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Socioeconomic status and functional ability among older adults

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Socioeconomic status and functional ability among older adults
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Disabilities ( jstor )
Diseases ( jstor )
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Modeling ( jstor )
Net income ( jstor )
Net worth ( jstor )
Older adults ( jstor )
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Thesis (Ph.D.)--University of Florida, 2000.
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Includes bibliographical references (leaves 118-127).
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Printout.
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Vita.
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by Dorothy Jean McCawley.

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SOCIOECONOMIC STATUS AND FUNCTIONAL
ABILITY AMONG OLDER ADULTS
















By

DOROTHY JEAN MCCAWLEY


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA




SOCIOECONOMIC STATUS AND FUNCTIONAL
ABILITY AMONG OLDER ADULTS
By
DOROTHY JEAN MCCAWLEY
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2000


Copyright 2000
by
Dorothy Jean McCawley


ACKNOWLEDGMENTS
None of my accomplishments have been due to my effort alone, nor would they
have been worthwhile if I accomplished them alone. I know that this dissertation might
have been possible as a lone effort, but it would not be the experience worth celebrating.
The most important influences in my life are my parents, Jean and Jim McCawley. My
mother and father taught me that I can accomplish anything and they imbued in me the
value of education. My life would not be the same without them. My brother and sister,
Mary Zinger and Rick McCawley, and their families have also been a great source of
support and joy. The admiration from my nieces and nephews gave me an additional
incentive to complete this process.
This is an academic endeavor and as such I owe a great debt of thanks to the
faculty and staff at the University of Florida. They are too many to name, but a few
deserve special mention. I was floundering for a time until my co-chairs, Drs. Barbara
Zsembik and Donna Berardo, took me under their wings and gave me a sense of
direction. Their mentoring inspired me and helped me maintain my enthusiasm
throughout the long process of creating this dissertation. In addition, for their assistance
and committee service during my graduate education, I gratefully acknowledge the
encouragement of Drs. Leonard Beeghley, Otto von Mering, and Chuck Peek. I
appreciate the time they have given to help me become a better scholar. The
administrative staff at the Department of Sociology saved me from the grief of
bureaucracy, especially Mary Robinson and Sheran Flowers.
iii


I also owe a great debt of gratitude to my support network of dear friends and co
workers. Diana Hannah and Paula Coley persevered in friendship despite my single-
minded focus on my academic pursuit. The staff at Fringe Benefit Coordinators,
especially Beth Lege and George Zinger, kept the office running in my absence. They
gave me peace of mind and a continuing source of income over the past seven years.
My diploma should include the names of my family and friends, the faculty and
staff at the University of Florida and F.B.C. who all helped in their own way to make this
dissertation a reality. I apologize to those I have not mentioned by name. Know that I
gratefully acknowledge your encouragement and support in my graduate career and in the
completion of my doctoral dissertation.
IV


TABLE OF CONTENTS
page
ACKNOWLEDGMENTS iii
LIST OF TABLES vii
LIST OF FIGURES ix
ABSTRACT x
CHAPTERS
1 HEALTH AMONG OLDER ADULTS 1
2 DETERMINANTS OF FUNCTIONAL STATUS 5
Introduction 5
Models of Functional Ability 7
Socioeconomic Status and Health 10
The Moderating Effect of Health Behaviors and Health Indicators 18
Covariates 29
Summary of Socioeconomic Status and Functional Ability in Older Adults 37
Hypotheses 39
3 THE SURVEY OF ASSETS AND HEALTH DYNAMICS AMONG THE OLDEST
OLD 41
Research Sample 41
Measures 48
Procedures 59
4 DESCRIBING CHANGE AND STABILITY IN FUNCTIONAL PERFORMANCE 62
Correlation of Measures 67
Response to Hypotheses 80
5 PREDICTING CHANGE IN FUNCTIONAL PERFORMANCE 83
Multinomial Logistic Regression Modeling 83
Model Comparisons 84
v


Models of Causation 94
Response to Hypotheses 95
6 FOCUS ON HEALTH RECOVERY 98
Summary 98
Economic Resources and Functional Limitations 100
Social Policy Recommendations 101
Covariates 104
Why Separate Functional Measures? 105
APPENDICES
A SELECTED CODEBOOK SURVEY QUESTIONS 113
B MULTINOMIAL REGRESSION COEFFICIENTS FOR FUNCTIONS 109
LIST OF REFERENCES 118
BIOGRAPHICAL SKETCH 128
vi


LIST OF TABLES
Table Page
1. Variable Description and Coding 49
2. Decile Ranges for Income and Net Worth 53
3. Descriptive Statistics of Sample 58
4. Transition Matrix of Walking Several Blocks 63
5. Transition Matrix of Climbing Stairs 64
6. Transition Matrix of Pushing/Pulling Large Objects 65
7. Transition Matrix of Lifting 10 Pounds 66
8. Transition Matrix of Picking Up a Dime 66
9. Correlation Matrix: Correlation with changes in Functional Performance Between
Waves 1 and 2 68
10. Correlation Matrix: Correlation with SES Variables 70
11. Correlation Matrix: Correlation with Health Behaviors 74
12. Correlation Matrix: Correlation with Genetic Endowments 75
13. Correlation Matrix: Correlation with Covariates 62
14. Correlation Matrix: Correlation between all variables 77
15. The Odds Ratios of Being in a Stable with Limitations State 85
16. The Odds Ratios of Declining Functional Status 89
17. The Odds Ratios of Recovery of Functional Status 93
18. Model Comparisons 95
vii


19. Net Worth Comparisons: Indirect Effects of SES through Health Behaviors 97
20. Summary of Transition Matrices 100
21. Health Behaviors and Functional Abilities 103
22. Predicting Health Recovery in Walking 113
23. Predicting Health Recovery in Climbing Stairs 114
24. Predicting Health Recovery in Pushing/Pulling 115
25. Predicting Health Recovery in Lifting 116
26. Predicting Health Recovery in Picking Up a Dime 117
viii


LIST OF FIGURES
Figure Page
1.A Model of the Disablement Process 10
2.Conceptual Model of the Effects of SES and Health Behaviors on Functional Ability ....38
3.Modification of the Model of the Disablement Process Showing Variables used in this
Research 47


Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
SOCIOECONOMIC STATUS AND FUNCTIONAL ABILITY AMONG OLDER
ADULTS
By
Dorothy Jean McCawley
December 2000
Chair: Dr. Barbara Zsembik
Major Department: Sociology
With increasing numbers of older adults in the population, research with a focus
on healthy recovery among older adults can have an impact on quality of life. Previous
research has examined the connection between socioeconomic status and health and
found that higher SES and better health are correlated.
This research examines the health-SES link specifically as regards the process of
disability in older adults as regards functional limitations, which include difficulty with
specific physical actions, which is the focus of this research. The Asset and Health
Dynamics Among the Oldest Old survey is a longitudinal data set that includes data about
difficulty walking, climbing stairs, lifting 10 pounds, pushing or pulling a large object
and picking up a dime from a table. It also includes detailed information about finances
and health behaviors. This data set offers the opportunity to analyze the specific
functional trajectories of the respondents through the first two waves of data collection.
x


Net worth proved a better indicator of health function over time than was annual
household income. Health behaviors such as exercising, not smoking, controlling weight,
and temperate drinking did moderate the relationship between net worth and functional
abilities. This group is mostly free of functional limitations, and between 18 40% of
older adults with functional limitations do recover from the first wave to the second.
xi


CHAPTER 1
HEALTH AMONG OLDER ADULTS
Today the dialogue about aging and the changing expectations of our later years
has become an urgent topic. The U.S. Bureau of the Census (1996) has followed the
growth in the population of the over age 65 group. In 1994, the over-65 group
represented one-eighth of our total population, which is an 11-fold increase from 1990.
The Bureau expects another dramatic increase in the number of older adults after 2010
when the Baby Boomers reach age 65.
As we look at the aging process and its theoretical understanding, we see that
successful aging may be linked to productivity no longer a time of disengagement, but
of increasing activity. As the number of adults over 65 years of age becomes a larger
percentage of our population in the next century this debate becomes increasingly
significant. The continuing debate over whether older adults are healthy and capable of
recovering if disabled is likely to intensify.
In order to prevent disease and disability (or promote recovery), it is useful to
examine how social factors affect the aging process and find ways to facilitate a healthy
aging process. We all desire a good quality of life in our later years, which includes high
satisfaction, health, material security and happiness (Fry, 1996). The growth in the over-
65 portion of the population points to the need to understand the dynamics of health,
aging and socioeconomic status (SES), since poor health can undermine the quality of life
of older adults.
1


2
As we age, our bodies may experience disease, injury or the exacerbation of a
congenital condition that results in physical impairment. The process of becoming
disabled has been described various times in attempts to arrive at some standard model
for analyzing the dynamics of disability. Verbrugge and Jette (1994) developed a model
that has several steps. First, individuals experience a medical condition that leads to
physical impairment, functional limitations, and eventually disability. They called this
the disablement process. Next, they added factors that impact the process, which they
label as risk factors, extra-individual factors, and intra-individual factors.
This research uses an expanded version of this model, one that includes health
recovery from functional limitations as an alternative outcome to disability. For this
study, recovery is defined as a reduction in the difficulty of performing specific
functions. Recovery is a significant event when evaluating the process of disability. If
we can examine the connections between social factors and health recovery, we can help
elders maintain their independence and focus attention to preventing functional
limitations, which should reverse the process away from disability.
Studying the incidence of disease is useful for tracking the history of a particular
condition. Looking at the functional consequences is more useful for public policy and
for the lifestyle of elders and their families. If a disease results in physical impairment,
the adult experiences difficulty in performing activities that allow for independent living
and self-care (George, 1996). Dependence clearly has a strong negative effect on the
quality of life satisfaction of elders, but recovery is possible. Previous studies have
demonstrated that recovery from functional disability at some point occurs considerably
often (Myers, Juster and Suzman, 1997).


3
A focus on recovery from functional limitations can help us generate a picture of
what resources we need to provide that will allow for continued successful aging of
elders today and of successive cohorts of elders. Health recovery is worth study since
reversing or delaying the last step in the process to disability means that older adults can
maintain their independence longer. Promoting healthy aging contributes to the quality
of life and active life expectancy of elders. As Charm az (1995) notes, recovery is the
goal of people with functional limitations. The initial reaction of people faced with
chronic illness is to plan to return to their prior level of ability, perhaps even to exceed
that level. She acknowledges that sufficient funds allow those who are functionally
limited flexibility in their recovery strategies. This research examines the connection
between SES and functional limitations in the context of the model of The Disablement
Process (Verbrugge and Jette, 1994).
House, Lepkowski, Kinney, Mero, Kessler, and Herzog (1994) note that
individuals in the lower SES strata experience greater levels of morbidity than do
individuals at higher levels of SES. We can view SES as a risk factor affecting the
functional levels of older adults. This has consequences for our public programs that
provide for treatment of illness, such as Medicare and Medicaid, and other entitlement
programs. As a result, we may realize that investing in prevention, screenings and a
focus on healthy aging will be socially beneficial.
Generally, one expects that in the retirement years economic stability is assured
through government programs and pension plans in place from previous work history.
However, most federal programs are designed to provide a floor of protection and often
do not provide coverage in the case of long-term illness (Estes, 1989; Wiener and Alston,


4
1996). In addition, if an older adult has had a history of poor health that began
significantly early in adulthood, he or she may not have had the opportunity to work in an
occupation that had fringe benefits such as a pension program. His or her health may
have affected the work history, and without consistent employment, older adults may not
have a strong pension program, if any.
One comprehensive source of information about the economic and functional
status of older adults is the Asset and Health Dynamics Among the Oldest Old (AHEAD)
survey. The AHEAD survey provides a longitudinal database for analysis of the
relationship between SES and health in the elderly population. The population under
study is adults aged 70 and older, despite the title of the survey as Oldest Old, which is
generally considered to refer to individuals 85 and older.
This longitudinal data set offers the opportunity to analyze the specific functional
trajectories of the respondents through the first two waves of data collection. Since the
data set includes unusually detailed financial information about household income and
wealth accumulation, it provides a unique ability to describe and examine the SES/health
link among the older adults, the fastest growing portion of our population. Future public
policy direction needs such information to guide us into having a healthier aged cohort in
the next century.


CHAPTER 2
DETERMINANTS OF FUNCTIONAL STATUS
This chapter includes an overview of the model of functional ability used in this
research and of past research relating to the connection between health and
socioeconomic status. The focus is on the theory of cumulative advantage and
disadvantage. This is followed by a section that covers determinations of SES as well as
the role of education in determining SES and health. Next, the moderating effects of
health behaviors such as smoking, drinking alcohol, exercise, diet and nutrition,
preventive care and social networks are examined. This is followed by a review of
literature relating to the covariates. These are age, sex, race/ethnicity, insurance, medical
conditions, doctor visits, prescriptions and genetic endowments. The final section of this
chapter is a summary and statement of hypotheses.
Introduction
As we pass from one century to another, our population and our experiences
present new challenges and opportunities. First we have seen evidence of dramatic
increases in our life expectancy, accompanied with the decrease in acute, infectious
disease and concomitant increase in chronic, debilitating disease. The result of chronic
conditions can be poor physical functioning. Physical functioning, along with adaptation
to disease, influences recovery from disease and is also a significant marker for older
adults quality of life. Understanding what social factors are related to the development
of poor functional status is informative to gain a full picture of health promotion among
older adults. (Guralnick and Lacroix, 1992)
5


6
Disability has gained prominence as an area of study because of changes in the
health of the population as a whole. For example, at the turn of the last century the
medical profession focused on acute, infectious diseases. At the turn of this century
chronic diseases have replaced acute conditions as the major cause of death, especially
for the elderly (Fried and Wallace, 1992). These conditions, while responsible for death
in the over age 65 age group, are also linked to limitations in functional ability, which can
be a precursor to disability.
The increasing incidence of chronic conditions leads us to study treatments, not
cures (Katz, 1999). Especially when it comes to disabling conditions, we are more
interested in increasing the length of time that one is stable or, if possible, the
improvement of a condition to avoid total disability. One consequential difference
between acute and chronic conditions is that an individual may live for a long time with
the limitations of a chronic condition. As Verbrugge and Jette (1994: 1) note, People
mostly live with chronic conditions rather than die from them.
Older adults arrive in their post-retirement years with differing resources,
including wealth, health, knowledge, and social networks. The resources that older adults
have at this point of their lives are the result of their previous life experiences. Aging is,
after all, the accumulation of life experiences and biological changes across an entire life,
and a holistic approach recognizes the influences of the past on the present (Wallace,
1992).
Not all older adults arrive at advanced age with disability. Advanced age is not an
automatic indicator of functional limitations and resulting disability. Additionally, older
adults who do have functional limitations sometimes get better (Mantn and Stallard,
1996). Knowing the dynamics of these processes of disability and recovery in


7
community dwelling older adults may help them maintain their independence. A study
such as this one also expands our understanding of the link between SES and health by
focusing on different health indicators, ones that are closely linked with independent
functioning (Wilkinson, 1996).
Arber and Evandrou (1993) point out that independence is perceived on three
dimensions. The first is physical independence or being independent in the domestic
sphere and being able to maintain ones own physical and personal care. The second is
autonomy, which implies the ability for self-direction free from interference. The third
aspect of independence is reciprocity or interdependence, which mitigates the negative
aspects of help if it is the result of a sense of mutuality. The point of this distinction is
that dependence and independence are endpoints of a spectrum and that we reach more
acceptable points on this spectrum during the life course. In addition, some loss of
independence is palatable if one can maintain a level of autonomy and a sense of
receiving that which is owed, rather than offered as charity. Underlying all this is the
ability to function at a level sufficient to maintain self-esteem.
Models of Functional Ability
The combination of an aging population, increases in chronic conditions and life
expectancy, and widening economic inequality points to the significance of
understanding the mechanisms of functional limitations in old age. If we can clarify the
connections between resources and functional ability in older adults, it may be possible to
modify social policy to protect older adults.
There are a variety of measures available to determine physical morbidity.
Activities of Daily Living (ADLs) focus on activities such as bathing, eating, and
toileting. Instrumental Activities of Daily Living (IADLs) measure activities such as


8
shopping, doing laundry, and talking on the telephone. Functional abilities include basic
body functions such as walking a short distance, climbing stairs, picking up a dime from
a table, lifting 10 pounds, or pushing/pulling an object. ADLs and IADLs are associated
with disability in Verbrugge and Jettes disablement model (1994). This differs from
other schema of disability, including one set forth by Johnson and Wolinsky (1993).
Health is a multi-dimensional phenomenon, and as Crimmins (1996) points out,
conclusions about trends in disability/functional ability will vary depending on the
definition of functional limitations used.
In the Verbrugge and Jette (1994) model, functional limitations occur in the step
before disability. The distinction is made that functional limitations reflect difficulty
with a specific action, one that is situation-free, and thus not a disability. This differs
from other research on disability, which may use measures of functional ability that are
context specific. A disability in the Verbrugge and Jette (1994) model is situational and
impacts an individuals social role, which is what ADLs and IADLs measure. Thus, this
research focuses on measures of function such as the ability to walk several blocks, climb
stairs, push or pull a large object, lift 10 pounds, and pick a dime up off a table. These
are actions, not activities.
Other definitions of functional limitations include self rated health (poor, fair,
good, excellent) or diagnosis by a medical provider. Using the functional limitations
listed in the previous paragraph to examine the health SES connection is useful because
they have a direct relation to issues of independence and are less subjective than self-
rated health. Another benefit of using functional ability is that it links disease states with
environmental influences (Guralnik and Lacroix, 1992). This serves as a valuable tool in
describing the needs of the aging population as well as helps in understanding the


9
influences on health status. It also avoids some of the traps of ADL and IADL, which are
influenced by socially defined roles and the sociocultural environment (Freedman and
Martin, 1998). Functional ability is no less objective than physicians ratings of
respondents health (Markides, Lee, Ray and Black, 1993) and is less subject to
complications due to limited access secondary to low SES. This research is focusing on
measures relating to physical functioning, especially those early in the disablement
process.
The model used in this research to analyze the connection between SES and
functional limitations is the model of the disablement process developed by Verbrugge
and Jette in 1994 (Figure 1). This model points to the connections between pathological
causes of functional limitations as well as the social factors that influence the
exacerbation or diminishment of limitations. Disability is a gradual process, and too
often we analyze the end result of the serious disability, but not at the impact of activity
limitations that affect participation in quality of life (or leisure) activities (Atchley, 1998).
This reflects the current thinking of aging as a social pathology (Arber and Ginn, 1991).
One additional risk factor that could possibly add to our understanding of the
disablement process that is not explicitly included in Verbrugge and Jettes model is the
accumulation of resources such as wealth, education, and income. These resources,
which determine ones socioeconomic status (SES), are not evenly distributed in our
society and previous research has linked SES and health (Deaton and Paxson 1998;
Feinstein 1993; Link and Phelan 1995; Nagi 1976; Preston and Taubman 1994). Risk
factors are considered pre-disposing because they occur before the disablement process
begins.


10
EXTRA-INDIVIDUAL FACTORS
Medical Care & Rehabilitation
Medications & Other Therapeutic Regimens
External Supports
Built, Physical & Social Environment
1
PATHOLOGY
(diagnoses of disease, injury,
congenital/ developmental
condition)
1
SK FACTORS
IMPAIRMENTS
(dysfunctions and structural
abnormalities in specific body
systems: musculoskeletal,
cardiovascular, neurological, etc.)
FUNCTIONAL
LIMITATIONS
(restrictions in basic physical and
mental actions: ambulate, reach,
stoop, climb stairs, produce
intelligible speech, see standard
print, etc.)
DISABILITY
(difficulty doing
activities of daily life:
job, household
management, personal
care, hobbies, active
recreation, clubs,
socializing with
friends and kin,
childcare, errands,
sleep, trips, etc.)
t
INTRA-INDIVIDUAL FACTORS
Predisposing characteristics:
demographic, social, lifestyle,
behavioral, psychological,
environmental, biological
Lifestyle & behavior changes
Psychosocial attributes & coping
Activity Accommodations
Figure 1. A Model of the Disablement Process (Verbrugge and Jette, 1994)
Socioeconomic Status and Health
Theoretical Implications
Older adults have accumulated advantageous resources in terms of savings,
accumulation of a pension, and access to governmental benefits due to the type of
employment they enjoyed in their younger years (Evandrou and Falkingham, 1993).
Their lack of accumulation may also be the result of disadvantages that they have faced
over their life course. As ORand and Henretta (1999: 10) point out, [cjumulative
advantage characterizes patterns of divergence or increased inequality over time.


11
Public programs reduce inequality in later years, but do not result in a total
leveling effect among older adults (ORand and Henretta, 1999). They cannot counteract
the accumulation of pension and wealth over the life course. Individuals with greater
income have the option of savings, which, over time, translate into greater wealth. The
resulting accumulation can ultimately produce a larger SES-health gap as the cohort ages
(Kubzansky, Berkman, Glass, and Seeman, 1998). The theory of cumulative
advantage/disadvantage points to consequent disadvantages, such as illness or functional
limitations, which can result in increases in inequality at older ages (ORand, 1996). In
other words, as individuals age, they have opportunities that make it possible for them to
accumulate resources. Alternatively, they may face barriers. The more the opportunities,
the greater the accumulated resources, the better health older adults will enjoy.
Another consequential aspect of this theory is that, even when negative events
occur, the accumulation of advantage may allow individuals with the benefit of resources
the ability to recover more quickly than individuals without resources. Aging is not a
story of inevitable decline. It is possible for some older adults to reach extreme old age
in good health with no functional limitations as well as to recover from health limitations
they experience. This research adds to this discussion by providing a description and
explanation for elders in the functional limitation stage of the disability process.
Disability is one of the most significant risk factors contributing to mortality among older
adults (Rogers, 1995). The goal of this research is to examine the relative contribution of
resources in the form of finances and in the form of health behaviors/health stock through
the mechanism of cumulative advantage/disadvantage theory.
This pattern of cumulative advantage intersects with issues of health and well
being. Individuals who enjoy good health in their middle years have formed a basis for


12
good health in their older years. Individuals who have accumulated disadvantages in
their middle years may find their older years even more difficult and as a result
experience an increase in inequality over time. ORand (1996) outlines the process of
cumulative advantage and disadvantage of resources (wealth and health) over the course
of ones life that produces stratification of our elders. Initial inequalities in the
distribution of resources (economic or health-related) result in barriers to individuals in
disadvantaged groups. As a result, over the course of the life cycle, any disruptive social
changes result in multiplying the disadvantages one experiences (Uhlenberg and Miner,
1996). Conversely, individuals who have access to resources have access to opportunities
to accumulate advantages over the life course. The result is stratification of our elders
with inequality in SES and health which may be exacerbated by negative health events.
Mortality and morbidity rates for the upper social classes have dropped in recent
decades (Feinstein, 1993) which has resulted in a widening of the health gap between the
rich and poor. Elders in the upper social classes may have access to resources that allow
for prompt treatment of health and the purchase of goods and services that promote good
health and a lifestyle free of violence and negative environmental influences. This gap
between rich and poor is also a result of cumulative disadvantage and advantage as we
see inequalities increasing with age (Deaton and Paxson, 1998) and for each succeeding
cohort of elders (ORand, 1996).
Socioeconomic Status Income and Wealth
Previous research has found a consistent negative correlation between health and
socioeconomic status (Arber and Cooper, 1999; Deaton and Paxson, 1998; Feinstein,
1993; Link and Phelan, 1995; Kington and Smith, 1997; Preston and Taubman, 1994;
Smith, 1995;), although the exact nature of the causal relationship is unknown


13
(Cartwright, 1992). One longitudinal study using income found that a period of
economic deprivation does predict future functional limitations (Lynch, Kaplan, and
Shema, 1997). They did not have information on wealth, and they measured economic
deprivation as the number of times individuals were below 200% of the poverty level.
The mean age of their study population was about 65 years of age. Income, wealth, and
older ages were not the focus of that research. How does SES link to health? Which
comes first?
One argument is that poor health limits ones ability to work consistently at better
paying jobs, is associated with unhealthy living conditions and limits access to good
health care and preventive services (Adler, Boyce, Chesney, Cohen, Folkman, Kahn and
Syme, 1994). Smith (1995) points out that ill health also has associated medical expenses
that deplete savings and the ability to save. Health in older ages reflects the life history of
the individual.
The opposite argument is that living with limited opportunities can result in
poorer health. If one has wealth, the household is able to afford access to a preventive
lifestyle and better medical care (Smith, 1995), while individuals in limited financial
circumstances may engage in less healthful behaviors due to a lack of resources or
limited access, or lack of knowledge. Arber and Cooper (1999) found that social class (as
measured by last main occupation) was a better predictor of health status than was age,
for men and for women. Access to money, and the desirable attributes that go with it,
such as car ownership and the power to buy services as they are needed, depends on life
history rather than any special circumstances related to old age (Wilson, 1993: 63).
Researchers using cross-sectional data to analyze the relationship between SES
and health can determine that health and SES are linked, in that individuals with higher


14
SES have better health (Adler et al., 1994, House 1994). However, cross-sectional
studies cannot provide us with information about temporal ordering of SES and health. It
is evident that we need to extend prior research to analyze causal inferences between SES
and health at older ages (House, Lepkowski, Kinney, Mero, Kessler, and Herzog, 1994).
Some researchers look at the relationship between socioeconomic status and
health and assume that we can improve health by adding to individuals income and
wealth (Smith and Kington, 1997). This would work if the assumption about the causal
connection is true. Older adults in the AHEAD have a certain stock of health that can
serve as a baseline for analysis, and with the longitudinal data available this research can
examine the direction of the SES effects on health recovery (Smith and Kington, 1977).
We can ask, does knowing SES in wave 1 of a longitudinal data set help us predict health
status in wave 2? This will not allow for conclusions about the causal association across
the lifespan, but does allow for uncovering some of the mechanisms underlying the
relationship between health and SES.
In considering the connection between SES and health, it is essential to determine
the composition of SES. The AHEAD data set includes information on all possible
sources of income for the individual respondent and for the household of the respondent.
Another determinant of SES is accumulated wealth.
Wealth, or net worth, includes all assets minus debt, including property
ownership, which reflects the legacy of working life (Wilson, 1993: 59). Income and
net worth are linked, especially for the upper percentiles of the wealthy. The disparity in
net worth is explained by looking at differences in income among the wealthier groups
(Smith, 1995). Smith (1995) also found that lower income people save less than higher
income people regardless of their race or ethnicity, which contributes to their lack of net


15
worth at older ages. In fact, the main source of net worth for the poor and the middle
income group is their Social Security benefit (Smith 1995).
Education Indicator of SES?
Education has been used as one of the factors determining SES. Education,
occupation and income tap into different features of SES position (Preston and Taubman,
1994). For older adults, education is stable and so the link between education, as a
measure of SES, and health diminishes by age (Beckett, 2000). In addition, education
may act in determining health status in other ways rather than directly as an indicator of
SES. As such, it may be linked to health behaviors more than SES at older ages.
A good education generally leads to favorable work and living conditions that can
have a direct effect of earnings potential and thus higher SES and better health. Education
can also indirectly influence health due to the connection between education and better
health habits and behaviors (Marmot, 1998). Educational attainment is associated with
the availability of information and with cognitive skills (Preston and Taubman, 1994, p.
282). As a result, individuals with a good education can obtain information about healthy
behaviors and comply with them.
Research by Beckett (2000) found that education and functional impairment are
significantly and negatively correlated for individuals over the age of 65. This may
reflect the fact that individuals in higher SES brackets and with better education live
longer, which may in part be due to behavioral factors related to education as an indicator
of self-efficacy. If education continues to hold the same relationship with mortality over
time, we can expect longer, healthier lives as better educated cohorts age (Elo and
Preston, 1996).


16
This is what Freedman and Martin (1998) found in their evaluation of functional
limitations of adults over the age of 50 evaluated in 1984 and 1993. They noted an
increase in educational attainment and a drop in functional limitations over that decade,
leading them to conclude that education operates in several ways to improve health. It is
associated with a beneficial lifestyle, but it may also be associated with the ability to
follow health care treatment plans and to modify the environment, whether that is the
physical environment or behaviors.
Mantn (1996) also speculates that education gives older adults the motivation
they need to stay functional as it also helps them avoid risk factors such as smoking and
high-fat diets. The results of his research show that older adults in a low education group
were 5.73% more likely to be disabled. Mantons (1996) work was with adults who
experienced impairments measured by IADL and ADL, and he found that being a high
school graduate makes a difference in disability rates. If disability rates for high school
drop-outs were the same as for high school graduates, disability rates would be reduced
by thirteen percent.
Education was a measure of healthful behavior by Smith and Kington (1997b)
also. They argue that education may... affect the way individuals can transform inputs
into good health (Smith and Kington, 1997b: 108). Greater education gives households
access to and awareness of preventive behaviors, avoiding environmental risks, better
problem-solving skills, and effective self-care. Reynolds and Ross (1998) found that
education has power in predicting good health other than as a credential or that it leads to
high status employment. Rather, they found that education is significant, especially if
individuals are economically disadvantaged. Other research on older Black adults also
found that individuals with more education engaged in health promotion activities


17
(Ferraro, 1993). In this research, education is used as an indicator of health promoting
behavior.
Another view of education that is apart from SES and is an indication of human
capital, or the ability to use education to solve a variety of problems, not just as a work-
related skill enhancement (Mirowsky, 1998). In this sense, education gives one a sense
of control over ones lifestyle, leading to healthful behaviors and fewer functional
problems. However, this effect is present only for individuals who enjoy general
prosperity. Educated individuals know how to maximize the usefulness of economic
resources to reduce any sense of economic hardship (Mirowsky and Ross 1999).
Multiple and Interlocking Mechanisms
Straus (1999, 106) points out that we need to appreciate that social and cultural
forces might be causing or complicating the patients illness. This is true when we look
at the relationship between socioeconomic status and health in the elderly. Parental
income has an effect on childrens access to education, which shapes employment and
earning history. Mirowsky and Hu (1996) found that education was linked to physical
impairment. They theorized that a lack of education is related to low income and as a
result individuals experience increased risk of physical impairment because they cannot
meet their basic physical need for food, clothing, shelter and care. Consequently their
limitations prevent them from improving their economic condition which results in
concentrating their economic hardship. Mirowsky and Hu (1996,1091) found a web of
reinforcing effects in that lack of income, poor health, and lack of exercise work
together to compound problems over time.
Life span accumulations of resources may increase the differential between
classes experiencing chronic illness. Material resources include current annual income


18
from various sources for respondents and their spouses. Past employment history has an
effect on current levels of income and asset accumulation, and health shocks during
employment could adversely and cumulatively result in greater inequalities in older ages
(Deaton and Paxson, 1994).
Few individuals over the age of 70 are employed, but they have income from
public sources, such as Social Security, and from pension plans. In addition to current
income, material resources include assets such as home ownership and savings. Older
adults who were at a disadvantage during their work life and who suffered illness start
their retirement with fewer resources than those who have accumulated advantages.
Smith (1997) found, in his study using the AHEAD data set, that net worth is very
concentrated in this sample of elders, much more so than is income. Half the population
owns 99% of the net worth and the other half has the remaining 1%. The concentration
of net worth seems to be among families who are White. The average minority (Black or
Latino) household has no financial wealth at all.
The Moderating Effect of Health Behaviors and Health Indicators
Health Behaviors
Verbrugge and Jettes (1994) model of the disablement process points to the
existence of lifestyle and behavior factors that moderate the course of functional
limitations. They point out that wealth can allow for access to sources of information or
services that can alter the impact of medical conditions. Material resources make it
possible for the individual to purchase goods and services that can increase the odds of
remaining healthy. This includes more than purchasing health care services. For
example, owning an automobile allows for freedom of movement to take advantage of
preventive medical services or health-related activities such as health club or a jogging


19
park. Wealth is also connected to behavioral factors such as smoking, lifestyle and
exposure to violence (Feinstein, 1993). In this way, SES is linked to behavioral factors
that can also determine morbidity.
Individual responsibility for health, especially in the face of increasing incidence
of chronic disease, can make the difference between functional independence and
disability. Mechanic (1995) points out that as chronic disease problems increase in the
aging population, the mainstream medical diagnostic disease model is inadequate to
address the needs of disabled elderly. Their own intervention and assertiveness to engage
in healthful behaviors may be the best source of prevention and recovery. As a result, the
wealthier older adults may enjoy higher functional status and be able to recover quicker
from limitations.
The quality of health goods and services such as access to providers of care and
information about healthy behaviors are usually positively related to price. Generally
better quality care costs more, and so individuals with greater economic resources can
afford to purchase better quality health. According to Feinstein (1993) researchers
have not reached a consensus about the relative contribution of SES and behavior to
health. This may be because SES and health behaviors are interrelated and that behavior
is a mediating factor between SES and health, although probably not as significant a
contributor as accumulated net worth and income (Wilkinson, 1996).
Individuals in higher SES levels may be able to recover from functional
limitations faster because they avoid risky health behaviors such as smoking, drinking
and eating less healthful foods. These individuals have access to educational information
that helps form their health behaviors. House et al. (1994) note that individuals in higher
SES levels are approaching the ideal in aging experiencing their older years free of


20
significant morbidity and functional limitations until the very last years of life. In
addition, they can avoid the stress of economic deprivation that influences the incidence
of health problems.
Smoking. Adler et al. (1994) note the strong link between SES components of
income, occupation, and education with smoking behavior. The connection is meaningful
due to the strong negative effect that smoking has on health. Wilkinson (1996) has noted
that smoking has become an identifier for socioeconomic stress. Smoking is closely
linked to cancer and cardiovascular disease, medical conditions that can cause functional
limitations or adversely affect recovery efforts from functional limitations.
Smoking behavior varies by race and education. Higher educated men and
women smoke less than do individuals who do not have a college degree (Berkman and
Mullen, 1997). Cessation rates are also higher for individuals with more education.
Berkman and Mullen (1997) also note that Black men are more likely to smoke than are
White men, but the opposite is true for women. Smoking is an indicator of risk-taking
behavior and of public health concern in light of its connection to chronic conditions and
subsequent functional limitations (Clark, Callahan, Mungai, Wolinsky, 1996).
Traditionally Mexican-Americans and Puerto Ricans have had lower rates of
smoking, but that rate has increased among males in the 1990s (Markides, Rudkin,
Angel, and Espino, 1997). Cuban-American males have the highest rate of smoking. As
a result, minority individuals (especially men) who are also poorer may have higher
incidences of chronic conditions relating to smoking and subsequent limitations.
Alcohol Use. Alcohol consumption follows the opposite pattern as smoking,
with individuals in the higher SES levels drinking more than individuals at lower levels
(Adler et al., 1994). Excessive alcohol consumption has negative consequences on


21
health, resulting in cirrhosis of the liver (Stoller, 1994). It can also exacerbate other
medical conditions, such as ulcers, respiratory disease, and heart conditions, and interact
with prescription medications (Council on Scientific Affairs, 1996). Alcohol is also
associated with a higher incidence of hip fractures. Not only does alcohol impair
balance, but heavier drinkers do not eat a balanced diet and are found to have a lower
bone density (Council on Scientific Affairs, 1996).
Previous research on elderly Danes found a U-shaped relation between alcohol
consumption and mortality. Both abstaining adults and heavy drinkers had a higher risk
of mortality than do light drinkers (Gronbaek, Deis, Becher, Hein, Schnohr, Jensen,
Borch, and Sorensen, 1998). The results of this study were the same for older adults as
for middle-aged adults and the mortality risk for women was 1.29 for abstaining women
as compared to light drinkers and 1.22 for abstaining men as compared to light drinkers.
These researchers concluded that light alcohol intake is associated with a lower mortality
than is abstaining or heavy drinking. The difference is the result of higher risk of
mortality from cardiovascular disease among abstaining adults. Light drinkers have
higher high-density lipoprotein (HDL cholesterol) and lower platelet aggregation
resulting in better cardiovascular health and lower mortality.
The Council on Scientific Affairs found similar patterns in research of older U.S.
adults (1996). Drinking behavior can be damaging to elders health at any level if it
interacts with medications or if the individual is a smoker or has hypertension (Moore,
Morton, Beck, Hays, Oishi, Partridge, Genovese and Fink, 1999). Thus, alcohol
consumption is a critical variable to consider in an overall view of the health of older
adults.


22
Exercise. Physical activities are no longer integral to work or commuting and are
now part of our leisure activity. As a result, lack of regular physical activity has become a
health issue. Individuals who do not exercise suffer from loss of muscle mass and are
more susceptible to functional limitations. In addition to the direct effect that exercise
has in enhancing our health, it also has an indirect effect in that individuals who do not
exercise often suffer from obesity (Adler et al., 1994). Ruchlin and Lachs (1999) found
that less than half of older adults walk (the most popular exercise of this age group) and
when they did walk, most spent less than 15 minutes per walk. Individuals with higher
SES and more education exercise more than do individuals at lower SES (Mirowsky and
Hu, 1996; Ruchlin and Lachs, 1999) and the older an individual becomes, the less he or
she exercises (Bennett and Morgan, 1993). SES and education are indicators of lifestyle,
one that includes regular exercise, and helps hinder the development of functional
limitations. Exercise is seen as part of a high-status lifestyle and is reinforced by the
availability of economic resources in reducing the incidence of functional limitations.
These behavioral characteristics have an effect on an individuals health in a
cumulative fashion over the course of the life span. Individuals who participate in
regular exercise are less likely to suffer functional impairments, and thus are able to
pursue employment on a regular basis and accumulate resources to maintain good health.
Exercise can benefit older adults even if they do not exercise when they are younger.
Research has shown that exercise, even started after age 70, can reduce physical decline
and enhance functional abilities (Cress, Buchner, Questad, Esselman, deLateur, and
Schwartz, 1999). Older adults who exercise with weights to increase their endurance see
increases in their muscle strength as well as their aerobic capacity. Consequently, these


23
adults move more quickly and are able to carry more weight. Their physiologic reserve is
improved, and this helps maintain an independent lifestyle free of functional limitations.
Diet and Nutrition. Additional measures relating lifestyle to health and SES
include waist-to-hip ratios and body-mass index measures. The waist-to-hip ratio is a
measure of where an individual carries body fat. It can be around the waist, an apple
shape, or around the hips, a pear shape. The more weight carried around the waist the
greater the risk of cardiovascular disease because it indicates a higher concentration of
body fat around the heart with resulting atherosclerosis of the blood vessels.
Body-mass index (BMI) is a measure related to waist-to-hip ratio. The BMI is
determined by converting height and weight to metric measures and dividing weight by
height squared (McBride, 1992). This is a measure of body fat, and higher results are
associated with physical disability (Visser, Harris, Langlois, Hannan, Roubenoff, Felson,
Wilson and Kiel, 1998). High body fat is associated with chronic disease and low
physical activity. High BMI also indicates an increased physical burden on the body.
This burden places a strain on joints and muscles and limits the bodys ability to move
easily. All of these factors increase the risk of functional limitations.
The risk of mortality also increases with greater BMI but is less of a risk at older
ages (Stevens, Cai, Pamuk, Williamson, Thun, Wood, 1998; Visser et al. 1998). A
moderate increase in BMI as one ages may provide physical resources in the event of
metabolic stress from disease. However, large increases in BMI are correlated with an
increased risk of disease, such diabetes, arthritis, hypertension and cardiovascular disease
(Clark, Callahan, Mungai, and Woiinsky, 1996).
The risk of cardiovascular disease is linked to social class, in that individuals in
the lower SES had worse indicators (Marmot et al. 1998). BMI is also correlated with


24
education, indicating that better educated older adults engage in healthier eating as well
as physical activity (Kubzansky et al., 1998; Stoller, 1994). The result is a lower risk of
functional limitations at older ages.
Similar to patterns in exercising, diet, the other component of BMt, also reflects
social class and education. Upper and lower class individuals all prefer healthy diets, but
usually upper class individuals know that low fat and high fiber diets are good for their
health (Howarth, 1993). Eating may also be a response to stress. Wilkinson (1996)
points out that the poor may eat for comfort and as a source of relaxation. The
combination of eating out of stress as well as eating the wrong foods puts individuals in a
lower SES at greater risk for health consequences of poor eating habits.
Risk also varies by race and ethnicity. Clark et al. (1996) found that 40% of
Black women aged 51 to 61 have a BMI considered to be obese and that 25% of Black
men were also in the obese category. Obesity was related to difficulty in physical
functioning, a link in the path of disability. For example, about 50% of the women and
30% of the men had arthritis and the same percentage reported taking medication for
hypertension. Visser et al. (1998) found BMI more predictive of difficulties with physical
functioning than changes in muscle mass. They point to weight loss intervention in an
attempt to improve the functional performance of older adults.
We cannot overlook the influence of the food culture and social structure in the
United States in the connections between social class and obesity. McKinlay (1997)
argues that we are surrounded by an increasing corporatization of our diet for profit We
are encouraged to eat processed synthetic foods, not the basic, natural, nutritious foods.
As a consequence our health care system must focus on downstream endeavors; that is,
fixing our health problems after they are manifested in functional limitations (McKinlay,


25
1997: 520). Efforts at increasing healthy eating at earlier ages could result in a
cumulative health advantage for older adults.
Preventive Services. Checkups, immunizations, routine screenings, and
preventive care are shown to be health enhancing behaviors (Stoller, 1994). However, the
amount and type of preventive care an individual receives is dependent on factors related
to SES, specifically insurance coverage and access to medical services (Feinstein, 1993).
Generally, individuals who participate in preventive care have ongoing
relationships with their physicians. They visit their doctors regularly and the doctors are
familiar with their health status. As a result, medical conditions may be detected at an
earlier stage and treated more effectively before they become serious. Feinstein (1993)
notes that individuals from lower SES admitted through emergency rooms are usually
sicker than those from higher SES are. This may be the result of poor quality,
intermittent health care received by individuals in lower SES. They may wait to receive
care until after the condition is more serious because they do not have a regular provider
of care.
Another limitation of U.S. preventive care programs is their focus exclusively on
individual behavior. It is a blaming the victim type of approach in its underlying
ideology (McKinlay, 1997: 529). All this is linked to social structural issues such as
moral uniformity that may stem from Puritan roots and are reflected in middle-class
values. The medical establishment operates as an instrument of social control
condemning certain behaviors and encouraging conformity to other behaviors without
considering social context and the wider cultural values. For example, McKinlay (1997)
points to the health goal of relaxing to avoid heart disease while the economic system
demands hard working dedicated employees. The resulting expectation of behavior


26
change is unrealistic in light of these conflicting values. Focusing on individual behavior
also keeps us from recognizing the cultural and economic influences that encourage
unhealthful behavior.
While McKinlay (1997) wants to focus on the beginning point of the process of
disability, that is, the point at which we experience influences that increase our risk of
illness, this research will focus on the midpoint. This is the point at which at-risk
behaviors are identified and intervention used at the individual level to prevent the arrival
at the endpoint, or actual disability. With the AHEAD data set we can analyze the
midpoint and determine if individuals who engage in preventive behaviors have more
success at avoiding functional limitations or even recovering once they occur. Whether
or not older adults participate in preventive care gives an indication of preventive health
behaviors and their connection with functional ability. The AHEAD provides
information about respondents participation in various health screenings, which will be
used in this research to indicate preventive care.
Social Networks. Social relationships have an influence of health and well-being
(Berkman, Oxman and Seeman, 1992) as well as functional ability (Harwood, Prince,
Mann, and Ebrahim, 1998). Berkman and Mullen (1997) note that supportive social
networks can reduce mortality risk and delay institutionalization. Part of the effect is the
result of improvement in the quality of life due to social connections. Additional factors
include monetary support and assistance with routine tasks. A study of womens health
found that membership in clubs and organization is especially significant as a predictor of
womens health (Moen, Dempster-McClain and Williams, 1992).
Unfortunately the AHEAD data set does not include information on the size,
structure, or perception of social relationships. In this case, the closest proxy is the


27
number of children of the respondent and the presence of a partner. These people can be
influential in the event of functional limitations, if the children live at home or nearby
(Morris, Sherwood, and Morris, 1996). Knowing only the number of children and
marital status does not give any sense of the strength of the network, nor its availability
or perceived adequacy.
Moen, Dempster-McClain and Williams (1992) found that number of children
had no significant contribution to predictions of duration of health for women, but other
measures of social support did have a positive relationship with good health. Individuals
in good health tend to accumulate roles as they remain active. So, we would expect older
adults with good functional abilities to have more children and a partner who can provide
the emotional and physical support for continued good health or recovery in the event of
a functional limitation.
Social support can be measured in several ways. The help pattern is a
determination of the source and types of help offered in through social networks.
Researchers have found variation in financial aid, types of services exchanged, and
generational linkages (Wilkinson, 1988). One focus of such research is the pattern of
help between daughters and their parents.
In the article by Wilkinson (1988) on mother-daughter bonds, she concludes that
the study of the help pattern between mother and daughter is complex and influenced by
the social environment, the structure of family life, and the changing roles of women.
She call for a more thorough scrutiny of generational ties encompassed by the help
pattern (Wilkinson, 1988: 189). The AHEAD data set does not allow for this thorough
analysis, although such a help pattern could be essential in the recovery from functional
limitations.


28
Another tie that contributes to social networks is the marriage bond. The data set
includes marital status and the presence of a partner in the household. The marital status
of elderly men and women differ, as a result of womens greater longevity. Only after
the age of 85 a small majority (54%) of men are widowed, while most women live alone,
due primarily to widowhood, starting in their mid-seventies (Arber and Evandrou, 1993).
Thus, marital status may help in determining who among older adults is likely to recover
from functional limitations.
The divorced, separated, never married, and widowed have much less than one-
half the household net worth of married couples (Smith, 1995). Smith (1995) also found
that marriage and savings behavior are positively correlated. However, when measuring
marital status and its predictive ability for functional limitations, Arber and Cooper
(1999) found no statistical relationship.
Marital status has a strong relationship with mortality, with currently married
individuals having lowest death rates (Elo and Preston, 1996), benefiting men slightly
more than women. Elo and Preston (1996) suggest that this is because of a selection
effect, in that the never-married most likely suffer from some health problems. This
research looks at functional ability, but we expect to see a similar relationship due to the
selection effect as well. Goldman, Korenman, and Weinstein (1995) researched the
connection between marital status and disability and found that there may be a survival
effect for older adults, such that the differences in disability by marital status are not as
expected. In fact, single women in their study were in better health than the married
women were. This may indicate that single women are more likely to recover from
functional limitations. Recovery was not included in their study. Their work did not
include data on net worth nor all sources of income either, which they note as a weakness


29
of their study. This research will include measures of SES to gain insight into the process
of recovery from functional limitations considering marital status as part of a social
network. This allows for counting members of a social network, but does not provide
measures of its instrumental or emotional nature.
Covariates
Demographic Variables
Gender. Women and minorities face multiple disadvantages as they age. Older
women and minorities are two or more times likely to be poor than are white men
(Choudhury and Leonesio, 1997). Past racial or gender discrimination puts them in a
disadvantaged position regarding pension accumulation. Pensions tend to favor people
who have had advantages during their lifetime (Uhlenberg and Miner, 1996).
Women live longer, but have more reported illness than men do (Johnson and
Wolinsky, 1994). Women are also more likely to suffer functional limitations than men
are as they age (Arber and Cooper, 1999; Daltroy et al. 1999) and are less likely to
recover (Beckett, Brock, Lemke, Mendes de Leon, Guralnik, Gillenbaum, Branch, Wetle,
and Evans, 1996). Above age 80, nearly 20% more women than men are functionally
disabled (Arber and Ginn, 1993: 37).
It is difficult to determine the exact mechanisms at work here. It may be that
there is a link with SES, which was not accounted for in the research referenced above.
This could also be the result of a survivorship curve, that the men who have lived to older
ages are stronger and less susceptible to disability. In addition, the older men who are
disabled are possibly removed from the community-based population and living in care
facilities, while women may be more likely to remain in the community.


30
Additional factors which leave women particularly vulnerable to health and SES
inequalities are marital instability (SES is generally the result of husbands work history),
lack of opportunities for consistent employment and lack of pension accumulation
(Choudhury and Leonesio, 1997). Again, the intersection of SES and various
demographic factors put individuals at greater risk of functional impairment. If women
have higher levels of disability and longer life expectancy, then the issue of SES is even
more critical since women could be living with longer periods of functional limitations
and eventual disability than men do. Issues of recovery from functional limitations may
also differ by gender. This research will attempt to gain insight into the health-SES
connection for men and women.
Race and Ethnicity. A combination of factors place ethnic minorities at a
disadvantage when determining functional status. Issues of SES and health are even
more essential for ethnic and racial minorities as their population is expected to increase
substantially in the next 50 years (Martin and Soldo, 1997). Biology is not the issue
here, rather social and economic circumstances affect race and ethnic groups as social
entities (Berkman and Mullen, 1997; Kington and Smith, 1997). Especially at older ages,
there is less of a race-based difference in mortality and morbidity; social and economic
variables explain more of the differences in death rates (Elo and Preston, 1996) and
functional limitations (Kington and Smith, 1997).
Previous studies have found that Puerto Ricans and African-Americans suffer
from greater disability and functional limitations when over the age of 60 than non-
Hispanic White older adults (Jette, Crawford and Tenestedt, 1996). Older African-
Americans are concerned about their health and rate their health as worse than Whites
(Ferraro, 1993; Berkman and Mullen, 1997). They also suffer from more functional


3)
limitations (women more so than men), even though they do not have a significantly
different number of chronic health conditions (Ferraro, 1993). Ferraros research did not
include income and net worth variables, but did include education, which was a
significantly negatively correlated with functional limitations.
Just as with any grouping by race and ethnicity, Latinos experience diversity
within subgroups (Whitfield and Baker-Thomas 1999). Generally Mexican-Americans,
Cubans and Puerto-Ricans experience differences in mortality and morbidity from each
other (Markides, Rudkin, Angel, Espino, 1997). Some of the differences are due to
location of birth (immigrants versus U.S.-born), but some is due to socioeconomic status.
Differences in education between racial and ethnic groups have a correlation with
the differences in health experiences. Older adults who are Black experience worse
health and generally have less formal education (Bound, Schoenbaum and Waidmann
1995). If they do have a health problem is it more likely to progress to disability than is a
similar health problem for a White older adult (Ferraro and Farmer, 1996). In a
longitudinal study using ADLs the researchers found that older Blacks had a health
disadvantage when compared to Whites but that considerations of SES mitigated the
difference somewhat (Mendes de Leon, Beckett, Fillenbaum, Brock, Branch, Evans, and
Berkman, 1997). This research used only three levels of income (low [<$5,000], middle
[$5,000 $10,000], income [>$10,000]) and did not include any measures of net worth.
Unfortunately, there is little consensus about how or why racial and ethnic groups
may differ in health due to age or SES as many studies do not examine these relationships
(Mantn and Stallard, 1997). New data is being generated and this research will add to
knowledge about differences in SES, functional ability, and race and ethnicity.


32
Previous studies using the AHEAD data have found net worth gaps by racial and
ethnic groups using household wealth (Smith, 1995). This is similar to the results that
Brown (1996) found in that the ratio of median earnings of Black to Whites is .6 to .8.
The income gap is significantly less than the net worth gap, which may reflect the
accumulation of advantage over the life course of non-Hispanic White older adults.
Smith (1995) also found that household net worth was unequally distributed across
percentiles of household net worth with the upper 5% holding seven times ($655,000) the
net worth of the average household among non-Hispanic White households. Brown
(1996) found the ratio of median net worth of Blacks to Whites to be .2 to .3. This
research looks at differences in functional ability, however, and race is used as a control
variable to avoid confounding findings by SES (Ferraro, 1993). Additionally, this
research will explore the effect of health promoting behaviors on functional ability.
Age. The oldest old are especially vulnerable to disabling medical conditions.
Poverty rates increase with age, especially for women (Soldo, Hurd, Rodgers, and
Wallace, 1997). The oldest old are defined as individuals age 85 or older. For the
AHEAD data set, which includes interviews with individuals bom 1923 and earlier and
age 70 at the time of the interview, the oldest old were bom in 1908 or earlier. The oldest
old may have outlived their resources or they may be experiencing more ill health as they
age. This particular cohort was growing up during the Depression era and may be at
additional cumulative disadvantage due to the historical period of their work life, which
probably started in 1926 (Soldo et al. 1997).
Studies of older adults find that there is a correlation between age and declining
physical function, but some older adults, even at the oldest ages, actually recover from
disability (Beckett et al. 1996). This is again a point against the problemitizing of old age


33
and assuming that aging is a process of inevitable decline. Beckett et al. (1996) did find
that decline increased with increasing age, but there is variability by individuals. Their
study did not include measures of SES or health behaviors, which could explain the
variability. The purposes of this research is to address this gap. As a result we may be
able to determine how older adults who recover from functional limitations differ from
those who do not.
Genetic Influences
Our inherited characteristics are not health behaviors, but they do point to the
potential of our heath state, so for this study they are characterized as covariates and
grouped with health behaviors. Health status in older ages reflects health at younger ages,
even back to fetal status and genetic endowments (Smith and Kington, 1997). Thus,
some families are healthier than others with cumulative advantages and disadvantages
shared by family members. Genetic endowments promote health recovery as they
represent early childhood environments (even fetal environments) and the advantages a
good environment can contribute to good health in later years. Good health starts in tero
with some families being healthier than others and passing this benefit on to children
(Smith and Kington, 1997).
Smith and Kington (1997) used the AHEAD wave 1 data set and found several
measures within the data set that could be used as proxy measures for genetic
endowments. The data set includes age of death and education level of the respondents
parents, which they used to measure the relative good health of the previous generation.
It also includes data on the number of surviving siblings and children. They used this all
information to create a proxy for genetic endowment and the promotion of health
behaviors. They found evidence of intergenerational health transmission in the positive


34
correlation of respondents functional abilities and parents age at death (Smith and
Kington, 1997, 165). Respondents with higher functional abilities also had parents who
lived long, even past the respondents 70th birthday. Similarly, higher functioning
respondents had long-lived siblings, another indication of intergenerational health
transmission.
Medical Utilization
Existing Medical Conditions: Co-Morbidities. The first step in the
disablement process is the presence of chronic conditions or medical events that can
result in impairments and then functional limitations. Changes in our environment have
reduced the incidence and prevalence of acute, infectious conditions. Limitations on
physical activity can have social consequences with the resulting loss of independence
and social interaction. Individuals who maintain an active lifestyle are more likely to
maintain good physical functioning and be able to recover from them when they do
occur. They will have the physiologic resources to regain good functional status (Clark
etal. 1996).
Today chronic conditions are increasingly the cause of health problems. Previous
studies found that approximately 40% of adults over the age of 65 report activity
limitations due to chronic conditions (Fried and Wallace, 1992). Individuals with
functional limitations and co-morbidities are more likely to remain functionally disabled
than individuals with no other chronic conditions (Chirikos and Nickel, 1986).
Researchers using data from the Framingham Study (Guccione, Felson, Anderson,
Anthony, Zhang, Wilson, Kelly-Hayes, Wolf, Kreger, and Kannel, 1994) found that knee
osteoarthritis, heart disease, and stroke were conditions most attributable to functional
limitations. They also noted that chronic obstructive pulmonary disease and heart disease


35
made significant associations with functional limitations. In the AHEAD data set, we can
control for cancer, diabetes, emphysema, heart condition, stroke and arthritis as well as
test connections between SES and functional ability and potential recovery of good
functional status.
Insurance Coverage. Medicare coverage is available for almost all adults over
the age of 65. The U.S. Census (1996) reports that 99.4% of elderly had continuous
coverage between Medicare, Medicaid and military health care. However, such coverage
is not distributed equally among elders, leaving some groups at greater risk. For
example, Mexican Americans have low levels of health insurance coverage and the
coverage they do have is minimal (Angel and Angel, 1996). Additionally, elders in poor
health or with functional limitations are less likely to have private health insurance
(Wilcox-Gok and Rubin 1994). Without this coverage, older adults may not seek care in a
timely fashion when it is possible to treat their medical conditions more effectively and
increase the probability of recovery from functional limitations. However, the presence
of publicly funded programs is no guarantee of adequate coverage for health care
expenses.
Unfortunately publicly funded programs such as Medicare and Medicaid do not
cover all health care costs. AARP estimates that the elderly will pay 43% of health care
costs out of their own pockets (Crystal, 1996). For some older adults this out of pocket
payment represents in excess of 16% of their annual income (Estes, 1989). SES status
has a direct effect on the amount and type of coverage that elders can afford. This may
affect their efforts to receive preventive care or treat chronic conditions that could result
in functional impairment and, thus, lower the odds of recovery.


36
So, the assumption that the elders of our population will successfully age even in
the face of chronic illness or disability since public programs will cover the cost of their
treatment may be erroneous. Without the full protection of public programs, individuals
must look to their own savings to help defray the expenses associated with treating
chronic illness and disability. Yet we know that financial assets are not evenly
distributed among the elderly (Crystal, 1996), and neither is public or private insurance
(U.S. Bureau of the Census, 1996). There is a direct link between the type of coverage
one enjoyed during employment years to the adequacy of coverage in the retirement
years, once again pointing to the importance of examining the connections between SES
and health (Angel and Angel, 1996).
Medical Services. Contact with medical providers and taking prescription
medications are indicators of possible pathology that leads to functional limitations.
Previous research has found that previous hospital stays are the biggest predictor of
continuing functional limitations (Chikiros and Nickels, 1986). Chikiros and Nickels
(1986) research did not include prescription medications as a variable, nor did they
include net worth as a SES variable (only income). Research using prescription
medications did find a strong statistical correlation between use of medication and lower
levels of functioning in older adults (Daltroy, Larson, Eaton, Phillips, Liang, 1999).
Another, subtle, economic factor may be at work here as well. Providers of health
care are socialized beings and susceptible to economic incentives and the perceived
opportunity cost of continuing disability. The subsequent patient care plan may vary
depending on the SES of the patient (Chikiros and Nickels, 1986).
Regular medical services are integral to ensuring good health and recovery from
functional imitations, since medical advances have the possibility of slowing or stopping


37
the progress of disease to disability (Crimmins, 1997). With increases in life expectancy,
researchers are asking if disability-free years are increasing, thus indicating a
compression of morbidity (Fries, 1989: 208). The concept of compressing morbidity
was re-introduced by Fries (1989) who analyzed the concept using data through the
1980s. He points out that individuals in lower SES are not enjoying the benefits of
reduced health risks and that their old age will be more expensive if they suffer more
disability over a longer period of time in their later years.
Summary of Socioeconomic Status and Functional Ability in Older Adults
Most older adults enjoy a relatively active life; however, about 14% of older
adults have some degree of activity limitation (Jette, 1996). This has a negative effect on
the quality of life they enjoy and the independence they are able to maintain. Jette (1996)
points out that disability prevention is possible and that recovery is a possible and
desirable goal for our elders as they experience longer life expectancy and increasing
numbers. At the very least, further decline may be stalled or delayed. Another
consideration in determining the possibility of health recovery, as well as delayed
deterioration, is understanding the mechanisms that increase or decrease the risk of
functional limitations at older ages.
Figure 2 is the conceptual model proposed for this research that incorporates the
correlates described above. Education is in the health behavior box. In this research
education will serve as an indication of self-efficacy regarding healthy behaviors.
Generally, individuals at lower SES suffer poorer health. They tend to adopt
more risky health behaviors (House et al. 1994) and have an accumulation of poor health
and a lack of resources such education, income and net worth. As House et al. (1994)
point out, the accumulated effect of psychosocial risk factors is harder on the physiology


38
of older adults due to biological declines that occur with age. The stratification that
accumulates over the course of a lifetime leaves elders particularly vulnerable.
Figure 2. Conceptual Model of the Effects of SES and Health Behaviors on Functional
Ability
There is a feedback loop between health and SES, and with cross-sectional data it
is difficult to see where the loop begins. Longitudinal data is useful in this endeavor to
determine the causal relationship between SES and functional status. Elders are a
heterogeneous population with great variation in their functional abilities, and studying
change in functional ability over time is a helpful tool in understanding the source of
variability and improving the ability to promote health recovery (Jette, 1996).


39
The disablement process provides a model for examining different outcomes of
functional limitations. This model is useful as a basis for determining the relationship of
SES and functional status as well as any moderating effects from preventive health
behaviors and the relative contribution of income versus net worth to the likelihood of
recovery from functional limitations.
Hypotheses
In light of previous research in this area, this research will focus on six
hypotheses, as follows:
1) A significant proportion of older adults are free of functional limitations and
some of those older adults who do suffer from functional limitation recover
from them within two years.
2) Individuals with greater economic resources have stronger functional status.
3) Individuals with greater economic resources are less likely to suffer a decline in
their functional status.
4) Individuals with greater economic resources are more likely to recover from
functional limitations when they occur.
5) Income and net worth will affect functional limitations differently depending on
the functional status of the individual:
a) Net worth is a better predictor of a stable state regarding functional limitations
across both waves of data. Individuals with a higher net worth will be more
likely to experience a stable state with no functional limitations, while the
opposite is likely to be true for individuals with lower net worth. This is due to


40
the accumulative advantage of a higher net worth or disadvantage of a lower
net worth.
b) Income is a better predictor of a transition state regarding functional limitations
across both waves of data. Individuals with a higher income will be more
likely to recover from functional limitations experienced in wave 1 by wave 2.
Individuals with a lower income will be more likely to suffer a decline between
Waves. This is due to the current onset of the disabled state and the need for
more accessible resources to improve the functional status.
6) Intervening health behaviors will modify the relationship between
socioeconomic status and health.


CHAPTER 3
THE SURVEY OF ASSETS AND HEALTH DYNAMICS AMONG THE OLDEST
OLD
Research Sample
Data
The Assets and Health Dynamics Among the Oldest Old (AHEAD) survey is a
national panel study designed to be used for analysis of older Americans and their
experiences with health, finances and families. This is an ongoing longitudinal survey of
community based individuals bom in 1923 and earlier. The initial sample of 7,447
respondents were taken from the Health and Retirement Survey (HRS) screenings of area
probability household sample. Additional respondents aged 80 and older were taken
from the Medicare Master Enrollment File (HCFA) for a total of 8,221 respondents. The
dual sampling frame was used to test for bias in the selection criteria. Additionally,
Mexican-Americans, African-Americans, and Floridians were sampled at 1.8 times the
probability as the general population.
The data are organized into three waves with plans to accumulate additional
waves of information merged as part of the FIRS. Wave 1 includes data that were
collected between October 1993 and July 1994 and wave 2 data that was completed in
May 1996. The survey was sponsored by the National Institute on Aging. The Institute
for Social Research at the University of Michigan oversees the data collection.
The designers of the AHEAD data set recognized the importance of evaluating the
interaction of health status and financial well being for setting public policy (Myers,
41


42
Juster and Suzman, 1997). As a result they have gathered detailed information at a
household level about health, finances and family relationships. This makes the AHEAD
an extremely useful data set for determining the direct effect of SES, and its indirect
effect through health behaviors, upon the health status of the elders of the United States.
The AHEAD data set is designed to examine the health and economic dynamics
of the oldest old. The data set includes detailed information on all sources of household
income and net worth, presence or absence of functional status, details of medical
diagnoses and health care services, participation in preventive health behaviors as well as
limited data on the respondents parents, siblings, children and grandchildren. This data
set makes a unique contribution to research in its richness of detail regarding the health
and economic condition that is critical to understanding the SES/health relationship.
An additional benefit of the AHEAD data set is that is it longitudinal.
Longitudinal data provide a good picture of onset and desistance of medical conditions
that provides us with models of individual health experiences not available with cross-
sectional data. With these data researchers can determine the relationship between SES
and health for individual cases and categorize respondents by stability or change over
time. It is possible to determine the order in which changes occur in functional status and
the relationship between functional status, SES and health behaviors among the oldest
old. We can then analyze the benefit of knowing SES at wave 1 and functional ability at
wave 2 and determine if that knowledge gives us insight into the causative relationship
between SES and health over this time period. Through longitudinal data we gain the
ability to examine the chronological and developmental course of the relationship
between SES and health.


43
The concepts of successful aging and compression of morbidity need careful
study if we want to encourage them in our rapidly aging society. What social factors
influence the continuing good health of the oldest old? Ensuring quality of life for older
adults as well as freedom from disability and dependence saves time, money, and
emotional distress.
Another advantage of the AHEAD data set and this research is that the functional
status will be unbundled into particular activities. Prior research tends to create indices
or scales combining various measures of ADL, IADL or functional limitations. Each of
these measures of functional ability (walking, climbing, pushing/pulling, lifting and
picking up a dime) are influenced by different environmental, social, and physical
factors. By analyzing the SES/health connection for each of these independently we
eliminate the possibility of complicating upper body versus lower body issues. In this
way it is possible to see if the relationship between SES and health varies by specific
functional abilities, since different pathologies may influence each of the functional
abilities diversely.
The SES/health link is a consequential one to study in this age group for several
reasons. First, we will all hopefully be in this category at some point in our lives, and the
number of the U S. population who are aging is increasing. To examine the factors
influencing a happy old age aids the oldest old as well as the following generations that
are responsible for their care and support. It is informative to see if the SES/health link is
the same for the oldest old as it is for other age groups. Secondly, this group is at a
unique stage. They have probably accumulated all the net worth and education possible


44
for them, so there is a limit to the question of reverse causation (that is, health causing
wealth) from this point forward.
The AHEAD data set also offers us information about health behaviors of the
oldest old and their impact on functional ability. Does a lower risk lifestyle have an
effect on functional ability at older ages? If so, it is not too late for even the oldest old to
make changes in health behaviors in order to improve their health status.
This group can also help us recognize the social influences on our health status.
They have experienced a lifetime of social forces and influences that have determined
their social standing and resulting good or bad health. Critically analyzing this through
measures of SES can help succeeding generations to choose a different course.
Sample
The AHEAD wave 1 includes interviews with 8,221 non-institutionalized
individuals from 6,047 different households, with a response rate of 80.4% and a dual
sampling frame for respondents aged 80 and older. The dual sampling frame, by the
HRS and HCFA for the over 80 portion of the sample, was used to eliminate sampling
bias. The households surveyed contained at least one individual 70 or older (bom prior to
1923) and his or her spouse. Some surveys were conducted face-to-face (especially if the
respondent was over age 80), and others took place over the phone depending on the
respondents preferences. The study was designed to over-sample individuals of Black
and Hispanic race/ethnicity as well as people living in Florida. Analyses use weighted
data unless otherwise indicated.
This study sample will include all individuals who completed the financial, health
and behavior sections of the survey in waves 1 and 2. Only survivors are included since


45
this research is trying to determine the influences on transitions in functional ability
between waves. The combined sample of wave 1 and wave 2 respondents used in this
study is 6,237 individuals. The variables used are summarized in Table 1 on page 49 and
the descriptive statistics are summarized in Table 3 on page 58.
One limitation of using this data set is that only three waves of data are available
at this time and the waves are only two years apart (this research uses the first two
waves). This, however, does allow us a first glimpse at the transitions between functional
states among the oldest old, recognizing that recovery from functional limitations may
take more time than two years. There is a possibility of the opposite situation as well.
With these data we do not know the picture in the intervening time frame. Verbrugge,
Reoma and Gruber-Baldini (1994) found that post-hospital older adults improved for a
month or two, but then their health declined. They noted that functional ability is
variable for the first year following a hospital stay. Respondents could experience
functional limitations and recovery several times between waves of data collection.
Other research found that lower body limitations were less likely to resolve than
upper body limitations (Wolinsky, Stump, Callahan, and Johnson 1996). Since most of
the functional measures in the research are of the lower body, we may have consistency
across waves. Any patterns shorter than two years in duration are obscured by the
schedule of interviews. As a result, researchers may overestimate the stability of
functional status.
Functional status is the third step in the main pathway of Verbrugge and Jettes
disablement process model. It is a point in the process where we can observe how
physical dysfunctions operate in the lived experiences of the oldest old. This point is also


46
before complete disability, as would be measured by ADLs and IADLs. As such, it is
divorced from social definitions and role responsibilities, and any biases this might cause
in answers from the respondents. Additionally, it may explain a point in the process
before the need for nursing homes or other institutional aids and at which recovery is
possible. Jette (1999) called for more longitudinal studies isolating the steps of the
disablement process for a more thorough analysis of the critical risk factors at each point
in the pathway. This research attempts to do that.
The AHEAD study only peripherally addresses issues of access to health care and
barriers to adequate rehabilitation from physical impairments. Respondents are asked
about their visits to various providers, but are not asked if they did not see a provider due
to access limitations or other barriers to care. The data do not include measures of
convalescence nor measures of social networks nor social support. These would be
beneficial additions to the study as would questions related to difficulties seeing a
provider, whether they are related to transportation issues, cost of care, or lack of
availability of providers. The Institute for Social Research has gathered thorough
information on the health and wealth status of the respondents.
The data set is also limited by problems common to all longitudinal research, such
as attrition due to death and those lost to follow-up. This could be a problem when
analyzing health care issues, as individuals more frail may die between waves of data
collection. As a result, their experiences are not included in this analysis which may
affect our understanding of the trajectory of individuals with greater functional
limitations. For the second wave of interviews, 9% of respondents had died between
waves, and 11.1% were lost through attrition or did not respond for other unspecified


47
reasons. However, 88.9% of the baseline respondents have provided interviews at all
waves in which they were eligible.
Additionally there are problems with confounding cohort effects. Historically,
this cohort grew up before we had the medical knowledge we have today which guides
our health behaviors. For example, smoking was a popular pastime; cigarettes were
passed out on airplane flights. Today, we know the detrimental effects of cigarette
smoking and we cannot know what health behaviors we would have seen among this
cohort if they had the knowledge we have today. In addition, they lived through a time of
segregated school and health care systems. As a result, it is difficult to know if the study
results will be applicable to this cohort only or generalizable to the older population of
succeeding cohorts or if patterns observed are typical or normative aging. Other
measurement limitations will be addressed specifically for each set of variables.
EXTRA-INDIVIDUAL FACTORS
Number of prescriptions, Doctor visits, hospital and nursing
Home stays, outpatient surgery, insurance, number of
Children for social support and assistance
PATHOLOGY IMPAIRMENTS FUNCTIONAL DISABILITY
{Number of medical LIMITATIONS
conditions) (WM me h¡xk a¡mh
stairs, Lift 10 pounds,
Push/Pull heavy object,
Pick up a Dime)
7 r
RISK FACTORS INTRA-INDIVIDUAL FACTORS
Net Worth, Years of Education, Sex, Race;
Genetic Factors: Father/Mothers age at death, number of Siblings;
Behavioral Factors: drink alcohol, smoking, weight, exercise,
social support network and preventive care
Figure 3. Modification of the Model of the Disablement Process (Verbrugge and Jette,
1994) Showing Variables used in this Research


48
Measures
The data from Waves 1 and 2 of the Assets and Health Dynamics Among the
Oldest Old (AHEAD) were used to analyze the relationship between socioeconomic
status (SES) and functional limitations. The wording for selected questions from the
AHEAD codebook is included in Appendix A. The model presented by Verbrugge and
Jette (1994) is used as the basis for the design of the study. V ariables are shown in Table
1.
Functional Performance
The response variable is functional performance. The functional performance
indicators in both waves are: walking several blocks, climbing one flight of stairs,
pushing or pulling large objects, lifting weights over 10 pounds, and picking up a dime
from the table. Respondents were eliminated if they said they Dont do the activity,
but not because of health reasons. The individuals may not have the occasion to perform
these activities, such as not being around stairs, for example. As a result, the response
does not provide meaningful information on physical ability, just on environmental
limitations and opportunities (Guralnik and Lacroix, 1992). Using functional
performance to measure health status has several advantages. In this research, each
measure is modeled separately. Aggregating the scales in a meaningful fashion is
difficult and may mask difficulties with specific tasks and therefore underestimate the
exact level of difficulty with functioning (Guralnik and Lacroix, 1992).
Measures of functional performance have advantages over basing measures of
health on medical diagnoses because the existence of a medical diagnosis may be
compromised by access to care issues, which are related to SES. Analyzing


49
improvements in functional ability allows for a description of the elders who avoid
moving into complete disability.
Table 1. Variable Description and Coding
Variable
Description
Functional Performance (0=no difficulty, l=difficulty)
Walking Kb difficulty walking several blocks
Climbinga,b difficulty climbing one flight of stairs
Pushing/ Pullinga,b difficulty pushing/pulling a large object
Lifting "h difficulty lifting 10 pounds
Pick up a Dime*,b difficulty picking up a dime off a table
Socioeconomic Status
Income8
Total annual household income
Net Worth"
All assets minus all debts, including any debt for mortgage in total dollar
amount
Health Behaviors
Smoking"
Alcohol Use"
Exercise1*
Never smoked, former smoker, current smoker
0=abstainer; Light drinker=l-2 drinks; Drinker=3+ drinks/day
Vigorous activity three times a week over the past 12 months
0=non exerciser l=exerciser
Body Mass Index"
Screenings1*
Weight & Height ratio: Underweight (BMI <18.5); Normal weight (BMI
between 18.5 and 24.9); Overweight (BMI >24.9)
Number of health care screenings for breast cancer, prostate cancer,
cholesterol, cervical cancer, or had a flu shot or breast self-exam:
Social networks"
Women: 3* the number of screenings; Men: 5*the number of screenings.
Number of children ever had
Education*
Marital Status: 0=no partner; l=married or co-habiting
Years of education completed
Covariates
Genetic Endowments
Mothers age"
Fathers age"
Siblings"
Sex"
Mothers age if living. If deceased, her age at death
Fathers age if living. If deceased, his age at death
Number of siblings still alive
Sex. 0=male; l=female
Age"
Race"
Medical Conditions*
Age at interview
Race/ethnicity: Non-Latino White, African-American, or Other (Latino, Asian,
or Native American)
Total number present of the following: cancer, diabetes, emphysema, heart
condition, stroke, or arthritis
Health Insurance*
Three variables: 1) Medicaid = presence or absence of Medicaid, 2)
Government Insurance = other Government sponsored ins (Medicare Part A,
CHAMPUS), or 3) private pay insurance = Medicare supplements or individual
Doctor Visit*
Prescriptions*
coverage.
Respondent visited a doctor in the past 12 months
0=no visits; 1= at least one visit
Number of prescription medications taken each month
Source: AHEAD
a Wave 1
b Wave 2


50
Studies have shown that these functional limitations are correlated with many
measures of health, such as self-reported health, work disability, IADLs, and ADLs
(Johnson and Wolinsky 1993; Waldron and Jacobs 1988). This measure is less subjective
than self-reported health, which is useful because it provides a specific level of
performance for comparison. It is easily observable to the respondent, compared to some
medical conditions, but has not progressed into disability, can be treated to maintain
community independence with less costly medical care.
The measures of functional performance include upper and lower body. Walking,
and climbing focus on the lower body and its functioning, while lifting 10 pounds and
pushing/pulling a heavy object is a measure of both upper and lower body, and picking
up a dime is upper body performance. Difficulty with any of these activities can lead to a
reduction in ADL or IADL performance. For example, if lifting is a problem, that could
reflect problems with reaching, which could mean the individual would eventually be
unable to dress or perhaps to grocery shop and unload groceries in the home.
For this research, we focus on the change in functional performance from wave 1
to wave 2. If an individual reports no problem with the measure of functional
performance in waves 1 and 2, then the variable is coded as stable with no limitations. If
a respondent has difficulty in waves 1 and 2, then the variable is coded as stable,
limitations. If an individual has difficulty in wave 1, but is recovered by wave 2, then the
variable is coded as recovered. If the respondent had no difficulty in wave 1, but
difficulty in wave 2 the variable is coded as declined.
Approximately one-third of respondents in wave 1 experienced some level of
difficulty with the functional performance measures, except the ability to pick up a dime


51
from a table (see Table 3). The most difficulty was experienced with walking several
blocks (37.5%) and pushing or pulling heavy objects (36.7%). The least difficulty was
experienced with picking up a dime from a table (8.3%). In wave 2 all measures
increased in the percentage of respondents reporting some level of difficulty with the
functional performance measures. Again, the most difficulty was reported with walking
several blocks (45.2%) and the least with picking up a dime (11.5%). The second most
reportedly difficult task was pushing or pulling a large object followed by lifting 10
pounds and finally climbing one flight of stairs.
Socioeconomic Status
Independent variables will include income and net worth for each household and
the educational level of older adults, all measured in wave 1. The mean household
income for the respondents in wave 1 was $25,191.94 and ranged from $0.00 to
$700,000. For this research income is divided into deciles as shown in Table 2.
Household income is measured with a variable constructed by AHEAD staff summing
across all household members the amount of income from the following sources:
Social Security
Retirement pension income
Supplemental Security
Stock income
Welfare
Income from bonds
Veterans benefits
Income from dividends or
Rental income
interest on savings or
checking accounts or CDs,
Business
bonds or treasury bills
Farm
Income from work (including
self-employment)
IRA
Any other sources of income.
Annuity income


52
Net worth includes all other forms of economic resources measured as the total
dollar value of all assets owned by household members minus any debts, including the
mortgage. This is also a derived variable using assets such as:
The value of real estate
holdings
Business holdings
IRA holdings
Retirement pensions
Stock ownership
Mutual fund ownership
Treasury bills and certificates
of deposit
Savings bonds
Ownership of means of
transportation
Value of jewelry and
collections
Checking and savings
accounts
Debts owed to respondent
Rights in a trust or estate
Debts were subtracted from assets to derive the variable for net worth.
Mortgage Life insurance policy loans
Utilities Loans from relatives
Credit card balances Real estate tax
Medical debts Home insurance
Net worth was measured as a total of all assets minus all debt, including home
mortgages. Net worth had a larger range and mean than household income. The mean
net worth was $182,765 and it ranged from -$285,000 to $14,655,000. Both income and
net worth were divided into deciles for regression analysis (Table 2).
Health Behaviors
The AHEAD data set includes information on preventive care behaviors that
affect health. The data for smoking, drinking, and BMI are used from wave 1 to establish
a baseline. Also, the questions were changed for wave 2 and did not include useful


53
information, such as status as a former smoker. Wave 2 questions included exercise
activity and participation in health screenings, which were not asked in wave 1.
Table 2. Decile Ranges for Income and Net Worth
Decile
Income Range
Net Worth Range
1
$0-
5,900
-$285,000 +$570
2
6,000
- 8,976
600-
13,200
3
9,000
-11,988
13,500
- 35,700
4
12,000
- 14,940
35,712
- 59,200
5
14,976
- 17,820
59,500
- 86,200
6
18,000
-21,842
86,500-
-120,900
7
22,000
- 26,960
121,000
-167,000
8
27,000
- 33,600
167,900
-247,000
9
34,000
-47,600
247,500
-422,500
10
48,000 -
- 700,000
423,000 -
14,655,000
The first health behavior is smoking. Respondents are coded into 1 of 3
categories: 1) non-smoker at wave 1,2) former smoker at wave 1, and 3) current smoker
at wave 1. The referent category is non-smoker.
Another health behavior is alcohol consumption. Details about alcohol
consumption include the number of drinks per day. Respondents were coded according
to the number of drinks consumed each day. This resulted in three categories: 1) abstains
from alcohol (0 drinks per day), 2) light drinker (2 or less drinks per day), and 3) drinker
(3+ drinks per day). The referent category is abstaining.
Respondents were also asked about their participation in vigorous activity in wave
2, but the question was not included in wave 1. The question covers physical activity
including sports, heavy housework, or a job that requires physical labor. Respondents


54
were asked whether they participated in physical activity three times a week or more over
the past 12 months.
An additional behavioral variable related to nutritional status and physical activity
is the imputed body mass index based on the height and weight of the respondents at both
waves. The following categories of body size were used: 1) underweight (BMI less than
18.5), 2) normal weight (BMI between 18.5 and 24.9), or 3) overweight (BMI over 24.9).
The referent category is normal weight.
The respondents were asked about preventive care services in wave 2, but not in
wave 1. They were asked if they had completed or received any of the following: a) flu
shot, b) blood test for cholesterol, c) self-test for breast cancer, d) mammogram, e) pap
smear, and f) prostate cancer screen since wave 1? Items a, b, c, d, and e will be included
for women. Items a, b, and f will be included for men. For each test completed, men will
receive 5 points and women will receive 3 points. The scoring calibrates a single scale.
The preventive care scale will range from 0 (no screens completed) to 15 (all screens
completed).
Education is measured as the number of years of formal schooling the respondents
had, with a maximum of 17+ years. The mean number of years of schooling is 10.8 with
a range of 0 (1.7%) to 17+ (5.6%) years. Approximately 11% of the respondents
completed eighth grade and 30% of the group completed high school and 27% had some
college or graduated from college.
The final measure used in this section is of social networks. Unfortunately neither
wave includes any information about social networks nor social support. The closest


55
proxy for this is the number of children, assuming that they interact with the respondents,
and marital status. The data used is number of children and presence of a partner.
Overall this group follows healthy behaviors, except for weight control. Only
9.7% of the respondents are current smokers with 48.2% never smoking and 42% quitting
before wave 1. Eighty-nine point two percent of the group abstains from alcohol or
consumes less than one alcoholic drink per day. Slightly less than one-third of the
respondents (29.3%), however, engaged in regular vigorous exercise. This could explain
the 44% of the respondents who remained overweight between Waves 1 and 2, with a
BMt over 24.9. Two point seven percent remained underweight and 11% lost weight
while nearly 5% gained weight. The remaining 38% maintained a BMI between 18.5 and
24.9, which is an appropriate weight for their height.
The AHEAD respondents indicated if they participated in preventive care, which
included screenings for cholesterol, prostate cancer, breast cancer, cervical cancer,
receiving a flu shot, or performing a breast self-exam. The mean score for preventive
screens was 9.47 overall. Men received an average score of 10.8, but because the value
applied to each screening was 5 points, this means they obtained approximately 2.2
screenings of the total 3 available (cholesterol, flu, prostate). The average score for the
women of 8.6, divided by the score per screening of 3, results in a slightly higher
participation in preventive screenings of 2.9 of the total 5 available (flu, cholesterol, pap
smear, breast self-exam, mammogram). The most popular screening for men was
prostate screen (47.5% of the men receiving preventive screens). For women, a
mammogram was the most used preventive screen (23.3% of women receiving
preventive screens).


56
The final health behavior used in this research is social networks. The data set
does not contain any overt measures of social support or social networks. As a substitute,
marital status and number of children ever had was used. The average number of
children reported by the respondents was 2.7 with a minimum of 0 and a maximum of 21.
Marital status categories include married or cohabiting and unmarried (divorced,
widowed, and never married). A slight majority of the respondents were married or
cohabiting (51.8%).
Control Variables
Demographic Information Control variables include sex, race/ethnicity, age,
insurance benefit coverage, medical conditions, doctor visits and prescription
medications. All controls will be measured at wave 1 to form a baseline of variables.
Sixty-one point seven percent of the sample respondents were women, which is
representative of this age cohort. Age was measured as exact age. The oldest respondent
at the time of the first interview was 103 years old and the youngest was 69, and the
mean age was 77.25 years.
Ethnic groups will consist of non-Latino Whites, non-Latino African -Americans,
and Other (includes Latino, Asian and American Indian). There are too few members of
other ethnic origins to form meaningful homogeneous groups with risking zero cells and
unstable estimates of coefficients and standard errors. The study sample is 80.3% non-
Latino White, 13.0% African-American, and 6.7% other.
Medical Utilization. A summary measure of the number of medical conditions
offers a measure of the prevalence of pathological conditions, the first phase in the
disablement process. The disability model focuses on the following conditions: cancer,


57
diabetes, emphysema, heart condition, stroke and arthritis. Respondents had an average
of .99 conditions. Categories of insurance coverage are: 1) Medicaid; 2) government
insurance (Medicare and CHAMPUS); and 3) private pay individual plan (basic, Medi-
gap, supplemental, etc.). Most of the respondents have some type of insurance coverage.
Only 10% have Medicaid, but 94.2% have government insurance and a private pay plan.
As far as accessing the medical system, respondents were asked if they had had a
doctors visit in the past 12 months. Eighty-nine point two percent of the respondents
had had at least one visit. The respondents were also asked the number of prescription
medications they were taking. The number ranged from 0 to 20, with an average of 2.78
prescriptions. Other measures of accessing the medical system, such as hospital stays or
nursing home stays or outpatient surgery were not used in this research. It was believed
that these would be confounding variables, measuring the same functional limitations as
the dependent variable.
Genetic Endowments. The AHEAD data set does not include any detailed
information regarding genetic background, yet such information is meaningful because
our genetic inheritance provides a starting point for our health in older ages and it
represents our childhood environment. Long life of family members is serving as an
indication of positive health genetic endowments. This research follows Smith and
Kingtons (1997) strategy to use substitute variables. The variables here are: 1) mothers
age (if alive, or age at death), 2) fathers age (if alive, or age at death) and 3) number of
living siblings.


58
Table 3. Descriptive Statistics of Sample
MEAN (S.D.)
SES
Household income ($0 $700,000
Net worth (-$285,000 $14,655,000)
HE.AL.TO BEHAVIORS
Smoking:
Never smoked
Former smoker
Current smoker
Alcohol use:
Abstainer
Light Drinker
Drinker
Exercise:
Regular exerciser
Diet/Nutrition
BMI sl8.5
BMI >18.5 s24.9
BMIi24.9
Screenings (0-15)
Females (0-15)
Males (0-15)
Social networks
$25,191.94 ($29,983.13)
$182,765 ($395,668.10)
9.47 (4.7)
8.6 (4.4)
10.8(4.8)
PERCENTAGE
FUNCUONAL PERFORMANCE
Wav^ I
Wave 2
Walking difficulty
37.5%
45.2%
Stair Climbing difficulty
29.8%
33.1%
Pushing/Pulling difficulty
36.7%
44.3%
Lifting difficulty
33.5%
40.1%
Picking up a dime difficulty
8.3%
11.5%
48.2%
42%
9.7%
54.3%
43.6%
2.1%
3.6%
43.9%
52.5%
Number of children ever had (0-21) 2.73 (2.3)
Married/Cohabiting 51.8%
Education (1 17+) 10.84 (3.7)
£OVARJATES
Women 61.7%
Men 38.3%
Non-Latino Whites 80.3%
African Americans 13.0%
Other (Latino, Asian, or Native American) 6.7%
Age (69-103) 77.25(5.68)
Unmarried 48.2%
Genetic endowments
Fathers age (20 -107) 71.6(15.1)
Mothers age (18-109) 74.2(17.1)
Number of surviving siblings (0-13) 2.07 (2.02)
Number of medical conditions (0 6) 0.99(0.97)
Government Insurance (Medicare, CHAMPUS) 94.2%
Medicaid 10.0%
Private pay insurance (Medi-gap, basic, supplemental) 94.2%
Doctor visit 89.2%
Prescription Medications (0 20) 2.78(2.1)
Source: AHEAD, Waves 1 and 2
a range in parentheses
b change between waves
c current age or age at death
Until sociological research and biomedical research merge data, we will not have
accurate measures of genetic endowments. As a proxy, this data set includes the age of
parents of the respondents. Some of the respondents parents were still alive, so their age


59
is reported. If the respondents parents are deceased, their age at death is reported. In
addition, the number of surviving siblings may provide some indication of the positive
biological influences on health in old age. The average age for fathers of the respondents
was 71.6 and ranged from 20 to 107. Mothers average age was slightly higher at 74.2
with a range of 18 to 109. The mean number of surviving siblings was 2.07 with a
minimum of 1 and a maximum of 13.
Procedures
The data will be analyzed in two phases, first through a transition matrix for each
functional limitation and, second, using multinomial logistic regression to analyze
correlates of changes in functional status. For each of the five performance measures,
there are four states that can occur over the two waves. One pair of states is stability, or
no change in functional status, whether the respondent is stable with limitations or stable
with no functional limitations in both waves. The second pair of states is a transition
between limitations and no limitations. Recovery occurs when the respondent cannot
perform the activity at wave 1, but can perform it by wave 2. Functional decline occurs
when the respondent was able to perform the activity without difficulty at wave 1 but in
wave 2 completes only with difficulty or no longer performs it at all. A transition matrix
is an origin-destination contingency table and provides descriptive statistics of the stasis
and change states in functional status of the respondents. The resulting matrix will be a 2
by 2 table for each of the measures of functional ability. This analytic step permits the
description of th elevels of change, decline and recovery in the sample.
In the second analytical phase, multivariate procedures provide insight into
correlates of decline, recovery and stasis. Specifically, the research will estimate the


60
effect of SES and health behaviors on functional performance using multinomial logistic
regression. This type of modeling is useful for describing the relationship between the
dependent and independent variables when the dependent variable is not continuous and
polytomous. In this case the dependent variable is arrayed across four categories. The
categories are: 1) decline; 2) recovery; 3) stable with functional limitations; and 4) stable
with no functional limitations. The basic model equation is as follows:
Ln[i/j (category 4, category 1)] = a + piXi + P2X2 + ... + PkXk
Ln[$ (category 4, category2)] = a + piXi + P2X2 + ... + PkXk
Ln[$ (category 4, category3)] = a + piX] + p2X2 + ... + pkXk
For each of the five functional performance measures (walking, climbing,
pushing, lifting, picking up a dime), the relevant transition matrix will be used to create a
four category dependent variable: maintenance of non-limited state, maintenance of
functionally limited state, recovery from limited state, decline into an limited state. The
reference category, category 4, is the maintenance of the non-impaired state. Three sets
of contrasts are enabled: 1) decline versus maintenance of non-impaired state (category 1,
category 4); 2) recovery versus maintenance of non-impaired state (category 2, category
4); and, 3) maintenance of impaired state versus maintenance of non-impaired state
(category 3, category 4).
This research will estimate two regression models for each functional
performance indicator. A positive coefficient indicates that a higher value of the
correlate is associated with either stasis in limitation, decline, or recovery compared to
maintenance of a non-impaired state. A negative coefficient indicates that a lower value
of the correlate is associated with either stasis in limitation, functional decline or
recovery, compared to maintenance of a non-impaired state.


61
The first model includes measures of SES, genetic endowments, and the
covariates. The second model adds the measures of health behaviors. Comparisons of
these two models describe the moderating effect of health promotion activities on the
relationship between SES and functional performance. Specifically, significant SES
coefficients describe a direct SES effect.


CHAPTER 4
DESCRIBING CHANGE AND STABILITY IN FUNCTIONAL PERFORMANCE
This chapter describes change and stability in functional performance. The first
section presents the transition tables for each of the five performance measures. The
second section evaluates the relationship of each of the correlates with the four states of
each functional performance measure.
Transition Tables
A transition matrix is useful for examining changes in functional ability over
time. First respondents are sorted into two categories with regards to each functional
measure using the wave 1 data. The two categories are no difficulty versus difficulty in
performing the function. This is the origin state. Next, it is determined if the respondents
continue to have difficulty or no difficulty in wave 2. This is the destination state.
A cross-tabulation is developed with the cells on the diagonal indicating stability.
That is, respondents who had no difficulty in wave 1 and no difficulty in wave 2 as well
as respondents who had difficulty in both waves 1 and 2.
The upper right hand cell holds information about respondents who deteriorated
over the two waves. The lower left-hand cells provide data about respondents who
actually improved between waves. The cell with the largest number of respondents is the
upper left hand cell indicating that the respondent had no difficulty with the functional
measure in wave 1 and in wave 2. Tables 4 through 8 contain the transition matrices for
the five measures of functional performance.
62


63
Table 4. Transition Matrix of Walking Several Blocks
Destination State
Origin State
No Difficulty
(0)
Difficulty
(1)
Total
No Difficulty
3100.8
854.14
3954.9
(0)
78.4
21.6
100.0
Difficulty (1)
402.62
17.5
1893.9
82.5
2296.6
100.0
Total
3503.37
56.04
2748.08
43.96
6251.46
100.00
Note: Analysis used weighted data. Each cell in the transition matrix
shows the cell size and the frequency percent. Source: AHEAD, Wave 1
and Wave 2
Walking several blocks. Table 4 shows that 79.9% of the respondents were
stable, either with or without limitations, between wave 1 and wave 2. Forty-nine point
six percent had no difficulty walking several blocks in wave 1 and in wave 2. They are
shown in the upper left cell, representing stability and no difficulty, which is the most
common of the cell states. The other stable state, continuing functional limitations, is
shown in the lower right cell. These respondents, about 30.2% of group, had difficulty in
wave 1 and continued to have difficulty in wave 2.
The upper right-hand and lower left-hand cells show the respondents who
experienced a transition in functional ability between waves. Interestingly enough, 17.5%
of respondents actually improved between waves 1 and 2, having difficulty performing
the function in wave 1 and no difficulty in wave 2. Twenty-one point six percent of the
respondents declined in functional ability between waves 1 and 2.


64
Climbing stairs. The transition matrix for stair climbing demonstrates similar
results with walking. Approximately 79% of the respondents were stable from one wave
to the next, with 75% of the stable group with no limitations, and 25% stable with
functional limitations. Among the groups experiencing transition in functional ability,
16.8% declined in ability and 24.4% improved between waves 1 and 2.
Table 5. Transition Matrix of Climbing Stairs
Origin Stale
Destination State
No Difficulty
(0)
Difficulty
(1)
Total
No Difficulty
3726.8
750.57
4477.4
(0)
83.2
16.8
100.0
Difficulty (1)
560.63
1213.4
2296.6
24.4
52.8
100.0
Total
4287.46
1964
6251.46
68.58
31.42
100.00
Note: Analysis used weighted data. Each cell in the transition matrix
shows the cell size and the frequency percent. Source: AHEAD, Wave 1
and Wave 2
Pushing/pulling large objects. This functional ability parallels the results of the
transition matrix for walking. Almost one-half of the group had no difficulty performing
this task in either wave 1 or wave 2. Eighty-six point five percent of respondents were
stable between the two waves of data, with the largest proportion of respondents being in
a state of stability, no difficulty with pushing/pulling large objects (47.09%). Once again,
a meaningful finding here is that almost 24.9% of respondents actually improved in
functional ability between wave 1 and wave 2.


65
Lifting ten pounds. More than one-half of the respondents (52.3%) reported no
difficulty in lifting 10 pounds in wave 1 and in wave 2. In addition, 24.8% of
respondents recovered by wave 2 from the difficulty they had in wave 1 in lifting 10
pounds. Consistent with walking and pushing/pulling, approximately 21.6% of the
respondents had a decline in functional ability and reported difficulty in lifting weight in
wave 2 when they had no difficulty in wave 1.
Table 6. Transition Matrix of Pushing/Pulling Large Objects
Destination State
Origin State
No Difficulty
(0)
Difficulty
(1)
Total
No Difficulty
2943.9
1024.5
3968.4
(0)
74.2
25.8
100.0
Difficulty (1)
568.13
24.9
1714.9
75.1
2283
100.0
Total
3512.08
56.18
2739.37
43.82
6251.46
100.00
Note: Analysis used weighted data. Each cell in the transition matrix
shows the cell size and the frequency percent. Source: AHEAD, Wave 1
and Wave 2
Picking up a dime. As noted previously, the responses to difficulty with this
functional ability differ from the other four. Here almost 85% of respondents had no
difficulty with this function in wave 1 and wave 2 and only 4% had difficulty in both
waves. Similarly to the other functional transition matrices a significant number of
respondents recovered from the functional limitation. Here one-half the number who
declined in ability recovered between waves of the data set.


66
Table 7. Transition Matrix of Lifting 10 Pounds
Origin State
Destination State
No Difficulty
(0)
Difficulty
(1)
Total
No Difficulty
3272.1
900.56
4172.7
(0)
78.4
21.6
100.0
Difficulty (1)
515.93
1562.9
2078.8
24.8
75.2
100.0
Total
3788.04
2463.41
6251.46
60.59
39.41
100.00
Note: Analysis used weighted data. Each cell in the transition matrix
shows the cell size and the frequency percent. Source: AHEAD, Wave 1
and Wave 2
Table 8. Transition Matrix of Picking Up a Dime
Destination State
Origin State
No Difficulty
(0)
Difficulty
(1)
Total
No Difficulty
5281.1
464.49
5745.6
(0)
91.9
8.1
100.0
Difficulty (1)
252.45
50
253.39
50
505.85
100.0
Total
5533.57
88.52
717.888
11.48
6251.46
100.00
Note: Analysis used weighted data. Each cell in the transition matrix
shows the cell size and the frequency percent. Source: AHEAD, Wave 1
and Wave 2
These transition matrices respond to the first hypothesis, that a significant
proportion of older adults are free of functional limitations and some of those older adults
who do suffer from functional limitations recover from them within two years. As


67
indicated in the matrices, half of the respondents have no limitations with walking,
climbing, lifting, or pushing/pulling. Over 80% have no difficulty with picking up a
dime, and 50% of those who have this limitation in wave 1 recovers the ability by wave
2. Respondents had the most difficulty with pushing/pulling and lifting. Both these
activities require both upper and lower body strength, and since this group is largely
female may reflect their lower upper body strength. Between 17% to 25% of those who
do have limitations with the other functional abilities recover between waves 1 and 2.
Correlation of Measures
Tables 9 through 13 contain the correlation coefficients of the measures used in
this study. The first table, Table 9, provides the correlations among the measures of
functional limitations. Functional limitations were measured in wave 1 and wave 2 and
the state in each wave is compared for the correlations. A decline state indicates that
the individual had no limitations in wave 1 but had limitations in wave 2. A recover
state indicates that the individual had limitations in wave 1 but had no limitations in wave
2. A stable, limited state indicates that the individual experienced functional
limitations in both waves. A stable, no limits state indicates that the respondent had no
functional limitations in either wave. Table 10 shows the correlations between changes
in functional limitations and SES measures and Table 11 between changes in functional
limitations and health behaviors. Table 12 summarizes the correlations between changes
in functional limitations and measures of genetic endowments and Table 13 correlations
with the covariates.


Table 9. Correlation Matrix: Correlation with changes in Functional Performance Between Waves 1 and 2
Pearson Correlation Coefficients/ N=6,237
Walking
Climbing
Pushing
/Pulling
Picking up a Dime
o
(2)
(3)
(4)
to
(2)
(31
(4)
ni
(2)
(31
(41
di
(21
(31
(41
Climbing
decline
.22**
-.03 *
.06**
-.2 **
recovery
.05 **
-.21
**
-.09
**
.15 **
stable
limited
-.06 **
-.04 *
.53 **
-.43
**
stable no
limits
-.07 *
-.07
**
-.52
**
.57**
Pushing/Pulling
decline
0.17
**
-.02
-.004
-.11
**
.19
-.04 *
-.07
**
-.06
**
recovery
.02
-.13
**
-.03 *
.08**
.04
**
-11
**
-.01
-.05
stable
limited
-.04
**
-.006
.47**
-.41
.002
.09
**
.47
.44
**
stable no
limits
-.08 **
-.05
**
-.44
**
.49**
-.12
**
-.12
**
-.38
.46
**
Lifting
decline
.17**
.003
.004
-.13
**
.2**
-.03 *
-.03 *
-.09
**
.26
**
-.05
**
-.01
-.16
**
recovery
.01
-.12
**
-.05
**
.1 **
.01
-.12
*
-.03 *
.09
**
.01
-.21
**
-.02
.13
**
stable
limited
-.03 *
-.02
.5**
.43 **
.006
.08
**
.47
**
-.44
-.04
**
-.008
.56
*
-.47
*
stable no
limits
-.09 **
-.05
-.5 **
.51 **
-.14
**
-.12
**
-.04
**
.5 **
-.14
**
-.07
**
-.49
.59
**
Picking up
decline
.06**
-.04
**
.13 **
-.14
*
.09
**
-.03 *
.13
-.15
06
**
-.03 *
.13
**
-.14
**
.07
-.03 *
-.15
**
-.16
**
recovery
03 *
-.01
-.14
**
-.12
**
.01
-.06
**
-.12
**
.12
**
.04 *
-.04 *
-.13
**
.11
**
-.003
-.03 *
-.12
**
.12
**
stable
limited
-.02
-.02
.18 **
-.15
**
-.01
.01
.18
-.15
**
-.03 *
.006
.16
**
-.13
**
-.005
-.002
.18
**
-.15
**
stable no
limits
-.02
.03 *
-.27
**
.24**
-.05
-.17
-.26
#*
.25
-.007
.0005
-.25
**
.23
**
-.05
.008
-.27
**
.26
**
*p<.001 p < .05
Source: AHEAD, Waves 1 and 2


69
Functional Limitations
As shown in Table 9, the changes in five functional limitations vary in the
strength and significance of their correlation with one another. The strongest correlation
is between the measure of being disabled in both waves. The correlation ranged from 18
between picking up a dime and walking, climbing, and lifting (between picking up a dime
and pushing/pulling the correlation was 16) to .56 between pushing/pulling and lifting.
This may indicate that respondents suffer from several functional limitations at one time
and that they follow a similar trajectory.
The correlations of the same functional state for each measure of functional
performance are, for the most part, strongly and significantly correlated with each other,
except for picking up a dime. This functional performance may be fundamentally
different from the other measures of functional ability. The other all involve the lower
body, and pushing/pulling and lifting involves the upper and lower body with grosser
muscle movements. Picking up a dime is the only measure that requires upper body
mobility solely and depends on dexterity and fine motor skills. It requires more discrete
movement and may indicate a more serious problem than the other measures of
functional performance.
We gain additional insight by looking at each of the functional abilities on its own
rather than as combined in an index. The functions have differences and shared features
that provide useful information about the respondents. For example, the stable state with
limitations is strongly correlated between walking and climbing (.53) and between lifting
and pushing/pulling (.56). These two functional abilities share characteristics in that they
use similar parts of the body. It appears that when a respondent has limitations with one
functional measure they may also have difficulty with another. Limitations with walking


70
are also strongly correlated with limitations in lifting (.5). This indicates that difficulty
with walking is connected with difficulties in other functions. Additionally, the
correlations demonstrate that while respondents have a tendency to have more than one
functional limitation at a time, the other strong correlations are between the functional
abilities in the stable state without limitations. The correlation there range from .59 to a
low of .23 with picking up a dime.
Table 10. Correlation Matrix: Correlation with SES Variables
Pearson Correlation Coefficients N = 6237
Total Household
Income
Net Worth
(assets-debt)
decline
-.044 **
-.046 **
recovery
.018
.025 *
stable limited
-.145 **
. 209 **
stable no limits
.173 **
.238 **
ox
decline
-.055 **
-.064 **
Z
recovery
.071 **
.088 **
E
a
stable limited
-.168**
-.231 **
U
stable no limits
.216 **
.284 **
> n
decline
-.036 *
-.046 **
.5 .5
recovery
.021
.016
cn a
= £
stable limited
-.141 **
. 193*.
stable no limits
.165 **
.217**
decline
-.064 **
-.061 **
1
recovery
.042 **
.053 **
2
stable limited
-.136**
-.228 **
stable no limits
.187**
.271 **
decline
-.048 **
-.072 **
3
recovery
.054 **
.093 **
£
stable limited
-.026 *
_ 049 **
stable no limits
.078 **
.130 **
**p<.001
* p < .05
Source: AHEAD, Waves
Socioeconomic Status
In Table 10, the correlations between SES and changes in functional limitations,
show a significant, if modest, relationship between income, net worth and the measures


71
of change in functional limitations. The correlation coefficients indicate that functional
abilities are less likely to be limited with increasing income and net worth. Maintenance
of non-limited state is correlated with higher SES. Similarly, stasis in a limited state is
associated with lower values of the SES measures. Transitions are more weakly
correlated with SES than static states. SES correlates more frequently with functional
decline than recovery, based on the number of significant correlations. Functional
declines are concentrated among lower values of SES. In contrast, higher values of
income and net worth are associated with recovery.
Health Behaviors
The measures for health behavior used in this study are smoking status in wave 1
(former, current, or non-smoker), number of alcoholic drinks consumed in an average day
at wave 1 (abstains, less than 1 2 is a light drinker, and 3 or more is a drinker), body
mass index (BMI) at wave 1, participation in vigorous physical exercise three times a
week in wave 2, and participating in preventive care (measured by number of preventive
health care screenings for cholesterol, breast, cervical or prostate cancer, high blood
pressure or a flu shot) in wave 2, social networks (number of children, marital status) and
years of education. The correlations are shown in Table 11.
Stable States. The strongest and significant correlations are between the stable
groups (no change in functional limitations between wave 1 and wave 2) and education,
drinking, exercise and participating in preventive screens. The stable states seem more
affected by health behaviors than are the transition states. Drinking behavior is measured
in wave 1, while exercise and preventive behavior were first asked of respondents in


72
wave 2. Abstaining and heavy drinking respondents are more likely to experience
functional limitations than are light drinkers, those who exercise vigorously, are educated
and those who engage in preventive behaviors. This is consistent with previous research
that pointed to a U-shaped relationship between drinking behavior and disability
(Gronbaek et al. 1998). In other words, light drinking is less correlated with health
problems than is abstaining or heavy drinking.
Additional health behaviors that are statistically correlated with the stable states
of most of the functional measures are weight and smoking. Being underweight or
overweight is associated with increases in functional limitations, especially with walking,
while normal weight is positively correlated with remaining stable with no functional
limitations over both waves. Being a former smoker is positively correlated with
remaining stable without functional limitations, but being a current non-smoker is not.
Smoking is correlated with difficulties in walking while being a former or non smoker is
more highly correlated with difficulties in climbing stairs.
Transition States: Decline and Recovery. Exercise is significantly correlated
with all states of functional limitations. The association differs depending on functional
ability. Thus, exercise and recovering from functional limitations go hand-in-hand, as
does lack of exercise and decline in functional ability. Drinking alcohol is significantly
correlated with these functional states as well. Just as with the stable group, respondents
who abstain from alcohol are more likely to experience functional limitations, while
those who drink fewer than three drinks per day are less likely to experience limitations.


73
Covariates
The covariates used in the study are sex, age, race/ethnicity, number of medical
conditions, insurance coverage, doctor visits, number of prescriptions and genetic
endowments. The positive, significant correlations shown in Table 13 for the stable
status with functional limitations indicate this state is more likely for respondents who are
female, Black, older, without insurance, have many medical conditions and visits to
doctors, and are taking more medications. The opposite trends are noted for the stable
status with no functional limitations.
Strong correlations are seen for stable states of walking between age and number
of medical conditions. The same is true for climbing except that prescription use is also
strongly correlated. Pushing/pulling stable states are strongly correlated with the
respondents sex, number of medical conditions and number of prescriptions. This may
reflect womens lesser upper body strength, as lifting follows a similar pattern, with the
addition of strong correlations with age. The strongest correlations among the stable
states of picking up a dime and the covariates are number of medical conditions and
number of prescriptions. The decline and recovery status groups follow the stable, with
limitations group, but the coefficients are not as large.
Genetic Endowments. The measures here are proxies for biomedical markers.
For this research the age of parents and the number of surviving siblings is used to
indicate the positive health benefits passed on from one generation to the next. Parents
age is measured as the age at death for deceased parents or the current age of living
parents.


Table 11. Correlation Matrix: Correlation with Health Behaviors
Pearson Correlation Coefficients N=6,237 unless noted otherwise
Educa- Non Former Smoker Abstain Light Drinker Normal Over Under Exercise Social Social Preventive
tion Smoker Smoker Drinker Weight weight weight N=6,235 network: network: Screens
marital children N=6,043
status
Walk
Decline
Recovery
Stable limited
Stable no
limitations
-.035*
.017
-.169**
.189**
-.004
-.01
.02
-.02
-.009
.006
-.03*
.04**
.02
.007
.02*
-.03*
.02
-.007
.17**
-.18**
-.03*
.01
-.16**
.17**
.02
-.007
-.04*
.02
.003
.02
-.07**
.07**
.004
-.02
.04**
-.05**
-.02
-.01
.06**
-.05**
-.09**
.03*
-.3**
.35**
.03*
-.03*
.31**
-.32**
.03*
-.00001
.05**
-.07**
.02
-.002
-.12**
.1**
Decline
-.054**
-.01
-.01
.04**
.04*
-.04**
.01
.003
-.003
.006
-.1**
.03*
-.002
-.03*
X)
Recovery
.082**
-.03*
.03*
.008
-.07**
.06**
.02
.02
-.02
-.006
.058**
-.07*
-.03*
.023
I
Stable limited
-.1%**
.04**
-.04**
.0004
.18**
-.17**
-.03*
-.04**
.01
.08**
-.22**
.27**
.07**
-.13**
Stable no
limitations
.244**
-.04**
.06**
-.02
-.2**
.2**
.03*
.05**
-.02
-.07**
.29**
-.28**
-.08**
.14**
Decline
-.045**
.006
-.02
.02
.04**
-.04*
-.02
.0005
-.008
.02
-.097**
-.005
-.008
-Oil
s
Recovery
.019
.009
-.007
-.005
-.04*
.04*
.003
.008
-.005
-.008
.04**
-.07**
-.006
-.016
i
Stable limited
-.126**
.1**
-.1**
-.004
.17**
-.15**
-.06**
.01
-.04**
.08**
-.24**
.21**
.06**
-.11**
Stable no
limitations
.157**
-.09**
.1**
-.01
-.2**
.18**
.07**
-.006
.04**
-.09**
.3**
-.27**
-.05**
.11**
Decline
-.027*
-.009
-.004
.01
.03*
-.03*
-.002
.03*
-.03*
-.004
-.09**
.004
.007
-.006
c e
Recovery
.043**
.001
-.006
.007
-.05**
.05**
.01
.02
-.008
-.03*
.02
-.06**
-.02
-.009
1 g
Stable limited
-.141**
.09**
-.09**
.008
.15**
-.14**
-05**
.01
-.04*
.07**
-.24**
.26**
.05**
-.09**
4.4.
Stable no
limitations
.166**
-.07**
.08**
-.01
-.19**
.17**
.05**
-.02
.05**
-.08**
.29**
-.27**
-.06**
.08**
Decline
-.035-
.03*
-.03*
.005
.05**
-.05**
-.006
-.01
-.003
.04**
-.07**
.08**
-.01
-.004
g3 a
Recovery
.082**
-.01
.01
.006
-.05**
.05**
.01
.02
-.02
-001
.056**
-.09**
-.04**
.032*
11
Stable limited
-.043**
-.005
.002
.002
.04**
-.04*
-.019
.01
-.02
.02
-.05**
.14**
.03*
-.02
s
Stable no
limitations
.094**
-.02*
.03*
-.001
-.09**
.08**
.02
.01
.004
.02
.11**
-.18**
-.03*
.03*
pc.001
Source: AHEAD, Wave 1
p < .05


75
Table 12. Correlation Matrix: Correlation with Genetic Endowments
Pearson Correlation Coefficients
Mothers Age
N=5,823
Fathers Age
N=5,657
# Siblings
N=6,220
decline
-.01
.007
-.006
recover
.005
-.004
.02
$
stable limited
-.06**
-.03*
-.04*
stable no limits
.07**
.02*
.05**
00
decline
-.005
.03*
-.005
.9
§
recovery
-.005
.03*
.0006
9
stable limited
-.07**
-.008
-.04**
stable no limits
.06**
.009
.04**
decline
-.002
.01
.02
.9 .9
recovery
.01
.03*
.002
!£
stable limited
-.04*
-.02
-.05**
stable no limits
.04*
.02
.03*
decline
-.04**
-.01
-.02
1
recovery
-.009
.01
-.002
stable limited
-.03*
-.01
-.05**
stable no limits
.05**
.03*
.05**
§
decline
-.04*
-.001
-.02*
00
recovery
.04*
-.008
-.003
1
stable limited
-.0001
.013
.002
a.
stable no limits
.05**
-.01
.02
** p < .001 p < .05
Source: AHEAD, Wave 1
Mothers age and number of siblings are negatively correlated with the measures
of functional limitation (Table 12). The strongest relationship is between walking,
climbing and pushing/pulling in the stable states. These tasks all require large muscle
groups. Fathers age is not as strongly related and is significant for the stable states in
walking function and the decline/recovery in climbing stairs. All the significant
correlations indicate that less functional limitations and recovery are related to increasing
age in parents and higher numbers of surviving siblings.


Table 13. Correlation Matrix: Correlation with Covariates
Pearson Correlation Coefficients Number of Observations^,237, Prescriptions N=5,262
Respond
ents Sex
Non-Latino
White
Black
Other
Age
Medical
Conditions
Insurance
Plan(s)
Doctor
Visit
Prescrip
tions
decline
-.007
-.04**
.04**
.01
.03*
.03*
-.02
-.004
-.002
1
recover
-.033*
.003
.004
-.01
-.002
-.03*
.004
-.02*
-.02
£
stable limited
.13**
-.06**
.07**
.01
.24**
.31**
-.05**
.08**
.3**
stable no limit
-.13**
.09**
-.09**
-.03*
-.24**
-.32**
.06**
-.08**
-.03**
decline
-.009
-.03*
.02
.008
.07**
.03*
-.05**
-.014
.02
1
recover
.-06**
.06**
-.05**
-.04*
-.03*
-.07**
.005
-.02
-.06**
CJ
stable limited
.14**
-.11**
*
*
r~-
O
.09**
.23**
.27**
-.07**
.06**
.25**
stable no limit
-.14**
.15**
-.1**
.1**
-.25**
-.28**
.09**
-.05**
-.25**
decline
.03*
-.03*
.03*
.002
.04*
.004
-.04**
.003
.004
f.F
recover
-.05**
.03*
-.01
-.02
-.009
-.06**
.01
-.03*
-.04*
12
stable limited
.23**
-.05**
.04**
.02*
.17**
.26**
-.03*
.08**
.24**
stable no limit
-.25**
.08**
-.07**
-.04*
-.18**
-.27**
.06**
-.09**
-.25**
decline
.07**
-.04**
.06**
.009
.05**
-.005
-.02
-.007
-.01
I
recover
-.05**
.03*
-.008
-.04*
-.02
-.07**
-.008
-.02
-.06
3
stable limited
.25**
-.06**
.06**
.01
.22**
.27**
-.05**
.08**
.26**
stable no limit
-.28**
.1**
.1**
-.03*
-.24**
-.27**
.06**
-.08**
-.26**

decline
.02
-.02
.03*
-.004
#
*
t''

.08**
-.02*
.01
.05**
00 u
g g
recover
-.02
.06**
-.05**
-.03*
-.07**
-.09**
.07**
-.03*
-.06**
II
stable limited
-.005
-.03*
.04*
-.004
.08**
.14**
-.02
.03*
.11**
stable no limit
-.02
.07**
.07**
-.013
-.13**
-.18**
.07**
-.04**
-.14**
** p<.001 *p<,05
Source: AHEAD, Wave 1 and Wave 2


Table 14. Correlation Matrix: Correlation between all variables
a)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(ID
(12)
(13)
(14)
(15)
(16)
(1) Income
1.0
(2) Net Worth
.5**
1.0
(3) Education
.4**
.5**
1.0
(4) Non Smoker
-.06**
-.06**
-.03*
1.0
(5) Former Smoker
.09**
.09**
.05**
-.82**
1.0
(6) Smoker
-.04**
-.05**
-.02*
-.32**
-.28**
1.0
(7) Abstainer
-.22**
-.27**
-.26**
.20**
-.17**
-.07**
1.0
(8) Light Drinker
.21**
.26**
.26**
-.17**
.15**
.03*
-.96**
1.0
(9) Drinker
.05**
.03*
.02
-.11**
.04**
.11**
-.16**
-.13**
1.0
(10) Exercise
.11**
.17**
.14**
-.02
.04**
-.04**
-.1**
.11**
-.009
1.0
(11) Screens
.19**
.23**
.18**
-.09**
.13**
-.08**
-.12**
.12**
-.009
.11**
1.0
(12) Normal Weight
.04**
.06**
.1**
.02
-.06**
.06**
-.02
.02
.002
.05**
-.05**
1.0
(13) Underweight
-.05**
-.07**
-.05**
.01
-.05**
.06**
.05**
-.05**
-.01
-.05**
-.08**
-.18**
1.0
(14) Overweight
-.06*
-.03*
-.08**
-.02
.07**
-.08**
-.0004
.001
.002
-.03*
.08**
-.9**
-.21**
1.0
(15) Mothers Age
.07**
.09**
.09**
.004
-.0004
-.007
-.03*
.03*
.01
.04*
.04*
.02
.009
-.03*
1.0
(16) Fathers Age
-.01
.002
.008
.003
.003
-.009
.02
-.01
-.03*
.009
-.004
-.001
.01
-.003
.08**
1.0
(17)# of Siblings
-.05**
-.05**
-.17**
.02
-.03*
.02
.06**
-.05**
-.02*
.05**
.03*
-.03*
-.02
.04*
.03*
.09**
(18)# of Children
-.08**
-.15**
-.24**
-.03*
-.02
-.006
.09**
-.08**
-.005
-.01
-.03*
-.09**
-.006
.09**
-.03*
.01
(19) Respondents Sex
-.17**
-.17**
-.02
.38**
.35**
-.06**
.17**
-.13**
-.11**
-.12**
-.23**
.05**
.09**
-.09**
-.04**
-.02*
(20) White
.22**
.35**
.39**
-.03*
.04**
-.02
-.17**
.17**
.004
.1**
.09**
.12**
-.03*
-.1**
.07**
-.01
(21) Black
-.17**
-.27**
-.24**
.04*
-.04**
.01
.14**
-.15**
.007
-.07**
-.06**
-.1**
.008
.09**
-.06**
.01
(22) Other
-.13**
-.19**
-.3**
.002
-.007
.009

X

-.08**
-.02
-.07**
-.05**
-.05**
.03*
.04*
-.03*
.003
(23) Age
-.15**
-.18**
-.15**
.16**
-.09**
-.12**
.12**
-.1**
-.07**
-.16**
-.21**
.11**
.07**
-.14**
-.05**
.02
(24) Married /Partner
.32**
.35**
.14**
-.14**
.15**
-.01
-.13**
.12**
.04*
.1**
.19**
-.02
-.05**
.04**
.04*
-.006
(25) # Medical Cond.
-.08**
-.13**
-.11**
-.06**
.06**
-.006
.12**
-.11**
-.02
-.14**
.09**
-.06**
.008
.05**
-.03*
-.007
(26) # Medications
-.03*
-.08**
-.03*
.004
.03*
-.06**
.11**
-.1**
-.04*
-.18**
.14**
-.06**
.004
.06**
-.06**
-.03*
(27) Gov. Insurance
.05**
.03*
.03*
-.02
.008
.03*
-.008
.005
.012
.007
.03*
.005
.003
-.006
.03*
.01
(28) Medicaid
-.26**
-.37**
-.35**
.02*
-.03*
.02
.16**
-.16**
-.02
-.10**
-.08**
-.07**
.06**
.05**
-.05**
.009
(29) Private Insurance
.13**
.18**
.19**
-.008
.03*
-.04*
-.08**
.09**
-.007
.05**
.1**
.03*
-.03*
-.02
.02
-.01
(30) Doctor Visit
.04*
.04**
.04*
-.005
.07**
-.11**
-.004
.011
-.02
-.04*
.25**
-.03*
-.02
.04*
-.05**
-.005
*p<.01 **p<001


Table 14. Correlation Matrix (continued)
cm
(1) Income
(2) Net Worth
(3) Education
(4) Non Smoker
(5) Former Smoker
(6) Smoker
(7) Abstainer
(8) Light Drinker
(9) Drinker
(10) Exercise
(11) Screens
(12) Normal Weight
(13) Underweight
(14) Overweight
(15) Mothers Age
(16) Fathers Age
(17) # of Siblings 10
(18) # of Children .08"
(19) Respondents Sex 03*
(20) White -.07**
(21) Black .01
(22) Other .09**
(23) Age -.17**
(24) Married/Partner 01
(25) # Medical Cond. -03*
(26) # Medications -.05**
(27) Gov. Insurance 02
(28) Medicaid .01
(29) Private Insurance 03*
(30) Doctor Visit-.03*
(18) (19) (20) (21)
1.0
-.009
1.0
-.19**
-.03*
1.0
.08**
.04**
-.8**
1.0
.2**
-.01
-.54**
-.1**
-.07**
.08**
.007
.0003
-.006
-.38**
.14**
-.14**
.06**
-.03*
-.05**
.04*
.02*
.07**
.01
-.009
-.02
-.06**
-.01
.02
.22**
.09**
-.32**
.19**
-.09*
.001
.22**
-.16**
.02
.02
-.004
.007
(22) (23) (24) (25)
1.0
-.01
1.0
-.03*
-.23**
1.0
.03*
.04*
-.05**
1.0
-.007
.04**
-.03*
.47**
-.007
-.04*
.04**
.01
.25**
.07**
-.17**
.12**
-.14**
-.01
.09* *
.003
-.004
.009
.02
.2**
(26) (27) (28) (29)
1.0
.00006
1.0
.09**
-.06**
1.0
.04*
-.05**
.2**
1.0
.27**
-.0006
.02
.09**
(30)
1.0
oo
p<.01 **p<.001


79
Overall Correlations
Table 14 shows the correlations between the variables used in this research. First
we note that some multicollinearity exists between certain variables, such as between
income, net worth and education. The resulting models may be less precise and have
larger standard errors. To offset this difficulty, regression models will be estimated and
tested in a step-wise fashion to determine the effects on the significance of the estimates
and the standard errors. As a result, the regression modeling will use either income or net
worth as an indicator of SES.
Education is used as a health behavior, as it indicates self-efficacy. Other
correlations with income and net worth show a positive correlation with respondents who
are White, male, married or co-habiting and of younger ages. Negative correlations
among health behaviors are seen among respondents who abstain from drinking, are
overweight or underweight and with increasing number of children. Among the
covariates, income and net worth are negatively correlated with Medicaid, number of
siblings, higher numbers of medical conditions, and higher numbers of prescriptions.
Other examples of multicollinearity exist between number of medical conditions
and number of medications and net worth, education and Medicaid coverage. However,
each measure adds to the theoretical basis of the model. These variables are included for
control purposes and so the research is not hampered by more conservative tests of
significance that may result from the multicollinearity
Indicators of the connection between education and positive health behaviors are
seen here with the significant positive correlation between education and normal weight,
light drinking, exercise, participation in preventive screens and former smoker, as well as
the negative correlation between education and number of medical conditions. This is an


80
additional indication that education can be used as an indicator of self-efficacy and
motivation to engage in preventive health behaviors.
Response to Hypotheses
The data indicate that the first hypothesis, that a significant proportion of older
adults are free of functional limitations and do experience recovery from limitations when
they do exist, is confirmed by the transition matrices. For walking, pushing/pulling, and
lifting, just under one-half of the respondents are free of functional limitations in both
waves of the AHEAD data set and between 17% to 25%, depending on the measure of
functional ability, experienced improvement in their functional status between waves.
Respondents had more difficulty with pushing/pulling and lifting and much less difficulty
with picking up a dime.
The second, third, and fourth hypotheses, that individuals with greater economic
resources: 2) have stronger functional status; 3) are less likely to suffer decline; and 4)
are more likely to recover, are also supported by the correlation coefficients. The
coefficients indicate the individuals with more economic resources are less likely to have
limitations in either wave. The largest coefficients are for climbing, lifting, and walking.
The signs of the coefficients also indicate that greater income and net worth are
associated with better functional ability, less likelihood of decline, and greater likelihood
of recovery.
Likewise, the fifth hypothesis is partially supported by the bivariate data. This
hypothesis is that income is a better predictor of recovery than is net worth for the
transition states of decline and recovery and net worth is a better predictor of stasis states.
According to the correlation coefficients, net worth is more strongly correlated with all


81
functional states than is income. The coefficients for the correlations are larger for net
worth than for income, indicating that net worth has more influence on functional
limitations than does income.
The sixth hypothesis, that intervening health behaviors will modify the
relationship between SES and health, will be addressed through multinomial logistic
regression. However the correlation coefficients in Table 14 show us the health
behaviors do vary by income and net worth. Education is strongly and positively
correlated with both income and net worth. Exercising, being a light drinker and
participating in preventive screens are also strongly, positively correlated with the
measures of SES. The measure of health behavior with a strong negative correlation
with SES is abstaining from alcohol. Other negative correlations are number of children,
being underweight or overweight, being a current smoker or a non-smoker. Being a
former smoker is positively correlated with SES.
Health behaviors also have influence on functional status, especially in the stable
states. Education, light drinking (versus abstaining), exercise and participating in
preventive screens are all positively correlated with remaining free of functional
limitations in both waves, and negatively correlated with the stable state with functional
limitations, especially for walking and climbing. Education, exercise and light drinking
are also strongly correlated with lifting and pushing/pulling, but not as strongly correlated
with picking up a dime. The presence of a social network is significantly correlated with
all the stasis states, but the correlation coefficient is smaller than for other measures of
health behavior. The same is true of the BMI and smoking variables. There are


82
indications even at the bivariate level that health behaviors have strong influence on
functional limitations.


CHAPTER 5
PREDICTING CHANGE IN FUNCTIONAL PERFORMANCE
Multinomial Logistic Regression Modeling
The correlation coefficients all indicate that recovery is more likely with greater
economic resources, as stated in the second hypothesis. In order to determine the exact
relationship as well as any intervening influences of health behaviors (the fourth
hypothesis), it is necessary to look at multiple variable modeling. In this case,
multinomial logistic regression modeling was used. The purpose of the modeling is to
determine the maximum likelihood of obtaining the particular data that are actually
observed (Power and Xie, 2000). This type of modeling is the comparison of
membership in the four categories established. As a result, membership in each category
is compared to the probability of membership in the reference category, in this case,
being stable in each wave with no functional limitations.
Through testing the models and looking at the correlation matrices, it appeared
that household income, net worth, and years of education were too correlated to use
together in the logistic regression model. First, education is kept in the models because,
while it is associated with SES, it is also a predictor that indicates motivation to engage in
preventive behavior, which may have a different affect on health recovery. It is a
measure of self-efficacy in this research and, thus, part of the set of variables measuring
health behaviors. Net worth is a stronger predictor of functional ability than is household
income in these models (p<,001). Therefore, the modeling was done using net worth as
the indicator of SES and years of education as a health behavior variable.
83


There are two regression models. The first model tests regressing functional
ability on net worth and the control variables. The second model adds preventive health
behaviors, including education. The results of comparing the first and second models
show that the second, saturated model is more predictive of functional limitations
(pc.001). Therefore, the following description is of the complete model that includes the
preventive health behavior variables.
Model Comparisons
Stable with Functional Limitations
Comparing the models of functional abilities illustrates the benefits of analyzing
each functional measure separately. Although walking and climbing are similar, there are
differences between them as among picking up a dime, lifting and pushing/pulling. This
section presents comparisons of both stable states (see Table 15). The only variables
statistically significant for all five functional abilities are age, number of medical
conditions, and number of prescriptions, which are all control variables.
By comparing odds ratios (coefficients are reported in Appendix B), we can
determine the risk of falling into the stable category with no limitations versus the stable
category with limitations. A positive coefficient and odds ratio grater than 1 signals
higher odds of be in the stable category with limitations A negative coefficient and odds
ratio less than one indicates higher odds of being stable with no functional limitations.
Socioeconomic Status. The measure of SES, net worth, is significant for all
functional limitations except for picking up a dime. The odds ratio indicates that the odds
of a stable state with functional limitations is higher among the older adults with a lower


85
net worth, especially for climbing stairs and lifting (.88). Walking and pushing/pulling
odds are slightly better at .93/.92.
Table 15. The Odds Ratios of Being in a Stable with Limitations State.
Walking
Climbing
Stairs
Lifting
Pushing/
Pulling
Picking up a
dime
SES
Net Worth
93**
.88**
.88**
.92**
.98
Health Behaviors (ref. categories: Smoking = non-smoker; Drinking = abstains; Weight = normal wt.)
Education
.97*
.97*
1.00
.98
1.01
Smoker
2.79**
1.69*
1.64*
1.64*
1.19
Former Smoker
1.47**
1.26*
1.22*
1.05
.99
Light Drinker
.67*
.60**
.67**
69**
.90
Drinker
.58
.41*
.28*
.36*
.30
Preventive Screens
.95**
.95**
.98
.99
.99
Exercise
.17**
.22**
.27**
.27**
.78
# Children
1.03
1.04*
1.05*
1.04
1.02
Married/ Cohabiting
1.05
1.11
1.16
1.25*
.78
Underweight
1.38
1.63*
2.12**
1.64*
1.20
Overweight
1.67**
1.37**
.83*
.83*
.87
COVARIATES
Genetic Endowments
Fathers Age
.99
1.00
1.00
1.00
1.01
Mothers Age
1.00
1.00
1.00
1.00
1.00
# Siblings
1.03
1.01
1.00
.98
1.10*
Respondents Demographics (referent category: Race = White)
Sex (l=female)
1.72**
1.96**
6.48**
4.69**
.71
Black
.86
.95
.90
.75
1.27
Other (Latino, Asian,
or Native American)
.61*
1.65*
.59*
.85
.40
Age
1.12**
1.11**
1.10**
1.06**
1.07**
Medical Conditions
2.09**
2.01**
1.91**
2.07**
1.63**
Utilization Issues (referent category: Insurance = Government Insurance (Medicare/CHAMPUS))
Medicaid
1.22
1.21
1.85**
1.21
1.55
Private Pay
.75
.71
.67*
.63*
.65
Doctor Visit
.99
1.08
1.08
.96
.99
Prescriptions
1.41**
1.28**
1.28**
1.31**
1.16**
Intercept Coefficient
Constant
-9.16
-9.81
-9.39
-6.33
-9.57
Model Log Likelihood
3689.853
3417.529
3365.676
3635.678
1430.555
N=4,977
*p<05 *p<001
Source: AHEAD, Waves 1 and 2.


86
Health Behaviors. Exercise is the most frequent significant health behavior
predictor. The odds ratio for exercise is approximately .2 for walking, climbing, lifting
and pushing/pulling, indicating that those who do not exercise are 80% more likely to
have functional limitations. Unfortunately the question regarding exercise was not asked
in wave 1, so it is difficult to determine if exercisers are more likely to be non-limited or
if the non-limited are more likely to exercise. However, this is an indication that
engaging in vigorous physical activity is beneficial to maintaining functional ability.
All the preventive health behaviors are significant for the functional ability of
climbing stairs, except for marital status. The highest odds ration of 1.69 is for smokers
as compared to non-smokers. This indicates that smokers are 69% more likely to report
difficulty in climbing stairs than non-smokers are. This is similar to the odds ratios for
being underweight as compared to normal weight. There, individuals who are
underweight are 63% more likely to report difficulty with climbing stairs than normal-
weight older adults are. This may reflect a loss of muscle when losing weight as one
ages. Similar results are seen with lifting and pushing/pulling, which may also reflect a
loss of muscle mass. Social networks is another health behavior that is positively
correlated with climbing and lifting limitations.
Overweight individuals have similar limitations with walking and smokers have
nearly 200% more difficulty walking than non-smokers do. Walking does not require as
much muscle, and lack of lung capacity may be more influential here reflected in the
odds ratios for smokers, even former smokers, and overweight older adults. Persons who
regularly complete preventive health screens are less likely to report walking and
climbing limitations; otherwise, preventive screens do not distinguish between stability in
limitations of upper body functions.


87
Drinking is also a significant predictor of stability in functional ability for all
functions, except for picking up a dime. None of the health behaviors odds ratios are
statistically significant for picking up a dime. The odds ratios for drinking indicate that
abstainers are 30 to 40% more likely to suffer functional limitations than light drinkers.
These results mirror the correlation coefficients that indicate that abstaining has more
negative consequences. This is also true of older adults who consume more than 3 drinks
a day, which is a very small proportion of the respondents, for climbing, lifting and
pushing/pulling.
Covariates. Measures of genetic predisposition included parents age at death, or
current age if still alive, and number of siblings. None of these measures are statistically
significant except for number of siblings with picking up a dime. The odds ratios here
are 1.10, indicating that persons who have more siblings have more difficulty picking up
a dime, This is counter to the expectations that a strong genetic heritage as measured by
many siblings would result in better health at older ages. This is also a change from the
bivariate analysis in which a number of the correlation coefficients, especially those
revealing the effects of mothers age at death, were significantly correlated with the
measures of functional limitations.
Unlike the correlation coefficients in the bivariate analysis there are no significant
differences between whites and African Americans. The race/ethnicity category of
Other, which includes Latino, Asian American, and Native Americans, is statistically
significant, and indicate that these respondents are less likely to have functional
limitations in walking and lifting than the white respondents, and more likely to have
climbing limitations than white respondents. The other demographic variables that are
significantly predictive are age and sex. Women are more likely than men to have


88
walking, lifting and pushing/pulling limitations. For all functional limitations, each
additional year of age raised the risk of having limitations in waves 1 and 2 by
approximately 10%.
Forms of insurance are also statistically significant for certain measures of
functional limitations. Limitations in walking, climbing or picking up a dime is unrelated
to type of insurance. Respondents with Medicaid are more likely to suffer limitations as
compared to the reference group, which has Medicare or CHAMPUS, for climbing (19%)
and lifting (79%). Individuals with private pay supplemental coverage are less likely to
have functional limitations with lifting (34%) and with pushing/pulling (37%) than
individuals with Medicare or CHAMPUS only. It may be that this is not significant for
all measures because so many of this group have some form of coverage. The presence
or absence of coverage does not vary much.
From the odds ratios, it appears that the extra- individual factors of the
disablement process model (Verbrugge and Jette, 1993), or the health care access and
utilization factors, are the most predictive of functional limitations. Each additional
prescription raises the odds by roughly 30% of remaining with functional limitations
(16% for picking up a dime), and each additional medical conditions doubles the risk,
holding all other variables constant.
Decline in Functional Status
This section describes the contrast between functional decline and stable with no
limitations respondents (see Table 16).


Full Text
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