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Falls among older adults: characteristics of fallers, predictors of falls, and the impact of falls on health care and long-term care utilization

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Falls among older adults: characteristics of fallers, predictors of falls, and the impact of falls on health care and long-term care utilization

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FALLS AMONG OLDER ADULTS:
CHARACTERISTICS OF FALLERS, PREDICTORS OF FALLS, AND THE
IMPACT OF FALLS ON HEALTH CARE AND LONG-TERM CARE
UTILIZATION

by

In Hee Choi
________________________________________________________

A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(GERONTOLOGY)

December 2008

Copyright 2008 In Hee Choi

ii
DEDICATION
To my father Dr. Kyu-Hong Choi and my mother Dr. Young-Joo Kim
with all my love and appreciation

iii
ACKNOWLEDGEMENTS
I would like to express my deepest and most sincere gratitude to my
dissertation committee for their thoughtful guidance and continuous support:
Dr. Timothy Biblarz, Dr. Eileen Crimmins, and Dr. Jon Pynoos. I would
especially like to thank Dr. Pynoos, my advisor, for his tremendous guidance,
encouragement, support and mentorship: I have been blessed with the
privilege and honor of working with him. I would also like to thank Dr. Larry
Rubenstein and Dr. Gerald Jones for their mentorship and willingness to share
their time in support of this project.
In addition, I would like to thank my colleagues, faculty members,
friends and staff members at the USC Davis School of Gerontology, and my
friends and mentors in both the United States and Korea for their
encouragement, friendship, and support.
I’m indebted to my parents, Dr. Kyu-Hong Choi and Dr. Young-Joo Kim,
and my brother Jong-Moon Choi, for their endless love, support, and prayers.
I would also like to thank my grandparents for their love and inspiration, and
aunt April Cho and uncle Joe Cho for their love and support. I would like to
extend special thanks to my fiancé, Won-Yong Kim, and my
parents-in-law-to-be for their love, patience, and prayers.
Lastly, I would like to thank God for being my Lord, and for His
unconditional love and guidance throughout my life.

iv
TABLE OF CONTENTS

Dedication

ii
Acknowledgements

iii
List of Tables

v
List of Figures

vii
Abstract

viii
Chapter I: Introduction

1
Chapter II: Characteristics of community-dwelling older adults who
fall

14
Chapter III: Predictors of falls among older adults from different
racial/ethnic backgrounds

47
Chapter IV: Impact of falls on health care and long-term care
utilization

86
Chapter V: Conclusion

143
Bibliography

156

v
LIST OF TABLES

Table 2.1: Characteristics of the Study Population by Fall Status (N
= 11,851)

29
Table 2.2: Odds Ratios for One-Time Fallers and Multiple-Time
Fallers Relative to Nonfallers (N = 11,851)

33
Table 2.3: Characteristics of Fallers Who Did or Did Not Talk about
Their Falls with Health Care Professionals (n = 2,634)

36
Table 3.1: Characteristics of the Study Population (N = 11,442)

67
Table 3.2: Multivariate Models of Fall Predictors (N = 11,442)

72
Table 3.3: Multivariate Models of Fall Predictors among Three
Racial/Ethnic Groups

77
Table 4.1: Baseline Characteristics of the Study Population by Fall
Status: Frequency of Falls (N = 7,287)

105
Table 4.2: Baseline Characteristics of the Study Population by Fall
Status: Severity of Falls (N = 7,287)

109
Table 4.3: Comparison of Health Characteristics in 2004 by
Frequency of Falls in 2002 (N = 7,287)

112
Table 4.4: Comparison of Health Care Use in 2004 by Frequency of
Falls in 2002 (N = 7,287)

113
Table 4.5: Comparison of Health Characteristics in 2004 by
Severity of Falls in 2002 (N = 7,287)

115
Table 4.6: Comparison of Health Care Use in 2004 by Severity of
Falls in 2002 (N = 7,287)

116
Table 4.7: Results of Multiple Logistic Regression Predicting the
Use of Hospital Care: Falls as Predictors of Any
Hospitalization in 2004 (N = 7,287)

118
Table 4.8: Results of Multiple Logistic Regression Predicting the
Use of Nursing Home: Falls as Predictors of Any Nursing
Home Use in 2004 (N = 7,287)

121

vi
Table 4.9: Results of Multiple Logistic Regression Predicting the
Use of Home Health Care: Falls as Predictors of Any
Home Health Care Use in 2004 (N = 7,287)

124
Table 4.10: Unstandardized Ordinary Least Squares Regression
Estimates Predicting Number of Hospitalizations in 2004
(N = 7,287)

127
Table 4.11: Unstandardized Ordinary Least Squares Regression
Estimates Predicting Number of Physician Visits in 2004
(N = 7,287)

130
Table 4.12: Regression Results on Health Care and Long-Term
Care Utilization in 2004: Frequency of Falls as
Predictors of Health Care and LTC Use (N = 7,287)

133
Table 4.13: Regression Results on Health Care and Long-Term
Care Utilization in 2004: Severity of Falls as Predictors
of Health and Long-Term Care Use (N = 7,287)
136

vii
LIST OF FIGURES

Figure 1.1: Falls Model

12
Figure 2.1: Percentage of Fallers among 2002 MCBS - Access to
Care Data (N = 11,851)

28
Figure 2.2: Percentage of Fallers Who Talked about Their Falls
with Health Care Professionals (n = 2,634)

35
Figure 2.3: Percentage of Fallers Whose Health Care
Professionals Tried to Understand Why They Had
Fallen When Fallers Reported Their Falls (n = 1,213)

38
Figure 2.4: Percentage of Fallers Who Talked about Their Falls
with Health Care Professionals and Received
Information on How to Prevent Falls (n = 1,213)

39
Figure 4.1: Analytic Model Predicting the Effect of Falls on Health
Care and Long-Term Care Utilization

103

viii
ABSTRACT
This dissertation profiles the characteristics of community-dwelling
older adults who fall (hereafter, fallers); investigates predictors of falls by
frequency of falls and by race/ethnicity; and examines the extent to which
fallers talk about their falls with health care professionals, and the
consequences of falls on subsequent health care and long-term care (LTC)
utilization.
By using nationally representative samples of the U.S. population, the
results demonstrate that fallers were more likely to be older, be female, be
non-Hispanic White, live alone, have poorer health characteristics, have
poorer socioeconomic characteristics, and have more supportive features at
home compared to their nonfaller counterparts. With respect to the predictors
of falls, the findings show that being female, having functional limitations, and
having certain diseases/chronic conditions were significant predictors of falls
for both one-time and multiple-time fallers. In addition, the study found that
being non-Hispanic African American or Hispanic decreased the likelihood of
experiencing a fall, and the relative magnitude of predictors of a fall varied
slightly across racial/ethnic groups.
With respect to the extent to which fallers talk about their falls with
health care providers, less than half of the fallers in this study talked about
them with health care providers, and those who did were more likely to be
older, to be female, to be unmarried, to live in a metropolitan area, to have

ix
poorer health, and to have higher numbers of falls and injurious falls compared
to those who did not talk about their falls. In terms of the consequences of falls
on health care and LTC utilization, falls in 2004 were significantly associated
with higher health care and LTC utilization in 2004. However, falls in 2002
were generally not associated with service utilization in 2004, whereas
injurious falls in 2002 had a significant positive association with higher number
of physician visits in 2004.
Given that falls among older adults are sentinel events, and in light of
the significant impact falls have on health care and LTC utilization, effective
and targeted fall prevention programs are needed to prevent and reduce falls
among older adults by ameliorating and modifying risk factors for falls.

1
CHAPTER I: INTRODUCTION
Falls among older adults are an important public health problem given
their high prevalence, serious consequences in terms of morbidity and
mortality, as well as high health care and long-term care (LTC) costs. The
objectives of my dissertation are threefold: (a) to profile the characteristics of
older adults who fall and how these adults communicate with health care
professionals about their falls; (b) to examine the predictors of falls among
older adults from different racial/ethnic backgrounds; and (c) to analyze the
effect of falls on health care and LTC utilization, using a nationally
representative sample of older adults in the United States.
This will help researchers and policy makers to (a) understand the
characteristics of community-dwelling fallers in order to identify the at-risk
segment of the population, which in turn will aid in the development of future
fall prevention programs and policies in terms of better targeting and better
design by providing specific socio-demographic patterns and comorbidities of
falls among older adults; and (b) examine the extent to which falls among older
adults are a public health problem by investigating the impact of falls on health
care and LTC utilization.
A. Definition of a Fall
Although a number of studies have developed definitions of a fall, there
is no universally accepted definition or consensus as to what the definition of a
fall should be (Zecevic, Salmoni, Speechley, & Vandervoort, 2006). One of

2
the most widely adopted definitions, developed by the Kellogg International
Working Group on the Prevention of Falls in the Elderly, defines a fall as “an
event which results in a person coming to rest inadvertently on the ground or
other lower level and other than as a consequence of the following: sustaining
a violent blow, loss of consciousness, sudden onset of paralysis as in a stroke,
or an epileptic seizure” (Gibson, Adres, Isaacs, Radebaugh, &
Worm-Peterson, 1987, p. 4).
Likewise, similar—but broader and simpler—definitions of a fall have
been developed by researchers. Such definitions include, but are not limited
to, the following: “falling all the way down to the floor or ground, or falling and
hitting an object like a chair or stair” (Nevitt, Cummings, & Hudes, 1991, p.
M164); “unintentionally coming to rest on the ground, floor, or other lower
level; excludes coming to rest against furniture, wall, or other structure”
(Buchner et al., 1993, p. 300); “losing your balance such that your hands,
arms, knees, buttocks or body touch or hit the ground or floor” (Berg, Alessio,
Mills, & Tong, 1997, p. 262); “any events in which a person inadvertently or
intentionally comes to rest on the ground or another low level such as a chair,
toilet or bed” (Tideiksaar, 2002, p. 15); “an unintentional change in position
resulting in coming to rest at a lower level or on the ground” (Stel et al., 2003,
p. 1357); “an unexpected event in which the participants come to rest on the
ground, floor, or lower level” (Lamb, Jørstad-Stein, Hauer, & Becker, 2005, p.
1619); and “an unexpected event in which some part of a person’s body

3
comes to rest on the ground or some lower level, like a bed or chair” (Zecevic
et al., 2006, p. 370).
A fall is not specifically defined in my dissertation because the survey
data for my analyses did not define what a fall is. Specifically, the Medicare
Current Beneficiary Survey - Access to Care (2002) and Health and
Retirement Study (2002, 2004) used the following questions, respectively,
when they asked respondents about their falls: “[Since (PREV. SUPP. RD.
INT. DATE)/In the past year], (have you/has SP) fallen down?” and “Have you
fallen down [since R’s LAST IW MONTH, YEAR/in the last two years]?”
Although having a clear definition of a fall is important in that it reduces the
variability in understanding the meaning of a fall and facilitates comparison
among studies, few previous studies have defined a fall and have instead left it
to the older adults themselves to decide what a fall is (Zecevic et al., 2006).
This is partly because developing one universal definition of fall may not be
possible or necessary. Yet for future studies, it would be helpful to have a
clear, standardized, appropriate, and operational definition of a fall because it
would overcome a serious methodological pitfall for the evaluation and
interpretation of prevention strategies. It would also allow for meaningful
comparisons across research studies (Hauer, Lamb, Jorstad, Todd, & Becker,
2006).

4
B. Prevalence and Costs of Falls among Older Adults
Studies have reported that more than one third of individuals aged 65
years and older fall each year (Hornbrook et al., 1994; Rubenstein et al., 2004).
Moreover, approximately 20% to 30% of older adults who fall suffer moderate
to severe injuries that reduce mobility and independence and increase the risk
of premature death (Alexander, Rivara, & Wolf, 1992). According to the
National Center for Injury Prevention and Control (2008), unintentional fall was
the leading cause (62.1%) of nonfatal unintentional injury among individuals
aged 65 and older in the United States in 2006, followed by unintentional
struck by/against (7.6%), unintentional overexertion (6.0%), unintentional
motor vehicle accidents (5.9%), and unintentional cut/pierce (4.0%). Likewise,
falls and fall-related injuries are the leading cause of death due to injury among
older adults (J. A. Stevens, 2002/2003). More than 15,800 people aged 65
and older died from unintentional fall-related injuries in 2005 (Centers for
Disease Control and Prevention, 2008).
Falls and fall-related injuries not only have serious consequences for
older adults in terms of morbidity and mortality but are also associated with
high health care and LTC costs (J. A. Stevens, 2002/2003). According to the
Centers for Disease Control and Prevention (2008), in 2005, more than 1.8
million adults aged 65 and older were treated in emergency departments for
nonfatal fall-related injuries, and more than 433,000 of these patients were
hospitalized. In 2000, direct medical costs totaled $19.3 billion for nonfatal fall

5
injuries and $179 million for fatal fall injuries (J. A. Stevens et al., 2006). Using
data from the 1997 Medical Expenditure Panel Survey, Carroll, Slattum, and
Cox (2005) reported that the total direct medical costs (i.e., inpatient
hospitalizations, emergency room visits, office-based medical visits, hospital
outpatient visits, home health care, dental visits, prescription drugs) of treating
fall-related injuries among community-dwelling older adults amounted to $6.2
billion in 1997 ($7.8 billion in 2002 dollars), with the mean cost per injured faller
totaling $2,039 in 1997 ($2,591 in 2002 dollars). Furthermore, Stevens (2005)
reported that the total cost of fall injuries for people aged 65 and older, which
does not include the long-term consequences of these injuries (e.g., disability,
decreased productivity, reduced quality of life), is expected to reach $43.8
billion (in current dollars) by 2020. Fall-related injuries also have significant
impacts on LTC costs because such injuries often profoundly limit the
independence and lifestyle of older adults. For example, a study by Alexander
et al. (1992) showed that noninstitutionalized individuals injured in falls were
discharged to nursing care 3 times more often than individuals hospitalized for
nontrauma cases.
C. Previous Research
From a medical and health perspective, falls constitute a prevalent and
complex geriatric syndrome that often results in considerable mortality and
morbidity as well as premature nursing home admissions among older people
(Rubenstein & Josephson, 2002/2003). Fall-related injuries often result in a

6
downward spiral that includes reductions in physical activity, independence,
and confidence (Rogers, Rogers, Takeshima, & Islam, 2004). Moreover, older
people may develop an increased fear of falling in response to a fall,
regardless of the degree of fall-related physical trauma they experience; this in
turn may result in serious emotional, psychological, or social changes as well
as limits on physical activities and reduced functional abilities (Kressig et al.,
2001; Vellas, Wayne, Romero, Baumgartner, & Garry, 1997).
In general, research has provided valuable insights into the risk factors
for falls by focusing on the association between physical functioning (e.g.,
balance, mobility), health characteristics (e.g., presence of certain
diseases/chronic conditions), and the experience of falls among older adults,
as well as the effectiveness of fall prevention interventions. Many of these
studies, however, used relatively restricted samples (e.g., small numbers of
participants in clinical or community studies, lack of comparison among
different racial/ethnic groups), which limits the generalizability of their findings
to broader populations.
Moreover, it has been suggested that current fall risk models that focus
on physical measures are often time intensive and expensive to implement for
all persons with fall risk potential (Cook, 2003). This study, with its focus on
demographics, socioeconomic factors, and health characteristics, will help
health care practitioners and policy makers to identify at-risk segments of the
population and target fall prevention interventions.

7
D. Falls Model
Studies have identified the risk factors for falls, which are generally
classified as either intrinsic/personal or extrinsic/environmental (Gillespie et
al., 2005). Intrinsic risk factors generally include age-related physiological
changes, impairments to the sensory-nervous system, disorders of the
musculoskeletal system, and specific acute and chronic diseases, all of which
can have additive effects (Perell et al., 2001; Steinberg, Cartwright, Peel, &
Williams, 2000; Tideiksaar, 2001). Many age-related physiological changes,
including declines in sensory and integrative systems in older adults, as well
as acute and chronic diseases/conditions, increase the risk of falling. Extrinsic
risk factors refer to environmental hazards and obstacles that interfere with
safe mobility or hazardous activities, the absence of supportive features, as
well as medication side effects. In general, researchers have reported that the
majority of falls among older adults result from a complex interaction between
intrinsic and extrinsic risk factors (Leslie & Pierre, 1999).
With respect to intrinsic risk factors, the following conditions among
older adults are generally associated with an increased risk of falling:
advancing age; gait and balance disorders; visual impairment that is mainly
caused by cataracts, glaucoma, and macular degeneration; functional
impairment, usually indicated by the inability to perform basic activities of daily
living (e.g., dressing, bathing, eating); cognitive impairment and depression,
which may increase susceptibility to falls by impairing judgment, visual and

8
spatial perception, and orientation ability; and history of falls or injurious falls
(Rubenstein & Josephson, 2002/2003). Specifically, using the data from 16
studies providing quantitative risk of falling for individuals, Rubenstein and
Josephson reported that (a) weakness in the lower extremities is the most
potent risk factor associated with falls, increasing the odds of falling, on
average, by more than 4 times; (b) gait and balance impairments are
significant risk factors for falls, associated with about a threefold increase in
the risk of falling; (c) visual impairments increase the risk of falling by about 2.5
times; and (d) cognitive impairment and depression increase the risk of falling
by about 2 times.
The American Geriatrics Society, British Geriatrics Society, and
American Academy of Orthopaedic Surgeons (AGS/BGS/AAOS) Panel on
Falls Prevention (2001) also identified the most common risk factors for falls
as the following: muscle weakness, history of falls, gait deficit, balance deficit,
use of assistive devices, visual deficit, arthritis, impaired activities of daily
living, depression, cognitive impairment, and age 80 years old and older.
Moreover, Rawsky (1998) investigated 100 articles published from 1979 to
1996 that were related to falls among the elderly population in a variety of
settings and identified the following as the most often cited intrinsic risk
factors: cognitive impairment/psychological status, acute or chronic illness,
mobility/gait/balance problems, medication, sensory deficits, environmental
factors, and fall history.

9
Extrinsic risk factors usually refer to environmental hazards or
behavioral risk factors (e.g., hazardous activities) and to medication side
effects. Extrinsic risk factors generally create conditions that lead to trips or
slips, and they thereby pose a greater risk for community-dwelling older adults,
who may already have multiple intrinsic risk factors for falls (Perell et al., 2001).
In particular, risk factors in the home include hazards (e.g., clutter on the floor,
hazardous rugs/carpet), dangerous areas (e.g., bathroom, stairs) and lack of
supportive features (e.g., absence of grab bars and/or handrails). At the
community level, outdoor hazards include uneven pavement or surfaces,
pavement cracks, tree roots, slippery ground, obstacles in walkways (e.g.,
bike racks, garbage cans), uneven steps, building mats, unsafe stairs, poor
lighting or contrasts, and snow or ice on walks or steps (Pynoos, Sabata, &
Choi, 2005).
In general, extrinsic risk factors for falls are prevalent in the homes and
communities of older persons. For example, in their randomized controlled
study, M. Stevens, Holman, and Bennett (2001) reported that 570 older adults
aged 70 and older living in the community all had homes with at least one fall
hazard such as floor rugs, mats, stopovers, and steps. Moreover, Cesari et al.
(2002) reported that domestic environmental hazards (e.g., poor lighting,
uneven floor surfaces, absence of grab bars in the bathroom) may increase
the prevalence of falls by more than 50%; they reached this conclusion using
data from the National Silver Network project in Italy, a population-based,

10
longitudinal, observational study that was performed on all patients (N = 5,570)
admitted from 1997 and 2001 to home care programs in 19 home health
agencies.
Another important extrinsic risk factor for falls is medication use in
terms of taking psychotropic medications and polypharmacy (Ganz, Bao,
Shekelle, & Rubenstein, 2007). The relationship between medication use and
falls has been investigated in many studies, which have found that (a) the use
of psychotropic medications, antidepressants, anti-arrhythmic heart
medications, and digoxin significantly increases the risk of falling; and (b) the
use of three or more medications is clinically and statistically related to the risk
of falling (Cumming, 1998; Leipzig, Cumming, & Tinetti, 1999; Rubenstein &
Josephson, 2002/2003).
Behavioral risk factors are associated with an increased risk of falling
among older adults, although there is only preliminary evidence that
risk-taking behaviors increase fall risk among older adults (Lord, Sherrington,
Menz, & Close, 2007). Behavioral risk factors, which are related to choices
that older adults make and how they interact with their environment, include
engaging in risk-taking behavior and neglecting preventive action. For
example, using a sample of 96 community-dwelling, independent participants
who were aged 60 years or older, Berg and colleagues (1997) reported that
among older adults who had fallen, the following were the five most frequently
cited reasons for falling: hurrying too much (31%), not looking where he or she

11
was going (21%), slipping on a wet or slippery surface such as a rug (19%),
tripping over something such as a cord or a curb (19%), and looking up instead
of down at the surface on which he or she was walking (14%). Along with
predisposing personal/intrinsic and environmental/extrinsic risk factors,
engaging in risk-taking behavior (e.g., rushing) and neglecting preventive
action (e.g., leaving water, oil, or liquid spills on the floor; wearing unsafe
shoes) may increase the risk of falling among older adults.
Based on the previous research, I examine the predictors of falls
among older adults using the following framework (see Figure 1). Personal
factors (or intrinsic factors) include demographic characteristics (e.g., age,
gender, race/ethnicity, marital status, living arrangement), socioeconomic
characteristics (e.g., education, income, Medicaid eligibility, private insurance
coverage), and health status (e.g., self-rated health, number of ADL and IADL
limitations, number of mobility limitations, presence of acute and chronic
diseases/conditions, vision problems, hearing problems). Environmental
factors (or extrinsic factors) include the presence of special railings and ramps
as well as bathroom modifications. However, given the fact that information on
environmental risk factors and medication use is limited in existing data sets,
more focus is given to personal (i.e., intrinsic) factors in this study.

12
Figure 1: Falls Model
Note: ADL = activities of daily living; IADL = instrumental activities of daily living

Personal Factors

Demographic Characteristics
• Age
• Gender
• Race/ethnicity
• Marital status
• Living arrangement
• Living in an urban area

Socioeconomic Characteristics
• Education
• Income
• Medicaid eligibility
• Private insurance

Health Status
• Self-rated health
• Number of ADL limitations
• Number of IADL limitations
• Number of mobility
limitations
• Acute and chronic conditions
o Alzheimer’s disease
o Arthritis
o Depression
o Diabetes
o Heart disease
o Hypertension
o Osteoporosis
o Parkinson’s disease
o Stroke
o Urinary incontinence
• Hearing problems
• Vision problems

Environmental Factors

• Having bathroom
modification
• Presence of railings
• Presence of ramps

Experience of fall(s)

13
E. Organization of the Dissertation
The dissertation begins with a brief overview of falls among older adults
in terms of definition, prevalence, impacts on individuals with respect to
morbidity and mortality, as well as impacts on health care and LTC costs. In
addition, the falls model that has been used in previous research is briefly
described in the Introduction. Three research chapters follow, each of which
has its own literature review and methods section. The Conclusion
summarizes the findings, limitations, and implications of the three research
chapters and suggests topics for future study.

14
CHAPTER II: CHARACTERISTICS OF COMMUNITY-DWELLING OLDER
ADULTS WHO FALL
A. Introduction
In response to the serious public health issues connected with falls,
numerous studies have investigated the risk factors for falls among older
adults. In general, studies have focused on the demographic and health
characteristics (e.g., age, gender, medical conditions, level of functioning) of
individuals who fall (hereafter, fallers).
With respect to the demographic characteristics of fallers, studies have
reported that being of advanced age, being female, being White, and living
alone are associated with a high risk of falling (Lord, Sherrington, Menz, &
Close, 2007; J.A. Stevens, 2005). In terms of health characteristics, studies
have reported that impaired balance, mobility, and gait; impaired functioning of
the sensory and neuromuscular systems; and medical conditions such as
stroke, arthritis, depressive symptoms, vision impairments, and Parkinson’s
disease are associated with a high risk of falling (Close, Lord, Menz, &
Sherrington, 2005; Tinetti, 2003). For example, Morris et al. (2004) reported
that occasional fallers were more likely to be women and were nearly twice as
likely to have more than three medical conditions compared to nonfallers; this
finding was obtained using samples from the Health Status of Older People
survey of 1,000 Australians aged 65 and older living in the community, which
was part of the baseline survey for the Melbourne Longitudinal Studies on

15
Healthy Ageing Program. De Rekeneire et al. (2003) also reported that fallers
were more likely to be female and White; to report having more chronic
diseases and using more medications; and to have less leg strength, poorer
balance, slower 400-meter walk times, and less muscle mass. De Rekeneire
et al. found these results using samples from the Health, Aging and Body
Composition Study, a longitudinal prospective study in Pittsburgh,
Pennsylvania, and Memphis, Tennessee. Likewise, Gill, Taylor, and Pengelly
(2005), using a representative sample of community-dwelling older adults
aged 65 years and older in South Australia, found that women were more likely
to fall than men, as were respondents who were not married, who were in the
older age groups, and who reported that their health was not as good now as it
was 12 months ago.
A relatively small number of studies, however, have investigated the
socioeconomic characteristics of fallers as well as the association between
socioeconomic characteristics and the incidence of falls. Numerous studies
have shown the pervasive role that socioeconomic status (SES) plays in
morbidity and mortality among older adults: There is a clear and inverse
relationship between SES and ill health (Williams & Wilson, 2001). Because
falls result from complex interactions between older adults’ physiological
systems and environmental characteristics, socioeconomically disadvantaged
older adults may be at higher risk for falling due to their poor health. Moreover,
older adults with low SES may be less aware of the risks associated with

16
falling and may lack important resources such as information about fall
prevention strategies (e.g., medication management, physical activity, home
modification); thus, they may be less likely to protect themselves against the
risk of falling. Yet, little is known about the socioeconomic characteristics of
community-dwelling older adults who fall.
The few studies that have investigated the association between
socioeconomic factors and the incidence of falls among older adults have
reported inconsistent findings. For example, Salvà, Bolibar, Pera, and Arias
(2004) evaluated the incidence of falls according to sociodemographic and
health factors with a representative cohort of 448 elderly community-dwelling
older adults in Spain, but did not find a significant relationship between
sociodemographic factors and the incidence of falls. However, Stel and
colleagues (2003), using data from the Longitudinal Aging Study Amsterdam
(N = 1,365 community-dwelling older persons), found that a high level of
education was a predictor for recurrent falling. However, using data from the
Social, Environmental and Risk Context Information System Survey
conducted in 2000 by the Department of Health (South Australia), Gill and
colleagues (2005) found that having an annual household income of more
than AUD 80,000 decreased the risk of falling among older adults, suggesting
that a lower level of income, which can be indicative of a lower SES, is
associated with a greater risk of poor health and chronic disease, which in turn
has been associated with an increased risk of falling.

17
With respect to environmental characteristics, studies have reported
that environmental factors that are prevalent in the homes of older adults may
increase the risk of falling. In a survey of 272 community-dwelling women
aged 70 and older who were at risk for falling, Wyman and colleagues (2007)
found that environmental hazards—defined as physical elements in the home
that pose potential fall risks (e.g., height of a toilet seat, absence of a night light,
tripping hazards, or lack of a grab bar)—were highly prevalent in the homes of
older adults in that each house had on average 10.7 modifiable hazards, with a
range of 4 to 17. Environmental risk factors such as hazards, dangerous
areas, and lack of supportive features in the home can contribute to falls
among older adults—who may already have multiple intrinsic risk factors for
falls—by creating conditions that lead to trips or slips (Perell et al., 2001).
Using the participants from the Minimum Data Set—Home Care (N = 2,304),
Fletcher and Hirdes (2002) found that older adults who had one or more
environmental hazards within their homes were more likely to experience a
fall. However, Gill et al. (2000), using a sample from Project Safety (N =
1,088), reported that there was no consistent association between 13 potential
trip or slip hazards and the timing of the first nonsyncopal fall in the homes of
community-living older persons, even after they categorized participants by
impairments in vision, balance/gait, and cognition—all of which are relevant to
mobility and fall risk. Likewise, using a population-based case control study
sample in Florida, Sattin, Rodriguez, DeVito, and Wingo (1998) reported that

18
although environmental hazards were commonly found in older adults’
dwelling units and there were some variations by subgroup in the likelihood of
a hazard being present (e.g., presence of cords/wires in the living room as a
risk factor for injurious falls among men), most home environmental hazards
(e.g., greater numbers of tripping hazards) assessed for home modification
were generally not associated with an increased risk of injurious falls among
most older people, controlling for demographics, functioning, and health
characteristics. The effect of environmental risk factors on the relative risk of
falling is complex and specific to an individual’s health and physical ability as
well as the challenges posed by the environment (Leslie & Pierre, 1999; Lord,
Menz, & Sherrington, 2006). Yet, few studies have examined the relationship
between the home environment and the prevalence of falls.
Another interesting question is whether older adults who fall are likely to
talk about their falls with health care professionals. Few studies have
investigated the extent to which fallers talk about their falls with their health
care professionals; previous studies have focused mainly on the health care
costs associated with falls. In addition, older adults often underreport their fall
experiences to their health care professionals partly due to denial,
embarrassment, or memory loss, even though discussing their falls with health
care professionals could address potentially modifiable fall risk factors and/or
provide information about how to prevent future falls. Investigating this issue
will help researchers understand how to improve communication and

19
education among fallers and health care professionals with regard to
preventing falls.
Objectives
This study profiles the demographic, socioeconomic, health, and
environmental characteristics of community-dwelling fallers and predictors of
falls by examining data from the 2002 Medicare Current Beneficiary Survey
(MCBS) - Access to Care, a nationally representative data set. Such
characteristics are analyzed based on fall status (i.e., nonfaller, one-time
faller, and multiple-time faller who have had more than one fall). Previous
studies have made comparisons between one-time fallers and multiple-time
fallers or between occasional fallers and recurrent fallers, as
one-time/occasional fallers and multiple-time/recurrent fallers generally have
distinctly different profiles in terms of gender, health, and medical conditions
(Morris et al., 2004; Pluijm et al., 2006). This study also describes the extent to
which fallers talk about their falls with their health care professionals.
Research questions
1. What are the demographic, socioeconomic, health, and environmental
characteristics of community-dwelling fallers?
2. Are there differences in demographic, socioeconomic, health, and
environmental characteristics among nonfallers, one-time fallers, and
multiple-time fallers?

20
3. What are the predictors of falls among one-time fallers and multiple-time
fallers?
4. Among fallers, which individuals are most likely to talk about their falls with
health care professionals?
B. Methods
Sample
Conducted by the Office of Strategic Planning of the Centers for
Medicare & Medicaid Services (CMS), the MCBS is a continuous,
multipurpose survey of a nationally representative sample of the Medicare
population. Focusing on economic and beneficiary issues (i.e., health care
use, expenditures, and factors that affect the use of care and the beneficiary’s
ability to pay in particular), the MCBS collects a variety of information about
demographic characteristics, health status and functioning, access to care,
insurance coverage, financial resources, and potential family support (CMS,
2007). Using a rotating panel design, each annual MCBS sample (referred to
as a panel) consists of an age-stratified random sample of aged and disabled
Medicare beneficiaries who were alive and eligible for the program as of
January 1 of the sampling year (Ferraro & Liu, 2005). Sample individuals are
interviewed three times a year at 4-month intervals for up to 4 years using a
computer-assisted personal interviewing instrument.
MCBS produces two files annually: (a) Access to Care (since 1991),
which is released within a year of the survey; and (b) Cost and Use (since

21
1992), which is released within 2 years of the survey. The Access to Care file
contains survey data on utilization of health services, medical expenditures,
health insurance coverage, sources of payment, health status and functioning,
access to care, information needs, and satisfaction with care, as well as a
variety of demographic and behavioral information such as income, assets,
living arrangements, family support, quality of life, gender, race, education,
military service, and marital status. As an ongoing panel survey of
approximately 12,000 Medicare beneficiaries, the survey sample is statistically
representative of the national Medicare population with the use of weights.
The MCBS has provided researchers with a rich source of data on health care
utilization and costs for the Medicare population since its inception in 1991
(Ferraro & Liu, 2005).
The 2002 Access to Care questions were administered from
September through December 2002 as part of the Round 34 interview for the
continuing sample and as part of the initial interview for the 2002 supplemental
sample. There was an initial response rate of 84% (CMS, 2007). The 2002
MCBS - Access to Care (N = 16,315) defined falls as incidents in which the
respondent had fallen down. The 2002 MCBS - Access to Care data, which is
the 12
th
wave, is the first wave that contains relatively detailed information
about falls among older adults. The survey asked whether a participant had
fallen down in the past year (n = 3,765), how many times the participant had
fallen (minimum = 0, maximum = 80), and if a fall had required medical

22
attention (yes = 1,193). The survey also asked whether the participant had
talked to a health professional about falls (yes = 1,829), whether a health
professional had tried to understand the reason why the participant fell (yes =
1,381), and whether a health professional had given information on ways to
avoid falls (yes = 1,147).
A total of 13,598 respondents met the age eligibility requirement (i.e.,
aged 65 and older) for this study, but the final analytic sample was reduced to
11,851 by deleting cases with item-level nonresponse and with missing values
for the analytic models. This number did not include interviews with
beneficiaries living in institutions (e.g., skilled nursing facilities). This study
focused only on community-dwelling populations because studies have shown
that many characteristics and risk factors overlap between
community-dwelling and institutional populations, although there are some
differences between these two groups (Rubenstein & Josephson, 2002/2003).
The question of whether a participant had talked about his or her falls
with health care professionals was a follow-up question intended only for those
who reported experiencing at least one fall (n = 2,950). After the deletion of
cases with item-level nonresponse and with missing values for the analytic
models, the final analytic sample consisted of 2,634 community-dwelling
individuals aged 65 years or older who had experienced a fall and had or had
not talked about their fall(s) with health care professionals.

23
Weights provided by the CMS were used to estimate for the total
Medicare population.
Measures
Four categories of predictors for falls were examined: demographic
characteristics, socioeconomic characteristics, health characteristics, and
environmental characteristics. The demographic variables included age,
gender, race/ethnicity, marital status, living arrangement, and urban residency
(i.e., lived in a metropolitan area). Age was measured in number of years.
Race/ethnicity was recoded as non-Hispanic (NH) White, NH African
American, Hispanic, and Other by combining the race and Hispanic/Latino
origin variables. “Other” racial/ethnic categories included American Indian or
Alaska Native, Asian, Native Hawaiian, or other Pacific Islander. For
multinomial logistic analysis, the race/ethnicity variable was recoded as a
dichotomous variable: racial/ethnic minority (i.e., non-NH White) = 1 versus
NH White = 0. Demographic variables other than age and race/ethnicity were
created as dichotomous variables: female = 1 versus male = 0, married = 1
versus not married = 0, lived alone = 1 versus lived with others = 0, lived in a
metropolitan area = 1 versus lived in a nonmetropolitan area = 0. Marital
status was not included in the multinomial logistic model due to its high
correlation with the living arrangement variable.
The socioeconomic variables included education, income, Medicaid
coverage, and the availability of private insurance. These four variables were

24
each coded as dichotomous variables: less than a high school education = 1
versus more than a high school education = 0, income less than $25,000 = 1
versus income more than $25,000 = 0, had Medicaid coverage = 1 versus did
not have Medicaid coverage = 0, had other insurance coverage = 1 versus did
not have other insurance coverage = 0.
Health characteristics included self-rated health, functional status as
measured by ADL and IADL limitations, number of mobility limitations, certain
medical conditions, vision problems, and hearing problems. Self-rated health
was assessed by asking respondents to rate their general health condition (1 =
excellent, 2 = very good, 3 = good, 4 = fair, 5 = poor). Number of ADL
difficulties was measured by calculating the sum of difficulties with six basic
ADLs (range = 0–6). Respondents were asked whether they had any difficulty
with bathing, dressing, eating, getting into/out of bed/chair, walking, or using a
toilet. If respondents had difficulty with any of these items, their response to
that item was coded as yes = 1. Number of IADL difficulties was measured as
the sum of difficulty with six IADLs (range = 0–6). Respondents were asked
whether they had difficulty with using the telephone, doing light housework,
doing heavy housework, preparing meals, shopping, or managing money. If
respondents had difficulty with any of these items, their response to that item
was coded as yes = 1. For the multinomial logistic model, the number of ADL
and IADL difficulties were combined due to multicollinearity: Previous studies
have justified the combining of ADL and IADL items into a single overall scale

25
(Spector & Fleishman, 1998). Mobility limitations were measured by asking
each respondent if he or she had difficulty stooping/crouching/kneeling,
lifting/carrying 10 pounds, extending his or her arms above the shoulder,
writing/handling objects, or walking a quarter mile or 2 to 3 blocks. If
respondents had difficulty with any of these items, the response to that item
was coded as yes = 1. Then, the number of mobility limitations was measured
by computing the sum of these five mobility limitations (range = 0–5).
With respect to medical conditions, a series of questions asked the
respondents if they had been told by a doctor that they had Alzheimer’s
disease, arthritis, diabetes, heart disease, hypertension, osteoporosis,
Parkinson’s disease, or stroke. These items were recoded as dichotomous
variables (1 = yes, 0 = no). Urinary incontinence was measured by asking
respondents how often they had lost urine beyond their control during the past
12 months. If a respondent had done so at any time during the period, it was
recoded as yes = 1. Depression was measured by asking respondents if they
had been depressed during the past 12 months. If a respondent had been
depressed at any time, it was recoded as depressed = 1. Hearing and vision
problems were measured by asking respondents if they had trouble hearing or
seeing, respectively. For both variables, responses were recoded as
dichotomous variables (1 = yes, 0 = no).
Environmental characteristics included the presence of special railings
and presence of ramps, as well as having bathroom modifications. Having

26
bathroom modifications was measured by asking “Does the survey
participant’s bathroom have modifications?” If a respondent had bathroom
modifications, it was coded as yes = 1. The presence of special railings and
ramps was measured by asking if a respondent had any of these items (yes =
1, no = 0).
Fall status was recoded as three groups by combining two variables
that asked (a) whether a participant had fallen down in the past year and (b)
how many times a participant had fallen. The first category consisted of older
adults who had not experienced a fall (i.e., nonfallers). The second category
consisted of older adults who had experienced one fall (i.e., one-time fallers).
The third category consisted of older adults who had experienced more than
one fall (i.e., multiple-time fallers).
Analysis
Descriptive statistics (e.g., means with standard deviations for
continuous variables and frequencies for categorical variables) were
computed to provide a profile of the sample. In order to assess the statistical
significance of associations, analysis of variance for continuous variables and
Pearson chi-square analysis for categorical variables was conducted to
examine differences among nonfallers, one-time fallers, and multiple-time
fallers with regard to demographic, socioeconomic, health, and environmental
characteristics.

27
Multinomial logistic regression was conducted to examine the
predictors of falls among one-time fallers and multiple-time fallers, with
nonfallers as the reference group in the model. Prior to running the model,
correlations within predictor variables were run in order to eliminate variables
that had high levels of overlapping variance with other variables (i.e.,
correlated with each other at a level of 0.6 or higher).
In addition, t tests for continuous variables and Pearson chi-square
analysis for categorical variables were conducted to examine differences
between fallers who talked about their falls with health care professionals and
those who did not do so with regard to demographic, socioeconomic, health,
and environmental characteristics, as well as the frequency and severity of
their falls.
Sampling weights were used to calculate the estimates of means (for
continuous variables) and proportions (for categorical variables) of the
Medicare population, as well as for multinomial logistic regression analyses.
All analyses were performed using SAS statistical software (Version 9.1; SAS
Institute, Cary, NC).
C. Results
Figure 2.1 shows that approximately 21.4% of the respondents had
fallen in the previous year.

28
Figure 2.1: Percentage of Fallers among 2002 MCBS - Access to Care Data (N = 11,851)

Table 2.1 shows the demographic, socioeconomic, health, and
environmental characteristics of fallers according to fall status. Among the
entire sample, the mean age was 75.1 (SD = 6.8). Approximately 57.7% were
female, 55.8% were married, 32.1% lived alone, and 76.8% lived in an urban
area. The sample included an ethnic mix of 81.6% NH White, 7.9% NH African
American, 6.8% Hispanic, and 3.8% other. About 29.1% had less than a high
school education, 56.3% had an annual income of less than $25,000, and 10%
were eligible for Medicaid. On average, the entire sample reported good to fair
self-rated health, 0.54 ADL and 0.72 IADL limitations, as well as 1.97 mobility
limitations.

29
Table 2.1: Characteristics of the Study Population by Fall Status (N = 11,851)
Variable
Entire
Sample
(N = 11,851)
Nonfallers
(n = 9,203)
One-Time
Fallers
(n = 1,465)
Multiple-
Time Fallers
(n = 1,183) p
a

Demographic characteristics
Age, M (SD) 75.10 (6.80) 74.75 (6.60) 76.34 (7.22) 76.44 (7.54) .00
Female gender, % 57.7 56.0 67.1 60.7 .00
Race/ethnicity, %
NH White 81.6 80.9 83.5 85.1 .00
NH African American 7.9 8.1 6.6 7.1
Hispanic 6.8 7.1 6.1 4.7
Other 3.8 3.8 3.8 3.1
Married, % 55.8 57.1 50.0 51.7 .00
Lives alone, % 32.1 31.1 37.8 33.2 .00
Lives in an urban area, % 76.8 77.5 75.4 72.7 .00
Socioeconomic characteristics
Education less than high school, % 29.1 28.2 28.9 36.7 .00
Income less than $25,000, % 56.3 54.7 61.4 63.0 .00
Medicaid eligibility, % 10.0 9.2 11.5 14.0 .00
Private health coverage, % 65.4 65.5 67.1 62.5 .04
Health characteristics
Self-rated health, M (SD) 2.61 (1.09) 2.53 (1.07) 2.76 (1.08) 3.14 (1.13) .00
ADL limitations, M (SD) 0.54 (1.14) 0.41 (0.98) 0.71 (1.24) 1.46 (1.65) .00
IADL limitations, M (SD) 0.72 (1.31) 0.57 (1.16) 0.97 (1.47) 1.65 (1.81) .00
Mobility limitations, M (SD) 1.97 (1.62) 1.77 (1.56) 2.33 (1.58) 3.16 (1.50) .00

30
Table 2.1: Continued
Alzheimer’s disease, % 2.2 1.8 2.6 4.8 .00
Arthritis, % 55.9 53.0 63.1 70.5 .00
Depression, % 60.3 57.1 68.9 76.2 .00
Diabetes, % 18.7 17.2 21.7 27.2 .00
Heart disease, % 12.3 11.5 12.9 18.6 .00
Hypertension, % 58.8 57.5 61.5 66.6 .00
Osteoporosis, % 17.7 16.6 20.8 23.0 .00
Parkinson’s disease, % 1.3 1.0 1.4 3.7 .00
Stroke, % 10.6 9.1 11.8 21.1 .00
Urinary incontinence, % 22.9 19.7 28.4 42.0 .00
Hearing problems, % 36.9 34.8 40.1 49.8 .00
Vision problems, % 31.9 29.4 35.6 47.9 .00
Environmental characteristics
Presence of bathroom modification, % 29.3 27.1 35.3 40.0 .00
Presence of handrails, % 2.7 2.4 3.4 4.2 .00
Presence of ramps, % 9.5 8.7 12.6 12.3 .00
Note: ADL = activity of daily living; IADL = instrumental ADL; NH = Non-Hispanic; SD = standard deviation.
a
p values are for the overall comparisons among study groups.

31

Among fallers, both one-time and multiple-time fallers were in general
slightly older than nonfallers, although there was no statistically significant age
difference between one-time and multiple-time fallers. Compared to
nonfallers, fallers were more likely to be female, be NH White, be unmarried,
and live alone, and they were less likely to live in a metropolitan area. With
respect to socioeconomic characteristics, fallers were more likely than their
nonfaller counterparts to have less than a high school education, to have an
annual income of less than $25,000, and to be eligible for Medicaid. With
respect to health characteristics, fallers were more likely than nonfallers to
report poorer self-rated health and to have a higher number of ADL, IADL, and
mobility limitations. They were also more likely to report a higher prevalence
of the following 12 diseases/conditions than their nonfaller counterparts:
Alzheimer’s disease, arthritis, depression, diabetes, heart disease,
hypertension, osteoporosis, Parkinson’s disease, stroke, urinary incontinence,
hearing problems, and vision problems. With respect to environmental
characteristics in terms of the presence of supportive features in the home,
fallers were more likely to have bathroom modifications, handrails, and ramps
in their home compared to nonfallers. All of these differences were statistically
significant.
Compared to one-time fallers, multiple-time fallers were more likely to
be NH White and to be married and were less likely to be female, live alone,
and live in a metropolitan area. With respect to socioeconomic characteristics,

32
multiple-time fallers were more likely to have less than a high school
education, have an income of less than $25,000, and be eligible for Medicaid;
they were less likely to have private health insurance coverage compared to
their one-time faller counterparts. With respect to health characteristics,
compared to one-time fallers, multiple-time fallers were more likely to report
poorer self-rated health; to have more ADL, IADL, and mobility limitations; and
to have higher rates of prevalence of the 12 previously listed chronic
conditions/diseases. With respect to environmental characteristics,
multiple-time fallers were more likely to have bathroom modifications and
handrails.
Table 2.2 shows the results of the multinomial logistic regression
predicting the odds of experiencing a one-time fall and multiple falls relative to
no falls, controlling for covariates. Nonfallers represented the reference
group. Being of advanced age; being female; being a racial/ethnic minority
(i.e., non-NH White); having less than a high school education; having a higher
number of ADL and IADL limitations; having a higher number of mobility
limitations; and having arthritis, depression, or diabetes were all significant
predictors of falls among one-time fallers as compared to nonfallers.
Specifically, individuals with advancing age (odds ratio [OR] = 1.02; 95%
confidence interval [CI] = 1.01, 1.03), female gender (OR = 1.34; 95% CI =
1.17, 1.53), a higher number of ADL and IADL limitations (OR = 1.05; 95% CI =
1.02, 1.09), mobility limitations (OR = 1.09; 95% CI= 1.03, 1.14), arthritis (OR =

33
1.18; 95% CI = 1.04, 1.33), depression (OR = 1.33; 95% CI = 1.17, 1.51), or
diabetes (OR = 1.22; 95% CI = 1.05, 1.41) were significantly more likely to
have experienced a fall as compared to nonfallers. However, being a
racial/ethnic minority (OR = 0.84; 95% CI = 0.71, 0.99) and having less than a
high school education (OR = 0.87; 95% CI = 0.75, 0.99) decreased the
likelihood of experiencing a fall.
Table 2.2: Odds Ratios for One-Time Fallers and Multiple-Time Fallers Relative to Nonfallers
(N = 11,851)
One-Time Fallers Multiple-Time Fallers
Variable
OR (95% CI) OR (95% CI)
Demographic characteristics
Age 1.02 (1.01, 1.03)** 1.00 (0.99, 1.01)**
Female gender 1.34 (1.17, 1.53)** 0.85 (0.73, 1.00)**
Non-White 0.84 (0.71, 0.99)** 0.63 (0.52, 0.77)**
Lives alone 1.08 (0.95, 1.22)** 1.02 (0.88, 1.18)**
Lives in an urban area 0.92 (0.81, 1.06)** 0.90 (0.78, 1.05)**
Socioeconomic characteristics
Education less than high school 0.87 (0.75, 0.99)** 1.06 (0.91, 1.23)**
Income less than $25,000 1.07 (0.94, 1.21)** 0.93 (0.80, 1.09)**
Medicaid eligibility 1.08 (0.88, 1.34)** 0.93 (0.74, 1.17)**
Private health coverage 1.12 (0.97, 1.28)** 0.96 (0.82, 1.12)**
Health characteristics
Self-rated health 1.03 (0.97, 1.10)** 1.04 (0.96, 1.11)**
ADL and IADL limitations 1.05 (1.02, 1.09)** 1.15 (1.11, 1.18)**
Mobility limitations 1.09 (1.03, 1.14)** 1.31 (1.24, 1.39)**
Alzheimer’s disease 0.93 (0.64, 1.35)** 0.94 (0.66, 1.35)**
Arthritis 1.18 (1.04, 1.33)** 1.29 (1.12, 1.50)**
Depression 1.33 (1.17, 1.51)** 1.39 (1.19, 1.62)**
Diabetes 1.22 (1.05, 1.41)** 1.21 (1.04, 1.42)**
Heart disease 0.96 (0.81, 1.14)** 1.06 (0.88, 1.26)**
Hypertension 0.98 (0.87, 1.11)** 1.03 (0.89, 1.19)**
Osteoporosis 0.92 (0.79, 1.07)** 0.98 (0.83, 1.17)**
Parkinson’s disease 1.08 (0.66, 1.76)** 1.50 (0.99, 2.26)**
Stroke 1.01 (0.84, 1.22)** 1.41 (1.18, 1.68)**
Urinary incontinence 1.13 (0.99, 1.29)** 1.61 (1.40, 1.86)**

34
Table 2.2: Continued
Hearing problems 1.09 (0.97, 1.23)** 1.21 (1.06, 1.39)**
Vision problems 1.03 (0.91, 1.16)** 1.21 (1.06, 1.39)**
Environmental characteristics
Bathroom modification 1.08 (0.95, 1.23)** 1.07 (0.92, 1.24)**
Presence of handrails 0.94 (0.68, 1.31)** 1.06 (0.75, 1.52)**
Presence of ramps 1.18 (0.98, 1.42)** 0.88 (0.71, 1.09)**
Cox and Snell 0.10
Nagelkerke 0.14
Note: ADL = activity of daily living; CI = confidence interval; IADL = instrumental ADL; OR
= odds ratio.
*p < .05. **p < .01.

Compared with nonfallers, being female; being a racial/ethnic minority;
having a higher number of ADL and IADL limitations; having a higher number
of mobility limitations; and having arthritis, depression, diabetes, stroke,
urinary incontinence, hearing problems, or vision problems were significant
predictors of falls among multiple-time fallers. Specifically, having a higher
number of ADL and IADL limitations (OR = 1.15; 95% CI = 1.11, 1.18) and a
higher number of mobility limitations (OR = 1.31; 95% CI = 1.24, 1.39)
increased the likelihood of experiencing multiple falls. The presence of the
following chronic conditions/diseases also increased the likelihood of
experiencing multiple falls: arthritis (OR = 1.29; 95% CI = 1.12, 1.50),
depression (OR = 1.39; 95% CI = 1.19, 1.62), diabetes (OR = 1.21; 95% CI =
1.04, 1.42), stroke (OR = 1.41; 95% CI = 1.18, 1.68), urinary incontinence (OR
= 1.61; 95% CI = 1.40, 1.86), hearing problems (OR = 1.21; 95% CI = 1.06,
1.39), and vision problems (OR = 1.21; 95% CI = 1.06, 1.39). However, being
female (OR = 0.85; 95% CI = 0.73, 1.00) and being a racial/ethnic minority (OR

35
= 0.63; 95% CI = 0.52, 0.77) decreased the likelihood of experiencing multiple
falls.
Figure 2.2 shows that approximately 46% of the respondents talked
about their falls with a health care professional.
Figure 2.2: Percentage of Fallers Who Talked about Their Falls with Health Care
Professionals (n = 2,634)

Table 2.3 shows the demographic, socioeconomic, health, and
environmental characteristics, as well as the fall status, of individuals who
talked and did not talk about their falls with health care professionals.
Compared to the respondents who did not talk about their falls with health care
professionals, individuals who talked about their falls were more likely to be
slightly older (76.9 years old vs. 75.9 years old), be female (68.2% vs. 61.3%),
and live in a metropolitan area (76.6% vs. 72.2%), whereas they were less
likely to be married (47.9% vs. 53.0%). With respect to health characteristics,
respondents who talked to health care professionals about their falls reported

36
poorer self-rated health (3.04 vs. 2.82); had a higher number of ADL, IADL,
and mobility limitations (1.31 vs. 0.81, 1.56 vs. 1.01, and 2.94 vs. 2.49,
respectively); and had a higher prevalence of Alzheimer’s disease (4.4% vs.
2.6%), arthritis (69.4% vs. 63.8%), depression (76.5% vs. 68.3%),
hypertension (66.8% vs. 61.0%), osteoporosis (25.4% vs. 18.6%), stroke
(19.3% vs. 13.0%), and urinary incontinence (38.6% vs. 30.7%). In terms of
environmental characteristics, respondents who talked to health care
professionals about their falls were more likely to have bathroom modifications
(42.2% vs. 33.3%) and ramps (14.5% vs. 10.8%) in their homes. With respect
to their fall experiences, respondents who talked with health care
professionals about their falls were more likely to have experienced multiple
falls (49.2% vs. 39.6%), injurious falls (63.1% vs. 4.5%), and a higher number
of falls (2.61 vs. 1.92) compared to those who did not talk with health care
professionals about their falls. All of these differences were statistically
significant.
Table 2.3: Characteristics of Fallers Who Did or Did Not Talk about Their Falls with Health
Care Professionals (n = 2,634)
Variable
All Fallers
(n = 2,634)
Did Not Talk
About Falls
(n = 1,404)
Talked
About Falls
(n = 1,230) p
a

Demographic characteristics
Age, M (SD) 76.38 (7.36) 75.91 (7.25) 76.92 (7.44) .00
Female gender, % 64.4 61.3 68.2 .00
Race/ethnicity, %
NH White 84.2 84.2 84.1 .62
NH African American 6.8 6.9 6.7
Hispanic 5.5 5.1 6.0
Other 3.5 3.8 3.2

37
Table 2.3: Continued
Married, % 50.7 53.0 47.9 .01
Lives alone, % 35.9 35.7 36.0 .88
Lives in an urban area, % 74.3 72.2 76.6 .01
Socioeconomic characteristics
Education less than high school, % 32.4 31.2 33.8 .15
Income less than $25,000, % 62.2 62.1 62.2 .97
Medicaid eligibility, % 12.6 11.6 13.8 .08
Health maintenance organization, % 19.0 19.1 18.9 .92
Private health coverage, % 65.0 65.5 64.5 .60
Health characteristics
Self-rated health, M (SD) 2.92 (1.12) 2.82 (1.10) 3.04 (1.12) .00
ADL limitations, M (SD) 1.04 (1.48) 0.81 (1.29) 1.31 (1.63) .00
IADL limitations, M (SD) 1.26 (1.65) 1.01 (1.49) 1.56 (1.79) .00
Mobility limitations, M (SD) 2.69 (1.60) 2.49 (1.60) 2.94 (1.57) .00
Alzheimer’s disease, % 3.5 2.6 4.4 .01
Arthritis, % 66.4 63.8 69.4 .00
Depression, % 72.0 68.3 76.5 .00
Diabetes, % 24.2 23.3 25.3 .23
Heart disease, % 15.4 14.2 16.8 .06
Hypertension, % 63.7 61.0 66.8 .00
Osteoporosis, % 21.7 18.6 25.4 .00
Parkinson’s disease, % 2.3 1.9 2.8 .09
Stroke, % 15.9 13.0 19.3 .00
Urinary incontinence, % 34.3 30.7 38.6 .00
Hearing problems, % 44.2 45.1 43.1 .30
Vision problems, % 40.9 41.2 40.6 .74
Environmental characteristics
Presence of bathroom
modification, % 37.4 33.3 42.2 .00
Presence of handrails, % 3.8 3.2 4.4 .10
Presence of ramps, % 12.5 10.8 14.5 .01
Falls
One-time fall, % 56.0 60.4 50.8 .00
Multiple-time falls, % 44.0 39.6 49.2 .00
Noninjurious falls, % 68.5 95.5 36.9 .00
Injurious falls, % 31.5 4.5 63.1 .00
Number of falls, M (SD) 2.24 (3.58) 1.92 (2.65) 2.61 (4.41) .00
Note: ADL = activity of daily living; IADL = instrumental ADL; NH = Non-Hispanic; SD =
standard deviation.
a
p values are for the overall comparisons among study groups.

38
Figure 2.3 shows that among the respondents who talked to a doctor or
other medical professional about their falls, approximately 74.9% of the health
care professionals talked with the respondents in an effort to understand why
they fell.
Figure 2.3: Percentage of Fallers Whose Health Care Professionals Tried to Understand Why
They Had Fallen When Fallers Reported Their Falls (n = 1,213)

Figure 2.4 shows that among the respondents who talked to a doctor or
other medical professional about their falls, approximately 61.1% of the health
care professionals talked with the respondents in order to give them
information on how to prevent future falls.

39
Figure 2.4: Percentage of Fallers Who Talked about Their Falls with Health Care
Professionals and Received Information on How to Prevent Falls (n = 1,213)

D. Discussion
Using a nationally representative sample from the Medicare population,
this study profiles the demographic, socioeconomic, health, and
environmental characteristics of older adults who experienced one fall or
multiple falls, as well as the extent to which fallers talk about their falls with
health care professionals. In addition, it investigates predictors of falls among
one-time fallers and multiple-time fallers.
The findings from this study confirm previous research showing that
fallers are more likely to be older, be female, be NH White, and live alone
compared to their nonfaller counterparts (Gill et al., 2005; Lord et al., 2007).
Compared to nonfallers, fallers in this study were more likely to have poorer
socioeconomic characteristics in terms of being more likely to have less than a
high school education, to have an annual income of less than $25,000, and to

40
be eligible for Medicaid; however, they were less likely to have private
insurance coverage. These findings are consistent with those of Gill and
colleagues.
In addition, fallers were more likely to have poorer health characteristics
such as worse self-rated health; higher numbers of ADL, IADL, and mobility
limitations; as well as a higher prevalence of chronic conditions/diseases. This
finding is not surprising, because numerous studies have reported that poor
physical health is associated with falls among older adults (Gaebler, 1993;
Gibson, Adres, Isaacs, Radebaugh, & Worm-Peterson, 1987).
With respect to environmental characteristics, fallers were more likely
to have supportive features such as bathroom modifications, handrails, and
ramps in their homes. This may be partly due to the fact that fallers with poorer
health and functioning could have made environmental modifications to
promote mobility and safety in their homes. Yet given the cross-sectional
nature of this study, it is not possible to establish a temporal order of
environmental modifications and fall experience.
With respect to predictors of falls, this study confirms previous findings
that being female; having functional limitations; and having certain
diseases/chronic conditions such as arthritis, depression, and diabetes are
significant predictors of falls for both one-time and multiple-time fallers as
compared to nonfallers (Gaebler, 1993; Morris et al., 2004). However, female
gender was negatively associated with experiencing falls among multiple-time

41
fallers compared to nonfallers. This could be partly due to the fact that frail,
older women may restrict their level of physical activity, thereby avoiding
hazardous situations that could result in a fall.
This study also found that being part of a racial/ethnic minority group
decreases the risk of falling for both one-time fallers and multiple-time fallers.
This finding is consistent with a study by Hanlon, Landerman, Fillenbaum, and
Studenski (2002) that reported that African Americans had a reduced risk of
experiencing a fall. J.A. Stevens and Dellinger (2002) also found that
fall-related death rates for both men and women were highest among Whites
and lowest among Blacks, and non-Hispanics had higher fall-related death
rates than Hispanics. This could be due to selection bias stemming from a
survivor effect, or such differences may reflect different lifestyle characteristics
related to habitual physical activity among racial groups (Lord et al., 2007).
Future studies need to investigate the underlying mechanism (e.g., biological
underpinnings, behavioral characteristics) of a negative association between
race/ethnicity and the experience of a fall.
For one-time fallers, being of advanced age and having less than a high
school education were significant predictors of falls compared to nonfallers.
For multiple-time fallers, the presence of stoke, urinary incontinence, hearing
problems, and vision problems were also significant predictors of falls
compared to nonfallers. This is consistent with a study by Morris et al. (2004)
that found that multiple fallers were more likely to have multiple medical

42
conditions, and a study by Graafmans et al. (1996) that reported that a history
of stroke increased the risk of recurrent falls. However, researchers should
further explore the reason why having less than a high school education has a
protective effect on experiencing falls among one-time fallers.
This study shows that having supportive features in the home is not
associated with experiencing a fall. There are three possible explanations for
this. First, the measurement of environmental factors was too limited in this
study, given the relatively small amount of information contained in the survey
data. Therefore, more objective and precise measurements of environmental
risk factors should be included in future studies. Second, most environmental
risk factors/conditions conceptualized in previous studies have been apparent
or extraordinary hazards (e.g., slippery walking surfaces, obstacles in walking
paths, inadequate lighting, loose carpets and rugs, structurally deficient stairs,
icy sidewalks). Because these studies often fail to consider how an individual
interacts with the surrounding environment (Connell, 1996), they seldom
identify environmental conditions that create situational risk. Third,
environmental hazards can change over time, which could weaken a true
association between the existence of environmental hazards and the
experience of a fall (Gill, Williams, & Tinetti, 2000). Future studies should
consider the fact that environmental conditions that may increase the risk of
falling are often specific to the individual and/or situation in accordance with

43
personal and behavioral factors (e.g., level of physical functioning, routine
activity patterns, and risk-taking behaviors) (Connell, 1996).
Compared to individuals who did not talk about their falls with health
care professionals, those who did talk about their falls were more likely to be
slightly older, be female, and live in a metropolitan area and were less likely to
be married. However, socioeconomic characteristics did not differ significantly
between individuals who talked about their falls with their health care
professionals and those who did not. With respect to health characteristics,
individuals who talked about their falls had poorer self-rated health; higher
numbers of ADL, IADL, and mobility limitations; and a higher prevalence of
certain chronic conditions/diseases. Moreover, fallers who talked about their
falls were more likely to have experienced multiple falls, injurious falls, and a
higher number of falls compared to those who did not talk about their falls.
Having communication with health care professionals about fall
experiences can help older adults—especially those at high risk for falling—to
reduce future falls by modifying potential risk factors for falls through having
multidisciplinary risk screening and assessment, learning fall prevention
strategies, and/or being referred to targeted fall prevention programs.
Although patient awareness of risk can contribute to improved fall prevention
management, less than 50% of the fallers in this study talked about their falls
with health care professionals. Physicians can play a critical role in identifying

44
older adults at high risk for falling by directly asking patients or family members
about fall experiences rather than waiting for older adults to report falls.
Educating health care professionals about the need for fall risk
assessment and management, and referral for specific interventions, can
further improve fall prevention efforts. Chou, Tinetti, King, Irwin, and Fortinsky
(2006) pointed out that the identification and management of elderly patients
at high risk for falling remain largely neglected in clinical practice. A study by
Salter et al. (2006) reported that the majority of community-dwelling older
adults who presented themselves to an emergency department due to a
fall—but who were not admitted to a hospital—did not receive any element of
the comprehensive geriatric or fall prevention assessment prescribed by the
AGS/BGS/AAOS Panel on Fall Prevention (2001) guidelines for the
prevention of falls for older people, which derived from prospective study of 54
older adults aged 70 and older in Vancouver, Canada. The Connecticut
Collaboration for Fall Prevention (CCFP), which was designed to address the
challenges of incorporating evidence-based fall risk assessment and
management strategies into the clinical practices of health care provider
groups (e.g., physicians, nurses, physical therapists) for the primary care of
older adults by educating and training them, had a favorable impact on
improving the knowledge, attitudes, and behaviors of health care providers
concerning fall risk factor assessment and management. A majority of health
care providers reported directly intervening with or referring older patients in

45
order to address fall risk factors after exposure to the CCFP implementation
team (Fortinsky et al., 2004). Although major barriers to such interventions
include time constraints, competing demands, lack of knowledge and skills,
fragmentation and lack of coordination, patients’ unwillingness to report
problems or follow through with recommendations, and reimbursement and
financial concerns (Baker et al., 2005; Chou et al., 2006; Tinetti, Gordon,
Sogolow, Lapin, & Bradley, 2006), improving communications between older
adults and health care professionals about fall experiences and how to prevent
future falls is an integral part of—and can serve as an initial step in—a
multifactorial fall prevention approach. Future studies are needed (a) to
explore the extent to which health care experiences (e.g., access barriers)
may influence the tendency to talk about fall experiences with health care
professionals and (b) to examine how effectively health care professionals
provide information on preventing future falls.
This study has several limitations. First, the data were not from a
prospective, randomized, controlled trial and relied on self-reports of falls. The
self-report and retrospective nature of this study, which relied on the recall of
the respondents, may have been subject to a certain amount of error and may
have led to the underestimation of the magnitude of fall experiences. Second,
given the limitations on available data, it was not possible to define a fall in this
study. Hence, this study may not have captured the full breadth and depth of a
fall because it relied on respondents’ interpretations of a fall. Lastly, findings

46
must be interpreted cautiously because the cross-sectional design of this
study does not indicate the cause/effect relationship between the independent
variables and falls.
Despite these limitations, the current study adds value to the literature
because it examined demographic, socioeconomic, health, and environmental
characteristics of fallers, as well as predictors of falls by frequency, using a
nationally representative sample of community-dwelling individuals. In
addition, the present study investigated the extent to which older adults who
experience falls communicate with health care professionals about their falls.

47
CHAPTER III: PREDICTORS OF FALLS AMONG OLDER ADULTS FROM
DIFFERENT RACIAL/ETHNIC BACKGROUNDS
A. Introduction
The racial/ethnic composition of the population aged 65 and older has
changed significantly, yet studies have noted that there is limited information
with regard to the prevalence of—and risk factors for—falls in different
racial/ethnic groups. The small number of previous studies available has often
focused on the incidence of falls among different racial/ethnic groups, and they
offer mixed findings. For example, Reyes-Ortiz, Al Snih, Loera, Ray, and
Markides (2004) reported that the proportion of persons who fall is lower in
some populations such as Japanese American older adults (11% for men and
17% for women), compared to Caucasian older adults (27% for men and 36%
for women). Moreover, the Duke site of the Established Populations for
Epidemiologic Studies of the Elderly study reported that African American
older adults had a 23% reduced risk of experiencing a fall compared to White
older adults (Hanlon, Landerman, Fillenbaum, & Studenski, 2002). However,
Faulkner et al. (2005) reported that differences in fall rates between older
Caucasian and African American community-dwelling women in their study
were not statistically significant, although ethnic differences in fall
circumstances in terms of fall location and surface, fall direction, and likelihood
of falling on the hand/wrist did occur based on samples from the Study of
Osteoporotic Fractures at the University of Pittsburgh Clinical Center.

48
With regard to risk factors, only a small number of studies have
compared the risk factors for falls among various racial/ethnic groups. Means,
O’Sullivan, and Rodell (2000), using a nonexperimental sample (N = 298)
recruited from the Central Arkansas Area Agency on Aging, reported
similarities between community-dwelling African American and White older
women in general health characteristics, history of falls and fall-related
injuries, and several balance and mobility measures. Schwartz et al. (1999),
using a sample of 152 community-dwelling Mexican American women aged 59
to 84 years, found that many of the risk factors for falls among older Mexican
American women are similar to those reported for NH Caucasian women (e.g.,
chronic conditions, poor neuromuscular function, and impaired functional
ability). However, research comparing risk factors for falls among older adults
from different racial/ethnic backgrounds has often been limited by restricted
samples (e.g., nonexperimental studies, gender-specific sampling), which
makes it difficult to draw coherent generalizations.
Race/ethnicity as a multidimensional construct can affect the risk of
falling among older adults through health status and resource disparities. In
general, race and ethnicity predict variations in health in the United States
(Williams, 1999). For example, African Americans have elevated mortality
rates for 8 of the 10 leading causes of death compared to Whites (Williams,
1999). Moreover, middle-aged Black men have a higher prevalence of
hypertension, stroke, diabetes, kidney and bladder problems, and stomach

49
ulcers, whereas middle-aged White men have a higher prevalence of heart
conditions, chronic obstructive pulmonary disease, back problems, high
cholesterol, and eye problems (Hayward, Crimmins, Miles, & Yang, 2000).
Likewise, older Hispanics have higher rates of death from diabetes and
chronic liver disease than their White counterparts (Markides & Wallace,
1996). Reyes-Ortiz et al. (2004) found that disability and certain medical
conditions such as diabetes mellitus, arthritis, and symptoms of depression
are all common among Mexican American populations. In addition, using
cross-sectional data on a national sample of 9,744 men and women aged 51
through 61 from the 1992 Health and Retirement Study, Kington and Smith
(1997) reported that African Americans and Hispanics generally had worse
functional ability than Whites, although this disadvantage was eliminated by
controlling for SES. Mendes de Leon and colleagues (2005), using data from
a longitudinal, population-based study of 6,158 African American and White
adults aged 65 and older from the south side of Chicago, noted that African
American older adults reported significantly higher disability levels than Whites
after adjustment for age and gender. Such variation is partly due to genetic
differences that predispose members of one race or another to specific
diseases, social factors that affect health through specific physiological
mechanisms and processes, and health behaviors. In addition, racial/ethnic
minority older adults are more likely to have fewer resources in terms of
wealth, income, and education (i.e., lower SES), which translates into reduced

50
access to health services, lower quality medical care, later diagnosis, and
greater severity of illness (Dunlop, Manheim, Song, & Chang, 2002; Kington &
Smith, 1997).
Given the fact that chronic medical conditions have been linked to an
increased risk of falling and fall-related injuries, this study focuses on
health-related characteristics such as functional limitations (including
balance/mobility impairment and weaknesses) and chronic
conditions/diseases, along with demographic and environmental
characteristics, as predictors of falls among older adults from three
racial/ethnic groups (Herndon et al., 1997).
Chronic conditions/diseases related to increased risk of falling
The following diseases/chronic conditions have been shown to
correlate with an increased risk of falling among older adults: arthritis,
cognitive impairment, depression, diabetes, heart disease, hypertension,
osteoporosis, stroke, urinary incontinence, hearing impairment, and visual
impairment.
Arthritis: As a leading cause of disability in the United States,
arthritis—lower limb arthritis in particular—has been identified as a risk factor
for falls because it often results in deficits such as impaired strength,
proprioception, and balance—as well as increased levels of pain—among
older adults (Sturnieks et al, 2004). Using 684 community-dwelling individuals
aged 75 to 98 years in their randomized controlled trial for falls prevention in

51
Sydney, Australia, Sturnieks et al. (2004) reported that older people with lower
limb arthritis are at increased risk for falling due to declines in muscle strength,
proprioceptive acuity, and standing balance associated with the disease.
Likewise, Kaz and colleagues (2004), in a case control study of older women
with rheumatoid arthritis (N = 103) and without rheumatoid arthritis (N = 203),
stressed that femoral neck osteoporosis was found in 31%—and increased fall
risk in 68%—of women with rheumatoid arthritis. Using the Australian
Longitudinal Study of Ageing (N = 1,947), Dolinis, Harrison, and Andrews
(1997) also reported that arthritis was an independent risk factor for falls, with
a possible causal relationship existing between arthritis and a fall in view of the
degradation of lower limb function caused by the deterioration of joints in the
lower limbs.
Cognitive impairment: Dementia and other cognitive impairments
increase the risk of falling by influencing how an individual perceives and
adapts to the environment and to activity demands in terms of misperceiving
environmental dangers, erring in judgment, or failing to discriminate between
safe and dangerous environmental conditions or activities (Tideiksaar, 2002).
Moreover, cognitive impairment associated with dementia may heighten the
risk of falling by increasing the tendency of an older adult to wander and by
altering gait patterns (Lord et al., 2007). Likewise, Parkinson’s disease, a
chronic, progressive neurodegenerative disorder that often affects postural
control mechanisms, is associated with increased risk of falling by leading to a

52
displaced center of gravity, loss of balance, as well as complications from
treatment (Gray & Hildebrand, 2000; Tideiksaar, 2002). Lord and colleagues
(2007) reported that many older adults with Parkinson’s disease suffer from
frequent falls due to their rigid posture, abnormal gait and impaired ability to
respond to external perturbations. For example, Paulson, Schafer, and
Hallum (1986) reported that more than 50% of 211 participants with
Parkinson’s disease experienced frequent falls, possibly due to gait
disturbance, visual disturbance, and loss of postural control associated with
the disease. In addition, Koller, Glatt, Vetere-Overfield, and Hassanein (1989)
reported that 38% of their 100 parkinsonian participants fell, with almost 13%
falling more than once a week; falling was correlated with age, duration of
disease, postural instability, bradykinesia, and rigidity. Ashburn, Stack,
Pickering, and Ward (2001), using a sample population (N = 63) recruited from
general practitioners, found that the risk of falling was greatly increased by the
presence of Parkinson’s disease. Using a univariate analysis of multiple risk
factors from 16 studies (8 of which were conducted within the
community-dwelling population, and the other 8 of which were conducted
within the nursing home population), Rubenstein and Josephson (2002/2003)
also reported that depression and cognitive impairment were associated with
an approximate twofold increase in the risk of falling.
Depression: Although the order of causation is not clear, depression is
associated with falls among older adults (Biderman, Cwikel, Fried, & Galinsky,

53
2002). Older people with depression are less likely to be physically active
(Penninx, Leveille, Ferrucci, van Eijk, & Guralnik, 1999), which in turn results
in reduced muscle strength, coordination, and balance, thereby posing a
greater risk for falls. Moreover, previous studies have reported that
depressive symptoms are associated with increased risk of disability or
physical impairment, although there is a reciprocal association between
depression and disability in that poor physical function itself is associated with
higher levels of depressive symptoms (Bruce, Seeman, Merrill, & Blazer,
1994; Everson-Rose et al., 2005; Ormel, Rijsdijk, Sullivan, van Sonderen, &
Kempen, 2002; Penninx et al., 1998). In addition, studies have shown that
depressive symptoms, as well as the psychotropic medications that are
commonly used in the treatment of late-life depression, appear to be
independently associated with an increased risk of falling among older adults
(Joo et al., 2002). Using data from the National Silver Network project in Italy,
Cesari et al. (2002) reported that depression is strongly correlated with a
higher tendency to fall.
Diabetes: Diabetes mellitus often results in complications such as
neuropathy, which is associated with lower extremity weakness and altered
proprioception; diabetic retinopathy; autonomic neuropathy manifesting as
orthostatic hypotension; and diabetic foot ulcers, all of which could be potential
mechanisms for increased risk of falling among older adults (Maurer,
Burcham, & Cheng, 2005; Tideiksaar, 2002). For example, Gregg et al.

54
(2000), using data from the third National Health and Nutrition Examination
Survey (N = 6,588), found that diabetes, which may increase the risk of
disability due to complications such as cardiovascular and peripheral vascular
disease, vision loss, and peripheral neuropathy, was associated with a two- to
threefold increase in older adults’ risk of being unable to perform
mobility-related tasks (e.g., walking, climbing steps, doing housework). They
suggested that impairments in lower extremity physical functioning in people
with diabetes are key contributors to loss of physical independence. Schwartz
et al. (2002) reported that older women with diabetes, especially those using
insulin, have an increased

risk of falling; their study was conducted using data
from the Study of Osteoporotic Fractures, which is a prospective cohort

study
of osteoporosis and fractures in older women (N = 9,249).
Heart disease: Studies have reported a causal association between
some cardiovascular disorders—particularly orthostatic hypotension, carotid
sinus syndrome, and vasovagal syndrome—and falls (AGS/BGS/AAOS Panel
on Fall Prevention, 2001). According to Davies and Kenny (1996),
cardiovascular disorders such as carotid sinus hypersensitivity and carotid
sinus syndrome are associated with unexplained falls as well as falls
associated with unexplained loss of consciousness among older adults
presenting themselves to accident and emergency departments (N = 188).
Hypertension: Hypertensive patients may be at higher risk for falling
resulting from fainting associated with diminished baroreflex sensitivity or

55
hypotension secondary to therapy (Pérez-Castrillón et al., 2005). Moreover,
older adults with hypertension and diabetes can face the additional burdens of
associated retinopathies along with age-related decline in visual acuity (Lord
et al., 2007). Another possible mechanism exists whereby hypertension is
indirectly related to a high risk of falling through the level of physical activity:
Studies have shown that a low level of physical activity or a lack of moderately
vigorous activity increases the risk of developing hypertension (Paffenbarger
& Lee, 1997).
Osteoporosis: Arnold, Busch, Schachter, Harrison, and Olszynski
(2005) reported that older women may have a greater fall risk if they are
afflicted with osteoporosis, a disease associated with decreased bone mineral
density, postural change, balance, and strength deterioration, all of which may
increase individuals’ fear of falling and subsequent self-limitation of activity.
Liu-Ambrose, Eng, Khan, Carter, and McKay (2003) suggested that postural
changes related to osteoporosis (e.g., kyphosis), inappropriate balance
strategies, self-imposed restrictions on physical activity secondary to fear of
falling, general frailty, and low body mass associated with osteoporosis may
increase the risk of falling. Liu-Ambrose et al. (2003) also reported that older
women with osteoporosis may constitute a group at particularly high risk for
fractures, as these women have both lower bone density and possibly a higher
fall risk compared to their age-matched healthy counterparts (N = 42).

56
Stroke: Studies have reported that people who have suffered a stroke
are at higher risk for falling than are those in the general population (Hyndman,
Ashburn, & Stack, 2002). As a common neurological event among older
adults, stroke often results in mild to moderate limitations on functioning.
These common impairments may affect balance and mobility, which in turn
increases the risk of falling. Hyndman and colleagues (2002) reported that
people who have fallen following a stroke often attribute their fall to loss of
balance, misjudgment/lack of concentration, or their feet dragging on the
ground, thus causing them to trip. In particular, brain stem and cerebellar
strokes may cause damage to areas of the brain closely associated with
balance (Lord et al, 2007). Using data from the Kansas City Stroke Study, a
prospective, large cohort study of stroke survivors (N = 280), J. S. Yates, Lai,
Duncan, and Studenski (2002) found that community-dwelling older adults
who have had a stroke—particularly those with motor and sensory
deficits—are at a higher risk for falling. In their cross-sectional, observational
study (N = 41), Hyndman et al. (2002) also found that stroke survivors living in
the community were at higher risk for falling than were those in the general
population of England. Likewise, Herndon et al. (1997), using a
population-based case control study (N = 1,158), reported that older adults
with a history of stroke had an increased risk of a fall injury event. Forster and
Young (1995) found that stroke was associated with a risk of falling among 108
patients in the Bradford Metropolitan community who were aged 60 or older

57
and who had some residual disability related to stroke. In addition, Jørgensen,
Engstad, and Jacobsen (2002) reported that falls were more frequent among
noninstitutionalized long-term stroke survivors (23%) than among community
controls (11%), and the risk of falling at least once was more than twice as high
for the individuals with stroke. The authors found these results using 111
home-living long-term stroke survivors and 143 control participants randomly
selected from the same municipality and matched with respect to age and
gender.
Urinary incontinence: Previous studies have reported that urinary
incontinence—urge incontinence in particular—is associated with increased
risk of falling by necessitating multiple urgent trips to the bathroom, especially
during the night (Brown et al., 2000). For example, De Rekeneire et al. (2003)
reported that urinary incontinence was associated with an increased risk of
falling in men and women from their cross-sectional analyses (N = 3,050) of
baseline data from the Health, Aging and Body Composition Study, a
longitudinal prospective study conducted in Pittsburgh, Pennsylvania, and
Memphis, Tennessee, field centers. Using participants from the Study of
Osteoporotic Fractures, which recruited community-dwelling women aged 65
and older (N = 6,049), Brown et al. (2000) found that weekly or more frequent
urge incontinence was associated independently with a 26% increase in the
risk of falling and a 34% increase in the risk of fractures.

58
Hearing impairment: Given the fact that decreases in hearing sensitivity
are often associated with decreased vestibular function, individuals with
hearing loss—who are more likely to have impaired balance—may be prone to
falls (Purchase-Helzner et al., 2004). For example, Crews and Campbell
(2004), using data from the 1994 Second Supplement on Aging (N = 9,447),
reported that older adults with hearing loss were 1.7 times more likely to have
experienced a fall, and those with both vision and hearing loss were 3 times
more likely to have fallen in the past 12 months than people without such
problems.
Visual impairment: Age-related vision changes such as greater
sensitivity of the aging eye to glare, restrictions on the visual field, loss of
visual acuity and contrast sensitivity, and decline in depth perception—as well
as visual disorders due to cataracts, macular degeneration, and
glaucoma—increase the likelihood of falls among older adults by interfering
with safe mobility, especially during the night (Lord, 2006; Tideiksaar, 2002).
In particular, Turano and colleagues (2004), using a population-based sample
of 1,504 older adults enrolled in the third round of the Salisbury Eye Evaluation
project, reported that decreases in the visual field were significantly associated
with a decline in mobility performance (e.g., increase in the number of bumps,
decrease in walking speed). Lord and colleagues (2007) reported that deficits
in acuity and contrast sensitivity, restriction of the visual field, increased
susceptibility to glare, and poor depth perception can lead to misjudgment of

59
distances and misinterpretation of spatial information, thereby increasing an
individual’s risk of falling. Lord and Dayhew (2001), using data from a
prospective cohort study of community-dwelling men and women aged 63 to
90 in Australia (N = 148), reported that visual impairment—impaired depth
perception in particular—is an important risk factor for falls among older
adults.
Objectives
This study examines whether predictors of falls among older adults
differ among different racial/ethnic groups. Information on the predictors of
falls among older adults from different racial/ethnic groups can help policy
makers develop more effective fall prevention strategies, given that the racial
and ethnic composition of the population aged 65 and older is changing
significantly. For example, African Americans and other minority groups now
represent a greater proportion of elders than in previous years; furthermore,
the proportion of older adults aged 65 and older who are members of racial
minority groups (i.e., Black, American Indian/Alaska Native, Asian/Pacific
Islander) is expected to increase from 11.3% to 16.5%, while the proportion of
Hispanics is expected to increase from 5.6% to 10.9%, between 2000 and
2030 (Goulding, Rogers, & Smith, 2003).
Research question and hypotheses
The aim of this study was to identify factors associated with
experiencing a fall among three racial/ethnic groups (i.e., NH White, NH

60
African American, Hispanic) and to investigate whether predictors of falls differ
among such groups. Specifically, this study tested the following hypotheses:
1) Predictors of a fall related to demographic characteristics do not differ in
three racial/ethnic groups.
2) Predictors of a fall related to socioeconomic characteristics differ in three
racial/ethnic groups in that having low SES in terms of education and income
increases the likelihood of experiencing a fall.
3) Predictors of a fall related to health differ in three racial/ethnic groups in that
self-rated health, functional limitations, and the presence of certain
diseases/chronic conditions have different impacts on the likelihood of
experiencing a fall.
4) Predictors of a fall related to environmental characteristics differ in three
racial/ethnic groups in that having more home modifications decreases the
likelihood of experiencing a fall.
B. Methods
Sample
The 2002 MCBS-Access to Care data were used to test the research
questions. Detailed descriptions of the data are given in Chapter 2. Initially,
13,598 MCBS-Access to Care (2002) respondents met the age eligibility
requirement (i.e., aged 65 and older) for this study, but the sample was
restricted to 13,076 by selecting respondents who identified themselves as
either NH White (n = 11,047), NH African American (n = 1,115), or Hispanic (n

61
= 914). The final sample for this study comprised 11,442 respondents (NH
Whites = 9,696, NH African Americans = 961, and Hispanics = 785) after the
exclusion of cases with item-level nonresponse and with missing data relevant
to the analytic models. Weights provided by the CMS were used to estimate
for the total Medicare population.
Measures
Dependent Variable
The dependent variable was a dichotomous measure of whether the
participant had had any falls during the previous 12 months (yes = 1, no = 0).
Independent Variables
Four categories of predictors for falls were examined: demographic
characteristics, socioeconomic characteristics, health characteristics, and
environmental characteristics. The demographic variables included age,
gender, race/ethnicity, living arrangement, and urban residency (i.e., lived in a
metropolitan area). Age was measured in actual number of years.
Race/ethnicity was coded as NH White, NH African American, Hispanic, or
Other. However, other racial/ethnic categories such as American Indian or
Alaska Native, Asian, Native Hawaiian or other Pacific Islanders were not
included in this study due to their small sample sizes. Demographic variables
other than age and race/ethnicity were created as dichotomous variables:
female = 1, male = 0; lived alone = 1, lived with others = 0; and lived in a
metropolitan area = 1, lived in a nonmetropolitan area = 0.

62
The socioeconomic variables included education, income, Medicaid
eligibility, and the availability of private insurance, each coded as dichotomous
variables: less than a high school education = 1, more than a high school
education = 0; income less than $25,000 = 1, income more than $25,000 = 0;
eligible for Medicaid = 1, not eligible for Medicaid = 0; had other insurance
coverage = 1, did not have other insurance coverage = 0.
Health characteristics included self-rated health, functional status as
measured by ADL and IADL limitations, number of mobility limitations, certain
medical conditions, vision problems, and hearing problems. Self-rated health
was assessed by asking respondents to rate their general health condition (1 =
excellent, 2 = very good, 3 = good, 4 = fair, 5 = poor). The number of ADL and
IADL limitations was measured by calculating the sum of limitations on six
basic ADL and six IADL limitations; the number of ADL limitations and the
number of IADL limitations were combined due to multicollinearity. The
respondents were asked whether they had any difficulty with (a) bathing, (b)
dressing, (c) eating, (d) getting into/out of bed/chair, (e) walking, (f) using the
toilet, (g) using the telephone, (h) doing light housework, (i) doing heavy
housework, (j) preparing meals, (k) shopping, or (l) managing money. If
respondents had difficulty with any of these items, their response to that item
was coded as yes = 1. Then the number of total ADL and IADL limitations was
measured by computing the sum of the 12 items. Mobility limitations were
measured by asking respondents if they had difficulty (a)

63
stooping/crouching/kneeling, (b) lifting/carrying 10 pounds, (c) extending their
arms above the shoulder, (d) writing/handling objects, or (e) walking ¼ miles
or 2 to 3 blocks. If respondents had difficulty with any of these items, the
response to that item was coded as yes = 1. Then the number of mobility
difficulties was measured by computing the sum of the five mobility limitations
(range: 0 – 5).
To assess respondents’ medical condition, a series of questions asked
the respondents if they had been told by a doctor that they had Alzheimer’s
disease, arthritis, diabetes, heart disease, hypertension, osteoporosis,
Parkinson’s disease, or stroke. These items were recoded as dichotomous
variables (1 = yes, 0 = no). Depression was measured by asking each
respondent if he or she had been depressed during the past 12 months: If a
respondent had been depressed at any time, it was recoded as 1 = depressed.
Hearing and vision problems were measured by asking each respondent if he
or she had trouble hearing or had trouble seeing, respectively. Responses for
both the hearing and the seeing variables were recoded as dichotomous
variables (1 = yes, 0 = no). Urinary incontinence was measured by asking
respondents how often they had lost urine beyond their control during the past
12 months. If a respondent had done so at any time during the period, it was
recoded as 1 = yes.
Environmental characteristics included the presence of special railings
and the presence of ramps, as well as having bathroom modifications. Having

64
bathroom modifications was measured by asking “Does the survey
participant’s bathroom have modifications?” If a respondent had bathroom
modifications, it was coded as 1 = yes. The presence of special railings and
ramps was measured by asking if a respondent had any of these items (yes =
1, no = 0).
Analysis
Descriptive statistics (e.g., means, standard deviations, and
frequencies) as well as multivariate models were obtained by using SAS
statistical software (Version 9.1; SAS Institute, Cary, NC) and Statistical
Package for Social Sciences (SPSS, Chicago, IL) Version 14.0 for Windows.
Comparisons of sample characteristics among NH White, NH African
American, and Hispanic respondents were made by conducting one-way
analyses of variance for continuous variables and chi-square tests for
categorical variables. Correlations were identified between potential
covariates within the major subcategories by using Kendall tau and Pearson
correlation coefficients before performing multivariate analysis.
A hierarchical logistic regression was conducted to examine the
independent effect of the following factors: demographic (i.e., age, gender,
race/ethnicity, living arrangement, urban residency), socioeconomic (i.e.,
education, income, Medicaid eligibility, availability of private insurance), health
(i.e., self-rated health, physical functioning as measured by ADL/IADL
limitations, number of mobility limitations, chronic conditions/diseases), and

65
environmental (i.e., bathroom modifications, presence of railings and ramps).
Independent variables were entered hierarchically, beginning with
demographic variables (Model 1), followed by socioeconomic variables (Model
2), health variables (Model 3), environmental variables (Model 4), and
interaction terms (Model 5).
Logistic regression models of the likelihood of falling, including a race
dummy variable and race interaction terms, were estimated. Model 5 tested
the interactions between (a) race/ethnicity and living arrangement (i.e., Black
× Live Alone, Hispanic × Live Alone), (b) race/ethnicity and education (i.e.,
Black × Education, Hispanic × Education), (c) race/ethnicity and income (i.e.,
Black × Income, Hispanic × Income), (d) race/ethnicity and Medicaid eligibility
(i.e., Black × Medicaid, Hispanic × Medicaid), (e) race/ethnicity and private
insurance availability (i.e., Black × Private Insurance, Hispanic × Private
Insurance), (f) race/ethnicity and self-rated health (i.e., Black × Self-Rated
Health, Hispanic × Self-Rated Health), (g) race/ethnicity and functional
limitations (i.e., Black × ADL/IADL Limitations, Hispanic × ADL/IADL
Limitations, Black × Mobility Limitations, Hispanic × Mobility Limitations), (h)
race/ethnicity and bathroom modifications (i.e., Black × Bathroom
Modifications, Hispanic × Bathroom Modifications), (i) race/ethnicity and the
presence of handrails (i.e., Black × Railings, Hispanic × Railings), and (j)
race/ethnicity and the presence of ramps (i.e., Black × Ramps, Hispanic ×
Ramps). Next, logistic regressions of predictors of a fall, stratified by the three

66
racial/ethnic groups (i.e., NH Whites, NH African Americans, and Hispanics),
were estimated.
C. Results
Descriptive analyses
Table 3.1 shows the demographic, socioeconomic, health, and
environmental characteristics for the entire analytic sample and for each
racial/ethnic group. As shown in Table 3.1, the mean age was 75.15 (SD =
6.81) among the 11,442 respondents who were aged 65 and older in this
sample. This included an ethnic mix of 84.7% NH White, 8.4% NH African
American, and 6.9% Hispanic. Approximately 58% were female, and 32.4% of
the respondents lived alone. The majority of (76.7%) respondents lived in an
urban area. Of the 11,442 respondents, less than one third had less than a
high school education, and more than 56% had an income of less than
$25,000. Almost 66% of the respondents had private health insurance, and
about 9% of the respondents were eligible for Medicaid benefits. On average,
respondents had 1.26 ADL/IADL limitations (SD = 2.26), and about 20% of the
respondents reported their health condition as fair (16.2%) or poor (4.7%).
About one third (29.6%) of the respondents had bathroom modifications in
their residences, and 9.4% of the respondents had ramps in their residences.
Approximately 21.6% of the respondents had experienced a fall.

67
Table 3.1: Characteristics of the Study Population (N = 11,442)
NH White NH AA Hispanic Entire Sample
Characteristic (n = 9,696) (n = 961) (n = 785) p
a
(N = 11,442)
Demographic characteristics
Age, M (SD) 75.29 (6.79) 74.56 (7.02) 74.13 (6.59) .00 75.15 (6.81)
Female gender, % 57.1 61.0 59.3 .04 57.6
Lives alone, % 32.5 35.4 27.1 .00 32.4
Lives in an urban area, % 74.8 83.2 91.3 .00 76.7
Socioeconomic characteristics
Education less than high school, % 24.1 53.7 57.4 .00 28.9
Income less than $25,000, % 52.0 78.5 79.0 .00 56.1
Medicaid eligibility, % 5.7 28.3 31.4 .00 9.3
Private insurance availability, % 71.3 39.2 35.7 .00 66.2
Health characteristics
Self-rated health, M (SD) 2.57 (1.08) 2.90 (1.08) 2.82 (1.08) .00 2.61 (1.09)
Sum of ADL/IADL limitations, M (SD) 1.19 (2.16) 1.72 (2.74) 1.62 (2.74) .00 1.26 (2.26)
Mobility limitations, M (SD) 1.94 (1.60) 2.31 (1.73) 1.97 (1.64) .00 1.97 (1.61)
Alzheimer’s disease or dementia, % 2.1 3.2 1.3 .02 2.2
Arthritis, % 55.9 61.1 52.7 .00 56.1
Depression, % 60.4 61.0 60.5 .93 60.4
Diabetes, % 16.9 31.2 23.6 .00 18.6
Heart disease, % 12.7 9.1 12.1 .00 12.4
Hypertension, % 57.4 73.5 56.7 .00 58.7
Osteoporosis, % 19.0 7.1 15.4 .00 17.7

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Table 3.1: Continued
Parkinson’s disease, % 1.4 1.0 0.7 .20 1.3
Stroke, % 10.7 11.9 8.2 .04 10.7
Urinary incontinence, % 23.5 19.5 18.3 .00 22.8
Hearing problems, % 39.1 26.1 22.8 .00 36.9
Vision problems, % 31.3 36.4 31.6 .01 31.7
Housing characteristics
Presence of bathroom modification, % 30.8 25.2 20.5 .00 29.6
Presence of handrails, % 2.8 2.7 1.3 .05 2.7
Presence of ramps, % 9.4 8.8 10.4 .48 9.4
Experience of a fall, % 22.2 18.9 17.7 .00 21.6
Note: AA = African American; ADL = activity of daily living; IADL = instrumental ADL; NH = non-Hispanic; SD = standard deviation.
a
p values are for the overall comparisons among study groups.

69

All three racial/ethnic groups (i.e., NH White, NH African American,
and Hispanic) had a similar distribution of mean age (75.3, 74.6, and 74.1,
respectively). However, NH White older adults were slightly—but statistically
significantly—older than their NH African American and Hispanic counterparts.
NH African American and Hispanic respondents were significantly more likely
to be female (61.0% and 59.3%, respectively) than NH Whites (57.1%). NH
White older adults were statistically significantly less likely to live in an urban
area (74.8%) compared to NH African American and Hispanic older adults
(83.2% and 91.3%, respectively).
NH African American and Hispanic older adults were more likely to
have less than a high school education (53.7% and 57.4%, respectively) and
income of less than $25,000 (78.5% and 79.0%, respectively) than their White
counterparts (24.1% for less than a high school education, 52.0% for income
of less than $25,000). Moreover, NH African American and Hispanic older
adults were more likely to be eligible for Medicaid (28.3% and 31.4%,
respectively) than NH Whites (5.7%) and were less likely to hold private health
insurance (39.2% and 35.7%, respectively) compared with NH Whites
(71.3%).
Compared to NH White respondents, NH African American and
Hispanic respondents were more likely to report poorer self-rated health as
well as more functional limitations in terms of higher ADL/IADL limitations (p <
.01). Moreover, the prevalence of many diseases/chronic conditions (e.g.,

70
heart disease, hypertension, osteoporosis, etc.) differed among these three
racial/ethnic groups (p < .01). In addition, NH White respondents were more
likely to have environmental modifications such as bathroom modifications
(30.8%) and handrails (2.8%) than their NH African American (25.2% and
2.7%, respectively) and Hispanic (20.5% and 1.3%, respectively)
counterparts.
Predictors of falls among older adults
As shown in Table 3.2, being of advanced age and being female
increased the likelihood of experiencing a fall, whereas being NH African
American, being Hispanic, and living in an urban area decreased the likelihood
of experiencing a fall in Model 1. In Model 2, being of advanced age, being
female, having less than a high school education, having income of less than
$25,000, and being eligible for Medicaid increased the likelihood of
experiencing a fall. However, being NH African American, being Hispanic, and
living in an urban area decreased the likelihood of experiencing a fall in Model
2. Once the health characteristics were included in Model 3, the effects of
living in an urban area, having less than a high school education, having
income of less than $25,000, and being eligible for Medicaid ceased being
statistically significant predictors of a fall. In Model 3, being of advanced age;
having a higher number of ADL/IADL limitations; having a higher number of
mobility limitations; and having arthritis, depression, diabetes, hearing
problems, stroke, or urinary incontinence increased the likelihood of

71
experiencing a fall, whereas being NH African American and being Hispanic
decreased the likelihood of experiencing a fall. All of these predictors in Model
3 remained significant predictors of experiencing a fall in Model 4, but none of
the environmental characteristics were significant predictors of a fall in Model
4. Once the interaction terms were included in Model 5, the effects of being
NH African American and being Hispanic ceased being statistically significant
predictors of a fall. However, being of advanced age; having a higher number
of ADL/IADL limitations; having a higher number of mobility limitations; and
having arthritis, depression, diabetes, hearing problems, stroke, or urinary
incontinence increased the likelihood of experiencing a fall in Model 5.

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Table 3.2: Multivariate Models of Fall Predictors (N = 11,442)
Model 1 Model 2 Model 3 Model 4 Model 5
Variable OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)
Demographic characteristics
Age 1.03 (1.03, 1.04)** 1.03 (1.02, 1.04)** 1.01 (1.00, 1.02)** 1.01 (1.00, 1.02)** 1.01 (1.00, 1.02)**
Female 1.36 (1.24, 1.50)** 1.31 (1.12, 1.45)** 1.09 (0.97, 1.21)** 1.08 (0.97, 1.21)** 1.08 (0.97, 1.21)**
NH African American 0.84 (0.71, 0.99)** 0.71 (0.59, 0.85)** 0.69 (0.58, 0.84)** 0.70 (0.58, 0.84)** 0.63 (0.30, 1.35)**
Hispanic 0.80 (0.66, 0.97)** 0.66 (0.55, 0.81)** 0.74 (0.60, 0.91)** 0.75 (0.61, 0.92)** 1.46 (0.70, 3.04)**
Lives alone 1.01 (0.92, 1.12)** 0.96 (0.87, 1.07)** 1.04 (0.94, 1.16)** 1.04 (0.93, 1.15)** 1.05 (0.94, 1.18)**
Lives in an urban area 0.83 (0.75, 0.92)** 0.89 (0.80, 0.98)** 0.91 (0.82, 1.01)** 0.91 (0.81, 1.01)** 0.91 (0.81, 1.01)**
Socioeconomic
characteristics
Education 1.11 (1.00, 1.24)** 0.95 (0.85, 1.06)** 0.95 (0.85, 1.07)** 1.00 (0.89, 1.13)**
Income 1.17 (1.06, 1.30)** 1.03 (0.93, 1.15)** 1.03 (0.93, 1.15)** 0.98 (0.87, 1.10)**
Medicaid eligibility 1.46 (1.24, 1.72)** 1.03 (0.86, 1.22)** 1.02 (0.86, 1.22)** 1.03 (0.83, 1.28)**
Private insurance 1.05 (0.94, 1.16)** 1.04 (0.94, 1.17)** 1.04 (0.93, 1.16)** 1.07 (0.95, 1.21)**
Health characteristics
Self-rated health 1.02 (0.97, 1.07)** 1.02 (0.97, 1.08)** 1.03 (0.98, 1.09)**
Number of ADL/IADL
limitations 1.11 (1.08, 1.14)** 1.11 (1.08, 1.13)** 1.10 (1.07, 1.13)**
Number of mobility
limitations 1.18 (1.13, 1.22)** 1.18 (1.13, 1.22)** 1.19 (1.14, 1.24)**
Alzheimer’s disease 0.92 (0.69, 1.24)** 0.93 (0.70, 1.24)** 0.94 (0.71, 1.27)**
Arthritis 1.21 (1.10, 1.34)** 1.21 (1.09, 1.34)** 1.21 (1.10, 1.34)**
Depression 1.37 (1.24, 1.52)** 1.37 (1.24, 1.52)** 1.37 (1.23, 1.52)**
Diabetes 1.21 (1.08, 1.36)** 1.21 (1.07, 1.36)** 1.22 (1.08, 1.37)**
Heart disease 1.01 (0.88, 1.15)** 1.00 (0.88, 1.15)** 1.01 (0.88, 1.16)**
Hypertension 1.00 (0.91, 1.10)** 1.00 (0.90, 1.10)** 1.00 (0.91, 1.10)**
Osteoporosis 0.96 (0.85, 1.09)** 0.96 (0.84, 1.08)** 0.95 (0.84, 1.07)**
Parkinson’s disease 1.30 (0.91, 1.84)** 1.28 (0.90, 1.83)** 1.28 (0.90, 1.83)**

73
Table 3.2: Continued
Stroke 1.21 (1.05, 1.39)** 1.20 (1.05, 1.39)** 1.19 (1.03, 0.37)**
Urinary incontinence 1.33 (1.20, 1.49)** 1.33 (1.20, 1.48)** 1.34 (1.20, 1.49)**
Hearing problems 1.13 (1.02, 1.25)** 1.13 (1.02, 1.25)** 1.12 (1.02, 1.24)**
Vision problems 1.08 (0.98, 1.20)** 1.08 (0.98, 1.20)** 1.09 (0.98, 1.20)**
Environmental
characteristics
Bathroom modifications 1.08 (0.97, 1.21)** 1.09 (0.98, 1.22)**
Presence of handrails 0.98 (0.75, 1.29)** 1.07 (0.81, 1.42)**
Presence of ramps 1.02 (0.87, 1.20)** 1.04 (0.88, 1.23)**
Interactions
Black × Education 0.61 (0.41, 0.90)**
Black × Income 3.03 (1.66, 5.52)**
Black × Private Insurance 0.66 (0.43, 1.00)**
Hispanic × Self-Rated
Health 0.81 (0.65, 1.00)**
Hispanic × ADL/IADL
Limitations 1.09 (1.00, 1.18)**
2 log likelihood 12,084.35 12,036.83 11,279.67 11,277.12 11,235.33
Chi-square 182.48 230.01 987.17 989.71 1,031.50
Note: ADL = activity of daily living; CI = confidence interval; IADL = instrumental ADL; NH = Non-Hispanic; OR = odds ratio.
*p < .05. **p < .01.

74
Independent of all other variables, every one year of increase in age
increased the odds of experiencing a fall by 0.9%. Holding everything else
constant, having a higher number of ADL/IADL limitations, a higher number of
mobility limitations, arthritis, depression, diabetes, hearing problems, stroke,
and urinary incontinence increased the odds of experiencing a fall by 9.9%,
19.0%, 21.4%, 37.0%, 21.6%, 12.3%, 18.9%, and 34.0%, respectively.
Interaction effects
Interaction effects were tested between race/ethnicity and (a)
demographic characteristics, (b) socioeconomic characteristics, (c) health
characteristics, and (d) environmental characteristics. Logistic regression
analysis revealed a significant main effect for age, a high number of ADL/IADL
limitations, mobility limitations, arthritis, depression, diabetes, hearing
problems, stroke, and urinary incontinence, with no significant interaction
effects between race/ethnicity and living arrangement, or race/ethnicity and
environmental characteristics (these are not shown in Model 5 in Table 3.2
because there were no significant effects).
However, of the 10 interactions, having less than a high school
education, having income of less than $25,000, and having private insurance
significantly interacted with being NH African American. Being NH African
American with income of less than $25,000 significantly increased the
likelihood of experiencing a fall (OR = 3.03, 95% CI = 1.66, 5.52) compared to
those individuals who were not NH African American and had an income of

75
more than $25,000. However, being NH African American with less than a
high school education (OR = 0.61; 95% CI = 0.41, 0.90) or with private
insurance (OR = 0.66; 95% CI = 0.43, 1.00) significantly decreased the
likelihood of experiencing a fall compared to those individuals who were not
NH African American and who had more than a high school education or had
private insurance. In addition, having poorer self-rated health and a higher
number of ADL/IADL limitations significantly interacted with being Hispanic.
Being Hispanic with poorer self-rated health significantly decreased the
likelihood of experiencing a fall (OR = 0.81; 95% CI = 0.65, 1.00). However,
being Hispanic with a higher number of ADL/IADL limitations significantly
increased the likelihood of experiencing a fall (OR = 1.09; 95% CI = 1.00,
1.18).
Multivariate analyses of a fall
Predictors of a fall among NH Whites. Among NH White respondents
(n = 9,696), age (p < .01); a higher number of ADL/IADL limitations (p < .01); a
higher numbers of mobility limitations (p < .01); and prevalence of arthritis (p <
.01), depression (p < .01), diabetes (p < .01), hearing problems (p < .05),
stroke (p < .01), and urinary incontinence (p < .01) all increased the likelihood
of experiencing a fall. However, living in an urban area decreased the
likelihood of experiencing a fall (p < .05). Independent of all other variables,
age increased the odds of experiencing a fall by 1.1% per year. Having a
higher number of ADL/IADL limitations and a higher number of mobility

76
limitations increased the odds of experiencing a fall by 9.8% and 18.6%,
respectively, when everything else was held constant. Similarly, when
everything else was held constant, having arthritis, depression, diabetes,
hearing problems, stroke, and urinary incontinence increased the odds of
experiencing a fall by 24.5%, 39.6%, 19.7%, 11.7%, 20.9%, and 32.3%,
respectively. Conversely, living in an urban area decreased the odds of
experiencing a fall by 10.9%, independent of all other variables.

77
Table 3.3: Multivariate Models of Fall Predictors among Three Racial/Ethnic Groups

NH White
(n = 9,696) NH AA (n = 961) Hispanic (n = 785)
Variable OR (95% CI) OR (95% CI) OR (95% CI)
Demographic characteristics
Age 1.01 (1.00, 1.02)** 0.98 (0.95, 1.01)** 1.01 (0.98, 1.04)**
Female gender 1.06 (0.94, 1.19)** 1.36 (0.91, 2.04)** 1.27 (0.83, 1.96)**
Lives alone 1.04 (0.93, 1.17)** 0.94 (0.65, 1.37)** 1.03 (0.66, 1.61)**
Lives in an urban area 0.89 (0.79, 1.00)** 0.88 (0.55, 1.40)** 1.56 (0.73, 3.33)**
Socioeconomic characteristics
Education less than high school 1.00 (0.88, 1.13)** 0.63 (0.42, 0.93)** 0.87 (0.56, 1.35)**
Income less than $25,000 0.97 (0.87, 1.09)** 3.01 (1.64, 5.51)** 1.09 (0.62, 1.89)**
Medicaid eligibility 1.03 (0.83, 1.28)** 0.83 (0.53, 1.30)** 1.22 (0.75, 2.00)**
Private insurance availability 1.07 (0.95, 1.20)** 0.74 (0.49, 1.13)** 1.15 (0.71, 1.86)**
Health characteristics
Self-rated health 1.03 (0.97, 1.09)** 1.02 (0.84, 1.24)** 0.85 (0.69, 1.05)**
Sum of ADL and IADL limitations 1.10 (1.07, 1.13)** 1.11 (1.03, 1.20)** 1.19 (1.10, 1.30)**
Mobility limitations 1.19 (1.14, 1.24)** 1.14 (0.99, 1.31)** 1.10 (0.93, 1.29)**
Alzheimer’s disease 0.95 (0.69, 1.30)** 0.61 (0.24, 1.57)** 1.30 (0.34, 4.92)**
Arthritis 1.25 (1.12, 1.39)** 1.01 (0.68, 1.49)** 1.08 (0.71, 1.65)**
Depression 1.40 (1.25, 1.56)** 1.44 (0.97, 2.14)** 1.06 (0.70, 1.61)**
Diabetes 1.20 (1.05, 1.36)** 1.36 (0.92, 2.01)** 1.26 (0.80, 1.96)**
Heart disease 1.03 (0.89, 1.19)** 0.92 (0.51, 1.67)** 0.76 (0.42, 1.40)**
Hypertension 1.05 (0.95, 1.17)** 0.48 (0.32, 0.73)** 0.93 (0.61, 1.41)**
Osteoporosis 0.97 (0.85, 1.11)** 0.95 (0.50, 1.81)** 0.58 (0.32, 1.05)**

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Table 3.3: Continued
Parkinson’s disease 1.27 (0.88, 1.84)** 2.95 (0.70, 12.40)** 0.83 (0.11, 6.23)**
Stroke 1.21 (1.04, 1.41)** 1.24 (0.74, 2.05)** 0.75 (0.38, 1.51)**
Urinary incontinence 1.32 (1.18, 1.49)** 1.91 (1.24, 2.93)** 1.23 (0.76, 1.99)**
Hearing problems 1.12 (1.01, 1.24)** 1.22 (0.82, 1.83)** 1.16 (0.74, 1.83)**
Vision problems 1.07 (0.96, 1.19)** 0.89 (0.61, 1.32)** 1.61 (1.07, 2.43)**
Housing characteristics
Bathroom modification 1.09 (0.97, 1.22)** 1.09 (0.70, 1.69)** 1.02 (0.61, 1.68)**
Presence of handrails 1.06 (0.80, 1.41)** 0.35 (0.08, 1.51)** 0.43 (0.07, 2.66)**
Presence of ramps 1.03 (0.87, 1.22)** 0.72 (0.37, 1.39)** 1.22 (0.66, 2.26)**
2 log likelihood 9,680.78 818.28 696.86
Cox and Snell R
2
0.08 0.11 0.09
Nagelkerke R
2
0.13 0.18 0.14
Note: AA = African American; ADL = activity of daily living; CI = confidence interval; IADL = instrumental ADL; NH = non-Hispanic;
OR = odds ratio.
*p < .05. **p < .01.

79
Predictors of a fall among NH African Americans. Among NH African
American respondents (n = 961), having an income of less than $25,000, a
higher number of ADL/IADL limitations, and the presence of urinary
incontinence increased the likelihood of experiencing a fall (p < .01). However,
having less than a high school education (p < .05) and having hypertension (p
< .01) decreased the likelihood of experiencing a fall. Independent of all other
variables, respondents who had an income of less than $25,000 had 3.01
times greater odds of experiencing a fall than those who had an income of
more than $25,000. With everything else held constant, having a higher
number of ADL/IADL limitations and having urinary incontinence increased the
odds of experiencing a fall by 11.3% and 90.6%, respectively. However,
independent of all other variables, having less than a high school education
and the presence of hypertension decreased the likelihood of experiencing a
fall by 37.3% and 51.9%, respectively.
Predictors of a fall among Hispanics. Among Hispanic respondents (n
= 785), having a higher number of ADL/IADL limitations (p < .01) and having a
vision problem (p < .05) increased the likelihood of experiencing a fall.
Independent of all other variables, Hispanic respondents with a higher number
of ADL/IADL limitations had 19.3% greater odds of experiencing a fall. With
everything else held constant, having a vision problem increased the odds of
experiencing a fall among Hispanic respondents by 61.1%.

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D. Discussion
The primary purpose of this study was to examine (a) whether
race/ethnicity has a statistically significant association with the experience of a
fall, and (b) whether the relative magnitude of predictors of a fall differs across
racial/ethnic groups.
The results show significant effects of advancing age, race/ethnicity,
number of ADL/IADL limitations, number of mobility limitations, arthritis,
depression, diabetes, hearing problems, stroke, and urinary incontinence on
the likelihood of experiencing a fall. With respect to demographic
characteristics, the findings from this study confirm previous research showing
that being of advanced age increases the likelihood of experiencing a fall,
whereas being part of a racial/ethnic minority decreases the likelihood of
experiencing a fall among older adults. In addition, previous findings that
noted the significant effect of health characteristics (e.g., poor functional ability,
poor mobility, depressive symptoms, urinary incontinence) on the likelihood of
experiencing a fall are clearly confirmed by this study in that a higher number
of ADL/IADL and mobility limitations and the presence of certain chronic
conditions/diseases increased the likelihood of experiencing a fall. In
particular, the positive association between higher level of functional
limitations and increased risk of falling is in accordance with a study by
Langlois et al. (1995), who, using data from the Study to Assess Falls Among
the Elderly (N = 991), reported that dependence in ADLs is associated with

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increased risk of falling in older persons. However, future studies need to
investigate whether the close associations between urinary incontinence,
depression, level of mobility and falls are due to causal connections or shared
risk factors (Lord et al., 2007).
With respect to the effect of race/ethnicity on the likelihood of
experiencing a fall, being NH African American and being Hispanic decreased
the likelihood of experiencing a fall (p < .01). This finding is consistent with the
results of a study by Hanlon et al. (2002) that reported that African American
older adults had a reduced risk of experiencing a fall compared to White older
adults. One possible explanation is due to selection bias in that healthier NH
African American and Hispanic older adults who survived to older age (i.e.,
age 65 and older) were included in this study. A detailed study that
investigates the association between race and falls in terms of prevalence, fall
circumstances, location, and consequences (e.g., injuries) should be
conducted using a well-designed, robust study design.
The relative magnitude of predictors of a fall among older adults varied
slightly across racial/ethnic groups. With respect to the association between
socioeconomic characteristics and falls, having an income of less than
$25,000 increased the likelihood of experiencing a fall among NH African
American respondents. This is not surprising given the fact that falls are
closely related to poor health conditions, and individuals within each race
group with a substantially higher risk of mortality and morbidity are generally

82
poorer, less educated, employed in occupations that are service oriented, or
not in the labor force at all (Sorlie et al., 1995). Yet having less than a high
school education decreased the likelihood of experiencing a fall among NH
African American respondents by 37%. This is somewhat consistent with a
study by Stel and colleagues (2003) that reported that a high level of education
was a predictor for recurrent falling. This could reflect a reporting bias among
better educated persons. However, the mechanism between level of
education and the likelihood of experiencing a fall should be explored further.
With respect to the association between health characteristics and falls,
certain chronic conditions/diseases differed across racial/ethnic groups in
predicting the likelihood of falls. Unlike among minority older adults, mobility
limitations, arthritis, depression, diabetes, hearing problems, and stroke were
significant predictors of falls among NH White respondents. One possible
explanation for this is that NH White older adults may be more aware than
minority older adults of health conditions/diseases because those measures
were based on doctors’ diagnoses, not self-reports or other subjective
appraisals/evaluations of one’s health characteristics, and therefore were
more sensitive indicators. For example, Kutner, Greenberg, Jin, and Paulsen
(2006) reported that White and Asian/Pacific Islander adults had higher
average health literacy—defined as the degree to which individuals have the
capacity to obtain, process, and understand basic health information and

83
services needed to make appropriate health decisions—than Black, Hispanic,
American Indian/Alaska Native, and multiracial adults.
Moreover, unlike among NH White respondents, having hypertension
decreased the likelihood of experiencing a fall among NH African American
respondents. This is a counterintuitive finding in that sedentary behavior,
which increases the risk of falling, was associated with the prevalence of
hypertension among Black women (Ainsworth, Keenan, Strogatz, Garrett, &
James, 1991). However, the negative association between high blood
pressure and fall injury events was reported in a previous study conducted by
Herndon et al. (1997), whose findings indicated that high blood pressure had a
protective effect on experiencing a fall. They suggested that their results were
consistent with previous studies that had found low systolic blood pressure to
be a risk factor for falls. Among Hispanic respondents, having a vision
problem increased the likelihood of experiencing a fall by 61%. One possible
explanation is that some vision problems such as glaucoma, which is the
second leading cause of blindness, are more prevalent with increasing age in
Hispanics than in other ethnic groups (National Eye Institute, 2002; Quigley et
al., 2001).
The major limitations of this study are as follows. First, the self-report
nature of this study, which relied on the recall of the respondents, may have
subjected the data to a certain amount of error. Second, the restriction of
study samples to individuals aged 65 and older may mean that the samples

84
represented hardier minority older persons who survived morbidity and
mortality at earlier ages compared to their NH White counterparts; Mendes de
Leon and colleagues (2005) pointed out that premature mortality due to
cumulative disadvantage among minority groups may result in the survival of a
comparatively healthier group of older adults. Third, the cross-sectional
nature of this study requires cautious interpretation of the findings.
Despite these limitations, the current study adds value to earlier
research because the relative magnitude of predictors of a fall among minority
older adults was examined by using a nationwide sample of
community-dwelling individuals. Moreover, this study explored the
comorbidities of falls among three racial/ethnic groups; differentiating the
relative contribution of preexisting disease to the risk of falling is important
because it enables clinicians to identify high-risk individuals who may benefit
from targeted intervention (Lord et al., 2007). This study confirms that physical
functioning in terms of ADL/IADL limitations increases the likelihood of
experiencing a fall among all NH Whites, NH African Americans, and
Hispanics, although by slightly different magnitudes. Yet socioeconomic
characteristics in terms of having less than a high school education and having
an income of less than $25,000 have an impact on the likelihood of
experiencing a fall for NH African Americans only. Given that SES influences
access to health services, the ability to act on health advice, and the capacity
to modify health risk factors, future fall prevention interventions should

85
consider the capacity of individuals and population subgroups to comply with
best practices. Future interventions should also attempt to overcome barriers
in terms of structural, material, economic, and environmental constraints
experienced by population subgroups (Aldrich et al., 2003).

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CHAPTER IV: IMPACT OF FALLS ON HEALTH CARE AND LONG-TERM
CARE UTILIZATION
A. Introduction
Falls among older adults have significant consequences in terms of
adverse functional outcomes (e.g., injury, decline in health, loss of function
and independence), negative psychological impact (e.g., fear of falling,
depression), and high health care utilization, as well as reduced quality of life.
For example, a study by Stel, Smit, Pluijm, and Lips (2004), which used data
from the Longitudinal Aging Study Amsterdam (N = 204), yielded the following
findings: Almost 70% of the respondents, who were community-dwelling older
adults who had reported at least one fall in the year before the interview,
suffered physical injury; more than one third (37.3%) of the respondents
reported a decline in functioning; and almost one quarter (23.5%) of the
respondents used health services as a consequence of their falls. Laird,
Studenski, Perera, and Wallace (2001) also reported that a history of falls in
the preceding year was a risk factor for functional decline, adverse health
events, and health service utilization among members of a Medicare managed
care population (N = 350); in their study, individuals with one or more falls were
3.5 times more likely to have a subsequent decline in functioning and were 2.4
times more likely to be hospitalized.
With respect to adverse functional outcomes, falls—especially injurious
or repetitive falls—contribute to immediate and ongoing functional

87
dependence. For example, Tinetti and Williams (1998), using a probability
sample of 957 community-dwelling residents of New Haven, Connecticut, who
were older than age 71, found that one noninjurious fall, at least two
noninjurious falls, and at least one injurious fall were each associated with a
decline in ADL and IADL functions over 3 years; two or more noninjurious falls
were associated with a decline in social activities; and at least one injurious fall
was associated with a decline in physical activity. The authors also reported
that multiple-time fallers and those with an injurious fall experienced a greater
decline in ADL and IADL functioning—and a substantial decline in social
activities—compared to one-time fallers. Katsumata, Arai, and Tamashiro
(2007), using data from a community-based longitudinal study of persons
aged 65 or older (N = 663), also reported that baseline falling and homebound
status (i.e., left home only once per week or less) were significantly associated
with a subsequent decline in ADLs over a 1-year follow-up and in ADLs,
IADLs, intellectual activity, and social role over a 2-year follow-up. Roudsari,
Ebel, Corso, Molinari, and Koepsell (2005) also pointed out that fewer than
one third of older adults are able to return to their pre-fall level of activity
following hospitalization for a fall, and a significant proportion of older adults
need long-term or even lifetime health care.
With respect to the negative psychological impact of falling, falls often
generate fear of falling, which has a range of consequences including
increased caution, excessive restrictions on—or avoidance of—activity,

88
increased dependency, depression, decreased social contact, and lower
quality of life (Delbaere, Crombez, Vanderstraeten, Willems, & Cambier, 2004;
Friedman, Munoz, West, Rubin, & Fried, 2002; Murphy, Williams, & Gill, 2002;
Scheffer, Schuurmans, van Dijk, van der Hooft, & de Rooij, 2008; Stel et al.,
2004). In their systematic review of 28 relevant studies concerning fear of
falling among community-dwelling elders, Scheffer et al. (2008) noted that
having had at least

one fall is reported in most studies as an independent risk

factor for developing fear of falling, and fear of falling often leads to a decline in
physical, functional, and psychological performance; an increased risk of
falling; and progressive loss

of health-related quality of life. Tinetti, Mendes de
Leon, Doucette, and Baker (1994), using a probability sample of 1,103
residents of New Haven, Connecticut, who were aged 72 years and older and
who were members of the Project Safety cohort, found that the proportion of
participants reporting a decrease in activity due to fear of falling was 24%
among fallers versus 15% among nonfallers. Likewise, Zijlstra and colleagues
(2007) found, in their sample of 4,031 older people randomly selected from the

general population of community-living older people, that older adults who had
fallen more than once were more than four times as likely to

avoid activities
compared to nonfallers. Murphy et al. (2002), using community-dwelling older
adult samples from the Project Safety project (N = 1,064), also reported that a
history of an injurious fall within the past year was independently associated
with activity restriction. Delbaere et al. (2004) reported that fear of falling and

89
restriction of ADLs predicted falls during a 1-year follow-up in their study,
which used 225 community-living individuals aged 60 years and older who
were recruited on a voluntary basis by mail. Friedman and colleagues (2002),
using samples from the Salisbury Eye Evaluation project (N = 2,212), also
reported that falls at baseline was an independent predictor of the onset of fear
of falling after 20 months, and reporting fear of falling at baseline was an
independent predictor of falling at 20 months follow-up.
With respect to health care utilization due to a fall, many studies have
reported that falls among older adults place heavy demands on the health care
system. In general, falls—repetitive and injurious falls in particular—are
associated with an increased likelihood of health care utilization (e.g.,
hospitalization) and institutionalization (e.g., nursing home use), and fallers
report higher health care costs compared to their nonfaller counterparts. For
example, Kiel, O’Sullivan, Teno, and Mor (1991), using data from the
Longitudinal Study of Aging (N = 4,113), found that one-time fallers, and
especially repeated fallers (i.e., those with two or more falls in the preceding
year), were at greater risk for subsequent hospitalization, nursing home
admission, and frequent physician contact than were nonfallers, after
controlling for age, sex, self-perceived health status, and difficulties with ADLs.
The authors also reported that fallers were at greater risk for reporting
subsequent difficulties with ADLs, IADLs, and more physically demanding
activities than nonfallers.

90
Using data from the Health Care Financing Administration and the
Connecticut Long-Term Care Registry (N = 1,017), and controlling for
sociodemographic characteristics and physical and mental health status,
Rizzo et al. (1998) reported that falls were associated with increased health
care costs and that these costs increased with the frequency and severity of
falls. In comparison with nonfallers, participants in this study who incurred one
or more injurious falls were (a) more than 3 times as likely to receive hospital
care, (b) 4 times as likely to use emergency room service, (c) more than 16
times as likely to receive nursing home care, and (d) 7 times as likely to use
home health care services. Moreover, Chu, Chiu, and Chi (2008) reported that
fallers had a significantly greater number of hospitalizations as well as a
greater number of visits to general outpatient departments, specialist clinics,
general practitioners, and emergency departments than nonfallers, using their
population-based sample of 1,517 older adults aged 65 years or older.
Furthermore, Wolinsky, Fitzgerald, and Stump (1997) found that hip fracture,
as the most serious fall injury, significantly increased the likelihood of
subsequent hospitalization and increased the number of hospital days by
21.3%.
Many previous studies have focused on the increased health care costs
associated with treating fall-related injuries; thus, research on the long-term
demand for services due to falling is relatively lacking (Wolinsky, Johnson, &
Fitzgerald, 1992). One piece of evidence demonstrating the enduring

91
relationships between falling and the use of health care and LTC services
appears in a study by Wolinsky and colleagues (1992). Using data from the
Longitudinal Study on Aging (N = 5,151), these authors reported that a
one-time fall was associated with an increased likelihood of nursing home
placement, whereas repetitive falling was associated with decreased health
status at both 2- and 4-year follow-ups; they also found that repetitive falling
was associated with an increased likelihood of hospitalization, nursing home
placement, and death at both 2- and 4-year follow-ups. Likewise, using 690
persons aged 75 and older who were hospitalized through the emergency
department of an academic hospital in Switzerland, Seematter-Bagnoud,
Wietlisbach, Yersin, and Büla (2006) reported that elderly patients hospitalized
after a noninjurious fall were twice as likely to be institutionalized as those
admitted for other medical conditions, independent of functional and health
status; these patients also incurred higher rehabilitation and LTC costs during
the 6-month follow-up period. Alexander, Rivara, and Wolf (1992) also
reported that noninstitutionalized persons injured in falls were discharged to
nursing care 3 times more often than were persons hospitalized for nontrauma
cases, using data from the population-based hospital discharge registry in the
State of Washington (N = 149,504). Similarly, using data from the Longitudinal
Study of Aging (N = 3,357 for the 2-year follow-up, N = 2,681 for the 4-year
follow-up), Dunn, Furner, and Miles (1993) found an association between
multiple falls and an increased risk of institutionalization at both 2-year and

92
4-year follow-ups, but the effect of being a multiple-time faller on increased risk
of institutionalization ceased being significant when the authors controlled for
demographic traits and measures of disability at baseline (i.e., number of
difficulties with ADLs).
Objective and research question
Previous studies have often focused on short-term service utilization
and related costs of falls, and therefore have not generally investigated the
enduring effect of falls and subsequent falls on health care and LTC service
utilization in the following years. In addition, some of the existing studies have
not controlled for the comorbidities of fallers, even though comorbid conditions
are as important as the demographic characteristics and functional limitations
of fallers in terms of their effect on health care and LTC utilization. Therefore,
the purpose of this study was to estimate the consequences of falls among
older adults in terms of the effect of past falls (i.e., history of falling in the past
year) and falls on subsequent health care and LTC utilization (i.e., any
overnight stay in a hospital, use of nursing home, use of home health care,
number of hospitalizations, number of physician visits), controlling for a
number of covariates that are associated with health care and LTC utilization
among older adults. In particular, socioeconomic characteristics, which are
closely related to an individual’s ability to use health care services or his or her
means of obtaining health care services, were included in the model along with
demographic and health characteristics.

93
B. Methods
Sample
Data from two waves of the Health and Retirement Study (HRS)—2002
HRS and 2004 HRS—were used in this study. Funded by the National
Institute on Aging, with supplemental support from the Social Security
Administration, and conducted by the Institute for Social Research Survey
Research Center at the University of Michigan, the HRS is a national
probability sample survey of noninstitutionalized older Americans with
supplemental oversamples of Blacks, Hispanics and residents of the State of
Florida. The HRS is designed to examine health and retirement decision
making as well as how older adults and their families respond to declining
health in later life.
In their original conceptualization, the HRS and the Asset and Health
Dynamics Among the Oldest Old (AHEAD) studies were created as separate
but related surveys (HRS, 2006a). The HRS was designed to collect
information on persons from pre-retirement into retirement as individuals
made the transition from active workers to retired persons, whereas the
AHEAD study was designed to examine the dynamic interactions between
health, family, and economic variables in the post-retirement period at the end
of life (HRS, 2006a). Since 1998, however, the HRS and AHEAD studies have
been merged, and respondents from each study have formed a cohort in a

94
combined interview. At the same time, two new cohorts were added: the
Children of the Depression Age and the War Babies.
Collected from February 2002 through March 2003, the 2002 HRS
sample includes 18,167 persons in 12,350 households (HRS 2006a). As a
nationally representative sample, the 2002 HRS sample is composed of four
sub-samples: (a) the AHEAD sub-sample (born 1923 or earlier), (b) the HRS
sub-sample (born 1931–1941), (c) the Children of the Depression Age (born
1924–1930), and (d) the War Babies (born 1942–1947). The 2004 HRS
sample was collected from March 2004 through February 2005, and it includes
20,129 persons in 13,645 households (HRS, 2006b). The 2004 HRS sample
is comprised of five sub-samples: (a) the AHEAD (born 1923 or earlier), (b) the
original 1992 HRS (born 1931–1941), (c) the Children of the Depression Age
(born 1924–1930), (d) the War Babies (born 1942–1947), and (e) the Early
Baby Boomers (born 1948–1953).
Both the 2002 and 2004 HRS encompass multiple domains in the lives
of persons older than age 50, including information on the demographic,
cognitive, economic, health, work, and family statuses of respondents and
their spouses. In the 2002 and 2004 HRS, falls were defined as incidents in
which the respondent had fallen down (HRS, 2006a, 2006b). The surveys
contained three questions related to falls: (a) “Have you fallen down in the last
two years?” (yes = 3,196 in 2002; yes = 3,642 in 2004), (b) “How many times
have you fallen in the last two years?” (minimum = 0, maximum = 50), and (c)

95
“In that fall and/or in any of these falls, did you injure yourself seriously enough
to require medical treatment?” (yes = 1,124 in 2002; yes = 1,201 in 2004).
Of the 23,630 respondents who were aged 65 and older in HRS 2002
and respondents of any age in HRS 2004, only those participants (N = 9,334)
who were aged 65 or older who were interviewed in both 2002 and 2004, and
those who answered at both waves whether they had fallen or not, were
selected for the present study. Among individuals aged 65 and older who fell
in 2002, approximately 4.5% (n = 482) were reported to be dead in 2004. It
should be noted that the respondents left out of the analyses were statistically
significantly older, male, and unmarried; had lower SES; poorer health status
(i.e., poorer self-rated health and lower Telephone Interview for Cognitive
Status [TICS] scores), higher numbers of ADL/IADL limitations and functional
limitations; and had a higher prevalence of the following chronic
conditions/diseases: broken hip, cancer, diabetes, heart disease, lung
disease, stroke, urinary incontinence, hearing problems, vision problems, and
depression. After the deletion of cases with item-level nonresponse and with
missing values for analytic models, the resulting sample size was 7,287.
Measures
Dependent variables
The primary dependent measures were as follows: (a) overnight stay in
a hospital: Respondents were asked “(In the last two years/since the previous
interview), have you been a patient in a hospital overnight?” (b) nursing home

96
use: Respondents were asked “(In the last two years/since the previous
interview), have you been a patient overnight in a nursing home, convalescent
home, or other LTC facility?” (c) home health care use: Respondents were
asked “(In the last two years/since the previous interview), has any
medically-trained person (e.g., professional nurse, visiting nurse’s aide,
physical or occupational therapist, chemotherapist, or respiratory oxygen
therapist) come to your home to help you?” (d) number of times a respondent
was a patient in a hospital overnight: Respondents were asked “How many
different times were you a patient in a hospital overnight since the last
interview/in the last two years?” and (e) number of physician visits:
Respondents were asked “(Aside from any hospital stays, outpatient surgery,
both hospital stays and outpatient surgery) how many times have you seen or
talked to a medical doctor about your health, including emergency room or
clinic visits, since the last interview/in the last two years?” Dichotomous
dependent variables were created for any overnight stay in a hospital, any
nursing home use, and any home health care use (yes = 1, no = 0); continuous
dependent variables were created for number of hospitalizations and number
of times that respondents had seen a doctor. Given the small number of
respondents who had multiple nursing home stays, the number of times a
respondent had an overnight stay in a nursing home was not included in the
analyses.

97
Independent variables
The major independent variables included the experience of falls in
2002 and in 2004. The experience of falls in each of these years was
measured according to whether the respondent had fallen down since the
previous interview or in the past 2 years. The experience of falls in 2002 was
further classified as a one-time fall, multiple falls, noninjurious fall(s), or
injurious fall(s).
The covariates were selected based on their potential association with
either the fall-related variables or with health care and LTC utilization. The
covariates were divided into four categories: demographic, socioeconomic,
health (in terms of chronic conditions and functioning), and changes in health
between 2002 and 2004. Demographic variables included age, gender,
race/ethnicity, and marital status. Age was measured in actual number of
years. Gender and marital status were recoded dichotomously, with female =
1 and male = 0, and married = 1 and not married = 0. Race/ethnicity was
recoded as NH White, NH African American, Hispanic, and Other by
combining the race and Hispanic/Latino origin variables. Other racial/ethnic
categories included American Indian or Alaskan Native, Asian, or Pacific
Islander.
Socioeconomic characteristics were measured by education, Medicaid
coverage, and availability of private insurance. The variables for education,
Medicaid coverage, and the availability of private insurance were each coded

98
as dichotomous variables: less than a high school education = 1 versus more
than a high school education = 0; had Medicaid coverage = 1 versus did not
have Medicaid coverage = 0; and had other insurance coverage = 1 versus did
not have other insurance coverage = 0.
Baseline health characteristics were measured by self-rated health,
number of ADL and IADL limitations, number of functional limitations, number
of chronic conditions/diseases, hearing problems, vision problems,
depression, and cognitive status. Self-rated health was assessed by asking
respondents to rate their general health condition (1 = excellent, 2 = very good,
3 = good, 4 = fair, 5 = poor). Number of ADL difficulties was measured by
calculating the sum of limitations on six basic ADL items. The respondents
were asked whether they had any difficulty with (a) dressing, (b) walking, (c)
bathing, (d) eating, (e) getting into and out of bed, or (f) using a toilet. If
respondents had difficulty with any of these items, their response to that item
was coded as yes = 1, and the number of difficulties was summed. The
number of IADL difficulties was measured by computing the sum of five IADL
items. The respondents were asked whether they had difficulty with (a)
preparing meals, (b) grocery shopping, (c) using the telephone, (d) taking
medication, or (e) managing money. If respondents had difficulty with any of
these items, their response to that item was coded as yes = 1, and the number
of difficulties was summed. The number of ADL and the number of IADL
difficulties were combined for regression models; combining ADL and IADL

99
items into a single overall scale has been justified in previous studies (Spector
& Fleishman, 1998). Functional limitations were measured by asking
respondents if they had difficulty (a) walking several blocks, (b) jogging 1 mile,
(c) sitting for 2 hours, (d) getting up from a chair, (e) climbing stairs, (f)
stooping, (g) reaching out his/her arms, (h) pulling/pushing large objects, (i)
lifting weights, or (j) picking up dimes. If respondents had difficulty with any of
these items, their response to that item was coded as yes = 1, and the number
of limitations was then measured by computing the sum of the 10 mobility
limitations (range = 0–10).
With respect to baseline chronic conditions/diseases, a series of
questions asking the respondents if they had been told by a doctor that they
had arthritis, broken hip, cancer, diabetes, heart disease, hypertension, lung
disease, stroke, or urinary incontinence was used to assess the respondents’
medical condition; these items were recoded as dichotomous variables (yes =
1, no = 0). Because the primary interest was to examine the effect of
controlling for multiple chronic conditions rather than examining the effects of
specific covariates upon the outcomes, the frequency of each chronic
condition/disease was calculated by summing the following items: arthritis,
cancer, diabetes, heart disease, hypertension, lung disease, and stroke
(range = 0–7). However, given the close associations of broken hip and
urinary incontinence with fall and with health care utilization, broken hip and
urinary incontinence were included as separate covariates. Hearing and

100
vision problems were measured by asking each respondent if he or she had
trouble hearing or seeing, respectively; for both the hearing problems and the
vision problems variables, responses were recoded as dichotomous variables
(yes = 1, no = 0).
Baseline depression was measured by using an 8-item truncated
version of the Center for Epidemiologic Studies–Depression scale (CES-D),
with higher scores indicating more depressive symptoms. Each respondent
was asked if he or she was depressed, felt like everything was an effort, slept
restlessly, was happy, felt lonely, enjoyed life, felt sad, or could not get going
for much of the time during the past week. All items worded in the positive
direction were reverse scored, and a summary score (range = 0–8) was
created by summing the number of “yes” answers across the eight items.
Cognitive status was measured by using the global cognitive score of the
TICS, which is a validated cognitive screening measure designed for use in
telephone surveys (Brandt, Spencer, & Folstein, 1988). The baseline TICS
scores, with higher scores indicating better cognitive status, were calculated
based on the answers given by respondents, who were asked to list the
month, day of the month, year, date, current president, and current vice
president; as well as to name the object that people usually use to cut paper,
name the kind of prickly plant that grows in the desert, and count backward
from 20 to 10. A score of 0 to10 was given based on the number of correct
answers.

101
In order to control for any health changes between HRS 2002 and HRS
2004, the following variables were included in the analyses: poorer self-rated
health, more ADL/IADL limitations, more functional limitations, more chronic
conditions/diseases, broken hip in 2004, urinary incontinence in 2004, vision
problems in 2004, hearing problems in 2004, more depression in 2004, and
lower TICS scores in 2004 compared to 2002. With respect to self-rated
health, one question asked respondents whether their health was better now,
about the same, or worse compared to their health in the previous interview. If
a respondent answered that his/her health was worse in 2004 compared to the
interview in 2002, his/her answer was recoded as 1, whereas answers of
better or about the same were recoded as 0. Each of the following scores in
HRS 2002 were subtracted from the corresponding score in HRS 2004 in
order to assess change over time: number of ADL/IADL limitations, number of
functional limitations, number of chronic conditions/diseases, CES-D score,
and TICS score. If a respondent had a higher number of limitations/difficulties,
a higher number of chronic conditions/diseases, higher depression (i.e.,
CES-D) scores, or lower TICS scores in 2004 compared to 2002, his/her
response was recoded as dichotomous variables (1 = yes, 0 = no). In addition,
broken hip in 2004, urinary incontinence in 2004, vision problems in 2004, and
hearing problems in 2004 were each included in the analyses as dichotomous
variables (yes = 1, no = 0).

102
Analysis
Preliminary descriptive analyses of the baseline data were used to
characterize the study sample. In addition, health characteristics in 2004, as
well as health care use in 2004, were compared by frequency of falls and
severity of falls in 2002. Pearson chi-square tests for categorical variables and
analyses of variance for continuous variables were conducted.
In order to examine the associations between baseline fall history in
2002, fall status in 2004 and health care and LTC utilization, a hierarchical
series of logistic regression and ordinary least squares regression models
were constructed to examine fall status in 2002 and 2004 as a predictor of
health care and LTC use in 2004, in which each category of covariates was
successively added. The first model included only the dummy variables for fall
status in 2002 and 2004, and the final model included all four groups of
covariates, in order of entry: demographic characteristics, socioeconomic
characteristics, health characteristics in 2002, and changes in health between
2002 and 2004 (see Figure 4.1). The associations between baseline fall
history by frequency and severity in 2002, fall status in 2004, and health care
and LTC utilization in 2004 were also examined.

103
Figure 4.1: Analytic Model Predicting the Effect of Falls on Health Care and Long-Term Care
Utilization

HRS 2002 HRS 2004 HRS 2004

Note: ADL = activity of daily living; HRS = Health and Retirement Study; IADL = instrumental
ADL; TICS = Telephone Interview for Cognitive Status.
Socioeconomic
characteristics
• Education
• Medicaid
eligibility
• Private
insurance
availability
Fall in 2002
Fall in 2004
Demographic
characteristics
• Age
• Gender
• Race/ethnicity
• Marital status

1. Any overnight
stay in a hospital in
2004

2. Any nursing
home use in 2004

3. Any home health
care use in 2004

4. Number of
hospitalizations in
2004

5. Number of
physician visits in
2004
Health
characteristics
• Self-rated
health
• Number of
ADL & IADL
limitations
• Number of
functional
limitations
• Number of
chronic
conditions/
diseases
• Broken hip
• Urinary
incontinence
• Vision
problems
• Hearing
problems
• Depression
• TICS scores
Health change
between 2002 and
2004
• Worse
self-rated health
• More ADL &
IADL limitations
• More functional
limitations
• More chronic
conditions/
diseases
• Broken hip
• Urinary
incontinence
• Vision problems
• Hearing
problems
• More
depression
• Lower TICS
scores

104
Statistical analyses were performed using the SPSS package, Version
14.0 for Windows. All analyses were weighted, and thus, results are
applicable to community-dwelling older adults aged 65 and older in the United
States.
C. Results
Descriptive analyses
Table 4.1 shows the demographic characteristics, socioeconomic
characteristics, health characteristics, and health care use of respondents with
respect to their fall status (i.e., nonfallers, one-time fallers, multiple-time
fallers). Among the entire sample, the mean age was 73.96, with a standard
deviation of 6.54. Approximately 59.5% were female, and 57% were married.
The sample included an ethnic mix of 85.9% NH White, 7.5% NH African
American, 4.8% Hispanic, and 1.7% Other. About 26.5% had less than a high
school education, 6% had Medicaid coverage, and 67.3% had at least one
form of private health insurance. A majority of the respondents (76.3%)
reported excellent to good self-rated health. The respondents had an average
of 0.26 ADL and 0.13 IADL limitations, as well as 2.45 functional limitations.

105
Table 4.1: Baseline Characteristics of the Study Population by Fall Status: Frequency of Falls (N = 7,287)
Variable
Entire
Sample
(N = 7,287)
Nonfallers
(n = 5,445)
One-Time
Fallers
(n = 964)
Multiple-Time
Fallers
(n = 878) p
a

Demographic characteristics
Age, M (SD) 73.96 (6.54) 73.50 (6.34) 75.06 (6.93) 75.53 (6.95) .00
Female gender, % 59.5 57.4 67.2 64.4 .00
Race/ethnicity .00
NH White, % 85.9 85.3 87.2 88.6
NH African American, % 7.5 7.9 7.4 5.0
Hispanic, % 4.8 4.8 4.0 5.8
Other, % 1.7 2.0 1.5 0.6
Married, % 57.0 58.5 54.3 50.8 .00
Socioeconomic characteristics
Education less than HS, % 26.5 26.3 25.8 29.1 .05
Medicaid coverage, % 6.0 5.9 5.4 7.5 .04
Private health availability, % 67.3 67.1 66.7 69.4 .18
Health characteristics
Self-rated health, M (SD) 2.76 (1.05) 2.67 (1.03) 2.84 (1.05) 3.20 (1.07) .00
ADL limitations, M (SD) 0.26 (0.75) 0.18 (0.62) 0.33 (0.86) 0.66 (1.14) .00
IADL limitations, M (SD) 0.13 (0.47) 0.10 (0.40) 0.15 (0.50) 0.30 (0.72) .00
Functional limitations, M (SD) 2.45 (2.49) 2.12 (2.31) 2.87 (2.52) 4.06 (2.81) .00
Sum of chronic conditions/diseases, M (SD) 2.01 (1.24) 1.90 (1.20) 2.15 (1.23) 2.48 (1.33) .00

106
Table 4.1: Continued
Arthritis, % 65.9 62.9 73.1 76.1 .00
Broken hip, % 0.8 0.2 2.6 2.0 .00
Cancer, % 16.7 16.1 18.2 18.7 .01
Diabetes, % 16.5 15.0 18.9 22.8 .00
Heart disease, % 28.1 25.9 28.0 41.4 .00
Hypertension, % 56.8 55.6 59.0 62.0 .00
Stroke, % 6.8 5.4 7.9 14.1 .00
Lung disease, % 9.8 9.2 9.7 13.1 .00
Urinary incontinence, % 21.0 17.3 27.1 36.9 .00
Vision problems, % 19.2 17.4 20.9 28.5 .00
Hearing problems, % 22.0 20.4 23.6 30.2 .00
Depression, M (SD) 1.37 (1.80) 1.24 (1.71) 1.50 (1.87) 1.97 (2.11) .00
TICS, M (SD) 9.36 (1.05) 9.40 (1.01) 9.32 (1.05) 9.17 (1.21) .00
Health care use
Hospitalization, % 27.3 24.8 30.9 38.4 .00
Nursing home use, % 2.5 1.8 3.5 5.3 .00
Home health care use, % 6.5 5.1 9.9 11.5 .00
Number of times in hospital, M (SD) 0.44 (0.99) 0.39 (0.96) 0.49 (0.98) 0.68 (1.15) .00
Number of physician visits, M (SD) 9.51(11.98) 8.74 (10.68) 10.44 (12.07) 13.27 (17.49) .00
Note: ADL = activity of daily living; HS = high school; IADL = instrumental ADL; NH = non-Hispanic; TICS = Telephone Interview for
Cognitive Status.
a
p values are for the overall comparisons among study groups.

107
Both one-time and multiple-time fallers were in general slightly—but
statistically significantly—older than nonfallers, although there was no
statistical age difference between one-time and multiple-time fallers.
Compared to nonfallers, fallers were more likely to be female, NH White, and
unmarried. With respect to health characteristics, fallers were more likely than
their nonfaller counterparts to report poorer self-rated health; to have higher
numbers of ADL, IADL, and functional limitations; and to report a higher
prevalence of the following chronic conditions/diseases: arthritis, broken hip,
cancer, diabetes, heart disease, hypertension, stroke, urinary incontinence,
vision problems, and hearing problems. Compared to nonfallers, fallers had
higher CES-D scores, which indicated more depression, and lower TICS
scores, which indicated poorer cognitive functioning. With respect to health
care utilization, fallers had higher utilization with respect to the following
services: hospitalization, nursing home, home health care, number of
hospitalizations, and number of physician visits (i.e., number of times
seen/talked to a medical doctor aside from any hospital stays). All of these
differences were statistically significant.
Compared to one-time fallers, multiple-time fallers were more likely to
be male, NH White, and unmarried. With respect to socioeconomic
characteristics, multiple-time fallers were more likely to have less than a high
school education and to have Medicaid coverage compared to their one-time
faller counterparts. With respect to health characteristics, compared to

108
one-time fallers, multiple-time fallers were more likely to report poorer
self-rated health; to have more ADL, IADL, and functional limitations; to have
higher rates of prevalence for the 10 previously listed chronic
conditions/diseases except broken hip; and to have higher CES-D scores and
lower TICS scores. With respect to health care use, multiple-time fallers had
higher utilization of the above aforementioned five services. All of these
differences were statistically significant.
Table 4.2 presents the demographic characteristics, socioeconomic
characteristics, and health characteristics of respondents with respect to
severity of a fall (i.e., nonfallers, fallers with no injury, fallers with injuries). In
general, compared to nonfallers, fallers both with and without injuries were
older, female, NH White, and unmarried and had poorer health status.
Compared to fallers without injuries, fallers with injuries were more likely to be
female; be unmarried; have Medicaid coverage; have poorer self-rated health;
have higher numbers of ADL, IADL, and functional limitations; and have a
higher prevalence of the following chronic conditions: arthritis, broken hip,
hypertension, stroke, and vision problems. In addition, fallers with injuries
reported higher CES-D scores compared to fallers without injuries. However,
fallers without injuries had a higher prevalence of the following chronic
conditions: cancer, diabetes, heart disease, lung disease, and hearing
problems. Fallers with injuries had higher health care utilization compared to
fallers with no injuries. All of these differences were statistically significant.

109
Table 4.2: Baseline Characteristics of the Study Population by Fall Status: Severity of Falls (N = 7,287)
Variable
Entire
Sample
(N = 7,287)
Nonfallers
(n = 5,445)
Fallers with No
Injury
(n = 1,258)
Fallers with
Injuries
(n = 584) p
a

Demographic characteristics
Age, M (SD) 73.96 (6.54) 73.50 (6.34) 75.12 (6.94) 75.64 (6.94) .00
Female gender, % 59.5 57.4 62.5 73.1 .00
Race/ethnicity .00
NH White, % 85.9 85.3 88.2 87.1
NH African American, % 7.5 7.9 6.8 5.0
Hispanic, % 4.8 4.8 3.9 6.8
Other, % 1.7 2.0 1.1 1.0
Married, % 57.0 58.5 53.2 51.4 .00
Socioeconomic characteristics
Education less than HS, % 26.5 26.3 26.9 28.4 .33
Medicaid coverage, % 6.1 5.9 5.4 8.6 .00
Private health availability, % 67.3 67.1 68.8 66.2 .24
Health characteristics
Self-rated health, M (SD) 2.76 (1.05) 2.67 (1.03) 2.97 (1.06) 3.09 (1.10) .00
ADL limitations, M (SD) 0.26 (0.75) 0.18 (0.62) 0.45 (0.96) 0.57 (1.13) .00
IADL limitations, M (SD) 0.13 (0.47) 0.10 (0.40) 0.20 (0.57) 0.27 (0.70) .00
Functional limitations, M (SD) 2.45 (2.49) 2.12 (2.31) 3.28 (2.70) 3.78 (2.78) .00
Sum of chronic conditions/diseases, M (SD) 2.01 (1.24) 1.90 (1.20) 2.32 (1.30) 2.28 (1.27) .00

110
Table 4.2: Continued
Arthritis, % 65.9 62.9 73.7 76.4 .00
Broken hip, % 0.8 0.2 0.3 6.6 .00
Cancer, % 16.7 16.1 19.5 16.2 .00
Diabetes, % 16.5 15.0 22.1 18.0 .00
Heart disease, % 28.1 25.9 34.8 33.4 .00
Hypertension, % 56.8 55.6 60.2 60.9 .00
Lung disease, % 9.8 9.2 11.6 10.9 .00
Stroke, % 6.8 5.4 10.4 11.8 .00
Urinary incontinence, % 21.0 17.3 31.8 31.8 .00
Vision problems, % 19.2 17.4 23.9 26.0 .00
Hearing problems, % 22.0 20.4 27.1 26.0 .00
Depression, M (SD) 1.37 (1.80) 1.24 (1.71) 1.67 (1.97) 1.85 (2.05) .00
TICS, M (SD) 9.36 (1.05) 9.40 (1.01) 9.25 (1.14) 9.25 (1.12) .00
Health care use
Hospitalization, % 27.3 24.8 31.1 42.0 .00
Nursing home use, % 2.5 1.8 2.8 7.8 .00
Home health care use, % 6.5 5.1 8.6 15.3 .00
Number of times in hospital, M (SD) 0.44 (0.99) 0.39 (0.96) 0.51 (0.98) 0.74 (1.22) .00
Number of physician visits, M (SD) 9.51 (11.98) 8.74 (10.68) 10.88 (13.95) 13.80 (16.85) .00
Note: ADL = activity of daily living; HS = high school; IADL = instrumental ADL; NH = non-Hispanic; TICS = Telephone Interview for
Cognitive Status.
a
p values are for the overall comparisons among study groups.

111
Table 4.3 shows the comparison of health and functioning in 2004 by
frequency of falls in 2002. Both one-time and multiple-time fallers in 2002
reported worse self-rated health; higher numbers of ADL, IADL, and functional
limitations; a greater sum of chronic conditions/diseases; as well as higher
depression scores and lower TICS scores in 2004 compared to nonfallers in
2002. Moreover, compared to nonfallers, both one-time and multiple-time
fallers in 2002 reported more changes in ADL and IADL limitations between
2002 and 2004 in terms of reporting more ADL and IADL limitations in 2004
than in 2002. Both one-time and multiple-time fallers reported a higher
prevalence of the following conditions in 2004 compared to nonfallers: broken
hip, urinary incontinence, vision problems, and hearing problems. Both
one-time and multiple-time fallers reported more depressive symptoms and
lower TICS scores compared to 2002. Compared to one-time fallers,
multiple-time fallers in 2004 reported poorer self-rated health; higher numbers
of ADL, IADL, and functional limitations; a greater number of chronic
conditions/diseases; higher CES-D scores; lower TICS scores; and a higher
prevalence of the following conditions: broken hip, urinary incontinence, vision
problems, and hearing problems. In addition, multiple-time fallers reported
negative health changes between 2002 and 2004 in terms of having more ADL
and IADL limitations, more depressive symptoms, and lower TICS scores. All
of these differences were statistically significant.

112
Table 4.3 Comparison of Health Characteristics in 2004 by Frequency of Falls in 2002 (N = 7,287)
Variable
Nonfallers
(n = 5,445)
One-Time
Fallers
(n = 964)
Multiple-Time
Fallers
(n = 878) p
a

2004 Self-rated health, M (SD) 2.81 (1.06) 2.94 (1.09) 3.26 (1.05) .00
2004 ADL limitations, M (SD) 0.23 (0.72) 0.36 (0.89) 0.72 (1.23) .00
2004 IADL limitations, M (SD) 0.13 (0.47) 0.21 (0.62) 0.36 (0.78) .00
2004 Functional limitations, M (SD) 2.37 (2.42) 3.11 (2.50) 4.05 (2.82) .00
2004 Sum of chronic conditions, M (SD) 2.09 (1.24) 2.37 (1.29) 2.66 (1.36) .00
2004 Depression, M (SD) 1.24 (1.72) 1.54 (1.91) 1.96 (2.12) .00
2004 TICS scores, M (SD) 9.38 (1.10) 9.21 (1.32) 9.12 (1.36) .00
More ADL limitations, % 9.0 13.2 21.8 .00
More IADL limitations, % 6.7 9.9 15.3 .00
More functional difficulties, % 37.7 41.2 36.7 .04
More chronic conditions/diseases, % 21.3 23.2 20.2 .17
Broken hip in 2004, % 0.7 1.4 1.2 .02
Urinary incontinence in 2004, % 20.1 28.3 35.8 .00
Vision problems in 2004, % 19.6 22.0 32.6 .00
Hearing problems in 2004, % 24.3 27.9 33.3 .00
More depression, % 27.6 30.3 31.1 .02
Lower TICS scores, % 21.2 26.5 28.5 .00
Note: ADL = activity of daily living; IADL = instrumental ADL; TICS = Telephone Interview for Cognitive Status.
a
p values are for the overall comparisons among study groups.

113
Table 4.4 shows the comparison of health care use with respect to any
overnight stay in a hospital in 2004, nursing home use in 2004, home health
care use in 2004, number of times in a hospital in 2004, and number of
physician visits in 2004 by frequency of falls in 2002. Compared to nonfallers,
both one-time and multiple-time fallers in 2002 reported higher utilization in
2004 of the following services: any overnight stay in a hospital, nursing home,
and home health care. Compared to one-time fallers, multiple-time fallers in
2002 reported higher utilization in 2004 of the following services: any overnight
stay in a hospital, nursing home, home health care, number of times in a
hospital, and number of physician visits. All of these differences were
statistically significant.

Table 4.4: Comparison of Health Care Use in 2004 by Frequency of Falls in 2002 (N = 7,287)
Variable
Nonfallers
(n = 5,445)
One-Time
Fallers
(n = 964)
Multiple-Time
Fallers
(n = 878) p
a

2004 Any hospitalization, % 26.8 29.8 40.3 .00
2004 Any nursing home use, % 2.0 3.2 5.7 .00
2004 Any home health care use, % 7.0 7.6 13.2 .00
2004 Number of times in hospital, M
(SD) 0.45 (1.33) 0.47 (0.95) 0.72 (1.14) .00
2004 Number of physician visits, M
(SD) 9.70 (16.35) 10.88 (13.21) 13.43 (19.05) .00
a
p values are for the overall comparisons among study groups.

Table 4.5 shows the comparison of health and functioning in 2004 by
severity of falls in 2002. Both fallers with no injury and fallers with injuries in
2002 reported worse self-rated health; more ADL, IADL, and functional
limitations; as well as a higher sum of chronic conditions in 2004 compared to

114
nonfallers in 2002. Moreover, compared to nonfallers in 2002, both fallers with
no injury and fallers with injuries in 2002 reported more changes in ADL and
IADL limitations between 2002 and 2004 in terms of reporting more ADL and
IADL limitations in 2004 than in 2002. Compared to nonfallers in 2002, both
fallers with no injury and fallers with injuries reported in 2004 a higher
prevalence of broken hip, urinary incontinence, vision problems, and hearing
problems; more depressive symptoms; and lower TICS scores. Compared to
fallers with no injury in 2002, fallers with injuries in 2002 had higher numbers of
ADL limitations and functional limitations; a higher prevalence of broken hip,
urinary incontinence, and vision problems; higher CES-D scores; and lower
TICS scores in 2004; they also reported negative health changes between
2002 and 2004 in terms of having more ADL limitations, more depression, and
lower TICS scores. All of these differences were statistically significant.

115
Table 4.5: Comparison of Health Characteristics in 2004 by Severity of Falls in 2002 (N = 7,287)
Variable
Nonfallers
(n = 5,445)
Fallers with No
Injury
(n = 1,258)
Fallers with
Injuries
(n = 584) p
a

2004 Self-rated health, M (SD) 2.81 (1.06) 3.07 (1.05) 3.14 (1.15) .00
2004 ADL limitations, M (SD) 0.23 (0.72) 0.50 (1.02) 0.60 (1.20) .00
2004 IADL limitations, M (SD) 0.13 (0.47) 0.26 (0.66) 0.31 (0.80) .00
2004 Functional limitations, M (SD) 2.37 (2.42) 3.46 (2.66) 3.75 (2.75) .00
2004 Sum of chronic conditions, M (SD) 2.09 (1.24) 2.52 (1.34) 2.49 (1.33) .00
2004 Depression, M (SD) 1.24 (1.72) 1.67 (1.98) 1.90 (2.11) .00
2004 TICS scores, M (SD) 9.38 (1.10) 9.21 (1.28) 9.07 (1.45) .00
More ADL limitations, % 9.0 17.1 17.7 .00
More IADL limitations, % 6.7 12.5 12.4 .00
More functional difficulties, % 37.7 40.3 36.5 .11
More chronic conditions/diseases, % 21.3 21.6 22.3 .78
Broken hip in 2004, % 0.7 1.0 2.2 .00
Urinary incontinence in 2004, % 20.1 31.2 33.2 .00
Vision problems in 2004, % 19.6 25.7 30.0 .00
Hearing problems in 2004, % 24.3 30.9 29.4 .00
More depression, % 27.6 29.8 32.6 .01
Lower TICS scores, % 21.2 26.6 29.3 .00
Note: ADL = activity of daily living; IADL = instrumental ADL; TICS = Telephone Interview for Cognitive Status.
a
p values are for the overall comparisons among study groups.

116
Table 4.6 shows the comparison of health care use with respect to any
overnight stay in a hospital in 2004, nursing home use in 2004, home health
care use in 2004, number of times in a hospital in 2004, and number of
physician visits in 2004 by severity of falls in 2002. Both fallers with no injury
and fallers with injuries in 2002 reported higher utilization of all of these
services compared to nonfallers in 2002. Compared to fallers with no injury in
2002, fallers with injuries in 2002 had higher utilization of the following services
in 2004: any nursing home, any home health care, number of hospitalizations,
and number of physician visits. All of these differences were statistically
significant.

Table 4.6: Comparison of Health Care Use in 2004 by Severity of Falls in 2002 (N = 7,287)
Variable
Nonfallers
(n = 5,445)
Fallers with
No Injury
(n = 1,258)
Fallers with
Injuries
(n = 584) p
a

2004 Any hospitalization, % 26.8 34.3 35.6 .00
2004 Any nursing home use, % 2.0 4.3 4.5 .00
2004 Any home health care use, % 7.0 9.3 12.5 .00
2004 Number of times in hospital, M
(SD) 0.45 (1.33) 0.58 (1.05) 0.59 (1.04) .00
2004 Number of physician visits, M
(SD)
9.70
(16.35) 11.30 (15.09)
13.84
(18.54) .00
a
p values are for the overall comparisons among study groups.

Multivariate regression analyses: Effects of falls in 2002 and falls in
2004 on health care and LTC utilization in 2004
Table 4.7 presents results of multiple logistic regression models
predicting any overnight stay in a hospital in 2004, focusing on the effects of
falls in 2002 and 2004 as predictors of any hospitalization in 2004. Falls at

117
baseline (i.e., falls in 2002) was a significant predictor of any hospitalization in
2004 in Models 1, 2, and 3. However, it ceased being statistically significant
once baseline health measures were included in Model 4. Although the
magnitude of the 2004 hospitalization odds ratio for falls in 2004 was
decreased by controlling for the demographic variables in Model 2, the
addition of socioeconomic characteristics in Model 3, the addition of 2002
health characteristics in Model 4, and the addition of changes in health
between 2002 and 2004 in Model 5, the association remained statistically
significant throughout the models. In the full model, falls in 2004, advancing
age, female gender, self-rated health in 2002, functional limitations in 2002,
number of chronic conditions/diseases in 2002, urinary incontinence in 2002,
more ADL/IADL limitations in 2004, more functional limitations in 2004, a
greater number of chronic conditions/diseases in 2004, broken hip in 2004,
urinary incontinence in 2004, hearing problems in 2004, and depression in
2004 were all significant independent predictors of any overnight stay in a
hospital in 2004. Independent of all other covariates, experience of a fall in
2004 increased the odds of having any overnight stay in a hospital in the same
year by 48% (p < 0.01).

118
Table 4.7: Results of Multiple Logistic Regression Predicting the Use of Hospital Care: Falls as Predictors of Any Hospitalization in 2004 (N =
7,287)
Model 1 Model 2 Model 3 Model 4 Model 5
Variable OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)
Fall in 2004 2.00 (1.81, 2.20)** 1.94 (1.76, 2.14)** 1.94 (1.75, 2.14)** 1.67 (1.51, 1.85)** 1.48 (1.33, 1.65)**
Fall in 2002 1.17 (1.05, 1.30)** 1.14 (1.02, 1.27)** 1.13 (1.02, 1.26)** 0.95 (0.85, 1.07) * 0.96 (0.85, 1.07)**
Demographic characteristics
Age in 2002 1.03 (1.02, 1.04)** 1.03 (1.02, 1.04)** 1.02 (1.02, 1.03)** 1.02 (1.01, 1.03)**
Female 0.82 (0.75, 0.91)** 0.83 (0.75, 0.91)** 0.76 (0.68, 0.84)** 0.73 (0.66, 0.82)**
NH White (reference)
NH African American 1.04 (0.87, 1.24)** 0.96 (0.79, 1.15) * 0.85 (0.70, 1.03)** 0.85 (0.70, 1.04)**
Hispanic 0.89 (0.71, 1.13)** 0.76 (0.59, 0.98)** 0.79 (0.61, 1.03)** 0.85 (0.65, 1.11)**
NH other 0.85 (0.58, 1.22)** 0.82 (0.57, 1.19) * 0.83 (0.57, 1.22)** 0.95 (0.64, 1.40)**
Married 0.96 (0.87, 1.06)** 0.99 (0.90, 1.10) * 1.04 (0.94, 1.15)** 1.02 (0.92, 1.14)**
Socioeconomic characteristics
Less than HS education 1.17 (1.05, 1.30)** 0.97 (0.87, 1.09)** 0.95 (0.84, 1.06)**
Medicaid 1.56 (1.28, 1.92)** 1.20 (0.97, 1.48)** 1.18 (0.95, 1.47)**
Other insurance 1.11 (1.00, 1.23)** 1.11 (1.00, 1.23)** 1.11 (1.00, 1.24)**
Health characteristics
Self-rated health in 2002 1.20 (1.14, 1.27)** 1.14 (1.08, 1.22)**
ADL & IADL limitations in 2002 0.98 (0.93, 1.03)** 1.00 (0.94, 1.05)**
Functional limitations in 2002 1.08 (1.05, 1.11)** 1.07 (1.04, 1.10)**
Number of chronic
conditions/diseases in 2002 1.26 (1.20, 1.31)** 1.34 (1.28, 1.40)**
Broken hip in 2002 0.62 (0.36, 1.09)** 0.69 (0.39, 1.20)**
Urinary incontinence in 2002 1.11 (0.98, 1.25)** 1.20 (1.06, 1.36)**
Vision problems in 2002 0.94 (0.83, 1.07)** 0.90 (0.79, 1.03)**
Hearing problems in 2002 0.96 (0.85, 1.08)** 0.97 (0.86, 1.10)**
Depression in 2002 1.02 (0.99, 1.05)** 1.02 (0.99, 1.05)**
TICS score in 2002 0.98 (0.93, 1.03)** 0.98 (0.93, 1.04)**

119
Table 4.7: Continued
Health changes between 2002
and 2004
Worse self-rated health 1.04 (0.92, 1.17)**
More ADL & IADL limitations 1.34 (1.17, 1.54)**
More functional limitations 1.26 (1.14, 1.40)**
More chronic
conditions/diseases 2.50 (2.23, 2.80)**
Broken hip in 2004
10.50 (5.29,
20.84)**
Urinary incontinence in 2004 1.28 (1.09, 1.50)**
Vision problems in 2004 0.96 (0.82, 1.13)**
Hearing problems in 2004 1.17 (1.00, 1.36)**
More depression 1.19 (1.07, 1.32)**
Lower TICS scores 1.00 (0.89, 1.12)**
2 log likelihood 11,060.58 10,972.21 10,941.19 10,501.04 10,048.73
Cox and Snell R
2
0.03 0.04 0.04 0.08 0.12
Nagelkerke R
2
0.04 0.05 0.05 0.12 0.18
Note: ADL = activity of daily living; CI = confidence interval; HS = high school; IADL = instrumental ADL; NH = non-Hispanic; OR = odds
ratio ; TICS = Telephone Interview for Cognitive Status.
*p < .05. **p < .01.

120
Table 4.8 shows the results of multiple logistic regression models
predicting any nursing home use in 2004. Falls at baseline stopped being
significantly associated with nursing home use, once health characteristics in
2002 were included in Models 4 and 5. However, falls in 2004 was a
statistically significant predictor of nursing home use in 2004 throughout the
models. Although the magnitude of the 2004 nursing home use odds ratio for
falls in 2004 was decreased by controlling for demographic and
socioeconomic characteristics, health characteristics in 2002, and health
changes between 2002 and 2004, the association remained significant. In the
full model, falls in 2004, advancing age, NH African American, Hispanic,
marital status, self-rated health, number of ADL/IADL limitations in 2002,
number of functional limitations in 2002, hearing problems in 2002, more
ADL/IADL limitations in 2004, more functional limitations in 2004, and more
chronic conditions/diseases in 2004, as well as broken hip in 2004 were all
significant independent predictors of nursing home use in 2004. Independent
of all other covariates, experience of a fall in 2004 increased the odds of
having any nursing home use in the same year by 76% (p < 0.01).

121
Table 4.8: Results of Multiple Logistic Regression Predicting the Use of Nursing Home: Falls as Predictors of Any Nursing Home Use in 2004 (N
= 7,287)
Model 1 Model 2 Model 3 Model 4 Model 5
Variable OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)
Fall in 2004 3.17 (2.41, 4.17)** 2.80 (2.12, 3.69)** 2.76 (2.09, 3.64)** 2.34 (1.75, 3.11)** 1.76 (1.31, 2.38)**
Fall in 2002 1.56 (1.19, 2.04)** 1.38 (1.05, 1.82)** 1.38 (1.05, 1.82)** 1.01 (0.75, 1.35)** 1.05 (0.77, 1.41)**
Demographic characteristics
Age in 2002 1.06 (1.04, 1.08)** 1.06 (1.04, 1.08)** 1.06 (1.04, 1.08)** 1.04 (1.02, 1.07)**
Female 1.15 (0.86, 1.54)** 1.17 (0.86, 1.57)** 0.89 (0.65, 1.21)** 0.83 (0.60, 1.15)**
NH White (reference)
NH African American 0.74 (0.41, 1.31)** 0.61 (0.34, 1.10)** 0.48 (0.26, 0.87)** 0.45 (0.24, 0.84)**
Hispanic 0.35 (0.12, 1.01)** 0.26 (0.09, 0.76)** 0.25 (0.09, 0.75)** 0.23 (0.08, 0.69)**
NH other 0.20 (0.02, 1.84)** 0.18 (0.02, 1.66)** 0.15 (0.02, 1.43)** 0.17 (0.02, 1.65)**
Married 0.60 (0.45, 0.80)** 0.63 (0.47, 0.84)** 0.67 (0.50, 0.90)** 0.64 (0.47, 0.87)**
Socioeconomic characteristics
Less than HS education 1.38 (1.04, 1.84)** 1.07 (0.80, 1.45)** 1.04 (0.76, 1.42)**
Medicaid 1.22 (0.72, 2.06)** 0.70 (0.40, 1.21)** 0.77 (0.44, 1.34)**
Other insurance 0.82 (0.62, 1.10)** 0.86 (0.64, 1.14)** 0.83 (0.62, 1.12)**
Health characteristics
Self-rated health in 2002 1.34 (1.14, 1.57)** 1.22 (1.03, 1.45)**
ADL & IADL limitations in 2002 1.07 (0.97, 1.18)** 1.12 (1.01, 1.24)**
Functional limitations in 2002 1.18 (1.10, 1.26)** 1.17 (1.09, 1.26)**
Number of diseases in 2002 1.03 (0.92, 1.16)** 1.06 (0.94, 1.19)**
Broken hip in 2002 1.09 (0.40, 2.96)** 1.39 (0.51, 3.76)**
Urinary incontinence in 2002 1.14 (0.84, 1.53)** 1.11 (0.81, 1.54)**
Vision problems in 2002 1.02 (0.74, 1.39)** 1.06 (0.75, 1.49)**
Hearing problems in 2002 0.52 (0.37, 0.73)** 0.48 (0.33, 0.69)**
Depression in 2002 1.01 (0.94, 1.08)** 1.01 (0.94, 1.09)**
TICS score in 2002 0.91 (0.81, 1.02)** 0.91 (0.80, 1.02)**

122
Table 4.8: Continued
Health changes between 2002
and 2004
Worse self-rated health 0.94 (0.68, 1.28)**
More ADL & IADL limitations 2.32 (1.70, 3.16)**
More functional limitations 1.54 (1.14, 2.07)**
More chronic
conditions/diseases 1.46 (1.07, 1.98)**
Broken hip in 2004
7.97 (4.50,
14.11)**
Urinary incontinence in 2004 0.81 (0.51, 1.26)**
Vision problems in 2004 1.30 (0.88, 1.91)**
Hearing problems in 2004 1.14 (0.78, 1.66)**
More depression 1.13 (0.84, 1.51)**
Lower TICS scores 1.01 (0.74, 1.38)**
2 log likelihood 2,161.66 2,078.35 2,069.21 1,951.70 1,827.97
Cox and Snell R
2
0.01 0.02 0.02 0.03 0.05
Nagelkerke R
2
0.05 0.09 0.10 0.15 0.21
Note: ADL = activity of daily living; CI = confidence interval; HS = high school; IADL = instrumental ADL; NH = non-Hispanic; OR = odds ratio;
TICS = Telephone Interview for Cognitive Status.
*p < .05. **p < .01.

123
Table 4.9 shows the results of multiple logistic regression models
predicting home health care use in 2004. Falls at baseline was not a
significant predictor of home health care use in 2004 in Models 1, 2, and 3, but
it became a statistically significant predictor once health measures were
included in Model 4. Falls in 2004 remained a statistically significant predictor
of home health care use in 2004, although the sequential adjustment for the
covariates lowered the odds ratios. In the full model, falls in 2002, falls in
2004, advancing age, Medicaid coverage, self-rated health in 2002, number of
ADL/IADL limitations in 2002, number of functional limitations in 2002, number
of chronic conditions/diseases in 2002, more ADL/IADL limitations in 2004,
more functional limitations in 2004, and more chronic conditions/diseases in
2004, as well as broken hip in 2004 were all significant independent predictors
of home health care use in 2004. Independent of all other covariates,
experience of a fall in 2002 decreased the likelihood of having any home
health care use by 23% (p < 0.01), whereas experience of a fall in 2004
increased the likelihood of having any home health care use in the same year
by 44% (p < 0.01).

124
Table 4.9: Results of Multiple Logistic Regression Predicting the Use of Home Health Care: Falls as Predictors of Any Home Health Care Use in
2004 (N = 7,287)
Model 1 Model 2 Model 3 Model 4 Model 5
Variable OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)
Fall in 2004 2.37 (2.02, 2.77)** 2.19 (1.87, 2.58)** 2.17 (1.84, 2.55)** 1.78 (1.50, 2.11)** 1.44 (1.20, 1.72)**
Fall in 2002 1.15 (0.97, 1.36)** 1.05 (0.89, 1.25)** 1.05 (0.88, 1.25)** 0.77 (0.64, 0.93)** 0.77 (0.63, 0.93)**
Demographic characteristics
Age in 2002 1.05 (1.04, 1.07)** 1.06 (1.04, 1.07)** 1.05 (1.03, 1.06)** 1.04 (1.02, 1.05)**
Female 1.10 (0.93, 1.30)** 1.08 (0.91, 1.28)** 0.96 (0.80, 1.15)** 0.91 (0.75, 1.10)**
NH White (reference)
NH African American 1.19 (0.89, 1.60)** 0.93 (0.68, 1.27)** 0.83 (0.60, 1.14)** 0.83 (0.60, 1.16)**
Hispanic 1.40 (0.98, 1.99)** 0.86 (0.58, 1.26)** 0.93 (0.62, 1.39)** 0.98 (0.65, 1.49)**
NH other 1.02 (0.55, 1.90)** 0.90 (0.48, 1.69)** 0.94 (0.50, 1.78)** 1.11 (0.58, 2.11)**
Married 0.81 (0.69, 0.96)** 0.90 (0.76, 1.07)** 0.96 (0.80, 1.14)** 0.95 (0.79, 1.14)**
Socioeconomic characteristics
Less than HS education 1.22 (1.03, 1.46)** 0.98 (0.81, 1.18)** 0.95 (0.78, 1.15)**
Medicaid 3.21 (2.44, 4.22)** 2.14 (1.60, 2.86)** 2.28 (1.69, 3.07)**
Other insurance 1.13 (0.95, 1.36)** 1.20 (0.99, 1.44)** 1.19 (0.98, 1.44)**
Health characteristics
Self-rated health in 2002 1.22 (1.11, 1.34)** 1.15 (1.04, 1.28)**
ADL & IADL limitations in 2002 1.14 (1.07, 1.21)** 1.18 (1.10, 1.26)**
Functional limitations in 2002 1.16 (1.11, 1.21)** 1.16 (1.11, 1.21)**
Number of diseases in 2002 1.19 (1.11, 1.28)** 1.27 (1.18, 1.37)**
Broken hip in 2002 0.71 (0.32, 1.57)** 0.84 (0.39, 1.85)**
Urinary incontinence in 2002 0.99 (0.82, 1.20)** 1.06 (0.87, 1.31)**
Vision problems in 2002 0.98 (0.81, 1.19)** 0.94 (0.76, 1.15)**
Hearing problems in 2002 1.00 (0.83, 1.20)** 0.95 (0.78, 1.16)**
Depression in 2002 0.97 (0.93, 1.01)** 0.97 (0.92, 1.02)**
TICS score in 2002 0.99 (0.91, 1.06)** 1.01 (0.93, 1.09)**

125
Table 4.9: Continued
Health changes between 2002
and 2004
Worse self-rated health 0.95 (0.78, 1.15)**
More ADL & IADL limitations 1.69 (1.39, 2.06)**
More functional limitations 1.45 (1.22, 1.73)**
More chronic
conditions/diseases 2.09 (1.74, 2.50)**
Broken hip in 2004
12.53 (7.33,
21.40)**
Urinary incontinence in 2004 1.20 (0.93, 1.55)**
Vision problems in 2004 0.92 (0.71, 1.19)**
Hearing problems in 2004 0.95 (0.73, 1.23)**
More depression 1.09 (0.91, 1.30)**
Lower TICS scores 1.01 (0.84, 1.23)**
2 log likelihood 5,039.11 4,929.70 4,851.47 4,545.12 4,295.14
Cox and Snell R
2
0.01 0.03 0.03 0.07 0.09
Nagelkerke R
2
0.03 0.06 0.08 0.15 0.21
Note: ADL = activity of daily living; CI = confidence interval; HS = high school; IADL = instrumental ADL; NH = non-Hispanic; OR = odds ratio;
TICS = Telephone Interview for Cognitive Status.
*p < .05. **p < .01.

126
Table 4.10 presents the hierarchical regression equations predicting
number of hospitalizations in 2004. Falls at baseline was inversely associated
with a higher number of hospitalizations in the full model, whereas falls in 2004
was positively associated with a higher number of hospitalizations. The effect
of falls in 2002 was suppressed in earlier models, but it became statistically
significant once health characteristics were included in Models 4 and 5. In the
full model, falls in 2002, falls in 2004, female gender, NH African American,
Hispanic, self-rated health in 2002, number of ADL/IADL limitations in 2002,
number of functional limitations in 2002, number of chronic
conditions/diseases in 2002, TICS score in 2002, more ADL/IADL limitations in
2004, more functional limitations in 2004, more chronic conditions/diseases in
2004, broken hip in 2004, as well as hearing problems in 2004 were all
significantly associated with number of hospitalizations in 2004. Controlling
for all other variables, experience of a fall in 2004 increased the number of
hospitalizations in 2004 by 0.15 (p < 0.01), whereas experience of a fall in
2002 decreased the number of hospitalizations in 2004 by 0.07 (p < 0.05).

127
Table 4.10: Unstandardized Ordinary Least Squares Regression Estimates Predicting Number of Hospitalizations in 2004 (N = 7,287)
Model 1 Model 2 Model 3 Model 4 Model 5
Variable b SE b SE b SE b SE b SE
Constant 0.38** 0.02 –0.35** 0.16 –0.37** 0.16 0.03** 0.22 0.14** 0.23
Fall in 2004 0.31** 0.03 0.29** 0.03 0.29** 0.03 0.20** 0.03 0.15** 0.03
Fall in 2002 0.04** 0.03 0.03** 0.03 0.03** 0.03 –0.07** 0.03 –0.07** 0.03
Demographic characteristics
Age in 2002 0.01** 0.00 0.01** 0.00 0.01** 0.00 0.00** 0.00
Female –0.09** 0.03 –0.08** 0.03 –0.12** 0.03 –0.12** 0.03
NH White (reference)
NH African American 0.01** 0.05 –0.06** 0.05 –0.15** 0.05 –0.14** 0.05
Hispanic –0.11** 0.07 –0.25** 0.07 –0.25** 0.07 –0.22** 0.07
NH other –0.09** 0.10 –0.12** 0.10 –0.13** 0.10 –0.08** 0.10
Married –0.05** 0.03 –0.03** 0.03 –0.01** 0.03 –0.01** 0.03
Socioeconomic characteristics
Less than HS education 0.10** 0.03 0.00** 0.03 –0.01** 0.03
Medicaid 0.28** 0.06 0.11** 0.06 0.10** 0.06
Other insurance –0.03** 0.03 –0.02** 0.03 –0.02** 0.03
Health characteristics
Self-rated health in 2002 0.09** 0.02 0.06** 0.02
ADL & IADL limitations in 2002 0.05** 0.02 0.06** 0.02
Functional limitations in 2002 0.05** 0.01 0.04** 0.01
Number of diseases in 2002 0.08** 0.01 0.10** 0.01
Broken hip in 2002 –0.30** 0.15 –0.28** 0.15
Urinary incontinence in 2002 0.01** 0.03 0.03** 0.03
Vision problems in 2002 –0.02** 0.04 –0.05** 0.04
Hearing problems in 2002 –0.04** 0.03 –0.03** 0.03
Depression in 2002 –0.01** 0.01 –0.01** 0.01
TICS score in 2002 –0.04** 0.01 –0.04** 0.01

128
Table 4.10: Continued
Health changes between 2002 and 2004
Worse self-rated health 0.06** 0.03
More ADL & IADL limitations 0.16** 0.04
More functional limitations 0.06** 0.03
More chronic conditions/diseases 0.29** 0.03
Broken hip in 2004 0.83** 0.14
Urinary incontinence in 2004 0.06** 0.05
Vision problems in 2004 –0.07** 0.04
Hearing problems in 2004 0.10** 0.04
More depression 0.03** 0.03
Lower TICS scores 0.02** 0.03
Adjusted R
2
0.01 0.02 0.02 0.06 0.08
Note: ADL = activity of daily living; HS = high school; IADL = instrumental ADL; NH = non-Hispanic; TICS = Telephone Interview for
Cognitive Status.
*p < .05. **p < .01.

129
Table 4.11 presents the hierarchical regression equations predicting
number of physician visits in 2004. Falls at baseline ceased being statistically
significantly associated with number of physician visits in 2004 once health
characteristics were included in Models 4 and 5. However, falls in 2004 was
positively associated with number of physician visits throughout the models.
In the full model, falls in 2004, female gender, having less than a high school
education, self-rated health in 2002, number of functional limitations in 2002,
number of chronic conditions/diseases in 2002, vision problems in 2002,
depression in 2002, TICS scores in 2002, more ADL/IADL limitations in 2004,
more functional limitations in 2004, more chronic conditions/diseases in 2004,
broken hip in 2004, hearing problems in 2004, and more depression in 2004
were all significantly associated with number of physician visits in 2004.
Independent of all other variables, experience of a fall in 2004 increased
number of physician visits in 2004 by 0.79 (p < 0.05).

130
Table 4.11: Unstandardized Ordinary Least Squares Regression Estimates Predicting Number of Physician Visits in 2004 (N = 7,287)
Model 1 Model 2 Model 3 Model 4 Model 5
Variable b SE b SE b SE b SE b SE
Constant 9.09** 0.21 6.26** 2.09 5.48** 2.11 –2.35** 2.88 –1.49** 2.95
Fall in 2004 2.58** 0.38 2.54** 0.38 2.52** 0.38 1.29** 0.38 0.79** 0.38
Fall in 2002 1.58** 0.40 1.55** 0.41 1.55** 0.41 0.38** 0.41 0.33** 0.40
Demographic characteristics
Age in 2002 0.04** 0.03 0.05** 0.03 –0.01** 0.03 –0.04** 0.03
Female –0.51** 0.36 –0.58** 0.36 –0.83** 0.37 –0.79** 0.37
NH White (reference)
NH African American 0.34** 0.67 0.44** 0.68 –0.03** 0.68 –0.07** 0.68
Hispanic 0.09** 0.85 –0.03** 0.89 0.27** 0.88 0.48** 0.88
NH other –2.13** 1.30 –2.11** 1.30 –1.72** 1.27 –1.21** 1.27
Married –0.02** 0.37 0.07** 0.37 0.45** 0.36 0.41** 0.36
Socioeconomic characteristics
Less than HS education –1.13** 0.40 –2.32** 0.41 –2.43** 0.41
Medicaid 2.99** 0.79 1.19** 0.78 1.02** 0.78
Other insurance 0.57** 0.38 0.49** 0.37 0.47** 0.37
Health characteristics
Self-rated health in 2002 1.66** 0.20 1.30** 0.21
ADL & IADL limitations in 2002 –0.02** 0.20 0.06** 0.20
Functional limitations in 2002 0.37** 0.10 0.32** 0.10
Number of diseases in 2002 1.48** 0.15 1.63** 0.15
Broken hip in 2002 –1.76** 1.95 –1.51** 1.94
Urinary incontinence in 2002 0.49** 0.43 0.39** 0.44
Vision problems in 2002 –0.90** 0.45 –0.99** 0.47
Hearing problems in 2002 0.71** 0.42 0.81** 0.43
Depression in 2002 0.35** 0.11 0.39** 0.11
TICS score in 2002 0.45** 0.18 0.48** 0.18

131
Table 4.11: Continued
Health changes between 2002 and 2004
Worse self-rated health 0.72** 0.43
More ADL & IADL limitations 1.51** 0.51
More functional limitations 1.16** 0.36
More chronic conditions/diseases 3.49** 0.41
Broken hip in 2004 3.90** 1.85
Urinary incontinence in 2004 –0.83** 0.58
Vision problems in 2004 0.55** 0.56
Hearing problems in 2004 1.12** 0.55
More depression 1.33** 0.38
Lower TICS scores –0.07** 0.40
Adjusted R
2
0.01 0.01 0.01 0.06 0.07
Note: ADL = activity of daily living; HS = high school; IADL = instrumental ADL; NH = non-Hispanic; TICS = Telephone Interview for
Cognitive Status.
*p < .05. **p < .01.

132
In this section, the regression results on health care and LTC utilization
in 2004 by frequency and severity of falls in 2002 are presented, focusing on
the effect of 2002 falls. Because all falls in 2004 were significantly associated
with high health care and LTC utilization in 2004, only results of falls in 2002
that were statistically significant are presented.
Table 4.12 shows the regression results on health care and LTC
utilization in 2004 by frequency of falls in 2002 as predictors of health care and
LTC use. Controlling for falls in 2004 and all of the covariates mentioned
previously revealed that a one-time fall in 2002 was inversely associated with
home health care use (OR = 0.68; 95% CI = 0.53, 0.87; p < 0.01) and number
of hospitalizations (b = –0.11, p < 0.01) in 2004.

133
Table 4.12: Regression Results on Health Care and Long-Term Care Utilization in 2004: Frequency of Falls as Predictors of Health Care and
Long-Term Care Use (N = 7,287)
Any Home Health Care Use No. of Hospitalizations
Logistic Regression OLS Regression
Variable OR (95% CI) b SE
Fall in 2004 1.41 (1.18, 1.69)** 0.15** 0.03
One-time fall in 2002 0.68 (0.53, 0.87)** –0.11** 0.04
Multiple falls in 2002 0.86 (0.68, 1.09)** –0.03** 0.04
Demographic characteristics
Age in 2002 1.04 (1.03, 1.05)** 0.00** 0.00
Female 0.91 (0.75, 1.10)** –0.12** 0.03
NH White (reference)
NH African American 0.84 (0.60, 1.17)** –0.14** 0.05
Hispanic 0.97 (0.64, 1.48)** –0.22** 0.07
NH other 1.12 (0.59, 2.14)** –0.08** 0.10
Married 0.95 (0.79, 1.14)** –0.01** 0.03
Socioeconomic characteristics
Less than HS education 0.95 (0.78, 1.15)** –0.01** 0.03
Medicaid 2.28 (1.69, 3.08)** 0.10** 0.06
Other insurance 1.18 (0.98, 1.43)** –0.03** 0.03
Health characteristics
Self-rated health in 2002 1.15 (1.04, 1.28)** 0.06** 0.02
ADL & IADL limitations in 2002 1.17 (1.10, 1.25)** 0.06** 0.02
Functional limitations in 2002 1.16 (1.11, 1.21)** 0.04** 0.01
Number of diseases in 2002 1.27 (1.18, 1.36)** 0.10** 0.01
Broken hip in 2002 0.85 (0.39, 1.86)** –0.27** 0.15
Urinary incontinence in 2002 1.06 (0.86, 1.30)** 0.03** 0.03
Vision problems in 2002 0.94 (0.76, 1.15)** –0.05** 0.04
Hearing problems in 2002 0.95 (0.78, 1.17)** –0.03** 0.03

134
Table 4.12: Continued
Depression in 2002 0.97 (0.92, 1.02)** –0.01** 0.01
TICS score in 2002 1.01 (0.93, 1.09)** –0.04** 0.01
Health changes between 2002 and 2004
Worse self-rated health 0.94 (0.78, 1.15)** 0.06** 0.03
More ADL & IADL limitations 1.69 (1.38, 2.06)** 0.16** 0.04
More functional limitations 1.46 (1.22, 1.74)** 0.06** 0.03
More chronic conditions/diseases 2.09 (1.74, 2.51)** 0.29** 0.03
Broken hip in 2004 12.75 (7.47, 21.77)** 0.84** 0.14
Urinary incontinence in 2004 1.20 (0.93, 1.55)** 0.06** 0.05
Vision problems in 2004 0.91 (0.70, 1.18)** –0.07** 0.04
Hearing problems in 2004 0.94 (0.73, 1.22)** 0.10** 0.04
More depression 1.09 (0.91, 1.31)** 0.03** 0.03
Lower TICS scores 1.01 (0.84, 1.22)** 0.02** 0.03
Adjusted R
2
0.08
2 log likelihood 4,292.61
Cox and Snell R
2
0.09
Nagelkerke R
2
0.21
Note: ADL = activity of daily living; CI = confidence interval; HS = high school; IADL = instrumental ADL; NH = non-Hispanic; OLS = ordinary
least squares; OR = odds ratio; TICS = Telephone Interview for Cognitive Status.
*p < .05. **p < .01.

135
Table 4.13 presents the regression results on health care and LTC
utilization in 2004 by severity of falls in 2002 as predictors of health care and
LTC use. Controlling for falls in 2004 and all of the covariates mentioned
previously revealed that noninjurious falls at baseline was inversely
associated with home health care use (OR = 0.70; 95% CI = 0.56, 0.87; p <
0.01) and number of hospitalizations (b = –0.08, p < 0.05) in 2004. However,
injurious falls in 2002 was positively associated with number of physician visits
(b = 2.12, p = 0.00) in 2004, controlling for falls in 2004 and all of the covariates
in the model.

136
Table 4.13: Regression Results on Health Care and Long-Term Care Utilization in 2004: Severity of Falls as Predictors of Health Care and
Long-Term Care Use (N = 7,287)
Any Home Health Care Use
No. of
Hospitalizations
No. of Physician
Visits
Logistic Regression OLS Regression OLS Regression
Variable OR (95% CI) b SE b SE
Fall in 2004 1.44 (1.21, 1.72)** 0.15** 0.03 0.83** 0.38
Noninjurious fall in 2002 0.70 (0.56, 0.87)** –0.08** 0.04 –0.43** 0.46
Injurious falls in 2002 0.93 (0.70, 1.23)** –0.07** 0.05 2.12** 0.64
Demographic characteristics
Age in 2002 1.04 (1.02, 1.05)** 0.00** 0.00 –0.04** 0.03
Female 0.90 (0.74, 1.09)** –0.12** 0.03 –0.85** 0.37
NH White (reference)
NH African American 0.84 (0.60, 1.17)** –0.14** 0.05 –0.02** 0.68
Hispanic 0.97 (0.64, 1.48)** –0.22** 0.07 0.41** 0.88
NH other 1.11 (0.58, 2.11)** –0.08** 0.10 –1.22** 1.27
Married 0.94 (0.79, 1.13)** –0.01** 0.03 0.39** 0.36
Socioeconomic characteristics
Less than HS education 0.95 (0.78, 1.15)** –0.01** 0.03 –2.42** 0.41
Medicaid 2.27 (1.68, 3.05)** 0.10** 0.06 0.96** 0.78
Other insurance 1.19 (0.98, 1.44)** –0.02** 0.03 0.47** 0.37
Health characteristics
Self-rated health in 2002 1.15 (1.04, 1.28)** 0.06** 0.02 1.30** 0.21
ADL & IADL limitations in 2002 1.17 (1.10, 1.25)** 0.06** 0.02 0.05** 0.20
Functional limitations in 2002 1.16 (1.11, 1.21)** 0.04** 0.01 0.31** 0.10
Number of diseases in 2002 1.27 (1.18, 1.37)** 0.10** 0.01 1.63** 0.15
Broken hip in 2002 0.75 (0.34, 1.66)** –0.28** 0.15 –2.64** 1.96
Urinary incontinence in 2002 1.07 (0.87, 1.31)** 0.03** 0.03 0.41** 0.44
Vision problems in 2002 0.94 (0.76, 1.16)** –0.05** 0.04 –1.00** 0.46
Hearing problems in 2002 0.96 (0.78, 1.17)** –0.03** 0.03 0.83** 0.43

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Table 4.13: Continued
Depression in 2002 0.97 (0.92, 1.02)** –0.01** 0.01 0.39** 0.11
TICS score in 2002 1.01 (0.93, 1.09)** –0.04** 0.01 0.47** 0.18
Health changes between 2002 and 2004
Worse self-rated health 0.94 (0.78, 1.15)** 0.06** 0.03 0.70** 0.43
More ADL & IADL limitations 1.70 (1.39, 2.07)** 0.16** 0.04 1.54** 0.51
More functional limitations 1.46 (1.22, 1.74)** 0.06** 0.03 1.17** 0.36
More chronic conditions/diseases 2.09 (1.74, 2.51)** 0.29** 0.03 3.48** 0.41
Broken hip in 2004 12.41 (7.26, 21.22)** 0.83** 0.14 3.83** 1.85
Urinary incontinence in 2004 1.20 (0.93, 1.55)** 0.06** 0.05 –0.85** 0.58
Vision problems in 2004 0.91 (0.70, 1.17)** –0.07** 0.04 0.49** 0.56
Hearing problems in 2004 0.95 (0.73, 1.23)** 0.10** 0.04 1.14** 0.55
More depression 1.08 (0.91, 1.30)** 0.03** 0.03 1.32** 0.39
Lower TICS scores 1.01 (0.84, 1.22)** 0.02** 0.03 –0.08** 0.40
Adjusted R
2
0.08 0.07
2 log likelihood 4,291.92
Cox and Snell R
2
0.09
Nagelkerke R
2
0.21
Note: ADL = activity of daily living; CI = confidence interval; HS = high school; IADL = instrumental ADL; OLS = ordinary least squares; NH
= non-Hispanic; OR = odds ratio; TICS = Telephone Interview for Cognitive Status.
*p < .05. **p < .01.

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D. Discussion
This study assessed the effect of falls on health care and LTC utilization
among community-dwelling older adults. In this study, older adults who had
experienced falls in 2004 had a significantly greater risk of having high health
care and LTC utilization in 2004 than did older adults reporting no falls.
Moreover, older adults who had experienced falls with injuries in 2002 had a
significantly positive association with a higher number of physician visits in
2004, after controlling for falls in 2004 and other covariates. This confirms the
study by Chu et al. (2008) that reported that fallers who had major injuries
(e.g., fractures or subdural hematomas) had the greatest number of general
outpatient department and total doctor clinic visits in the 1-year follow-up.
The associations of falls in 2004 with health care and LTC utilization in
2004 were weakened when controlling for demographic, socioeconomic, and
health characteristics, as well as changes in health between 2002 and 2004,
suggesting that decline in functioning and health status might partially explain
the increased health care and LTC use. However, the results showing that
falls—and falls with injuries in particular—contribute substantially to health
care and LTC utilization suggest that falls and injurious falls should be
considered as sentinel events, and therefore, underlying risk factors for falls
should be addressed in terms of assessment and modifications/treatments.
Given the fact that a number of fall prevention programs have proven effective
(Clemson et al., 2004; Day et al., 2002; Tinetti, Baker, et al, 1994; Yates &

139
Dunnagan, 2001), multifactorial fall prevention intervention programs that
include risk assessment, physical activity, and environmental modifications
can reduce falls among older adults, and thereby improve older adults’
independent functioning and enhance their quality of life.
The lack of a statistically significant effect of falls in 2002 on any
hospitalization or any nursing home use could have resulted from the fact that
the effect of falls has been dissipated over time. A study by
Seematter-Bagnoud et al. (2006), using a study population consisting of
elderly patients hospitalized through the emergency department of an
academic hospital in Switzerland who were enrolled in two observational
cohort studies (N = 690), reported that noninjurious falls were not associated
with greater risk of hospital readmission. The authors suggested that other
health indicators (e.g., comorbidity) might better predict such service
utilization.
However, falls in 2002 did have a negative association with any home
health care use and number of hospitalizations, controlling for falls in 2004 and
other covariates. Specifically, one-time fall in 2002 decreased the likelihood of
home health care use in 2004 and had a negative association with number of
hospitalizations in 2004. Likewise, noninjurious falls in 2002 decreased the
likelihood of home health care use in 2004 and had a negative association with
number of hospitalizations in 2004. These results do not confirm the initial
hypotheses linking falls in 2002 to greater risk of health care and LTC

140
utilization in 2004. Although these findings are unexpected, one possible
explanation for such findings is that a one-time fall and a noninjurious fall may
not necessarily be markers of frailty, and therefore, these types of falls have an
inverse association with home health care use and number of hospitalizations.
Studies have reported that older adults who experience a single fall differ in
some respects from those who have multiple falls; this is partly due to the fact
that single or occasional falls often result from extrinsic factors such as
environmental hazards, whereas repeated falls are primarily the result of
intrinsic factors such as chronic disease and disability (Dunn, Rudberg,
Furner, & Cassel, 1992). Dunn and colleagues (1992) also reported that a
single fall was not associated with increased functional disability over a 2-year
follow-up, whereas multiple falls were significantly associated with functional
disability over a 2-year follow-up, controlling for covariates such as
demographic, chronic conditions, and disabilities. Another possible
explanation is that older adults who experienced a one-time fall or noninjurious
falls might adopt protective behaviors as well as healthier lifestyles to guard
against future falls and hence reduce their utilization of such services.
Several limitations to this study should be noted. First, the self-report
nature of the study, which relied on the recall of the respondents, may have
underestimated the frequency and severity of falls. Second, with respect to
home health care utilization, respondents were asked only whether they had
had a medically trained person come to their home to help them during the

141
past 2 years; this question fails to capture the extent to which various types of
services were provided to the older adults, and the volume and intensity of
those services. Lack of such information may have caused important
elements to be overlooked in the assessment of differences in question
meaning and perception of services. Third, establishing the temporal
precedence between falls in 2004 and health care and LTC utilization in 2004
is difficult due to the nature of cross-sectional research. Further studies are
needed to investigate the complex interactions among falls, decline in health
and functioning, as well as health care and LTC utilization; this can be done by
examining whether falling is a marker of frailty, or whether falling is a sentinel
event on a pathway leading to functional decline that triggers a downward
spiral response, or whether both of these are true, with the end result of all of
these interactions being increased health care and LTC utilization.
Despite these limitations, the current study shows that
falls—especially injurious falls—have a significant and enduring effect on older
adults in terms of increased health care and LTC utilization. This finding
suggests that developing effective intervention programs to reduce falls and
fall-related injuries should be an area of importance in public policy; this is
because such efforts hold promise in terms of reducing the incidence of falls
and improving quality of life among older adults, as well as reducing potential
health care and LTC service spending associated with falls and fall-related
injuries (Rizzo et al., 1998). Health care providers who have developed a

142
rapport with their elderly patients can address fall prevention issues within
their established patient–practitioner relationships by identifying risk factors for
falls among their elderly patients and providing information to patients and
their caregivers regarding available, effective strategies for reducing or
modifying those risks (Wolinsky et al., 1992).

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CHAPTER V: CONCLUSION
A. Summary
By using nationally representative samples of the U.S. population, the
three papers in this dissertation attempt to fill gaps in researchers’ knowledge
about falls among community-dwelling older adults. The first paper profiled
the demographic, socioeconomic, health and environmental characteristics of
community-dwelling older adults who had experienced one-time fall or multiple
falls, as well as the extent to which they talked about their falls with health care
professionals. In addition, predictors of falls among one-time fallers and
multiple-time fallers were investigated. The second paper examined
predictors of falls among three racial/ethnic groups (i.e., NH Whites, NH
African Americans, Hispanics) and investigated whether predictors of falls
differ by these groups. The third paper examined the consequences of falls
among older adults in terms of the effect of past falls (i.e., history of falling in
the past year) and the effect of falls on subsequent health care and LTC
utilization (i.e., any overnight stay in a hospital, use of nursing home, use of
home health care, number of hospitalizations, number of physician visits),
controlling for covariates that are associated with health care and LTC
utilization among older adults.
B. Limitations
The data sets utilized for the analyses have several weaknesses that
should be noted. First, none of the data sets (i.e., 2002 MCBS-Access to

144
Care, 2002 and 2004 HRS) defined a fall; therefore, what was meant by a fall
was left to the older adults’ interpretation. This may have led to
inconsistencies regarding how researchers and older adults defined a fall.
Using a sample from a telephone survey prior to the implementation of a
comprehensive community-based fall prevention program for older adults in
Ontario, Canada (N = 477), Zecevic et al. (2006) reported that fall definitions
from the literature were similar to those provided by older adults, yet older
adults and health care providers associated falls with antecedents and
consequences, whereas research studies focused mainly on a description of
the event. It may be possible that noninjurious falls are disregarded and
underreported by older adults.
Second, the data for the analyses relied on self-reports of falls and
other measures. Although many epidemiologic studies and national health
surveys commonly rely on data collected via face-to-face interviews or
self-administered questionnaires, it is not always possible to guarantee the
accuracy of respondents’ self-reports. Kriegsman, Penninx, Van Eijk, Boeke,
and Deeg (1996) reported that the concordance regarding the presence or
absence of specific chronic diseases (e.g., diabetes mellitus, cardiac disease,
malignant neoplasm, cerebrovascular disease, chronic non-specific lung
disease) between patient and general practitioner information was generally
satisfactory with the exception of peripheral atherosclerotic disease and
arthritis, for which the percentages were considerably lower. Although

145
self-reports remain a crucial source of information about falls among older
adults, retrospective self-reporting of falls and injuries is likely to be less
accurate than calendar methods, largely due to underreporting (Mackenzie,
Byles, & D’Este, 2006). Mackenzie et al. (2006) found that although the
percentage of agreement between retrospective self-reporting and calendar
reporting of falls was 84%, self-reporting of fall-related injuries demonstrated
poor validity compared with calendar methods in their prospective study of 264
older adults living in New South Wales and Queensland in Australia. In
addition, older adults may be reluctant to admit their fall experiences due to
negative perceptions associated with falls (such as social embarrassment,
indignity, and damage to their confidence), or they might just forget past falls if
they were not significant. Therefore, the self-reporting method and
retrospective nature of the data may have subjected the present study to a
certain amount of error and possibly underestimated the magnitude of fall
experiences among older adults.
Third, the cross-sectional nature of the first and second studies requires
cautious interpretation because it does not allow for cause/effect in the
relationship between risk factors and fall experiences.
C. Findings, Implications, and Future Research
Characteristics of fallers
With respect to characteristics of fallers, findings from the first paper
(Chapter 2) confirm those of previous studies in that fallers were more likely to

146
be older, be female, be NH White, live alone, and have poorer health
characteristics compared to their nonfaller counterparts. In addition, the first
paper investigated socioeconomic and environmental characteristics that
have been investigated less frequently in previous studies. Findings show that
fallers, compared to nonfallers, had poorer socioeconomic characteristics (i.e.,
more likely to have less than a high school education, to have an annual
income of less than $25,000, as well as to be eligible for Medicaid). With
respect to environmental characteristics, fallers were more likely to have
supportive features at home such as bathroom modifications, handrails, and
ramps. Although it is not possible to establish a temporal order of
environmental modifications and the experience of a fall due to the
cross-sectional nature of the study described in Chapter 2, this could be due to
the fact that fallers with poorer health and functioning could have made
environmental modifications to enhance mobility and safety in their homes.
Predictors of falls among older adults
With respect to the predictors of falls, findings from the first study
(Chapter 2) confirm those of previous studies that being female, having
functional limitations, as well as having certain diseases/chronic conditions
(such as arthritis, diabetes, and depression) are significant predictors of falls
for both one-time and multiple-time fallers. It should be noted that predictors of
falls were slightly different for one-time fallers and multiple-time fallers in that
more health-related characteristics (e.g., urinary incontinence, stroke, vision

147
problems, hearing problems) were predictors of falls among multiple-time
fallers, whereas these were not significant predictors among one-time fallers.
This could be partly due to the fact that multiple falls often result from intrinsic
risk factors for falls such as chronic disease and disability, whereas a one-time
fall is often associated with extrinsic risk factors such as environmental
hazards.
Moreover, the first (Chapter 2) and second (Chapter 3) papers found
that being part of a racial/ethnic minority group (i.e., NH African American,
Hispanic) decreased the likelihood of experiencing falls, which is in line with
the few available studies that have investigated race/ethnicity in predicting
falls among older adults. This could be due to selection bias stemming from a
survivor effect. Further investigation should be directed toward the underlying
mechanism for why being part of a racial/ethnic minority decreases the
likelihood of falls.
The second study adds knowledge to the field by investigating falls
among racial/ethnic minority older adults, a population that has previously
been overlooked. The study found that being NH African American or
Hispanic decreased the likelihood of an older adult experiencing a fall and that
the relative magnitude of predictors of a fall varied slightly across racial/ethnic
groups. Specifically, being of advanced age; living in an urban area; and
having a higher number of ADL and IADL limitations, mobility limitations,
arthritis, diabetes, urinary incontinence, stroke, depression, and hearing

148
problems were significant predictors of falls among NH White older adults.
Among NH African American older adults, having less than a high school
education, an income of less than $25,000, higher numbers of ADL and IADL
limitations, and urinary incontinence were significant predictors of falls.
Among Hispanic older adults, having higher numbers of ADL and IADL
limitations and vision problems were significant predictors of falls. A number
of chronic conditions/diseases were significant predictors of falls only among
NH White older adults; one possible explanation could be that NH White older
adults may be more aware than racial/ethnic minority older adults of their
health conditions/diseases—especially if these are based on a doctor’s
diagnosis.
It is interesting to note that certain socioeconomic characteristics were
predictors of falls only among NH African American older adults. Among NH
African Americans, having an income of less than $25,000 increased the
likelihood of experiencing a fall, whereas having less than a high school
education decreased the likelihood of experiencing a fall. Although one study
by Stel et al. (2003) reported that a high level of education was a predictor for
recurrent falling, this finding is somewhat unexpected. Further research is
needed to explore the association between falls and level of education.
Regarding environmental factors, having supportive features at home
was not a significant predictor of falls. This could be due to the fact that (a) the
current study did not allow for the determination of temporal precedence or

149
cause-effect in the relationship between environmental modifications and the
experience of a fall, or (b) the three measures available from the current data
set were not sufficient to capture the environmental conditions in older adults’
homes.
Consequences of falls on health care and LTC utilization
The third paper (Chapter 4) reports that older adults who experienced
falls in 2004 had a significantly greater risk of having high health care and LTC
utilization in 2004. Falls in 2002 were generally not associated with health
care and LTC utilization in 2004. Only injurious falls in 2002 had a significantly
positive association with higher number of physician visits in 2004 after
controlling for falls in 2004, sociodemographic and health covariates, as well
as changes in health between 2002 and 2004. Given the significant impact of
injurious falls on health care utilization, fall prevention programs should target
at-risk populations such as individuals prone to injurious falls, individuals who
have experienced recurrent falls, or individuals with a history of falling.
However, the experience of a one-time fall in 2002 was inversely associated
with higher number of hospitalizations and any home health care use in 2004.
In addition, the experience of a noninjurious fall in 2002 was inversely
associated with number of hospitalizations and any home health care
utilization in 2004. Such unexpected findings may be due to the fact that
one-time fallers or noninjured fallers, who have relatively better health status
than multiple-time fallers or injured fallers, might adopt strategies to prevent

150
future falls into their healthy lifestyles, and their fall experiences could thus
play a protective role in terms of health care and LTC service utilization.
Nevertheless, further studies should explore why one-time falls and
noninjurious falls have only inverse associations with number of
hospitalizations and decrease the likelihood of home health care utilization.
The first paper (Chapter 2) reports that less than half of the fallers in the
study talked about their falls with health care professionals. Individuals who
talked about their falls were more likely to be older, to be female, to be
unmarried, to live in a metropolitan area, to have poorer health, and to have
higher numbers of falls and injurious falls compared to those who did not talk
about their falls. Although the majority of health care professionals talked with
their patients to understand why they fell (74.9%) and gave their patients
information on how to prevent future falls (61.1%), there is a need to develop a
better connection between fallers and health care professionals. Because
health care professionals (e.g., physicians, nurses) have a rapport with their
patients, they can serve as vital links for referring fallers to various
interventions (e.g., exercise, home modification, cognitive–behavioral
interventions) in the community or to rehabilitation and for providing fall
prevention strategies.
Importance of fall prevention interventions
This dissertation confirms and adds knowledge to earlier research by
examining characteristics of fallers and predictors of falls, as well as the

151
impact of falls on health care and LTC utilization, using nationally
representative samples. In particular, the first (Chapter 2) and second
(Chapter 3) papers report that risk factors for falls among older adults are
complex in that they often have multiple, interactive risk factors for falls. In
general, advancing age, female gender, living alone, functional limitations in
terms of having ADL, IADL, and mobility limitations, as well as certain chronic
conditions/diseases are significant predictors of falls. However, risk factors for
falls vary slightly depending on older adults’ fall status (i.e., frequency of falls,
severity of falls) as well as their race/ethnicity. This enables us to identify
at-risk populations and target fall prevention interventions to these individuals.
Given that falls among older adults are sentinel events, and in light of the
significant impact falls have on health care and LTC utilization, appropriate
and effective fall prevention intervention programs are needed to prevent and
reduce falls among older adults by ameliorating and modifying risk factors for
falls.
A number of studies have shown that fall prevention programs are
effective at reducing falls among older adults. A recent publication from the
Centers for Disease Control and Prevention (2008) titled Preventing Falls:
What Works summarizes a number of exercise-based, home modification, and
multifaceted fall prevention intervention programs that have rigorous scientific
evidence of effectiveness. In general, studies have reported that
multidisciplinary, multifactorial fall prevention approaches that include fall risk

152
assessment and management (e.g., medication review); physical activity
programs tailored to enhance gait and balance, muscle strength, and
flexibility; and environmental modifications are most effective at reducing falls
(Gillespie et al., 2005; RAND, 2003; Roudsari, Ebel, Corso, Molinari, &
Koepsell, 2005). However, challenges and barriers associated with
implementing such fall prevention programs should be noted. For example,
multi-factorial interventions often require extensive resources and
coordination as well as a multidisciplinary team of providers (Pynoos, Rose,
Rubenstein, Choi, & Sabata, 2006). Shumway-Cook et al. (2007) reported
that it was feasible to implement a community-based fall prevention program
using existing resources such as senior centers, parks and recreation
programs, and assisted and independent living facilities with the capacity to
offer group exercise programs to seniors. They suggested that the
development of public–private and state–local partnerships and linkages
between senior service, health care, and public health organizations is critical
to the successful implementation of such programs. After evaluating the
implementation of a fall prevention program based on the Yale Frailty and
Injury Cooperative Studies of Intervention Trials in senior centers located in
Connecticut, Baker, Gottschalk, and Bianco (2007) also noted the importance
of collaboration among senior center administrators, health care providers,
senior center members, and researchers as key to conducting
comprehensive, multi-factorial fall prevention interventions in senior centers.

153
Moreover, resources are required to sustain and expand existing fall
prevention programs and services for older adults. In a study by Wagner et al.
(1994), a modest intervention program designed to reduce risk factors for
disability and falls reported a significantly lower incidence of new disability and
fewer falls over a 1-year period compared with usual-care controls among
ambulatory senior health maintenance organization enrollees. However, the
differences diminished after 2 years of follow-up, suggesting that any effects
will dissipate if the intervention is not sustained. This serves as an example of
the need for sustainable and long-term strategic approaches for implementing
feasible, evidence-based fall prevention intervention programs in the
community (Hendrie, Hall, Arena, & Legge, 2004).
Another important area is the need for post-fall intervention programs.
The post-fall period is a critical and ideal time for interventions because fallers
may be more open to making changes to prevent future falls (Nachreiner,
Findorff, Wyman, & McCarthy, 2007). However, the first paper (Chapter 2)
reports that less than 50% of older adults discussed their falls with health care
professionals. A study by Salter et al. (2006) also underscored the need
for—and importance of—comprehensive fall prevention assessment in health
care settings. Comprehensive discharge planning that includes information on
fall prevention, referral, and home follow-up can be helpful
(Seematter-Bagnoud, Wietlisbach, Yersin, & Büla, 2006). In addition, Tinetti,
Mendes de Leon, Doucette, and Baker (1994) suggested that the most

154
successful approach to prevention, rehabilitation, or geriatric evaluation and
management may combine simultaneous attempts to improve both the
efficacy and physical skills of older adults. Therefore, post-fall intervention
programs should also take into account improving physical skills and
fall-related efficacy to counteract excessive fear and avoidance behaviors
(Murphy, Williams, & Gill, 2002).
It should also be emphasized that there is “no-one-size-fits-all” fall
prevention program, given older adults’ high degree of heterogeneity in terms
of demographic, socioeconomic, and health characteristics. Older adults’
compliance with the design and implementation of intervention programs is
another important issue, given the fact that many believe that they are not at
risk and some of them are therefore unlikely to participate in specific fall
prevention activities (Pynoos et al., 2006). Future research should explore
how to make fall prevention programs personally relevant events for older
adults, given the fact that older adults often have an overly positive perception
of their risk of falling (Hughes et al., 2008). In addition, future fall prevention
programs should consider individual-centered care and choices rather than
designing discrete disease-based intervention programs for specific
conditions, because the former could better meet the overall needs of older
adults who are likely to face multiple health conditions (Lord et al., 2007).
Future research is also needed to tailor interventions to populations with
different characteristics and risk factors for falls. In particular, there is a

155
growing need to develop culturally appropriate intervention programs for
racial/ethnic minority older adults, which is an overlooked area.
Accommodating differences in language, customs, cultures, and/or health
beliefs among diverse older populations could enhance the effectiveness of
fall prevention programs.

156
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Asset Metadata

Creator Choi, In Hee (author)

Core Title Falls among older adults: characteristics of fallers, predictors of falls, and the impact of falls on health care and long-term care utilization

School Leonard Davis School of Gerontology

Degree Doctor of Philosophy

Degree Program Gerontology

Publication Date 09/11/2008

Defense Date 04/30/2008

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

Tag fall,fall prevention,OAI-PMH Harvest,older adults

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Pynoos, Jon (committee chair), Biblarz, Timothy J. (committee member), Crimmins, Eileen M (committee member)

Creator Email inhchoi@gmail.com,inhchoi@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m1594

Unique identifier UC1390483

Identifier etd-Choi-2331 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-112054 (legacy record id),usctheses-m1594 (legacy record id)

Legacy Identifier etd-Choi-2331.pdf

Dmrecord 112054

Document Type Dissertation

Rights Choi, In Hee

Type texts

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

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu