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AN ABSTRACT OF THE DISSERTATION OF
Marc B. Schure for the degree of Doctor of Philosophy in Public Health presented on
May 2, 2013.
Title: Prevalence and Association of Depressive Symptoms with Physical Disability and
Long-Term Care Use among Older American Indians.
Abstract approved:
_______________________________________________________________________
R. Turner Goins
Health officials have recently been sounding the alarm that depression will soon
surpass many of the major medical conditions in causing disability among adults. Recent
demographic and health trends are generating public health concern about depression.
First, the prevalence of chronic conditions has dramatically increased over the last several
generations. Second, depression often accompanies chronic conditions and can
exacerbate physical health outcomes. Third, the recent entry of the baby-boomers into
the older age bracket is spawning an substantive expansion of the older adult population
in this country, an age group that disproportionately experiences greater number of
chronic conditions, physical disabilities, and associated long-term care needs. These
health trends are even greater among older American Indians. However, despite greater
prevalence of morbidity, depression, and physical disability among older American
Indians, very little is known about the relative impact of morbidity and depression on
physical disability. Therefore, the first manuscript examines 1) the prevalence and
correlates of depressive symptoms and 2) the relationship of depressive symptoms with
chronic conditions and physical disability among older American Indians, using the
Native Elder Care Study data.
The presence of depressive symptoms in older adults have been implicated in the
need for and use of greater medical care, greater number of caregiving hours received,
and increased medical expenditures above and beyond the severity of chronic conditions
or physical disability. No published studies have examined the difference in amount of
long-term informal and formal care receipt between older American Indians with
different levels of depressive symptoms. Therefore, the second manuscript examines the
difference in amount of informal and formal long-term care receipt between older
American Indians with different levels of depressive symptoms.
Examining the contribution of depressive symptoms to physical disability and
long-term care use among older adults is important because it is a preventable and
treatable condition afflicting a substantive proportion of this population. As the number
of older adults increases, long-term care will be in greater demand. However, it is
unclear whether our long-term care system will be able to meet the growing demand.
Thus, this research contributes to our understanding of the factors leading to greater
physical disability and long-term care use.
© Copyright by Marc B. Schure
May 2, 2013
All Rights Reserved
Prevalence and Association of Depressive Symptoms with Physical Disability and Long-
Term Care Use among Older American Indians.
by
Marc B. Schure
A DISSERTATION
Submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Doctor of Philosophy
Presented May 2, 2013
Commencement June 2013
Doctor of Philosophy dissertation of Marc B. Schure presented on May 2, 2013.
APPROVED:
_____________________________________________________________________
Major Professor, representing Public Health
_____________________________________________________________________
Director of the School of Social and Behavioral Health Sciences
_____________________________________________________________________
Dean of the Graduate School
I understand that my dissertation will become part of the permanent collection of
Oregon State University libraries. My signature below authorizes release of my
dissertation to any reader upon request.
_____________________________________________________________________
Marc B. Schure, Author
ACKOWLEDGEMENTS
No sane graduate student gets through a doctoral program without the support of
family, friends, colleagues, or mentors. Thus, by inference, I must consider myself (at
least partially) of sound mind. As such, I must first thank my mentor, Dr. Turner Goins,
who kindly took me under her wing to provide thoughtful and diligent guidance on how
to critically think and write about the context of health issues among culturally-distinct
populations. I could not have asked for a better, more genuine and respected researcher
to guide me through the nuances of this field and yet provide me the space to explore and
develop my own research interests. For this, I am greatly indebted to her.
Second, I believe that having a few special colleagues to commiserate with and
hold each other up has been essential to surviving the demands of such an endeavor. The
value of their fellowship is still somewhat immeasurable to me. I must extend my
gratitude to friends, past and present, who have been a reminder of staying grounded in
the things that really matter in life. I hope that I can live by their loving example. Last,
but certainly not least, is the respect owed to my family. I owe any success that I might
claim to my mother, who has been a constant source of support and inspiration of living
fully, no matter what life throws at you. As with other major endeavors, this one has not
been without sacrifices. But it has been a temporary sacrifice with the hope of a better
life. It is with this hope that I envision a promising future where my wife and daughter
may live and age well, with the rest of society, both successfully and with a sense of
purpose, belonging, security, and well-being. Then, maybe, I’ll consider retirement.
CONTRIBUTION OF AUTHORS
Marc B. Schure conceptualized the organization and content of material in this
dissertation, performed all data analyses, and organized results in the initial draft of
manuscripts presented.
Dr. R. Turner Goins provided editorial comments and advice on all manuscripts and was
the Principal Investigator for the Native Elder Care Study funded by the award
#AG022336, from the National Institute on Aging (NIA).
Drs. Adam Branscum, Sheryl Thorburn, and Viktor Bovbjerg provided editorial
comments, analytic advice, and suggestions on the interpretation of the results in both
manuscripts.
TABLE OF CONTENTS
Page
Chapter 1. General Introduction …………………………………………………….......1
Background and Significance ……………………………………………….......1
Study Rationale …………………….….………...………………………….......6
Specific Aims ……………………………………………………………….......7
Chapter 2. First Manuscript ..……………………………………………………..…......9
Prevalence and association of depressive symptoms with physical disability
among older American Indians
Chapter 3. Second Manuscript …………………………………………………………42
Informal and formal long-term care use among older American Indians
by levels of depressive symptoms
Chapter 4. General Conclusions .……………………………………………………….63
Future Directions …………………………………………………………….....64
Bibliography …………………………………………………………………………....66
Appendix ……………………………………………………………………………….78
LIST OF FIGURES
Figure Page
2.1 The Disablement Process Model ..……………………………………………….39
2.2 Conceptual diagram of a mediation model ……………………………………...40
2.3 Pathway analysis of physical disability ………………………………………….41
3.1 The Behavioral Model …………………………………………………………...61
3.2 Determination of assistance need with activities of daily living and
Instrumental activities of daily living …………………………………………....62
LIST OF TABLES
Table Page
2.1 Prevalence of depressive symptoms by sample characteristics ………..………..31
2.2 Adjusted associated risk factors of depressive symptoms ……………...……….33
2.3 Prevalence of physical disability by sample characteristics ………………….....34
2.4 Adjusted association of depressive symptoms with physical disability ……..….37
2.5 Standardized direct, indirect, and total effects of physical disability ……….…..38
3.1 Prevalence of informal and formal long-term care use by level of
depressive symptoms …………………………………………………………….58
3.2. Percent of older adults with physical disabilities using informal long-term
care use by source of caregiving ………………………………………………...59
3.3 Unadjusted and adjusted association of depressive symptoms with informal
care use …………………………………………………………………………...60
DEDICATION
I cannot think of a more appropriate person to dedicate this to than my father who, with
the endless support from my mother, gracefully succumbed to the effects of a
progressively physically-disabling disease in his later life. His legacy inspires me in
ways I have yet to fully understand or realize. Perhaps this endeavor reflects my desire to
better understand and respect my father’s experience in a way that honors one’s struggles
and respective resilience in life.
1
Prevalence and Association of Depressive Symptoms with Physical Disability and Long-
Term Care Use among Older American Indians.
CHAPTER 1. INTRODUCTION
“The term clinical depression finds its way into too many conversations these days. One
has a sense that a catastrophe has occurred in the psychic landscape.”
(Leonard Cohen)
Background and Significance
The World Health Organization (2012) has predicted that, by the end of this
decade, depression will be the second leading contributor to the global burden of disease,
surpassing many other serious diseases as a cause of physical disability. In the U.S., the
prevalence of clinically significant depressive symptoms among community-dwelling
older adults ranges from 8% to 25% (Blazer, 2003). Many chronic medical conditions
largely contribute to the onset of late-life depression (Alexopoulos, 2005, 2006;
Chapman, Perry, & Strine, 2005), as well as to physical disability in later life (Brault,
Hootman, Helmick, Theis, & Armour, 2009; Gill, Allore, Holford, & Guo, 2004; Stuck et
al., 1999), contributing to the need for and use of informal (unpaid) and formal (paid)
long-term care (Katon, 2003; Langa, Valenstein, Fendrick, Kabeto, & Vijan, 2004).
Recent demographic and health trends support predicted increases in the
prevalence of depression, physical disability, and associated long-term care needs within
the U.S. population. First, the number of U.S. adults aged ≥65 years is projected to more
than double from 40.2 million in 2010 to 88.5 million in 2050, with an even greater rate
increase among the oldest old (aged ≥85 years), from 5.8 million in 2010 to 19 million in
2
2050 (Vincent & Velkoff, 2010). Second, increased age is associated with increased
prevalence of single and multiple morbidities and physical and cognitive disabilities
(Berlau, Corrada, & Kawas, 2009; Ukraintseva & Yashin, 2001). Recent estimates show
that approximately 80% of persons aged ≥65 years have at least one chronic condition
and 50% have two or more chronic conditions (Velkoff, He, Sengupta, & DeBarros,
2005). More importantly, the prevalence of severely disabling chronic conditions such as
Type 2 diabetes, arthritis, and Alzheimer’s disease is predicted to dramatically increase
among older adults over the next several decades (Centers for Disease Control and
Prevention, 2003; Hebert, Scherr, Bienias, Bennett, & Evans, 2003; Velkoff et al., 2005).
Recent evidence from national surveys suggests that the number and proportion of older
adults with physical disabilities will dramatically increase along with need for associated
medical and public health services (Brault et al., 2009; Seeman, Merkin, Crimmins, &
Karlamangla, 2010).
Many older adults with comorbid depressive symptoms are at increased risk for
adverse medical events, physical disability onset, institutionalization, and mortality
(Bagulho, 2002; Cronin-Stubbs et al., 2000; Katon et al., 2005; Lyness et al., 2007). The
presence of depressive symptoms among older adults with chronic conditions is
independently associated with nearly 50% greater medical care costs (Katon et al., 2005)
and greater number of hours of received informal caregiving (Langa et al., 2004).
Therefore, it is not surprising that public health officials are targeting older adults with
evidence-based prevention and treatment programs to improve both their physical and
mental health (Centers for Disease Control and Prevention and National Association of
Chronic Disease Directors, 2009).
3
As an ethnic minority, older American Indians are particularly susceptible to
greater number of chronic conditions, greater depressive symptoms, and physical
disability. They disproportionately suffer from poorer physical and mental health
compared to other racial and ethnic groups (Barnes, Powell-Griner, & Adams, 2005;
Centers for Disease Control and Prevention, 2011). Similarly, older American Indians
experience some of the highest rates of physical disability compared to all other racial
and ethnic groups (Denny, Holtzman, Goins, & Croft, 2005; Goins, Moss, Buchwald, &
Guralnik, 2007; Moss, Schell, & Goins, 2006). Limited evidence suggests that
prevalence of depressive symptoms among older American Indians is also higher
compared to their same-aged racial and ethnic counterparts (Curyto et al., 1998; John,
Kerby, & Hennessy, 2003). Because the population of older American Indians is
expected to increase dramatically over the next several decades (Vincent & Velkoff,
2010), understanding physical and mental health and physical disability trends, their
relationship to each other, and their risk factors specific to this segment of the population
is essential for guiding public health programming and policy.
The Disablement Process Model
Drawing upon prior conceptual models of disability (Nagi, 1969; World Health
Organization, 1980), the Disablement Process Model emerged as a theoretical framework
to better understand mechanistic pathways to physical disability (Verbrugge & Jette,
1994). This model suggests that physical disability onset is the combined or independent
effect of risks imposed by chronic conditions, as well as demographic, environmental,
and psychosocial factors. Because evidence shows that prevalence of depressive
symptoms is much higher among adults living with chronic medical conditions (Jones,
4
Marcantonio, & Rabinowitz, 2003; Lyness et al., 2002; Lyness et al., 2007; Teresi,
Abrams, Holmes, Ramirez, & Eimicke, 2001), researchers have increasingly examined
the association and effect of comorbid depression among older adults with the onset and
progression of physical disability.
Evidence of the Relationship between Chronic Conditions, Depression, and Disability
Cross-sectional studies have demonstrated positive correlations of depressive
symptoms with physical disability in older adults (da Silva, Scazufca, & Menezes, 2013;
Hatfield, Hirsch, & Lyness, 2013; Jeste et al., 2013; Kiosses, Klimstra, Murphy, &
Alexopoulos, 2001). A number of longitudinal studies, many population-based, have
provided evidence that late life depression is one causal factor for increased risk of
physical disability onset (Barry, Allore, Bruce, & Gill, 2009; Bosworth, Hays, George, &
Steffens, 2002; Braungart, 2005; Cronin-Stubbs et al., 2000; Penninx, Leveille, Ferrucci,
Van Eijk, & Guralnik, 1999; Reynolds, Haley, & Kozlenko, 2008; van Gool et al., 2005).
Furthermore, evidence suggests that among those undergoing post-stroke rehabilitation,
depression increases the odds of long-term stroke-induced physical disability by 2.5 times
compared to those without depression (Pohjasvaara, Vataja, Leppävuori, Kaste, &
Erkinjuntti, 2001).
Two studies have demonstrated a bi-directional relationship, or a reciprocal effect,
of depressive symptoms and physical disability among older adults (Chen et al., 2012;
Ormel, Rijsdijk, Sullivan, Van Sonderen, & Kempen, 2002). Specifically, structural
equation models demonstrated a strong, more immediate effect of physical disability on
depression and a weaker one-year lagged effect of depression on physical disability
among adults aged ≥57 years (Ormel et al., 2002). This finding was also supported in
5
another cohort of older adults aged ≥65 years where both physical disability and
depressive symptoms were significant predictors of each other, with physical disability
more so than depressive symptoms (Chen et al., 2012). Results from these studies
support the idea of a reciprocal relationship between depression and physical disability
(Ormel & Von Korff, 2000).
The Behavioral Model
The Behavioral Model of Health Services Use was developed and refined over the
last half-century to better understand and predict the use of a range of health services
(Andersen, 2008; Andersen & Davidson, 2007). According to this model, three types of
factors are considered core predictors of service use, including (1) predisposing
characteristics, (2) enabling resources, and (3) need. These factors exist and operate at
the individual and aggregate level, directly and indirectly influence and are influenced by
personal health practices, process of care, current health service use, and health
outcomes. Within this model, chronic medical conditions, depressive symptoms, and
physical disability can be viewed as need factors at the individual level, whereas the
prevalence of each of these conditions in a population may be viewed as need factors at
the aggregate level (also cited as contextual indicators; Andersen & Davidson, 2007).
Relationship between Comorbid Depressive Symptoms and Medical and Long-Term Care
Service Use and Need
Older adults with comorbid depression are more likely to use long-term care and
ambulatory services compared to those without accompanying depressive symptoms. For
example, after controlling for disease severity, comorbid depression is associated with a
nearly 50% increase in medical care costs (Katon, 2003). Also, depressive symptoms in
6
older adults is associated with higher levels of informal caregiving use even after
adjusting for other existing major chronic conditions (Langa et al., 2004). Adjusting for
physical health impairment, one study found that compared to older adults without
depression, those with mild and severe depression had significantly more service use and
need, home help, and hospitalizations (Badger, 2007). After controlling for comorbidity,
older adult patients with depression were significantly more likely to have increased use
of outpatient services (Luber et al., 2001). Such evidence of greater service need among
adults with depression is supported by related research showing higher incidence of
physical disability and disease complications among those with comorbid depression (Lin
et al., 2010).
Study Rationale
Evidence suggests that, compared to their racial and ethnic counterparts, older
American Indians have greater prevalence of depressive symptoms (Curyto et al., 1998;
John et al., 2003). Yet, no recent studies have been identified that have examined the
prevalence and associated risk factors among older American Indians. In addition, no
identified studies have examined the association of depressive symptoms with chronic
conditions and physical disability in this population. Cultural factors distinct to
American Indians (Loftin, 1983; Moss, 2005) can have a differential impact on the
relationship between depressive symptoms and physical disability and other studies
suggest racial and ethnic differences in regards to these two conditions (Dunlop, Song,
Lyons, Manheim, & Chang, 2003; Russell & Taylor, 2009). Therefore, manuscript 1
examines the prevalence, risk factors, and association of depressive symptoms with
physical disability among a sample of older American Indians.
7
Data suggest that older adults with depressive symptoms have substantial
increased use of ambulatory and long-term care services and greater need for care
compared to their counterparts without depressive symptoms (Badger, 2007; Katon,
2003; Langa et al., 2004). No identified studies have specifically examined these
associations among older American Indians. Others have posited that, culturally,
American Indians are more reluctant to report needing assistance than their racial and
ethnic peers (Loftin, 1983; Moss, 2005). In addition, due to geographic isolation and
economic factors, many older American Indians may face greater challenges to accessing
care compared to their racial and ethnic peers (Centers for Disease Control and
Prevention, 2011). Therefore, manuscript 2 examines differences in informal and formal
long-term care use among older American Indians with physical disabilities and different
levels of depressive symptoms.
Specific Aims
The overall objective of this dissertation is to understand the association of
comorbid depression on physical disability and long-term care use among older
American Indians. This will be accomplished with secondary data analyses of the cross-
sectional data from the Native Elder Care Study. The following specific aims were
examined:
1. Determine the prevalence of and identify risk factors for depressive symptoms
among older American Indians. Based on prior research demonstrating correlated
risk factors of depressive symptoms, it was hypothesized that the presence of greater
depressive symptoms in older American Indian adults will be significantly associated
with greater chronic pain, greater physical disability, greater number of chronic
8
conditions, greater prescription medication use, smoking, physical inactivity, fewer
hours of sleep, lower education, lower social support, lower personal mastery, lower
self-efficacy, being unmarried, living alone, and female sex.
2. Examine the association between the number of chronic conditions and physical
disability and determine if it is modified or mediated by the presence of greater
depressive symptoms. Based on the conceptualization of disablement, as illustrated
by the Disablement Process Model (Verbrugge & Jette, 1994), it was hypothesized
that greater depressive symptoms in older American Indians living with chronic
medical conditions will mediate its effect on physical disability, and increase the
probability of physical disability.
3. Compare informal and formal long-term care use among those with physical
disabilities by levels of depressive symptoms. Based on the Behavioral Model and
prior studies, it was hypothesized that older American Indian adults living with
physical disabilities and higher levels of depressive symptoms will use more hours of
long-term care services compared to those without or with lower depressive
symptoms.
9
CHAPTER 2. FIRST MANUSCRIPT
Prevalence and association of depressive symptoms with physical
disability among older American Indians
Marc B. Schure1
R. Turner Goins1
1School of Social and Behavioral Health Sciences, College of Public Health and Human
Sciences, Oregon State University
10
Abstract
Older American Indians disproportionately suffer from physical and mental health
and have greater physical disability compared to their racial and ethnic counterparts.
This study uses cross-sectional data from the Native Elder Care Study. Data were
collected on physical disability, health conditions, health behaviors, and psychosocial
resources. The purpose of this study was to examine the prevalence of depressive
symptoms and its unadjusted and adjusted risk factors among American Indians aged ≥55
years. Furthermore, we examined the role of peripheral mediating factors, such as
depressive symptoms, as delineated in the Disablement Process Model. Overall, the
prevalence of clinically significant depressive symptoms in the sample was 13%. Results
supported the mediating role of several peripheral variables, including depressive
symptoms, physical activity, chronic pain, and personal mastery. Findings support the
potential positive effect of behavioral and psychosocial interventions on both depression
and physical disability outcomes.
Keywords: depressive symptoms, physical disability, American Indians, older adults
Acknowledgments
We would like to thank the tribe and its study participants for their role in making this
study possible. Furthermore, the analytic support from Drs. Adam Branscum and Alan
Acock has been essential to the completion of this work. This study was funded in part
from the National Institute of Aging (Funding # AG022336) and from Oregon State
University’s College of Public Health and Human Sciences.
11
“That's the thing about depression: A human being can survive almost anything, as long
as she sees the end in sight. But depression is so insidious, and it compounds daily, that
it's impossible to ever see the end. The fog is like a cage without a key.”
(Elizabeth Wurtzel—From Prozac Nation)
INTRODUCTION
Trends in depression and physical disability prevalence are likely to have a large
impact on prospective care needs of older adults. By 2020, depression is predicted to
become the second leading contributor, following heart disease, to the global burden of
disease and physical disability (World Health Organization, 2012). The prevalence of
clinically significant depressive symptoms ranges from 8-25% among U.S. older adults
(Blazer, 2003). Prevalence of depressive symptoms is found to be even higher (16-35%)
among older primary care patients and institutionalized older adults (Jones et al., 2003;
Lyness et al., 2002; Teresi et al., 2001).
Data for older U.S. adults indicate that the number and proportion of the younger
older adults (aged 60 to 69 years) with physical disabilities is dramatically increasing
(Seeman et al., 2010). Longitudinal comparisons of the 1988-1994 and 1999-2004
National Health and Nutrition Examination Survey data show that this cohort of adults
had 40-70% increases of all types of physical disability over the course of a decade, with
accompanying increases in body mass index and chronic disease prevalence (Seeman et
al., 2010). Older adults are more likely to experience physical disability. The 2005
Survey of Income and Program Participation data show that physical disability
12
prevalence doubles from middle-age to older age, with nearly 52% of adults aged ≥65
years having a physical disability (Brault et al., 2009).
Irrespective of prevalence, the number of older adults with chronic diseases,
depressive symptoms, and physical disability is expected to dramatically increase over
the coming decades as a direct result of our aging population. The number of U.S. adults
aged ≥65 years is projected to more than double from 40.2 million in 2010 to 88.5 million
in 2050 (Vincent & Velkoff, 2010). Increased age is associated with increased
prevalence of morbidities and physical disabilities (Berlau et al., 2009; Ukraintseva &
Yashin, 2001). Over the next several decades, the prevalence of severely disabling
chronic conditions, such as Type 2 diabetes, arthritis, and Alzheimer’s disease is
predicted to substantially increase among older adults (Centers for Disease Control and
Prevention, 2003; Hebert et al., 2003; Narayan, Boyle, Geiss, Saaddine, & Thompson,
2006). Furthermore, these chronic conditions are significantly associated with later life
depression (Alexopoulos, 2005; Chapman et al., 2005).
Substantial evidence supports the negative impact of comorbid depression on
physical disability among older adults. Longitudinal studies have demonstrated that late
life depression increases the risk for physical disability onset (Barry et al., 2009;
Braungart, 2005; Reynolds et al., 2008). Two longitudinal studies demonstrated a
reciprocal effect between physical disability and depression, with a much stronger
immediate effect on the latter (Chen et al., 2012; Ormel et al., 2002). Together, these
studies show strong positive correlations between depressive symptoms and physical
disability and support a reciprocal relationship.
13
The Disablement Process Model (Verbrugge & Jette, 1994) offers a framework
for conceptualizing the comorbid depression-disability relationship. Figure 2.1 shows the
main pathway by which chronic conditions may (or may not) lead to physical disability.
Within the main pathway, impairments and functional limitations are mediating
conditions that eventually result in physical disability. Certain un-modifiable risk factors,
such as age and sex, may also contribute to the development of any of the main pathway
variables. Environmental and psychosocial factors may also buffer or facilitate the
disablement process. As the Disablement Process has been modeled, its suggests that
depressive symptoms moderate the successive impact of each main pathway variable.
However, its contribution as a moderator is not clear and preliminary evidence suggests it
may actually mediate the relationship between chronic conditions and physical disability
(Braungart, 2005). Determining mediating and moderating effects is important to
verifying theory and informing practice (Baron & Kenny, 1986).
--Insert Figure 2.1 about here--
As with other racial and ethnic groups, the number of older American Indians is
projected to substantially increase over the next several decades (Vincent & Velkoff,
2010). Compared to their racial and ethnic counterparts, older American Indians have
some of the highest rates of physical disability (Denny et al., 2005; Goins et al., 2007;
Moss et al., 2006). Similarly, limited evidence exists showing higher prevalence of
depressive symptoms among this racial group compared their racial and ethnic peers
(Curyto et al., 1998; John et al., 2003). No recent studies have been identified that have
examined the prevalence and associated risk factors of depressive symptoms among older
American Indians. Furthermore, none have specifically examined the association of
14
chronic conditions, adjusted for depressive symptoms, with physical disability in this
racial group. Therefore, the purpose of this study was to: (1) determine the prevalence of
depressive symptoms, (2) identify significant risk factors for depressive symptoms, (3)
examine the association of chronic conditions, adjusted for depressive symptoms, with
physical disability in a sample of older American Indian adults, and, (4) determine if
depressive symptoms moderates or mediates the association.
METHODS
Study Design and Data Collection
Data for this study originate from the Native Elder Care Study, a cross-sectional
study of community-dwelling older adult members of a federally-recognized American
Indian tribe located in the Southeast region of the U.S. (Goins, Garroutte, Leading Fox,
Geiger, & Manson, 2011). Using a tribal participatory research approach, study
investigators collaborated with tribal members to examine social and health care needs
for older members of the tribe residing in the tribal service area (Goins et al., 2011). Data
were collected from 2006 to 2008 using in-person interviewer-administered surveys and
included information about physical disability, mental and physical health, personal
assistance needs, health care use, and psychosocial resources. The tribe’s institutional
review board, tribal council, tribal elder council, and West Virginia University
institutional review board approved the project. All study participants provided informed
consent and received a $20 gift card for completing the interview. The Oregon State
University institutional review board approved the secondary data analyses for this study.
Sample
15
Eligibility criteria for study inclusion included being an enrolled tribal member,
aged ≥55 years, a resident in the tribal service area, non-institutionalized, and having the
capacity to demonstrate adequate cognitive ability. The lower older age threshold of 55
years was used (compared to 65 years) as evidence suggests more rapid declines in health
status and shorter life expectancy among American Indians compared to other racial and
ethnic groups (Hayward & Heron, 1999; Indian Health Service, 2013). Based on age and
residential location, tribal enrollment records showed 1,430 persons as potentially eligible
for study enrollment. From this generated list, members were randomly selected for
study recruitment from three age groups: 55-64, 65-74, and ≥75 years. Recruitment
methods included calling and visiting potential participants’ homes. From this random
sample, 47 could not be located, 78 declined participation, and 50 were deemed to be
ineligible for study inclusion. The final sample included 505 participants.
Measures
Depressive Symptoms
The Centers for Epidemiologic Studies—Depression (CES-D) Scale was used to
measure depressive symptoms (Radloff, 1977). This scale has been widely used among a
number of population-based studies, and the scale’s validity and reliability has been
confirmed among older adults and across different racial groups (Mui, Burnette, & Chen,
2002) and has been validated with older American Indians (Chapleski, Lamphere,
Kaczynski, Lichtenberg, & Dwyer, 1997). The full version of the CES-D scale includes
20 items comprised of four factors: depressed affect, positive affect, somatic symptoms,
and perceptions regarding interpersonal relationships (Radloff, 1977). Participants were
asked how often they felt each symptom in the past week, with sample items including:
16
(1) I felt fearful, (2) I enjoyed life, and (3) people were unfriendly; and to respond on a
scale of 0 to 3 (0 = rarely or none of the time, 1 = some or a little of the time, 2 =
occasionally or a moderate amount of time, 3 = most or all of the time). Positive affect
items were reverse coded. Therefore, the total sum score ranges from 0 to 60 comprising
both the count and frequency of experiencing each of the CES-D items. The internal
consistency of the scale for the study sample was high (α = 0.89). This tool has been
used to screen for depression with a commonly accepted cut-off score of ≥16 indicating
clinically significant depressive symptoms (Radloff, 1977; Weissman, Sholomskas,
Pottenger, Prusoff, & Locke, 1977). As a more stringent approach to classifying
subsyndromal (i.e., depressive symptoms not quite meeting diagnosis for major
depression) depression among older adults, others have used a tiered (low: 0-9, moderate:
10-19, and high: ≥20) method to scoring the CES-D (Barry et al., 2009).
Physical Disability
We defined physical disability as having difficulty with any of eight activities of
daily living (ADLs) and eight instrumental activities of daily living (IADLs) (Fillenbaum,
1988; Lawton & Brody, 1969). Difficulty with any ADL infers disability with any of the
following activities: bathing/showering, dressing, eating, transferring, walking, toileting,
grooming, and getting outside. IADL difficulty infers disability with the following
activities: using the telephone, light housework, heavy housework, preparing meals,
shopping, managing money, managing medications, and transportation. Participants
were asked how much difficulty they have in doing each ADL and IADL activity with a
response option of 1 to 5 (1 = no difficulty, 2 = some difficulty, 3 = a lot of difficulty, 4 =
unable/cannot do, 5 = do not do). The latter response item includes a follow-up question
17
on whether it is because of a health or physical problem. If participants indicated no
difficulty performing an activity or indicated they do not do it but stated it was not
because of a health or physical problem, they were then deemed not to have a disability
for that particular activity (coded 0). If participants indicated either they had some
difficulty, a lot of difficulty, unable/cannot do, or do not do because of a health or
physical problem, then they were deemed to have a disability for that particular activity
(coded 1). Thus, the total sum score across all ADLs and IADLs ranged from 0 to 16.
Health Conditions
Chronic Conditions. This measure included the number of 12 common chronic
medical conditions: heart disease, stroke, angina, congestive heart failure, heart attack,
lung disease, Parkinson’s disease, cancer, diabetes, high blood pressure, kidney disease,
and liver disease. Respondents were asked if, since age 55, a doctor had told them they
had one of the listed 12 conditions with “yes” and “no” response options. Therefore, the
total sum score range of this measure is 0 to 12.
Chronic Pain. Chronic pain was measured with an adapted self-report scale
rating the intensity of chronic pain and its impact on daily physical and social functioning
(Von Korff, Ormel, Keefe, & Dworkin, 1992). Three items measuring pain intensity
have a response scale from 0 to 10, with higher scores indicative of higher chronic pain
intensity. Two items measuring pain-related disability have a response scale from 0 to
10, with higher scores reflective of how much their chronic pain contributes to greater
disability. Thus, the three chronic pain intensity items and the two disability items were
averaged separately. The last item asks…“About how many days in the last 6 months,
have you been kept from your usual activities because of physical pain?” This item was
18
scored on a scale from 0 to 180 days. An overall composite scale was then generated
based on the Guttman scaling method, whereas a chronic pain classification, with a range
from a grade of 0 to 4, was developed via the following scheme: 1) The mean chronic
pain intensity scale was coded as low (< 5) and high (5-10) intensity; 2) Disability days
were coded as 0 points = 0-6 days, 1 point = 7-14 days, 2 points = 15-30 days, and 3
points = ≥31 days; and, 3) Disability score values generated 0 points = 0-2, 1 point = 3-4,
2 points = 4-5, and 3 points = 7-10. Disability days and disability scoring was then
combined into an overall count variable with a range from 0 to 6. The overall composite
scale was then coded in the following fashion: Grade 0 indicating no chronic pain (e.g.,
all items = 0), Grade 1 = low disability-low intensity chronic pain; Grade 2 = low
disability-high intensity chronic pain, Grade 3 = high disability-moderately limiting (3-4
disability points) regardless of chronic pain intensity, and Grade 4 = high disability-
severely limiting (5-6 disability points) regardless of chronic pain intensity.
Health Behaviors
Selected health behavior questions originate from the Behavioral Risk Factor
Surveillance System questionnaire and have been previously assessed for reliability
(Nelson, Holtzman, Bolen, Stanwyck, & Mack, 2001). Participants were asked whether
they currently drink alcohol and whether they currently smoke, both with a “yes” and
“no” response option. They were also asked whether or not they participated in any
physical activities in the past month with a “yes” and “no” response option. Participants
were asked to provide the number of hour/minutes that they, on average, sleep per night.
Last, they were asked how many prescription medications they regularly took during the
last three months.
19
Psychosocial Factors
Social Support. Social support was measured with the Medical Outcomes Study
Social Support Survey (Sherbourne & Stewart, 1991). This is a 19-item survey with a 5-
point response selection (0 = none of the time, 1 = a little of the time, 2 = some of the
time, 3 = most of the time, 4 = all of the time) and a sum score range of 0 to 76.
Participants were asked how often each of the following types of support is available
when needed. Sample items include: (1) someone you can count on to listen to you when
you need to talk, (2) someone who understands your problems, and (3) someone to do
something with for relaxation. The internal consistency of this scale for the study sample
was very high (α = 0.96).
Self-Efficacy. A general self-efficacy scale was used to assess participants’
perceived self-efficacy (Jerusalem & Schwarzer, 1992). This is a 9-item response scale
with a 4-point response selection (0 = not at all true, 1 = hardly true, 2 = moderately true,
3 = exactly true) and a sum score range of 0 to 27. Sample items include: (1) I can solve
most problems if I try hard enough and (2) If I am in trouble, I can usually think of a
solution. The internal consistency of this scale for the study sample was high (α = 0.90).
Personal Mastery. A personal mastery scale was used to assess participants’
perceived mastery over life events (Pearlin & Schooler, 1978). This scale consists of
seven items with a 4-point response selection (0 = strongly disagree, 1 = disagree
somewhat, 2 = agree somewhat, 3 = strongly agree) and a sum score range from 0 to 21.
Sample items include: (1) I have little control over the things that happen to me, and (2)
What happens to me in the future mostly depends on me. Two positive items were
20
reverse coded. The internal consistency of this scale for the study sample was
moderately high (α = 0.79).
Demographics
Demographic characteristics included age, sex, marital status (married/live partner
versus unmarried), educational attainment (<12 years versus ≥12 years), and living
arrangements (living alone versus living with others).
Statistical Analyses
We first examined the prevalence of low, moderate, and high depressive
symptoms by specific sample characteristics using a chi-squared test. Then, we used
forward stepwise regression, adding covariates to test for significant associations of
selected risk factors for depressive symptoms. Variables were removed from the models
when found not to be significant with depressive symptoms after adjustment for other
covariates. We treated depressive symptoms as a count variable in the regression models.
Because depressive symptoms had a high number of zero counts, we selected zero-
inflated negative binomial regression models using physical disability to predict
excessive zero counts. We excluded 14 cases that did not provide any response to the
depressive symptoms scale. Comparisons of the 14 missing cases with the rest of the
sample showed significant differences in age and educational attainment. Those with
missing data on depressive symptoms were more likely to be older (p <0.001) and have
<12 years of education (p <0.001).
To test whether comorbid depressive symptoms moderates the association
between chronic conditions on physical disability, we used a moderation model for
physical disability proposed by Wang and colleagues (2006). First, we used chi-squared
21
tests to examine unadjusted associations of physical disability with demographic, health,
health behavior, and psychosocial variables. Second, we ran a bivariate regression model
to test the unadjusted association of chronic conditions with physical disability. Then, we
created an interaction term between depressive symptoms and chronic conditions to test
the moderation of depressive symptoms on physical disability, running a model that
included the two main independent variables and the interaction term, treating physical
disability as the dependent variable. Last, we added other covariates to test whether the
moderation effect lasted. Physical disability was treated as a count variable in the
regression analyses. Like depressive symptoms, physical disability had a high number of
zero counts, and therefore, we selected the zero-inflated negative binomial regression
model using age to predict excessive zero counts. We used post-estimation commands to
determine the best model fit.
--Insert Figure 2.2 about here--
We used StataCorp statistical analysis software’s (Stata Statistical Software,
2007) structural equation modeling tools to create pathway models for further examining
the relationship of the independent variables on physical disability, and to test depressive
symptoms as a mediator (Acock, 2013). First, we tested for mediation between variables,
including depressive symptoms, found to be significantly associated in the regression
models. Figure 2.2 shows a simple model (Model A), the bivariate effect (c) of the
independent variable (X) on the dependent variable (Y), and a mediation model (Model
B) in which the mediator (M) mediates the effect of X on Y. The mediation model
simultaneously estimates the direct effect (c’) of X on Y and the indirect effect of X on
the mediator (a) and the mediator on Y (b). If the direct effect (c’) in Model B is
22
significant and smaller than the direct effect (c) in Model A, then the mediator is said to
partially mediate the effect of X on Y. If the direct effect (c’) in Model B is small and
insignificant, then the mediator is said to fully mediate the effect of X on Y (Acock,
2013).
--Insert Figure 2.3 about here--
We used the multiple imputation, then deletion method for imputing missing data
and dropping any cases for which the dependent variable was completely missing (von
Hippel, 2007). Specifically, the multiple imputation by chained equations method was
used to impute any remaining missing data for the independent variables (Royston &
White, 2011). The variance inflation factor was then estimated to test for
multicollinearity among the independent variables, which was not found to be a
substantive issue in the regression models. Data were imputed on 14 cases for depressive
symptom variables, 7 cases for personal mastery, 17 cases for self-efficacy, 9 cases for
social support, 14 cases for prescription medication use, 6 cases for number of sleep
hours, 6 cases for current smoker, 2 cases for current alcohol use, 4 cases for physical
activity, 1 case for chronic conditions, 6 cases for living arrangements, and 2 cases for
educational attainment. For all multivariate analyses and pathway analyses, all
continuous independent variables were standardized. For all multivariate models, we
used sensitivity analyses with depressive symptoms as binary (<16 versus ≥16) and
categorical (0 -9, 10-19, ≥20). All analyses were completed using StataCorp’s statistical
software package version 12.0 (Stata Statistical Software, 2007).
RESULTS
23
Table 2.1 presents the prevalence of depressive symptoms by demographic
characteristics, health conditions, health behaviors, and psychosocial variables (n = 491).
The overall prevalence of clinically significant (≥16) depressive symptoms in this sample
was 13%. Overall, the prevalence of moderate and high depressive symptoms was 17%
and 7%, respectively. Bivariate analyses demonstrated significant unadjusted
associations between higher levels (≥10 symptoms) of depressive symptoms and the
younger old age group, female sex, those unmarried, lower education, greater number of
chronic conditions, greater chronic pain, greater physical disability, current smokers,
physical inactivity, fewer hours of sleep, greater number of prescription medication use,
lower personal mastery, lower self-efficacy, and lower social support.
--Table 2.1 about here—
Table 2.2 shows the unadjusted and adjusted risk factors for depressive
symptoms. Model 1 shows that physical disability had a significant unadjusted
association with depressive symptoms. Adjusting for chronic pain, physical disability
remained significantly associated with depressive symptoms (Model 2). Model 3
supports the addition of physical activity, current smoker, and hours of sleep as
significant behavioral factors associated with depressive symptoms. Three of the other
psychosocial measures were added last to test a best fit model (Models 4 to 6). Social
support, self-efficacy, and personal mastery all showed significant associations with
depressive symptoms, with the latter showing the best model fit (AIC = 2765.2; BIC =
2807.1). However, physical disability subsequently became insignificant with the
addition of personal mastery into the model. Therefore, Model 7 omits physical
disability and subsequently produces the best fitted model (AIC = 2763.2; BIC = 2800.9).
24
According to this model, having higher levels of personal mastery decreases the expected
number of depressive symptoms by a factor of 0.67, and having greater chronic pain
increases the expected number of higher depressive symptoms by 1.19, holding all other
factors constant. Sensitivity analyses supported these findings.
--Table 2.2 about here--
Table 2.3 shows the prevalence of physical disability by demographic
characteristics, health condition, health behaviors, and psychosocial factors. Chi-square
tests show significant bivariate effects between these variables and degree of physical
disability. Specifically, greater number of physical disabilities was significantly
associated with older age, being female, being unmarried, lower education, greater
number of chronic conditions, more severe chronic pain, physical inactivity, fewer hours
of sleep, greater prescription medication use, greater depressive symptoms, lower
personal mastery, lower self-efficacy, and lower social support.
--Table 2.3 about here--
Table 2.4 presents results from the adjusted associations of physical disability
with and without the interaction term of chronic conditions and depressive symptoms.
Model 1 indicates significant associations of chronic conditions, depressive symptoms,
and its interaction term with physical disability. Model 2 subsequently omits the
interaction term and introduces sex and educational attainment, and indicates that
physical disability has significant positive associations with being female and having
lower education. Subsequent models support significant adjusted associations with
chronic pain, physical activity, self-efficacy, and personal mastery. However, as Model 6
indicates, after adjustment for personal mastery, the association of depressive symptoms
25
with physical disability loses statistical significance. According to this model, greater
personal mastery decreases the expected number of ADL & IADL disabilities by a factor
of 0.75, holding other factors constant. Model 7 reintroduces the interaction term and
indicates marginal significance of the interaction between depressive symptoms and
chronic conditions. Model 8 drops the interaction term and depressive symptoms,
producing the best conservative fit model (BIC = 1636.8).
--Table 2.4 about here--
Figure 2.3 presents a theorized recursive (without feedback loops) pathway model
by which significantly associated independent variables relate to the onset of physical
disability. This model treats depressive symptoms as a partial mediating variable,
whereas chronic conditions has a significant direct effect on depressive symptoms (β =
0.18, p <0.001) and depressive symptoms’ subsequent marginal effect on physical
disability (β = 0.07, p <0.10). This pathway also shows that personal mastery is a strong
mediator of depressive symptoms and physical disability with depressive symptoms
direct effect on personal mastery (β = -0.37, p <0.001) and personal mastery’s subsequent
direct effect on physical disability (β = -0.15, p <0.001). The three arched lines between
depressive symptoms, physical activity, and chronic pain indicate significant correlations
between each of these three variables’ error terms. The model also indicates significant
mediating effects of chronic pain and physical activity on the effect of chronic conditions
on physical disability. Personal mastery has a significant mediating effect on all other
independent variables in the model and accounts for the greatest amount of variance of
the independent variables (R2 = 0.28).
--Figure 2.3 about here--
26
Table 2.5 presents the standardized direct, indirect, and total effects of all of the
independent variables on physical disability and each other and indicates a good model fit
(CFI = 0.99; TLI = 0.98; RMSEA = 0.04). Initial mediation analyses supported the
selection of mediator variables (results not shown). All of the independent variables’
total effect is statistically significant. The total (direct + indirect) effect of depressive
symptoms on physical disability is 0.13 (p <0.001) with the indirect effect accounting for
46% of the total effect and the direct effect for 54% of the total effect.
--Table 2.5 about here--
DISCUSSION
This study is one of few to test the role of depressive symptoms on the
disablement process among older adults (Braungart, 2005; Braungart Fauth, Zarit,
Malmberg, & Johansson, 2007) and the first known to do so among older American
Indians. We first examined the prevalence and risk factors for depressive symptoms in
our study sample. The overall prevalence of clinically significant depressive symptoms
was within the range as found in other studies of older adults (Blazer, 2003; Horowitz,
Reinhardt, & Kennedy, 2005) but less than found in other samples of older American
Indians (Curyto et al., 1998; John et al., 2003). For example, Horowitz and colleagues
(2005) reported that among adults aged ≥65 year, seven percent had major depression and
nearly 27% had subsyndromal depressive symptoms. However, in our study, we were
unable to determine whether any of the participants had major depressive disorders.
We identified a number of unadjusted and adjusted risk factors for depressive
symptoms. Our results showed significantly higher prevalence of depressive symptoms
among those of female sex, lower educational attainment, and unmarried status, thus
27
supporting previous studies among older adults (Ariyo et al., 2000; Bisschop, Kriegsman,
Beekman, & Deeg, 2004; Blazer, 2002; Curyto et al., 1998; Djernes, 2006; Hybels,
Blazer, & Pieper, 2001). However, after adjustment for the presence of chronic
conditions, health behaviors, and other psychosocial measures, these were no longer
significantly associated with depressive symptoms. Our findings that the younger old
were more likely to experience depressive symptoms than the older old are supported by
previous evidence (Blazer, Burchett, & George, 1991). Greater number of chronic
conditions, chronic pain, and physical disability as significant correlates of increased
depressive symptoms has been consistently supported by other studies of older adults
(Alexopoulos, 2005; Ariyo et al., 2000; Braam et al., 2005; Chapman et al., 2005; Hybels
et al., 2001; Jang, Haley, Small, & Mortimer, 2002; Jorm et al., 2005). One identified
longitudinal study of older American Indians showed that comorbidity was the best
predictor of increased depressive symptoms (Chapleski, Kaczynski, Gerbi, &
Lichtenberg, 2004). Physical inactivity, smoking, and fewer hours of sleep were found to
be significantly associated with higher depressive symptoms in our study as has been
found in others (Adams, Sanders, & Auth, 2004; Cho et al., 2008; Gazmararian, Baker,
Parker, & Blazer, 2000; Jorm et al., 2005; Kritz-Silverstein, Barrett-Connor, & Corbeau,
2001). As these are modifiable risk factors, health promotion efforts should be focused
on changing key lifestyle habits that impact the onset and reoccurrence of depressive
symptoms among older adults. Indeed, recent studies have shown that physical activity
programs for older adults can have significant impacts on both physical functioning and
depressive symptoms (Hughes et al., 2010; Ip et al., 2013)
28
Consistent with previous findings (Bisschop et al., 2004; Jang et al., 2002; Jang,
Mortimer, Haley, & Graves, 2004), social support, self-efficacy, and personal mastery all
demonstrated strong negative associations with depressive symptoms suggesting that
deficits in each of these leaves older adults more vulnerable to depressive symptoms.
Bisschop and colleagues (2004) found that psychosocial factors, such as self-efficacy and
personal mastery, play a mediating role between only certain chronic conditions and
depressive symptoms. For example, they found that personal mastery had a buffering
effect of Type 2 diabetes on depressive symptoms but not on other chronic conditions, as
did self-efficacy for cancer. Others have called for psychosocial interventions among
older adults as a means for buffering the onset of depressive symptoms (Blazer, 2002;
Seeman, Berkman, Lusignolo, & Albert, 2001). This framework suggests that older
individuals can learn skills to assist in coping with challenges that often arise in later life.
As some evidence suggests (McAuley, Jerome, Marquez, Elavsky, & Blissmer, 2003;
Raji, Ostir, Markides, & Goodwin, 2002; Seeman et al., 2001), psychosocial
interventions hold the promise to not only improve mood, but other dimensions in one’s
life, such as cognitive, social, and physical functioning.
The contribution of depressive symptoms to the disablement process has been
modeled in various ways and therefore has not been clear as to whether it moderates or
mediates the effect of pathology on physical disability. Our results supported depressive
symptoms as a partial mediator, contributing to greater chances of having a physical
disability. Interestingly, our models supported the inclusion of personal mastery and self-
efficacy as other strong mediating factors, thus supporting the latter argument for
implementing psychosocial interventions to improve not only mood, but physical
29
functioning. These findings validate results from other studies exploring the mediating
role of psychosocial factors in the disablement process (Braungart, 2005; Braungart Fauth
et al., 2007).
The study findings should be regarded in the context of certain limitations.
Because this study was cross-sectional, causality cannot be assumed. Even when
constructing pathway analysis, the theorized direction of relationships cannot be proven.
Future studies would benefit by using a longitudinal approach to disentangling these
relationships and to better understand both the risk factors for depressive symptoms and
its relationship to physical disability. In regards to our mediation and moderation
analyses of the disablement process, it should be noted that we only operationalized one
type of pathology—the number of chronic conditions. It is plausible that if
operationalized in different ways or another measure of pathology (i.e., injury) was
selected, different results might be found. Furthermore, it is important to clarify that our
disablement models did not include mediator variables (i.e., impairment and physical
functioning measures) from the main pathway, and emphasized the peripheral variables.
Our data were all self-reported and therefore subject to recall bias. Similarly, those 14
cases for which complete depressive symptom information was missing were
significantly older and had less education compared to those for whom we had data.
Finally, the results from this study are limited in its generalizability to older American
Indian adults of a single tribe, and therefore, cannot be inferred to be representative of
other older adult populations or other American Indian tribes.
In sum, the present research showed the prevalence of depressive symptoms in
our sample of older American Indians to be consistent with other studies of community-
30
dwelling older adults, but substantially less than found in other studies examining older
American Indians. Findings also support depressive symptoms as a partial mediating
factor in the disablement process, contributing to the odds of being physically disabled.
This evidence supports the plausibility and relevancy of disseminating psychosocial and
behavioral interventions to prevent both depression and physical disability among older
adults.
31
Table 2.1. Prevalence of depressive symptoms by sample characteristics (n=491)
Depressive Symptoms
Total Sample
Low (0-9)
n=372 (76%)
Moderate (10-19)
n=82 (17%)
High (20)
n=37 (7%)
n (%) n (%) p
value1
Demographics
Age
0.023
55-64 166 (34%) 114 (31%) 33 (40%) 19 (51%)
65-74 184 (37%) 152 (41%) 22 (27%) 10 (27%)
>75 141 (29%) 106 (28%) 27 (33%) 8 (22%)
Sex
0.023
Female 316 (64%) 227 (61%) 62 (76%) 45 (69%)
Male 175 (36%) 145 (39%) 20 (24%) 20 (31%)
Marital Status
0.050
Married/Life Partner 225 (54%) 182 (49%) 29 (35%) 14 (38%)
Unmarried 266 (46%) 190 (51%) 53 (65%) 23 (62%)
Educational Attainment
0.005
< 12 years 182 (63%) 123 (33%) 41 (50%) 18 (49%)
> 12 years 308 (63%) 248 (67%) 41 (50%) 19 (51%)
Living Arrangements
Lives Alone 352 (72%) 270 (73%) 56 (70%) 26 (72%) 0.846
Lives With Others 133 27%) 99 (27%) 24 (30%) 10 (28%)
Health Conditions
No. of Chronic
Conditions
0.003
0 to 1 236 (48%) 194 (52%) 26 (32%) 16 (43%)
2 to 3 192 (39%) 141 (38%) 38 (47%) 13 (35%)
4 or more 62 (13%) 37 (10%) 17 (21%) 8 (22%)
Chronic Pain
<0.001
Grade 0 (Pain free) 107 (22%) 95 (28%) 8 (13%) 4 (14%)
Grade 1 175 (36%) 147 (44%) 19 (30%) 9 (32%)
Grade 2 51 (10%) 44 (13%) 7 (11%) 0 (0%)
Grade 3 48 (10%) 27 (8%) 15 (23%) 6 (21%)
Grade 4 46 (11%) 22 (7%) 15 (23%) 9 (32%)
Physical Disability
<0.001
0 (None) 242 (49%) 212 (57%) 22 (27%) 8 (22%)
1-2 (Mild) 109 (22%) 87 (23%) 15 (18%) 7 (19%)
3-4 (Moderate) 52 (11%) 33 (9%) 13 (16%) 6 (16%)
5 or more (Severe) 88 (18%) 40 (11%) 32 (39%) 16 (43%)
32
Table 2.1 continued. Prevalence of depressive symptoms by sample characteristics (n=491)
Health Behaviors
Drinks Alcohol
0.828
Yes 53 (11%) 40 (11%) 8 (10%) 5 (14%)
No 438 (89%) 332 (89%) 74 (90%) 32 (86%)
Current Smoker
0.004
Yes 104 (21%) 68 (18%) 21 (26%) 15 (41%)
No 383 (78%) 301 (82%) 60 (74%) 22 (59%)
Physically Active
<0.001
Yes 306 (62%) 255 (69%) 40 (49%) 11 (31%)
No 182 (37%) 115 (31%) 42 (51%) 25 (69%)
No. of Hours of Sleep
0.001
<6 Hours 79 (16%) 48 (13%) 19 (24%) 12 (32%)
≥6 Hours 407 (83%) 322 (87%) 60 (76%) 25 (68%)
No. of Prescription
Medications
<0.001
None 62 (13%) 54 (15%) 3 (4%) 5 (14%)
1 to 2 84 (17%) 76 (21%) 6 (8%) 2 (5%)
3 to 4 95 (19%) 74 (20%) 14 (18%) 7 (19%)
5 or more 237 (48%) 159 (44%) 55 (71%) 23 (62%)
Psychosocial Factors
Social Support
<0.001
Low 183 (37%) 116 (31%) 46 (56%) 21 (57%)
Medium 143 (29%) 116 (31%) 18 (22%) 9 (24%)
High 165 (34%) 140 (38%) 18 (22%) 7 (19%)
Self-Efficacy
<0.001
Low 178 (36%) 107 (29%) 49 (60%) 22 (61%)
Medium 147 (30%) 121 (33%) 18 (22%) 8 (22%)
High 158 (32%) 138 (38%) 14 (17%) 6 (17%)
Personal Mastery
<0.001
Low 170 (35%) 94 (25%) 48 (59%) 28 (76%)
Medium 145 (30%) 116 (31%) 21 (26%) 8 (22%)
High 174 (35%) 160 (43%) 13 (16%) 1 (3%)
Note. Number of depressive symptoms measured by the Center for Epidemiologic Studies
Depression Scale. 14 cases omitted from analyses due to missing data.
1Based on Chi-square test
33
Table 2.2. Adjusted associated risk factors of depressive symptoms (n = 491)
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
β SE β SE β SE β SE β SE β SE β SE
Health Conditions
Physical Disability 0.31*** 0.046 0.19** 0.062 0.16** 0.059 0.15* 0.060 0.12* 0.057 0.03 0.059 --- ---
Chronic Pain --- --- 0.14** 0.052 0.11* 0.049 0.11* 0.050 0.12* 0.047 0.15** 0.043 0.17*** 0.035
Health Behaviors
Current Smoker --- --- --- --- 0.28** 0.106 0.21
‡ 0.110 0.32** 0.106 0.29** 0.101 0.28** 0.101
Physically Active --- --- --- --- -0.33** 0.112 -0.30* 0.118 -0.22* 0.107 -0.17‡ 0.098 -0.17
‡ 0.097
Sleep --- --- --- --- -0.09* 0.043 -0.09* 0.044 -0.16*** 0.045 -0.09* 0.040 -0.09* 0.040
Psychosocial Factors
Social Support --- --- --- --- --- --- -0.14** 0.047 --- --- --- --- --- ---
Self-Efficacy --- --- --- --- --- --- --- --- -0.29*** 0.050 --- --- --- ---
Personal Mastery --- --- --- --- --- --- --- --- --- --- -0.39*** 0.049 -0.40*** 0.047
AIC1 Value 2856.3 2843.1 2826.8 2819.8 2792.2 2765.2 2763.2
BIC2 Value 2877.4 2868.2 2864.6 2861.7 2834.2 2807.1 2800.9
Note: ‡p < 10, *p < .05, **p < .01, ***p < .001; Physical disability used to predict the presence of zero counts in dependent variable. 1Akaike Information
Criterion, 2Bayesian Information Criterion. Best model fit in bold. Physical disability, hours of sleep, social support, self-efficacy, and personal mastery all
treated as continuous.
34
Table 2.3. Prevalence of physical disability by sample characteristics (N=505)
Degree of Disability
Total Sample
None
n = 243
(48%)
Mild
n = 110
(22%)
Moderate
n = 54
(11%)
Severe
n = 98
(19%)
n (%)
n (%) p value1
Demographics
Age
<0.001
55-64 167 (33%)
90 (37%) 32 (29%) 16 (30%) 29 (30%)
65-74 185 (37%)
105 (43%) 38 (35%) 20 (37%) 22 (22%)
>75 153 (30%)
48 (20%) 40 (36%) 18 (33%) 47 (48%)
Sex
0.004
Female 326 (64%)
138 (57%) 75 (68%) 39 (72%) 74 (76%)
Male 179 (36%)
105 (43%) 35 (32%) 15 (28%) 24 (24%)
Marital Status
<0.001
Married/Life Partner 228 (45%)
132 (54%) 42 (38%) 24 (44%) 30 (31%)
Unmarried 277 (55%)
111 (46%) 68 (62%) 30 (56%) 68 (69%)
Educational Attainment
<0.001
< 12 years 193 (38%)
74 (31%) 37 (34%) 23 (43%) 59 (60%)
> 12 years 310 (61%)
168 (69%) 73 (66%) 30 (57% 39 (40%)
Living Arrangements
0.468
Lives Alone 363 (72%)
180 (75%) 74 (67%) 40 (74%) 69 (72%)
Lives With Others 136 (27%)
59 (25%) 36 (33%) 14 (26%) 27 (28%)
35
Table 2.3 continued. Prevalence of physical disability by sample characteristics (N=505)
Health Conditions
No. of Chronic Conditions
<0.001
0 to 1 240 (48%)
144 (59%) 44 (40%) 19 (35%) 33 (34%)
2 to 3 196 (39%)
86 (35%) 56 (51%) 19 (35%) 35 (36%)
4 or more 68 (13%)
13 (5%) 10 (9%) 16 (30%) 29 (29%)
Chronic Pain
<0.001
Grade 0 (Pain free) 107 (21%)
81 (33%) 15 (14%) 7 (25%) 4 (7%)
Grade 1 175 (35%)
122 (50%) 53 (48%) 0 (0%) 0 (0%)
Grade 2 52 (10%)
30 (12%) 22 (20%) 0 (0%) 0 (0%)
Grade 3 48 (10%)
3 (1%) 13 (12%) 14 (50%) 18 (31%)
Grade 4 58 (11%)
7 (3%) 7 (6%) 7 (25%) 37 (63%)
Health Behaviors
Drinks Alcohol
0.458
Yes 53 (11%)
30 (12%) 12 (11%) 4 (7%) 7 (7%)
No 450 (89%)
212 (88%) 98 (89%) 50 (93%) 90 (93%)
Current Smoker
0.891
Yes 104 (21%)
49 (20%) 25 (23%) 12 (23%) 18 (19%)
No 395 (78%)
191 (80%) 85 (77%) 41 (77%) 78 (81%)
Physically Active
<0.001
Yes 312 (62%)
178 (74%) 73 (68%) 26 (48%) 35 (36%)
No 189 (37%)
64 (26%) 35 (32%) 28 (52%) 62 (64%)
No. of Hours of Sleep
0.003
<6 Hours 81 (16%)
32 (13%) 14 (13%) 7 (14%) 28 (29%)
≥6 Hours 418 (83%)
210 (87%) 95 (87%) 44 (86%) 69 (71%)
36
Table 2.3 continued. Prevalence of physical disability by sample characteristics (N=505)
No. of Prescription Medications
<0.001
None 62 (12%)
47 (20%) 10 (9%) 2 (4%) 3 (3%)
1 to 2 85 (17%)
57 (24%) 15 (14%) 6 (12%) 7 (8%)
3 to 4 97 (19%)
49 (20%) 25 (24%) 7 (13%) 16 (17%)
5 or more 247 (49%)
87 (36%) 56 (53%) 37 (71%) 67 (72%)
Psychosocial Factors
Self-Efficacy
<0.001
Low 181 (36%)
63 (26%) 42 (40%) 27 (51%) 49 (54%)
Medium 148 (30%)
72 (30%) 36 (34%) 16 (30%) 24 (26%)
High 159 (32%)
103 (43%) 28 (26%) 10 (19%) 18 (20%)
Personal Mastery
<0.001
Low 178 (35%)
49 (20%) 46 (42%) 25 (47%) 58 (62%)
Medium 146 (29%)
71 (29%) 33 (30%) 18 (34%) 24 (26%)
High 174 (34%)
122 (50%) 30 (28%) 10 (19%) 12 (13%)
Depressive Symptoms
<0.001
Low 372 (74%)
212 (88%) 87 (80%) 33 (63%) 40 (45%)
Medium 82 (16%)
22 (9%) 15 (14%) 13 (25%) 32 (36%)
High 37 (7%)
8 (3%) 7 (6%) 6 (12%) 16 (18%)
Social Support
<0.001
Low 187 (37%)
75 (31%) 42 (39%) 21 (40%) 49 (54%)
Medium 143 (28%)
60 (25%) 40 (37%) 19 (36%) 24 (26%)
High 166 (33%)
108 (44%) 27 (25%) 13 (24%) 18 (20%)
Note. Number of depressive symptoms measured by the Center for Epidemiologic Studies Depression Scale. 16 or greater symptoms =
clinically significant depressive symptomatology; 14 cases omitted from analyses due to missing data. 1Based on Chi-square test
37
Table 2.4. Unadjusted and adjusted association of depressive symptoms with physical disability (n = 491)
Model 11 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
2 Model 8
β SE β SE β SE β SE β SE β SE β SE β SE
Demographics
Sex --- --- 0.41**
0.155 0.37* 0.145 0.34
* 0.144 0.36
* 0.140 0.36
* 0.141 0.38
** 0.141 0.37
** 0.142
Educational Attainment --- --- 0.40** 0.138 0.26* 0.127 Ns. --- --- --- --- --- ---
---
Health Conditions
Chronic Conditions 0.30***
0.064 0.31***
0.061 0.22**
0.069 0.21**
0.065 0.22**
0.067 0.17**
0.062 0.20**
0.063 0.17**
0.062
Chronic Pain --- --- --- --- 0.51***
0.064 0.48***
0.066 0.48***
0.066 0.49***
0.062 0.49***
0.061 0.51*** 0.059
Health Behaviors
Physically Active --- --- --- --- --- --- -0.35* 0.140 -0.34
* 0.136 -0.28
* 0.134 -0.26
* 0.135 -0.30
* 0.135
Psychosocial Measures
Self-Efficacy --- --- --- --- --- --- --- --- -0.17***
0.068 --- --- ---
---
Personal Mastery --- --- --- --- --- --- ---
---
-0.29***
0.068 -0.29***
0.067 -0.33***
0.064
Depressive Symptoms 0.43***
0.072 0.37***
0.074 0.18**
0.059 0.15**
0.058 0.14* 0.058 0.07 0.060 0.09 0.062 --- ---
Interaction Term -0.08* 0.039 --- --- --- --- --- --- --- --- --- --- -0.17
‡ 0.039 --- ---
AIC2 Value 1735.4 1729.5 1624.8 1615.5 1606.9 1600.5 1592.8 1599.0
BIC3 Value 1764.7 1763.1 1662.5 1657.4 1648.9 1642.5 1638.9 1636.8
Note: ‡p < 10, *p < .05, **p < .01, ***p < .001; Age used to predict the presence of zero counts in dependent variable.
1Unadjusted Moderation Model,
2Full
adjusted Moderation Model, 3Akaike Information Criterion,
4Bayesian Information Criterion. Best model fit in bold.
38
Table 2.5. Standardized direct, indirect, and total effects of physical disability pathway (N = 505)
Direct Effect Indirect Effect Total Effect
β (standardized)
Depressive Symptoms
Chronic Conditions Depressive Symptoms 0.18*** --- 0.18***
Physically Active
Chronic Conditions Physical Activity -0.09* --- -0.09*
Chronic Pain
Chronic Conditions Chronic Pain 0.31*** --- 0.32***
Personal Mastery
Depressive Symptoms Personal Mastery -0.37*** --- -0.37***
Physically Active Personal Mastery 0.11** --- 0.11**
Chronic Pain > Personal Mastery -0.11* --- -0.11*
Chronic Conditions Personal Mastery -0.08‡ -0.11*** -0.19***
Age Personal Mastery -0.18***
-0.18***
Disability
Depressive Symptoms Disability 0.07‡ 0.06*** 0.13***
Physically Active Disability -0.11*** -0.02*** -0.13***
Chronic Pain Disability 0.55*** 0.02* 0.57***
Personal Mastery Disability -0.15*** --- -0.15***
Chronic Conditions Disability --- 0.23*** 0.23***
Age Disability 0.15*** 0.03** 0.18***
Note: The significance levels shown are for the unstandardized solution. ‡p < .10, * p < 0.05, ** p < 0.01,
and *** p < 0.001. CFI = 0.99; TLI = 0.98; RMSEA = 0.04.
39
Figure 2.1: The Disablement Process Model
40
Figure 2.2: Conceptual diagram of a mediation model
41
Figure 2.3: Pathway analysis of physical disability
42
CHAPTER 3. SECOND MANUSCRIPT
Informal and formal long-term care use among older
American Indians by levels of depressive symptoms
Marc B. Schure1
R. Turner Goins1
1School of Social and Behavioral Health Sciences, College of Public Health and Human
Sciences, Oregon State University
43
Abstract
Older adults with comorbid depressive symptoms are more likely to use long-term
care and medical services compared to their non-depressed peers. Current evidence
shows higher prevalence of physical and mental health conditions among older American
Indians. The purpose of this study was to examine informal and formal long-term care
use among older American Indians with physical disabilities by level of depressive
symptoms. We used data from the Native Elder Care Study, a cross-sectional study of a
sample (n = 505) of older American Indians aged ≥55 years. Our sample had a high
prevalence of physical disability (52%), and need for long-term care (74%). Results
indicated significant associations of greater informal care use among those with higher
levels of depressive symptoms compared to those without or fewer depressive symptoms.
Older American Indians with physical disabilities and depressive symptoms use more and
would benefit by more accessible informal long-term care services compared to their
counterparts without or fewer depressive symptoms.
Key words: depressive symptoms, physical disability, long-term care, unmet need
Acknowledgments
We would like to thank the tribe and its study participants for their role in making this
study possible. Furthermore, the analytic support from Drs. Adam Branscum and Alan
Acock has been essential to the completion of this work. This study was funded in part
from the National Institute of Aging (Funding # AG022336) and from Oregon State
University’s College of Public Health and Human Sciences.
44
“I feel thin, sort of stretched, like butter scraped over too much bread.”
(J. R. R. Tolkien, Fellowship of the Ring, Bilbo Baggins on the effects of very old age)
INTRODUCTION
According to the World Health Organization, depression will surpass many other
serious diseases as the second leading contributor to the global burden of disease by the
end of this decade (World Health Organization, 2012). Studies among U.S. community-
dwelling older adults show the prevalence of clinically significant depressive symptoms
typically ranges from 8% to 16% (Blazer, 2003). Later life depression is often a
consequence of living with chronic conditions, physical disabilities, and their impact on
quality of life (Alexopoulos, 2005, 2006; Brault et al., 2009; Chapman et al., 2005; Gill et
al., 2004).
Chronic conditions and physical disabilities accompanied by greater depressive
symptoms are associated with greater need for and use of informal (unpaid) and formal
(paid) long-term care (Katon, 2003; Langa et al., 2004). Long-term care (LTC) is the
direct help provided to those in need of assistance with daily activities (Friedland, 2004),
and covers medical care and social services for persons with chronic conditions and
disability (Feder, Komisar, & Niefeld, 2000). Over 6 million adults aged ≥65 years need
LTC services (Kaye, Harrington, & LaPlante, 2010), with approximately two-thirds of
them eventually needing some type of LTC service for an average of two years (Wysocki
et al., 2012). Among U.S. older adults who died in the community setting from 2000-
2002, an average of 65.8 informal care hours per week was received during the last year
of life (Rhee, Degenholtz, Lo Sasso, & Emanuel, 2009). Data from the Health and
45
Retirement Study show that formal long-term care use increases one’s probability of
earlier mortality, whereas availability of informal long-term care reduces one’s
probability of earlier mortality (Weaver, Stearns, Norton, & Spector, 2009).
Older adults with comorbid depressive symptoms are more likely to use
ambulatory and LTC services than their counterparts without depressive symptoms
(Badger, 2007; Katon, 2003; Langa et al., 2004). One review study concludes, that after
controlling for disease severity, comorbid depression is associated with an approximately
50% increased cost in medical care in all adults (Katon, 2003). Similarly, after
adjustment for physical health, older adults with comorbid depressive symptoms are more
likely to need and use more LTC services compared to those without comorbid
depressive symptoms (Badger, 2007; Luber et al., 2001). One study shows that adults
aged ≥70 years with depressive symptoms receive twice the number of informal
caregiving hours than those without depressive symptoms (Langa et al., 2004).
The prevalence of depression, chronic conditions, physical disability, and
associated LTC needs among older adults will likely increase in both numbers and
proportion over the next several decades. Increased age is associated with increased
prevalence of single and multiple morbidities and physical and cognitive disabilities
(Berlau et al., 2009; Ukraintseva & Yashin, 2001), with estimates showing approximately
80% of persons aged ≥65 years with at least one chronic condition and 50% with two or
more chronic conditions (Velkoff et al., 2005). Furthermore, recent evidence from
national surveys suggests that the expected increase in the population of older adults and
those with physical disabilities will dramatically increase the need for associated medical
and public health services (Brault et al., 2009; Seeman et al., 2010).
46
Older American Indians experience some of the highest rates of physical
disability (Denny et al., 2005; Goins et al., 2007; Moss et al., 2006) and suffer from
poorer physical and mental health compared to other racial and ethnic groups (Barnes et
al., 2005; Centers for Disease Control and Prevention, 2011). Prevalence of depressive
symptoms among older American Indians has been shown to be higher than that of the
general population of older community-dwelling adults (Curyto et al., 1998; John et al.,
2003). Such evidence suggests that older American Indians may have a greater need for
and use of LTC services compared to the general population.
Figure 3.1 shows how certain predisposing, enabling, and need components may
affect the use of services as delineated in the Behavioral Model of Health Services Use
(Andersen, 2008; Andersen & Davidson, 2007). These components directly and
indirectly influence and are influenced (via feedback arrows) by personal health
practices, current use of health services, and health outcomes. This model distinguishes
between individual and aggregate level characteristics that influence the use of certain
medical and LTC services. Thus, the Behavioral Model provides a conceptual
framework upon which to understand and predict the use of these services.
--Insert Figure 3.1 about here--
As with other racial and ethnic groups, the number of older American Indians is
projected to substantially increase over the next several decades (Vincent & Velkoff,
2010). No identified studies have examined the association of depressive symptoms with
informal and formal LTC use among older American Indians with physical disabilities.
There is an urgent need to assess LTC service use among this vulnerable population, as
evidence suggests a high unmet need for LTC services (Goins, Bogart, & Roubideaux,
47
2010). Therefore, the purpose of this study was to compare informal and formal LTC use
among older American Indians with physical disabilities by level of depressive
symptoms.
METHODS
Study Design and Data Collection
Data for this study originate from the Native Elder Care Study, a cross-sectional
study of community-dwelling older adult members of a federally-recognized American
Indian tribe located in the Southeast regions of the U.S. (Goins et al., 2011). Using a
tribal participatory research approach, the investigators collaborated with tribal members
to examine social and health care needs for older members of the tribe residing in the
tribal service area (Goins et al., 2011). Data were collected from 2006 to 2008 using in-
person interviewer-administered surveys and included information about physical
disability, mental and physical health, personal assistance needs, health care use, and
psychosocial resources. The tribe’s institutional review board, tribal council, tribal elder
council, and West Virginia University institutional review board approved the project.
All study participants provided informed consent and received a $20 gift card for
completing the interview. The Oregon State University institutional review board
approved the secondary data analyses for this study.
Sample
Eligibility criteria for study inclusion included being an enrolled tribal member,
aged ≥55 years, a resident in the tribal service area, non-institutionalized, and having the
capacity to demonstrate adequate cognitive ability. The lower older age threshold of 55
years was used (compared to 65 years) as evidence suggests more rapid declines in health
48
status and shorter life expectancy among American Indians compared to other racial
groups (Hayward & Heron, 1999; Indian Health Service, 2013). Based on age and
residential location, tribal enrollment records showed 1,430 persons as potentially eligible
for study enrollment. From this generated list, members were randomly selected for
study recruitment from three age groups: 55-64, 65-74, and ≥75 years. Recruitment
methods included calling and visiting potential participants’ homes. From this random
sample, 47 could not be located, 78 declined participation, and 50 were deemed to be
ineligible for study inclusion. The final generated sample included 505 participants.
Measures
Informal and Formal Care Use
Study participants who indicated that they had difficulties in performing one or
more activities of daily living (ADLs) or instrumental activities of daily living (IADLs),
were asked how many hours of help per week they receive for these activities from a
friend or family member (i.e., unpaid persons) and how many hours per week they
receive for these activities from a paid person. Each of these measures was treated as a
count variable.
Depressive Symptoms
The Centers for Epidemiologic Studies—Depression (CES-D) Scale was used to
measure depressive symptoms (Radloff, 1977). This scale has been widely used among a
number of population-based studies and its validity and reliability has been confirmed
among older adults and across different racial groups (Mui et al., 2002) and validated
with older American Indians (Chapleski et al., 1997). The full version of the CES-D
scale includes 20 items comprised of four factors: depressed affect, positive affect,
49
somatic symptoms, and perceptions regarding interpersonal relationships (Radloff, 1977).
Participants were asked how often they felt each symptom in the past week, with sample
items including: (1) I felt fearful, (2) I enjoyed life, and (3) people were unfriendly; and
to respond on a scale of 0 to 3 (0 = rarely or none of the time, 1 = some or a little of the
time, 2 = occasionally or a moderate amount of time, 3 = most or all of the time).
Positive affect items were reverse coded. Therefore, the total sum score ranges from 0 to
60 comprising both the count and frequency of experiencing each of the CES-D items.
The internal consistency of the scale for the study sample was high (α = 0.89). This tool
has been used to screen for depression with a commonly accepted cut-off score of ≥16
indicating clinically significant depressive symptoms (Radloff, 1977; Weissman et al.,
1977). As a more stringent approach to classifying subsyndromal (i.e., depressive
symptoms not quite meeting the diagnosis for major depression) depression among older
adults, others have utilized a tiered (low: 0-9, moderate: 10-19, and high: ≥20) method to
scoring the CES-D (Barry et al., 2009).
Physical Disability
We defined physical disability as having difficulty with any of eight activities of
daily living (ADLs) and eight instrumental activities of daily living (IADLs) (Fillenbaum,
1988; Lawton & Brody, 1969). Difficulty with ADLs infers disability with any of the
following activities: bathing/showering, dressing, eating, transferring, walking, toileting,
grooming, and getting outside. IADL difficulty infers disability with any of the
following activities: using the telephone, light housework, heavy housework, preparing
meals, shopping, managing money, managing medications, and transportation. For each
activity, participants were asked how much difficulty they have in doing them with a
50
response option of 1 to 5 (1 = no difficulty, 2 = some difficulty, 3 = a lot of difficulty, 4 =
unable/cannot do, 5 = do not do). The latter response item includes a follow-up question
on whether it is because of a health or physical problem. If participants indicated no
difficulty performing an activity or indicated they do not do it but stated it was not
because of a health or physical problem, they were then deemed not to have a disability
for that particular activity (coded 0). If participants indicated either they had some
difficulty, a lot of difficulty, unable/cannot do, or do not do because of a health or
physical problem, then they were deemed to have a disability for that particular activity
(coded 1). Thus, the total sum score across all ADLs and IADLs ranged from 0 to 16.
Health Conditions
Chronic Conditions. This measure included the number of 12 common chronic
medical conditions: heart disease, stroke, angina, congestive heart failure, heart attack,
lung disease, Parkinson’s disease, cancer, diabetes, high blood pressure, kidney disease,
and liver disease. Respondents were asked if, since age 55, a doctor had told them they
had one of the listed 12 conditions with “yes” and “no” response options, yielding a sum
score range of 0 to 12.
Psychosocial Factors
Personal Mastery. A personal mastery scale was used to assess participants’
perceived mastery over life events (Pearlin & Schooler, 1978). This scale consists of
seven items with a 4-point response selection (0 = strongly disagree, 1 = disagree
somewhat, 2 = agree somewhat, 3 = strongly agree, with a sum score range from 0 to 21.
Sample items include: (1) I have little control over the things that happen to me, and (2)
What happens to me in the future mostly depends on me. Two positive items were
51
reverse coded. The internal consistency of this scale for the study sample was
moderately high (α = 0.79).
Self-Efficacy. A general self-efficacy scale was used to assess participants’
perceived self-efficacy (Jerusalem & Schwarzer, 1992). This is a 9-item response scale
with a 4-point response selection (0 = not at all true, 1 = hardly true, 2 = moderately true,
3 = exactly true) and a sum score range of 0 to 27. Sample items include: (1) I can solve
most problems if I try hard enough and (2) If I am in trouble, I can usually think of a
solution. The internal consistency of this scale for the study sample was high (α = 0.90).
Social Support. Social support was measured with the Medical Outcomes Study
Social Support Survey (Sherbourne & Stewart, 1991). This is a 19-item survey with a 5-
point response selection (0 = none of the time, 1 = a little of the time, 2 = some of the
time, 3 = most of the time, 4 = all of the time) and a sum score range of 0 to 76.
Participants were asked how often each of the following types of support is available
when needed. Sample items include: (1) someone you can count on to listen to you when
you need to talk, (2) someone who understands your problems, and (3) someone to do
something with for relaxation. The internal consistency of this scale for the study sample
was very high (α = 0.96).
Assistance Need
Respondents were asked whether or not they needed assistance with eight ADL
tasks and eight IADL tasks. Figure 3.2 presents how assistance need was classified.
Respondents were classified as having no need with any ADL or IADL if they reported
having some level of difficulty performing a task or if they did not perform the task due
to a health condition and did not report a need for assistance. Respondents were
52
classified as having a met need if they reported having some level of difficulty
performing an activity or did not perform the activity due to a health condition, received
assistance for that activity, and did not report needing more assistance. Respondents
were classified as having an unmet need if they reported some level of difficulty
performing an activity or did not perform the activity due to a health condition and
reported needing assistance not currently receiving at all or reported needing more
assistance than they were currently receiving. Assistance need included both met and
unmet assistance need outcomes. Assistance need was operationalized as the count of
ADL and IADL activities for which older adults with physical disability needed
assistance.
Demographics
Demographic characteristics included age, sex, marital status (married/live partner
versus unmarried), educational attainment (<12 years versus ≥12 years), and living
arrangements (living alone versus living with others).
Statistical Analyses
First we examined the unadjusted associations of depressive symptoms with each
of the dependent variables. We then conducted multivariate models to determine
depressive symptoms’ adjusted associations with the dependent variables showing
significant associations in the unadjusted model. Because of the high zero counts of each
dependent variable, we selected zero-inflated negative binomial regression models, using
age to predict excessive zero counts. Dependent variables were treated as continuous in
the multivariate regression models. Those variables which became insignificant when
53
adjusted for others were omitted from the final models. We ran sensitivity analyses with
depressive symptoms as bivariate (<16 versus ≥16) and tiered (0-9, 10-19, ≥20).
We imputed missing data and then dropped cases for which the dependent
variable was completely missing (von Hippel, 2007). Specifically, we used the multiple
imputation chained equations method to impute missing data (Royston & White, 2011).
We then estimated the variance inflation factor to test for multicollinearity among the
independent variables, which was not found to be a substantive issue. Data were imputed
on 14 cases for depressive symptom variables, 7 cases for personal mastery, 17 cases for
self-efficacy, 9 cases for social support, 1 case for chronic conditions, 6 cases for living
arrangements, and 2 cases for educational attainment. We excluded cases that did not
provide any response to the dependent variable of interest. We compared the completely
missing cases on the dependent variable with the others by demographic variables.
Comparisons showed that those with completely missing data on the dependent variables
were more likely to be younger age (p = 0.002), male sex (p = 0.009), and higher
educational attainment (p = 0.008). For all multivariate analyses, all continuous
independent variables were standardized. All analyses were completed using StataCorp’s
statistical software package version 12.0 (Stata Statistical Software, 2007).
RESULTS
Table 3.1 presents the prevalence of informal and formal LTC use by levels of
depressive symptoms. Thirty-four percent of respondents indicated that they use formal
care (n = 50) and 82% informal care (n = 148). Bivariate analyses showed significant
unadjusted associations of depressive symptoms with informal care use only.
Specifically, those who used informal LTC were more likely to have moderate and high
54
depressive symptoms (p = 0.028). Table 3.2 shows the percent of older adults with
physical disabilities who rely on informal care use by source of informal caregiving. The
greatest sources of informal long-term care were daughters (31%), spouses (22%), and
sons (21%).
--Insert Table 3.1 about here--
--Insert Table 3.2 about here--
Table 3.3 presents the unadjusted and adjusted association of depressive
symptoms with informal care use. Model 1 shows the unadjusted association of
depressive symptoms with informal care use. This significant association is retained
when adjusted for care assistance need (Model 2). Of the demographic characteristics,
only age and living arrangement were significantly associated with informal care use
(Model 3), with those who were older and living alone being more likely to use informal
care. The number of chronic conditions was not found to be significantly associated with
informal care use. Model 4 shows the best fit model in which, of the other psychosocial
variables, only self-efficacy was significantly associated with informal care use after
controlling for the other covariates. This model indicates that, after adjustment of
demographic characteristics, assistance need, and self-efficacy, depressive symptoms
remained significantly associated with informal care use. Compared to those without or
lower depressive symptoms, those with higher depressive symptoms had an increased
expected number of informal care use per week by a factor of 1.42, holding all other
factors constant. Sensitivity analyses supported these results.
--Insert Table 3.3 about here--
DISCUSSION
55
Our findings show mixed results of depressive symptoms’ associations with
informal and formal LTC use. Significant associations were found between depressive
symptoms and informal care use. Even after controlling for demographic characteristics,
assistance need, and self-efficacy, this relationship remained statistically significant,
suggesting that having greater depressive symptoms increases one’s odds of using more
informal care by nearly 50%. Only one known population-based study (Langa et al.,
2004) examined the effect of depressive symptoms on informal care use among the older
old (≥70 years) and supports our findings regarding informal care use. Similarly, Badger
and colleagues (2007) found significantly higher use of home help among older adults
with moderate and high depressive symptoms compared to those with none.
We did not find a significant association of depressive symptoms with formal care
use in this study, despite other studies showing greater medical care use and
hospitalizations among older adults with greater depressive symptoms (Badger, 2007;
Luber et al., 2001). Although we cannot confirm, we speculate that cultural expectations
of caring for elders in American Indian communities may suggest that informal care
networks (versus formal care networks) would be more responsive to observed mental
and physical health concerns of its older members. Indeed, the much higher prevalence
informal care use would support this hypothesis. Future studies are warranted to test
whether traditional care networks for elders exist and function today according to
customary belief. The most commonly used sources of informal care found in this study
are consistent with previous findings of informal care networks for older community-
dwelling adults (Christianson, 1988; Hoover & Rotermann, 2012).
56
Over half of the study sample reported one or more difficulties in ADLs and
IADLs, suggesting a high prevalence of physical disability among older American
Indians. Of those, over a quarter reported a need for care, and nearly half of those
reported an unmet need. Clearly, evidence from this study suggests a need for more
accessible LTC services in this population, a finding supported by a nationwide study of
older American Indians (Goins et al., 2010; Jervis, Jackson, & Manson, 2002). The
Indian Health Care Improvement Act Amendments of 2009 (U.S. Congress, 2009) was
put into place to improve access to health care for American Indians. The translation of
this initiative, and other related policies and programs, to improved health outcomes
should be evaluated by health researchers.
Presented findings should be viewed in the context of some study limitations. As
a cross-sectional study, causality cannot be established. Longitudinal designed studies
will contribute to our understanding of depressive symptoms’ relative contribution to
LTC use and need. It is particularly worth noting that data were self-reported and
therefore subject to participant bias. Others have suggested that American Indians are
less likely to report using or needing assistance than their racial and ethnic peers (Loftin,
1983; Moss, 2005), suggesting that our study participants may have actually under-
reported both use and need of care. Another related concern is differential recall bias
between those with low or none versus higher levels of depressive symptoms. Evidence
exists to suggest that persons with higher levels of distress are more likely to over-report
use of services compare to those with low levels of distress (Rhodes & Fung, 2004;
Rhodes, Lin, & Mustard, 2002) Such bias would inflate the significant findings in our
study. Finally, the generalizability of our results are limited to older American Indian
57
adults of a single tribe and are not representative of other older adult populations or other
American Indian tribes.
In sum, our study shows that older American Indians with physical disabilities
and greater depressive symptoms are more likely to use greater informal care compared
to their peers without or less depressive symptoms. Our findings indicated a high
prevalence of physical disability, need for care, and unmet need for care in the study
sample, supporting evidence on the necessity to evaluate specific long-term care needs
for older tribal members with physical disabilities. Needs assessment studies will help
guide future programming and policies aimed at the provision and accessibility of long-
term care services. Future research is needed to track the effectiveness of these policies
and programs on long-term health outcomes in these populations.
58
Table 3.1. Prevalence of informal and formal long-term care use by level of depressive symptoms
Depressive Symptoms
Low
(0-9)
Moderate
(10-19)
High
(20 or more)
Total n (%) n (%) p value1
No. of Hours of Care Use Per Week
Informal (unpaid) 180
0.028
Yes 148 (82%) 82 (76%) 36 (86%) 20 (100%)
No 32 (18%) 26 (24%) 6 (14%) 0 (0%)
Formal Care (paid) 148 0.149
Yes 50 (34%) 30 (34%) 12 (38%) 2 (12%)
No 98 (66%) 58 (66%) 20 (63%) 15 (88%)
Note: Significance levels determined by Chi-squared tests.
59
Table 3.2. Percent of older adults with physical disabilities using
informal long-term care use by source of caregiving (n = 180)
Source of informal caregiving Number Percent
Daughter 56 31.1%
Spouse 40 22.2%
Son 37 20.6%
Granddaughter 28 15.6%
Grandson 14 7.8%
Daughter-in-law 8 4.4%
Neighbor 3 1.7%
Son-in-law 2 1.1%
60
Table 3.3. Unadjusted and adjusted association of depressive symptoms with informal care use (n = 180)
Model 1 Model 2 Model 3 Model 4
β SE β SE β SE β SE
Demographics
Age
--- --- --- --- 0.57*** 0.119 0.49*** 0.105
Lives Alone
--- --- --- --- 1.04*** 0.282 0.99*** 0.277
Assistance Need
--- --- 0.37** 0.116 0.47*** 0.115 0.39*** 0.110
Psychosocial Measures
Self-Efficacy
--- --- --- --- --- --- -0.25* 0.109
Depressive Symptoms 0.46*** 0.030 0.36** 0.126 0.37*** 0.104 0.35*** 0.102
AIC1 Value
1507.8 1497.5 1465.4 1461.7
BIC2 Value 1524.6 1517.7 1492.3 1492.0
Note: ‡p < 10, *p < .05, **p < .01, ***p < .001; Age used to predict the presence of zero counts in dependent variable.
1Akaike Information Criterion,
2Bayesian Information Criterion. Best fit model in bold.
61
Figure 3.1. The Behavioral Model (adapted from Andersen, 2008)
62
Figure 3.2. Determination of assistance need with activities of daily living and
instrumental activities of daily living
63
CHAPTER 4. GENERAL CONCLUSIONS
As the Disablement Process and Behavioral Models and current evidence suggest,
psychosocial characteristics, such as depressive symptoms, have a contributive effect on
the onset and trajectory of physical disability and informal long-term care use. The
presented work in this dissertation contributes to the scientific knowledge and
understanding the health, physical functioning, and long-term care use of older American
Indians with comorbid depressive symptoms. In addition, it highlights health disparities
among older American Indians.
In manuscript 1, we examined the prevalence of depressive symptoms by
associated risk factors, and sought to determine if it is a significant moderator or mediator
of chronic conditions’ relation to physical disability. Specifically, we used the
Disablement Process Model as a framework to examine the role of depressive symptoms
in the disablement process. We identified a number of significantly associated
unadjusted and adjusted, modifiable and non-modifiable risk factors for depressive
symptoms. Our moderation and mediation tests supported depressive symptoms’ role as
a significant mediating factor (marginally so as a direct mediator), particularly indirectly
through personal mastery. We used structural equation modeling to test a theorized
pathway analyses by which chronic conditions and other factors significantly impact
physical disability outcomes. These findings support existing limited evidence of
depressive symptoms as a significant mediator of chronic conditions on physical
disability. Similarly, they support the potential positive preventive effects of
interventions focusing on positive reinforcement of psychosocial skills and supports.
64
In manuscript 2, we used the Behavioral Model to conceptualize the relationship
of key factors, including depressive symptoms, to predict informal and formal long-term
care use. Specifically, we found higher depressive symptoms to be significantly
associated with greater number of informal care hours used, but not formal care hours.
These findings are consistent with existing evidence of long-term care use among older
adults. We surmise that American Indian traditional informal support networks are more
sensitive to the mental and physical health needs of its elders than formal support
networks. We conclude by observing the high prevalence of physical disability, need,
and unmet need for long-term care in this sample of older American Indians. This
evidence supports that of nationwide studies of American Indians in highlighting the need
for programs and policies aimed at increasing the availability and accessibility of long-
term care services for older adults in American Indian tribal communities.
Future Directions
There are several key points to highlight for future related research. First, more
longitudinal studies are needed to validate existing theoretical models of the disablement
process and service use. Similarly, there is a need to clarify the causal relationships of
key psychosocial variables with each other, physical disability, and associated long-term
care use trajectories among older adults. Mediation and moderation analyses are critical
to achieving both aims, and to guide health promotion and intervention programs.
Second, researchers must take into account contextual factors (i.e., cultural, historical,
economic, etc.) that have subsequent impacts on American Indians’ health, and medical
service and long-term care use. Doing so will help to better understand and devise
culturally appropriate programs and policies for reducing existing health disparities in
65
this ethnic population. Third, pilot intervention studies should test whether targeting key
psychosocial and behavioral factors will have a meaningful impact on physical disability
trajectories and other health outcomes among older adults.
With the expected dramatic increase in older adults with physical disabilities and
chronic conditions, we as a nation are likely to face a long-term care crisis. More and
more, older adults will become increasingly reliant on informal care providers, who are
likely already overburdened by work demands. Population research is needed to track the
growing demand for long-term care services and to provide scientific evidence for future
policies that affect important disability programs such as Medicaid.
Finally, it is commendable and heartening that public health has broadened its
focus beyond communicable and chronic physical diseases to include mental health as an
essential component of health and well-being. There are plenty of fruitful discoveries to
be made in research that examines the intricate relationships of physical and mental
health across a lifespan. From the mapping of the brain to the discoveries of bio-
molecular structures of the physical body, future public health practitioners are likely to
have an increasing selection of tools to prevent and/or delay those things which afflict the
body and mind
66
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APPENDIX
79
LITERATURE REVIEW
Depressive Symptoms among Older Adults
Depression remains the primary mental health concern among older adults. In
conjunction with other major chronic conditions it is acknowledged to worsen social,
emotional, and physical functioning. The presence of depression in late life significantly
decreases the quality of life of older adults and increases risk of mortality (Blazer, 2003).
Therefore, older adults’ wellbeing and longevity may benefit from strategic prevention
and robust diagnostic and treatment efforts.
Definitions and Measurement of Depression and Depressive Symptoms
While there are commonly used case definitions of depression, there is not entire
agreement on what constitutes clinically significant depression (Blazer, 2003). Major
depression is diagnosed by clinicians using the Diagnostic and Statistical Manual, Fourth
Edition (DSM-IV), in which one exhibits at least one of two core symptoms (depressed
mood and lack of interest) accompanied by four or more related listed symptoms (APA,
1994). The DSM-IV also classifies those having one of the core symptoms along with
three other related symptoms as having minor depression (also termed subsyndromal or
subthreshold). Regardless of the number of operationalized measures used to diagnosis
depressive symptoms, evidence suggests that the presence of minor symptoms has
equally, if not more, of a substantive impact than the presence of major depression on
impairment, functioning, and physical disability among older adults (Lyness et al., 2007).
80
A tool frequently used to measure depressive symptoms is the Epidemiologic
Studies Depression Scale (CES-D). A cut-off score of ≥16 has been established to
determine clinically significant depressive symptoms (Radloff, 1977; Weissman,
Sholomskas, Pottenger, Prusoff, & Locke, 1977). As a more stringent approach to
classifying subsyndromal (i.e., depressive symptoms not quite meeting diagnosis for
major depression) depression among older adults, others have used a tiered (low: 0-9,
moderate: 10-19, and high: ≥20) method to scoring the CES-D (Barry et al., 2009). This
scale has been widely used among a number of population-based studies. Its validity and
reliability has been confirmed among older adults and across different racial groups (Mui,
Burnette, & Chen, 2002) and has been validated with older American Indians (E.E.
Chapleski, Lamphere, Kaczynski, Lichtenberg, & Dwyer, 1997).
Prevalence and Incidence of Depression among Older Adults
Prevalence of depression, like other chronic conditions, varies widely among
older adults. While prevalence estimates of major depression in older community-
dwelling adults remain relatively low (1-4%), prevalence estimates of minor depression
typically range from 8-16% in these populations (Blazer, 2003). Studies of older adults
visiting a primary care provider and those in long-term care facilities often find higher
prevalence estimates of both major and minor depression compared to other samples of
older adults. For example, Lyness and colleagues (2002) found that 9% of older primary
care patients were diagnosed with major depression and 16% with other significant
depressive symptoms not meeting criteria for major depression. Studies with
institutionalized older adults show a prevalence range of 12-14% diagnosed with major
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depression and 20% to 35% diagnosed with minor depression (Jones, Marcantonio, &
Rabinowitz, 2003; Teresi, Abrams, Holmes, Ramirez, & Eimicke, 2001). Yet, as
researchers acknowledge, stigma and under-diagnosis of depression likely result in
underestimates of the actual prevalence of depression among older adults (Alexopoulos et
al., 2002; Charney et al., 2003).
Prevalence of Depression among Older American Indian Adults
Mental health disparities are substantial among American Indian adults (Barnes et
al., 2005). Few studies have specifically examined depressive symptom prevalence
among older American Indians. One study among Great Lakes American Indians aged
≥55 years found the prevalence of depressive symptomatology, as measured by the CES-
D, to be 18% (Curyto et al., 1998). Another study of American Indians aged ≥60 years
identified 41% of respondents with depression (John et al., 2003). These rates suggest
that older American Indian adults suffer disproportionately from depression compared to
other racial and ethnic groups.
Modifiable and Non-Modifiable Risk Factors of Depression among Older Adults
Evidence suggests a number of modifiable risk factors for depression among older
adults. Primarily, chronic conditions, associated pain, and disability are largely
implicated for the emergence of late-onset depression (Alexopoulos, 2005; Chapman,
Perry, & Strine, 2005). Studies have provided evidence for the “vascular depression”
hypothesis in which cerebrovascular disease increases one’s vulnerability to depression
and other emotional and cognitive syndromes (Alexopoulos, 2006; Kales, Maixner, &
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Mellow, 2005). Other chronic conditions have also been associated with depression.
Particularly, evidence has accumulated to suggest that depressive disorders are prominent
among those with asthma, arthritis, cardiovascular disease, stroke, heart attack, cancer,
and Type 2 diabetes (Chapman et al., 2005). Physical disability and pain are also highly
predictive of the onset of depressive symptoms (Bair, Robinson, Katon, & Kroenke,
2003; Chen et al., 2012). Furthermore, greater overall medical burden from chronic
conditions and physical disability is predictive of higher risk for depression in older
adults (Alexopoulos et al., 2002).
Other factors associated with depression among older adults include lower
income, social isolation, relocation, caregiving, medication use, and bereavement from a
recently lost spouse (Alexopoulos, 2005; Berg, Palomäki, Lönnqvist, Lehtihalmes, &
Kaste, 2005; Friedmann et al., 2006; Kendler, Myers, & Zisook, 2008; Perlick et al.,
2007; Wilson, Chen, Taylor, McCracken, & Copeland, 1999). Among older American
Indians, lower education and living in an urban area have been identified as significant
correlates of depression (Curyto et al., 1998). Only two non-modifiable risk factors, sex
(female) and genetic vulnerability, have been shown to be associated with the onset of
depression in older adults (Blazer, 2003; Djernes, 2006; Sullivan, Neale, & Kendler,
2000).
Impact of Depression among Older Adults
Late life depression is a risk factor for a number of serious chronic cognitive and
physical health conditions, increased complications from medical illness, and mortality.
Studies have shown that a history of depression places one at significantly increased risk
83
of dementia and Alzheimer’s disease (Green et al., 2003; Jorm, 2001). Depression has
been shown to increase the risk of cardiovascular disease and Type 2 diabetes (Knol et
al., 2006; Lett et al., 2004; Williams et al., 2002) and decrease the chance of recovery
from medical incidences (Cronin-Stubbs et al., 2000). Furthermore, depression is
associated with non-adherence to healthy lifestyle regimens and increased risk for Type 2
diabetes complications (DiMatteo, Lepper, & Croghan, 2000; Lin et al., 2004; Lin et al.,
2010). Depression is also an independent predictor of physical disability in older adults
(Barry et al., 2009; Chen et al., 2012; Gill, Allore, Holford, & Guo, 2004; Greenglass,
Fiksenbaum, & Eaton, 2006) and in combination with other chronic diseases (Reynolds,
Haley, & Kozlenko, 2008). Comorbid depression is associated with mortality in older
adults (Katon et al., 2005; Romanelli, Fauerbach, Bush, & Ziegelstein, 2002) and has
been found to be a robust, consistent independent predictor of mortality in older
community-dwelling adults (Ganguli et al., 2002).
Late life depression impacts the amount of care and treatment, and incurred
medical expense related to medical conditions. Older adults with comorbid depression
have nearly a 50% increase in medical care costs compared to those without depression,
controlling for disease severity (Katon, 2003; Luber et al., 2001; Unützer et al., 1997).
Depressed older adults have significantly increased outpatient service use, controlling for
comorbidity (Luber et al., 2001). Similarly, the presence of depressive symptoms
substantially impacts informal long-term care use. For example, one study found that the
presence of depressive symptoms among adults aged ≥70 years was independently
associated with the number of hours of informal caregiving received (Langa et al., 2004).
Researchers estimate the economic impact of the depressive symptom-associated
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caregiving needs for older adults alone to be over $9 billion U.S. dollars annually (Langa
et al., 2004).
Intervention and Treatment of Depression
Since depression is effectively treatable but under-diagnosed in older community-
dwelling adults, it is a suitable area for targeting prevention programs. Two key
objectives of Healthy People 2020 directly relate to depression screening and prevention:
1) to increase the proportion of primary care physician visits that screen adults for
depression and 2) to substantially increase the proportion of primary care facilities that
provide mental health treatment onsite or by paid referral (U.S. Department of Health &
Human Services, 2011). Based on existing evidence, an expert panel of the Task Force
on Community Preventive Services recommended two intervention models for
depression in older adults: depression care management (DCM) model and cognitive
behavioral therapy (CBT) (Snowden, Steinman, & Frederick, 2008). The DCM model is
characterized as a systematic team-based approach to diagnosing and treating depression
with a combination of psychotherapy and antidepressant pharmalogical drugs, whereas
CBT emphasizes therapy and teaching sessions that help the patient to learn coping skills.
DCM models parallel collaborative care for depression models, which have also
demonstrated to be more effective than standard care (Gilbody, Bower, Fletcher,
Richards, & Sutton, 2006). Inherent in all of these models is the need to engage the
patient in treatment above and beyond the administration of medicine. A systematic
review of depression treatment concludes this very statement. Researchers found that of
the existing randomized clinical trials, patients receiving both drug treatment and
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psychological intervention had significantly improved affect compared to those with drug
treatment alone (Pampallona, Bollini, Tibaldi, Kupelnick, & Munizza, 2004).
Studies have demonstrated the effect of physical inactivity on depression among
older adults. After adjusting for age, sex, ethnicity, chronic conditions, and disability,
physical inactivity was found to be predictive of higher prevalent depression and
subsequent incident depression (Strawbridge, Deleger, Roberts, & Kaplan, 2002).
Similarly, researchers found that depressive symptoms in older adults were predicted by
lower intensity physical activity at baseline, eight years prior to follow-up (Lampinen,
Heikkinen, & Ruoppila, 2000). These findings suggest that physical activity, particularly
at higher intensities, is an important lifestyle component of good mental health, and
provides a fruitful intervention target for preventing and alleviating depression among
older adults. Indeed, the American College of Sports Medicine and American Heart
Association recommends regular moderate-intensity physical activity with aerobic and
anaerobic activities for older adults to achieve and maintain overall good health (Nelson
et al., 2007).
Physical Disability among Older Adults
While physical disability can emerge at any point in life, one’s risk for physical
disability tends to increase in late life, either due to the presence of chronic conditions,
onset of frailty, or injury. The Disablement Process Model suggests that disability is
preceded by impairment and functional limitations associated with chronic conditions or
injury (Jette, 2009; Jette et al., 2002; Verbrugge & Jette, 1994). However, not all chronic
conditions or injuries result in disability and a host of other personal, social and
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environmental factors matter in whether or not these result in physical disability.
Physical disability in late life, as in earlier stages of life, is a significant development that
has implications for oneself, personal caregivers, and the care provider service system.
Therefore, understanding the antecedents of physical disability is critical for the
development of strategies aimed at its prevention and delay of onset.
Definition and Measurement of Physical Disability
In past, the definition of physical disability has overlapped with the term
functional limitation, which refers to restrictions in performing specific tasks. However,
physical disability is distinguished from functional limitations in that it represents an
inability or restricted ability to perform fundamental daily activities in a community
setting (Verbrugge & Jette, 1994). Physical disability is distinguished from cognitive
disability to indicate restrictions in physical functioning versus mental capacity. Some of
the most commonly used measures of physical disability include self-reported activities
of daily living (ADLs) and instrumental activities of daily living (IADLs), with the
former capturing more severe disability (Albert & Freedman, 2009; Jette et al., 2002).
ADL tasks include basic activities such as bathing, dressing, eating, walking, and
toileting while IADL tasks entail more independent activities such as cooking, managing
medications and finances, and shopping. Some level of physical disability is inferred if
the respondent states some difficulty or inability to perform the specific task because of a
functional limitation. These measures are also commonly used to gather information on
the use of personal assistance or assistive devices to compensate for the physical
disability.
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Operationalization and classification of disability using ADL and IADL measures,
as well as the number of activities, has varied widely in disability studies leaving to
question actual physical disability trends among older adults in the U.S. (Albert &
Freedman, 2009; Freedman et al., 2002). Further complicating estimates, physical
disability has been classified according to degree of severity (Brorsson & Asberg, 1984;
Ormel, Rijsdijk, Sullivan, Van Sonderen, & Kempen, 2002) and use of various cut-off
scores based on the number of restricted activities (Desai, Lentzner, & Weeks, 2001).
Therefore, researchers interested in physical disability among older adults should be
aware of the variability of physical disability measurement and classification.
Prevalence of Physical Disability among Older Adults
Data for older U.S. adults indicate that the number and proportion of the younger
older adults (aged 60 to 69 years) with physical disabilities is dramatically increasing
(Seeman et al., 2010). Longitudinal comparisons of the National Health and Nutrition
Examination Survey data show that this cohort of adults had 40-70% greater prevalence
of all types of physical disability over the course of a decade, with accompanying
increases in body mass index and chronic disease prevalence (Seeman et al., 2010).
Older adults are more likely to experience physical disability. The 2005 Survey of
Income and Program Participation data show that physical disability prevalence doubles
from middle-age to older age, with nearly 52% of adults aged ≥65 years having a
physical disability (Brault et al., 2009).
Prevalence of Physical Disability among Older American Indian Adults
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A nationally representative sample of adults aged ≥55 years in 2000 showed that
among American Indian elders, 21% reported mobility disability and 12% reported self-
care disability (Goins et al., 2007). With the use of a geographical information system,
physical disability prevalence for American Indian elders aged ≥65 years was estimated
at 58% (Moss et al., 2006). Similarly, data from the 2003-2005 Behavioral Risk Factor
Surveillance System indicated that 38% of older American Indians aged ≥50 years had a
reported disability, substantially higher than all other ethnic groups (Okoro et al., 2007).
Modifiable and Non-Modifiable Risk Factors for Physical Disability among Older
Adults
Several modifiable risk factors for physical disability exist for older adults and
include chronic medical conditions, falls, depression, and physical inactivity. The top
three causes of physical disability include arthritis, back or spine problems, and heart
conditions (Brault et al., 2009). Other chronic conditions and medical events are also
predictive of physical disability. Specifically, arthritis, Type 2 diabetes, myocardial
infarction, stroke, and congestive heart failure have been highly associated with physical
disability (Gill et al., 2004). A systematic literature review on functional status decline
(including physical disability) concluded that chronic disease burden in general is
strongly associated with physical disability (Stuck et al., 1999). Fall-related injuries have
also been shown to predict persistent physical disability outcomes in older adults (Gill et
al., 2004; Kannus, Sievänen, Palvanen, Järvinen, & Parkkari, 2005). Depression (Barry
et al., 2009; Chen et al., 2012; Gill et al., 2004; Greenglass et al., 2006), lower physical
activity (Motl & McAuley, 2010; Stuck et al., 1999) and lower educational attainment
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(Chiu & Wray, 2011; Goins et al., 2007; Melzer, Izmirlian, Leveille, & Guralnik, 2001)
have also been significant predictors of physical disability.
Age, sex, and race/ethnicity are three non-modifiable factors that infer risk for
physical disability. Increasing age has been shown to raise the risk of physical disability
onset (Brault et al., 2009; Gill et al., 2004), with one study showing a 45% increase risk
for incident physical disability among older adults for every 5 years increase in age.
Females have been shown to have a significantly higher prevalence of physical disability
compared to men. In 2005, physical disability prevalence in the U.S. among women was
24.4% compared to men at 19.1% (Brault et al., 2009). Other studies confirm that
regardless of age, women have both higher physical disability prevalence and incidence
compared to men (Leveille, Fried, McMullen, & Guralnik, 2004; Murtagh & Hubert,
2004; Warner & Brown, 2011). Several studies provide evidence of racial and ethnic
disparities in physical disability. Data from the 1994-2006 Health and Retirement Study
show that Blacks and Hispanics have significantly higher physical disability rates
compared to Whites (Chiu & Wray, 2011; Warner & Brown, 2011). Furthermore, other
national survey data show that physical disability disparities are even greater for older
American Indian and Alaska Native adults (Goins et al., 2007; Moss et al., 2006).
2.2.5 Impact of Physical Disability among Older Adults
The impact of physical disability on older adults, caregivers, and caregiving
resources is substantive. Evidence suggests that physical disability is a strong predictor
for depression among those with the disability (Chen et al., 2012; Ormel et al., 2002;
Ormel & Von Korff, 2000) and their informal caregivers (Haley, LaMonde, Han, Burton,
90
& Schonwetter, 2003; Van Wijngaarden, Schene, & Koeter, 2004). Physical disability is
significantly associated with lower health-related quality of life (Peek, Patel, &
Ottenbacher, 2005), and places individuals at increased risk for assistive device use,
institutionalization, and mortality (Gaugler, Duval, Anderson, & Kane, 2007; Inouye et
al., 1998; Leibson, Tosteson, Gabriel, Ransom, & Melton, 2002; Pressler & Ferraro,
2010).
Older adults with physical disabilities disproportionately use health care
resources. In 2000, “dual-eligibles” (those who qualify for both Medicare and Medicaid),
consumed 24% of total Medicare expenditures, and in 2002, consumed 42% of Medicaid
expenditures (Komisar, Feder, & Kasper, 2005). Yet, nearly 30% of the dual-eligible
persons reported needing more long-term care assistance. In addition chronic illness-
associated physical disability remains predictive of substantially greater use of mental
health care use (Yoon & Bernell, 2012). A micro-simulation model estimates that the
current cohort of 65-year olds will require, on average, three years of long-term care such
as formal or informal home care or facility care (Kemper, Komisar, & Alecxih, 2005).
Projections of the next cohort of baby-boomers suggest that long-term care needs may be
even greater (Brault et al., 2009).
2.2.6 Prevention and Management of Physical Disability
Three related areas are critical for preventing and/or delaying the onset of
physical disability in late life: fall prevention, obesity prevention, and physical activity
promotion. A number of fall prevention programs have been designed and implemented
with some limited success in older adults (Rubenstein & Josephson, 2006). Some
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balance programs, such as Tai Chi, have demonstrated to be effective at preventing falls
(Li et al., 2005). Given obesity’s strong association with chronic conditions, obesity
prevention has also become a critical area to physical disability prevention (Lemmens,
Oenema, Klepp, Henriksen, & Brug, 2008). There is increasing evidence that physical
activity plays a direct role towards reducing physical disability (Keysor, 2003; Penninx et
al., 2001). Provided the strong association between depression and physical disability,
studies have shown that depression treatment also reduces physical disability and
associated chronic pain (Lenze et al., 2001; Lin et al., 2003; Unützer et al., 2002)
Depending on the nature of the physical disability, two distinct tertiary prevention
strategies are often used to improve physical functioning and/or to adjust to physical
disability: rehabilitation and management. Rehabilitation programs are specific to the
type of physical disability affected by the injury or condition, and often require a number
of rehabilitation and medical specialists. For example, post-stroke patients may suffer
from a number of mental, sensory, linguistic, and physical disabilities and require
medical expertise from physicians, rehabilitation specialist, physical therapists, speech
pathologists, and vocational therapists (National Institutes of Neurological Disorders and
Stroke, 2011). Other rehabilitation programs for older adults are specific to
cardiovascular events and fall-related hip fractures (Balady et al., 2007; Binder et al.,
2004). Lifestyle management programs are essential for diabetics with disabilities to
prevent further complications and disability from the condition (Renders, Valk, Griffin,
Wagner, & Assendelft, 2001). Lastly, assistive devices, such as canes and walkers, have
become essential for older adults suffering from physical disabilities (Agree & Freedman,
2003; Pressler & Ferraro, 2010).
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Disability-Associated Long-Term Care Use among Older Adults
Long-term care is essential for the most physically disabled older adults.
Estimates have suggested that at on average, older adults of this generation will require
three years of long-term care whether at home, an assisted living facility, or skilled
nursing facility (Kemper et al., 2005). Recent trends in long-term care spending suggest
a rapidly growing burden on both public and private finances for years to come
(Jacobzone, 2000; Levit et al., 2003). Combined with projections of a rapidly aging
society and decreased taxpayer base for public funding (referred to as the dropping
“dependency” ratio, whereas there will be fewer working adults to help fund health care
government spending), the demand for long-term care services will increase while the
burden of long-term care may increasingly fall upon informal caregivers and support
networks (Friedland, 2004; Goulding, Rogers, & Smith, 2003).
Definition and Measurement of Disability-Associated Long-Term Care Use
Long-term care (LTC) is the direct help provided to those in need of assistance
with daily activities (Friedland, 2004), and covers medical care and social services for
persons with chronic conditions and disability (Feder, Komisar, & Niefeld, 2000). LTC
can be home-based or at an institution, such as assisted living facilities and nursing
homes, but the majority needing these services reside in the home and rely on unpaid
(informal) assistance (Thompson, 2004). LTC among community-dwelling older adults
is most often assessed with responses to the ADL and IADL questions about needing
personal assistance to complete a specific task or activity (Pressler & Ferraro, 2010). It
93
has also been assessed with the number of reported informal caregiving hours received
per week (Langa et al., 2004). Both types of information can provide good estimates of
older adults’ long-term care receipt.
Estimates of Disability-Associated LTC Use and Need among Older Adults
Over 6 million adults aged ≥65 years need LTC services (Kaye, Harrington, &
LaPlante, 2010), with approximately two-thirds of them eventually needing some type of
LTC service for an average of two years (Wysocki et al., 2012). Among U.S. older
adults who died in the community setting from 2000-2002, an average of 65.8 informal
care hours per week was received during the last year of life (Rhee, Degenholtz, Lo
Sasso, & Emanuel, 2009). Use of informal caregiving is higher among adults aged ≥70
years reporting depressive symptoms with those with higher depressive symptoms
received twice the number of informal caregiving hours than those with without
depressive symptoms (Langa et al., 2004).
Estimating LTC need and unmet need is critical as unmet need has been identified
as a predictor of a number of adverse events including injury, weight loss, burns, and
mortality (Blazer, Sachs-Ericsson, & Hybels, 2005; LaPlante et al., 2004). Unmet need
has also been associated with the number of ADL difficulties, living alone, and being a
racial and ethnic minority member (Desai et al., 2001; Kennedy, 2001). There are three
common classifications used for examining LTC need estimates among older adults with
physical disabilities: 1) met need (assistance is needed and received), 2) under-met need
(more assistance is needed than currently received), and 3) unmet need (assistance is
needed but none is received) (Desai et al., 2001; Kennedy, 2001). Researchers have
94
estimated that among disabled older adults, 58% have some unmet need (Komisar et al.,
2005). However, evidence suggests that older adults who use assistive devices to
compensate for their disability are less likely to report an unmet need (Agree &
Freedman, 2003).
Disability Models: Empirical Evidence for the Relationships Between Depression,
Chronic Disease, and Physical Disability
Disability models have emerged as a means to better understand conceptually
how and why disability differentially presents itself among persons with similar chronic
conditions. Thus, the question researchers recently began to ask was not whether a
chronic condition (or injury) leads to physical disability, but what other factors contribute
to whether or not a chronic condition leads to physical disability. Implicit in this question
is the idea that there may be other mediating and moderating factors involved in the
disablement process.
The Disablement Process Model
Drawing upon prior models for disability (Nagi, 1969; World Health
Organization, 1980), Verbrugge and Jette (1994) proposed their own, called the
Disablement Process Model. Similar to the other models, the Disablement Process
Model suggests a main pathway leading towards disability. When specific pathologies
emerge, these will ultimately cause some level of bodily impairment, which in turn may
lead to functional limitations, or restrictions in actions such as mobility or speech.
Whether or not disability emerges as an outcome is seen as a complex interaction of
95
functioning and a host of other internal and external factors that impact the process.
Impairments and functional limitations in this process are viewed as mediating variables.
Each of these stages can be measured in a number of different ways. Pathology may be
measured according to specific disease diagnosis, the severity of the disease, number of
chronic conditions, or a combination of the disease count and severity. Impairments are
commonly measured according to the diagnosed conditions and their known impact on
functioning. Functional limitations can be assessed with a variety of physical and
cognitive tests. Physical disability is often measured through self-reported difficulty in
doing daily activities of living.
Distinct from previous models is the added emphasis on contextual personal and
external moderating factors that ultimately may influence this process. For example,
there may be several modifiable and non-modifiable risk factors that may accelerate,
decelerate or halt the process towards disability. Rehabilitative care, medications, and
other external supports may even reverse the process. The intra-individual factors
emphasize that the individual has a very influential role in shaping outcomes through
psychosocial attributes, adopted coping strategies, and lifestyle behaviors.
Conceptual Models of Depression, Chronic Conditions, and Disability
Provided evidence for the reciprocal and synchronistic changes in physical
disability and depression (Ormel et al., 2002; Ormel & Von Korff, 2000), others have
offered conceptual models upon which to view the interactive relationship of depression
and chronic conditions on physical disability. These models share three features: 1)
chronic conditions may lead to physical disability and vice versa; 2) depression
96
potentially leads to poorer health behaviors which in turn increase vulnerability to
chronic conditions; and, 3) physical disability may increase risk of further depression
(Katon, 2003; Lenze et al., 2001). More recently Wang and colleagues (2006) have
provided clarity on how to define and categorize the relationship of contextual factors
(such as depression) within disability models. Provided the known relationship of
chronic conditions on depression and the potential relationship of chronic conditions on
disability outcomes, depression can be viewed as a “responsive” moderating factor of the
latter relationship. Specifically, chronic conditions can directly lead to depression and
depression also has an independent effect on the disablement process leading towards
disability outcomes. Alternatively, others have demonstrated the mediating role of
depression on physical disability (Braungart, 2005). Therefore, depression’s role on the
disablement process has been conceptualized in varying ways by researchers.
Empirical Evidence for the Relationship between Depression and Physical Disability
among Older Adults
A number of cross-sectional and longitudinal studies have provided empirical
evidence for the positive association between depression and physical disability among
older adults, and some on the causal nature of this relationship (Ormel & Von Korff,
2000). Most studies showing causality have examined only the unidirectional
relationship between depression and physical disability. Only a select few studies have
provided evidence for the reciprocal dynamic relationship between depression and
physical disability (Chen et al., 2012; Ormel et al., 2002).
97
Cross-sectional studies have demonstrated positive associations of depressive
symptoms with IADL disability (Kiosses, Klimstra, Murphy, & Alexopoulos, 2001;
Steffens, Hays, & Krishnan, 1999) and combined ADL/IADL disability in older adults
(Wallsten, Tweed, Blazer, & George, 1999). One longitudinal study demonstrated that
physical disability is a causal factor for onset of depression in older adults (Zeiss,
Lewinsohn, Rohde, & Seeley, 1996). A number of longitudinal studies, many
population-based, have evidenced late life depression as a causal factor for physical
disability. Several show that higher depressive symptomatology, as measured by the
CES-D, is predictive of greater risk for incident ADL disability among older adult
cohorts, suggesting an “accelerating” effect of depression on physical disability onset
(Barry et al., 2009; Braungart, 2005; Cronin-Stubbs et al., 2000; Penninx, Leveille,
Ferrucci, Van Eijk, & Guralnik, 1999; Reynolds et al., 2008; van Gool et al., 2005).
Following a racially-diverse high functioning cohort of adults aged 70-79 years, one
study found that high levels of depressive symptoms at baseline were highly predictive of
subsequent ADL disability 2.5 years later for both men (OR = 2.87) and women (OR =
4.27) (Bruce, Seeman, Merrill, & Blazer, 1994). Evidence suggests that among those
undergoing post-stroke rehabilitation, depression increases the odds of long-term stroke-
induced disability by 2.5 times compared to those without depression, suggesting that
depression decreases one’s chances of recovery from physical disability (Pohjasvaara,
Vataja, Leppävuori, Kaste, & Erkinjuntti, 2001).
Two studies have demonstrated the bidirectional relationship, or the reciprocal
effect, of depression and physical disability among older adults (Chen et al., 2012; Ormel
et al., 2002). Specifically, structural equation models demonstrated a strong, more
98
immediate effect of ADL/IADL disability on depression and a weaker one-year lagged
effect of depression on ADL/IADL disability among adults aged ≥57 years (Ormel et al.,
2002). This finding was also supported in another cohort of older adults aged ≥65 years.
Both physical disability and depressive symptoms were significant predictors of each
other, but physical disability more so than depressive symptoms (Chen et al., 2012).
These studies support the idea of a sychronistic relationship between depression and
physical disability (Ormel & Von Korff, 2000).
The Behavioral Model: Factors of Disability-Associated Long-Term Care Use
among Older Adults
The Behavioral Model was developed and refined over the last half-century to
understand and predict health service use in the hope of making access to care equitable
(Andersen & Davidson, 2007; Andersen, 2008). Since then, it has been widely used by
researchers to understand numerous types of service use and other health behaviors.
According to this model, three types of factors are viewed as core component predictors
of service use: predisposing characteristics, enabling resources, and need. These
components directly and indirectly influence and are influenced (via feedback arrows) by
personal health practices, current use of health services, and health outcomes. This
model distinguishes between individual and aggregate level characteristics that influence
the use of certain medical and LTC services. Thus, the Behavioral Model provides a
conceptual framework upon which to understand and predict the use of these services.
Researchers have also tried to estimate the amount of LTC services used by
disabled older adults. Data from the 1994-1997 National Health Interview Survey
99
indicated that, on average, older adults use nearly 36 hours of personal care assistance per
week (LaPlante et al., 2002). Data from the 2000 Health and Retirement Study suggests
that older adults dying in the community setting use, on average, nearly 66 hours per
week of informal care during the last year of life (Rhee et al., 2009). A nationally
representative survey of adults aged ≥70 years found that the number of informal
caregiving received significantly increased with the presence of depressive symptoms
even after adjusting for social and demographic factors, caregiver network, and other
chronic health conditions (Langa et al., 2004). More so, these researchers estimated a
yearly cost of $9 billion dollars of caregiving costs associated with depressive symptoms
(Langa et al., 2004) suggesting that depressed older adults differ from those without
depression in caregiving needs and use.
Conclusion
Late life depression can have a substantial impact on physical disability
trajectories and long-term care use and need. These, in turn, are associated with
increased medical care costs, injuries, and premature mortality among older adults.
Prevalence of physical disability increases with age, and is found to be even higher
among certain racial populations such as American Indians. Similarly, limited evidence
indicates higher depressive symptoms among older American Indians compared to their
racial and ethnic counterparts. Recent studies suggests that health and functioning trends
fueled by an aging adult population with greater morbidities will lead to a substantially
greater need for long-term care services. If formal (paid) care is largely inaccessible to
100
most adults with comorbid depression and physical disabilities, the bulk of this burden
will fall to those providing informal (unpaid) care.
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