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m m LIBRARY
100 2? Michigan State
Unwersrty

 

 

 

This is to certify that the
dissertation entitled

HEALTH DISPARITIES IN ACCESS TO HEALTH CARE
FOR OLDER PEOPLE WITH DISABILITIES

presented by

Jae Chul Lee

has been accepted towards fulfillment
of the requirements for the

Ph.D. degree in Rehabilitation Counselor
Education

 

 

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PLACE IN RETURN BOX to remove this checkout from your record.
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5/08 K./Prolecc&Pres/ClRC/DateDue.indd

 

HEALTH DISPARITIES IN ACCESS TO HEALTH CARE
FOR OLDER PEOPLE WITH DISABILITIES

By

Jae Chul Lee

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Rehabilitation Counselor Education

2008

ABSTRACT

HEALTH DISPARITIES IN ACCESS TO HEALTH CARE
FOR OLDER PEOPLE WITH DISABILITIES

By

Jae Chul Lee

Given the substantial number of individuals with disabilities and the sheer rapid
growth of US. aging population, research on health disparities of older people with
disabilities is vital for improving quality of life for society in general, as well as people
with disabilities. Nonetheless, there has been a great lack of research on health
disparities for older individuals with disabilities in the literature. To reduce and further
eliminate health disparities of older people with disabilities, as well as to provide
effective interventions and policies for eliminating and preventing health disparities, it is
imperative to investigate the existence of health disparities of this older population and
elucidate mechanisms in which health disparities occur.

The purpose of this study was to investigate the existence of health disparities in
access to health care between older people with and without disabilities and to what
extent such disparities could be accounted for by the behavioral model variables. Health
disparities in access to health care were examined in the following areas: cost barrier to
health care, health care provider, and routine physical checkup. In examining
mechanisms of health disparities, Andersen’s (1995) behavioral model variables—
predisposing characteristics, enabling resources, and perceived and evaluated health

needs—were used. Given a lack of research employing a national data set for health

disparities of older persons with disabilities, this study examined older people aged 65
and over in 50 states and the District of Columbia from the BRFSS for 2006 employing
chi-square tests and binary and multinomial logistic regression.

Chi-square tests found that significant disparities existed in access to health care
between older individuals with and without disabilities in the three aspects of health
disparities. In addition, hierarchical multivariate logistic regression analyses revealed
that such disparities occurred through intricate mechanisms; the mechanisms for the three
aspects of health disparities showed intricate patterns, respectively. In addition, the
extent of the effect of the behavioral model variables on the disparities was various in
each mechanism. The limitations of this study and suggestions for future studies are

also presented.

Copyright by
JAE CHUL LEE
2008

To my late father, Yong Heon Lee; my mother, Seong Nam J ang; my wife, Hyun Ju Jun;
and my daughters, Hyewon and Hyein Lee.

ACKNOWLEDGEMENT

First of all, I would like to express my heartfelt thanks to my family. First, I
would give my sincere thanks to my late father, Yong Heon Lee for providing me with
opportunity to work on social justice and quality of life for vulnerable populations
including people with disabilities. His traumatic brain injury due to a car accident made
him a totally different person and had an enormous toll on our family physically,
emotionally, and financially. However, I am deeply appreciative of my father because
his accident has made me what and who I am. His great love that I received before his
accident has been cherished deep in mind all the time, and I dearly dream a day to have a
talk with him in heaven. His silent but big smile, as well as his big chest and arms into
which I used to fling myself in the air when he came home after work, have been and will
be in my memory forever.

Second, my deep love and appreciation go to my mother, Seong Nam Jang for
her sacrifice to her husband and children all her life. I cannot dare to fathom the
enormous challenge she went through due to my father’s accident. I do still vividly
remember her pain, ordeal and cries I witnessed. Despite colossal challenges she faced
with a great lack of support, however, my mother dearly loved her husband and children.
For all her sacrifice and love, I would like to give my warmest thanks to my mother: “I
LOVE YOU MOTHER, AND THANK YOU FOR EVERYTHING YOU DID FOR US.
MY APPRECIATION FOR YOU IS WAY BEYOND DESCRIPTION.”

Third, I give thanks to my brother and sisters who shared with me all difficult

times due to father’s disability. Although there was not much talk about our problems

vi

among us, we could understand one another. The simple fact that we went through the
same challenge was considerable encouragement for me.

Finally, I would give my sincere thanks to my three ladies who have been by me
all the time: wife, Hyun Ju Jun, and daughters, Hyewon and Hyein. They brought a
bright light to my life. Moreover, they made me truly realize what a great joy it is to
love and be loved by my loved ones and to be a man and father. Their simple presence
as well as their big smile and laugh have given me enormous strength and support. My
lack of availability for them made me feel terribly sorry, which in turn made me focus
more on my study.

In relation to this study, I would like to thank all my dissertation committee
members. Without their assistance, I could not have completed this study. First, Dr.
John Kosciulek emphasized clear conceptualization and writing throughout not only the
dissertation process but also my doctoral study. These emphases, constructive criticism,
and detailed editing greatly improved this study. His stress on the clarity of
conceptualization and writing will be a great asset for my scholarly career. Second, Dr.
Michael Leahy and Dr. Chandra Donnell encouraged me to narrow down my original
broader interests to focus on access to health care and assisted me with a better
conceptualization of this study. Dr. Leahy also encouraged me to go beyond our
academic discipline and broad my horizons by taking many courses in several disciplines.
This extensive study outside our discipline greatly helped me to work on this study.
Third, Dr. Alexander von Eye contributed to the improvement of the quality of this study
through his advice and suggestions pertaining to statistical analysis and interpretation, as

well as his critical eye on the conceptual framework. Lastly, all the committee members

vii

should be given thanks for their helpful comments on the final draft of this study.

In addition, I would like to thank the following individuals outside the
dissertation committee for their kind replies to my e-mail questions: Dr. John Van
Hoewyk and Dr. Trivellore Raghunathan at the Survey Research Center of the University
of Michigan, and Mr. Bill Bartoli at the Center for Disease Control and Prevention
Information Technology Support (CITS). In particular, Dr. Van Hoewyk and Dr.
Raghunathan helped me to have a better understanding of IVEware and multiple
imputation, and Mr. Bartoli assisted me with the structure of the BRFSS. It should be
mentioned, however, that, despite their generous assistance from my committee members
and other experts, all potential errors in this study lie solely with me.

Great assistance from Project Excellence (PE) also should be mentioned. My
work experience at PE, which made me believe that my choice of MSU for doctoral study
was excellent, developed my skills about and confidence in working on state and national
data sets. In addition, I thank Dr. Darlene Groomes for her emotional support during
this study and Dr. Virginia Thielsen for her. generous support for letting me take summer
courses at the University of Michigan for the dissertation.

Lastly, my deepest thanks go to Heavenly Father for his unceasing love and grace.
He made it possible for me to be thankful for my entire life. In addition, through Him, I
could build great fi’iendship with John and Bonnie Bankson and Dick and Elaine
Ranshaw, who provided me and my family with considerable help. In particular, the

Banksons have been not only great friends but also family for us.

viii

TABLE OF CONTENTS

LIST OF TABLES ............................................................................................................. xi

LIST OF FIGURES ......................................................................................................... xiii
CHAPTER 1

INTRODUCTION .............................................................................................................. 1

Conceptual Framework for the Study ........................................................................... 4

Significance of the Problem .......................................................................................... 8

Purpose of the Study and Research Questions .............................................................. 9
CHAPTER 2

LITERATURE REVIEW .................................................................................................. 15

Health Disparities ........................................................................................................ 15

Definitions and Measurement of Health Disparities ......................................... 16

Determinants of Health Disparities in Access to Health Care .................................... 23

Sociodemographic Characteristics. ................................................................... 23

Health Care Practices and Personal Lifestyle and Behaviors ........................... 26

Cultural Factors ................................................................................................. 28

Other Factors ..................................................................................................... 30

Health Disparities of People with Disabilities. ........................................................... 30

Rehabilitation Counseling ................................................................................. 32

Health Disparities for People with Disabilities ................................................. 36
CHAPTER 3

METHDOLOGY .............................................................................................................. 41

Data Set ofthe Study ................................................................... 43

Analysis of Complex Survey Data .............................................................................. 47

Procedures ................................................................................................................... 53

Exploratory Data Analysis. ............................................................................... 53

Re-coding .......................................................................................................... 54

Missing Data ..................................................................................................... 54

Criterion and Predictor Variables. ............................................................................... 60

Criterion Variables ............................................................................................ 61

Predictor Variables ............................................................................................ 62

Predisposing Characteristics. .................................................................. 63

Enabling Resources ................................................................................. 64

Health Needs ........................................................................................... 65

Perceived Health Needs. ............................................................... 65

Evaluated Health Needs ................................................................ 66

Data Analyses .............................................................................................................. 66

Statistical Analysis ............................................................................................ 68

Logistic Regression ................................................................................. 69

ix

CHAPTER 4

RESULTS .......................................................................................................................... 71
Characteristics of Older People with and without Disabilities in the Study. .............. 71
Predisposing Characteristics ............................................................................. 75
Enabling Resources ........................................................................................... 75
Health Needs. .................................................................................................... 76
Existence of Disparities in Access to Health Care ...................................................... 76
Cost Barrier to Health Care ............................................................................... 77
Personal Health Care Provider. ......................................................................... 78
Routine Physical Checkup ................................................................................ 79
Relationships of Predictor Variables with Health Disparities in Access to Health Care
..................................................................................................................................... 80
Cost Barrier to Health Care ............................................................................... 81
Personal Health Care Provider. ......................................................................... 86
Routine Physical Checkup ................................................................................ 96
CHAPTER 5
DISCUSSION. ................................................................................................................ 105
Findings of the Study ................................................................................................ 105
Discussion of the Disparities in Access to Health Care ............................................ 107
Cost Barrier to Health Care ............................................................................. 109
Personal Health Care Provider. ........................................................................ 114
Routine Physical Checkup ............................................................................... 119
Limitations of the Study ............................................................................................ 121
Suggestions for Future Research .............................................................................. 125
Conclusions ............................................................................................................... 126
REFERENCES ............................................................................................................... 128

Table

3.1

3.2

4.1

4.2

4.3

4.4

4.5

4.6

4.7

4.8

4.9

4.10

4.11

LIST OF TABLES

Page
Distribution of Access to Health Care and its Determinants by Imputed and
Un-imputed Data in 91,005 Older People Age 65 and Older from the BRFSS
for 2006 .................................................................................... 58
Criterion and Predictor Variables ...................................................... 61
Characteristics of Older People with and without Disabilities by Predictor
Variables from the BRFSS for 2006 ................................................... 72
Cost Barrier to Health Care between Older People with and without
Disabilities ................................................................................. 77

Chi-Square Test for Disability Status and Cost Barrier to Health Care by Age.. 78

Personal Health Care Provider between Older People with and without
Disabilities ................................................................................. 78

Chi-Square Test for Disability Status and Personal Health Care Provider Care
by Age ..................................................................................... 79

Routine Physical Checkup between Older People with and without
Disabilities ................................................................................. 79

Chi-Square Test for Disability Status and Routine Physical Checkup by Age... 80

Odds Ratios (95% CI) for Cost Barrier to Health Care by Disability Status
from the BRFSS for 2006 ............................................................... 82

Adjusted Odds Ratios (95% CI) for Cost Barrier to Health Care by Predictor
Variables from the BRF SS for 2006 ................................................... 82

Odds Ratios (95% CI) for Personal Health Care Provider by Disability Status
for ‘No Personal Health Care Provider’ versus ‘More than One Health Care
Provider’ from the BRFSS for 2006 ................................................... 87

Odds Ratios (95% CI) for Personal Health Care Provider by Disability Status

for ‘Only One Personal Health Care Provider’ versus ‘More than One Health
Care Provider’ from the BRFSS for 2006 ............................................ 88

xi

Table Page

4.12 Adjusted Odds Ratios (95% CI) for Personal Health Care Provider by
Predictor Variables from the BRF SS for 2006 ....................................... 89

4.13 Odds Ratios (95% CI) for Routine Physical Checkup by Disability Status for
‘More than 5 Years ago or Never’ versus ‘Less than 2 Years ago’ fi'om the
BRFSS for 2006 .......................................................................... 97

4.14 Odds Ratios (95% CI) for Routine Physical Checkup by Disability Status for
‘2 Years to 5 Years ago’ versus ‘Less than 2 Years ago’ from the BRFSS for
2006 ........................................................................................ 98

4.15 Adjusted Odds Ratios (95% CI) for Routine Physical Checkup by Predictor
Variables from the BRF SS for 2006 ....... . ............................................ 99

xii

LIST OF FIGURES

Figure Page

1.1 Conceptual Framework for Access to Health Care ................................... 7

xiii

HEALTH DISPARITIES IN ACCESS TO HEALTH CARE
FOR OLDER PEOPLE WITH AND WITHOUT DISABILITIES

CHAPTER 1

INTRODUCTION

Health assumes a critical and fundamental role in actualizing people’s dreams and
maximizing their lives, whether they have a disability or not. Since Friedrich Engels
(1845/1958) raised an issue of society inequalities in health in The Condition of the
Working Class in England in 1845, one and a half centuries have passed. However, it is
well documented that health disparities prevail in the United States (e.g., Smedley, Stith,
& Nelson, 2003; Thomson, Mitchell, & Williams, 2006; US. Department of Health and
Human Services, 2006; Ver Ploeg & Perrin, 2004). In efforts to reduce and eliminate
health disparities, the US. has placed a primary focus on racial and ethnic minorities
(Braveman, 2006; Goldberg, Hayes, & Huntley, 2004; Ver Ploeg & Perrin, 2004). In a
stark contrast, people with disabilities have been given meager attention in health
disparities research, despite the sheer large number of people with disabilities and their
marginalized social status.

According to the US. Census Bureau (2005), as of July 2003, 35.9 million people
who are 65 and older accounted for 12 percent of the total US. population. The number
of older people is projected to be 72 million in 2030, representing 20 percent of the total
population (U .S. Census Bureau, 2005). The dramatic increase of the older population
in the US. becomes more evident, when compared to 3.1 million for the same age group
in 1900. Regarding life expectancy, in 2003, that of Americans reached 77.6 years for

both sexes with 74.8 years for male and 80.1 years for females (Hoyert, Kung, & Smith,

2005). When it comes to individuals with disabilities, they can be expected to live as
long as those without disabilities due to enhanced medical practice and advanced science
and technology.

In 2000, 49.7 million people with some kind of long-lasting condition or disability
represented 19.3 percent of 257.2 million non-institutionalized civilians age 5 and older
(US. Census Bureau, 2003). In addition, 14 million older people with disabilities age
65 and older accounted for 41.9% of 33.3 million people in the same age range in 2000
(U .S. Census Bureau, 2003). Moreover, National Center for Health Statistics (2006)
indicates that increased longevity is accompanied by increased prevalence of chronic
diseases and conditions that are positively associated with aging. Furthermore, the
“oldest-old” population age 85 and older is the fastest-growing subset of the older
population in the United States (Himes, 2002). This oldest-old group is more likely to
have various physical or mental illnesses and disabilities.

These statistics and studies show that a significant portion of the US. population
is living with a disability. Further, they indicate that the number of older people will
continuously increase in the future and that the older population is more apt to have
illnesses and disabilities whether physical or mental. In sum, given the mounting older
people, their increased life expectancy, and their high likelihood of having illnesses or
disabilities, health disparities of older individuals with disabilities warrant rigorous and
extensive research.

The sheer growing number of older people poses great challenges for society.
The graying of society requires our preparation for unexperienced issues pertaining to

economic well-being, health, and long-term care (Clark, Burkhauser, Moon, Quinn, &

Smeeding, 2004). Given the further health issues for people with disabilities and
society’s unpreparedness for the issues, extensive and thorough research on the health of
older people with disabilities is of critical importance.

Research on health disparities involves ethical judgment because health disparity
includes determination of whether a disparity is avoidable, unjust, or unfair (Braveman,
2006; Carter-Pokras & Baquet, 2002; Whitehead, 1990). Health disparities deal with
social values pertaining to distributive justice and human rights (Braveman, 2006; Hunt,
2006; Universal Declaration of Human Rights, 1948; World Health Organization, 1946).
This approach to health disparities in terms of social justice and human rights indicates
that the disparities should be broadly addressed and well applied to people with
disabilities. However, the US has focused its main attention on racial and ethnic groups
to ensure them enhanced quality of life. Nevertheless, given the fact that people with
disabilities are the biggest minority group in the U.S.—almost one in five US. residents
age 5 and over has a disability (US. Census Bureau, 2003)—it is a true irony that it was
not until recently that health disparities of people with disabilities started to be
investigated. In particular, since Healthy People 2010 included disability and secondary
conditions in its 28 focus areas (USDHHS, 2000), more studies on the health disparities
of people with disabilities have been conducted (e.g., Drum, Krahn, Culley, & Hammond,
2005; Havercamp, Scandlin, & Roth, 2004; Ouellette-Kuntz, 2005; USDHHS, 2002a,
2005)

Health disparities have been typically measured through comparisons between
disadvantaged and advantaged groups (Bravemen, 2006), such as majority population and

minority p0pulation in terms of specific health indicators (e.g., Minority Health and

Health Disparities Research and Education Act of 2000; Satcher, 1999). In this study,
health disparities in access to health care of older people with disabilities were
investigated through comparisons with their counterparts without disabilities. To render
policies further effective to eliminate and prevent health disparities, scientific
understanding of determinants of health disparities is important (Adler, 2006). This can
be reflected in recent health disparities studies that place their foci on examining
determinants of health disparities rather than simply the existence of health disparities
(e.g., Baum, Garofalo, & Yali, 1999; Brunner & Marmot, 1999; House, 2002; House &
Williams, 2000; Kaplan, 1999; Kuh & Ben-Shlomo, 1997). In this study, the
determinants of health disparities of older population with disabilities were examined in
access to health care.
Conceptual Framework for the Study

Because a wide array of factors plays a role in health disparities, clarifying
determinants of health disparities is demanding and challenging. For the development
of a conceptual framework for health disparities in access to health care, main attention
was focused on the behavioral model of health services use, specifically the phase 4 of
the model (Andersen, 1995), given its comprehensiveness and long-term use in health
care access research (Phillips, Morrison, Andersen, & Aday, 1998; Ricketts & Goldsmith,
2005). This behavioral model was developed and has been evolved by Andersen and
colleagues (Aday & Andersen, 1974; Andersen, 1995; Andersen & Newman, 1973;
Davidson, Andersen, Wyn, & Brown, 2004).

In the behavioral model of health services use (Andersen, 1995), population

characteristics are composed of three main elements: 1) predisposing characteristics, 2)

enabling resources, and 3) need. The predisposing characteristics consist of the
following factors: a) demographic factors, such as age, gender, or marital status, b) social
structure, such as education, race/ethnicity, occupation, or religion, and c) health beliefs,
such as attitudes toward health services, knowledge about disease, and values about
health and illness. These predisposing characteristics influence enabling resources in
person, family, and community. Personal/family enabling resources include knowledge
about access to and use of health care and means to access health care, whereas
community enabling resources incorporate available health personnel and facilities.
These enabling resources affect one’s perceived and evaluated health need. Perceived
health need refers to how people think of their health status, whereas evaluated health
need indicates professional judgment about people’s health conditions or illnesses and
their need for medical treatment.

The aforementioned population characteristics—predisposing characteristics,
enabling resources, and health need—impact one’s health behavior: personal health
practices and use of health services. Finally, one’s health behavior affects health
outcomes: (perceived and evaluated) health status and consumer satisfaction. In the
phase 4 of the behavioral model (Andersen, 1995), environmental factors—health care
system and external environment—were newly added, and these factors influence the
population characteristics and health outcomes.

It needs to be noted that the processes of health service utilization in the
behavioral model are not simply linear but dynamic and recursive (Andersen, 1995).
That is, health outcomes—the final stage of the model—affect population characteristics

and health behavior, whereas health behavior influences population characteristics.

Moreover, environmental factors influence not only population characteristics but also
health outcomes. Further, population characteristics impact health outcomes directly, as
well as through health behavior.

Davidson, Andersen, Wyn, and Brown (2004) present a framework of contextual
variables to extend the behavioral model (Andersen, 1995). These contextual variables
include social, economic, structural, and public policy environment. This framework
was employed in an empirical study toward low-income adults (Brown, Davidson, Yu,
Wyn, Andersen, Becerra, & Razack, 2004). The study shows that public policies and
community environment impact access to health care for both insured and uninsured
adults. Given all the aforementioned factors and the data set of this study, a conceptual

framework for access to health care in this study is illustrated in Figure 1.1.

  
   

   

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In this conceptual framework, predisposing characteristics—sex, age, race,
marital status, and education level—affect one’s enabling resources—annual household
income, social and emotional support, and health care coverage. The enabling resources
influence one’s perceived and evaluated health needs, which impact one’s access to
health care. More specifically, while perceived health needs are one’s self-rated general
health status, evaluated health needs refer to chronic diseases, such as diabetes, asthma,
and cardiovascular diseases——heart attack, coronary heart disease, and stroke. In this
framework, one’s access to health care was conceptualized as a dynamic process, rather
than a one-way process over one’s life course. Therefore, one’s access to health care
affects all the aforementioned prior factors: predisposing characteristics, enabling
resources, and health needs. In addition, it was conceptualized that one’s access to
health care could change over his or her life span in this framework. Thus, changes in
many factors can impact one’s access to health care. For example, changes in people’s
SES, location of residence, health status, values and attitudes about health and health care
services, or health policy can influence whether people can access health care or not.

Significance of the Problem

For individuals with disabilities, studies on health disparities are crucial to
maximize likelihood to lead their later life minimizing additional health-related
challenges. Moreover, given that people who do not have a disability as youth tend to
have illness or disability in their later life, research on health disparities for people with
disabilities will increase the quality of life of the general public. In all, given the large
number of individuals with disabilities and the rapidly aging US. society, research on

health disparities of older population with disabilities is essential for enhancing quality of

life for society in general, as well as people with disabilities.

To reduce and ultimately eliminate health disparities of older population with
disabilities, it is imperative to investigate the existence of health disparities of this
population and elucidate mechanisms in which health disparities occur. Findings from
such research as this can provide viable ideas for effective interventions and policies for
eliminating and preventing health disparities. Moreover, those findings can promote
healthy aging for both people with disabilities and those without disabilities. Given that
people with disabilities is one of the most disadvantaged groups in society in terms of
education, income, occupation, employment, or living condition, health disparities could
be expected to exist, as compared to people without disabilities. In this case, it is vital
to find which factors contribute to the health disparities. Despite the importance of the
examination of the health disparities, there has been no extensive research focusing on
health disparities of people with disabilities, employing a national data set.

This study is expected to spark further interest in health disparities and healthy
aging of individuals with disabilities among rehabilitation counseling professionals,
disability scholars, and policy makers. Specifically, this study will encourage
researchers to discuss how health disparities of people with disabilities should be
effectively studied, and which factors should be most critically measured and monitored.
More rigorous and extensive research will help society better understand the current
picture of health disparities of people with disabilities and their causes. As a result, the
elimination and prevention of the disparities of the population and their resultant
enhanced quality of life will be expected.

Purpose of the Study and Research Questions

The purpose of this study was twofold: 1) to examine the existence of health
disparities in access to health care between older people with disabilities and older people
without disabilities, and 2) to investigate the impacts of predisposing characteristics,
enabling resources, and health needs on the health disparities, if such disparities exist.
To achieve these purposes, the existence and determinants of the health disparities were
examined in these areas: cost barrier to health care, personal health care provider, and
routine physical checkup. In this study, individuals aged 65 and older were specified as
older people, and older people without disabilities served as a reference group. These
specific questions were employed to examine the health disparities:

1. Do health disparities in access to health care exist between older people with
disabilities and older people without disabilities?

1) Are there significant differences in cost barrier to health care between older

people with disabilities and older people without disabilities?

2) Are there significant differences in personal health care provider between

older people with disabilities and older people without disabilities?

3) Are there significant differences in routine physical checkup between older

people with disabilities and older people without disabilities?

2. If health disparities in access to health care exist between older people with
disabilities and older people without disabilities, to what extent are such
disparities accounted for by predisposing characteristics, enabling resources, and
perceived and evaluated health needs?

1) Cost Barrier to Health Care

a) Are predisposing characteristics (sex, age, race, marital status, and

10

b)

d)

education level) related to cost barrier to health care? If so, to what
extent?

Are enabling resources (annual household income, social and
emotional support, and health care coverage) related to cost barrier to
health care after predisposing characteristics are accounted for? If so,
to what extent?

Are perceived health needs (general health status, physical health, and
mental health) related to cost barrier to health care afier predisposing
characteristics and enabling resources are accounted for? If so, to
what extent?

Are evaluated health needs (diabetes, asthma, heart attack, coronary
heart disease, and stroke) related to cost barrier to health care after
predisposing characteristics, enabling resources, and perceived health

needs are accounted for? If so, to what extent?

2) Personal Health Care Provider

3)

b)

C)

Are predisposing characteristics (sex, age, race, marital status, and
education level) related to personal health care provider? If so, to
what extent?

Are enabling resources (annual household income, social and
emotional support, and health care coverage) related to personal
health care provider after predisposing characteristics are accounted
for? If so, to what extent?

Are perceived health needs (general health status, physical health, and

11

mental health) related to personal health care provider after
predisposing characteristics and enabling resources are accounted for?
If so, to what extent?

(I) Are evaluated health needs (diabetes, asthma, heart attack, coronary
heart disease, and stroke) related to personal health care provider after
predisposing characteristics, enabling resources, and perceived health
needs are accounted for? If so, to what extent?

3) Routine Physical Checkup

a) Are predisposing characteristics (sex, age, race, marital status, and
education level) related to routine physical checkup? If so, to what
extent?

b) Are enabling resources (annual household income, social and
emotional support, and health care coverage) related to routine
physical checkup after predisposing characteristics are accounted for?
If so, to what extent?

c) Are perceived health needs (general health status, physical health, and
mental health) related to routine physical checkup after predisposing
characteristics and enabling resources are accounted for? If so, to
what extent?

(I) Are evaluated health needs (diabetes, asthma, heart attack, coronary
heart disease, and stroke) related to routine physical checkup after
predisposing characteristics, enabling resources, and perceived health

needs are accounted for? If so, to what extent?

12

Given the questions above, this study employed descriptive statistics, chi-square
test and logistic regression. The data for this study were drawn from the Behavioral
Risk Factor Surveillance System (BRFSS). This national survey is a collaborative
project of the Centers for Disease Control and Prevention (CDC) and US. states and four
territories. The on-going annual national telephone survey examines health conditions
and health risk behaviors mainly related to chronic disease and injury toward non-
institutionalized civilian population age 18 and older (CDC, n.d.d).

This study represents a first attempt to investigate health disparities of older
people with disabilities employing a national data set. Many studies on people with
disabilities have been conducted using the BRFSS (e. g., Brown, Yore, Ham, & Macera,
2005; McGuire, Strine, Okoro, Ahluwalia, & Ford, 2007; Havercamp et al., 2004; Kinne,
Patrick, & Doyle, 2004). However, these studies have some limitations. First, most of
the studies did not place their primary attention on health disparities. Brown et al.
(2005) compared adults with disabilities age 50 and older with their counterparts without
disabilities, but they attempted to assess physical activity patterns among the older adults.
Moreover, in comparing older people with disabilities age 65 and older with their
counterparts without disabilities, McGuire et al. (2007) examined the relationship
between healthy lifestyle behaviors and chronic diseases. Further, Kinne and colleagues
(2004) investigated prevalence of secondary conditions among individuals with
disabilities in the state of Washington. Second, although Havercamp et al. (2004)
focused on health disparities and compared adults with developmental disabilities with
adults with other disabilities and those without disabilities, their data sets were limited

only to North Carolina. Given these limitations, this study addressed data from all 50

13

states and the District of Columbia. US. territories were not included in the study since
their unique culture and lifestyle may impact one’s health conditions, health behavior and

practices, and access to health care.

14

CHAPTER 2

LITERATURE REVIEW

In the previous chapter, it has been clearly pointed out that health takes on great
importance in every individual’s life. The importance can be further immense for
individuals with disabilities. It has been well known that individuals with disabilities
have been exposed to marginalized treatment in the US, compared to their counterparts
without disabilities. Despite the critical significance of health for people with
disabilities and their marginalized social status, however, the rehabilitation counseling
profession has not given much attention to health disparity issues until now. Society
also has not extensively addressed health disparities issues pertaining to people with
disabilities. Given the lacking interest in and scanty research on such disparities of
people with disabilities, especially in the rehabilitation profession, this chapter deals with
major concepts of health disparities, such as their definition, measurement, and
determinants. In addition, health disparities of people with disabilities are addressed.
In so doing, how the rehabilitation counseling profession has dealt with health issues for
people with disabilities is discussed.

Health Disparities
A difficult task of defining health disparity could be aggravated by confusion
from similar words, such as health inequality and health inequity. Whilst the US.
prefers a term health disparity, other countries more frequently use such terms as health
inequity or health inequality (Carter-Pokras & Baquet, 2002). In particular, the term

health inequalities are more frequently used in Great Britain and Europe. In addition,

15

the use of the term health disparities has exponentially increased in the US. medical
literature in the 20005 (Adler, 2006).

Health disparity includes elements of both health inequality and health inequity
(Harper & Lynch, 2005). Inequality is a difference that can be measurable and
observable whereas inequity includes an ethical judgment involved in decision of the
difference (Harper & Lynch, 2005). According to Whitehead (1990), inequity is
differences that are unnecessary, avoidable, unfair, and unjust, so a moral and ethical
dimension is involved in the decision of health inequity. As a result, health disparity
research involves ethical judgment, and, more specifically, whether a disparity is
avoidable and unjust needs to be determined (Carter-Pokras & Baquet, 2002). The
ethical determination is about what a society believes is unfair, which is related to many
social, political, and ethical issues (Peter & Evans, 2001).

The aforementioned ethical issues on health disparity clearly show that it is a
great challenge to reach an unambiguous definition of health disparity. Moreover, the
ethical judgment in health disparity necessitates finding determinants of the disparity
(Harper & Lynch, 2005). That is, whether we can intervene to avoid health disparity
requires us to know what its determinants are. Since definitions and measurement of
health disparity are closely related, they are addressed together in the following.
Definitions and Measurement of Health Disparities

Sociologist Louis Wirth (1945) presented the following definition of a minority
group: “a group of people who, because of their physical or cultural characteristics, are
singled out from the others in the society in which they live for differential and unequal

treatment, and who therefore regard themselves as objects of collective discrimination” (p.

16

347). This definition shows that although there have been many efforts to reduce and
eradicate prejudice and discrimination against people with disabilities and to enhance
their social status and participation in society, people with disabilities are still a minority
group in the US. The marginalized status of this population in society could be
reflected in the lack of attention to and extensive research on health disparities of
individuals with disabilities. The lacking attention and research may indicate challenges
pertaining to measurement of health disparities of population with disabilities, such as
what specific areas should be examined for health disparities of people with disabilities,
and how the disparities should be measured. A literature review of how previous
primary studies defined and approached health disparities could serve as a guideline for
overcoming these challenges.

Regarding what specific areas should be examined for health disparities, Smedley,
Stith, and Nelson (2003) investigated racial and ethnic disparities from health care quality,
as well as access to health care. In addition, Ver Ploeg and Perrin (2004) studied
minority health disparities in terms of health and access to health care. Moreover,
Thomson, Mitchell, and Williams (2006) approached health disparities of racial and
ethnic minority populations from three aspects: health status, health care access and
quality, and health outcome. Braveman (2006) also shows through an extensive
literature review that a big body of US. literature on health disparities centers on two
areas for racial and ethnic disparities: health status and health care.

All the aforementioned studies placed main foci on racial and ethnic minorities.
In addition, these studies indicate that health disparities research has centered on the

following areas: 1) health in health status and health outcome, 2) access to health care,

17

and 3) quality of health care. However, it should be noted that these approaches could
vary due to the focus and goal of research. Since detailed information on health
outcomes, such as incidence, mortality, or the severity of diseases, and quality of health
care is not available in the core section of the BRFSS, health disparities of older people
with disabilities in this study were focused on access to health care. When it comes to
the measurement of health disparities of older people with disabilities, reviews of
definitions of health disparities should be helpful. Despite a great challenge of finding a
clear definition of health disparity, many attempts have been made to clarify what health
disparity is. However, these definitions are not all straightforward and explicit. While
dealing with definitions of health disparities, measurement issues are also discussed.

First, National Institute of Health (U SDHHS, 2002b) presents the following
definition of health disparities: “differences in incidence, prevalence, morbidity, mortality
and burden of diseases and other adverse health conditions that exist among specific
population groups in the United States” (p. 7). In this definition, health disparities
among specific population groups are measured in specific health outcomes. The health
disparities are not determined through a comparison with the general population or a
majority population. Moreover, the magnitude of the differences is not specified in the
definition. As a result, the definition does not serve as a clear guideline on how to
measure health disparities.

Second, Minority Health and Health Disparities Research and Education Act of
2000 defines health disparity as follows: “A population is a health disparity population
if. . .there is a significant disparity in the overall rate of disease incidence, prevalence,

morbidity, mortality, or survival rates in the population as compared to the health status

18

of the general population” (p. 2498). In this act, a health disparity is a significant
difference in health using the general population as a reference group. Therefore, if a
specific population has significant differences in health outcomes compared to the

general population, the specific population is determined to have health disparities. In
the process, the magnitude of the differences must be significant (Harper & Lynch, 2005).

Third, Satcher (1999), who was Assistant Secretary for Health and Surgeon
General and became US. Surgeon General later, made a comment on the elimination of
health disparity, through which the definition of health disparities could be obtained:

For all the medical breakthroughs in the past century, there remain significant

disparities in the medical conditions of racial groups in this country—a country

that is growing more and more diverse every day. What we have done through
this initiative is to make a commitment—really for the first time in the history of
our government—to eliminate, not just reduce, some of the health disparities

between majority and minority populations. (pp. 18-19)

From the above quotation, health disparities can be defined as differences in
health outcomes among racial groups. Satcher (1999) provided six focal areas for the
measurement of the disparities: infant mortality, immunization of children and adults, the
AIDS epidemic, cardiovascular disease, cancer, and diabetes. Therefore, health
disparities are measured as significant differences in the six areas between majority and
minority populations.

In contrast to the definition of National Institute of Health (U SDHHS, 2002b), the
last two definitions employ comparisons between a racial and ethnic minority group and

a majority group or the general population. However, these definitions do not clarify

19

what the minority group and the majority group are. Instead, a racial and ethnic
minority group is implicitly indicated as a minority group. Thus, health disparities of
racial and ethnic minority populations are determined through comparisons with the
majority population or the total population in specific health indicators. Bravemen
(2006) corroborates this finding. In his extensive literature review, Bravemen shows
that health disparities have been typically measured through comparisons between
disadvantaged and advantaged groups.

As compared to people with disabilities, those without disabilities are considered
an advantaged group, and, therefore, serve as a reference group in this study. As a result,
older people with disabilities were compared with their counterparts without disabilities
to examine health disparities in access to health care in this study.

It is important to note that the aforementioned definitions of health disparities do
not address people with disabilities. Although those definitions present some guideline
on the measurement of health disparities, their main attention is focused on racial and
ethnic minority groups. In contrast, Healthy People 2000 and Healthy People 2010
included disability to reduce and eliminate health disparities.

Healthy people 2000 (USDHHS, 1995) is a national health promotion and disease
prevention initiative, and reducing health disparities was one of three broad goals.

The second goal is to close thegaps in health status and health outcomes between

racial and ethnic minorities and the total population. Across many health

measures—mortality, morbidity, and health services utilization—the differences

between whites and minorities continue to be substantiated. (p. 6)

As indicated above, health disparities in Healthy People 2000 are differences in

20

health status and health outcomes between racial and ethnic minorities and the total
population. Moreover, Healthy People 2000 focused on access to preventative services
(U SDHHS, 1993). In Healthy People 2000, people with disabilities were included in
risky populations for health problems, with racial and ethnic groups and poor people

(U SDHHS, 1995). For disabilities, it should be noted, however, that Healthy People
2000 focused only on diabetes and chronic disabling conditions as priority areas.

Reduction of health gaps set forth as a goal in Healthy People 2000 was further
emphasized in Healthy People 2010 as indicated in the following: “The second goal of
Healthy People 2010 is to eliminate health disparities among segments of the population,
including differences that occur by gender, race or ethnicity, education or income,
disability, geographic location, or sexual orientation (p. 11).”

Compared to the predecessor, Healthy People 2010 shows considerable progress
in efforts to tackle health disparities. First, Healthy People 2010 has a goal of
eliminating health disparities beyond reducing them. Second, Healthy People 2010
specifies various factors for differences in health: gender, race and ethnicity, education or
income level, disability status, geographic location, or sexual orientation. On the other
hand, Healthy People 2000 specified three risky groups for health, including people with
disabilities. However, it is important to note that the aforementioned factors in Healthy
People 2010 do not necessarily cause health disparities (Keppel, Pearcy, & Klein, 2004).
Rather, the second goal of Healthy People 2010 serves to call attention to differences in
health which could be caused by those factors. Third, when it comes to disability,
Healthy People 2010 includes disability and secondary conditions in its 28 focus areas

(USDHHS, 2000). In Healthy People 2010, individuals are identified as having

21

disabilities if they have an activity limitation, use special equipment, or perceive
themselves as having a disability (USDHHS, 2000). In contrast, Healthy People 2000
placed its focus on diabetes and chronic disabling conditions as priority areas. Lastly,
Healthy People 2010 focuses on access to quality health care for eliminating health
disparities, as well as health status (USDHHS, 2007).

As compared to the previous three definitions, Healthy People 2000 and Healthy
People 2010 include disability as a risk factor for health. In particular, Healthy People
2010 deals with disability as a potential factor in health disparity, requiring an attention
to the factor. This is a noticeable progress because the US. has placed its primary focus
on racial and ethnic differences in health or health care in health disparities research (e.g.,
Kelley-Moore & Ferraro, 2004; Liao, McGee, Cao, & Cooper, 1999; Schoenbaum &
Waidmann, 1997; USDHHS, 2005). However, despite its progress in Healthy People
2010, the national health promotion initiative still has limitation. Contrary to the other
factors mentioned previously, disability status and geographic location are optional
characteristics in monitoring selected objectives and subobjectives (U SDHHS, 2007).
Regarding the measurement of health disparities, the best group rate is employed as a
reference in Healthy People 2010 (Keppel et al., 2004). For instance, the percentage
differences obtained through comparisons of the best racial and ethnic group with other
racial and ethnic group rates for given characteristics are measured for health disparities
(Keppel et al., 2004).

In sum, health disparities were defined in this study as avoidable differences in
access to health care between older people with and without disabilities. In addition, the

differences in access to health care were measured using older people without disabilities

22

as a reference group.
Determinants of Health Disparities in Access to Health Care

In this study, health disparities among older people with and without disabilities
were examined in access to health care. More specifically, the existence and
determinants of such disparities were focused on. In the previous chapter, predisposing
characteristics, enabling resources, and perceived and evaluated health needs were
conceptualized to impact one’s access to health care. In the conceptual framework, the
process of access to health care was dynamic and interactive, not as linear. As a result,
it should be noted that access to health care itself influences one’s predisposing
characteristics, enabling resources, and health needs. Nonetheless, more attention is
here focused on the linear impacts of the determinants on health care access. Given the
intricate and dynamic mechanism of health care access, the determinants of access to
health care are broadly addressed in the following areas: 1) sociodemographic
characteristics, 2) health care practices and personal lifestyle and behaviors, 3) cultural
factors, and 4) other factors. The relationship between health status and access to health
care is discussed in describing other determinants.
Sociodemographic Characteristics

A range of sociodemographic characteristics, such as race and ethnicity, income,
employment, social stratification, age, and sex, are involved in access to health care.
These sociodemographic characteristics are interconnected with not only one another but
also other determinants in different areas. Since health insurance status and coverage
issues play a significant role in access to health care and these issues are closely related to

other sociodemographic factors, discussion on sociodemographic characteristics is

23

centered on health insurance issues.

A national healthcare disparities report for 2006 illustrates that health disparities
in access to health care remain prevalent, and that racial and ethnic minorities and poor
people have less access to health care (U SDHHS, 2006). However, this report shows
that the access issues tend to be enhanced in older population than their young
counterparts due to Medicare provided for almost all older people in the United States.
This may suggest that age is positively associated with health care access in the U.S.,
compared to young counterparts. However, Gluck and Hanson (2001) present a finding
that a larger portion of people who rely on Medicare alone reported delaying care due to
cost, compared to people with private health insurance coverage. This indicates that the
universal coverage of Medicare for older people has limitation to dealing with health
disparities issues among the older population and that the impact of age on access to
health care merits further examination.

When it comes to sex, a significantly higher portion of females has a specific
source of ongoing care than male (USDHHS, 2006). Gender differences impact job
tenure, labor force participation, life course patterns, retirement, and self-rated health, to
name a few (George, 2001; Henretta, 2001; Moen, 2001). These differences may lead
to disparities in access to health care between males and females.

Insurance coverage, race and ethnicity, and income were found to be important
for access to health care, and their relationships to access to health care were complex
(e.g., Becker & Newsom, 2003; Berk & Schur, 1998; Glaser, 1993; Mueller, Patil, &
Boilesen, 1998; Schur & Berk, 1998; Smedley et al., 2003). Mueller and colleagues

(1998) examined people under 65 using data from the 1992 National Health Interview

24

Survey (N HIS) to investigate the impact of health insurance status, race and ethnicity,
and place of residence on access to health care. Mueller et al. (1998) demonstrated that
insurance status had the biggest impact on utilization of healthcare services. Moreover,
Muntaner and Parsons (1996) revealed that sociodemographic variables and social
stratification were associated with health insurance. More specifically, people who had
full-time employment, a bachelor’s degree or more, and higher income households, and
worked in larger firms and production industries were more likely to be covered by
private health insurance.

In research on health insurance, health care coverage also should be paid careful
attention. Using the 1994 Robert Wood Johnson Foundation National Access to Care
Survey, Berk and Schur (1998) showed that not only having health insurance but also
type of health insurance were associated with access to health care. In this study, people
in Medicaid reported half of difficulty of obtaining services compared to people without
health insurance. On the other hand, those in Medicaid reported twice more difficulties
of accessing services than individuals with private insurance. Millman (1993) noted
that undercoverage of health insurance impacts one’s access to health care. More
specifically, the depth or adequacy of coverage can delay people’s utilization of health
care or prevent them from accessing and using the care.

Lastly, it is important to note that racial and ethnic minority members have not
only poor access to health care but also poor quality of care (Smedley et al., 2003). Low
socioeconomic status and lack of health insurance lead to substandard medical care,
which in turn affects one’s health. Burstin, Lipsitz, and Brenna (1992) demonstrate that

people without insurance are exposed to greater risk for medical injury, caused by

25

substandard medical care. Millman (1993) points out two issues that poor people and
minority members are exposed to: difficulty of obtaining services and poor health
condition in general, which may be attributable to the content, quality, and continuity of
health care they receive, as well as the amount of the care.

In sum, a wide variety of sociodemographic characteristics plays a significant role
in one’s access to health care. Sociodemographic contributors affect one another and
also interact with other determinants in different areas, necessitating a cautious and
meticulous approach to investigating determinants of access to health care. In addition,
the sociodemographic contributors affect one’s health insurance status and coverage as
well as quality of health care.

Health Care Practices and Personal Lifestyle and Behaviors

It is well documented that health care practices, health behaviors and lifestyle—
for instance, medical care use, safety practices, cigarette smoking, alcohol and drug use,
sexual behavior, exercise patterns, dietary practices, and food consumption—impact
health and aging (e.g., Ferraro, 2006; Gross, Quinan, Rodstein, LaMontagne, Kaslow,
Saah, Wallenstein, Neufield, Denning, & Gaerlan, 1988; House, 2002; Land & Yang,
2006; Olshansky, Carnes, Rogers, & Smith, 1997; USDHHS, 2000; Young, 2005).
Those impacts on health and aging will in turn lead to one’s access to health care.

Young (2005) presents the following primary individual behaviors for diseases and health
problems: smoking, diet, alcohol and drug use, physical activity, and safety practice.
Smoking, the most important lifestyle determinant of health, increase risks of developing
certain cancers and respiratory diseases, as well as issues pertaining to pregnancy and

child growth retardation (Young, 2005). Additionally, alcohol and drug use can cause

26

diseases and injuries to the users, and also bring social and economic losses to the users’
families, community, and society in general (Hanson, Venturelli, & Fleckenstein,
2005).

Good nutrition and physical activity are vital for successful aging, as well as
health and quality of life (U SDHHS & US. Department of Agriculture, 2005). More
specifically, a nutritious diet and regular physical activity will more likely promote health
and reduce risk for main chronic diseases, such as cardiovascular disease, type 2 diabetes,
hypertension, osteoporosis, and certain cancers, as well as overweight and obesity
(Kurnanyika, 1997; USDHHS & USDA, 2005). Conversely, a poor diet and physical
inactivity are primary risk factors for developing those diet- or weight-related chronic
diseases and conditions. Moreover, malnutrition is associated with impaired immune
firnction, poor wound healing, and involuntary weight loss (Fischer & Johnson, 1999).

Simple as they may sound, a nutritious diet and regular physical activity are
connected with other sociodemographic and environmental factors. Poverty, living
alone, inadequate or poor social network and social relationships, or loneliness influence
one’s food access and a proper diet (Kurnanyika, 1997). In addition, an unsafe
environment can restrict individuals’ regular exercise, as indicated previously.

It should be noted that culture is involved in one’s health. Acculturation can
play a role in one’s health through westemized diet and access to health care (V er Ploeg
& Penin, 2004). In addition, cultural aspects are involved in dietary practices, behavior,
lifestyle, and family environment, and, therefore, culture should be taken into account for
one’s health (Kumanyika, 1997; Ver Ploeg & Perrin, 2004). Furthermore, cultural

norms and family socialization early in life may affect the formation of health promoting

27

and risky behaviors among individuals (Thomson et al., 2006). These cultural aspects
highlight their role on one’s health. However, it is obvious that culture also takes a big
role in one’s access to health care, which is addressed in the next subsection.

As with the impacts of sociodemographic contributors to access to health care, the
aforementioned studies reveal that health care practices and personal lifestyle and
behaviors are intricately involved in one’s access to health care. In particular, those
factors impact health care access through their influences on health status. In addition,
culture can play a part in the complex mechanism of health care access.

Cultural Factors

Various aspects of culture impact one’s access to health care and quality of
health care. Culture forms one’s belief and attitude about health and access to and
utilization of health care. Fiscella, Franks, and Clancy (1998) reveal that attitude toward
care system impacts people’s access to health care. In particular, given the more and
more racially and ethnically multicultural US. society and continual influx of immigrants
into the U.S., the cultural aspects need to be given more weight in health care access
research. For instance, it is well known that culture plays a role in immigrants’ help-
seeking behavior and attitudes about mental health problems (e.g., Shin, 2002; Vega,
Kolody, & Aguilar-Gaxiola, 2001; Yi, 2005).

First, Vega et al. (2001) investigated help-seeking patterns for mental health
problems among immigrant and US. born Mexican Americans. The study found that
Mexican immigrants had limited experiences and knowledge about mental health services
due partly to stigma. This was corroborated by a fact that public health insurance did

not increase utilization of mental health services for Mexican immigrants.

28

Second, Shin (2002) examined Korean immigrants’ help-seeking behaviors for
depression using 70 Korean immigrants in New York City through focus group
discussions and in-depth interviews. Shin suggested that traditional Asian practices and
prolonged care within family served to deter and delay the participants from seeking
mental health services for their depression. In the study, the following seven factors
were found as barriers to obtaining mental health services: stigma; gender (less
willingness of males); intervention focusing on explicit, overt and open communication;
lack of available services with bilingual and bicultural therapists; finances; lack of
information about available services; and geographic proximity.

The studies above show that although many immigrants were struggling with
mental health problems, their utilization rates of mental health services were low, which
could not be accounted for just by financial issues. This clearly suggests that these
challenges are associated with immigrants’ cultures toward mental health problems, and
that this cultural aspect can be differently manifested depending on individual
characteristics, such as level of acculturation and sociodemographic factors.

Language use and proficiency also affect people’s access to, utilization of, and
quality of health care (e. g., Fiscella, Franks, Doescher, & Saver, 2002; Perez-Stable,
Napoles-Springer, & Miramontes, 1997; Ver Ploeg & Perrin, 2004; Yi, 1995). Ver
Ploeg and Perrin (2004) indicate that acculturation and language use can impact one’s
health through access to and quality of health care. Compared to language use,
acculturation has broader impact on one’s health. Culture influences various
dimensions of one’s life, such as behavior, diet, and family environment (V er Ploeg &

Perrin, 2004). In contrast, language use and proficiency can affect one’s health through

29

access to, utilization of, and quality of health care.
Other Factors

Other determinants of health care access can be generally divided into
environmental factors and individual factors. First, environmental factors can primarily
affect one’s access to health care. Whether people live in rural areas or urban areas
influences an individual’s health care access (Committee on the Future of Rural Health
Care, 2005; Hartley, 2004; Thomson et al., 2006). Thomson et al. (2006) show that
people who live in disadvantaged neighborhoods and rural locations as well as the poor
have health disparities in access to health care. Lack of transportation also can be a
barrier to gaining access to health care (Millman, 1993). Location of residence and
socioeconomic status may be involved in the transportation issue. Furthermore,
available health personnel and facilities can impact one’s access to health care (Andersen,
1995). This issue is more likely to be encountered in rural areas and less affluent
community. Lastly, public policies and community environment impact access to health
care for both insured and uninsured adults (Davidson et al., 2004).

Second, various individual factors are associated with health care access.
Perceived and evaluated health status and consumer satisfaction affect one’s access to
health care (Andersen, 1995). Moreover, Fiscella et al. (1998) reveal that attitude
toward health care system impact people’s access to health care. Many individual
factors can be related to cultural aspects, which was previously mentioned.

Health Disparities of People with Disabilities
A plethora of research has documented that people with disabilities suffer from

unemployment, underemployment and, as a result, economic distress (e.g., Kaye, 1998;

30

Lustig, Strauser, & Donnell, 2003; Rehabilitation Research and Training Center on
Disability Demographics and Statistics, 2005; Stapleton & Burkhauser, 2003). This
economic distress for people with disabilities more likely leads to living in unhealthy
living conditions and environments, as well as poor diet and stress. Unsafe
neighborhoods make it difficult to regularly do exercises (Sallis, Johnson, Calfas,
Caparosa, & Nichols, 1997; Weinstein, Feigley, & Pullen, 1999). All these
aforementioned factors are potent contributors to poor health. This indicates that people
with disabilities have great risk factors for health.

The economic distress for people with disabilities also impacts their access to and
quality of health care. Related to health insurance, individuals with disabilities can be
further burdened with the following issues: exclusion waivers for preexisting health
conditions, higher premiums, and denials (Millman, 1993). That sociostructural factors
affect health status and access to health care for people with disabilities deserves careful
scrutiny of health disparities for older population with disabilities.

Nonetheless, there has been no publication on health disparities of people with
disabilities, whether young or old, in three primary rehabilitation counseling journals:
Rehabilitation Counseling Bulletin, Journal of Rehabilitation, and Journal of Applied
Rehabilitation Counseling. Given these limitations, how the rehabilitation counseling
profession has addressed health-related issues for people with disabilities are discussed in
greater detail to have a deep understanding of necessity and urgency of research on health
disparities of people with disabilities. Even beyond the rehabilitation counseling
profession, the US. has focused scanty attention to health disparities of this population,

compared to other populations, especially racial or ethnic minorities. Therefore,

31

reviews of health disparities for people with disabilities are not restricted to older
individuals with disabilities and access to health care for health disparities.
Rehabilitation Counseling

Overlooked interest in and attention to health and its sociostructural factors in the
rehabilitation counseling profession can be reflected in a lack of extensive and empirical
research on healthy aging in the aforementioned major rehabilitation counseling journals.
In 1981, Journal of Rehabilitation dealt with rehabilitation of older people in its special
issue (Volume 47, issue 4). This special issue approached aging and older individuals
with disabilities from comprehensive areas: ageism, rehabilitation process for older
population with disabilities, legislation, and aging-related issues such as leisure, sexuality,
independent living and employment. For health, this issue had an article on loss of
physical fimction and disability of older population (Jemigan, 1981). However, the
article simply presented an overview of physical health issues for older people, focusing
on quality of life and activities of daily living. Therefore, this special issue did not
sufficiently address health, despite its comprehensive approach to aging issues.

Recently, Journal of Rehabilitation published another special issue featuring
contemporary issues that aging Americans face (Volume, 69, issue 2, 2003). This
special issue dealt with a variety of challenges and concerns related to older population
and their implications for rehabilitation counseling. However, the special issue
approached aging centering on mental health, employment, and service delivery. As a
result, the issue did not go further to comprehensively address healthy aging of the older
population, not to mention health disparities.

Rehabilitation Counseling Bulletin also addressed aging issues in a special issue

32

(Volume 29, issue 2, 1985). This special issue adopted a life span perspective, and
discussed transition and disability issues from adolescents to older people. In spite of its
unique approach, however, this special issue did not address health issues.

This literature review shows that although the rehabilitation counseling profession
made efforts to deal with aging issues of people with disabilities, the profession has not
focused its main attention to healthy aging for people with disabilities. This fact also
can be corroborated through Mary E. Switzer Memorial Seminars. Until now, three
Mary E. Switzer Memorial Seminars have been held on older people with disabilities: in
1987, “the aging workforce: implications for rehabilitation”; in 1991, “Aging, disability
and the nation’s productivity”; and in 2003, “the aging American workforce: the impact
on persons with disabilities.” As the themes of these seminars clearly indicate, those
seminars restricted their attention to the population’s employment issues. This may
demonstrate that the rehabilitation counseling profession has maintained its primary focus
on productivity and employment issues for older people with disabilities.

Nevertheless, the literature review above does not necessarily mean that the
rehabilitation profession has had skimpy attention to aging and health issues. As a
matter of a fact, a multitude of topics pertaining to aging and health for individuals with
disabilities were addressed in the three major rehabilitation counseling journals: meaning
of aging and ageism (Bearden & Head, 1986; Benedict & Ganikos, 1981; Reiner, 1974;
Saxton & Spitznagel, 1992); grave aging issues for the general public and older people
with disabilities (Lewis, 1989; Zola, 1989); aging-related psychological issues (Fromm,
1966; Kivnick, 1985); promoting cognitive functioning (Abramson & Wehman, 1975);

loss of physical function (Jemigan, 1981); physical education for people with significant

33

disabilities (Rizzo & Kloepping, 1976); health promotion (Brandon, 1985; Taegue,
Cipriano, & McGhee, 1990; Tate, 1987; Wong & Neulicht, 1994); and health and
wellness (Putnam, Greenen, Powers, Saxton, F inney, & Dautel, 2003).

Of the above literature, studies on health merit further extensive review, because
they indicate that the rehabilitation counseling profession has made efforts to maintain
and promote health for people with disabilities, including older population with
disabilities. First, Brandon (1985) addressed health promotion and wellness, but he
simply discussed how those concepts could be applied to rehabilitation services and what
principles were applicable for health promotion and wellness. Second, Tate (1987) dealt
with health promotion, but she addressed it in the contexts of disability management and
workplaces. Thus, Tate approached health promotion to make workforce healthy.
Third, Teague et al. (1990) stressed comprehensive health promotion for health
maintenance and enhancement of people with disabilities. Although Teague and
colleagues recognized that health care was oftentimes overlooked for people with
disabilities, they did not go further to address what caused the lack of health care for the
population. Instead, they just discussed models and concepts of health promotion and
their applicability in the rehabilitation profession. Fourth, Wong and Neulicht (1994)
addressed a definition and model of health promotion, and presented barriers to health
promotion. Their focus was placed on discussing application and utility of health
promotion to people with disabilities. Finally, Putrnan and colleagues (2003) dealt with
barriers and facilitators to health and wellness of individuals with disabilities. Through
a qualitative study, Putrnan et al. revealed how people with long-term disabilities defined

and conceptualized health and wellness.

34

This extensive literature review shows that even though many studies in
rehabilitation counseling have addressed health promotion and wellness for individuals
with disabilities, they have several limitations. First, not all these studies focused
specifically on older individuals with disabilities; studies on health promotion put their
focus on the general population with disabilities (Brandon, 1985; Teague et al., 1990;
Wong & Neulicht, 1994). Additionally, health promotion was not discussed in a
comprehensive and continual framework, from childhood or youth to old age. Second,
those studies put their main focus on enlightening rehabilitation counseling professionals
on the significance of health issues and presenting the concepts and models of health
promotion. As a result, most of the studies did not empirically examine health and
wellness issues. Third, although some studies (e.g., Putrnan et al., 2003; Wong &
Neulicht, 1994) identified barriers to health and wellness of people with disabilities, they
did not extensively investigate what factors could contribute to the obstacles to health
maintenance and promotion. Most studies placed their primary focus on individual
factors, rather than on sociostructural factors. However, given the interconnectedness of
individuals and society, and the long history of stigma, prejudice, and discrimination
against individuals with disabilities, health issues of people with disabilities need to be
approached more broadly and comprehensively beyond individual levels. Therefore,
comprehensive and empirical research on health disparities is crucial.

To sum up, the rehabilitation counseling profession has traditionally placed its
focus on employment issues for people with disabilities, which may have served to divert
its attention from health. In addition, attention to health and health promotion in

rehabilitation counseling did not go beyond individual factors to investigate

35

sociostructural factors. This fact could be one of the reasons that there has been greatly
lacking research on health disparities for people with disabilities in rehabilitation
counseling. Given the great dearth of health disparities research in the rehabilitation
counseling profession, the following discussion on health disparities of individuals with
disabilities moves beyond rehabilitation counseling.

Health Disparities for People with Disabilities

It has been mentioned earlier that people with disabilities have been marginalized
in society, and that they have been exposed to great disadvantage in many aspects of life,
such as employment, income, education, or marriage. Further, individuals with
disabilities are more likely to encounter health issues, which impact their socioeconomic
status, and vice versa. In spite of the fact that people with disabilities sustain poor
health status caused by preventable secondary conditions, they may be the largest
underserved subpopulation (Drum et al., 2005). Given the big impact of health
insurance status and coverage on access to health care (e. g., Berk & Schur, 1998;
Millman, 1993; Mueller et al., 1998; Muntaner & Parsons, 1996), marginalized social
status can bring about great barriers to health care access for people with disabilities.
Therefore, people with disabilities are more likely to have health disparities in health
status and access to, utilization of, and quality of health care.

There exist compelling data suggesting that individuals with disabilities sustain
poor health status and secondary health conditions, which are preventable (e. g., Campbell,
Sheets, & Strong, 1999; Turk, Geremski, Rosenbaum, & Weber, 1997; USDHHS, 2000).
Turk et al. (1997) show that although women with cerebral palsy in the community think

of themselves as healthy, they have various secondary conditions, such as pain, bowel

36

and bladder problems, or poor dental health, all of which were independent of
participants’ type of cerebral palsy.

In addition, employing the data from the Aging with Disability (AwD) study and
the 1994-1995 NHIS, Campbell et al. (1999) show that people aging with polio,
rheumatoid arthritis, and stroke, who are 45 to 64 years old at the measurement. and
randomly selected from a county rehabilitation hospital and a community pool, have
clearly distinct disparities, compared to their counterparts without disabilities. More
specifically, those from the AwD study have appreciably higher rates of secondary health
conditions, such as arthritis, diabetes, high blood pressure, digestive disorders, and
respiratory disorders, than population estimates for the same-age cohort from the NHIS.

Furthermore, USDHHS (2000) shows that people with disabilities have health
disparities in health status and access to health care. Specifically, compared to people
without activity limitation, those with disabilities have more days of depression and
fewer days of vitality, as well as lower rates of physical activity and higher rates of
obesity. Regarding health care access, many individuals with disabilities have poor
access to health services and medical care.

USDHHS (2002a) clearly indicates that people with mental retardation suffer
fiom health disparities in health status and access to basic health care. Health disparities
for people with mental retardation are further serious not only because access to basic
health care is taken for granted for people without disabilities but also because the unmet
needs of those with mental retardation are too oftentimes overlooked (USDHHS, 2002a).
According to USDHHS (2002a), these issues are further exacerbated by the following

issues. First, most of health care providers are not trained to appropriately treat people

37

with mental retardation. Second, even for those providers with adequate training, they
are faced with few incentives from the present health care system to provide adequate
health care services for individuals with mental retardation.

Disability is a significant risk factor to access to preventative, primary, and
secondary health care (e. g., Chan, Doctor, MacLehose, Lawson, Rosenblatt, Baldwin, &
Jha, 1999; Fisher, 2004; Iezzoni, McCarthy, Davis, & Siebens, 2000; Sutton & DeJong,
1998). First, through an extensive literature review, Fisher (2004) shows that people
with mental retardation sustain health disparities in wide areas: general health screening
and care for physical symptoms, mental health, women’s care, dental services, health
maintenance, and prevention.

In addition, Chan and colleagues (1999) investigated preventative services of
Medicare patients with disabilities, using the 1995 Medicare Current Beneficiary Survey.
In the study, women with most significant disabilities reported fewer rates of
mammograms and Pap smears. Thus, disability was a significant risk factor to those
preventative services. It is important to note, however, that severity of disability is not
directly associated with receipt of certain preventative services (Diab & Johnston, 2004).
Thus, the relationship between the severity of disability and preventative health services
is not simple and linear (Diab & Johnston, 2004).

Using the 1994 NHIS and supplements from the NHIS-D, and Healthy Pe0ple
2000, Iezzoni et al. (2000) also show that people with mobility impairments—difficulty
walking, climbing stairs, or standing for extended period—are not given much attention
to screening and preventive services. Given the findings from the aforementioned

studies, it is not surprising that people with disabilities face great challenges to access to

38

preventive, primary, and secondary health care services (Sutton & DeJong, 1998).

In relation to barriers to access to health care, people with disabilities face a
variety of challenges, which are distinct from those for counterparts without disabilities.
Using focus group, O’Day, Dautel, and Scheer (2002) show that people with mobility
impairments—spinal cord injury, cerebral palsy, multiple sclerosis, and arthritis—
encounter the following hurdles for their access to health care: physical access to doctors’
offices and office equipment; establishing trusting relationships with physicians;
physicians’ lacking knowledge and sensitivity to disability; coverage of durable medical
equipment, medications, and rehabilitation; and negotiating managed care system to get
wanted service when they need it.

While Kroll, Jones, Kehn, and Neri (2006) employed focus group in examining
utilization of primary preventative services for individuals with physical disabilities, they
corroborated the aforementioned challenges to access to health care, except for the
negotiation with managed care system. Additionally, Kroll et al. (2006) found a short
appointment issue and extended the lack of professional behavior and sensitivity beyond
physicians to include the issue from other health care providers. Furthermore, Iezzoni et
al. (2000) pointed out that shortened appointment time, inaccessible physical
environment, and inadequate equipment could keep people with mobility impairments
from utilizing preventive care services.

In sum, people with disabilities have suffered from health disparities in health
status and access to health care, compared to people without disabilities. Individuals
with disabilities still face an accessibility issue to physical environments, despite the

passage of Americans with Disabilities Acts (ADA) in 1990. In addition, even in the

39

doctors’ offices, those with disabilities encounter inaccessible medical equipments and
health care providers with insensitive and unprofessional attitude and demeanor.

Coupled with these barriers to health care access, socioeconomic factors due to
marginalized social status of people with disabilities adversely impact their health status.
Their poor health status could then result in unemployment or underemployment, or serve
as an obstacle to gain and maintain quality employment. Low socioeconomic status due
to the employment issues will limit access to and quality of health care for people with
disabilities through health insurance status and coverage. Last but not least, health
disparities of people with disabilities have not been addressed rigorously and
comprehensively. Many studies merely explored health disparity issues, using a
convenient sample (Turk et al., 1997), focus groups (Kroll et al., 2006; O’Day et al,
2002), or literature review (Fisher, 2004; Ouellette-Kuntz, 2005). In particular,
determinants of the health disparities have not been extensively investigated, in spite of

widespread health disparities of people with disabilities.

40

CHAPTER 3

METHODOLOGY

For eliminating and preventing health disparities in access to health care of older
people with disabilities, this study investigated the existence and determinants of the
health disparities. For health care access, this study examined these factors: cost barrier
to health care, personal health care provider, and routine physical checkup. To achieve
the purposes of this study, the following questions were employed:

1. Do health disparities in access to health care exist between older people with
disabilities and older people without disabilities?

1) Are there significant differences in cost barrier to health care between older

people with disabilities and older people without disabilities?

2) Are there significant differences in personal health care provider between

older people with disabilities and older people without disabilities?

3) Are there significant differences in routine physical checkup between older

people with disabilities and older people without disabilities?

2. If health disparities in access to health care exist between older people with
disabilities and older people without disabilities, to what extent are such
disparities accounted for by predisposing characteristics, enabling resources, and
perceived and evaluated health needs?

1) Cost Barrier to Health Care

a) Are predisposing characteristics (sex, age, race, marital status, and

education level) related to cost barrier to health care? If so, to what

41

b)

d)

extent?

Are enabling resources (annual household income, social and
emotional support, and health care coverage) related to cost barrier to
health care after predisposing characteristics are accounted for? If so,
to what extent?

Are perceived health needs (general health status, physical health, and
mental health) related to cost barrier to health care after predisposing
characteristics and enabling resources are accounted for? If so, to
what extent?

Are evaluated health needs (diabetes, asthma, heart attack, coronary
heart disease, and stroke) related to cost barrier to health care after
predisposing characteristics, enabling resources, and perceived health

needs are accounted for? If so, to what extent?

2) Personal Health Care Provider

a)

b)

Are predisposing characteristics (sex, age, race, marital status, and
education level) related to personal health care provider? If so, to
what extent?

Are enabling resources (annual household income, social and
emotional support, and health care coverage) related to personal health
care provider after predisposing characteristics are accounted for? If
so, to what extent?

Are perceived health needs (general health status, physical health, and

mental health) related to personal health care provider after

42

d)

predisposing characteristics and enabling resources are accounted for?
If so, to what extent?

Are evaluated health needs (diabetes, asthma, heart attack, coronary
heart disease, and stroke) related to personal health care provider after
predisposing characteristics, enabling resources, and perceived health

needs are accounted for? If so, to what extent?

3) Routine Physical Checkup

a)

b)

d)

Are predisposing characteristics (sex, age, race, marital status, and
education level) related to routine physical checkup? If so, to what
extent?

Are enabling resources (annual household income, social and
emotional support, and health care coverage) related to routine
physical checkup after predisposing characteristics are accounted for?
If so, to what extent?

Are perceived health needs (general health status, physical health, and
mental health) related to routine physical checkup afier predisposing
characteristics and enabling resources are accounted for? If so, to
what extent?

Are evaluated health needs (diabetes, asthma, heart attack, coronary
heart disease, and stroke) related to routine physical checkup after
predisposing characteristics, enabling resources, and perceived health
needs are accounted for? If so, to what extent?

Data Set of the Study

43

The Behavioral Risk Factor Surveillance System (BRFSS) is a collaborative
project of the CDC and all U.S. states and territories. State health departments conduct
the cross-sectional telephone survey monthly in accordance with a protocol with
technical and methodological assistance from the CDC, and each state submits monthly
collected surveillance data to CDC for editing and summary updates (CDC, 2003). At
the end of a year, CDC’s Behavioral Surveillance Branch (BSB) and states publish
analyses of data (CDC, 2003). BSB is involved in a yearly distribution of nationwide
summaries of state-specific risk-factor prevalence estimates and state-specific response
rates, as well as risk-factor prevalence estimates for statewide core and optional module
data and state-specific, standard cross-tabulations of responses (CDC, 2006a).

Since 1984, the BRF SS has collected information on health conditions and
behavioral risk factors mainly related to chronic disease and injury in the adult population
age 18 years and older (CDC, n.d.d). More specifically, this annual national
surveillance system examines health status, health conditions, health risk behaviors, as
well as preventative health practices and access to health care. In 1984, only 15 states
participated in collecting the surveillance data, but all 50 states, the District of Columbia,
Puerto Rico, the U.S. Virgin Islands, and Guam have participated in this surveillance
system since 2001 (CDC, n.d.d).

In this cross-sectional national survey, the information is collected through
random-digit telephone interview of civilian non-institutionalized adults using computer-
assisted telephone interviewing. The BRF SS uses a probability sample of all
households with a telephone, and one adult per household is recruited. But the BRFSS

does not employ a proxy respondent in telephone interviews. These types of household

44

are considered negligible in the BRF SS: group homes, institutions, and homes in which
household members do not live for more than 30 days per year (CDC, 2006a).

Telephone numbers used to conduct the survey are provided to each state by CDC,
which purchases the telephone numbers from a database company (CDC, 2003). In the
database, the sampling frame used for the BRF SS is ensured to have a non-zero
probability of being selected (CDC, 2003). For managing quality of the BRF SS, each
state needs to submit to CDC a record for all telephone numbers actually called by the
data collector, and the telephone numbers not used for the survey since they were pre-
identified as non-working or business by the sample provider (CDC, 2003). In addition,
when states submit complete or incomplete data record to BSB, since data year 2003 the
following information must be included, as applicable, for quality assurance studies, data
weighting, and analyses of sample quality: the number of sample records selected from a
stratum by the sample provider, the number of telephone numbers in the stratum from
which the sample was selected, annual sequence number, replicate number, replicate
depth, number of attempts, precalling identification status, and density strata (CDC,
2003).

Regarding a sampling design, the BRFSS employs a disproportionate stratified
random sampling since the 2003 data year. That is, the BRFSS uses stratification by
density of residential telephone numbers, which are divided into two strata: high-density
and medium-density. Telephone numbers in the high-density stratum are oversampled
(CDC, 2006a). Aside from the density strata, some states have geographic strata. In
the BRFSS for 2006, all U.S. states and territories employed a disproportionate stratified

sample design except for Puerto Rico and the U.S. Virgin Islands, which used a simple

45

random sample design (CDC, n.d.d). In 2006, Guam did not participate in the data
collection (CDC, n.d.c).

When it comes to the BRF SS questionnaire, it consists of three parts: core
component section, optional modules, and state-added questions. The questionnaire is
annually constructed through the collaboration of CDC and states. More specifically,
program representatives from National Center for Chronic Disease Prevention and Health
Promotion (N CCDPHP) and other parts of CDC propose additional and emerging BRF SS
questions for the core component and optional modules to BSB, and states provide their
input and feedback on the proposed content (CDC, 2006a). All states and territories
must ask core questions in the core component section in its entirety, without
modification. However, states and territories can ask any, all, or none of the optional
modules and state-added questions only after the core questions are asked (CDC, 2006a).
The optional modules consist of several units of standardized questions on specific topics,
which states can include in their questionnaire. If a module chosen is not used in its
entirety or modified in any way, those questions are considered state-added questions
(CDC, 2006a). In this study, only core questions were employed.

For the quality of the BRFSS data sets, many studies demonstrate that the BRFSS
data sets are reliable and valid. Nelson, Holtzrnan, Bolen, Stanwyck, and Mack (2001)
comprehensively review and summarize more than 200 studies on reliability and validity
of measures on the BRF SS. They conclude that the BRF SS measures, including those
in the core component section, are of moderate to high reliability and validity.

In addition, the BRFSS measures respondents’ perceived health status: general

health status, physical health, mental health, and usual activity limitation. The last three

46

aspects of health are examined through health-related quality of life measures (HRQoL).
These I-IRQoL questions are valid and reliable (Andresen, Catlin, Wyrvvich, & Jackson-
Thompson, 2003; CDC, 2000; Ware, Kosinski, & Keller, 1996). Andresen et al. (2003)
show that the HRQoL measures are moderate to excellent for retest reliability.
Moreover, Andresen, Vahle, and Lollar (2001) investigate proxy reliability by using the
HRQoL measures for individuals with disabilities. They caution against the use of
proxies for HRQoL based on the findings that proxies overestimated impairments of
people with disabilities and underestimated HRQoL for the individuals with disabilities.
This finding is consistent with other studies that proxy respondents overreport disability
(Andresen, Fitch, McLendon, & Meyers, 2000) and functional limitations (Magaziner,
Sirnonsick, Kashner, & Hebel, 1988; Weinberger, Samsa, Schmader, Greenberg, Carr, &
Wildman, 1992). As indicated previously, the BRFSS does not employ proxy
respondents.
Furthermore, Nelson, Powell-Griner, Town and Kovar (2003) show that the
BRF SS provides national estimates generally comparable to those of NHIS, and, as a
result, they conclude that the BRFSS could be readily available for informing national
policy makers. Finally, Kinne and Topolski (2005) demonstrate that people with
disabilities are not underrepresented in population telephone surveys including the
BRFSS. These various studies illustrate that the BRFSS is an appropriate data set to
investigate health disparities between older people with and without disabilities.
Analysis of Complex Survey Data
As indicated in the previous section, the BRF SS has a disproportionate stratified

sampling design. For the appropriate analysis, understanding of design features of

47

complex surveys is essential. Therefore, design features of the BRF SS and their
associated issues in the analysis are addressed in this section. This also is necessary for
effective discussion about data analysis and its procedure in the following sections.

In the BRFSS, residential telephone numbers are divided into two strata: high-
density and medium-density. The telephone numbers in the two strata are sampled
disproportionately, and the numbers in the high-density stratum are oversampled (CDC,
2006a). This complex survey design in the BRFSS warrants special attention to the
analysis of the data set. Because most general statistical software packages assume
simple random sampling and, as a result, do not take into account complex survey
sampling design, analysis of complex survey data necessitates specialized statistical
software (Brogan, 2005; Graubard & Korn, 1996; LaVange, Steams, Lafata, Koch, &
Shah, 1996; Lee & F orthofer, 2006).

Simple random sampling assumed in most statistics textbooks and standard
statistical software packages does not appropriately deal with complex survey data
primarily due to the following reasons: 1) unequal probabilities of selection, 2) clustering
of observations, 3) stratification, and 4) nonresponse and other adjustments (Brogan,
2005). That is, design features of complex survey and weighting adjustment need to be
taken into consideration in the analysis of complex survey data (Graubard & Korn, 1996;
Lee & Forthofer, 2006). This indicates that most standard statistical software packages
do not appropriately take into account in the analysis process the complex sampling
design and weighting, including poststratification, used in the BRF SS.

Despite its sophisticated sampling design, complex surveys still have several

sources of survey error, such as nonresponse error, noncoverage error, sampling error, or

48

measurement error. Of the survey errors, noncoverage error and nonresponse error are
focused on in this section due to the significance of their implications to data analysis.
Weights are assigned to the responding units in order to compensate for the following
factors: unequal selection probabilities, unit nonresponse, or noncoverage (Brogan, 2004;
Sturgis, 2004). In the BRFSS, weighting is used to reduce or eliminate bias in the
sample. Specifically, weighting is employed in order to increase generalizability of the
findings to the population by accounting for nonresponse and noncoverage errors, and
adjusting for such demographic variables as age, race, and sex between the BRFSS
sample and the population (CDC, 2006a). In the BRFSS, data are directly weighted for
these factors: the probability of selection of a telephone number, the number of adults in a
household, and the number of residential telephones in a household (CDC, 2006a). In
addition, nonresponse and noncoverage of households without telephones are accounted
for through poststratification adjustment (CDC, n.d.a).

Biemer and Lyberg (2003) indicate that weighting needs to be used to adjust for
unequal selection of observations, but that weighting is also used to adjust for
nonresponse and frame noncoverage. Calculating weighting in the complex survey is
complex and demanding when various factors are taken into consideration to obtain the
final weight. In the BRFSS, a final weight for a state’s data is gained by multiplying
weights for all relevant factors, and the computational formula for the final weight is as
follows: FINALWT = GEOWT * DENWT * (l/NPH) * NAD * CSA * POSTSTR (CDC,
n.d.b).

In the formula, FINALWT indicates the final weight assigned to each respondent,

and when a factor does not apply, one is set to its value. Whilst GEOWT adjusts for

49

different probability of selection among geographic regions of a state, DENWT adjusts
for different probability of selection between a high density stratum and a medium
density stratum. Also, NPH denotes the number of residential telephone numbers in the
respondent’s house, and NAD indicates the number of adults in the respondent’s
household. CSA is “the ratio of the expected cluster size to the actual cluster size”
(CDC, n.d.b). Lastly, POSTSTR denotes poststratification, which is used to adjust for
age, sex, and race distribution and noncoverage and nonresponse. The formula for the
final weight shows that for enhancing generalizability of the findings to the entire
population, the BRF SS employs weighting to adjust for variation in selection and
sampling probability, as well as such demographic factors as age, race, and sex.

Weighted analyses are necessary in order to gain unbiased or nearly unbiased
estimates of population parameters with complex survey data (e.g., Brogan, 2004; Korn
& Graubard, 1995; Lee & Forthofer, 2006). Brogan (2004, 2005) demonstrates that
general standard statistical software packages provide biased point estimates of
population parameters in an unweighted analysis, or underestimation of standard errors
for point estimates in a weighted analysis. It should be noted that using most standard
statistical packages with weighted variables in the analysis yields correct point estimates,
but the packages still do not provide correct estimated standard error because sampling
design features of complex survey, such as stratification, or clustering, are not typically
taken into account in the variance estimation procedure (Brogan, 2005; Lee & Forthofer,
2006).

In sum, unweighted analysis in most standard statistical software packages yields

biased point estimates of parameters, whereas weighted analysis in the packages provides

50

underestimation of standard errors for point estimates due to complex survey design, such
as unequal probabilities of selection of elements, clustering, stratification, or unit
nonresponse. This indicates that for gaining unbiased point estimates of population
parameters and correct standard error for point estimates, analyses of complex survey
data must be conducted with specialized statistical software, taking complex sampling
design into account (Brogan, 2004, 2005; Graubard & Korn, 1996; Lee & Forthofter,
2006)

Brogan (2004) indicates that variance estimation is important for precision of
estimators, as well as for correct confidence intervals for and hypotheses testing about
population parameters. Although there are various approaches to approximating the
estimated variance, Taylor series linearization and replication techniques are most
frequently used (Brogan, 2004; Lee & Forthofter, 2006). Taylor series linearization is
designed to gain an approximation to a nonlinear estimator, which is linearized using a
Taylor series expansion (Brick, Morganstein, & Valliant, 2000; Lee & Forthofter, 2006).
Standard sample survey variance estimation methods are then used to estimate the
variance of this linear approximation (Brick et al., 2000; Rust, 1985). There are various
replication methods, such as Balanced Repeated Replication, J ackknife Repeated
Replication, or Bootstrap. But these methods have a common basic idea: to take
multiple pseudo subsamples from an entire sample and estimate the variability across the
replicate subsamples (Brick et al., 2000; Lee & Forthofer, 2006). Brick et al. (2000)
show an array of advantages of the replication methods, which include but are not limited
to: intuitively easy understanding of the method, a sound theoretical basis, inclusion of

survey design information in replicate weights, and reflecting adjustment and estimation

51

methods. Ignoring complex sampling design and estimation methods in complex survey
analysis will lead to incorrect confidence intervals and statistical tests (Brick et al., 2000).

To appropriately take into account the complex sampling design of the BRFSS,
this study employed SAS 9.1.3, SUDAAN 9.0.3, and IVEware. First, SAS 9.1.3 (SAS
Institute, Inc, Cary, NC) was used due to its prevalence in the data management and its
PROCS for complex survey data analysis. SAS 9.1.3 was used in the exploratory
analysis, and Taylor series linearization is used in SAS 9.1.3 for variance estimation.
Second, SAS-callable SUDAAN 9.0.3 (Research Triangle Institute, Triangle Park, NC)
was used to properly analyze five multiply imputed data sets, as well as to analyze
subpopulations of focus in a convenient and appropriate way. Taylor series
linearization and replication methods such as BR and Jackknife are available for
variance estimation in SUDAAN. For this study, Taylor series linearization was
employed.

Third, IVEware developed by the Survey Methodology Program at the Institute of
Social Research of the University of Michigan is a SAS callable software application
built on the SAS Macro Language (Raghunathan, Solenberger, & Van Hoewyk, 2002).
IVEware has a multiple imputation feature for dealing with missing data. IVEware uses
a multivariate sequential regression approach for imputing values of missing items, and
the software can deal with various types of variables—continuous, binary, multi-
categorical, count, and mixed—in complex survey (Raghunathan, 2004; Raghunathan,
Lepkowski, Van Hoewyk, & Solenberger, 2001; Raghunathan et al., 2002). Although a
variety of ways is available to deal with missing data, two approaches are considered

appropriate: maximum likelihood (ML) and multiple imputation (MI) (Collins, Schafer,

52

& Karn, 2001; F ichman & Cummings, 2003; Little & Rubin, 1989; Schafer & Graham,
2002). The two approaches present comparable results, but MI has several benefits.
MI is more robust to the violation of model assumptions (Allison, 2000; King, Honaker,
Joseph, & Scheve, 2001; Schafer, 1997). Also, additional variables that are not included
in the analysis can be used in the imputation process of MI (Schafer & Graham, 2002).
In contrast, ML requires analysts to have an explicit and correct model for analyses, and
use the same variables in the imputation and analysis process (Fichman & Cummings,
2003; Schafer & Graham, 2002). Furthermore, IVEware was used to deal with missing
data in national public-use data sets, such as the NHIS or the National Health and
Nutrition Examination Survey (NHANES) (Parker & Schenker, 2007; NCHS, 2001;
Schenker, Raghunathan, Chiu, Makuc, Zhang, & Cohen, 2006). Given the various
functions of IVEware and the benefits of MI, this study employed IVEware to multiply
irnpute missing data.
Procedures
Although discussion in this section focuses on processes up to multiple
imputation of missing data, it should be noted that these procedures are not mutually
exclusive. They are divided merely for the convenience of discussion. The analyses
employed are addressed in a data analysis section.
Exploratory Data Analysis
As a first step of data analysis, an exploratory data analysis was conducted. This
exploratory data analysis was necessary to make sure if the data set was appropriate for
data analyses planned (Lee & Forthofer, 2006). The basic distribution and summary

statistics of key variables were explored to check for unusual values or missing cases.

53

In addition, it was examined whether sample sizes were large enough for analyses
planned and some categories of key values needed collapsing. It was also examined
whether the sufficient number of observations was available in various subgroups.
Further, demographic distributions in the analytic data set were checked for

reasonableness, compared to official census estimates. According to the Census Bureau
(2008), the number of older people age 65 and older was estimated at 37.3 million on
July 1, 2006. In contrast, the BRFSS for 2006 has an estimate of 38 million for older
people. This indicates that the BRFSS’s estimates for the older people are reasonable.

While the BRFSS for 2006 has 355,710 cases, the data set has 92,808 cases for
older people age 65 and older. The number of older people was firrther decreased to
91,005 after only 50 states and the District of Columbia were targeted in this study. As
a result, a total of 91 ,005 cases were focused on for all data analyses. However, it is
important to note that the entire data set was employed in all statistical analyses after
giving weight of zero to data outside the analytic domain of interest.
Re-coding

Re-coding was carried out prior to data analyses to enhance the quality and ease
of the data analyses. All questions of interest in the BRFSS include these two answers:
‘Don’t know/Not Sure’ and ‘Refused’. In this study, these answers were treated as
missing data to avoid distorted results in statistical analyses (N CHS, 2008). In addition,
given the exploratory data analyses, some variables in the data set were re-coded or re-
categorized for the convenience of analyses, which is further described in the criterion
and predictor variables section.

Missing Data

54

Weighting and imputation are two primary methods to adjust for nonresponse
(Biemer & Lyberg, 2003). Weighting is used to adjust for unequal selection of
observations and compensate for nonresponse and noncoverage. In this study,
weighting was taken into consideration by incorporating the design feature of the BRF SS
in the data analyses.

Regarding imputation, many imputation methods are available to compensate for
the bias of item nonresponse (Kalton, 1983). It is not rare that empirical social science
researchers encounter missing data or incomplete data in their research. In fact, missing
data or incomplete data are a pervasive problem in most empirical social science research.
There is a variety of imputation methods, which includes but is not limited to: hot deck
imputation, nearest-neighbor imputation, direct modeling, missing-indicator method,
mean or median imputation, single imputation of unconditional and conditional mean,
and multiple imputation (Biemer & Lyberg, 2003; Van der Heijden, Donders, & Moons,
2006; Zhou, Eckert, & Tierney, 2001). These methods use auxiliary information
obtained fi'om other items to fill in values for missing data (Kalton, 1983). For item
nonresponse, 3 MI technique was employed through IVEware to take care of missing data
in this study.

Allison (2002) indicates that MI can be used with almost any kind of data and
model to deal with missing data. When the data are missing at random, appropriate use
of MI yields estimates consistent and asymptotically efficient and normal (Allison, 2002).
MI has been shown to be effective for handing missing data in real studies, real study-
based simulations, or simulations (e.g., Barnard & Meng, 1999; Clogg, Rubin, Schenker,

Schultz, & Weidman, 1991; Harper, Lynch, Hsu, Everson, Hillemeier, Raghunathan,

55

Salonen, & Kaplan, 2002; Rubin, Stern, & Vehovar, 1995; Taylor, Cooper, Wei, Sarma,
Raghunathan, & Heeringa, 2002; van Buuren, Boshuizen, & Knook, 1999; van der
Heijden et al., 2006; Yang, Lynch, Raghunathan, Kauhanen, Salonen, & Kaplan, 2007;
Zhou et al., 2001). Further, MI has been used in national public-use data sets, such as
the NHIS or the NHANES, in which IVEware was used for M1 for missing data (Parker
& Schenker, 2007; NCHS, 2001; Schenker et al., 2006).

Rubin (1996) indicates that when complex patterns of nonresponse exist in survey
data, which is oftentimes true in complex surveys, MI is more flexible in the analysis,
compared to replication and reweighting. In MI, missing data are filled in using
nonmissing observed data. The values of the missing. data are drawn from a distribution
that is obtained from conditional relation of the imputed variable to other non-missing
variables, that is, conditioned on all observed variables (Raghunathan et al., 2001; Taylor
et al., 2002). To create multiple complete data sets, the process of drawing the values is
iteratively repeated multiple times. After imputation, each complete data set that was
created through the repeated imputed process is analyzed separately (Raghunathan, 2004).
Based on the results combined, statistical inferences about quantities of interest—
measures of association or summary statistics—are made (Taylor et al., 2002).

National Center for Health Statistics (2008) suggests that, as a general rule, 10
percent or less for missing data in a variable from an analytic data set could be acceptable
to continue an analysis without further adjustment or evaluation. Among variables of
interest in this study, an annual household income had the greatest percentage of missing
cases of 22.5 percent. All other variables of interest showed less than five weighted

percent for missing data except for a social and emotional support variable with 7.5

56

percent for missing data. However, it is challenging to decide what percent of missing
data is acceptable since a small percent of missing cases in each variable can add up to a
considerable amount in regression analyses. Therefore, all missing data in variables of
interest were imputed in this study.

According to Little and Raghunathan (1997), M1 theory require that all available
variables related to missing data should be included in imputations. In this study, as
many auxiliary variables as possible relative to missing data focusing more on annual
household income were employed in the imputation process for better imputation results.
Specifically, 44 variables including criterion and predictor variables were employed in
the imputation for missing cases. In addition, some design-based variables were
included in the imputation process in order to lessen the risk of mis-specifying an
imputation model and better account for the complex sampling design of the BRFSS
(Reiter, Raghunathan, & Kinney, 2006). Further, in the imputation process, the entire
data set was used rather than a subset of older people to increase any likelihood of
obtaining better imputation results. Five multiply imputed data sets were obtained
through ten iterations for each data set by IVEware. The literature shows that five sets
of imputed data sets and ten iterations are appropriate for most imputation unless the
amount of missing data is extreme (Pedlow, Luke, & Blumberg, 2007; Raghunathan et al.,
2002; Schafer, 1999).

Table 3.1 presents the distribution of respondents’ health care access and its
determinant variables for both the imputed data used in analysis and the un—imputed data,
as well as frequencies of missing data in the un-imputed data. For the distribution of the

imputed data sets, a separate analysis of each imputed data set was combined for five data

57

sets through SUDAAN. In Table 3.1, it is illustrated that the imputation process did not

have a significant impact on the distribution of the data set. The distribution of the
imputed data sets is considerably identical to the un-imputed data set except for annual
household income. The income variable showed differences from 1.6 percent to 7.3
percent in its distribution before and after imputation. These differences could be
attributed to a significant portion of missing data, 22.5 percent, in the variable before
imputation. The examination of the distribution of the variable in multiply imputed data
sets indicates that many poor or affluent interviewees did not respond to the income
question. In particular, through the imputation process, weighted percentage increased
by 4.6 and 7.3 for a ‘$15,000 to less than $25,000’ and a ‘850,000 or more’ category,

respectively.

Table 3.1. Distribution of Access to Health Care and its Determinants by Imputed and

Un—imputed Data in 91 ,005 Older People Age 65 and Older from the BRFSS for 2006

 

 

Un-imputed
Imputed Data Data
No. % No. % M.N°.- %
rssrng
Sex 0 0
Male 32,243 41.9 32,243 41.9
Female 58,762 58.1 58,762 58.1
Age 0 O
65 to 74 48,885 51.7 48,885 51.7
75 to 99 42,120 48.3 42,120 48.3
Race 1,332 1.5
Non-Hispanic White 78,837 81.3 77,990 81.6
Non-Hispanic African American 5,373 7.7 5,287 7.7
Hispanic 2,864 6.2 2,755 6.2
Other race or multiracial 3,931 4.8 3,641 4.5
Marital 268 0.3
Married 41,245 56.8 41,144 56.9

 

58

 

Table 3.1 (cont’d).
Divorced/widowed/separated
Never married or a member of an
unmarried couple

Education Level
Less than high school
High school or GED
Some college or technical school
More than college

Annual Household Income
Less than $15,000
$15,000 to less than $25,000
$25,000 to less than $35,000
$35,000 or more
$50,000 or more

Social and Emotional Support
Usually or always
Sometimes
Rarely or never

Health Care Coverage
Yes
No

General Health Status
Good and better
Fair and poor

Physical Health
0 day
1 day to 14 days
15 days to 30 days

Mental Health
0 day
1 day to 14 days
15 days to 30 days

Diabetes
Yes
No

Asthma
Yes
No

Heart Attack
Yes
No

Coronary Heart Disease
Yes
No

Stroke

45,790
3,970

14,946
32,995
21,061
22,003

13,428
20,660
14,591
16,546
25,779

71,739
8,636
10,630

89,230
1,775

65,298
25,707

55,879
19,671
15,454

74,211
11,200
5,594

16,087
74,918

10,089
80,916

12,169
78,836

12,589
78,416

59

39.3
3.8

16.4
34.9
22.5
26.2

12.6
21.5
15.5
18.5
31.9

79.0
9.2
11.8

98.0
2.0

71.4
28.7

60.3
22.9
16.8

81.2
12.7
6.1

18.7
81.3

10.9
89.1

14.0
86.0

14.6
85.4

45 ,671
3,922

14,941
32,883
20,924
21,890

13,168
19,443
12,467
11,048
14,426

65,949
8,085
10,131

89,023
1,747

64,688
25,684

54,556
17,570
15,092

72,206
10,788
5,499

16,071
74,818

10,056
80,638

12,062
78,149

12,178
77,088

39.3
3.8

16.5
34.9
22.5
26.1

15.8
26.1
17.1
16.4
24.6

78.5
9.4
12.2

98.1
1.9

71.2
28.8

61.3
21.7
17.0

81.2
12.6
6.2

18.7
81.3

10.9
89.1

14.0
86.0

14.5
85.5

367

20,453

6,840

235

633

3,787

2,512

116

311

794

1,739

387

0.4

22.5

7.5

0.3

0.7

4.2

2.8

0.1

0.3

0.9

1.9

0.4

 

Table 3.1 (cont’d).

Yes 7,704 8.4 7,694 8.4
No 83,301 91.6 82,924 91.6
A Cost Barrier to Health Care 288 0.3
Yes 3,882 4.3 3,858 4.3
No 87,123 95.7 86,858 95.7
A Personal Health Care Provider 289 0.3
Yes, only one 75,401 83.0 75,161 83.0
More than one 10,112 11.3 10,082 11.4
No 5,492 5.6 5,473 5.6
A Routine Physical Checkup 1,559 1.7
Within past 2 year 83,218 92.3 81,882 92.4
2 years to less than 5 years ago 3,358 3.5 3,248 3.4
More than 5 years or never 4,429 4.2 4,316 4.1

 

Criterion and Predictor Variables

In this study, health disparities between older people with and without disabilities
were investigated in access to health care. Older people with disabilities were defined
through the following processes. First, any individual who was 65 years old or over was
considered an older person in this study. Second, the BRF SS employs the following
two questions to find whether a respondent has a disability or not: 1) “Are you limited in
any way in any activities because of physical, mental, or emotional problems?”; and 2)
“Do you now have any health problem that requires you to use special equipment, such as
a cane, a wheelchair, a special bed, or a special telephone? Include occasional use or use
in certain circumstances” (CDC, 2006b, p. 10). A person who answers yes to either
question is defined as an individual with a disability in the BRF SS. In sum, older
people with disabilities were defined in this study as follows: people age 65 and older
who answered yes to either question of the two disability questions. In this study,
dependent variables are referred to as criterion variables, and independent variables as

predictor variables for clarity. Criterion and predictor variables in this study are

60

illustrated in Table 3.2, followed by detailed discussion on each variable.

Table 3.2. Criterion and Predictor Variables

 

Criterion and Predictor Variables BRFSS Variables

Cost Barrier to Health Care
Criterion Variables Personal Health Care Provider
Routine Physical Checkup

 

Sex

Age
Predisposing Characteristics Race

Marital Status
Education Level

 

Annual Household Income
Enabling Resources Social and Emotional Support
Predictor Health Care Coverage

 

Variables General Health Status
Perceived Health Needs Physical Health
Mental Health

 

Diabetes

Asthma

Evaluated Health Needs Heart Attack

Coronary Heart Disease
Stroke

 

 

 

Criterion Variables

The BRFSS examines access to health care in the four parts: 1) health care
coverage, 2) a cost barrier to health care, 3) a personal health care provider, and 4) a
routine physical checkup. However, given that the target group of this study was older
people age 65 and older, and that a huge majority of the population in the US is coved by
Medicare, this study did not include health care coverage in the examination of access to
health care. As a result, the remaining three parts were focused on as criterion variables
in this study. The questions of the three parts employed in the BRFSS are as follows

(CDC, 2006b):

61

a. A cost barrier to health care: “Was there a time in the past 12 months
when you needed to see a doctor but could not because of cost?” (p. 6).

b. A personal health care provider: “Do you have one person you think of
as your personal doctor or health care provider?” (p. 6).

c. A routine physical checkup: “About how long has it been since you last
visited a doctor for a routine checkup? A routine checkup is a general
physical exam, not an exam for a specific injury, illness, or condition.”
(p. 7).

These three parts were examined to see whether there were significant differences
between older people with and without disabilities. Determinants of health disparities in
access to health care were investigated in the same parts. While a cost barrier to health
care had a binary answer of yes or no, a personal health care provider and a routine
physical checkup had multiple answers. For a personal health care provider, this study
had three categories for the answers: 1) yes, only one; 2) more than one; and 3) no. For
a routine physical checkup, the BRFSS uses the following answers: 1) within past year
(anytime less than 12 months ago); 2) within past 2 years (1 year but less than 2 years
ago); 3) within past 5 years (2 years but less than 5 years ago); 4) 5 or more years ago;
and 5) never. This study grouped them into three categories: 1) within past 2 years; 2) 2
years to less than 5 years ago; and 3) more than 5 years or never.

Predictor Variables
For effectively investigating contributors to health disparities between older
people with disabilities and older people without disabilities, a wide range of potential

determinants was investigated in this study.

62

Predisposing Characteristics

The following predisposing characteristics were examined for determinants of
health disparities: sex, age, race, marital status, and education level. Age is measured in
years up to 99 in the BRFSS. This study targeted older people age 65 and older. In
order to better examine the impacts of age on the health disparities, age was divided into
two categories: one category of people age 65 to 74 years and the other category of
people age 75 and older. According to a recent study by Karnimoto, Easton, Maurice,
Husten, and Macera (1999), people age 75 and older had the least physical activity.
Given that physical activity can be indicative of and associated with various health issues,
the division of older people was made.

The BRFSS measures race with these three questions (CDC, 2006b). First, the

BRFSS asks respondents if they are Hispanic or Latino: “Are you Hispanic or Latino?” (p.
11). Second, the BRFSS measures the respondents’ multiple races by allowing them to
choosing all categories that apply to them: “Which one or more of the following would
you say is your race?” (p. 11). Third, the BRFSS asks what race the respondents think
they belong to through this question: “Which one of these groups would you say best
represents your race?” (p. 12). Respondents are provided the following categories for
the last two questions: White; Black or African American; Asian; Native Hawaiian or
Other Pacific Islander; American Indian or Alaska Native; or other. Given the small
percentage of other categories other than White, Afiican American, and Hispanic, race
was grouped in the following four categories in this study: 1) Non-Hispanic White; 2)
Non-Hispanic African American; 3) Hispanic; 4) Non-Hispanic Other Race or

Multiracial.

63

Given that this study targeted older people age 65 and older, marital status was
collapsed into three categories: I) married; 2) divorced/widowed/separated; and 3) never
married or a member of an unmarried couple. While an “a member of an unmarried
couple” category presented the small weighted percent of 0.56, a “never married couple”
category showed 3.25 for weighted percent. As a result, the two categories were
merged into one category.

Regarding education level, the BRF SS collects information from respondents the
highest grade or year of school they completed. In this study, education level was re-
coded into four categories: 1) less than high school (up to grade 11); 2) high school
graduates (grade 12 or GED); 3) some college or technical school (some college or
technical school); and 4) college graduate (college 4 years or more).

Enabling Resources

According to the poverty threshold 2006 of the Census Bureau (DeNavas-Walt,
Proctor, & Smith, 2007), when there are no related children under 18 years old, $9,669
was the poverty threshold for one person age 65 and older. If there is one related child
under 18 years old, and the size of family unit is two, the poverty threshold was $12,186
for householder 65 years and older. In addition, the median income of households with
householders age 65 and over was estimated at $27,798 (DeNavas-Walt et al., 2007).
Given the information, the annual household income from all sources was grouped into
three categories in this study: 1) less than $15,000; 2) $15,000 to less than $25,000; 3)
$25,000 to less than $35,000; 4) $35,000 to less than $50,000; and 5) $50,000 or more.

For social and emotional support, the BRFSS uses this question (CDC, 2006b):

“How often do you get the social and emotional support you need?” (p. 26). The

64

respondents are provided the following answers: Always, Usually, Sometimes, Rarely, or
Never. This study regrouped the questions into three categories: Usually or always;
Sometimes; and Rarely or Never.

Finally, health care coverage, this question is employed in the BRFSS (CDC,
2006b): “Do you have any kind of health care coverage, including health insurance,
prepaid plans such as HMOs, or government plans such as Medicare?” (p. 6). The
answers to the question were dichotomized into Yes or No.

Health Needs
Perceived Health Needs
In the BRF SS, health status is measured through self-rated health—excellent, very
good, good, fair, or poor. Health status is also measured through the number of healthy
days in terms of physical health, mental health, and limitations in usual activities such as
self-care, work, or recreation in the BRFSS. Thus, the BRF SS uses healthy days
methods to measure health-related quality of life (HRQoL), and the healthy days are
measured during the recent 30 days (CDC, 2000). The following questions are used in
the BRFSS to measure health status (CDC, 2006b):
a. Self-rated health: “Would you say that in general your health is—“ (p. 5).
b. Physical health: “Now thinking about your physical health, which includes
physical illness and injury, for how many days during the past 30 days was
your physical health not good?” (p. 5).
c. Mental health: “Now thinking about your mental health, which includes stress,
depression, and problems with emotions, for how many days during the past 30

days was your mental health not good?” (p. 6).

65

d. Usual activity limitation: “During the past 30 days, for about how many days
did poor physical or mental health keep you from doing your usual activities,
such as self-care, work, or recreation?” (p. 6).

This study employed general health status, physical health, and mental health for
perceived health needs. Self-rated general health status was dichotomized into 1) Good
and Better, and 2) Fair and Poor. For physical health and mental health, the number of
healthy days for each health was grouped into three subsets: 1) 0 day, 2) 1 day to 14 days,
and 3) 15 days to 30 days.

Evaluated Health Needs

 

This study used the following chronic diseases for evaluated health needs:
diabetes, asthma, and cardiovascular diseases (a heart attack, coronary heart disease, and
a stroke), which are the only available chronic illnesses in the BRFSS. Since prostate
cancer is a gender-specific illness, it was not included in this study. Except for diabetes,
respondents in the BRFSS are asked if they have ever been told by a doctor, nurse, or
other health professional that they have the chronic diseases. The responses to these
questions were categorized into Yes or No. For diabetes, respondents are asked if they
have ever been told by a doctor that they have diabetes, and they are given the following
answers: 1) Yes, 2) Yes, but female told only during pregnancy, 3) No, and 4) No, pre-
diabetes or borderline diabetes. In this study, responses to the second to fourth answer
were all re-categorized into No. That is, females who had diabetes only during
pregnancy as well as people with pre-diabetes or borderline diabetes were not considered
diabetics. As a result, this study used two categories for diabetes: Yes or No.

Data Analyses

66

This study was to investigate the existence and determinants of health disparities
in access to health care among older people with disabilities. To better understand the
health disparities, older population with and without disabilities was divided into two
categories: a group age 65 to 74 and the other group age 75 and older. It is important to
note that, in this study, the BRFSS data set for 2006 was not analyzed after dividing the
whole data set into two discrete data sets depending on the status of disability and age—
older people with disabilities and their counterparts without disabilities—in order to
sustain the design effect of the survey. Instead, the entire data set was employed in all
analyses and weights of zero were assigned for observations outside the analytic domain
of interest. In this way, the design feature of the entire data set is maintained, which
leads to correct estimation of variance (Lee & F orthofer, 2006).

To get a general picture of the health disparities in the U.S., this study employed
the core component sections of the BRF SS questionnaire. Questions in the core
component alone are required of all U.S. states and territories in the BRF SS. For
analysis, data from only all 50 states and the District of Columbia were used because two
U.S. territories—Puerto Rico and the U.S. Virgin Islands—can be considered to have
their own unique culture and lifestyle, which may impact one’s health conditions and
health risk behaviors, including preventative health practices and access to health care.
Guam did not collect any data in BRFSS for 2006.

While the BRFSS for 2006 has 355,710 cases, the data set has 92,808 cases for
older people age 65 and older. The number of older people was further decreased to
91,005 after only 50 states and the District of Columbia were targeted in this study. As

a result, a total of 91 ,005 cases were focused on for all data analyses. However, as

67

mentioned previously, the entire data set was employed in all statistical analyses after
giving a weight of zero to data outside the analytic domain of interest.

Lastly, analyses of the data set were performed with a primary focus on a design-
based approach in this study. Conversely, a model-based approach was minimally
employed in the analyses because it was greatly challenging to find all variables relevant
to a topic of this study and include them in the model (Lee & Forthofer, 2006).
Therefore, design features of the BRFSS and weighting were taken into consideration in
all the analyses.

Statistical Analysis

This study employed descriptive statistics, chi-square test, and logistic regression.
Descriptive statistics (proportions for dichotomous or polychotomous variables) of the
older population by disability status were done for predictor variables in predisposing
characteristics, enabling resources, and health needs. Chi-square test was employed to
examine whether differences between older people with and without disabilities were
statistically significant. P-values of chi-square statistics are based on Wald chi-square
in SUDAAN (RTI, 2004). Furthermore, logistic regression was employed to assess
associations between criterion variables and predictor variables. A series of multivariate
analyses using logistic regression were performed to investigate the extent of the impact
of predictor variables on access to health care. For all analyses, the significance level
of .05 and 95% confidence interval (CI) were employed.

For logistic regression, separate models were run in hierarchical stages in order to
more effectively find the effects of the predictor variables on health care access. First,

predisposing characteristics were entered in model 1. Second, enabling resources

68

variables were added to model 2. Regarding health care needs, the needs variables were
divided into two subsets—perceived needs and evaluated needs—to more effectively
investigate the impact of health needs on access to health care. As a result, perceived
health needs variables were first entered in model 3, and evaluated health needs variables
were then added to model 4.

Logistic Regression

Logistic regression is similar to multiple regression in that logistic regression
investigates association between one criterion variable and multiple predictor variables.
In logistic regression, however, the relationship between one categorical criterion
variable and a set of predictor variables is examined. The categorical criterion variable
can be dichotomous, polychotomous, or ordinal. Two types of logistic regression were
employed in this study. Binary logistic regression was used for one dichotomous
categorical criterion variable whereas multinomial logistic regression for two multiple
categorical criterion variables.

Binary logistic regression analysis is appropriate when criterion variables are
dichotomous and predictor variables are continuous or categorical (Agresti & Finlay,
1997; Menard, 2002; Tabachnick & Fidell, 2001). Multinomial logistic regression is an
extension of binary logistic regression in that a criterion variable in multinomial logistic
regression has multiple categories instead of dichotomous categories. This study
employed binary logistic regression in examining contributors to a cost barrier to health
care whereas multiple logistic regression was used for a personal health care provider and
a routine physical checkup.

The logic behind conducting and interpreting logistic regression analysis is

69

similar to that of multiple regression, which is used when the criterion variable is
measured on an interval or ratio scale of measurement (Brace, Kemp, & Snelgar, 2000).
While multiple regression uses Ordinary Least Squares (OLS) to estimate parameters
fiom the sample data, logistic regression employs maximum likelihood estimation
(Agresti & Finlay, 1997; Tabachnick & Fidell, 2001). In logistic regression analyses of
complex survey data sets, however, maximum likelihood estimation should be modified
due to a sample weight of each observation, and, therefore, an adjusted Wald test statistic
was used instead of the likelihood ratio test (Hosmer & Lemeshow, 2000; Lee &

Forthofer, 2006).

70

CHAPTER 4

RESULTS

This study had a two-fold purpose of investigating the following: 1) whether
there are health disparities in access to health care between older people with disabilities
and their counterparts without disabilities, and 2) to what extent predisposing
characteristics, enabling resources, and perceived and evaluated health needs account for
such disparities if the disparities exist. To address these purposes, this study employed
chi-square test and logistic regression. Chi-square test was used to address the first
purpose and logistic regression to address the second.

This chapter begins with descriptive statistics of characteristics of the two
populations, followed by the findings of chi-square tests of the existence of the health
disparities. Because significant differences between older people with and without
disabilities were found, potential determinants of the disparities were explored. More
specifically, logistic regression analyses were hierarchically performed to examine to
what extent such disparities were accounted for by predisposing characteristics, enabling
resources, and perceived and evaluated health needs. Results of the logistic regression
analyses are described for the following criterion variables: cost barrier to health care,
personal health care provider, and routine physical checkup.

Characteristics of Older People with and without Disabilities in the Study

Table 4.1 illustrates the distribution of predisposing characteristics, enabling
resources, and perceived and evaluated health needs for these three groups: the entire

sample, older people with disabilities, and older people without disabilities. Of the

71

entire sample for this study, who are 65 years old and over in 50 states and the District of

Columbia, older people with disabilities accounted for 37.78 percent (SE = .33; 95% CI =

37.15 to 38.43). Given that the BRFSS is a national survey with a complex sampling

design, percentages in this study hereinafter indicate weighted percent. The differences

between older people with and without disabilities were considered statistically

significant when confidence intervals between the two groups did not overlap, as well as

when p-values were significant at the significance level of .05. For the confidence

interval, this study took a conservative approach. As a result, when a confidence

interval started from 1.0 or ended at 1.0, it was considered insignificant. In Table 4.1,

significant differences between the two groups by disability status are presented in bold.

Table 4.1. Characteristics of Older People with and without Disabilities by Predictor

Variables from the BRFSS for 2006

 

 

Entire Sample Disability No Disability
N=91,005 N=35,133 N=55,872
. . %' (SE) %' (SE) %" (SE) 1?-
Predrctor Vanables 95% CI 95% CI 95% CI value
Predisposing Characteristics

Sex < .001
Male 41.88 (0.33) 39.26 (0.54) 43.47 (0.43)
41.22, 42.54 38.21, 40.32 42.64, 44.30
Female 58.12 (0.33) 60.74 (0.54) 56.53 (0.43)
57.46, 58.78 59.68, 61.79 55.70, 57.36

Age < .001
65 to 74 51.72 (0.34) 45.38 (0.54) 55.57 (0.43)
51.06, 52.38 44.32, 46.45 54.74, 56.40
75 to 99 48.28 (0.34) 54.62 (0.54) 44.43 (0.43)
47.62, 48.94 53.55, 55.68 43.60, 45.26

Race < .007
. . . 81.28 (0.35) 82.16 (0.55) 80.75 (0.45)
N°“'H‘Spam° Whlt" 80.59, 81.96 81.06, 83.21 79.85, 81.62
Non-Hispanic African 7.69 (0.20) 8.03 (0.35) 7.48 (0.25)
American 7.30, 8.10 7.37, 8.75 7.01, 7.99

 

72

 

Table 4.1 (cont’d).

Hispanic 6.24 (0.28) 5.10 (0.41) 6.94 (0.37)
5.72,6.81 4.36, 5.96 6.25, 7.70
. . 4.78 (0.19) 4.71 (0.28) 4.83 (0.25)
Other race °r mum’am' 4.42, 5.17 4.18, 5.29 4.35, 5.35
Marital < .001
. 56.83 (0.33) 50.39 (0.55) 60.74 (0.41)
Mame“ 56.19, 57.47 49.32, 51.47 59.94, 61.53
Divorced IWidowed/ 39.33 (0.32) 45.92 (0.54) 35.32 (0.39)
Separated 38.70, 39.95 44.87, 46.99 34.55, 36.09
Never married or a member 3.84 (0.13) 3.68 (0.20) 3.94 (0.18)
of an unmarried couple 3.59, 4.12 3.32, 4.08 3.60, 4.31
Education Level < .001
. 16.43 (0.27) 19.11 (0.48) 14.80 (0.33)
Less “‘3" mg" “hm“ 15.90, 16.97 18.18, 20.08 14.16, 15.45
. 34.88 (0.31) 33.86 (0.50) 35.50 (0.39)
H‘gh ““001 0’ GED 34.28, 35.48 32.88, 34.85 34.75, 36.27
Some college or 22.48 (0.28) 22.98 (0.45) 22.18 (0.35)
technical school 21.95, 23.03 22.12, 23.87 21.50, 22.88
College or more 26.21 (0.31) 24.05 (0.46) 27.52 (0.41)
25.61, 26.82 23.15, 24.97 26.73, 28.32
Enabling Resources
Annual Household Income < .001
12.58 (0.22) 16.57 (0.40) 10.16 (0.26)
Less than “5’0“" 12.16, 13.03 15.80, 17.37 9.67, 10.69
815,000 to less than 21.51 (0.28) 23.68 (0.49) 20.20 (0.34)
825,000 20.97, 22.07 22.73, 24.65 19.54, 20.87
825,000 to less than 15.50 (0.23) 15.50 (0.40) 15.51 (0.29)
$35,000 1505,1597 14.72, 16.30 14.94, 16.09
$35,000 to less than 18.53 (0.32) 16.72 (0.42) 19.63 (0.40)
850,000 17.89, 19.19 15.90, 17.57 18.84, 20.45
31.86 (0.39) 27.54 (0.51) 34.49 (0.49)
$5030“ ”r "‘0" 31.08, 32.66 26.56, 28.54 33.51, 35.49
Social and Emotional < .001
Support
79.02 (0.28) 76.58 (0.47) 80.50 (0.35)
”mu” 0' always 78.46, 79.56 75.66, 77.48 79.81, 81.18
Sometimes 9.23 (0.19) 11.51 (0.35) 7.85 (0.23)
8.86, 9.62 10.85, 12.21 7.41, 8.31
11.75 (0.23) 11.91 (0.36) 11.65 (0.29)
Rarely °’ “ever 11.31, 12.20 11.22, 12.63 11.09, 12.24
Health Care Coverage < .057
Yes 98.04 (0.10) 98.27 (0.13) 97.91 (0.13)
97.85, 98.22 97.99, 98.51 97.63, 98.15
No 1.96 (0.10) 1.73 (0.13) 2.09 (0.13)
1.78, 2.15 1.49, 2.01 1.85, 2.37

 

73

 

Table 4.1 (cont’d).
Perceived Health Needs
General Health Status

Good and better

Fair and poor
Physical Health
0 day

1 day to 14 days

15 days to 30 days
Mental Health
0 day

1 day to 14 days

15 days to 30 days

Evaluated Health Needs
Diabetes

Diabetes

No Diabetes
Asthma
Asthma

No Asthma
Heart Attack
Heart Attack

No Heart Attack
Coronary Heart Disease

Heart Disease

No Heart Disease
Stroke
Stroke

71.35 (0.32)
70.71, 71.97
28.65 (0.32)
28.03, 29.29

60.30 (0.34)
59.64, 60.96
22.94 (0.31)
22.35, 23.55
16.75 (0.25)
16.26, 17.26

81.20 (0.28)
80.65, 81.73
12.71 (0.23)
12.27, 13.17
6.09 (0.18)
5.74, 6.46

18.71 (0.27)
18.18, 19.26
81.29 (0.27)
80.74, 81.82

10.86 (0.21)
10.45, 11.28
89.14 (0.21)
88.72, 89.55

14.01 (0.24)
13.55, 14.48
85.99 (0.24)
85.52, 86.45

14.62 (0.24)
14.16, 15.09
85.38 (0.24)
84.91, 85.84

8.42 (0.18)
8.08, 8.77

74

51.05 (0.55)
49.97, 52.12
48.95 (0.55)
47.88, 50.03

38.76 (0.53)
37.72, 39.81
27.45 (0.52)
26.44, 28.49
33.79 (0.53)
32.77, 34.83

72.76 (0.52)
71.73, 73.77
16.54 (0.43)
15.73, 17.39
10.69 (0.39)

9.96, 11.48

23.96 (0.48)
23.03, 24.92
76.04 (0.48)
75.08, 76.97

15.83 (0.42)
15.03, 16.67
84.17 (0.42)
83.33, 84.97

19.97 (0.44)
19.11, 20.85
80.03 (0.44)
79.15, 80.89

21.33 (0.45)
20.46, 22.22
78.67 (0.45)
77.78, 79.54

13.58 (0.37)
12.88, 14.32

83.67 (0.36)
82.95, 84.37
16.33 (0.36)
15.63, 17.05

73.39 (0.40)
72.59, 74.17
20.21 (0.38)
19.48, 20.96
6.41 (0.21 )
6.00, 6.83

86.32 (0.31)
85.71, 86.19
10.39 (0.26)
9.88, 10.91
3.29 (0.17)
2.97, 3.65

15.53 (0.33)
14.89, 16.19
84.47 (0.33)
83.81, 85.11

7.84 (0.22)
7.41, 8.28
92.16 (0.22)
91.72, 92.59

10.39 (0.26)

9.88, 10.92
89.61 (0.26)
89.08, 90.12

10.55 (0.26)
10.05, 11.06
89.45 (0.26)
88.94, 89.95

5.29 (0.17)
4.97, 5.62

< .001

< .001

< .001

< .001

< .001

< .001

< .001

< .001

 

Table 4.1 (cont’d).

No Stroke 91.58 (0.18) 86.42 (0.37) 94.71 (0.17)

91.23, 91.92 85.68, 87.12 94.38, 95.03
' Percentages were weighted.

Predisposing Characteristics

As compared to their counterparts without disabilities, older people with
disabilities were more likely to be female and older. In addition, older individuals with
disabilities were less likely to be married. Further, a higher percentage of older
individuals with disabilities were divorced, widowed, or separated. Regarding race,
older individuals with disabilities were not statistically different from their counterparts
with one exception, Hispanics. Older Hispanics were less likely to have disabilities.
Regarding education level, older persons with disabilities had lower levels of education
as compared to their counterparts. A higher proportion of older people with disabilities
reported “less than high school” education, whereas older persons without disabilities
were more likely to receive higher education, “College or more.”
Enabling Resources

Older individuals without disabilities were more likely to have higher annual
household income and to receive more timely social and emotional support. More
specifically, as compared to their counterparts, a larger proportion of older persons with
disabilities reported lower income, less than $25,000. In contrast, older individuals
without disabilities were more likely to receive higher income, $35,000 or more.
Regarding social and emotional support, a higher percentage of older people without
disabilities were more likely to “usually or always” receive support when they needed it.
In contrast, a higher proportion of older individuals with disabilities received social and

emotional support “sometimes.” Regarding health care coverage, the two groups did

75

not show significant differences as anticipated. This result may be due in part to the
virtually universal coverage of Medicare for older persons.
Health Needs

Older people with disabilities showed significant differences in all categories of
health needs compared to their counterparts without disabilities. Older individuals with
disabilities showed poorer health conditions in perceived health needs. In particular,
those with disabilities were considerably more likely to report their general health status
as “fair or poor.” In addition, regarding physical health and mental health older
individuals with disabilities reported significantly more unhealthy days. More
considerable discrepancies were found between the two older populations in physical
health than mental health. Older people without disabilities were almost twice as likely
to report no unhealthy day for physical health within the past 30 days. In relation to
evaluated health needs, older people with disabilities were more likely to have diabetes,
asthma, heart attack, coronary heart disease, or stroke.

Existence of Disparities in Access to Health Care

Chi-square tests were conducted to investigate the existence of disparities in
access to health care between older people with and without disabilities. Such
disparities were examined in each of these criterion variables: cost barrier to health care,
personal health care provider, and routine physical checkup. To better investigate
whether disabilities were associated with the health disparities, chi-square tests were
conducted twice in each criterion variable. Differences in health disparities between the
two groups were first investigated using disability status. Given high prevalence of

disabilities among older people, the existence of the disparities was then exarrrined

76

controlling for age.

The results of chi-Square tests are presented for the criterion variables by each

test. As in the previous section of characteristics of older people with and without

disabilities, differences between the two older groups were considered significant when

p-values were significant at a = .05 and confidence intervals between the two groups did

not overlap. When a confidence interval started from 1.0 or ended at 1.0, it was

determined to be insignificant. Significant differences are shown in bold in the

following tables.

Cost Barrier to Health Care

Table 4.2 illustrates that when compared to older individuals without disabilities, the

counterparts with disabilities were more likely to experience cost barrier to health care.

Thus, older people with disabilities were more likely to face a situation in which they

could not see a doctor due to cost.

Table 4.2. Cost Barrier to Health Care between Older People with and without

 

 

Disabilities
No Yes
N = 87,123 N = 3.882
%* (SE) %“ (SE)
C1 CI )8 df p-value
Disability 103 .43 1 < .001
Status
. . . 93.60 (0.30) 6.40 (0.30)
Disablllty 92.97, 94.17 5.83, 7.03
. . . 97.02 (0.14) 2.98 (0.14)
N" D‘sab‘my 96.74, 97.28 2.72. 3.26

 

’ Percentages were weighted.

Table 4.3 shows that these differences between the two groups were still

77

significant after controlling for age. Therefore, older persons with and without
disabilities were significantly different on cost barrier to health care even after taking age

into account.

Table 4.3. Chi-Square Test for Disability Status and Cost Barrier to Health Care by Age

 

 

Age x2 df p-value
Total 103.43 1 < .001
65 to 74 94.00 1 < .001

 

Personal Health Care Provider

As shown in Table 4.4, older people with disabilities were more likely than their
counterparts to have more than one personal health care provider. On the other hand, a
larger proportion of older individuals without disabilities had only one or no personal

health care provider.

Table 4.4. Personal Health Care Provider between Older People with and without

 

 

Disabilities
No Yes, only one More than one
N=5,492 N=75,401 N=10,112
%' (SE) %' (SE) %" (SE) X2 (if p-
C1 C1 C1 value
Disability
Status 93.84 2 < .001
. . . 4.07 (0.21) 81.22 (0.43) 14.70 (0.39)
D’sab‘h‘y 3.67, 4.51 80.37, 82.05 13.96, 15.48
No Disability 6.58 (0.21) 84.13 (0.32) 9.29 (0.26)

6.18, 7.00 83.50, 84.74 8.80, 9.81
' Percentages are weighted.

78

In Table 4.5, another chi-square test controlling for age reveals that the
differences between older people with and without disabilities are still significant. That

is, the significant differences could not be accounted for by age.

Table 4.5. Chi-Square Test for Disability Status and Personal Health Care Provider by

 

 

Age
Age 12 df p-value
Total 93.84 2 < .001
65 to 74 42.39 2 < .001
>= 75 46.57 2 < .001

 

Routine Physical Checkup

Significant differences in routine physical checkup between older individuals
with disabilities and without disabilities are presented in Table 4.6. More specifically,
older people who had disabilities were more likely to have routine physical checkup
within less than 2 years. In contrast, a higher proportion of older persons without

disabilities had their physical checkup more than 5 years ago or never had a checkup.

Table 4.6. Routine Physical Checkup between Older People with and without Disabilities

 

>=5yrs or never 2yrs to 5yrs ago < 2 yrs ago

 

N = 4,429 N = 3,358 N = 83,218
%* (SE) %* (SE) %* (SE) )3 df p-
C1 C1 CI value
Disability

Status 6.62 2 .001
. . . 3.65 (0.20) 3.26 (0.19) 93.09 (0.27)
D‘sab'h‘y 3.28, 4.06 2.90, 3.66 92.54, 93.60
. . . 4.52 (0.17) 3.60 (0.15) 91.88 (0.23)
No D‘sab‘h‘y 4.19, 4.87 3.31, 3.92 91.42, 92.31

 

* Percentages are weighted.

79

Table 4.7 illustrates the results of another chi-Square test which adjusted for age
in order to further investigate the differences in routine physical checkup. After taking
age into account, the differences between older people with and without disabilities age
75 and over were still significant. In contrast, controlling for age made the differences
among older people age 65 to 74 insignificant. In sum, older individuals with and
without disabilities had significant differences in routine physical checkup only for

people age 75 and older.

Table 4.7. Chi-Square Test for Disability Status and Routine Physical Checkup by Age

 

 

Age )8 df p—value
Total 6.62 2 .001
65 to 74 1.02 2 .361
>= 75 4.99 2 .007

 

Relationships of Predictor Variables with Health Disparities in Access to Health Care

This study employed binary and multinomial logistic regression to examine
whether predisposing characteristics, enabling resources, and perceived and evaluated
health needs were associated with health disparities in cost barrier to health care, personal
health care provider, and routine physical checkup. While binary logistic regression
was used for cost barrier to health care, multinomial logistic regression was employed for
personal health care provider and routine physical checkup. The significance decision
was made using the same criteria as those for chi-square tests: significant p-values at 01
=.05 and confidence intervals without including 1.

For the logistic regression analyses, multivariate analyses were hierarchically run
to better examine the impacts of the aforementioned predictor variables on each of the

80

criterion variables. For cost barrier to health care, model I started with predisposing
characteristics variables. In subsequent models, other predictor variables were
sequentially entered, and model 2 included enabling resources variables. Health care
needs were investigated separately after being divided into perceived needs and evaluated
needs. As a result, perceived health needs variables and evaluated health needs
variables were respectively entered into model 3 and model 4. Thus, the final model
included all the aforementioned predictor variables. This process was repeated for
personal health care provider and routine physical cheekup. It should be noted that odds
ratios in all the models were adjusted for disability status.

In the following tables, odds ratios are presented for the three criterion variables
by disability status and predictor variables. In tables for odds ratios by disability status,
changes in odds ratios in each model are presented. Through these tables, to what
extent predisposing characteristics, enabling resources, and perceived and evaluated
health needs impacted criterion variables can be straightforwardly examined. In
addition, other tables for odds ratios by all predictor variables are presented so that the
impact of each predictor variable on each criterion variable could be examined. Since
this study employed hierarchical multivariate analyses for logistic regression, this study
presents adjusted odds ratios (AOR); in each model the extent of the effects of predictor
variables on criterion variables was investigated controlling for potential contributions
from other variables.

Cost Barrier to Health Care
Table 4.8 shows odds ratios by disability status in each model. This data presents the

extent predisposing characteristics (model 1), enabling resources (model 2), perceived

81

health needs (model 3), and evaluated health needs (model 4) impacted cost barrier to
health care. Table 4.9 illustrates whether each predictor variable was associated with
the cost barrier and that, if so, to what extent it had an impact on the criterion variable.
Moreover, the extent of the impacts of the predictor variables in each model and their
changes across the four models was examined. Therefore, as per Table 4.9, each

predictor variable’s association with and its impact on the cost barrier was investigated.

Table 4.8. Odds ratios (95% CI) for Cost Barrier to Health Care by Disability Status from

the BRF SS for 2006

 

Model 1 Model 2 Model 3 Model 4
0R8 AOR" AOR" AORb AOR"
(95% CI) (95% CI) (95% CI) (95% CI) (95% (:1)
Disability

(1.95, 2.56) (1.92, 2.54) (1.81, 2.43) (1.30, 1.77) (1.25, 1.72)
Older PW/ODd Reference Reference Reference Reference Reference

aUnadjusted odds ratio
bAdjusted odds ratio
COlder People with Disabilities

dOlder PeOple without Disabilities
* p < .05

Table 4.9. Adjusted Odds Ratios (95% CI) for Cost Barrier to Health Care by Predictor

Variables from the BRFSS for 2006

 

 

Model 1 Model 2 Model 3 Model 4
Predictor Variable AOR AOR AOR AOR
(95% CI) (95% CI) (95% CI) (95% CI)
Predisposing
Characteristics
Sex
Male
(referent) 1.0 1.0 1.0 1.0

 

82

 

Table 4.9 (cont’d).

Female 1.01 1.09 1.03 1.05
(0.88, 1.17) (0.94, 1.26) (0.89, 1.20) (0.91, 1.22)
Age
65 to 74
(referent) 1.0 1.0 1.0 1.0
75 to 99 0.66* 0.67* 0.69* 0.68“
(0.58, 0.77) (0.57, 0.77) (0.59, 0.80) (0.59, 0.79)
Race
Non-Hlspanlc Whlte 1.0 1.0 1.0 1. 0
(referent)
Non-Hispanic 1.83* 1.53* 1.52“ 1.56*
African American (1.51, 2.23) (1.26, 1.87) (1.24, 1.85) (1.28, 1.90)
Hispanic 249* 2.09* 1.90* 1.97*
(1.82, 3.39) (1.49, 2.94) (1.36, 2.66) (1.41, 2.76)
Other race or 1.71 * 1.42* 1.40"‘ 1.41 *
multiracial (1.30, 2.25) (1.07, 1.89) (1.05, 1.86) (1.06, 1.87)
Marital Status
Married
(referent) 1.0 1.0 1.0 1.0
Divorced/ 1.33* 1.02 1.01 1.01
Widowed/Separated (1.15, 1.54) (0.87, 1.20) (0.86, 1.19) (0.86, 1.19)
$33232“ 0’ a 1.07 0.78 0.72 0.74
unmarried couple (0.79, 1.43) (0.58, 1.06) (0.50, 1.02) (0.52, 1.03)
Education Level
College or more
(referent) 1.0 1.0 1.0 1.0
Some college or 1.64* 1.36“ 1.32”“ 131*
technical school (1.29, 2.07) (1.07, 1.73) (1.04, 1.68) (1.03, 1.66)
. 1.74* 1.26* 1.19 1.19
H‘gh S°h°°1 0’ GED (1.39, 2.17) (1.01, 1.59) (0.95, 1.49) (0.95, 1.49)
. 3.07* 1.89* 1.66* 165*
Less than h‘gh “mm (2.45, 3.85) (1.47, 2.41) (1 .29, 2.12) (1 .29, 2.12)
Enabling Resources
Annual Household
Income
$50,000 or more
(referent) 1.0 1.0 1.0
$35,000 to less than 1.07 1.06 1.06
$50,000 (0.82, 1.40) (0.80, 1.39) (0.80, 1.40)
$25,000 to less than 1.23 1.21 1.21
$35,000 (0.98, 1.54) (0.96, 1.53) (0.96, 1.53)
$15,000 to less than 2.03* 1.94* 1.94*
$25,000 (1.62, 2.55) (1.55, 2.45) (1.54, 2.44)
Less than $15,000

 

83

 

Table 4.9 (cont’d).

2.72* 2.56“ 2.52*
(2.17, 3.41) (2.04, 3.21) (2.01, 3.16)
Social and Emotional
Support
Usually or always 1.0 1.0 1.0
(referent)
Sometimes 1.98* 1.68* 1.68*
(1.64, 2.38) (1.38, 2.05) (1.38, 2.05)
Rarel or never 1.91 * 1.73* 1.73*
V (1.57, 2.31) (1.42, 2.11) (1.42, 2.10)
Health Care Coverage
Yes
(referent) 1.0 1.0 1.0
No 3.46* 3.47* 3.45*
(2.64, 4.53) (2.61, 4.62) (2.60, 4.57)
Perceived Health Needs
General Health Status
Good or better
(referent) I '0 1'0
Fair or oor 127* 123*
p (1.09, 1.48) (1.05, 1.46)
Physical Health
0 day
(referent) l '0 l '0
1.52"“ 1.51*
1 day 1" 14 days (1.28, 1.81) (1.27, 1.79)
1.53* 1.50*
15 days ‘0 30 days (1.25, 1.88) (1.22, 1.85)
Mental Health
0 day
(referent) 1 '0 1 '0
1.43* 1.43*
1dayt°14days (1.19,1.71) (1.19,1.71)
2.03“ 2.02*
15 days 1° 30 days (1.57, 2.61) (1.56, 2.61 )
Evaluated Health Needs
Diabetes
No
(referent) l '0
Y 0.89
is (0.74, 1.07)
Asthma
No
(referent) 1 '0

 

84

 

Table 4.9 (cont’d)

 

1.15
yes (0.95, 1.38)
Heart Attack
No
(referent) 1 ’0
1.14
yes (0.94, 1.38)
Coronary Heart
Disease
No
(referent) l '0
Yes 1.07
(0.88, 1.30)
Stroke
No
(referent) l '0
Yes 129*
(1.06, 1.56)
* p < .05

Table 4.8 shows that, as compared to their counterparts, older people with
disabilities were more likely to face difficulty accessing health care due to cost by 2.23
with no predictor variable included (OR=2.23, 95% CI=1.95 to 2.56). However, as
shown in model 1 (Table 4.8), predisposing characteristics variables had little impact on
cost barrier to health care (AOR=2.21, 95% CI=1.92 to 2.54). In the model, age, race,
and education level independently influenced one’s cost barrier to health care.

On the other hand, Table 4.8 shows that enabling resources variables in model 2
attenuated the risk of the cost barrier to a minor extent (AOR=2. 10, 95% CI=1.81 to 2.43)
with predisposing characteristics controlled for. As illustrated in Table 4.9,
predisposing characteristics significant in model 1 still remained significant in model 2.
In addition, low annual household income, specifically less than $25,000, social and

emotional support, and no health care coverage are respectively strong predictors of cost

85

barrier to health care.

In contrast to predisposing characteristics and enabling resources, perceived
health needs (model 3, Table 4.8) decreased the risk for the cost barrier by almost 60%
with predisposing characteristics and enabling resources variables adjusted for
(AOR=1.52, 95% CI=1.30 to 1.77). All perceived health needs variables—general
health status, physical health, and mental health—were independently related to cost
barrier to health care, and mental health showed a graded association. Thus, the more
unhealthy mental health days one had, the more likely he or she was to encounter the cost
barrier (AOR=1.43, 95% CI=1.19 to 1.71 for 1 day to 14 days; AOR=2.03, 95% CI=1.57
to 2.61 for 15 days to 30 days). In contrast, the extent of the association of physical
health with the cost barrier was almost the same in each category compared to 0 day
(AOR=1.52, 95% CI=1.28 to 1.81 for 1 day to 14 days; AOR=1.53, 95% CI=1.25 to 1.88
for 15 days to 30 days).

Lastly, Table 4.8 illustrates that evaluated health care needs variables in model 4
had little impact on the cost barrier when all other independent variables were controlled
for (AOR=1.47, 95% CI=1.25 to 1.72). Table 4.9 demonstrates that of the five chronic
illnesses, only a stroke was significantly associated with the cost barrier (AOR=1.29,
95% CI=1.06 to 1.56). Also, physical health and mental health were still significant in
model 4, but only mental health still showed a graded association (AOR=1.43, 95%
CI=1.19 to 1.71 for 1 day to 14 days; AOR=2.02, 95% CI=1.56 to 2.61 for 15 days to 30
days). The extent of the associations was almost identical across models 3 and 4.
Personal Health Care Provider

For personal health care provider, multinomial logistic regression was employed.

86

Therefore, while investigating the impact of predictor variables on the criterion variable,
“no personal health care provider” and “only one personal health care provider” were
respectively compared to “more than one personal health care provider,” which was a
referent. In Tables 4.10 and 4.11, changes in odds ratios for predisposing characteristics,
enabling resources, perceived health needs and evaluated health needs were examined.
Table 4.12 illustrates the effect of each predictor variable on the criterion variable and its

changes across models.

Table 4.10. Odds Ratios (95% CI) for Personal Health Care Provider by Disability Status
for ‘No Personal Health Care Provider’ versus ‘More than One Health Care Provider’

from the BRF SS for 2006

 

Model 1 Model 2 Model 3 Model 4
0Ra AORb AOR" AOR" AOR"
(95% CI) (95% CI) (95% CI) (95% CI) (95% CI)

Disability

c 0.39' ‘ 0.39’ 0.38" 0.57’ 062'
Older PWD (0.34, 0.45) (0.33, 0.45) (0.32, 0.44) (0.48, 0.67) (0.53, 0.74)
Older PW/ODd Reference Reference Reference Reference Reference

aUnadjusted odds ratio

bAdjusted odds ratio

cOlder People with Disabilities
dOlder People without Disabilities
* p < .05

Table 4.10 shows that as compared to “more than one health care provider,” predisposing
characteristics variables in model 1 did not have an impact on “no personal health care
provider” (AOR=O.39, 95% CI=0.33 to 0.45), and enabling resources variables also made
almost no impact to the criterion variable (AOR=0.3 8, 95% CI=0.32 to 0.44) with

predisposing characteristics adjusted for. In contrast, perceived health needs made

87

modest impact to the criterion variable by almost 20% (AOR=0.57, 95% CI=0.48 to 0.67),
after controlling for predisposing characteristics and enabling resources. Finally, model
4 indicates that evaluated health needs had a small impact (AOR=0.62, 95% CI=0.53 to

0.74) after taking all the predictor variables into account.

Table 4.11. Odds Ratios (95% CI) for Personal Health Care Provider by Disability Status
for ‘Only One Personal Health Care Provider’ versus ‘More than One Health Care

Provider’ from the BRFSS for 2006

 

. Model 1 Model 2 Model 3 Model 4
P d t
V2231]? 0Ra AOR" AORb AOR" AOR"
(95% CI) (95% c1) (95% c1) (95% c1) (95% or)

Disability

C 0.61 * 0.61* 0.61* 0.77* 079*
Older PWD (0.56, 0.66) (0.56, 0.67) (0.56, 0.67) (0.70, 0.84) (0.71, 0.87)
Older PW/ODd Reference Reference Reference Reference Reference

aUnadjusted odds ratio
bAdjusted odds ratio
cOlder People with Disabilities

dOlder People without Disabilities
* p < .05

Table 4.11 illustrates that compared to “no personal health care provider” (Table
4.10), “only one personal health care provider” showed almost identical findings in terms
of changes in odds ratios across models. That is to say, predisposing characteristics in
model 1 and enabling resources in model 2 did not impact personal health care provider.
However, perceived health care needs in model 3 had some impact to the criterion
variable although the extent of influence was modest at 16% (AOR=0.77, 95% CI=O.70
to 0.84). Lastly, evaluated health needs in model 4 had little impact (AOR=0.77, 95%

CI=0.71 to 0.87).
88

 

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93

In Table 4.12, two comparisons were made for each model: “no personal health
care provider” versus “more than one personal health care provider” and “only one
personal health care provider” versus “more than one personal health care provider.” In
both models, “more than one personal health care provider” was a referent. As
illustrated in Table 4.12, older people with disabilities were less likely to have no or only
one personal health care provider across all models.

Models 1 and 2, females were significantly related to no or one personal health
care provider. All other predisposing variables—age, race, and marriage—were
individually strongly associated with no personal health care provider alone in model 1.
Minorities and people who were not in marriage were more likely to have no personal
health care provider. However, Tables 4.10 and 4.11 show that the inclusion of
predisposing characteristics in model 1 did not have any impact on the risk of having no
or only one personal health care provider, compared to unadjusted odd ratio (AOR=O.39,
95% CI=0.34 to 0.45 for no health care provider; AOR=0.61, 95% CI=0.56 to 0.66 for
only one health care provider).

In model 2 including enabling resources with predisposing characteristics
controlled for, females and older people age 75 and over were continuously less likely to
have no personal health care provider. In addition, people who had low annual
household income, particularly less than $25,000, received social and emotional support
“rarely or never,” and had no health care coverage were individually strong predictors of
having no personal health care provider. Thus, older people with these enabling
resources were more likely to have no personal health care provider. In contrast, for

only one personal health care provider, only “$25,000 to less than $35,000” for an annual

94

household income was a significant predictor among enabling resources variables. As
illustrated in Tables 4.10 and 4.1], however, enabling resources in model 2 did not have
any effect on the outcomes (AOR=O.3 8, 95% CI=0.32 to 0.44 for no health care provider;
AOR=O.61, 95% CI=0.56 to 0.67 for only one health care provider).

Table 4.12 demonstrates that general health status and physical health in model 3
slightly attenuated the risks of having no or only one health care provider by 19% and
16% with predisposing characteristics and enabling resources adjusted for (AOR=0.57,
95% CI=0.48 to 0.67 for no health care provider in Table 10; AOR=O.77, 95% CI=O.70
to 0.84 for only one health care provider in Table 11). However, Table 4.12 further
shows that mental health was not significantly associated with the criterion variables
when predisposing characteristics, enabling resources, and other perceived health needs
variables were controlled for.

In the final model, evaluated health needs had a very slight impact on the criterion
variables (AOR=O.62, 95% CI=0.53 to 0.74 for no health care provider in Table 4.10;
AOR=O.79, 95% CI=0.71 to 0.87 for only one health care provider in Table 4.11).

Table 4.12 illustrates that compared to more than one health care provider, people with
diabetes, asthma, heart attack and coronary heart disease were less likely to have no
personal health care provider, whereas individuals with heart attack and coronary heart
disease were less likely to have only one health care provider.

Across all four models for a personal health care provider, females were
individually a strong predictor of having no or only one health care provider. Thus,
compared to more than one personal health care provider, females were less likely to

have no personal health care provider, or they were more likely to have only one health

95

care provider. In addition, individuals aged 75 and older, Hispanics, and people who
were divorced, widowed, or separated were significantly associated with no personal
health care provider. Specifically, Hispanics or people who were divorced, widowed or
separated were more likely to have no personal health care provider. In contrast, the
risk of having no personal health care provider was lower for people age 75 and over
across all the four models, as compared to older people age 65 to 74.

Routine Physical Checkup

For routine physical checkup, multinomial logistic regression was employed. As a
result, two comparisons were made: “routine physical checkup more than 5 years ago or
no routine physical checkup” versus “routine physical checkup less than 2 years ago”;
and “routine physical checkup 2 years ago to 5 years ago” versus “routine physical
checkup less than 2 years ago.”

In contrast to cost barrier to health care and personal health care provider,
however, routine physical checkup did not show significant results for all models. As
demonstrated in Tables 13, only models 1 and 2 presented significant results when “More
than 5 Years ago or Never” was compared to “Less than 2 Years ago.” Thus, health
needs variables (Models 3 and 4) had no significant effect on routine physical checkup.
However, it should be noted that predisposing characteristics in model 1 and enabling
resources in model 2 with predisposing characteristics adjusted for had almost no impact
on routine checkup (AOR=O.79, 95% CI=0.69 to 0.91 in model 1; AOR=O.78, 95%

CI=0.68 to 0.89 in model 2).

96

Table 4.13. Odds Ratios (95% CI) for Routine Physical Checkup by Disability Status for

‘More than 5 Years ago or Never’ versus ‘Less than 2 Years ago’ from the BRFSS for

 

 

2006
Model 1 Model 2 Model 3 Model 4
0R3 AORb AORb AOR" AOR"
(95% CI) (95% CI) (95% CI) (95% CI) (95% CI)
Disability
Older PWDC 0.80“ 0.79* 0.78* 0.88 0.93

(0.70, 0.91) (0.69, 0.91) (0.68, 0.89) (0.77, 1.01) (0.81, 1.07)
Older PW/ODd Reference Reference Reference Reference Reference

 

a‘Unadjusted odds ratio

bAdjusted odds ratio

cOlder People with Disabilities
dOlder People without Disabilities
* p < .05

In relation to a comparison between “2 years to 5 years ago” versus “Less than 2
years ago,” Table 4.14 illustrates that no model had any significant impact on routine
physical checkup. In addition, as previously discussed, significant differences in routine
physical checkup for older groups with and without disabilities were found only for older
people age 75 and over. Therefore, the following discussion on routine checkup is
centered on a comparison between “More than 5 Years ago or Never” and “Less than 2
Years ago” only for older persons age 75 and older. Further, the focus is placed on
model 1 and model 2 in the discussion because perceived and evaluated health needs had

no significant effect on the criterion variable.

97

Table 4.14. Odds Ratios (95% CI) for Routine Physical Checkup by Disability Status for

‘2 Years to 5 Years ago’ versus ‘Less than 2 Years ago’ from the BRFSS for 2006 a

 

Model 1 Model 2 Model 3 Model 4
OR" AOR" AORb AOR" AORb
(95% CI) (95% CI) (95% c1) (95% CI) (95% CI)

 

 

Disability

C 0.89 0.91 0.89 1.03 1.08
01"“ FWD (0.77, 1.04) (0.78, 1.05) (0.76, 1.04) (0.87, 1.23) (0.91, 1.28)
Older PW/ODd Reference Reference Reference Reference Reference
aUnadjusted odds ratio
bAdj usted odds ratio

COlder PeOple with Disabilities
dOlder People without Disabilities
* p < .05

98

 

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103

Table 4.15 shows that predisposing characteristics in model 1 had little effect on
routine physical checkup (AOR=O.79, 95% CI=0.69 to 0.91). Sex, age, and marital
status were statistically significant predictors of the criterion variable. More specifically,
females and older people age 75 and over were less likely to have routine physical
checkup more than 5 years ago or have no physical checkup as compared to “less than 2
years ago.” It should be noted, however, that this study found significant differences
between older people with and without disabilities only for those aged 75 and over. On
the other hand, older people who were not in marriage or had an education level—“high
school or GED” or “less than high school”—were more likely to have no physical
checkup or have it more than 5 years ago.

In model 2, predisposing characteristics significant in model I remained to be
strong predictors except an education level when enabling resources were accounted for.
When enabling resources were controlled for, any education level became insignificant.
In relation to enabling resources, older people who did not receive social and emotional
support they needed “usually or always” or did not have health care coverage were more
likely to have their routine physical checkup more than five years ago or to have no
physical checkup. Furthermore, older people who reported their annual household
income as “$25,000 to less than $35,000” and “Less than $15,000” were more likely to
have a physical checkup more than five years ago or have no checkup. However, these
significant enabling resources had almost no impact on the criterion variable with

predisposing characteristics controlled for (AOR=O.78, 95% CI=0.68 to 0.89).

104

CHAPTER 5

DISCUSSION

The two-fold purpose of this study was as follows: 1) to examine the existence of
health disparities in access to health care between older people with and without
disabilities, and 2) to what extent such disparities could be accounted for by predisposing
characteristics, enabling resources, and perceived and evaluated health needs.
Employing older individuals age 65 and over in 50 states and the District of Columbia
from the BRFSS for 2006, health disparities in access to health care of older people with
disabilities were investigated in cost barrier to health care, personal health care provider,
and routine physical checkup. After findings of this study are concisely presented, in-
depth discussion of the disparities ensues. The limitations of this study and suggestions
for future studies, as well as a conclusion follow after the discussion.

Findings of the SIM?

This study revealed that significant differences in access to health care existed
between older people with and without disabilities. First, older individuals with
disabilities were more likely than their counterparts without disabilities to face cost
barrier to health care. In relation to the extent of the effects of the behavioral model
variables—predisposing characteristics, enabling resources, and perceived and evaluated
health needs—on cost barrier to health care, predisposing characteristics had little effect
on the cost barrier, and enabling resources accounted for the cost barrier by a modest
portion with predisposing characteristics controlled for. In contrast, perceived health

needs explained the cost barrier the most after controlling for predisposing characteristics

105

and enabling resources, while evaluated health needs little accounted for cost barrier to
health care with all other variables adjusted for.

Second, this study found that older individuals with disabilities were more likely
to have more than one personal health care provider, whereas older people without
disabilities were more likely to have no or only one personal health care provider.
Regarding the effects of the behavioral model variables on personal health care provider,
predisposing characteristics and enabling resources did not impact “no personal health
care provider” and “only one personal health care provider” when they were both
compared to “more than one health care provider.” However, perceived health needs
had the biggest effect in the two comparisons when predisposed characteristics and
enabling resources were taken into consideration. This finding is consistent with cost
barrier to health care; perceived health needs had the biggest effect on the cost barrier.
But evaluated health needs had extremely little effect on personal health care provider in
the two comparisons while all other variables were controlled for.

Third, regarding routine physical checkup, older persons with disabilities were
found to be more likely to have routine checkup within less than 2 years compared to the
counterparts without disabilities. In contrast, older people without disabilities were
more likely to have no routine checkup or have it more than five years ago. However, it
should be noted that these differences were significant only for those aged 75 and older.
In addition, routine physical checkup showed significant findings only for a comparison
of “more than 5 years ago or never” versus “less than 2 years ago”; the other
comparison—“2 years to 5 years ago” versus “less than 2 years ago”—was not

significant. Furthermore, even in the first comparison, only model 1 (predisposing

106

characteristics) and model 2 (enabling resources) were statistically significant, but the
two models did not make noticeable change in adjusted odds ratios. That is, routine
physical checkup was little explained by predisposing characteristics and enabling
resources with predisposing characteristics controlled for. In cost barrier to health care
and personal health care provider, perceived health needs explained each aspect of health
care access the most. However, this was not applied to routine physical checkup since
only model 1 and model 2 showed significant associations with the routine checkup. As
indicated above, this study found that health disparities occurred through intricate
mechanisms, and that the mechanisms of such disparities had intricate patterns in each
aspect of health disparities.
Discussion of the Disparities in Access to Health Care

To facilitate an in-depth discussion of the findings of this study, general
discussion on the findings are first provided. Detailed discussion about the three aspects
of access to health care is then followed. This study suggests that as compared to the
counterparts without disabilities older people with disabilities paid more attention to and
they were more concerned about their health status given the findings of personal health
care provider and routine physical checkup. Based on a substantial review of studies on
the impact of health insurance on health-related outcomes for adults age 18 through 64,
Institute of Medicine (2002) indicates that adults with chronic conditions and those in late
middle age benefit most from health insurance coverage due to their greater need for
health care. This could be applied to the findings of this study: older persons with
disabilities were more likely to have more than one personal health care provider and

have routine physical checkup within less than 2 years, compared to their counterparts

107

without disabilities. Thus, the relatively greater need for health care for older
individuals with disabilities and their more attention to and concern about health status
due to their existing health issues could make them take full advantage of public health
care coverage such as Medicare or Medicaid.

Given the employment-related issues, economic distress and their impacts on
medical insurance for people with disabilities, favorable results of personal health care
provider and routine physical checkup may be much ascribed to public health care
coverage. Nonetheless, it is important to note that the effect of public health insurance
on health care access for older people with disabilities could not be investigated in this
study because the types and sources of health care coverage were not dealt with in the
BRF SS. Despite the potential favorable effect of public health care coverage on access
to health care for older individuals with disabilities, this study revealed that older persons
with disabilities were more likely to face cost barrier to health care. In other words,
they were more likely to have difficulty seeing a doctor in a timely manner because of
cost. This illustrates that in spite of the near-universal coverage of Medicare for older
people age 65 and over and supplemental coverage of Medicaid for those with low
incomes older individuals with disabilities still face challenge in accessing timely health
care.

This unfavorable finding can be inversely interpreted that public health care
coverage is still necessary to enhance access to health care for older people with
disabilities. Individual account and privatization has been highlighted among the recent
political debates on Social Security and universal health care coverage (Herd & Kingson,

2005; Moon, 2005). Concerns about financial crisis, as well as the sheer rapid increase

108

of older population due to the aging of the baby boomer generation have made debates
over reforms of old age policies on the top of political agenda. People for privatization
claim that a market solution will solve the financial crisis that Social Security faces.
However, a variety of valid arguments exist against the market solution and they
emphasize the significance of existence of Social Security and public health care
coverage run by the government. In relation to individuals with disabilities, it is highly
likely that older people with chronic illnesses or disabilities are precluded or denied by
private insurers, or that, if those with illnesses or disabilities are enrolled in private health
insurance, they have challenge in paying high premiums and co-pays due to their health
issues (Mills, 1993; Moon, 2005; Williams, Dulio, Claypool, Perry, & Cooper, 2004).
With regard to Medicare, increasing pressure for individual account and raising

the age of eligibility should be taken into consideration for older people with disabilities.
According to Moon (2005), raising the age of eligibility for Medicare can go favorably
for older individuals with disabilities as long as a new standard is created for increasing
assistance for older persons with great needs for health care due to chronic illnesses or
disabilities. Moon (2005) suggests that a new standard be created for qualifying for
disabilities and two-year waiting period for eligibility be eliminated in order to buffer the
effects of increasing the eligibility age.
C 0st Barrier to Health Care

This study found that older people with disabilities were more likely to face cost
barrier to health care than their counterparts without disabilities. Thus, older individuals
with disabilities had more difficulty seeing a doctor due to cost within the previous 12

months. This result was anticipated given that, despite the ADA, people with

109

disabilities are still one of the most disadvantaged groups in society from various aspects,
such as education, income, employment, or living condition. At the same time, however,
cost barrier to health care due to the disadvantaged situation could be anticipated to
ameliorate among older people given the near-universal coverage of Medicare. This
study revealed that although health disparities in access to health care are enhanced
among older people due to Medicare compared to young counterparts (U SDHHS, 2006),
older individuals with disabilities were more likely to face difficulty in accessing health
care due to cost than their counterparts without disabilities.

It has been very well documented that people with disabilities suffer fi'om
unemployment, underemployment and, as a result, economic distress (e. g., Kaye, 1998;
Lustig et al., 2003; Stapleton & Burkhauser, 2003). The economic distress for people
with disabilities can prevent them from purchasing supplemental health care coverage,
which impacts their access to and quality of health care. Aside from the economic
distress, people with disabilities can be further burdened with the following issues
Millman, 1993; Williams et al., 2004): exclusion waivers for preexisting health
conditions, too high premiums and co-pays, and denials. This suggests that despite the
near-universal Medicare older people with disabilities can still face enormous challenge
in accessing quality health care.

Other studies illustrate that lack of supplemental health coverage is related to
challenge of accessing health care. When compared to individuals with supplemental
health insurance, people who rely on Medicare alone were more likely to delay care due
to cost and have difficulty obtaining care within the previous year, and those also were

less likely to have usual source of care (Gluck & Hanson, 2001). More specifically, the

110

study shows that 21% of people with Medicare alone experienced delaying care due to
cost in comparison to 5% of individuals with private supplemental insurance. In
addition, 8% of individuals with Medicare alone had difficulties obtaining care compared
to 3% of people with private coverage.

Employing data of the population aged less than 65 from the 1994 RWJF
National Access to Care Survey, Berk and Schur (1998) showed that people in Medicaid
were twice as likely to have difficulties accessing services compared to individuals with
private insurance. Although this study restricted its attention to people aged less than 65
to effectively examine the impact of health insurance on access to health care, the study
revealed that as compared to those with private health coverage, individuals in Medicaid
were twice as likely to report difficulties in these areas: having a usual source of care,
obtaining needed care, and number of physician visits.

These studies indicate that people with Medicare or Medicaid alone have a great
risk of accessing quality and timely health care. Given the prevalent unemployment and
underemployment among people with disabilities as well as their poor quality of
employment, individuals with disabilities can be less likely to have other supplemental
health insurance, such as employer-sponsored retiree insurance or Medigap. In fact, as
of 2002, 12 percent of Medicare beneficiaries do not have any supplemental health
coverage (Cubanski, Voris, Kitchman, Neuman, & Potetz, 2005). In particular, in an era
of continuously advancing science and technology and enhanced medicine, older people
with chronic/fatal illnesses or disabilities are highly likely to sustain their life longer,
requiring more spending on their health care. Nonetheless, Medicare covers only 45

percent of health care expenses (total medical and long-term care expenses) for both

111

institutionalized and non-institutionalized beneficiaries in 2002 (Cubanski et al., 2005).

But older persons with disabilities who have extremely low incomes can have fill-
in benefits from Medicaid as well as Medicare; these people are “full-benefit dual
eligibles” (Cubanski et al., 2005). Cubanski et al. show that 56 percent of Medicare
beneficiaries with the lowest income, “$10,000 or less” are provided supplemental health
coverage through Medicaid. The benefits for older individuals with extremely low
incomes are generally generous and comprehensive (Clark et al., 2004; Moon, 2006).
However, these benefits can be offset by much greater need for health care among older
individuals with disabilities in great poverty. As compared to those with higher income,
Medicare beneficiaries with low incomes residing in the community self-reported that
they were in poor or fair health (Cubanski et al., 2005). Moreover, those beneficiaries
are highly likely to be uninsured before receiving Medicare or Medicaid, and, as a result,
their health condition are more likely to be worse (Hadley, 2002; IOM, 2002). Given a
marginalized status in society for people with disabilities, many of older individuals with
disabilities can have considerable and dire need for health care and be in poor health
condition. Hence, public health coverage may not be sufficient to cover their health
issues for older persons with disabilities. In this context, the finding that older people
with disabilities were more likely to be exposed to cost barrier to health care in spite of
public health coverage can be taken without great surprise.

In relation to the behavioral model variables, this study revealed that older aging
people age 75 and over were less likely to face cost barrier to health care. This finding
can seem perplexing since, given the positive association of age with chronic disease or

disability among older people, older aging people should have faced more cost barrier to

112

health care. The finding might be related to the fact that this study focused only on
community-dwelling individuals and excluded older people residing in institutions such
as nursing homes or other long-term care settings, who were more likely to have severe
and chronic diseases or disabilities. The percentage of older people who live in nursing
homes increases substantially with age (Administration on Aging, 2006). As a result,
older individuals aged 75 and over could be relatively healthy in this study. Or older
people in the age range were more apt to take it granted that they have some health issues,
and, as a result, they might be less likely to seek for assistance from medical
professionals compared to those aged 65 to 74. But perceived and evaluated health
needs in this study could not explain the difference between older people in the two age
ranges. Further studies are warranted for a better understanding of the finding.

In addition, this study shows that health care coverage had a considerable effect
on a timely access to health care for older persons with disabilities. Although older
people age 65 and over are eligible for Medicare, this study found that 1.73% of those
with disabilities and 2.09% of those without disabilities did not have any health care
coverage. Thus, this study demonstrates that Medicare is not a universal health care
coverage for all older people age 65 and over. Medicare provides basic acute care
services for over 97 percent of older individuals aged 65 and older (Clark et al., 2004).
Some older persons may not have Medicare since they are recent immigrants who are not
eligible for Medicare (DeLia, 2006), or they are in the process of eligibility (Williams et
al., 2004).

Furthermore, this study examined five chronic diseases for evaluated health

needs: diabetes, asthma, heart attack, coronary heart disease, and stroke. Of the five

113

chronic diseases, however, only stroke showed a significant association with the cost
barrier. When older individuals with disabilities had a stroke, they were more likely to
face the cost barrier. The reason why the other chronic diseases showed insignificant
results warrants further studies, but this might be related to Medicare coverage.
Medicare recently showed some improvements in preventative services, hospital
treatment, or outpatient health care for chronic conditions (Leatherrnan & McCarthy,
2005). As a result, hospital treatment of heart attack and outpatient care for chronic
health conditions such as diabetes have been possible through Medicare. In essential,
Medicare coverage has a primary focus on acute and post-acute health care services
(Clark et al., 2004; Moon, 2006). Stroke may require long-term care and rehabilitation
for older persons with disabilities, resulting in the exposure to the cost barrier.
Personal Health Care Provider

This study showed favorable findings on personal health care provider for older
people with disabilities; older individuals with disabilities were more likely to have more
than one personal health care provider, whereas older persons without disabilities were
more likely to have no or only one personal health care provider. The near-universal
coverage of Medicare for older people age 65 and over, as well as Medicaid for those
with low incomes should play a role in the results. Leatherman and McCarthy (2005)
show through a comprehensive review of recent studies that Medicare played a
significant part in reducing preeixisting disparities in screening, preventive care, and
seeking care among older people who did not have health insurance before 65.

Regarding health care coverage, this study revealed that those without health care

coverage were more than five times as likely to have no personal health care coverage,

114

while it was not found whether health care coverage played a significant role in having
one personal health care provider compared to more than one health care provider. This
finding can be interpreted that health care coverage can help stop older persons with
disabilities from having no personal health care provider, but the effect of health
insurance was not enough to encourage older individuals with disabilities to have more
than one health care provider. This may result from the lack of supplemental health
insurance with comprehensive coverage for older people with disabilities.

In spite of Medicare, older people are still burdened with considerable out-of-
pocket costs (Gonyea, 2005). In fact, Medicare does not provide universal benefit
coverage for older individuals although the eligibility of Medicare is almost universal
(Gonyea, 2005). Long-term care, preventive care, Part B premiums, deductibles, co-
payments, dentures and dental care, eyeglasses, and hearing aids are among gaps in
Medicare coverage (Moon, 2006; Social Security Administration, 2006). For older
individuals, cost sharing and premiums can be huge and they are also increasing, which
makes it necessary for older people to purchase additional private health insurance
(Meyer, 2005; Moon, 2006). In addition, health insurance coverage can increase
likelihood that people can have appropriate health care through a primary care provider
or a regular source of health care (IOM, 2002). Given the aforementioned findings of
the studies, it is possible that the lack of supplemental health insurance prevented older
persons with disabilities from having more than one personal health care provider. It is
important to note, however, that the BRFSS does not collect information on the types and
sources of health insurance fi'om interviewees, so this study could not investigate the

relationship between types and sources of health care coverage and personal health care

115

provider in further detail.

Gender was significantly related to personal health care provider; older females
with disabilities were less likely to have no personal health care provider compared to
males. Given this finding, older females with disabilities seemed to pay more attention
to their health. The finding might stem from the fact that older females with disabilities
were likely to have greater needs for health care. It should be noted that the gender
difference was not explained by perceived and evaluated health needs. Gender-specific
preventive care services might account for the gender difference in personal health care
provider. That is, women interviewees might think of professionals for preventative
care services as health care provider. But this study did not examine the effect of
gender-specific preventive care services on health care provider, such as a mammography,
aaclinical breast exam, a pap test, prostate cancer screening, or colorectal cancer
screening.

In the prior comparison between “no personal health care provider” versus “more
than one personal health care provider,” older females with disabilities were less likely to
have no personal health care provider. However, the finding of a comparison between
“one personal health care provider” versus “more than one health care provider” showed
that older females with disabilities were more likely to have only one health care provider.
This study revealed that older females with disabilities were less likely to have no
personal health care provider, but they were more likely to have only one health care
provider. These findings make it difficult to reach a conclusion that older females with
disabilities paid more attention to their health. As previously discussed, these findings

may also be associated with supplemental health care coverage. Nonetheless, this was

116

not examined due to the limitation of the BRFSS.

Song, Chang, Manheim, and Dunlop (2006) found that women had more
physician contact after controlling for variables in sociodemographics, health needs, and
economic access. However, physician contact could not be examined in this study
given the limitation of the BRFSS. In addition, different research methodologies should
be noted. While Song et al. (2006) investigated physician contact through binary
logistic regression, this study examined personal health care provider employing
multinomial logistic regression. These differences merit caution in the comparison of
the findings of the two studies.

Age had an independently significant effect on personal health care provider in a
comparison between “no personal health care provider” versus “more than one personal
health care provider.” Older persons with disabilities aged 75 and older were less likely
to have no personal health care provider compared to those aged 65 to 74. Aging shows
a positive association with greater prevalence of chronic diseases and conditions (N CHS,
2006). Thus, the older aging individuals become, the more likely they are to have
various physical or mental illnesses and disabilities. Higher age can make older
individuals with disabilities more apt to perceive their health status as poor, or sustain
various health issues. This should make the older aging people less likely to have no
personal health care provider compared to the younger aging people aged 65 to 74.

In relation to race, all non-Whites were significantly related to personal health
care provider in model 1 with predisposing characteristics. That is, all minority groups
were more likely to have no personal health care provider compared to non-Hispanic

Whites. However, non-Hispanic African Americans were more likely to have no

117

personal health care provider in only model 1 with predisposing characteristics, but they
did not present a significant association in all other models. This indicates that the
significant association in model 1 was explained by enabling resources, and perceived
and evaluated health needs.

This finding of African Americans is different from the result of a study by
Dunlop, Manheim, Song, and Chang (2002). Dunlop et al. (2002) found that compared
to non-Hispanic Whites, minority men, especially African Americans were less likely to
have any physician contact, which was not accounted for by variables in health need and
economic access. However, this study revealed that the significant association for
Afiican Americans in model 1 was accounted for by enabling resources and perceived
and evaluated health needs. Several differences between the two studies should be
noted. First, Dunlop et al.’s (2002) study included the interaction between gender and
race, whereas this study did not examine the interaction. In addition, Dunlop and
colleagues employed data from the 1993-1995 study on the Asset of Health Dynamics
Among the Oldest Old (AHEAD). Further, although Dunlop et a1. examined many
aspects of health care utilization, the number of a personal health care provider was not
investigated. Therefore, these differences make difficult a direct comparison of the
findings of the two studies.

With regard to social and emotional support, this study found that the less likely
timely social and emotional support older individuals with disabilities received, the more
likely they were to have no personal health care provider. Social connectedness and
social support present several benefits to health in the following aspects: a) finding

resources when one is exposed to problems and threats, b) offsetting the effects of stress,

118

and 0) being more likely to access health-related information (Thomson et al., 2006).
Older persons can learn greater knowledge of health from others through social support,
which includes information on health promotion and skills of a doctor (Adler, 2006).
Given these benefits of social support, the finding of this study makes sense.

Routine Physical Checkup

This study revealed that while older people with disabilities were more likely to
have routine physical checkup within less than 2 years compared to the counterparts
without disabilities, those without disabilities were more likely to have no routine
checkup or have it more than five years ago. However, these differences were
significant only for those aged 75 and older. Therefore, the following discussions are
centered only on older people aged 75 and over.

Older females with disabilities were less likely to have routine physical checkup
more than five years ago or have no physical checkup in both model 1 (predisposing
characteristics) and model 2 (enabling resources). In the discussion about personal
health care provider, older females with disabilities were less likely to have no health
care provider. This can suggest that older females with disabilities paid more attention
to their health and that they had greater needs for health care. This suggestion can be
applied to routine physical checkup as well.

With regard to race, only non-Hispanic African Americans showed significant
results, and they were less likely to have routine physical checkup more than five years
ago or have no physical checkup, compared to non-Hispanic Whites. That is, this study
revealed that, compared to Whites, African Americans were more likely to have routine

physical checkup. It should be noted that only older people age 75 and over showed

119

significant results in routine checkup. Hence, it may be possible that Afi'ican Americans
in this age range were in poorer health status, which made them pay more attention to
their health. Cubanski et al. (2005) show that as compared to Medicare beneficiaries
with higher incomes living in the community those with lower incomes have poorer
health, and that African American (41%) and Hispanic (3 7%) beneficiaries are much
more likely to be in fair or poor health compared to non-Hispanic Whites (25%).
However, this study’s finding on African Americans was not accounted for by perceived
and evaluated health needs.

Regarding marital status, older individuals with disabilities who were not in
marriage were more likely to have their routine physical checkup more than five years
ago or have no checkup in both model 1 and model 2. This unfavorable effect of being
out of marriage on routine physical checkup may be associated with social network.
More direct and reciprocal interrelations may be more likely to happen in marriage
especially for older people with disabilities. Given the double challenge of disability
and old age, it can be particularly demanding for older persons with disabilities who are
not in marriage to find continuous and meaningful relationships. In contrast, those with
disabilities in marriage could have more direct and continual encouragement for the
routine physical checkup from their spouse.

In relation to social and emotional support, this study found that older people
with disabilities in “sometimes” and “rarely or never” categories were more likely to
have no routine physical checkup or have it more than five years ago. This can illustrate
the importance of social support among older people as previously described. At the

same time, however, it should be noted that although the BRFSS examines social and

120

emotional support, the concept of the support is crude and general since the BRF SS
simply asks the interviewees to include support received from any source. This can
serve as a limitation to effectively investigate the impact of social support on access to
health care in this study.

Finally, older people with disabilities who did not have any health care coverage
were more than three and a half times as likely to have the checkup more than five years
ago or have no checkup. According to Institute of Medicine (2002), health insurance
helps insured individuals to have enduring relationship with a professional health care
provider, and uninsured status will make individuals with chronic diseases less likely to
have regular checkup. In this regard, public health insurance coverage such as Medicare
and Medicaid can help older persons with disabilities have a regular relationship with
health care providers, which could in turn assist them with routine physical checkup.

Limitations of this Study

For investigating health disparities in access to health care between older people
with and without disabilities, this study employed a secondary data analysis, using the
BRFSS for 2006. Although the BRFSS employs complex sampling design and this
study appropriately used statistical analyses to examine such disparities, this study had
some drawbacks resulting from the national survey data set and resultant secondary data
analyses. The following are potential limitations of this study.

First, the BRFSS collects health condition and health risk behaviors of adults in
the U.S. households, but did not include people in institutions, such as nursing homes or
other long-term care settings. As a result, this data set may not reflect the full reality of

the older population in terms of chronic disease or disability. In particular, given that

121

some older people, especially a significant portion of oldest-old population spend their
later life in nursing homes, the BRF SS may not truly capture health disparities among the
oldest-old people. The Administration on Aging (2006) illustrates that the percentage of
older people living in nursing homes in 2000 increases considerably with age: 1.1% for
people age 65 to 74, 4.7% for people age 75 to 84, and 18.2% for people age 85 and older.
This substantial portion of 18.2% for the oldest-old population may serve as a barrier to
investigating health care access in this group.

Second, general limitations related to the participation of people with disabilities
in national population surveys should be carefully considered. Kinne and Topolski
(2005) compared the 2001 BRFSS with the Census 2001 supplementary survey, the 2001
and 2003 BRFSS disability supplements, and the Washington state population survey.
All the surveys were telephone surveys except for the census survey, all focusing on the
state of Washington. Kinne and Topolski (2005) demonstrated that people with
disabilities were not underrepresented in all the population telephone surveys. In
addition, adults with disabilities in the 2001 BRFSS did not show less severe limitations
in sensory, mental, self-care, and multiple limitations, compared to adults in the Census
survey. These findings could not be accounted for by differences in survey
methodologies.

Despite this finding, however, the design and conduction of the BRFSS still
warrant great consideration. Given that the BRF SS is a telephone survey, the nature of
the survey may serve as an obstacle for the participation of people with disabilities,
especially people with deafness or hearing problems. Moreover, individuals with severe

cognitive or intellectual disabilities can have difficulty understanding survey questions,

122

more likely resulting in cessation, avoidance, or denial of participation in the survey.
These limitations can further increase among older population given their high
prevalence of chronic or severe illnesses and disabilities. As a result, the BRFSS may
not accurately reflect access to health care among the population with disabilities.

Third, the BRFSS employs a crude and broad concept of disability, which may
serve as a significant barrier for applying the findings of this study to people with
disabilities in general. The BRFSS measures interviewees’ disability, chronic diseases,
and health status through the telephone interview. The BRFSS uses only two questions
to define disability, which focus on any activity limitation and use of special equipment
due to any health problem. In this concept of disability, individuals with temporary
injuries using special equipment such as a wheelchair can be considered people with
disabilities. This concept is considerably different from the concept of disability
accepted by society or laws. Disability is a significantly complex concept and there
exist a variety of definitions. If we refer to the ADA as a conservative approach, a
person is considered having a disability when he or she meets one of the following
criteria: (3) having a physical or mental impairment significantly limiting one’s major life
activity(ies), (b) having a record of the impairment, or (c) being regarded as having such
an impairment. This definition of the ADA illustrates that the BRF SS employs a crude
concept of disability stressing activity limitation, which can make people with disability
in the BRFSS considerably different from those considered in society and the law.

In addition, the measures of disability in the BRFSS have not been validated
(Okoro, Balluz, Campbell, Holt, & Mokdad, 2005). However, the clear concept of

disability is essential to elucidate intricate mechanisms of health disparities for

123

individuals with disabilities. Further, functional limitations due to disability or chronic
diseases are not measured in the core component section of the BRF SS. As a result, the
severity of disability and chronic diseases cannot be examined in the BRFSS. These
limitations prevented further investigations into determinants of health disparities among
older people with disabilities.

Fourth, this study employed a cross-sectional data set for secondary data analysis.
Cross-sectional analysis can have limitations in unraveling mechanisms of health
disparities. Many health issues among older people are associated with various factors
in their youth, such as lifestyle, socioeconomic status, food consumption, or nutrition.
Hence, studies without considering these factors associated with younger days of older
population should have some limitation to finding causality of health disparities. The
drawback pertaining to causality limits generalizing the findings of this study to a broader
population.

Fifth, this study examined health disparities in access to health care through cost
barrier to health care, personal health care provider, and routine physical checkup. Due
to a lack of information in the BRFSS, more direct aspects of health care access could not
be investigated. For instance, this study examined the number of personal health care
providers, and it was found that older individuals with disabilities were more likely to
have more than one personal health care provider. However, the actual use and volume
of health care, such as the number of physician visits or hospital admission, could not be
appropriately investigated through the number of health care providers.

Finally, this study investigated health disparities of older people with disabilities

on a national level. This approach could present a national picture of the health

124

disparities. On the other hand, it may have drawbacks in terms of a given state because
such disparities can be different from state to state, but the variability cannot be
adequately captured in the aggregate approach. As a result, the applicability of the
findings in this study to the state level is limited.
Suggestions for Future Research

Given the findings and limitations of this study, the following suggestions are
presented for future studies that investigate the determinants. of health disparities for older
people with disabilities and mechanisms of such disparities. First, it is clear that access
to and utilization of health care are intricately intertwined with a great variety of factors.
With regard to the behavioral model, access to health care itself is conceived to influence
predisposing characteristics, enabling resources, and health needs. Thus, access to
health care is dynamic and recursive in the model. However, this study restricted its
attention to the linear effects of the behavioral model variables—predisposing
characteristics, enabling resources, and perceived and evaluated health needs—on access
to health care. Health disparities in access to health care for older persons with
disabilities could not be effectively accounted for by only those behavioral model
variables considered in this study. This finding suggests that future studies need to
account for dynamic and recursive aspects of access to health care to better understand
the intricate mechanisms of health disparities.

Second, this study placed attention on the examination of the existence of health
disparities among older individuals with disabilities due to a great lack of emphasis in the
literature on this issue. This study found the existence of the health disparities. This

finding warrants further studies to elucidate and unravel intricate mechanisms of such

125

disparities. Future studies should focus attention on the following points:
0 Interaction between gender and race/ethnicity
o Subdivision of health care coverage including private health insurance
0 The effect of functional limitations on access to health care
0 The impact of aging on people with disabilities, and, if possible, the further
differentiation of the impact of aging on individuals with disabilities who
acquired their disabilities at older age and those who had disabilities in their youth
0 A lifelong approach of the impact of disabilities on health status and access to
health care for older people with disabilities
0 The actual use and volume of health care for older people with disabilities
Given the widely heterogeneous compositions of a group of people with
disabilities and the challenge of defining and measuring disability, the operationalization
of individuals with disabilities need to be clearly stated and discussed in future studies.
Third, a rigorous and comprehensive approach needs to be taken in future studies
on health disparities of people with disabilities. Future studies should go beyond simply
exploring health disparities issues to rigorously investigate determinants and mechanisms
of the health disparities. In doing so, research on health disparities should be
comprehensively carried out on the wide spectrum of disabilities. At the same time,
given challenge of the participation of people with disabilities in surveys, qualitative
studies also should be employed in tandem with quantitative studies to gain deeper
understanding of access to and utilization of health care among individuals with
disabilities.

Conclusions

126

The two purposes of this study were to examine whether health disparities in
access to health care existed between older individuals with and without disabilities, and,
if such disparities existed, to what extent such disparities could be accounted for by the
behavioral model variables. This investigation demonstrated that significant disparities
in access to health care—cost barrier to health care, health care provider, and routine
physical checkup—existed between the two older populations. In addition, this study
illustrated that such disparities were accounted for by the behavioral model variables in
each aspect of health care access, and that each mechanism had intricate patterns. Thus,
this study demonstrated that health disparities in access to health care for older people
with disabilities occurred through intricate mechanisms.

This study indicated that as compared to their counterparts without disabilities
older individuals with disabilities paid more attention to their health, as evidenced by the
finding of personal health care provider and routine physical checkup. This study also
demonstrated that in spite of the near-universal coverage of Medicare for older people
age 65 and over and supplemental coverage of Medicaid for those with low incomes
older individuals with disabilities were more likely than their counterparts without

disabilities to face challenge in accessing timely health care.

127

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