this care (like a
service member does), so there is no “healthy soldier effect” when understanding health
outcomes (Borah & Fina, 2017; McLaughlin et al., 2008; Military Spouse and Family Benefits |
Military.Com, n.d.). In order to maintain employment in the military, soldiers have to meet
physical health requirements, including getting regularly screened for substances while on duty,
therefore health research done on the military population is often skewed because they are a
generally healthy population (McLaughlin et al., 2008).
Health research on military spouses is primarily about how the unique psychosocial stressors
from being partnered with someone in the military can impact their mental health (Holliday et
al., 2016; Meadows et al., 2017; Trail et al., 2019). Recent literature highlights how specific
military stressors such as a recent relocation to a new duty station or recent reunification after a
deployment can significantly impact mental health symptoms in the absence of protective factors
such as social support (Sullivan, et al., 2020). Previous literature has examined these stressors
and contextualized them into cycles or rhythms of military family life (Marini, et al., 2018;
Pincus, et al., 2001). These theories highlight the numerous transitions military families have to
19
go through during deployments, relocations, as well as when they eventually leave the military
and how a failure to consistently cope with these transitions can have a negative impact on
mental wellbeing (Marini, et al., 2018). However, there is minimal literature that conceptualizes
how socio-economic factors outside of the military could be impacting military spousal mental
wellbeing or physical wellbeing.
There are ecological frameworks that have been adapted to the military context and highlight
how a variety of environmental factors from the micro to the macro level simultaneously interact
to affect spousal health, but do not highlight pathways for those interactions (National
Academies of Sciences Engineering and Medicine, 2019). Furthermore, there is overall minimal
research on how socio-economic or military factors impact the physical health of military
spouses. Civilian and veteran literature highlight the importance of understanding physical health
to paint a complete picture of overall health and that there is a linear relationship between
adverse mental and physical health (Cohen et al., 2010; Ohrnberger et al., 2017).
Conceptualizing how socio-economic factors are impacting military spouses’ overall health is
important as research is showing that it is life stressors outside of the military that impact spousal
health more than military stressors of deployments and relocations (Sullivan, Park, & Riviere,
2021; Sullivan, Park, Cleland, et al., 2021). To address these gaps, this current study adapted the
World Health Organization’s (WHO) SDoH conceptual framework to a population of Army
wives to explore the specific socio-economic factors contributing to or protecting health in the
military environment.
The Unique Military Environment
20
The benefits afforded to military personnel and their families are in place to minimize
military life stressors. For the spouse, these may include frequent relocations that uproot them
from their community and employment, prolonged separations from their partner through
deployments and training, and worrying about their partner’s wellbeing during deployments
(Lester et al., 2016; Marini et al., 2018). Though these stressors may contribute to poorer health
outcomes, the benefits of equal access to healthcare through TRICARE eliminates a critical
factor that perpetuates health inequalities in the U.S. (CDC, 2020). The military extends these
healthcare benefits to the service member’s family to ensure the mission readiness and retention
of service members (Soeters et al., 2006; U.S. Department of Defense Office of People
Analytics, 2019).
Adapting the World Health Organization’s Social Determinants of Health Framework
This study adapted the WHO’s SDoH conceptual framework to the social and economic
factors contributing to military spouse’s health. The WHO SDoH framework was chosen was
selected three reasons: (1) The military has universal access to healthcare, therefore making it
more aligned with a global framework than a domestic one, (2) how it incorporates the important
role social connections have on health trajectories, and (3) it is a comprehensive model that can
be tailored to any context and accounts for modifiable and non-modifiable determinants. The
WHO breaks down the SDoH into three categories: (1) structural determinants, (2) social
cohesion and capital, and (3) intermediary determinants (Solar & Irwin, 2010). The framework
specifies the paths between determinants and health inequalities (Solar & Irwin, 2010; see Figure
1 for adapted conceptual framework).
Structural Determinants
21
Structural determinants are the socio-economic, cultural, and societal values that determine
social hierarchies—an individual’s cultural and socio-demographic position (Solar & Irwin,
2010). Prior military history is an important cultural factor that could impact a spouse’s health.
For example, if the spouse grew up in the military or had a previous relationship with a service
member could indicate how long a spouse has been exposed to military life. Military culture's
strength and uniqueness have been documented to impact help-seeking behavior (Westphal &
Convoy, 2015), which has potentially significant consequences for health.
In the military, sex, education, rank, and race/ethnicity are socio-demographic factors that
could impact Army wives’ health. As nine out of ten military spouses are self-identified as
women, this study will eliminate sex as a socio-demographic determinant by focusing on self-
identified women (Department of Defense, 2019). Prior mental health research documents socio-
demographic disparities in military spouse’s mental wellbeing. Specifically, large scale studies
have found military spouses with a junior rank status, less educational attainment, large families
(four or more children), experiencing unemployment, or who have previously served in the
military to negatively affect mental health (Donoho et al., 2018; Trail et al., 2019).
There are conflicting findings when examining racial/ethnic disparities in military wives'
health outcomes. When examined in large sample sizes, there appears not to be an effect on
mental health (Donoho et al., 2018; Trail et al., 2019). However, when used as a control variable
in empirical studies with smaller sample sizes, it has been a risk factor for mental health issues
(Sinclair et al., 2019; Sullivan et al., 2020). Prior literature documents smaller maternal-child
health disparities by race/ethnicity in the military than in the civilian community (Barfield et al.,
1996; Lundquist, Xu, et al., 2014; Lundquist, Elo, et al., 2014). Specifically, studies found Black
women in military hospitals (spouses and service members) to have more equal birth weight for
22
baby, prenatal care use for mom, and breastfeeding outcomes over time than Black civilian
women living in the same area and national datasets (Barfield et al., 1996; Lundquist, Xu, et al.,
2014; Lundquist, Elo, et al., 2014). The conflicting racial/ethnic disparity findings demonstrate a
need for a framework to conceptualize how social and economic environmental factors could be
contributing to their overall health.
Social Cohesion and Capital Determinants
The most commonly cited protective factors for military spouse's mental health are
access and a sense of social support (Donoho et al., 2017; Lester et al., 2013; Sumner et al.,
2016; Wang et al., 2015). Perceived formal and informal social supports have been associated
with improved health for spouses and the family (Saltzman et al., 2011; Skomorovsky, 2014;
Sumner et al., 2016). Prior literature documents that one of the most important social support
sources is an individual’s spouse/partner (Dehle et al., 2001; Gardner & Cutrona, 2004).
Literature both within and outside the military highlight how a violent relationship, such as
Intimate Partner Violence (IPV), is not a supportive one and can negatively impact health (see
Jones, 2012 for review of literature on IPV in the military; Satyanarayana et al., 2015). Lastly, a
review of 150 studies assessing the social determinants of mental health highlighted that a safe,
non-violent environment is essential in understanding environmental factors impacting overall
mental health (Silva et al., 2016).
Intermediary Determinants
Intermediary determinants include various factors that directly contribute to an
individual’s health status (Solar & Irwin, 2010). For this paper, these factors are grouped into
three categories outline by the WHO SDoH framework, (1) material circumstances, (2)
23
psychosocial factors, and (3) physical factors. An important material circumstance in the military
is living on or off a military installation (Donoho et al., 2017, 2018), which determines ease of
access to resources, including healthcare services.
Physical factors that could impact military wives' health include recent childbirth and
history of an illness or injury (Godier-Mcbard et al., 2019). Research has shown that military
spouses with a deployed partner are at greater risk for poor perinatal mental health (Godier-
Mcbard et al., 2019). Research outside of the military documents how the prevalence of health
issues significantly corresponds to evaluation of both mental and physical health symptoms
(O’Donnell et al., 2013).
Known psychosocial factors that could impact spouse health are the health of the service
member, family size and work-family conflict (Sinclair et al., 2019; Sullivan et al., 2020; Trail et
al., 2019). For example, a service members’ adverse health can create additional caregiving
burden for their spouse, which can lead to spouse mental health symptoms (Lambert et al.,
2012). Prior military spouse literature found larger family sizes to be significantly related to
Major Depressive Disorder in spouses (Donoho et al., 2018). Lastly, the military and their family
have been referred to as “greedy institutions” for the demands they both place on service
members (Segal, 1986). Work-family conflict may be more likely in military families because of
stressors associated with relocating, prolonged separations, and heavy workloads (Segal, 1986;
Wadsworth & Southwell, 2011). Work-family conflict has been shown to impact parents' health
outcomes as well as the family as a whole (Frone et al., 1997; Wadsworth & Southwell, 2011).
The Current Study
24
The present study is one of the first to explore military wives’ overall health and the socio-
economic factors impacting it. To guide factor selection and the relationship between factors on
overall health, this study adapted the WHO SDoH conceptual framework to the military
environment and then tested it. Two exploratory questions guided this analysis:
1. Does the WHO SDoH adapted conceptual framework produce significant associations
between socio-economic factors with Army wives’ health?
2. Which of the SDoH categories (structural, social cohesion/capital, or intermediary) most
explains Army wives' health?
Materials & Methods
Data & Participants
The Land Combat Study provided the data used for this secondary analysis study. These
data were collected in 2012 by Walter Reed Army Institute for Research (WRAIR). The original
project focused on soldiers from one Army unit who had recently completed a combat
deployment to Afghanistan and their spouses (Donoho et al., 2017). Within this group, 343 Army
spouses were recruited through coordinated efforts with the unit’s Family Readiness Group
(FRG) leaders. The FRG is an organization of family members, volunteers, and soldiers
belonging to a command-sponsored unit that provides an avenue of mutual support and
assistance to family members. The spouse survey included health and wellbeing measures and all
initial project procedures and secondary analysis of the project’s data were reviewed by the
Human Subjects Protection Branch of WRAIR. Spouses were informed about the study and
asked to provide informed consent before participating. Surveys were administered in-person and
online. Twenty-three percent of FRG spouses responded to recruitment, and 98% agreed to
25
participate. Roughly 75% of individuals completed a web-based version of the survey, and 25%
completed a paper version. In order to focus on civilian, female spouses, who may experience
unique determinants of health (Barnett & Hyde, 2001b; Mary Clare Lennon & Sarah Rosenfield,
1992) (Woodall et al., 2020), nine male spouses and seven female spouses that indicated they
were active-duty service members were dropped for a final analytic sample of 327 female
spouses. The majority of our sample was White (75.15%), married or partnered with an enlisted
service member (78.15%), unemployed and not looking for work (n=125, 38.46%), had less than
a bachelor’s degree (66.46%), and had no prior history with the military (71.02%; see Table 1).
Measures
Structural Determinants
Cultural & Socio-Demographic Position. History with the military was assessed with
one item, which asked spouses to indicate if any of the following experiences applied to them,
including: “I am/was a military service member,” “I grew up in a military family,” or “I was a
military spouse in a prior marriage.” A yes response on any of these three items was indicative of
having a personal history with the military. Five demographic measures of socio-demographic
position were included: 1) Education (less than a bachelor’s degree/bachelor’s degree or more);
2) Employment status (employed/unemployed); 3) Race/ethnicity (White/Black/Non-white
Hispanic/ Other [Asian/Pacific Islander & those that marked “Other”]); 4) Rank
(enlisted/officer); 5) Age (18-39 years/30+ years).
Social Cohesion & Capital Determinants
Social cohesion and capital were assessed through one measure of intimate partner
violence and three measures of social support. Intimate partner violence was assessed by a 10-
item screener for clinically significant intimate partner violence, similar to the Physical Assault
26
subscale of the revised Conflict Tactics Scales (Heyman et al., 2013, 2020). Each item is a self-
report measure with seven past-year response options never, not this past year but has happened,
once, twice, 3-5 times, 6-10 times, more than ten times. This screener was dichotomized to 1
(there is clinically significant IPV; a single physical or sexual abuse in the past 12 months;
Heyman et al., 2013) or 0 (not).
There were two measures used to assess social support and one dichotomous question.
The first measure consisted of three items from the Medical Outcomes Study (MOS) Social
Support Survey. The MOS questions begin with the prompt: "How often is each of the following
kinds of support available to you if you need it?" Responses are on a five-point Likert scale
ranging from 1 (none of the time) to 5 (all of the time). The types of support include "Someone
you can count on to listen to you when you need to talk,” "Someone to give you good advice
about a crisis," and "Someone to take you to the doctor if you needed it." This scale demonstrates
good reliability and validity (Sherbourne & Stewart, 1991). Scores were averaged with a high
score indicating a heightened sense of social support. This scale's internal consistency was good
in this sample (Cronbach’s alpha = .87; Sherbourne & Stewart, 1991; Sinclair et al., 2019)). The
second measure assessed a spouse’s sense of belonging to an Army community. This measure
was developed by the Army for this survey and used in prior studies (Donoho et al., 2017). This
measure includes four statements about the Army community such as, "I feel I am part of the
Army community," and "I have friends from the Army community with who I spend time
socializing." Responses are on a five-point Likert scale ranging from 1 (strongly disagree) to 5
(strongly agree) with a higher score indicating an increased sense of Army community. This
scale's internal consistency was good in this sample (Cronbach’s alpha = .81; Donoho et al.,
27
2017). Lastly, spouses were asked, “Do you belong to a church, temple, or other religious
group?” 1 (yes)/ 0 (no).
Intermediary Determinants
Material Circumstance. Distance to military installation was assessed through a question,
“How far to you live from the nearest military installation (or the one you use the most)?” with
responses of I live on post, 10 miles or less, 11-25 miles, 26 or more miles, Do not know. This
question was recoded to be 0 (On post) vs. 1 (Off post). In the context of the Army, the military
installation is referred to as “post.” Only three respondents (n=3) marked, “Do not know,” and
they were recoded as missing.
Physical Factors. Recent history of either illness or childbirth was assessed with two items
beginning with the prompt: “Within the past year, did any of following stressful events occur?”
Items included “personal injury or illness” and “birth of a child” and included two response
options: 1 (yes) / 0 (no).
Psychosocial Factors. Work-family conflict was assessed through a version of the validated
Work-Family Conflict five-item scale (Netemeyer et al., 1996), modified to reference the service
members' job and duties affecting home life. Example items include “the demands of my
spouse’s work interfere with my home and family life.” Responses were recorded on seven-point
Likert scale from 1 (strongly disagree) to 7 (strongly agree). This scale's internal consistency
was strong in this sample (Cronbach’s alpha = .92; Netemeyer, 1996). The mental health of the
military spouse was assessed through the question, “Have you noticed any behavior(s) in your
spouse that makes you think they need mental health treatment?” 1 (yes) / 0 (no). Family size
was measured through the question, “How many children to you have?” with a continuous
response ranging from 0—7+.
28
Health Outcomes
Physical Health. Physical health was measured by the Patient Health Questionnaire 15
(PHQ-15), a 15-item scale that assesses an individual’s general physical symptoms and
demonstrates strong validity and reliability (Kroenke et al., 2002). Respondents are asked how
often they have been bothered by physical problems, with response options ranging from 0 (not
at all) to 3 (bothered a lot). Some of the physical symptoms include “stomach pain,” “back
pain,” and “headaches.” Internal consistency was strong in this sample (Cronbach’s alpha = .85;
Kroenke et al., 2002).
Mental Health. Mental health was measured by the Patient Health Questionnaire Anxiety-
Depression Scale (PHQ-ADS). The PHQ-ADS combines the widely use Patient Health
Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder-7 (GAD-7) for a composite
measure of anxiety and depression (Kroenke et al., 2016). The PHQ-ADS is a 16-item
questionnaire that asks about how often the respondent has been bothered by the following
problems over the past month, with response options ranging from 0 (not at all) to 3 (nearly
every day). Items include, "Little interest or pleasure in doing things,” "Feeling down, depressed,
or hopeless,” "Feeling nervous, anxious, or on edge,” or "Becoming easily annoyed or irritable"
(Kroenke et al., 2016). Internal consistency of this mental health measure was good in this
sample (Cronbach’s alpha = .94; Kroenke et al., 2016).
Analytic Plan
Analysis was conducted on STATA 16.1. All potential variables of interest had an
acceptable level of missingness (less than 6%; Long & Freese, 2005). Due to high skewness and
kurtosis, PHQ-15 and PHQ-ADS scores were recoded, so responses ranged from 1-4 and the
natural log was computed; transformed variables were included in all analyses. In order to
29
interpret the natural log, the coefficients must be exponentiated and interpreted as percentages
similar to a logistic regression output (Gelman & Hill, 2007). Bivariate correlations were run to
assess for possible multicollinearity within the dataset and no correlations indicated any concerns
with either outcome variable (Table 2). Two separate hierarchical multivariable linear
regressions (HLR) were run to assess the different categories of socio-economic variables on
Army wives’ physical and mental health respectively. Three successive models were run for each
outcome to evaluate the contribution of the three SDoH categories: (1) structural determinants
alone; (2) structural and social cohesion/capital determinants, and (3) structural, social
cohesion/capital, and intermediary determinants.
Results
Sample demographics are presented in Table 1. The majority of spouses did not belong to
a religious group (56.04%) and did not have clinically significant IPV (84.52%). The majority of
spouses lived off post (61.30%), did not give birth to a child (77.47%), or have an injury or
illness (80.00%) in the past year. On average, spouses reported having at least one child of any
age with a mean of 1.22 (SD: 1.25). Furthermore, most spouses did not think their service
member spouse/partner needed mental health treatment (82.04%). The overall sample had low
mental health symptoms with a mean score of 7.31 (SD: 8.21), with about a third (31.21%)
meeting the clinical cut point for mild mental health symptoms (Kroenke et al., 2016). The
overall sample indicated mild physical health symptoms with a mean of 5.43 (SD:5.01); 49.52%
met the clinical cut point for mild physical health symptoms (Kroenke et al., 2002).
At the bivariate level (Table 2), there were significant relationships among socio-
economic factors within each SDoH category and physical health symptoms. Significant
relationships were evident only in the social cohesion/capital and intermediary categories for
30
mental health symptoms. In the structural category, being married to a junior ranking enlisted
service member was associated with increased physical health symptoms (r = .11, p <.05), and
higher educational attainment was associated with decreased physical health symptoms (r = -.14,
p <.01). In the social cohesion/capital category, having clinically significant IPV was associated
with more mental and physical health symptoms (r = .22, p <.01; r = .18, p <.01, respectively),
while reporting greater social support (mental: r = -.34, p <.01; physical: r = -.25, p <.01) and
sense of Army community (mental: r = -.32, p <.01; physical: r = -.27, p <.01) were associated
with fewer symptoms. Belonging to a church, temple, or other religious group was also
associated with fewer physical health symptoms (r = -.17, p <.01).
At the intermediary category, having a recent childbirth, recent injury/illness, thinking
your spouse needs mental health treatment, and categories of work-family conflict were all
significantly associated with mental and physical health symptoms. Having a recent injury/illness
(Mental health, r = .21, p <.01; Physical health, r = .23, p <.01), thinking your spouse needs
mental health treatment (Mental health, r = .31, p <.01; Physical health, r = .24, p <.01), and
high work-family conflict (Mental health, r = .23, p <.01; Physical health, r = .21, p <.01) were
all significantly associated with increased mental and physical health symptoms. Having a recent
childbirth was significantly associated with decreased mental and physical health symptoms
(Mental health, r = -.12, p <.05; Physical health, r = -.18, p <.01).
HLR results are presented in Table 3 (mental health) and Table 4 (physical health). Since
the natural log was used for analyses the exponentiated coefficients (EXP) are also presented
(Gelman & Hill, 2007). For mental health symptoms, model 1 (structural determinant variables
only) was not significant (F[6, 293] = .69, p = .660) and explained limited variance in our
outcome (R
2
= 0.014). Model 2 (structural and social cohesion/capital determinant variables), had
31
a better fit (ΔF = 15.29, p < .001) and explained significantly more variance in our outcome (R
2
= 0.196). In this model, clinically significant IPV (β = .155, p = .001, exponentiated coefficient
(EXP) = 16.76%), social support (β = -.020, p < .001, EXP = -2.0%), and sense of Army
community (β = -.017, p =.001, EXP = -1.70%) were significantly associated with mental health
symptoms. Those who had clinically significant IPV had about a 17% increase in mental health
symptoms than those that did not. For every one-unit increase in social support or sense of Army
community, mental health symptoms decreased by 2.0% and 1.7% respectively.
Model 3 (structural, social cohesion/capital, and intermediary determinants) had the best
fit (ΔF = 4.89, p < .001) and explained over a quarter of the variance in our outcome (R
2
=
0.279). In this model, social support (β = -.015, p = .008, EXP = -1.47%), sense of Army
community (β = -.013, p = .010, EXP = -1.32%), recent childbirth (β = -.082, p =.048, EXP = -
7.90%), thinking their spouse needed mental health treatment (β = .155, p =.001, EXP =
16.76%), and work-family conflict (β = .006, p =.019, EXP = .537%) were all significantly
associated with mental health symptoms. For every one-unit increase in social support or sense
of Army community mental health symptoms decreased by 1.45% and 1.43% respectively.
Wives that had a childbirth within the past year had 7.90% less mental health symptoms
compared to those that did not. Lastly, wives that thought their spouse needed mental health
treatment had 16.64% higher mental health symptoms and for every one-unit increase in work-
family conflict mental health symptoms increased less than a percentage.
For physical health symptoms (Table 4), model 1 (structural determinant variables only)
was not significant (F[6, 295] = 1.75, p = .1092) and explained limited variance in our outcome
(R
2
= 0.014). Model 2 (structural and social cohesion/capital variables), had a better fit (ΔF =
15.29, p <.001) and explained significantly more variance in our outcome (R
2
= 0.20). In this
32
model, clinically significant IPV (β = .095, p = .008, EXP = 9.94%), sense of Army community
(β = -.011, p = .003, EXP = -1.12%), and belonging to a religious group (β = -.063, p = .027,
EXP = -6.09%) were significantly associated with physical health symptoms. Those who had
clinically significant intimate partner violence had a 9.94% increase in physical health symptoms
than those that did not. For every one-unit increase in sense of Army community, physical health
symptoms decreased by 1.12%. Lastly, wives who belonged to a religious group had 6.09% less
physical health symptoms than those who did not.
Model 3 (structural, social cohesion/capital, and intermediary determinants) had the best fit
(ΔF = 6.14, p < .001) and explained over a quarter of the variance in our outcome (R
2
= 0.271).
In this model, sense of Army community (β = -.008, p = .031, EXP = -0.80%), belonging to a
religious group (β = -.070, p = .014, EXP = -6.51%), having a recent child (β = -.089, p = .003,
EXP = -8.49%), having a recent illness or injury (β = .085, p = .005, EXP = 8.96%), thinking
their spouse needs mental health treatment (β = .072, p = .036, EXP = 7.43%), and levels of
work-family conflict (β = .004, p = .003, EXP = .490%) were significantly associated with
physical health symptoms. For every one-unit increase in sense of Army community, physical
health symptoms decreased by less than a percentage, and those that belonged to a religious
group had 6.51% fewer physical health symptoms compared to those that were not. Wives that
had a child within the past year had 8.49% lower physical health symptoms than those who did
not. Wives that had an injury or illness in the past year had 8.96% more physical health
symptoms compared to those that did not. Furthermore, spouses that thought their service
member needed mental health treatment had 7.43% more physical health symptoms compared to
those that did not, and for every one-unit increase in work-family conflict, physical health
symptoms increased less than a percentage.
33
Discussion
We answered our first exploratory question of does the WHO SDoH adapted conceptual
framework produce significant associations between socio-economic factors with Army wives’
health was assessed through factor associations at each determinant level with overall health with
pairwise correlation analyses and regressions. Correlational findings show the applicability for
each determinant category for physical health symptoms and the social cohesion/capital and
intermediary categories for mental health symptoms. The prevalence of significant associations
between socio-economic factors and health outcomes supports the use of the SDoH conceptual
model in the military.
Regression results showed the protective nature of a sense of community on both physical
and mental health. This could be for a variety of reasons, including knowledge sharing about
resources, feelings of connectedness and lack of loneliness (Crouch et al., 2017). Regression
results also showed that a sense of social support can also protect against mental health
symptoms and that belonging to a religious group can protect against physical health symptoms.
This finding supports prior research that has documented the importance of social support on
military spouses' mental and physical health outcomes, as well as literature outside the military
the document the importance of social support on health outcomes (Donoho et al., 2017; Sumner
et al., 2016; Wang et al., 2015; Holt-Lundstad & Uchino, 2015). This study adds to this literature
by documenting that even in the presence of other socio-economic factors such as a recent
injury/illness, childbirth, clinically significant intimate partner violence, and thinking their
service member spouse needs mental health treatment, different types of social support still
protect both mental and physical health. Furthermore, these findings suggest that certain types of
social connections could be more beneficial for physical health than mental health. For example,
34
feeling that you have people to call and count on may be more important in protecting mental
health, whereas having an actual group that meets regularly is more important in protecting
physical health. Future research should seek to better understand the impact of specific types of
social support on a variety of health aspects in order to appropriately guide interventions.
These findings also point out the importance of the service member’s mental health on a
spouse’s overall health. Our results showed that spouses who thought their service member
needed mental health treatment had significantly more physical and mental health symptoms.
This finding could be due to the military's high mental health stigma and associated negative
impact on care-seeking behavior (see Michalopoulou et al., 2017 for review). For example, the
stress of supporting a service member who needs mental health treatment but refuses to seek it,
could negatively impact the military spouse’s overall health. Such a dynamic can breed a sense
of helplessness about how to help the service member, and thereby help themselves. Further,
these findings suggest employing spouses as a source of mental health treatment resources for
the service member. Building best practices for including spouses as sources of information for
the service member is an area that warrants further research.
The current study found that high levels of work-family conflict were significantly related to
adverse mental and physical health. However, these findings demonstrate work-family conflict
has a minimal significant change relative to the other predictors. The significance of the
relationship supports prior literature which found that the stress of work duties and caring for
one’s family, especially in a military setting, can be demanding on the service member;
similarly, family-friendly military work environments were found to support the wellbeing of the
service member (Huffman et al., 2008; Segal, 1986; Wadsworth & Southwell, 2011). Prior
literature has suggested innovative ways to help minimize work-family conflict. For example,
35
increasing the flexibility of work arrangements through extensions of work obligations
(Wadsworth & Southwell, 2011). The Navy provides their sailors more control of their time at
key periods, such as after a child's birth, by exchanging more work flexibility for a specific
amount of time for extensions of service (Wadsworth & Southwell, 2011). This type of work
flexibility provides the service member and their family with more control over prolonged
absences, and the military retains a highly trained worker.
Our study found that recent childbirth (within the past year) was significantly related to
decreased physical and mental health symptoms. This finding contrasts prior literature that
documents military spouses with a deployed partner are at greater risk for perinatal mental health
(Godier-Mcbard et al., 2019). However, these data were collected after a deployment when the
service member was home. A recent childbirth's positive impact on health could be related to
increased doctors visits and the care a mother is likely to receive after giving birth (Geissler et
al., 2020). This finding could also be a side effect of having universal healthcare, meaning there
are minimal barriers in receiving perinatal and postnatal care, which could improve overall
health. The finding could also be due to the six-week paid parental leave the active-duty service
member typically receives following the birth of a child, which can lead to increased family time
and a sense of support for the spouse (Lidbeck & Bernhardsson, 2019; Military.com, 2021).
Our findings suggest that structural determinants of military history, education, age,
race/ethnicity, employment status, or rank were not significantly related to either physical or
mental health symptoms when examined all together. This finding contrasts with other large-
scale studies that have found employment and rank to be significant predictors of military
spousal mental health (Donoho et al., 2018; Trail et al., 2019). However, these previous large-
scale studies did not examine socio-demographic factors with social cohesion/capital or
36
intermediary determinant categories such as social support, sense of community, or the service
member's mental health state. It is possible that when the SDoH categories are examined all
together, equal access to healthcare may attenuate the strength of associations between structural
determinants and health, as universal access to both coverage and care level out the effects of
structural inequalities that prevent individuals from seeking and receiving care in other
populations.
Similar to our findings, several past studies on military spouses’ mental and physical health
have failed to find a significant relationship between race/ethnicity and health outcomes
(Barfield et al., 1996; Donoho et al., 2018; Lundquist, Xu, et al., 2014; Lundquist, Elo, et al.,
2014). These consistent findings may indicate that in a context where there is equal access to
healthcare, little to no racial/ethnic disparities in health outcomes exist. Although not directly
comparable, recent research evaluating the Affordable Care Act, which put in place regulations
to constrain healthcare costs and improve quality in the US, documents less socio-economic
disparities in insurance coverage (including race/ethnicity; Angier et al., 2019; Chaudry et al.,
2019). This study adds to this literature by documenting that in an environment with universal
healthcare, there are not significant racial/ethnic differences in health outcomes in the presence
of other social determinants of health such as a recent illness/injury, childbirth, mental health of
service member, intimate partner violence, or social support.
To address our second exploratory question (identifying which determinant category best
explains Army wives’ health), we employed HLR analyses. These results showed that the mental
and physical health models with structural, social cohesion/capital, and intermediary
determinants were the best fitting. This finding indicates that Army wives’ physical and mental
health is best explained when all determinant categories are examined together. Furthermore,
37
while the social cohesion/capital factors such as feelings of social support, sense of Army
community, and clinically significant IPV were statistically meaningful when considered
alongside structural determinants, their effects were attenuated when intermediary determinants
were included. This suggests that some of the variance captured by the social cohesion/capital
factors is also captured in the intermediary factors. Together, this suggests that to understand the
social and economic context that could impact a military spouse’s overall health, it is important
to include factors across the SDoH categories.
Limitations
Findings from this study should be considered in light of several limitations. First, the
present study was a secondary analysis; therefore, many potentially relevant variables were not
available for inclusion in models. Second, a non-probability sampling approach was used for
data collection with about 23% response rate from eligible spouses, potentially limiting the
generalizability of findings. Third, data were cross-sectional so the temporality of associations
described here cannot be established. Though initial power calculations suggested the study was
powered to detect medium effect sizes, a number of decisions were made to preserve power,
including collapsing race/ethnicity and employment status demographic categories. As a result,
comparisons across these subgroups were not possible. Furthermore, the dichotomization of
race/ethnicity fails to account for the fact that the various groups incorporated into “non-White”
may not have the same social determinants of health. When statistically possible, disparities by
racial subgroup should be explored.
Implications
Though preliminary, these findings highlight the social determinants of health still exist
within a population with equal access to universal healthcare coverage, however, the impact of
38
the structural social determinants on health appear to be attenuated. Our findings suggest that
structural determinants of military history, education, age, race/ethnicity, employment status, or
rank were not significantly related to either physical or mental health symptoms when examined
all together. It is possible that when the SDoH categories are examined all together, equal access
to healthcare may attenuate the strength of associations between structural determinants and
health, as universal access to both coverage and care level out the effects of structural
inequalities that prevent individuals from seeking care in other populations. Furthermore, these
findings highlight similarities in socio-economic factors that impact Army wives’ mental and
physical health. Military medical providers should be aware that when addressing issues such as
the mental health of the service member, a recent childbirth from the spouse, or work-family
conflict, they are aiding the spouse’s overall health. In addition, by increasing a spouse’s sense of
community, providers support their physical and mental health. Lastly, our results support the
use of the adapted SDoH model to understand Army wives’ health. Future research with this
model should be tested on larger sample sizes, on other branches, and with both sexes.
39
Determinant Category Characteristic N (%) / M (SD) Range
Structural Determinants
Race/Ethnicity White 245 (75.15%)
Black 22 (6.75%)
Non-white Hispanic 34 (10.43%)
Other 25 (7.67%)
Rank Enlisted 254 (78.15%)
Officer 71 (21.85%)
Employment Status Employed 96 (29.54%)
Unemployed Looking For
Work
71 (21.85%)
Unemployed Not Looking For
Work
125 (38.46%)
Other 33 (10.15%)
Education Bachelor’s Degree or More 109 (33.54%)
Less than Bachelors 216 (66.46%)
Age 30 + years old 157 (48.16%)
18-29 years old 169 (51.84%)
Personal History with
Military
No 223 (71.02%)
Yes 91 (28.98%)
Social Cohesion & Social
Capital Determinants
Social Support 11.1 (3.43) 3-15
Army Community 13.62 (3.70) 4-20
Religious Group Involvement No 181 (56.04%)
Yes 142 (43.96%)
Clinically Significant IPV No 262 (84.52%)
Yes 48 (15.48%)
Intermediary Determinants
Distance to Military
Installation
Live on Post 125 (38.70%)
Live Off Post 198 (61.30%)
Childbirth in past year No 251 (77.47%)
Yes 73 (22.53%)
Injury/Illness in past year No 260 (80.00%)
Yes 65 (20.00%)
Does Service Member need
Mental Health Treatment?
No 265 (82.04%)
Yes 58 (17.96%)
Number of Children 1.22 (1.25) 0-5
Work-Family Conflict 22.10 (7.74) 5-35
Health
*Spouse Mental Health 7.31 (8.21) 0-39
*Spouse Physical Health 5.43 (5.01) 0-28
Table 2.1 Demographics of Analytic Sample of Active-Duty Army Wives.
*These are the non-transformed values. To correct for issues of skewness and kurtosis the natural log of these variables was
used for analyses. The exponentiated mean for mental health = 6.13 and the exponentiated mean for physical health = 4.88
40
Bold p ≤0.05 Bold Underlined p ≤0.01
(1) = PHQ-ADS mental health symptoms measure (2) PhQ-15 physical health symptom measure (3) = If spouse is from military family, previously served or
previously dated a service member vs. not (4) = Spouse received a bachelors or more vs. Received less than a bachelor’s degree (5) = ≤ 29 years vs. ≥ 30 years
(6) = vs. Non-White (7) = vs. Unemployed (8) = vs. Officer/Warrant Officer (9) = Has a clinically significant Intimate Partner Violence (IPV) vs. Not (12) =
Belong to church, temple, or other religious group vs. Not (13) = vs. On post (14) = Pregnancy in past year vs. Not (15) = Injury/Illness in past year vs. Not (18)
= Thinking service member needs mental health treatment vs. Not (19) = Work-family conflict
Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18)
(1) Mental Health Sym 1.00
(2) Phys. Health Sym 0.67 1.00
(3) Military History -0.04 0.01 1.00
(4) Bachelors + -0.04 -0.14 0.04 1.00
(5) Age 0.04 -0.08 -0.08 -0.14 1.00
(6) White -0.02 -0.10 0.01 0.06 0.09 1.00
(7) Employed -0.00 -0.01 0.04 0.16 -0.09 0.01 1.00
(8) Enlisted 0.10 0.11 -0.04 -0.59 -0.16 0.17 0.01 1.00
(9) Clin. Sig. IPV 0.22 0.18 -0.03 -0.03 0.02 -0.14 0.02 0.04 1.00
(10) Social Support -0.34 -0.25 -0.01 0.16 0.04 -0.08 0.04 -0.14 -0.17 1.00
(11) Army Community -0.32 -0.27 0.13 0.16 -0.01 0.07 -0.02 -0.16 -0.09 0.42 1.00
(12) Religious Group -0.10 -0.17 0.08 0.36 -0.16 -0.05 0.12 -0.33 0.02 0.11 0.18 1.00
(13) Off Post 0.10 0.01 0.01 0.16 0.16 0.05 0.14 -0.14 0.11 -0.01 -0.17 -0.10 1.00
(14) Recent Childbirth -0.12 -0.18 0.03 0.06 0.19 0.03 -0.12 -0.13 -0.02 0.05 0.03 -0.08 0.07 1.00
(15) Recent Injury 0.21 0.23 -0.05 -0.03 0.02 0.05 0.06 -0.01 0.16 -0.09 -0.06 -0.10 0.11 -0.09 1.00
(16) Num of children 0.02 0.02 0.03 -0.01 -0.33 -0.04 -0.18 -0.06 -0.03 -0.08 0.04 0.09 -0.25 0.01 -0.10 1.00
(17) SM. M. Health 0.31 0.24 0.01 0.01 -0.04 0.04 -0.00 0.05 0.26 -0.18 -0.17 0.03 0.08 -0.06 0.13 0.08 1.00
(18) WkFm. Conflict 0.23 0.21 0.03 0.20 -0.01 0.08 0.06 -0.22 0.12 -0.21 -0.12 0.12 0.02 0.02 0.01 0.10 0.22 1.00
Table 2.2 Pairwise Correlation Coefficients among Social Determinants of Mental Health and Health Outcomes among Army Wives.
41
*p ≤0.05 **p ≤0.01 ***p ≤0.001
●SM = Service Member, Tx = Treatment
Note. Due to issues of skewness and kurtosis the natural log of the mental health measure was used for analysis, therefore beta
coefficients close to zero can be interpreted as rough percentage estimates (i.e. -.020 should be interpreted as for every one-unit
increase in social support mental health symptoms decrease by roughly 2%). For coefficients not close to zero, please reference
results section for exponentiated value.
Model 1 Model 2 Model 3
B S.E. B S.E. B S.E.
Has Military History (vs. not) -.019 .039 .008 .037 .008 .036
Bachelors or more (vs. less than Bachelors) .020 .048 .073 .046 .044 .046
Young (vs. older—30 +) .013 .037 .018 .034 .011 .038
White (vs. non-White) -.016 .043 .009 .040 -.016 .039
Employed (vs. unemployed) -.000 .040 -.005 .037 -.026 .037
Enlisted (vs. officer/warrant officer) .088 .055 .061 .052 .056 .051
Clinically significant IPV (vs. not) .155*** .048 .082 .037
Social Support -.020*** .006 -.015** .006
Army Community -.017*** .005 -.013** .005
Belong to Religious Group (vs. not) -.028 .038 -.038 .037
Off Post (vs. on post) .032 .037
Recent childbirth (vs. not) -.080* .042
Recent Injury/Illness (vs. not) .079 .041
Number of Children -.003 .014
Thinking SM Needs Mental health Tx (vs. not)● .154*** .046
Work-Family Conflict .006* .002
R
2
.014 .196 .279
F .688 6.63*** 6.30***
Δ F 15.288*** 4.89***
Δ R
2
.182 .083
Table 2.3. Social Determinants of Mental Health Hierarchical Linear Regression Results.
42
*p ≤0.05 **p ≤0.01 ***p ≤0.001
●SM = Service Member, Tx = Treatment
Note. Due to issues of skewness and kurtosis the natural log of the mental health measure was used for analysis, therefore beta
coefficients close to zero can be interpreted as rough percentage (i.e. -.019 should be interpreted as for every one-unit increase in
social support mental health symptoms decrease by roughly 1.9%). For coefficients not close to zero, please reference results
section for exponentiated value.
Model 1 Model 2 Model 3
B S.E. B S.E. B S.E.
Military History (vs. not) .010 .028 .022 .028 .030 .026
Bachelors or more (vs. less than Bachelors) -.051 .034 -.020 .034 -.039 .033
Young (vs. older—30 +) -.018 .026 -.028 .026 -.025 .027
White (vs. non-White) -.039 .030 -.019 .030 -.036 .028
Employed (vs. unemployed) .002 .029 -.002 .028 -.015 .027
Enlisted (vs. officer/warrant officer) .038 .039 .012 .039 .012 .038
Clinically significant IPV (vs. not) .095** .036 .050 .035
Social Support -.008 .004 -.005 .004
Army Community -.011** .004 -.008* .004
Belong to Religious Group (vs. not) -.063* .028 -.067* .027
Off Post (vs. on post) .008 .026
Recent childbirth (vs. not) -.089** .030
Recent Injury/Illness (vs. not) .085** .030
Number of Children -.004 .010
Thinking SM Needs Mental health Tx (vs. not)● .072* .034
Work-Family Conflict .005** .002
R
2
.034 .162 .271
F 1.75 5.26*** 6.08***
Δ F 10.18*** 6.14***
Δ R
2
.127 .109
Table 2.4. Social Determinants of Physical Health Hierarchical Linear Regression Results.
43
Figure 2.1. Military Spouses Social Determinants of Health Conceptual Framework.
This is the adapted World Health Organization’s Social Determinants of Health Conceptual Framework to
the military spouse’s environment. This conceptual model was used to guide analyses to better understand
the socio-economic factors impacting Army wives’ overall health.
44
Chapter 3. (Study 2): Exploring the Social Determinants of Mental Health by Race and
Ethnicity in Army Wives
Abstract
Objective: To explore the Social Determinants of Mental Health (SDoMH) by race/ethnicity in a
sample with equal access to healthcare. Method: Using an adaptation of the World Health
Organization’s SDoMH Framework, this secondary analysis examines the combination of the
socio-economic factors that make up the SDoMH by race/ethnicity. This paper employed
Configurational Comparative Methods to analyze various racial/ethnic subsets from quantitative
survey data from (N=327) active-duty Army wives. Data was collected in 2012 by Walter Reed
Army Institute of Research. Results: Two solutions were found for each of the following
racial/ethnic groups: non-Hispanic Black, Hispanic non-White, Junior Enlisted non-Hispanic
White, and non-Hispanic other, for a total of eight solutions with varying pathways. Four
solutions consistently explained clinically significant depression symptoms and four solutions
consistently explained non-significant depression symptoms providing a solution for each
racial/ethnic minority group. Discussion: While preliminary, these findings highlight the
potential use of a configurational approach to understand mental health outcomes. Clinical
implications discussed.
45
Introduction
All active-duty military spouses experience unique stressors, including prolonged separations
from their partner during deployments or training, worrying about their partner's safety, and
frequent relocation (Borah & Fina, 2017). While most military wives cope well with these
unique stressors, there is conflicting evidence on whether racial/ethnic minorities are at greater
risk for adverse mental health issues in the military (Donoho et al., 2018; Sinclair et al., 2019;
Sullivan et al., 2020). This is in large part because of the minimal research focused on the mental
health outcomes of racial and ethnic minority military spouses (National Academies of Sciences
Engineering and Medicine, 2019). One reason for the lack of research could be the lack of a
framework to guide an understanding of how socio-demographic factors, such as race/ethnicity,
interact with the surrounding environment to impact mental health. The military is a unique
environment in that the barrier of insurance coverage and access to care are removed through
TRICARE insurance coverage to all service members and their dependents (Military Spouse and
Family Benefits | Military.Com, n.d.). Therefore, the military environment provides a unique
opportunity to understand health disparities with a key determinant of health removed. Using an
adaptation of the World Health Organization’s (WHO) Social Determinants of Health
framework, this paper examines the complex interrelationship between various environmental
factors on mental health among different racial/ethnic groups of military wives.
Unique Military Context
The military is a unique environment to study the social determinants of health in that
one of the key determinant factors, access to healthcare, is widely available. In the United States
military, every service member and their families are provided healthcare, including mental
health care, through TRICARE (Military Spouse and Family Benefits | Military.Com, n.d.). This
46
widespread access to coverage could explain why several large-scale studies have not found
racial/ethnic minority differences in mental health outcomes (Donoho et al., 2018; Trail et al.,
2019). However, other empirical research studies have examined military spouses’ mental health
and found race/ethnicity to negatively affect coping skills and more likely to be in a higher risk
profile group for developing mental health issues (Sinclair et al., 2019; Sullivan et al., 2020).
Furthermore, military spousal mental health research has not included race as a control variable
in their analysis (Eaton et al., 2008; Lester et al., 2010). There is a need for research to
understand the potential mental health difference by race/ethnicity and the environmental factors,
outside of access to healthcare, that could contribute to mental well-being.
Military Social Determinants of Mental Health Conceptual Framework
The social determinants of health have been conceptualized as a broad set of conditions that
impact overall health, including—but not limited to—the treatment of different demographic
groups such as racial/ethnic groups or genders, to the level of social capital an individual has, to
the access to healthcare (Solar & Irwin, 2010). The WHO framework highlights (1) structural
determinants, such as demographic characteristics including race/ethnicity, sex, socio-economic
status, educational attainment, and age, (2) social cohesion and capital, such as the social
supports that can prevent or exacerbate an illness, and (2) intermediary determinants, such as
access to resources, psychosocial factors, physical factors, and healthcare coverage. Here, the
model has been adapted to focus on mental health (SDoMH) and includes features unique to
military families; the adapted framework may address inconsistencies in prior research on the
relationship between racial/ethnic minorities and mental health among military wives (see Figure
1 for adapted conceptual model).
Structural Determinants
47
Structural determinants are factors that determine the social hierarchies that can impact
an individual’s mental health trajectory, such as an individual’s socio-economic position or
cultural/societal values (Solar & Irwin, 2010). In the military wives' context, socio-economic
position may include race/ethnicity, rank, and employment (Borah & Fina, 2017; Donoho et al.,
2018; Trail et al., 2019). Specifically, prior research suggests spouses who are a racial or ethnic
minority, unemployed, married to a junior ranking service member, or have less education are at
greater risk for Major Depressive Disorder (MDD) and increased stress levels (Donoho et al.,
2018; Trail et al., 2019). Further, in this population, both personal history with the military and
mental health stigma are cultural experiences or values which may impact spouse mental health.
For example, prior research found that spouses who have previously served in the military are at
greater risk for MDD, which could be due to the increased exposure to military stressors such as
prolonged periods away from loved ones and consistent relocations (Donoho et al., 2018).
Additionally, mental health stigma is high in the military and could be a barrier to seeking
treatment (Kim et al., 2011; Sharp et al., 2015a).
Adverse Childhood Experiences (ACES) that encompasses various aspects of an adverse
childhood home environment—including the psychological, physical, sexual, and four different
types of household dysfunction—have been shown to have a negative health trajectory for the
general population as well as military spouses ability to cope with stressors (Felitti et al., 1998;
Sinclair et al., 2019). ACES are an important structural determinant for racial/ethnic minorities
because research documents that children from different races/ethnicities do not experience
ACES equally. For example, a report that used a nationally representative sample of children
found that 61% of non-Hispanic Black children and 51% of Hispanic non-White children
48
experienced at least one ACE compared to 40% of non-Hispanic White children and 23% of
non-Hispanic Asian children (Sacks & Murphey, 2018).
Social Cohesion and Capital Determinants
Social cohesion and capital are conceptualized as the “extension of social relationships
and the norms of reciprocity, influencing health by way of the social support mechanisms that
these relationships provide to those who participate in them.” (Solar and Irwin 2010, p.41). Silva
et al.'s (2016) review of the social determinants of mental health literature highlights the
importance of safe social connections in preventing mental illness initiation and continuation,
specifically how experiences of violence can have a negative impact on mental health. In the
context of the military, social connections may include the presence of social supports, sense of
community, or the absences of violence. Military spouses with access to formal and informal
social support, including religious groups, community, friends or family, during military
separations had lower levels of anxiety (Bowen & Martin, 2011; Burrell et al., 2003).
Additionally, in a sample of Army wives, a greater sense of community connection to the Army
protected against mental health symptoms (Donoho et al., 2017). Lastly, an unsafe social
connection, including the experience of intimate partner violence (IPV), is a known risk factor
for adverse mental health both within and outside the military (see Jones 2012 for review of IPV
in the military population).
Intermediary Determinants
Intermediary determinants of mental health directly influence an individual’s mental
health status and can include: an individual’s material circumstances, physical factors,
psychosocial factors, and their interaction with the health system. For military wives, material
49
circumstances may include living on or off a base, post, or installation. Services, including
healthcare, as well as subsidized food, gas, and financial assistance are all available on military
installations; living off base could impact access to these goods and services. Among military
spouses, recent childbirth and prior history of illness are physical factors that have been
associated with adverse health (Godier-Mcbard et al., 2019).
Known psychosocial factors associated with mental health among military spouses
include the mental health of the military service member, family size, the health of the family,
and work-family conflict (Schwartz et al., 2005; Trail et al., 2019; Wadsworth & Southwell,
2011). Military spouses who reported having a large family (four or more children) were at
greater risk for MDD and increased stress levels (Donoho et al., 2018; Trail et al., 2019). A
service members’ adverse health can create additional strain on the spouse as they may
experience additional caregiver burden, which can increase depression (Conforte et al., 2017;
Lambert et al., 2012; Thandi et al., 2018). The military environment potentially lends itself to
high work-family conflict due to heavy workloads for military service members, and prolonged
periods away from home through deployments and trainings, which has been shown to have
consequences for spousal mental health (Kelly et al., 2008; Wadsworth & Southwell, 2011).
Finally, in the military context, service members and their families receive free or heavily
subsidized health care that is generally readily available, so fewer barriers to access mental
health care exists (Military Spouse and Family Benefits | Military.Com, n.d.). Spouses may still
experience barriers to care, such as not knowing what services are available to them, not having
the time to access them, or not being able to find a clinician they trust (Lewy et al., 2014; Schvey
et al., 2021). These factors may be associated with the choice not to pursue mental health
treatment (Schvey et al., 2021). Furthermore, a spouse could experience psychological barriers to
50
care such as mental health stigma. Stigma surrounding seeking and using mental health care is
higher in military wives when compared to a national sample of women (Lewy, et al., 2014).
The Current Study
This paper will use descriptive statistics and CCMs to explore how Army wives’
structural, social cohesion & capital, and intermediary determinants from the adapted SDoMH
framework explain the mental health of Army wives, specifically depression. This question can
prove difficult to explore through traditional correlational approaches to quantitative data, which
are designed to detect net effects using probabilistic methods. Correlational approaches assess
cause and effect relationships by controlling variables (i.e. holding them constant) to evaluate the
net effects of unit differences in independent variables on the outcome. In other words, the
outcome relies on the incremental difference in the dependent variable Y (on average) given a
unit difference in independent variable X, controlling for all other variables. These
methodologies typically rely on larger sample sizes to detect an effect. Since racial/ethnic
minorities generally comprise smaller groups in the military, there is often not a large enough
sample size to explore these groups in detail. Knowing the limitations of correlational
methodologies, Ragin and Fiss (2017) used fsQCA to examine the configurations of race,
gender, family background, educational achievement when addressing social inequality in the
US (Ragin & Fiss, 2017).
CCMs are a set-theoretic analytic method that use Boolean algebra to evaluate how
combinations of specific conditions can yield an outcome of interest (Ragin, 1987, 2014). In
other words, with all other things being equal, A is a cause of B, if and only if A is part of a set of
conditions AX that is regularly followed by B (D. Cragun, 2020; Furnari et al., 2020; Whitaker et
al., 2020). If A does not make a difference to B in any set of conditions, then A is redundant to
51
account for B, and is therefore not a cause of B. In CCMs the outcome relies on configurational
patterns that identify necessity (conditions always present when the outcome is present, but alone
do not guarantee the outcome) and sufficiency (conditions co-occur with outcome). These
methodologies do not have the same sample size requirement as correlational methodologies; for
example, a sample with ten cases (one case could be equivalent to one participant), can be
suitable for CCMs (Schneider & Wagemann, 2012).
CCMs identify how multiple conditions work together in configurations that operate
jointly as a whole and allows for assessing for equifinality, when multiple paths lead to an
outcome, as well as how a condition may only be relevant to an outcome if it is paired with
another condition (known as complex causality) (Furnari et al., 2020; Ragin, 2014). In other
words, there is more than one path to an outcome, and paths typically consist of specific
conditions that must appear together to yield the outcome. The ability of CCMs to detect causal
complexity and equifinality make it ideal in understanding the interplay of Army wives’
structural, social cohesion & capital, and intermediary determinants on their mental health.
Furthermore, CCMs are useful in exploring potential inequalities that entail the intersection of
causal conditions such as the structural, social cohesion and capital, and intermediary
determinants of mental health (Ragin & Fiss, 2017; Rich et al., 2020; Spangaro et al., 2016).
This paper represents one of the first to use Coincidence Analysis (CNA) as an
exploratory method for factor selection prior to modeling with Qualitative Comparative Analysis
(QCA). CNA is a relatively new member of the larger family of CCMs, featuring a unique
bottom-up approach (Whitaker et al., 2020; Yakovchenko et al., 2020). QCA has been employed
in hundreds of studies dating back to 1987 as a distinct CCM approach (Deborah Cragun et al.,
2016; Harris et al., 2019; Knittel et al., 2019; Myers et al., 2019; Palinkas et al., 2019; see
52
COMPASSS.org for a bibliography of CCM related articles). QCA determines whether a
combination of conditions is sufficient to produce an outcome through the truth table.
Specifically, the truth table displays all logically possible combinations of the causal conditions,
and then assigns each case to a truth table row of which it is a set member. For this paper, each
case represents a military wife participant. Through logical minimization and Boolean algebra,
the rows are compared and reduced to produce a variety of solutions with varying complexity
(complex, intermediate, and parsimonious). The solutions express different pathways of
combinations of the causal conditions that are sufficient to consistently produce the outcome of
interest.
For this paper, the outcome will be clinically significant depression symptoms. Two
exploratory questions guided the analysis:
1. How do the social determinant factors distribute across the three determinant categories
(structural, social cohesion & capital, intermediary) by race and/or ethnicity?
2. Which social determinant factors across the three determinate categories (structural,
social cohesion & capital, intermediary) combine to produce and not produce clinically
significant depression symptoms for Army wives by race and/or ethnicity?
Methods
Data & Participants
Data initially collected in 2012 by Walter Reed Army Institute of Research (WRAIR)
were used for this secondary analysis. Survey data were collected from the spouses of one
military unit approximately 16 months after this unit returned from Afghanistan. In-person and
online surveys were administered to participants in the continental United States (see (Donoho et
53
al., 2017) for already published recruitment methods). Spouses were informed about the study
and asked to provide informed consent before completing the survey. The survey included
measures of demographics, military stressors, and health and well-being. All initial project
procedures and secondary analysis of the project’s data were reviewed by the Human Subjects
Protection Branch of the Walter Reed Army Institute of Research (WRAIR). Each survey took
between 30 and 45 minutes to complete. Twenty-three percent of spouses responded to
recruitment, and 98% agreed to participate in the study. The majority (74.2%) completed a web-
based version of the survey, and the remainder completed a paper version. Nine male spouses
and seven female spouses that indicated they were in active-duty service were dropped for a final
analytic sample of 327 female spouses. The majority of spouses were White (74.7%) and had at
least one child (63.8%), with the most respondents in a middle-ranged rank of E5-E9 (43.9%),
unemployed (60.3%), and have some college/associates (49.8%); see Table 1).
Measures
Mental Health Outcome
To assess mental health, depression was used as it is a good measure of general mental
well-being and is often comorbid with other measures of mental health such as anxiety
(Naragon-Gainey, 2010). Mental health was measured by the Patient Health Questionnaire-8
(PHQ-8; Kroenke et al. 2009). The PHQ-8 is a diagnostic screening and severity measure for
depression. The PHQ-8 is based on a 4-point scale, ranging from 1 (not at all) to 4 (nearly every
day) and summed with higher scores indicating greater severity. Items include: “little interest or
pleasure in doing things” and “feeling tired or having little energy.” Internal consistency for the
PHQ-8 was good in this sample (Cronbach’s alpha = .89; Kroenke et al., 2009). For calibration,
the established clinical cut point of five (clinically significant mild depression symptoms) and
54
ten (probably diagnosis of depression) were used as guides (Kroenke et al., 2009). The fully in
score was 9.9 so a score of 10 or greater would be considered fully in, the cross-over point was
4.9, so score of 5 or more would be considered closer to fully in. Lastly, the fully out score was
4.1 so a score of 4 or less would be considered fully out.
To preserve space all explanatory factors that were considered as well as their calibration
are shown in Tables 2 & 3. For further description of measures used for final analyses, see
supplemental materials.
Analytic Plan
The first step for analysis was to look at the distribution of each potential SDoMH
category for each racial/ethnic identity. STATA 16.1 was used to understand the average scores
as well as frequencies for each factor of interest.
The non-Hispanic “other” category consisted of those that marked, “other” in response to
being asked their racial/ethnic identity. For this paper, it also included those that marked
“Asian/Pacific Islander,” as both categories had a relatively low number of respondents;
Asian/Pacific Islander n=13, other=12. This combination made the sample sizes across the
minority groups similar and increased the number of complete cases (no missingness) necessary
for analysis (N=18 complete cases). Furthermore, the decision to examine Junior Enlisted non-
Hispanic White participants was a result of when analyses were run on non-Hispanic White
participants and there was too much heterogeneity in the pathway leading to clinically significant
mental health (a limitation of CCMs) to find a solution that consistently covered respondents in
that group. Therefore, we examined another subgroup within the non-Hispanic White group that
is typically not reported on and has been known to be at greater risk for MDD—Junior Enlisted
spouses (Donoho et al., 2018). This subgroup is at higher risk for MDD and the majority of non-
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Hispanic White respondents (74%; N=87 complete cases), as there are more Junior Enlisted
service members in the military than senior enlisted (Department of Defense, 2019). The other
two racial/ethnic groups of non-Hispanic Black (N=19 of complete cases) and Hispanic non-
White (N=29 of complete cases) demonstrated a good sample size for complete cases as well as
some homogeneity in the pathways that led to clinically significant depression symptoms.
This paper is one of the first to pair CNA with QCA, with CNA used to inform factor
selection and QCA used for model development. R Studio, R and the R packages “cna”, “QCA”,
and “SetMethods” packages were used for the CCMs analysis to identify pathways that best
explained how the structural, social cohesion and capital, and intermediary factors collectively
impact a racial and ethnically diverse sample of Army wives’ mental health. This study utilized
fuzzy set QCA (fsQCA) so each condition or outcome was assigned a set membership value
ranging from full-set membership (1) to full-set nonmembership (0), allowing for partial
membership in one or more sets. The choice to use fsQCA was to best capture the continuous
nature of the outcome of clinically significant depression symptoms and some of the factors,
including measures of psychological barriers to mental health care and social support. For the
present analysis, we used CNA to inform factor selection among the original 20 SDoMH factors
(see Table 2) and then fsQCA to understand the complex pathways that produce and not produce
clinical depression symptoms in the context of the military.
Step One
The first step in the fsQCA analysis is calibration—the process of assigning membership
scores to cases based on empirical and theoretical knowledge (Schneider & Wagemann, 2012).
For results based on quantitative scales, we conducted direct method calibration (Ragin & Fiss,
2017; Schneider & Wagemann, 2012). With direct method calibration, cut points are established
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through theory, substantive knowledge, and prior empirical work (Ragin & Fiss, 2017; Schneider
& Wagemann, 2012). We first determined the value of the interval scale of measurement score at
which a case is “fully in,” a specified set (i.e., set membership score .95); the crossover point, or
threshold for maximum ambiguity or at which point the case membership score is .5 in the set
and .5 out of the set; and finally, the value at which a case is “fully out” of a set (i.e., set
membership score of .05). We then transformed the original interval scale values into fuzzy set
membership scores by transforming these data based on the log odds of full membership, using
the “calibrate” command in QCA package in R Studio. For a full list and description of the 20
factors tested in the factor selections process, as well as their calibration, see Tables 2 & 3.
For continuous factors, dual calibration was conducted. In dual method calibration, the
direct method is extended even further so a single factor is categorized into more than one
condition to illustrate qualitative differences in the scale used (Ragin & Fiss, 2017). For
example, the variable of social support is divided up into conditions “low social support” and
“high social support,” with appropriate cut-points of fully in, crossover, and fully out that are in
line with the adjective. To assist with the calibration process so the data is accurately captured,
the “skew.check” command in QCA package in R Studio was used to ensure that that the cut
point accurately captured the data. This function allows for the user to set additional crossover
points (hypotheticals) to compare if the different crossover points would skew the data on who is
considered “in set” vs. “out of set.” This is considered a robustness test that ensures a suitable
crossover point for the data (Schneider & Wagemann, 2012). See Table 3 for calibration of all 20
factors considered for analysis. The same calibration of conditions was put into CNA for factor
selection for each racial and ethnic subgroup examined.
Step Two
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The second step utilized a bottom-up approach for factor selection. This bottom-up
approach operates within the same regularity framework as fsQCA and has been used in other
peer-reviewed CCM-related work within health care (Hickman et al., 2020; Whitaker et al.,
2020; Yakovchenko et al., 2020) as well as in the military veteran population (Yakovchenko et
al., 2020). While this approach has been previously used, this is one of the first papers to use it in
combination with fsQCA. The bottom-up approach of CNA is well suited to inform factor
selection by searching the entire dataset and identifying minimally sufficient conditions (mscs)
that meet selection criteria. Conditions can be thought of as an individual or a collection of
calibrated factors within the data.
Consistency and coverage are two parameters often used to measure the strength of set
relationships, and both were used as key specifications in this data reduction phase of the
analysis (Yakovchenko et al., 2020). To be included in the final fsQCA analyses, the condition(s)
met the following criteria as outlined by Yakovchenko et al. (2020): (1) Configuration met the
consistency threshold of .75. Consistency is the number of cases that have the outcome present
and are covered by a given configurational solution divided by all cases covered by that given
configurational solution (expressed as a number between 0 and 1). (2) Coverage score for that
configuration uniquely distinguishes it from all other configurations sharing the same complexity
level; this is to ensure the maximum amount of cases are explained by the end solution.
Coverage is the number of cases that have the outcome present and are covered by a given
configurational solution divided by all cases with the outcome (also expressed as a number
between 0 and 1). Configurations with coverage scores lower than .25 were not considered for
final analysis. (3) The configuration aligns with the adapted WHO SDoMH framework and prior
published work about military spouse’s mental health. If a configuration meets the consistency
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threshold of .75 and the coverage threshold of .25 but does not make logical sense, it was
excluded. For example, if a high sense of social support was consistently in the presence of
clinically significant depression symptoms, then it would not be considered as that is not in line
with the conceptual framework or prior literature about the importance of social support in
protecting military spouses’ mental well-being.
The msc function was used to look across all 20 factors within each racial/ethnic
subsample to identify strategy configurations with the strongest apparent connections to
clinically significant depression symptoms. For example, within the non-Hispanic Black
subsample, the msc function was used to look across all 20 factors and the 19 complete cases (no
missingness) at once and examined the Boolean output to identify strategy configurations with
the strongest apparent connections to clinically significant depression symptoms. This is an
iterative process where first, single-condition mscs were examined, then two-condition mscs, and
then three-condition mscs in order to identify configurations of conditions with the highest
coverage and consistency scores in relation to the outcome.
Step Three
After factor selection, standard fsQCA procedure was used (Schneider & Wagemann,
2012). Necessity and sufficiency analysis were conducted on relevant factors from the mscs
factor selection process to produce pathways that produce our outcome of interest—clinically
significant depression symptoms by racial/ethnic subsamples of Army wives. Necessity analyses
help identify any causal conditions that must be present for the outcome to occur. Consistent
with other fsQCA literature, we used the consistency level (how consistently a condition is
present in the presence of the outcome) of .9 or above as the threshold for determining if a causal
59
condition was necessary (Schneider & Wagemann, 2012). Sufficiency analyses help identify
which causal condition or combination of conditions consistently produce an outcome.
Sufficiency analyses primarily rely on truth tables that display all logically possible
combinations of causal conditions. For this study’s outcome of clinically significant depression
symptoms, a consistency threshold of .8 was used which is also consistent with other fsQCA
literature (Schneider & Wagemann, 2012). Standard analysis using the Quine-McCluskey
algorithm was applied to the truth table to find the pathways that consistently lead to clinically
significant depression for the majority of each racial/ethnic subsample of Army wives.
Consistent with other fsQCA literature this analysis utilized the pathways generated by the
intermediate solution (Rich et al., 2020; Schneider & Wagemann, 2012).
Since fsQCA does not assume the opposite solution for the absence of the outcome (in
this case low depression symptoms), it is recommended that the negation of the outcome be
conducted to explore any further explanations of causal conditions that may be different than
configurations that explain the presence of low mental health (Schneider & Wagemann, 2012).
Lastly, the Proportional Reduction Inconsistency (PRI) scores were checked to avoid relations of
configurations in both the outcome and its absence, which would not make logical or conceptual
sense. The PRI score should be high and close to the raw consistency score (Greckhamer et al.,
2018).
Results
To address our first question of how do the SDoMH factors considered for analysis
distribute across the racial/ethnic groups in our sample we created Table 1. Table 1 shows more
similarities than differences in the SDoMH factors across all races and ethnicities. For example,
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among the structural determinants, the majority of wives were married to a service member with
an enlisted rank (non-Hispanic White: 74%, non-Hispanic Black: 90%, Hispanic non-White:
85%, non-Hispanic other: 68%), had a personal history with the military (Junior Enlisted non-
Hispanic White: 76%, non-Hispanic Black: 64%, non-Hispanic Other: 80%), and had a history of
an exposure to one or more ACES (Junior Enlisted non-Hispanic White: 58%, non-Hispanic
Black: 55%, Hispanic non-White: 76%, non-Hispanic other: 64%). Among the social cohesion
and capital the majority of wives did not have clinically significant IPV (Junior Enlisted non-
Hispanic White: 82%, non-Hispanic Black: 62%, Hispanic non-White: 79%, non-Hispanic other:
83%). Among the intermediary determinants, the majority of wives lived off post (Junior
Enlisted non-Hispanic White: 63%, non-Hispanic Black: 64%, Hispanic non-White: 52%, non-
Hispanic other: 60%), did not have a child in the past year (Junior Enlisted non-Hispanic White:
82%, non-Hispanic Black: 82%, Hispanic non-White: 79%, non-Hispanic other: 78%), or a
recent injury or illness (Junior Enlisted non-Hispanic White: 76%, non-Hispanic Black: 77%,
Hispanic non-White: 82%, non-Hispanic other: 92%), did not think their service member needed
mental health treatment (Junior Enlisted non-Hispanic White: 84%, non-Hispanic Black: 68%,
Hispanic non-White: 91%, non-Hispanic other: 100%), and were not currently receiving mental
health treatment (Junior Enlisted non-Hispanic White: 87%, non-Hispanic Black: 96%, Hispanic
non-White: 91%, non-Hispanic other: 100%).
There are some unique differences that Table 1 also shows. For example, non-Hispanic
Black Army wives (9%) and non-Hispanic other (4%) had the lowest percentages of officers
present, compared to non-Hispanic White (26%). Non-Hispanic Black wives were more likely to
be employed (55%) than all other racial/ethnic groups (Junior Enlisted non-Hispanic White:
28%, Hispanic non-White: 12%, non-Hispanic other: 29%). Non-Hispanic Black (M:10.6) and
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Hispanic non-White (M:10.2) wives had the lowest averages of social support compared to
Junior Enlisted non-Hispanic White (M: 11.0) and non-Hispanic other (M:11.3). Non-Hispanic
Black wives had the highest rates of exposure to IPV (38%) compared to the rest of the
subgroups that ranged from 13%-21% (see paragraph above for breakdown). The non-Hispanic
other racial/ethnic minority group reported the lowest prevalence of a recent injury or illness
(8%) compared to the rest of the subgroups which ranged from 18%-24% (see paragraph above
for breakdown). Lastly, Black wives had the highest rate of thinking their service member
needed mental health treatment (38%) while all others ranged from 8%-19% (see paragraph
above for breakdown).
Results from the exploratory CNA on each racial/ethnic subgroup revealed different
potential factors to consider for final analysis. For non-Hispanic Black participants, nine
potential factors were considered for the final fsQCA analysis: (1) High psychological barriers to
care, (2) High social support, (3) Employed, (4) Bachelor’s degree, (5) Clinically significant
Intimate Partner Violence (IPV), (6) Officer, (7) Recent Illness/injury, (8) High work-family
conflict, and (9) Adverse Childhood Experiences. For Hispanic non-White participants, six
potential factors were considered for final fsQCA analysis: (1) Junior Enlisted (E1E4), (2) Living
on post, (3) Recent childbirth, (4) High logistical barriers to care, (5) High social support, and (6)
Clinically significant IPV. For Junior Enlisted non-Hispanic White participants, six potential
factors were considered for final fsQCA analysis: (1) Low work-family conflict, (2) High work-
family conflict, (3) Living on post, (4) ACES, (5) Low sense of Army community, and (6) High
sense of Army community. For Non-Hispanic other participants, six potential factors were
considered for final fsQCA analysis: (1) Low sense of Army community, (2) High perceived
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logistical barriers, (3) High work-family conflict, (4) Recent injury/illness, (5) High social
support, and (6) Military history (see Table 4 for results).
Each candidate factor was considered separately for fsQCA analysis. Necessity analysis
on all the conditions and their negation for each subgroup, including items with specified
directionality (i.e., high social support vs. low social support), revealed that few met the .9
threshold. However, the highest-scoring factors that most aligned with the SDoMH framework
for each racial/ethnic subgroup were: Non-Hispanic black: High psychological barriers to care
(.27), Employed (.50), History of ACES (.69), High social support (.45); Hispanic non-White:
E1E4 rank (.54), Living off post (.97), Recent childbirth (.87); Junior Enlisted non-Hispanic
White: High work-family conflict (.75), Living off post (.68), High Army community (.69),
ACES (.77); Non-Hispanic other: High work-family conflict (.83), Low Army community (.55),
Not having a recent injury/illness (.63), Not having a military history (.80).
Sufficiency analysis involves constructing truth tables to evaluate whether combinations
of the highest-scoring factors were sufficient to produce clinically significant depression
symptoms for each racial/ethnic subgroup. Sufficiency analysis revealed different pathways for
each racial/ethnic group for clinically significant depression symptoms. Consistent with best
practices, we also report the characteristics of each truth table, including the number of rows and
cases with consistency values of .75 or higher and the number of rows with no cases (see
supplementary materials). Tables 4 show the results of the fuzzy set analysis of clinically
significant depression, and Table 5 shows the reverse, non-clinically significant depression.
Clinically Significant Depression Symptoms Pathways
All solutions demonstrated good consistency (>.75) and coverage (>.25). However,
Junior Enlisted non-Hispanic White had the lowest coverage score of .26, indicating that the
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number of participants that these pathways cover in describing the outcome is low but acceptable
(Schneider & Wagemann, 2012). The strongest fitting solution was for non-Hispanic Black
Army wives (consistency: .92, coverage: .60). Results show that each race/ethnicity has different
pathways adversely affecting their depression symptoms. For example, results indicate two
pathways that led to clinically significant depression symptoms for non-Hispanic Black Army
wives. One pathway shows that a history of ACES and being employed and experiencing high
psychological barriers to mental health care collectively led to clinically significant depression
symptoms and the other shows a history of ACES, not being employed, not having high social
support and not having high psychological barriers to mental health care also collectively led to
clinically significant depression symptoms. This is different from the pathway for Hispanic non-
White that showed being a Junior Enlisted rank, having a recent childbirth (within the past year)
and living off post led to clinically significant depression symptoms.
Junior Enlisted non-Hispanic White Army wives had similar conditions as non-Hispanic
Black and Hispanic non-White with a pathway that outlined a history of ACES, the absence of a
high sense of Army community, living off post, and experiencing high work-family conflict led
to clinically significant depression symptoms. Non-Hispanic other Army wives had one similar
condition that led to clinically significant depression symptoms to the Junior Enlisted non-
Hispanic White wives, but none of the other racial or ethnic groups. The non-Hispanic other
group of Army wives pathway to clinically significant depression showed a combination of not
having a military history combined with a low sense of Army community, and a high sense of
work-family conflict. While there were more differences than similarities, there seem to be
similar conditions that emerged across racial and ethnic groups: History of ACES, absence of
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high social support, living off post, and high work-family conflict. These conditions seem to
interact in unique ways for more than one racial/ethnic group.
Non-Clinically Significant Depression Symptoms Pathways
All solutions demonstrated very strong consistency (>.81) and coverage (>.46). The
highest consistency score was for non-Hispanic Black participants (.94), and the highest
coverage score was .88 for both Hispanic non-White and non-Hispanic other. Results show that
each race/ethnicity has different pathways positively affecting their depression. For example,
there were two pathways that led to not clinically significant depression symptoms for non-
Hispanic Black Army wives. The first pathway highlighted that having a history of ACES, being
employed combined with a high sense of social support and not having a high sense of
psychological barriers to mental health care led to not having clinically significant depression
symptoms. The second pathway highlights that not having ACES, being unemployed, and not
having psychological barriers to mental health care combined with a high sense of social support
led to not clinically significant depression symptoms. For Hispanic non-White Army wives, not
being married to a junior enlisted service member, living on post, or not having a recent
childbirth each individually led to not significant depression symptoms. For Junior Enlisted non-
Hispanic White Army wives, one pathway highlighted how living off post combined with not
experiencing high work-family conflict, or living off post combined with not having ACES, or
having a high sense of Army community combined with living on post each led to not clinically
significant depression symptoms. For non-Hispanic other Army wives, not having a low sense of
Army community combined with not having a recent injury or illness led to not having clinically
significant depression symptoms.
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There were conflicting findings of whether living on or off post was related to lower
depression symptoms. Results show that living on post in the presence of a high sense of Army
community is supportive of low depression symptoms, for Junior Enlisted non-Hispanic White
Army wives. There is also a pathway where not living on post combined with no history of
ACES led to low depression symptoms for Junior Enlisted non-Hispanic White Army wives.
The pathways for clinically significant depression symptoms were not the pathways for
the absence of clinically significant depression symptoms which support the strength of the
solutions and pathways found for clinically significant depression. No PRI scores were below .6,
indicating that there are not significant inconsistencies with the pathways we found (Greckhamer
et al., 2018).
Discussion
This paper adapted the WHO SDoMH conceptual framework to the military context that
conceptualizes how the structural, social cohesion & capital, and intermediary determinants
interact to affect mental health. With the WHO SDoMH conceptual framework as a guide, this
paper utilized CCMs and found eight different solutions that consistently covered the majority of
four different racial/ethnic groups of Army wives (non-Hispanic Black, Hispanic non-White,
Junior Enlisted non-Hispanic White, non-Hispanic other). Four solutions consistently explained
clinically significant depression symptoms and four solutions consistently explained non-
significant depression symptoms representing a solution for each racial/ethnic minority group.
While preliminary, the solutions highlight several pathways to understand what determinant
factors combine to lead to clinically significant depression symptoms for different racial and
ethnic groups of Army wives.
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Both the solutions that led to clinically significant depression and its absence demonstrate
that it is not one determinant factor that leads to adverse mental health or its absence; it is a
combination of factors working together. For example, conditions that interacted in unique ways
for more than one racial or ethnic group that led to clinically significant depression symptoms
included a history of adverse childhood events, absence of high social support, living off post,
and high-work-family conflict. These common conditions represent each of the three
determinant groups. These findings highlight that Army wives are not a monolithic group in
terms of exposure to stressors. In addition to the stressors of being partnered with someone in the
military, Army wives bring potential stressors associated with their everyday life such as their
race/ethnicity, history of ACES, lack of social support, living far away from a military
installation, and high levels of work-family conflict.
While preliminary, these findings demonstrate that even within a group with similar
exposure to unique stressors (in this case, military-specific stressors), causal conditions combine
in ways that create different pathways to affect and protect mental health. For example, while
there were common conditions found across racial and ethnic groups of Army wives, none of
them interacted in the same way. A common condition that was found across racial and ethnic
groups was a history of ACES. However, for non-Hispanic Black Army wives, it was a history
of ACES, being employed and experiencing high psychological barriers to mental health care
that collectively led to clinically significant depression symptoms, where for Junior Enlisted non-
Hispanic White Army wives it was a history of ACES, absence of a high sense of Army
community, living off post, and experiencing high work-family conflict that led to clinically
significant depression symptoms. These findings suggest that it is not one single avenue that
affects the mental health of Army wives, indicating a need for holistic mental health assessments
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to incorporate the SDoMH factors that could be impacting mental well-being. An example of a
holistic assessment is the biopsychosocial approach to address health, primarily mental health,
issues (Campbell & Rohrbaugh, 2013; Sperry, 2007). This approach incorporates an individual's
biological, psychological, and social history to inform the best course of treatment (Campbell &
Rohrbaugh, 2013; Sperry, 2007).
How these determinants interact demonstrate what would be attenuated by supports and
what may need extra support. For example, as seen for non-Hispanic Black and Junior Enlisted
non-Hispanic White Army wives, the stressor of having a history of ACES leads to high
depression symptoms in the absence of social support. Furthermore, a history of ACES is
attenuated with it is in the presence of high social support. This finding is in line with the
SDoMH framework that shows social support intersects both structural and intermediary
determinants, meaning social support can change an individual’s health trajectory (Solar, &
Irwin, 2010). This finding is in line with other research that has found social support to buffer the
effects of adverse childhood events on later life depression symptoms (Cheong et al., 2017). This
study shows this is also true for the Army wives’ population, which has additional military life
stressors to cope with. This is useful for military providers to know that potential support that
could help military wives with a history of adverse childhood events could be linking them up
with social connections.
The conflicting finding of living on post vs. off post and how that leads to low mental
health symptoms could be described by a spouse’s relationship or view of the military,
something that was not assessed in this study. For example, living on post could serve as a form
of social support for military wives by making it easier for them to access military resources such
the commissaries, social clubs, schools, medical services, as well as living among other military
68
families, which may otherwise be hard to connect with if living off post. However, it could be
unsupportive if a spouse has a negative view of the military and perhaps feel they need distance
from the military culture (Borah & Fina, 2017). Living on post can also isolate military wives
from their civilian friends and family members (Booth, 2003).
The results would support this interpretation of the results to a certain extent. For
example, living on post in the presence of a high sense of Army community was supportive for
Junior Enlisted non-Hispanic Army wives’ depression symptoms. Indicating that it is the
combination of living on post with a high sense of Army community that is supporting Junior
Enlisted non-Hispanic Army wives, and a high sense of Army community could be tied to a
positive view of the military. Unfortunately, sense of Army community was not assessed with
proximity to post with any other racial/ethnic group as it did not meet the consistency and
coverage thresholds to be included in the final analyses. Altogether, the various solutions support
the use of the adapted SDoMH framework in understanding key socio-economic factors that can
impact a diversity of military spouse’s mental health.
Our study uniquely contributes to the prior literature by using CNA in combination with
fsQCA to consider a variety of potentially relevant structural, social cohesion and capital, and
intermediary determinants on mental health outcomes for different groups of Army wives. Using
fsQCA this study described and illustrated the causal complexity among conditions that may
affect clinically significant depression symptoms for a diversity of racial and ethnic Army wives.
Fuzzy set QCA has demonstrated promising applications to research understanding racial
disparities (Ragin & Fiss, 2017; Rich et al., 2020). For example, rather than attributing Black and
White differences in poverty to test scores in school, Ragin and Fiss (2017) used fsQCA to show
how accumulated advantage favors White students and accumulated disadvantage disfavors
69
Black students. Applying similar methods in heath research could uncover important causal
pathways for health inequalities rather than focusing on specific demographic categories such as
race or gender that are often investigated in insolation of their socioecological context (Krieger,
2012; Rich et al., 2020).
Limitations
Findings should be interpreted in light of several limitations. The present study was a
secondary analysis; therefore, several potentially relevant variables were not available for
inclusion in models, such as the use of anti-depressant medications or substance use. This study's
generalizability is limited because a non-probability sampling approach was used for data
collection with about 23% response rate from eligible spouses. Furthermore, analysis is all based
on self-report data so there could be common method bias. For example, spouses with poor
mental health may be more likely to think to report that they have low social support.
Calibration is a critical step in the fsQCA process, and results are sensitive to different
calibrations of the causal conditions and of the outcomes. We provided details about how we
arrived at these calibrations based on our in-depth knowledge of quantitative scales. Nonetheless,
other researchers who employ other frameworks or have other perspectives might construct
different calibrations with different set membership parameters. To address this concern, we used
the skew.check function when creating the calibration cut points of fully in, crossover, and fully
out. This function allowed us to test how different cut points could skew the data +/- 10% to
ensure we were accurately capturing the data.
When using CCMs there are limitations such as heterogeneity of data. While
heterogeneity is a good thing, in the case of CCMs if there is too much, there are too many
70
potential pathways to the outcome, therefore making it difficult to understand the main pathways
for a substantial number of cases. This was the case for non-Hispanic White Army wives.
Consequently, we had to create another subsample of Junior Enlisted non-Hispanic White Army
wives where there was less heterogeneity in the SDoMH factors that combined to produce
depression symptoms.
Implications
Though preliminary, using CCMs, this paper identified complex causal pathways for
clinically significant depression symptoms in Army wives. Our study revealed that it is not just
one social or economic factor impacting a diversity of Army wives; it is a combination of
environmental and life factors, the SDoMH, interacting to affect depression symptoms. Findings
also show that stressors such as the structural determinant of ACES can be buffered by social
support for certain races/ethnicities. These findings collectively suggest that mental health
assessments should incorporate the unique context from where an Army spouse is coming from
to fully understand the factors that could impact their mental health, such as a biopsychosocial
assessment.
Lastly, our results support using the adapted SDoMH model to understand the variety of
environmental and life factors that can impact Army wives’ mental health. Future research with
this model and CCMs could incorporate qualitative data with quantitative surveys to better
highlight qualitative differences in environmental and life factors affecting mental health.
Additionally, future research with this model should be tested with both sexes and on other
branches.
71
Table 3.1. Demographic Characteristics of Whole Sample and Each Race/Ethnicity Subsample.
Determinant
Category
Characteristic Whole
Sample
N (%) / M
(SD)
Range
NH White
N (%) / M
(SD)
Range
Junior
Enlisted
NH White
N (%) / M
(SD)
Range
NH Black
N (%) / M
(SD)
Range
Non-White
Hispanic
N (%) / M
(SD)
Range
NH Other
N (%) / M
(SD)
Range
Structural
Determinants
Race/Ethnicity
White 245 (75%) 245(100%) 87 (100%) - - -
Black 22 (7%) - - 22 (100%) - -
Non-white
Hispanic
34 (10%) - - - 34 (100%) -
Other 25 (8%) - - - - 25 (100%)
Rank
E1-E4 112 (35%) 87 (36%) 87 (100%) 3 (14%) 14 (41%) 8 (32%)
E5-E9 142 (44%) 94 (39%) - 17 (77%) 15 (44%) 16 (64%)
Officer 71 (22%) 63 (26%) - 2 (9%) 5 (15%) 1 (4%)
Employment
Status
Employed 96 (30%) 73 (30%) 24 (28%) 12 (55%) 4 (12%) 7 (29%)
Unemployed
Looking For
Work
71 (22%) 48 (20%) 16 (18%) 7 (32%) 12 (35%) 4 (17%)
Unemployed
Not Looking For
Work
125 (39%) 99 (40%) 37 (43%) 2 (9%) 13 (38%) 11 (46%)
Other 33 (10%) 25 (10%) 10 (12%) 1 (5%) 5 (15%) 2 (8%)
Education
Less than
Bachelors
216 (67%) 158 (65%) 76 (88%) 13 (59%) 18 (82%) 17 (68%)
Bachelor’s
Degree or More
109 (34%) 86 (35%) 10 (12%) 9 (41%) 6 (18%) 8 (32%)
Age
18-29 years old 169 (52%) 133 (54%) 59 (68%) 5 (23%) 21 (62%) 10 (40%)
30 + years old 157 (48%) 112 (46%) 28 (32%) 17 (77%) 13 (38%) 15 (60%)
Personal
History with
Military
No 223 (71%) 169 (71%) 64 (76%) 14 (64%) 24 (73%) 16 (80%)
Yes 91 (29%) 70 (29%) 20 (24%) 8 (36%) 9 (27%) 4 (20%)
Social
Cohesion and
Capital
Determinants
Social Support 11.1 (3.4)
3-15
11.2 (3.5)
3-15
11 (3.3)
3-15
10.6 (3.6)
3-15
10.2 (4.0)
3-15
11.3 (3.3)
6-15
Army
Community
13.6 (3.7)
4-20
13.7 (3.8)
4-20
13.2 (3.9)
4-20
12.7 (2.9)
8-18
13.6 (3.3)
4-19
13.4 (3.4)
5-20
Religious
Group
Involvement
No 181 (56%) 136 (56%) 61 (71%) 10 (46%) 19 (56%) 16 (64%)
Yes 142 (44%) 106 (44%) 25 (29%) 12 (55%) 15 (44%) 9 (36%)
72
Clinically
Significant IPV
No 262 (85%) 203 (88%) 67 (82%) 13 (62%) 26 (79%) 20 (83%)
Yes 48 (16%) 29 (13%) 15 (18%) 8 (38%) 7 (21%) 4 (17%)
History of any
ACES
No 128 (40%) 102 (43%) 36 (42%) 9 (45%) 8 (24%) 9 (36%)
Yes 189 (60%) 137 (57%) 50 (58%) 11 (55%) 25 (76%) 16 (64%)
Intermediary
Determinants
Distance to
Military
Installation
Live on Post 125 (39%) 91 (38%) 32 (37%) 8 (36%) 16 (49%) 10 (40%)
Live Off Post 198 (61%) 152 (63%) 54 (63%) 14 (64%) 17 (52%) 15 (60%)
Childbirth in
past year
No 251 (78%) 188 (77%) 71 (82%) 18 (82%) 27 (79%) 18 (78%)
Yes 73 (23%) 57 (23%) 16 (18%) 4 (18%) 7 (21%) 5 (22%)
Injury/Illness
in past year
No 260 (80%) 193 (79%) 66 (76%) 17 (77%) 28 (82%) 22 (92%)
Yes 65 (20%) 52 (21%) 21 (24%) 5 (23%) 6 (18%) 2 (8%)
Does Service
Member need
Mental Health
Treatment?
No 265 (82%) 196 (81%) 71 (84%) 15 (68%) 31 (91%) 23 (92%)
Yes 58 (18%) 46 (19%) 14 (17%) 7 (32%) 3 (9%) 2 (8%)
Number of
Children
1.2 (1.3)
0-5
.55 (.87)
0-5
.40 (6.5)
0-3
.38 (6.2)
0-2
.52 (.75)
0-2
.2 (.56)
0-2
Work-Family
Conflict
22.1 (7.7)
5-35
22.5 (7.4)
5-35
20.3 (7.8)
5-35
22.1 (7.7)
10-34
20.9 (9.4)
5-35
20.1 (8.3)
5-25
Current MH
Tx received
No 299 (92%) 223 (91%) 76 (87%) 21 (96%) 30 (91%) 25 (100%)
Yes 26 (8%) 22 (9%) 11 (13%) 1 (5%) 3 (9%) 0
Logistical
barriers to care
8.1 (3.1)
4-18
8.1 (3.1)
4-18
8.0 (3.5)
4-18
7.1 (2.7)
4-12
8.5 (3.2)
4-14
8.4 (3.1)
4-14
Psychological
barriers to care
12.8 (5.3)
7-30
12.6 (5.3)
7-30
12 (5.2)
7-30
13 (4.8)
7-26
14.3 (5.7)
7-26
12.9(4.6)
7-21
Health
*Spouse
Depression
Score < 5 221 (68%) 168 (69%) 52 (61%) 12 (55%) 24 (71%) 17 (68%)
Score ≥ 5 102 (36%) 74 (31%) 34 (40%) 10 (46%) 10 (29%) 8 (32%)
Note: NH=Non-Hispanic
*A score of ≥ 5 is indicative of clinically significant depression symptoms
73
Table 3.2. All Measures Considered for Final Analysis.
Determinant
Category
Construct Level of
Measurement
Operationalization & Variable Description
Structural
Determinant
Personal history with
military
Dichotomous (Yes) to any of the following: grew up in military
family, prior military spouse, prior personal military
history vs. (No)
Race/ethnicity Categorical White, Black, Non-white Hispanic, Other
Sex NA All female sample
Rank Categorical E1-E4, E5-E9, Officer/Warrant Officer
Employment status Categorical Employed full or part time, unemployed and seeking
work, unemployed not seeking work
Education Dichotomous Bachelors + vs. < Bachelors
Age Dichotomous 30 + years vs. 18-29 years
History of ACES Dichotomous Yes exposure vs. No exposure
Social Capital
& Social
Cohesion
Social support 5-point Likert
Scale
3-items; e.g. People sometimes look to others for
companionship, assistance, or other types of support.
How often is each of the following kinds of support
available to you if you need it?
Army community 5-point Likert
Scale
4-items; e.g. Please rate how much you agree or
disagree with the following statements. Examples items
include, “"I feel I am part of the Army community
Religious group
involvement
Dichotomous Do you belong to a church, temple, or other religious
group (yes/no)
Intimate Partner
Violence (IPV)
Dichotomous Yes vs. No to clinically significant IPV
Intermediary
Determinant
Distance to military
installation
Dichotomous I live on-post vs. Not
Recent childbirth Dichotomous During the past year, did any of the following stressful
events occur? Birth of child (yes/no)
History of illness Dichotomous During the past year, did any of the following stressful
events occur? Personal injury or illness (yes/no)
Familial health Dichotomous Did you spouse have a serious illness while they were
deployed? (no, yes most recent deployment, yes earlier
deployment, yes most recent and earlier deployment)
Family size Dichotomous No children vs. 1+
Work-family conflict 7-point Likert
Scale
5-items: e.g. Please rate how much you agree or
disagree with the following statements. Example items
include, “the demands of my spouse’s work interfere
with my home and family life.”
Treatment received Dichotomous Are you currently in mental health treatment? (yes/no)
Logistical barriers to
care
5-point Likert
Scale
4-items; How much to do you agree or disagree with the
following factors related to receiving mental health
counseling or services.
74
Psychological barriers
to care
5-point Likert
Scale
7-items; e.g. How much to do you agree or disagree
with the following factors related to receiving mental
health counseling or services.
Outcome Depression symptoms 4-point Likert
Scale
8-items; e.g. Over the past month how often have you
been bothered by any of the following problems?
75
Table 3.3. Calibration of Conditions Considered for Final Analysis.
Determinant
Category
Construct Fuzzy sets Fully
in
Cross
over
Fully
out
Structural Determinant
Personal
history with
military
Yes, has a personal history with military 1 .49 0
Race/ethnicity NA, only subsets of each racial group were examined NA NA NA
Sex NA, all female sample NA NA NA
Rank Yes, is a specific rank (i.e., Officer, E1E4, E5E9) 1 .49 0
Employment
status
Yes, has a certain employment status (i.e., Employed, Unemployed
not looked for work, Unemployed Looking for work)
1 .49 0
Education Yes, has a bachelors + 1 .49 0
Age Yes, 18-29 years old (young) 1 .49 0
History of
ACES
Yes, has a history of ACES 1 .49 0
Intermediary Determinant
Distance to
military
installation
Lives on post 1 .49 0
Recent
childbirth
Yes, had a recent childbirth 1 .49 0
History of
illness
Yes, had a recent injury/illness 1 .49 0
Familial health Yes, thinks their spouse service member needs MH TX 1 .49 0
Family size Yes, has at least one child 1 .49 0
*Work-family
conflict (5-item
scale with 7-
point Likert
response)
Low conflict: The fully in score was set at the equivalent
“disagree” of for the majority of items. The crossover was set
between “neutral” and “agree.” The fully out score was set at the
equivalent of “agree” on the majority of items.
18 19.1 25
High conflict: The fully in score was set at the equivalent “agree”
for the majority of items. The crossover was set so the majority of
the items were “neutral.” The fully out score was set at the
equivalent of “disagree” on the majority of items.
22 18.1 17
Treatment
received
Yes, is receiving MH TX 1 .49 0
*Logistical
Barriers to care
(4-items, 5-
point Likert
scale)
Low Log. Barriers: The fully in score was set at the equivalent
“strongly disagree” for the majority of items. The crossover was set
so the majority of the items were “neutral.” The fully out score was
set at the equivalent of “agree” on the majority of items.
5 9.5 14
High Log. Barriers: The fully in score was set at the equivalent
“agree” for the majority of items. The crossover was set so the
majority of the items were “neutral.” The fully out score was set at
the equivalent of “strongly disagree” on all items.
11 10.5 4
*Psychological
barriers to
Low Psych. Barriers: The fully in score is set at the equivalent of
“disagree” on each item. The crossover was set between “neutral”
14 16.9 23
76
care (7-item
scale with 5-
point Likert
response)
and “agree.” The fully out score was set at the equivalent of
“agree” on the majority of items.
High Psych Barriers: The fully in score was set at the equivalent of
“agree” on each item. The crossover was set between “neutral” and
“disagree.” The fully out score was set at the equivalent of
“disagree” on the majority of items.
20 17.5 13
Social Capital & Social Cohesion
*Social
support (3-
items, 5-point
Likert scale)
Low support: The fully in score was set at the equivalent of “None
of the time,” and “a little of the time,” for the majority of items.
The crossover was set between “a little of the time” and “some of
the time.” The fully out score was set at the equivalent of “Most of
the time,” and “All of the time” for the majority of items.
5 6.5 13
High support: The fully in score was set at the equivalent of “Most
of the time,” and “All of the time” for the majority of items. The
crossover was set between “some of the time” and “most of the
time.” The fully out score was set between “None of the time,” and
“a little of the time” for the majority of item.
13 11.5 6
*Army
community (4-
items, 4-point
Likert scale)
Low community: The fully in score was set at the equivalent
“disagree/strongly disagree” for the majority of items. The
crossover was set between “disagree” and “neutral.” The fully out
score is set at the equivalent “agree” for the majority of items
7 11.1 15
High community: The fully in score was set at the equivalent
“agree” for the majority of items. The crossover was set between
“neutral” and “agree.” The fully out score was set at the equivalent
“disagree” for the majority of items
15 12.5 8
Religious group
involvement
Yes, belongs to a religious group 1 .49 0
Intimate
Partner
Violence (IPV)
Yes, has clinically significant IPV 1 .49 0
Outcome
*Mental
health
symptoms
Clinically Significant Depression Symptoms: The established
clinical cut point of 5 and 10 for mild symptoms and a probable
diagnosis of depression was used to inform fully in, crossover, and
fully out cut points. Fully in was set at 9.9 so all scores of 10 or
more would be considered fully in. Cross over was set to 4.9 so a
score of 5 or more would be considered closer to fully in than out.
and fully out was set to 4.1 so a score of 4 or less would be
considered fully out.
9.9 4.9 4.1
*Indicates a fuzzy set calibration of a continuous scale and therefore requires an explanation for fully in/out and crossover
point selections. The dichotomous variables do not. The crossover point was always selected to error on the side of capturing
the adjective in front of the variable name (i.e. high vs. low). See methods section for explanation of dual method calibration
(i.e. having high vs low for one variable). Since we were only interested in low mental health symptoms we did not do a dual
calibration for our outcome.
MH=Mental health, TX=Treatment
Bold indicates the constructs used for final analysis.
77
Table 3.4. Relevant Conditions for Clinically Significant Depression Symptoms for Army Wives
Condition(s) Consistency Coverage Complexity of Condition
Non-Hispanic Black
High psychological barriers to care .84 .27 1
~Bachelors degree * History of ACES .82 .59 2
~High social support * ~Employed .81 .50 2
High work-family conflict * Recent
Illness/Injury
.88 .43 2
~Officer * ~High social support .81 .37 2
~Officer * ~Bachelors degree * Clinically
significant IPV
.84 .43 3
Hispanic non-White
E1E4 * ~On post * Birth of recent child .78 .54 3
~High logistical barriers to care*~High
social support *Clinically significant IPV
.78 .37 3
Junior Enlisted non-Hispanic White
High work-family conflict * ~On post
*History of ACES *Low Army Community
.76 .26 4
~Low work-family conflict * ~On post
*History of ACES *~High Army
Community
.76 .26 4
Non-Hispanic Other
High work-family conflict*Recent
injury/illness
.87 .39 2
Recent injury/illness*~High social support .82 .31 2
Low Army community * ~Military History .80 .55 2
High work-family conflict*High logistical
barriers * Low Army community
.86 .52 3
* Denotes the logical sign and meaning both conditions must be present together in the presence
of the outcome of clinically significant depression symptoms.
~ = the absence of the factor
Underlined condition indicates the conditions that made the final QCA analysis in Table 3. See
results section for why other conditions did not make analysis.
Note. Complexity of condition means the number of factors in the condition. This table is a
subset of the full CNA MSC results that produced hundreds of combinations of conditions for
each racial/ethnic group. Only conditions that met the consistency threshold of .8 and coverage
threshold of .25 were considered for QCA analysis.
78
Table 3.5. Pathways for Clinically Significant Depression Symptoms by Race/Ethnicity.
Non-Hispanic Black Hispanic non-White Junior Enlisted non-
Hispanic White
Non-Hispanic Other
Causal Conditions 1 2 1 1 1
Structural
Determinant
History of ACES ● ● - ● -
Employed ●

- - -
E1E4 - - ● - -
Military History - - - -

Social
Support &
Social
Cohesion
Social Support
(High)

- - -
Army Comm. (High) 

-
Army Comm. (Low) - -  - -  ●
Intermediary
Determinant
Psychological Barriers to
Mental Health Care (High)
●

- - -
Recent Childbirth - - ●  - -
Live on post - -
 
-
Work-family Conflict (High) - - -  ●  ●
Recent Injury/Illness - - - - 

Consistency .88 .92 .78 .76 .87
Raw coverage
a
.24 .40 .54 .26 .53
Unique coverage
b
.20 .36 NA NA NA
Overall solution consistency .92 .78
.54
.76 .87
Overall solution coverage
c
.60 .26 .53
●indicates the presence of the condition, ○indicates the absence of the condition, a blank space indicates that it does not matter if the
condition is present or absent.
a
Indicates how much of the outcome clinically significant depression symptoms is covered by the solution.
b
Indicates how much of
the outcome clinically significant depression symptoms is uniquely covered by the solution.
c
Indicates how much of the outcome
clinically significant depression symptoms is covered by all solutions taken together. If there is an “NA” that means there was only
79
one solution to consider so the raw and unique coverage are the same. “-“ indicates condition not even considered for specified
racial/ethnic group.
80
Table 3.6. Pathways for Not Clinically Significant Depression Symptoms by Race/Ethnicity.
Non-Hispanic Black Hispanic non-White Junior Enlisted non-Hispanic White Non-Hispanic Other
Causal Conditions 1 2 1 2 3 1 2 3 1
Structural
Determinant
History of ACES ●


- -  - 
 
-
Employed ●

- - - - - - -
E1E4 - -

- - - -
Military History - - -  - - - - -
Social
Support &
Social
Cohesion
Social Support
(High)
●  ● - - - - - - -
Army Comm. (High) - - - - -

● -
Army Comm. (low)
 
 
Intermediary
Determinant
Psychological Barriers to
Mental Health care (High)
 
- - - - - - -
Recent Childbirth - -


- - - -
Live on post - -

●
 
● -
Work-family Conflict
(High)
- - - - -


Recent Injury/Illness

 
Consistency .91 .92 .84 .99 .92 .86 .80 .81 .89
Raw coverage
a
.27 .26 .61 .99 .91 .31 .31 .39 .88
Unique coverage
b
.20 .20 .23 .56 .38 .16 .14 .32 NA
Overall solution consistency .94 .89 .81 .89
Overall solution coverage
c
.46 .88 .79 .88
81
●indicates the presence of the condition, ○indicates the absence of the condition, a blank space indicates that it does not matter if the
condition is present or absent.
a
Indicates how much of the outcome not clinically significant depression symptoms is covered by the solution.
b
Indicates how much
of the outcome not clinically significant depression symptoms is uniquely covered by the solution.
c
Indicates how much of the
outcome not clinically significant depression symptoms is covered by all solutions taken together. If there is an “NA” that means there
was only one solution to consider so the raw and unique coverage are the same. “-“ indicates condition not even considered for
specified racial/ethnic group.
82
Figure 3.1. Social Determinants of Mental Health Conceptual Framework for Military Spouses.
83
Supplemental Materials
Supplemental Table 3.1. Truth Table Characteristics.
Rows (cases) with
Consistency ≥ 0.80
Rows (cases) with
Consistency ≤ 0.80
Row with No Cases
(Logical
Remainders)
Non-Hispanic Black
Clinically Significant
Depression
3 (8) 5 (8) 0
Not Clinically
Significant
Depression
2 (8) 6 (8) 0
Hispanic non-White
Clinically Significant
Depression
1(8) 7(8) 0
Not Clinically
Significant
Depression
7(8) 1(8) 0
Junior Enlisted non-Hispanic White
Clinically Significant
Depression
1 (14) 13 (14) 0
Not Clinically
Significant
Depression
10 (14) 4 (14) 0
Non-Hispanic Other
Clinically Significant
Depression
2(7) 5(7) 0
Not Clinically
Significant
Depression
4(7) 3(7) 0
Logical remainders= rows in the truth table without any cases
84
Description of Variables Used in Final Analyses
Structural Determinants. Five demographic measures were included: 1) Education (less
than a bachelor’s degree/bachelor’s degree or more); 2) Employment status
(employed/unemployed); 3) Race/ethnicity (White/Black/Non-white Hispanic/ Other
[Asian/Pacific Islander & those that marked “Other”]); 4) Rank (enlisted/officer); 5) Age (18-39
years/30+ years). History with the military was assessed with one item, which asked spouses to
indicate if any of the following experiences applied to them, including: “I am/was a military
service member,” “I grew up in a military family,” or “I was a military spouse in a prior
marriage.” A yes response on any of these three items was indicative of having a personal history
with the military.
ACEs were measured with a modified version of the scale from Felitti et al. (1998). This
version has been used in other studies with military populations (Cabrera, et al., 2007; Clarke-
Walper, 2014). Questions on the survey assessed each of the seven categories of childhood
exposure (abuse on a 1-psychological, 2-physical, or 3-sexual level, and household behaviors of
4-substance abuse, 5-mental illness, 6-mother treated violently, or 7-criminal history) (Felitti et
al., 1998). Three questions were 1 (yes)/0 (no) questions that started with, “When you were
growing up as a child was there anyone living in your household who...” Examples include,
“went to prison,” “was depressed or mentally ill,” and “was a problem drinker or alcoholic.” The
remaining four questions used for analysis were items that started with, “When you were
growing up, how often did a parent or adult living in your home…” Responses are on a five-
point Likert scale ranging from 1 (never) to 5 (very often). Examples include, “Swear at you,
insult you, or put you down?” and “push, grab shove, slap or throw something at your mother?”
The total score from this questionnaire was recoded to assess 1 (any exposure; marked yes or
more than never on a question) compared to 0 (no exposure; marked no or never on a question).
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Social Cohesion & Capital Determinants. Two measures within the social cohesion and
capital determinants made the model building process—social support and sense of Army
community. Social support was assessed through the Medical Outcomes Study (MOS) Social
Support survey. The MOS survey is a 3-item questionnaire that starts with, "How often is each of
the following kinds of support available to you if you need it?" Responses are on a five-point
Likert scale ranging from 1 (none of the time) to 5 (all of the time). Examples of support include
"Someone you can count on to listen to you when you need to talk,” "Someone to give you good
advice about a crisis," and "Someone to take you to the doctor if you needed it." This scale
demonstrates good reliability and validity (Sherbourne & Stewart, 1991). A high score indicated
a heightened sense of social support. Internal consistency of this scale was good in this sample
(Cronbach’s alpha = .87; Sherbourne and Stewart 1991).
A spouse’s sense of belonging to an Army community was assess through a measure
developed by the Army and has been used in prior studies (Donoho et al., 2017). This measure
includes four statements about the Army community such as, "I feel I am part of the Army
community," and "I have friends from the Army community with who I spend time socializing."
Responses are on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly
agree) with a higher score indicating an increased sense of Army community. This scale's
internal consistency was good in this sample (Cronbach’s alpha = .81; Donoho et al., 2017).
Lastly, spouses were asked, “Do you belong to a church, temple, or other religious group?” 1
(yes)/ 0 (no).
Intermediary Determinants. Five intermediary determinant factors made the model
building process—psychological barriers to mental health treatment, distance to military
installation, recent injury or illness, recent childbirth, and work-family conflict. Psychological
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barriers to mental health treatment consisted of seven items that starts with, “Please rate how
much you agree or disagree with the following factors related to receiving mental health
counseling or services.” Responses are on a five-point Likert scale ranging from 1 (strongly
disagree) to 5 (strongly agree). Examples include “It would be too embarrassing,” “It would
harm my career,” “I would be seen as weak,” and “my spouse would disapprove of me receiving
help.” This scale was originally validated for active duty service members and was adapted for
this survey and demonstrated good internal consistency in this sample (Cronbachs alpha = .91;
Kim et al., 2011).
Distance to military installation was assessed through a question, “How far to you live from
the nearest military installation (or the one you use the most)?” with responses of I live on post,
10 miles or less, 11-25 miles, 26 or more miles, Do not know. This question was recoded to be 0
(On post) vs. 1 (Off post). In the context of the Army, the military installation is referred to as
“post.” Only three respondents (n=3) marked, “Do not know,” and they were recoded as missing.
Recent history of either illness or childbirth was assessed with two items beginning with the
prompt: “Within the past year, did any of following stressful events occur?” Items included
“personal injury or illness” and “birth of a child” and included two response options: 1 (yes) / 0
(no).
Work-family conflict was assessed through a version of the validated Work-Family
Conflict five-item scale (Netemeyer et al., 1996). The version used was modified to reference the
service members' job and duties affecting home life instead of the spouses. Example items
include “the demands of my spouse’s work interfere with my home and family life.” Responses
were recorded on seven-point Likert scale from 1 (strongly disagree) to 7 (strongly agree). This
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scale's internal consistency was strong in this sample (Cronbach’s alpha = .92; Netemeyer,
1996).
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Chapter 4. (Study 3): Army Wives’ Mental Health Treatment Engagement: Logistical and
Psychological Barriers to Care
Abstract
Introduction: Military spouses are exposed to unique stressors that could put them at
greater risk for developing mental health issues. Understanding how to support them is important
for the military family's well-being and service member retention. This study examines the
impact of logistical and psychological barriers to mental health care on Army wives’ treatment
engagement, controlling for socio-demographic variables and mental health symptoms.
Methods: The present study is a secondary analysis of survey data collected in 2012 from 327
US Army wives from a previously deployed Army unit. Examples of logistical barriers assessed
include, “I don’t have adequate transportation,” and “it’s difficult to schedule an appointment.”
Examples of Psychological barriers assessed include “I would be seen as weak,” “my spouse
would disapprove of me receiving help,” and “it would harm my spouse’s career.” Results: The
multivariable logistic regression model showed two significant predictors of being in mental
health treatment: (1) depression symptoms (OR: 1.19, CI:1.08-1.31) and (2) psychological
barriers to care (OR: .82, CI:.72-.94). Conclusion: While preliminary, these results highlight that
Army wives experiencing depression symptoms are more likely to be in mental health care
treatment, meaning those that need care are more likely to receive it. Furthermore, those
experiencing higher psychological barriers to mental health care, including stigma around mental
health, are less likely to seek care. These findings support the military’s current efforts to create a
culture of support for psychological health as well as research innovative ways to minimize
stigma such as rebranding services or tele-health service.
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Key words: Military Spouse, Mental Health Care, Treatment Engagement, Socio-demographic
Controls
Impact statement: Psychological barriers to mental health care are preventing Army wives from
participating in mental health treatment. The military should continue their efforts to create a
culture of support for psychological health as well as research innovative ways to minimize
stigma such as rebranding services or tele-health service.
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Introduction
Close to one million military spouses experience unique stressors related to their partner’s
military service (Department of Defense, 2019). These stressors may include prolonged
separations during their partner’s deployment or training, worrying about their partner's health
and well-being during deployments, and frequent relocations that can uproot them from their
community and employment (MacDermid Wadsworth, 2013; Marini et al., 2018). Recent
research has shown that most spouses cope well with the stressors of being married to someone
in the military (Sullivan et al., 2020). However, research has also documented an increased
prevalence of mental health issues in female spouses when compared to non-military affiliated
women in the U.S (Steenkamp et al., 2018).
Supporting military spouses in receiving mental health care is important not only for their
health but also for the health of the military family and retention of the service member (Green et
al., 2013; Rosen & Durand, 1995). Previous research has explored logistical and psychological
barriers military spouses experience when accessing mental health care treatment (Eaton et al.,
2008; Schvey et al., 2021). While this work has focused on perceived barriers to treatment, it has
not examined treatment usage. (Schvey et al., 2021). Furthermore, prior work has not controlled
for a variety of socio-demographic variables, including, for example, spouse employment.
Understanding how different barriers could impact mental health care engagement while
accounting for socio-demographic characteristics will offer a more nuanced understanding of
mental healthcare usage for a diversity of military spouses. Understanding the nuances will help
paint a more complete picture of factors that could be contributing to treatment engagement for
military spouses in addition to typical barriers to care. The present study builds off prior work
that has examined causes and factors that contribute to barriers to mental health care by taking it
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a step further to understand the impact of logistical and psychological barriers on Army wives
actually using mental health care. This paper seeks to understand if certain barriers are actually
preventing care usage for Army wives controlling for several socio-demographic variables and
mental health symptoms.
Logistical Barriers to Mental health Care
Logistical barriers to mental health care include organizational as well as other barriers
outside of the organization, such as personally not having time to go to an appointment (Schvey
et al., 2021). Considering that all military spouses are afforded equal access to healthcare
through TRICARE, one may think that there would be minimal logistical barriers to mental
health care treatment (Military Spouse and Family Benefits | Military.Com, n.d.). However, even
in a context with equal access to care, there can nevertheless be obstacles to seeking treatment.
Previous research has highlighted several potential logistical barriers to mental health care
treatment, including difficulty attending daytime appointments, availability of counselors trained
to meet the needs of military families, lack of transportation, and lack of knowledge about where
to get services (Eaton et al., 2008; Lewy et al., 2014; Schvey et al., 2021). One of the most
common logistical barriers was the inability to get away from work or household responsibilities
to attend a daytime appointment (Lewy et al., 2014; Schvey et al., 2021). A number of contextual
factors may account for these findings, including increased childcare and household
responsibilities placed on the spouse during deployment or training separations (Borah & Fina,
2017; Sharp et al., 2015b). Collectively, these results indicate that even in a context with equal
access to treatment, logistical barriers can still be substantial.
Psychological Barriers to Mental Health Care
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The stigma surrounding mental health care is extensive and has several different types
(Schvey et al., 2021). For the purposes of this study, we define all types of stigma in general as
psychological barriers to mental health care. Psychological barriers are broadly defined as a
thought or fear of a perception that prevent an individual from seeking or receiving care (Schvey
et al., 2021). Psychological barriers to seeking mental health treatment have been a documented
problem for both spouses and service members seeking care (Eaton et al., 2008; Hoge et al.,
2004; Sharp et al., 2015b). Spouses report concerns that seeking mental health treatment will
adversely affect others’ opinions of and confidence in them (Eaton et al., 2008; Hoge et al.,
2004), a concern which carries significant consequences in the military context where personnel
and their families are expected to be “mission ready” (Schvey et al., 2021; Sharp et al., 2015b).
Further, spouses express concerns that seeking treatment could limit their partners’ opportunities
for promotion, advancement, security clearance and could lead to separation or removal (Sharp
et al., 2015b). In the Lewy et al. (2014) article, they were able to compare some psychological
barriers for military wives with a national sample of non-military affiliated women and found
military wives to have significantly more psychological barriers to mental health treatment such
as, community negative opinion and information might not be kept confidential than the national
sample.
More recently, Schvey et al. (2021), found that internalized stigma (the degree to which
one accepts negative societal attitudes and stereotypes about a particular identity or conditions
and incorporates the negative belief into their own self-concept) was significantly different for
spouses with a military history and a provisional psychiatric disorder. This highlights the
importance of understanding a spouse’s personal history with the military as that could impact
their perceptions of mental health treatment due to their exposure to military culture which may
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stigmatize treatment-seeking behavior (Borah & Fina, 2017; Schvey et al., 2021). Collectively,
the minimal research documenting military spouses’ psychological barriers to mental health
treatment indicates a need for further research on how these barriers impact the engagement of
mental health services.
Socio-demographic Factors and Military Spouses’ Mental Health Care
Schvey and colleagues (2021) examined both logistical and psychological perceived
barriers to mental healthcare in a large sample of just under 10,000 military spouses and found
significant differences across gender, race, military service experience, and provisional
psychiatric disorders. For example, racial and ethnic minority spouses were less likely to report
logistic barriers and internalized mental health stigma than non-Hispanic white respondents.
Spouses who had previously served or where currently serving in the military were significantly
more likely to report negative beliefs about mental health care as well as negative consequences
(Schvey et al., 2021). Furthermore, spouses meeting clinical thresholds on self-report measures
of mood and anxiety disorders were significantly more likely to report logistical and
psychological barriers to mental healthcare, suggesting that those who would most benefit from
care are less likely to seek it (Eaton et al., 2008; Schvey et al., 2021).
The large scale Schvey and colleagues (2021) study did not assess the relationship
between barriers and actual treatment engagement as well as failed to account for socio-
demographic factors that could impact perceived barriers such as where spouses live, their
employment status, and their military history in general. Living on or off a military installation is
a critical socio-demographic factor that could impact mental health care utilization, as it impacts
ease of access to resources, including healthcare services (Donoho et al., 2017, 2018).
Employment is another important socio-demographic variable that could impact healthcare
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utilization as employment may increase demands on a spouses’ time, potentially restricting their
access to services (Burke & Miller, 2018; Castaneda & Harrell, 2008). Knowing if a spouse grew
up in a military family as well as if they had previously served are important as these experiences
could have increased their exposure to military culture which has been known to stigmatize
mental health treatment and could impact treatment usage (Ben-Zeev et al., 2012; Michalopoulou
et al., 2017). Considering these issues, the present study considers how close a spouse lives to a
post (military installation), employment status and history with the military as critical socio-
demographic actors that could impact the engagement of mental healthcare. Two hypotheses
guided analysis for this study:
1. The more reported logistical barriers an Army wife reports, the less likely they will be
in mental health treatment.
2. The more psychological barriers an Army wife reports, the less likely they will be in
mental health treatment.
Methods
Data & Participants
Secondary data from the Land Combat Study was used for this study. The Land Combat
Study was a part of a series of data collections in 2012 conducted by Walter Reed Army Institute
of Research (WRAIR). All initial project procedures and secondary analysis of the project’s data
were reviewed by the Human Subjects Protection Branch of WRAIR. Spouses were recruited
through coordinated efforts with the brigades Family Readiness Group (FRG) leaders in 2012.
The FRG is a command-sponsored organization of family members, volunteers, and soldiers
belonging to a unit that provides an avenue of mutual support and assistance to family members.
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Military spouses were informed about the study and asked to provide informed consent. Twenty-
three percent of FRG spouses responded to recruitment. Surveys were administered in-person
and online with all participants in the continental United States. Each survey took between 30
and 45 minutes to complete. Of the spouses that responded, 98% agreed to take part in the study.
Approximately 74.2% of individuals completed a web-based version of the survey, and 25.8%
completed a paper version. In order to focus on civilian, female spouses, who may have unique
experiences gaining and maintaining employment as well as developing mental health issues
related to balancing work and family life when compared to males (Barnett & Hyde, 2001b;
Corry et al., 2019; Mary Clare Lennon & Sarah Rosenfield, 1992; Woodall et al., 2020), nine
male spouses and seven female spouses that indicated they were active-duty service members
were dropped, for a final analytic sample of 327 female spouses. The majority of our sample was
White (75.15%), married to or partnered with an enlisted service member (78.15%), unemployed
(n=229, 70.46%), had less than a bachelor’s degree (66.46%), and had no prior history with the
military (71.02%; see Table 1).
Measures
Logistical Barriers
Logistical barriers to mental health treatment consisted of five items that start with,
“Please rate how much you agree or disagree with the following factors related to receiving
mental health counseling or services.” Responses are on a five-point Likert scale ranging from 1
(strongly disagree) to 5 (strongly agree). Examples include, “I don’t know where to get help,”
“It’s difficult to schedule an appointment,” and “I don’t have adequate transportation.” This scale
was originally five items, but one item (“mental health services are not available”) was dropped
due to high missingness (74%), which could be because this population has universal health care
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coverage and access to mental health services. The Cronbach’s alpha score for the remaining
four items demonstrates adequate internal consistency (α=.71). This scale was originally
validated for active duty service members and was adapted for this spousal survey (Kim et al.,
2011).
Psychological Barriers
Psychological barriers to mental health treatment were measured with seven items that
began with the prompt: “Please rate how much you agree or disagree with the following factors
related to receiving mental health counseling or services.” Responses are on a five-point Likert
scale ranging from 1 (strongly disagree) to 5 (strongly agree). Examples include “It would be
too embarrassing,” “It would harm my career,” “I would be seen as weak,” and “my spouse
would disapprove of me receiving help.” This scale was originally validated for active duty
service members and was adapted for this spousal survey and demonstrated good internal
consistency in this sample (Cronbachs alpha = .91; Kim et al., 2011).
Socio-demographic Variables
Six demographic measures were included: 1) Education (less than a bachelor’s
degree/bachelor’s degree or more); 2) Employment status (employed/unemployed); 3)
Race/ethnicity (White/Black/Non-white Hispanic/ Other [Asian/Pacific Islander & those that
marked “Other”]); 4) Rank (enlisted/officer); 5) Age (18-39 years/30+ years); Proximity to
military installation (On post vs. Off post). History with the military was assessed with one item,
which asked spouses to indicate if any of the following experiences applied to them, including:
“I am/was a military service member,” “I grew up in a military family,” or “I was a military
spouse in a prior marriage.” A yes response on any of these three items was indicative of having
a personal history with the military.
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Mental Health
To assess mental health, depression was used as it is a good measure of general mental
well-being and is often comorbid with other measures of mental health such as anxiety
(Naragon-Gainey, 2010). Depression was measured by the Patient Health Questionnaire-8 (PHQ-
8; Kroenke et al. 2009). The PHQ-8 is a diagnostic and severity measure for depression. The
PHQ-8 is based on a 4-point scale, ranging from 1 (not at all) to 4 (nearly every day). Item
responses are summed with higher scores indicating greater severity. Items include: “little
interest or pleasure in doing things” and “feeling tired or having little energy.” Internal
consistency for the PHQ-8 was good in this sample (Cronbach’s alpha = .89; Kroenke et al.,
2009). A score of five on this measure is the cut point for clinically significant depression
symptoms (Kroenke et al., 2009).
Mental Health Treatment
Mental health treatment of the spouse (non-military) was assessed through the question,
“Are you currently in mental health treatment?” with response options: 1 (yes)/ 0 (no).
Data Analysis
STATA 16.1 was used for data cleaning and all analyses. Data cleaning included
recoding variables to be dummy variables for analysis, including recoding four participants that
marked “other” for employment into the “employed” category because they indicated in the
other description that they worked. Data cleaning also included dropping spouses that marked
they were dual service or male, as well as creating the continuous barrier measures and ensuring
approximately normal distribution. As this sample was comparatively small, several choices
were made in recoding to preserve power. For example, race/ethnicity is represented as
White/non-White and education is represented as less than a bachelor’s degree/bachelor’s degree
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or more. These choices were consistent with how these data have previously been used (Donoho
et al., 2017). All potential variables of interest had an acceptable level of missingness (less than
8%; Long & Freese, 2005). Bivariate correlations were run to assess for possible
multicollinearity within the dataset and no correlations indicated any concerns among our
independent variables. To assess the relationship between our dichotomous outcome variable of
being in mental health treatment or not with several predictors (i.e., barriers, depression
symptoms, and socio-demographic variables) multivariable logistic regression was used (Long &
Freese, 2005). Basic descriptive statistics were used to understand the distribution of barriers
among Army wives with unmet mental health care needs.
Results
The majority of spouses indicated that they were not currently in mental health treatment
(92%). The majority (68%) also scored below the cut point for clinically significant depression
symptoms (Kroenke et al., 2009). Of those that marked that they were currently not in mental
health treatment, 29% (n=85) met or exceeded the clinical cut point for clinically significant
depression symptoms, indicating about a third of Army wives in our sample who could
potentially benefit from some form of mental health treatment but are not currently receiving
any.
The multivariable logistic regression model was run in the full sample of N=327, to
examine the relationship between mental health symptoms, socio-demographic characteristics,
and barriers to care on the odds of participating in mental health services. This model was
significant (p<.001; Pseudo R²=.24) and results are displayed in Table 3. Two predictors were
significantly related to mental health services utilization: (1) depression symptoms (OR: 1.19,
CI: 1.08-1.31), (2) psychological barriers to care (OR: .82, CI: .72-.94). Holding all other
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variables constant, these results indicate that for each additional depression symptom, the odds of
being in mental health care treatment increase by 19%. Further, for each additional psychological
barrier to care, the odds of being in mental health treatment decrease, on average, by about 18%.
Among the 85 spouses in our sample who were not currently in mental health treatment
and indicated clinically significant depression symptoms, 51% indicated minimal logistical and
psychological barriers to care. The two highest rated psychological barriers to care among
spouses with unmet mental health needs were “it would be embarrassing” (13%) and “it would
harm my spouses’ career” (14%). The two highest rated logistical barriers to care were, “its
difficult to schedule an appointment” (20%) and “it would be difficult getting time away from
my other responsibilities (work, children, etc.) for treatment” (31%).
Discussion
The current study assesses how logistical and psychological barriers to mental health
treatment directly affect current mental healthcare utilization. This study builds upon previous
research that has explored health care engagement in this population by including a variety of
socio-demographic factors such as race/ethnicity, rank, age, education level, employment status,
proximity to the post where services are typically utilized, personal history with the military
along with current depression symptoms.
Our first hypothesis that the more logistical barriers an Army wife reports, the less likely
they are to be in mental health treatment, was not supported. The findings show that spouses with
higher depression symptoms have increased odds of being in mental health treatment, indicating
that those who do need care are more likely to receive it. This preliminary finding suggests
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perceived logistical barriers may not be predictive of actual service use. In addition to studying
barriers to care, future studies may also want to focus on intent to pursue treatment.
Our second hypothesis that the more psychological barriers an Army wife reports, the
less likely they are to be in mental health treatment was supported. The main factor preventing
spouses from being in mental healthcare, controlling for a variety of socio-demographic
variables as well as mental health symptoms, were psychological barriers to treatment. These
findings are consistent with prior research that found military wives had significantly higher
psychological barriers to mental health treatment when compared to non-military affiliated
women (Lewy et al., 2014). Our finding highlights that perceived psychological barriers to
mental health treatment are significantly contributing to Army wives not being in treatment in
the presence of socio-demographic variables such as employment status, education level,
personal history with the military, their proximity to the post, as well as current mental health
symptoms. This finding emphasizes the importance of continued efforts in decreasing mental
health stigma in the military for spouses through building a culture of support for psychological
health within the military (Defense Health Board Task Force on Mental Health, 2007).
Another strategy to reduce psychological barriers to mental health services could be to
describe these services using terms that are not as stigmatizing for this population. For example,
the family-based resiliency intervention, Families OverComing Under Stress (FOCUS) is used
across the globe on various Naval and Marine bases to provide preventative mental health
support for military families (Lester et al., 2011, 2013; Mogil et al., 2015). While the FOCUS
program is an adaptation of several family-based treatments to support the mental well-being and
functioning of the family, FOCUS markets itself as a resiliency-building intervention to support
the mission readiness of military families, in acknowledgment of the potential stigma that
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surround family therapy in this population (Lester et al., 2011). However, no formal studies have
been conducted to determine if this rebranding has led to greater mental health care engagement
over other programs that use more traditional health service terms.
In our sample, there were 29% of spouses who met the clinical cut point for clinically
significant depression symptoms and were not currently receiving any mental health treatment,
indicating a significant subset of spouses with unmet mental health care needs. This finding
complements prior literature that found spouses with significant mental health symptoms are at
increased odds of experiencing more barriers to care, which could prevent them from seeking or
receiving care (Schvey et al., 2021). The most reported barrier within this group was difficulty
getting time away from responsibilities such as work or children for treatment. This is consistent
with previous research that highlighted similar barriers for military wives compared to a national
sample of non-military affiliated women as well being the number one reported barrier in a
large-scale sample of military spouses (Lewy et al., 2014; Schvey et al., 2021). However, in the
present study, logistical barriers, like the availability of time away from other responsibilities,
did not significantly predict mental health services utilization (hypothesis 1). Again, this
preliminary finding highlights how perceived barriers may not be predictive of actual service
use.
Limitations & Future Research
These findings should be considered in light of several limitations. This present study
was a secondary analysis of data collected ten years ago; findings may not be as relevant to
military spouses today. The sample used for this analysis was limited in its racial/ethnic, gender,
and rank diversity, however, this sample is representative of the military spouse community by
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being primarily white, junior ranking, and female (Department of Defense, 2019). A non-
probability sampling approach was used for data collection with a 23% response rate from
eligible spouses, potentially limiting the generalizability of findings. Though initial power
calculations suggested the study was powered to detect medium effect sizes, several decisions
were made to preserve power, including collapsing race/ethnicity and employment status
demographic categories. As a result, comparisons across these subgroups were not possible.
Furthermore, the dichotomization of race/ethnicity fails to account for the fact that the various
groups incorporated into “Hispanic non-White” may not have the same experiences with mental
health treatment usage. When statistically possible, disparities by racial subgroup should be
explored.
Future research should assess both logistical and psychological barriers to care after the
COVID-19 pandemic as access to telehealth services have increased and proven to be effective at
treating mental illness (see Rauschenberg et al., 2021 for meta-analysis). Access to telehealth
services may increase utilization in the military spouse population, as this mode of service
delivery may minimize some logistical and psychological barriers. For example, a military
spouse does not have to worry about being seen in or near the treatment office if they are able to
receive services at home (Moreau et al., 2018). Furthermore, as telehealth services become more
frequently used, this may begin to address continuity of care for military spouses. Continuity of
care among military spouses is an important but understudied area, considering how frequently
military families relocate. One of the first studies to examine the continuity of care among
military beneficiaries, including spouses, documents how discontinuous care is an ongoing and
prominent issue in the military which can impact satisfaction and potentially lead to poorer
health outcomes (Gleason & Beck, 2017). These findings suggest a need for solutions to promote
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enduring and trusting relationships with military providers, and telehealth could be a promising
solution.
Conclusion
While preliminary, these results highlight that Army wives with increased depression
symptoms are likely to be in mental health care treatment and that Army wives with higher
psychological barriers to mental health care, including stigma around mental health, are less
likely to seek care. These findings support the military’s continued efforts to reduce the stigma
surrounding mental health treatment by creating a culture of support for psychological health.
Furthermore, in addition to a positive mental health culture, the military may want to evaluate if
rebranding mental health treatment, such as resiliency building, increases treatment engagement.
Lastly, these results highlight the need for future research on mental health care utilization of
telehealth services as a means to reducing barriers to mental health care among military spouses.
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Table 4.1. Demographic Characteristics.
Characteristic N (%) / M
(SD)
Range
Race/Ethnicity White 245 (75%)
Black 22 (7%)
Non-white Hispanic 34 (10%)
Other 25 (8%)
Rank Enlisted 254 (78%)
Officer 71 (22%)
Employment Status Employed 96 (30%)
Unemployed Looking For
Work
71 (22%)
Unemployed Not Looking
For Work
125 (39%)
Other 33 (10%)
Education Bachelor’s Degree or More 109 (34%)
Less than Bachelors 216 (67%)
Age 18-29 years old 169 (52%)
30 + years old 157 (48%)
Personal History with
Military
No 223 (71%)
Yes 91 (29%)
Distance to Military
Installation
Live on Post 125 (39%)
Live Off Post 198 (61%)
Currently in mental health
treatment
No 299 (92%)
Yes 26 (8%)
Depression Symptoms 3.65 (4.43) (0-24)
Logistical Barriers to
Mental Health Care
8.07 (3.11) (4-18)
Psychological Barriers to
Mental Health Care
12.81 (5.27) (7-30)
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Table 4.2. Barriers to Care Among Army Wives with Unmet Mental Health Care Needs (n=85).
Psychological
Barriers to Care
Barrier Statement Response N(%)
It would be too embarrassing. Strongly disagree/disagree 58 (69%)
Neither agree nor disagree 15 (18%)
Strongly agree/agree 21 (13%)
It would harm my career. Strongly disagree/disagree 67 (80%)
Neither agree nor disagree 10 (12%)
Strongly agree/agree 7 (8%)
I would be seen as weak. Strongly disagree/disagree 64 (76%)
Neither agree nor disagree 10 (12%)
Strongly agree/agree 10 (12%)
It would harm my spouses’ career. Strongly disagree/disagree 53 (63%)
Neither agree nor disagree 19 (23%)
Strongly agree/agree 12 (14%)
Others would think less of me. Strongly disagree/disagree 57 (70%)
Neither agree nor disagree 15 (18%)
Strongly agree/agree 10 (12%)
Others would blame me. Strongly disagree/disagree 60 (73%)
Neither agree nor disagree 19 (23%)
Strongly agree/agree 3 (4%)
My spouse would disapprove of me
receiving help.
Strongly disagree/disagree 63 (77%)
Neither agree nor disagree 10 (12%)
Strongly agree/agree 9 (11%)
Logistical
Barriers
Barrier Statement Response N(%)
I don’t know where to get help. Strongly disagree/disagree 60 (71%)
Neither agree nor disagree 16 (19%)
Strongly agree/agree 9 (11%)
I don’t have adequate transportation. Strongly disagree/disagree 70 (85%)
Neither agree nor disagree 9 (11%)
Strongly agree/agree 3 (4%)
It’s difficult to schedule an
appointment.
Strongly disagree/disagree 49 (59%)
Neither agree nor disagree 17 (20%)
Strongly agree/agree 17 (20%)
It would be difficult getting time away
from my responsibilities (work,
children, etc.) for treatment.
Strongly disagree/disagree 42 (50%)
Neither agree nor disagree 16 (19%)
Strongly agree/agree 26 (31%)
106
Table 4.3. Logistic Regression Predicting Current Mental Health Services Use
Variable B SE OR 95% CI for OR
Non-Hispanic White (vs. Hispanic non-White) 0.93 0.66 1.15 0.96 1.37
Officer (vs. enlisted) -1.71 0.90 0.18 0.03 1.06
Employed (vs. unemployed) -0.83 0.59 0.44 0.14 1.38
Bachelors (vs. less than bachelors) 0.81 0.57 2.25 0.74 6.84
Young (vs. older, 30+ yrs.) -0.09 0.52 0.91 0.33 2.52
Military History (vs. none) -0.02 0.54 0.98 0.34 2.82
Live on post (vs. live off post) -0.58 0.55 0.56 0.19 1.64
Depression symptoms (PhQ8) 0.18 0.05 1.19** 01.08 1.31
Logistical barriers to care 0.14 0.09 1.15 0.96 1.37
Psychological barriers to care -0.20 0.07 0.82* 0.72 0.94
Constant 0.10 0.10 -2.22 0.01 0.76
Model Characteristics
Chi-Square = 40.66
Degrees of Freedom = 10
Pseudo R² < 0.00
Abbreviations: CI, confidence interval; OR, odds ratio; SE, standard error; yrs, years; PhQ8,
Patient Health Questionnaire 8-item.
*p≤.01
**p≤.001
107
Supplemental Materials
Supplemental Table 4.1. Pairwise Correlation Coefficients Among Barriers to Mental Health Care and Treatment Usage among Army Wives.
Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
(1) In MH Tx 1.000
(2) Log. Barriers 0.050 1.000
(3) Psyc. Barriers -0.143* 0.619** 1.000
(4) Employed -0.067 0.019 0.020 1.000
(5) Mil. History -0.014 -0.093 -0.068 0.042 1.000
(6) White 0.063 -0.001 -0.080 0.010 0.012 1.000
(7) Officer -0.102 -0.049 0.046 -0.001 0.050** 0.167 1.000
(8) Bachelors + 0.006 -0.052 -0.007 0.158** 0.035 0.063** 0.586 1.000
(9) Young 0.058 0.034 -0.005 -0.089 -0.078 0.085** -0.160* -0.135** 1.000
(10) On Post -0.072 -0.064 -0.071 -0.143* -0.006 -0.045* -0.139** -0.161 -0.161 1.000
(11) Dep. Symp. 0.258** 0.216** 0.176** -0.012 -0.024 -0.089 -0.070 -0.028 0.016 -0.069 1.000
*p≤.05 **p≤.01
(1) Currently in Mental Health Treatment (2) Logistical Barriers to Mental Health Care (3) Psychological Barriers to Mental
Health Care (4) Employed vs. Unemployed) (5) Has a History with the Military vs. Not (6) Non-Hispanic White vs. Hispanic & non-White) (7) Officer vs. Enlisted (8)
Bachelors or more vs. Less than Bachelors (9) Young (18-29 years old) vs. Older (30+ years old) (10) Lives on Post vs. Not (11) Depression Symptoms
108
Supplemental Table 4.2. Proportions of Cases Classified in Logistic Regression.
Characteristic N (%) / M
(SD)
Race/Ethnicity Non-Hispanic White 245 (75%)
Hispanic non-white 81 (25%)
Rank Enlisted 254 (78%)
Officer 71 (22%)
Employment Status Employed 96 (30%)
Unemployed 229 (70%)
Education Bachelor’s Degree or More 109 (34%)
Less than Bachelors 216 (67%)
Age 18-29 years old 169 (52%)
30 + years old 157 (48%)
Personal History with
Military
No 223 (71%)
Yes 91 (29%)
Distance to Military
Installation
Live on Post 125 (39%)
Live Off Post 198 (61%)
Currently in mental health
treatment
No 299 (92%)
Yes 26 (8%)
109
Chapter 5. Conclusion
Introduction
This dissertation seeks to expand the knowledge of the social and economic environmental
factors that impact health (the SDoH) in a context with equal access to healthcare in the U.S. The
U.S. military is a unique population for many reasons, one of which is all of its members,
including their dependents, have equal access to universal healthcare coverage (Military Spouse
and Family Benefits | Military.Com, n.d.). Using the WHO SDoH framework with a sample of
Army wives, this dissertation explores various social and economic environmental factors that
appear to impact physical and mental health in a context with equal access to care. This
dissertation shows three papers each exploring a different aspect of the holistic WHO SDoH
framework through three studies. As outlined in Figure 1.1, Study 1 explores the main SDoH
factors that impact Army wives’ physical and mental health, Study 2 explores what SDoMH
factors work together to lead to and prevent adverse mental health for specific racial/ethnic
groups of Army wives, and Study 3 explores specific barriers that are impacting mental health
treatment usage among Army wives.
Review of Major Findings and Integration with Existing Research
Using data from Walter Reed Army Institute of Research, this dissertation shows there
are significant SDoH that can impact Army wives’ physical and mental health. Further, our
results also show that when examined by racial and ethnic groups, Army wives have different
pathways of SDoMH factors that lead to poor and healthy mental health symptoms. Finally, our
results demonstrate that even in an environment with equal access to universal healthcare
coverage, there can still be barriers to mental health care, such as the psychological barrier of
110
stigma to seek or receive care. The following subsections will review the independent findings of
each paper in relation to existing research and will then provide a crosscutting discussion of key
findings across the three studies that add depth to military health providers’ understanding of
how to better support military spouses’ overall health.
Army Wives: Exploring the Social Determinants of Health in a Military-Adjacent Population
with Universal Healthcare: Paper 1
Paper 1 documents the social determinants of health still exist within a population with
equal access to universal healthcare coverage, however, the impact of the structural social
determinants on health appear to be attenuated. Our findings suggest that structural determinants
of military history, education, age, race/ethnicity, employment status, or rank were not
significantly related to either physical or mental health symptoms when examined all together.
This finding contrasts with other large-scale studies that have found employment and rank to be
significant predictors of military spousal mental health (Donoho et al., 2018; Trail et al., 2019).
However, these previous large-scale studies did not examine socio-demographic factors with
social cohesion/capital or intermediary determinant categories such as social support, sense of
community, or the service member's mental health state. It is possible that when the SDoH
categories are examined all together, equal access to healthcare may attenuate the strength of
associations between structural determinants and health, as universal access to both coverage and
care level out the effects of structural inequalities that prevent individuals from seeking care in
other populations.
Similar to our findings, several past studies on military spouses’ mental and physical health
have failed to find a significant relationship between race/ethnicity and health outcomes
111
(Barfield et al., 1996; Donoho et al., 2018; Lundquist, Elo, et al., 2014; Lundquist, Xu, et al.,
2014). These consistent findings may indicate that in a context where there is equal access to
healthcare, little to no racial/ethnic disparities in health outcomes exist. Although not directly
comparable, recent research evaluating the Affordable Care Act, which put in place regulations
to constrain healthcare costs and improve quality in the U.S., documents less socio-economic
disparities in insurance coverage (including race/ethnicity; Angier et al., 2019; Chaudry et al.,
2019). This study adds to this literature by documenting that in an environment with universal
healthcare, there are not significant racial/ethnic differences in health outcomes in the presence
of other social determinants of health such as a recent illness/injury, childbirth, mental health of
service member, intimate partner violence, or social support.
While there was not a significant relationship between the structural determinants and health
outcomes, there were significant differences between the social cohesion and capital, and
intermediary determinants. For example, results show the protective nature of a sense of
community on both physical and mental health. This could be for various reasons, including
knowledge sharing about resources, feelings of connectedness, and lack of loneliness (Crouch et
al., 2017). Regression results show that a sense of social support can also protect against mental
health symptoms and that belonging to a religious group can protect against physical health
symptoms. This finding supports prior research documenting the importance of social support on
military spouses' mental and physical health outcomes (Donoho et al., 2017; Sumner et al., 2016;
Wang et al., 2015). This study adds to this literature by documenting that even in the presence of
other socio-economic factors such as a recent injury/illness, childbirth, clinically significant
intimate partner violence, and thinking their service member spouse needs mental health
treatment, different types of social support still protect both mental and physical health.
112
Furthermore, these findings suggest that certain types of social connections could be more
beneficial for physical health than mental health. For example, feeling that you have people to
call and count on may be more important in protecting mental health, whereas having an actual
group that meets regularly is more important in protecting physical health. Future research
should seek to better understand the impact of specific types of social support on various health
aspects to appropriately guide interventions.
The findings highlighted three intermediary determinant factors that significantly impact
Army wives’ health: perceptions of service members mental health, work-family conflict, and
having a recent childbirth (within the past year). Results show that spouses who thought their
service member needed mental health treatment had significantly more physical and mental
health symptoms. This finding could be due to the military's high mental health stigma and
associated negative impact on care-seeking behavior (see Michalopoulou et al., 2017 for review).
For example, the stress of supporting a service member who needs mental health treatment but
refuses to seek it could negatively impact the military spouse’s overall health. Such a dynamic
can breed a sense of helplessness about how to help the service member, and thereby help
themselves. Further, these findings suggest employing spouses as a source of mental health
treatment resources for the service member. Building best practices for including spouses as
sources of information for the service member is an area that warrants further research.
The current study found that high levels of work-family conflict were significantly related to
adverse mental and physical health. However, these findings demonstrate work-family conflict
has a minimal significant change relative to the other predictors. The significance of the
relationship supports prior literature which found that the stress of work duties and caring for
one’s family, especially in a military setting, can be demanding on the service member;

113

similarly, family-friendly military work environments were found to support the well-being of
the service member (Huffman et al., 2008; Segal, 1986; Wadsworth & Southwell, 2011). Prior
literature has suggested innovative ways to help minimize work-family conflict. For example,
increasing the flexibility of work arrangements through extensions of work obligations
(Wadsworth & Southwell, 2011). The Navy provides their sailors more control of their time at
key periods, such as after a child's birth, by exchanging more work flexibility for a specific
amount of time for extensions of service (Wadsworth & Southwell, 2011). This type of work
flexibility provides the service member and their family with more control over prolonged
absences, and the military retains a highly trained worker.
Finally, our study found that recent childbirth (within the past year) was significantly related
to decreased physical and mental health symptoms. This finding contrasts prior literature that
documents military spouses with a deployed partner are at greater risk for perinatal mental health
(Godier-Mcbard et al., 2019). However, these data were collected after a deployment when the
service member was home. A recent childbirth's positive impact on health could be related to
increased doctor visits and the care a mother is likely to receive after giving birth (Geissler et al.,
2020). This finding could also be a side effect of having universal healthcare, meaning there are
minimal barriers in receiving perinatal and postnatal care, which could improve overall health.
The finding could also be due to the six-week paid parental leave the active-duty service member
typically receives following the birth of a child, which can lead to increased family time and a
sense of support for the spouse (Lidbeck & Bernhardsson, 2019; Military.com, 2021).
Exploring the Social Determinants of Mental Health by Race and Ethnicity in Army Wives:
Paper 2
114
Paper 2 documents that the different groups of racial/ethnic Army wives have different
SDoMH pathways that lead to clinically significant poor mental health symptoms. While no
statistically significant differences between races and ethnicities were found in Paper 1, that
could be a result of the limitations of correlational methods. Correlational methods rely on net
effects where only the most prominent relationship is seen. This is important when trying to
understand factors that most impact an outcome, in this case, health. However, when trying to
understand different experiences and the diversity of experiences, it can be limiting. Therefore,
Paper 2 examines the SDoMH factors and how they interact among different racial/ethnic groups
of Army wives. Paper 2 is an exploratory analysis and preliminarily highlights how it is not just
one social or economic factor that is impacting the diversity of Army wives, it is a combination
of environmental and life factors, the SDoMH, interacting to affect depression symptoms. While
preliminary, these findings demonstrate that even within a group with similar exposure to unique
stressors (in this case, military-specific stressors), causal conditions combine in ways that create
different pathways to affect and protect mental health. These findings suggest that it is not one
single avenue that affects the mental health of Army wives, indicating a need for holistic mental
health assessments to incorporate the SDoMH factors that could be impacting mental well-being.
An example of a holistic assessment is the biopsychosocial approach to address health, primarily
mental health, issues (Campbell & Rohrbaugh, 2013; Sperry, 2007). This approach incorporates
an individual's biological, psychological, and social history to inform the best course of treatment
(Campbell & Rohrbaugh, 2013; Sperry, 2007).
How these determinants interact demonstrate what would be attenuated by supports and
what may need extra support. For example, as seen for non-Hispanic Black and Junior Enlisted
non-Hispanic White Army wives, the stressor of having a history of ACES leads to high
115
depression symptoms in the absence of social support. Furthermore, a history of ACES is
attenuated with it is in the presence of high social support. This finding is in line with the
SDoMH framework that shows social support intersects both structural and intermediary
determinants, meaning social support can change an individual’s health trajectory (Solar, &
Irwin, 2010). This finding is in line with other research that has found social support to buffer the
effects of adverse childhood events on later life depression symptoms (Cheong et al., 2017). This
study shows this is also true for the Army wives’ population, which has additional military life
stressors to cope with. This is useful for military providers to know that potential support that
could help military wives with a history of adverse childhood events could be linking them up
with social connections.
Paper 2 uniquely contributes to the prior literature by using Coincidence Analysis (CNA)
in combination with fuzzy set Qualitative Comparative Analysis (fsQCA) to consider a variety of
potentially relevant structural, social cohesion and capital, and intermediary determinants. Using
fsQCA this study described and illustrated the causal complexity among conditions that may
affect clinically significant depression symptoms for a diversity of racial and ethnic Army wives.
Fuzzy set QCA has demonstrated promising applications to research understanding racial
disparities (Ragin & Fiss, 2017; Rich et al., 2020). For example, rather than attributing Black and
White differences in poverty to test scores in school, Ragin and Fiss (2017) used fsQCA to show
how accumulated advantage favors White students and accumulated disadvantage disfavors
Black students. Applying similar methods in health research could uncover important causal
pathways for health inequalities rather than focusing on specific demographic categories such as
race or gender that are often investigated in insolation of their socio-ecological context (Krieger,
2012; Rich et al., 2020).

116

Army Wives’ Mental Health Treatment Engagement: Logistical and Psychological Barriers to
Care: Paper 3
Paper 3 specifically examines treatment engagement controlling for a variety of socio-
demographic factors such as employment, history with the military, and living proximity to the
military installation where services are typically utilized. Paper 3 documents that Army wives
with increased depression symptoms are likely to be in mental health care treatment and that
Army wives with higher psychological barriers to mental health care, including stigma around
mental health, are less likely to seek care. Paper 3 findings show that spouses with higher
depression symptoms have increased odds of being in mental health treatment, indicating that
those who do need care are more likely to receive it. This preliminary finding suggests perceived
logistical barriers may not be predictive of actual service use. In addition to studying barriers to
care, future studies may also want to focus on intent to pursue treatment.
The main factor preventing spouses from being in mental healthcare, controlling for a
variety of socio-demographic variables as well as mental health symptoms, were psychological
barriers to treatment. These findings are consistent with prior research that found military wives
had significantly higher psychological barriers to mental health treatment when compared to
non-military affiliated women (Lewy et al., 2014). Our finding highlights that perceived
psychological barriers to mental health treatment significantly contribute to Army wives not
being in treatment in the presence of socio-demographic variables such as employment status,
education level, personal history with the military, and proximity to the post well as current
mental health symptoms. This finding emphasizes the importance of continued efforts to
decrease mental health stigma in the military for spouses through building a culture of support
for psychological health (Defense Health Board Task Force on Mental Health, 2007).
117
Future research should assess both logistical and psychological barriers to care after the
COVID-19 pandemic as access to telehealth services have increased and are proven to be
effective at treating mental illness (see Rauschenberg et al., 2021 for meta-analysis). Access to
telehealth services may increase utilization in the military spouse population, as this mode of
service delivery may minimize some logistical and psychological barriers. For example, a
military spouse does not have to worry about being seen in or near the treatment office if they are
able to receive services at home (Moreau et al., 2018). Furthermore, as telehealth services
become more frequently used, this may begin to address continuity of care for military spouses.
Continuity of care among military spouses is an important but understudied area, considering
how frequently military families relocate. One of the first studies to examine the continuity of
care among military beneficiaries, including spouses, documents how discontinuous care is an
ongoing and prominent issue in the military which can impact satisfaction and potentially lead to
poorer health outcomes (Gleason & Beck, 2017). These findings suggest a need for solutions to
promote enduring and trusting relationships with military providers, and telehealth could be a
promising solution.
Clinical Implications
All three papers highlight how even in an environment with equal access to universal
healthcare coverage, there are still social determinant factors that negatively impact health as
well as engagement with universal care. If there is one takeaway from this dissertation, it is this,
military spouses are not a monolithic group in terms of exposure to stressors or how they cope
with them. Prior research has focused primarily on the impact of military specific stressors (e.g.
deployment) on health and has potentially obscured the impact of stress exposure unrelated to
military service (e.g. adverse childhood experiences, recent childbirth). This is seen in Paper 1
118
through the prevalence of the variety of significant determinant factors leading to health
outcomes (social support, a recent childbirth, thinking the service member needs mental health
treatment, and work-family conflict). This is seen in Paper 2 through our case study that showed
no two racial or ethnic groups of Army wives had the same pathway to clinically significant
depression symptoms. Finally, this is shown in Paper 3 where even in an environment with equal
access to universal coverage, there are still significant psychological barriers that can prevent a
spouse from engaging in care.
There are three main clinical takeaways from this dissertation. First, military medical
providers should be aware that when addressing issues such as the service member's mental
health, a recent childbirth from the spouse, or work-family conflict, they are aiding a spouse’s
overall health. In addition, by increasing a spouse’s sense of community, providers support
military spouses physical and mental health. Secondly, spouses bring their unique social and
economic environments with them while interacting with the military's additional stressors. To
best support the diversity of military spouses, assessments that enable clinicians to engage with
spouses across all determinant factors (structural, social cohesion and capital, intermediary)
should be utilized and studied, such as biopsychosocial assessments. Finally, psychological
barriers to care significantly prevent spouses from seeking mental health care. These findings
support the military’s continued efforts to reduce the higher than the average population stigma
surrounding mental health treatment by creating a culture of support for psychological health. In
addition to a positive mental health culture, the military may want to evaluate two things. One, if
rebranding mental health treatment, such as resiliency building, increases treatment engagement.
Two, if telehealth services reduce barriers to mental health care among military spouses.

119

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Abstract (if available)

Abstract Lack of access to healthcare perpetuates health inequalities in the United States (US) and is a key social determinant of health. Military spouses are a unique population within the U.S. in that they have universal healthcare. With guidance from the World Health Organization’s (WHO) Social Determinants of Health (SDoH) conceptual framework, the purpose of this dissertation was to explore various aspects of the SDoH in a sample with universal healthcare. Through three different studies, outlined through three different papers, this dissertation highlights how even in an environment with equal access to universal healthcare coverage, there are still social determinant factors that negatively impact health as well as engagement with universal care. Paper 1 documents how the social determinants of health still exist within a population with equal access to universal healthcare coverage, however, the impact of the structural social determinants on health appear to be attenuated. Paper 2 documents that the different groups of racial/ethnic Army wives have different determinant pathways that lead to clinically significant poor mental health symptoms. While preliminary, these findings demonstrate that even within a group with similar exposure to unique stressors (in this case, military-specific stressors), causal conditions combine in ways that create different pathways to affect and protect mental health. Paper 3 documents that Army wives with increased depression symptoms are likely to be in mental health care treatment and that Army wives with higher psychological barriers to mental health care, including stigma around mental health, are less likely to seek care. Collectively, this dissertation supports the use of the adapted WHO SDoH conceptual framework to understand various aspects of health for Army wives.

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Asset Metadata

Creator Dodge, Jessica Rampton (author)

Core Title Exploring the social determinants of health in a population with similar access to healthcare: experiences from United States active-duty army wives

School Suzanne Dworak-Peck School of Social Work

Degree Doctor of Philosophy

Degree Program Social Work

Degree Conferral Date 2021-08

Publication Date 07/31/2021

Defense Date 06/10/2021

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

Tag coincidence analysis,configurational comparative methodologies,mental health stigma,military spouses,OAI-PMH Harvest,qualitative comparative analysis,social determinants of health,universal healthcare access

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Castro, Carl (committee chair), Fiss, Peer (committee member), Henwood, Benjamin (committee member)

Creator Email ramptonjessica@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC15670632

Unique identifier UC15670632

Legacy Identifier etd-DodgeJessi-9957

Document Type Dissertation

Format application/pdf (imt)

Rights Dodge, Jessica Rampton

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given.

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu