[2011]
Susan Bodnar Deren
ALL RIGHTS RESERVED
PERCEIVED ILLNESS BURDEN, A KEY TO UNDERSTANDING
ADVANCE CARE PLANNING IN ADULTS
NEARING THE END OF LIFE
SUSAN BODNAR DEREN
A Dissertation submitted to the
Graduate School-New Brunswick
Rutgers, The State University of New Jersey
in partial fulfillment of the requirements
for the degree of
Doctor of Philosophy
Graduate Program in Sociology
written under the direction of
Deborah S. Carr
and approved by
_________________________
_________________________
_________________________
_________________________
_________________________
New Brunswick, New Jersey
[October, 2011]
ii
ii
ABSTRACT OF THE DISSERTATION
Perceived Illness Burden, a Key to Understanding
Advance Care Planning in Adults
Nearing the End of Life
By SUSAN BODNAR DEREN
Dissertation Director:
Deborah S. Carr
Despite nationwide legislation encouraging advance care planning (ACP), rates of
completion are low. A substantial body of work has examined ACP and the use of
advance directives; studies have identified the benefits of ACP, the efficacy of ACP, the
low rates of and barriers to ACP, the types of treatments patients are willing to accept,
and characteristics of those who engage in planning. However, ACP is still underutilized
and not fully understood. Questions remain about how to increase rates of ACP and what
factors influence those who have (or have not) engaged in the ACP process. To answer
these questions, recent analyses of ACP have called for researchers, policymakers, and
practitioners to examine ACP as a health behavior, using a biopsychosocial approach
based on patient perspectives that motivate such behavior.
The Common Sense Model of Self-Regulation (CSM) is a widely used health
behavior model asserting that an individual‟s health preferences and behaviors are not
only affected by their actual condition, but also by their perceptions about their health
condition. The likelihood that an individual prepares for end of life through ACP may
iii
iii
reflect their illness representations or beliefs about the duration, severity, consequence
and controllability of their health condition. Health representations may trigger a health
behavior which, in this case, is ACP. Therefore, the CSM may be useful in helping to
explain why individuals engage in ACP.
Using data from the New Jersey End-of-Life (NJEOL) study (N=293) (2006-
2008), an ethnically diverse sample of non-institutionalized older adults (≥ age 55), I
explore the extent to which patient perspectives or illness representations motivated them
to plan for the end of life. I focus on the consequences of illnesses and compromised
health at the end of life, specifically perceived illness burden. I examine if, how, and for
whom perceived burden motivates patients to engage in the process of advance care
planning. The findings have significant implications for health care practice and policy.
These findings suggest that functional impairments and perceptions of burden are
important factors in ACP; eliciting patient perceptions about the consequences of their
illness may facilitate increased levels of ACP.
iv
iv
ACKNOWLEDGEMENTS
I would like to take this time to thank those who have helped me to reach this
point in my academic and professional career. I must first acknowledge and thank my
dissertation committee members: Deborah Carr, Howard Leventhal, Ellen Idler, Allan
Horwitz, and Biren Saraiya. I would have not be here if it was not for your guidance,
mentorship, and support. I have learned so much from each of you. I must say a special
thank you to everyone at the Center for the Study of Health Beliefs and Behaviors. To my
dear friend, Carmelen Chiusano, your encouragement and friendship has been invaluable;
and to Joanne Hash-Converse, your camaraderie and mentorship has enabled me to get
this far. Thank you very much. And to Howard Leventhal, for your support, guidance
and leadership. It has been an honor to be part of the Center. Thank you to all the faculty,
staff, and students at the Institute for Health, Health Care Policy, and Aging Research and
the Department of Sociology. Especially Dianne Yarnell for helping me adjust to and
navigate graduate school.
I also need to thank my family; I know it has been difficult for each of you, as I
have had to try to juggle graduate school, work and being a mom. Steve, thank you for
your patience and understanding, I know this has been difficult. To my best friend,
Michael Brady – I would never have been able to do this without your instrumental and
emotional support. Thank you, you mean so much to me.
I dedicate this project to my children – Suzi, Maggie, Jamie, Noel and Stevie. I
love you more than anything and would not be here if it was not for your help and
support. I am forever grateful for your encouragement and patience.
v
v
Table of Contents
Title page…………………………………………………………………………………i
Abstract…………………………………………………………………………..………ii
Acknowledgment…………………………………………………………………..……iv
Table of contents……………………………………………………………………...…v
CHAPTER 1 – Introduction
Introduction…………………………………………………………………........1
Aims………………………………………………………………………………8
Background……………………………………………………………...……….10
Outline of the Dissertation……...…..……………………………………………14
References…………………………………………………….………………….16
CHAPTER 2 – Understanding Patients‟ Perspectives on Advance Care Planning: A
Qualitative Study of the Perceptions of Older Adults at the End of Life.
Abstract……………………………………………………………….……….…21
Introduction………………………………….……………………….…………..22
Methods……………………………….………………………………………….29
Results……………………………………………………………………………34
Discussion………………………………………………………………………..47
References………………………………………………………………………..58
Tables…………………………………...………………………………………..66
vi
vi
CHAPTER 3 - Perceived Illness Burden and its Associated Correlates: A measure of
objective and subjective consequence at the end of life.
Abstract……………………………………………………………….……….…71
Introduction………………………………….……………………….…………..72
Methods……………………………….………………………………………….87
Results……………………………………………………………………………95
Discussion………………………………………………………………...…….101
References…………………………………………………………………...….110
Tables…………………………………...………………………………………118
CHAPTER 4 – Using the Common Sense Model to Understand the Relationship between
Perceived Illness Burden and the Likelihood of Advance Care Planning.
Abstract……………………………………………………………….……..….134
Introduction………………………………….……………………….…………135
Methods……………………………….………………………………………...152
Results………………………………………………………………………..…158
Discussion………………………………………………………………………168
References………………………………………………………………………180
Tables…………………………………...……………………………...……….189
Chapter 5 – Conclusion
Structure of Dissertation and Key Findings…………………………………….216
Limitations…………………...…………………………………………………222
Implications and Directions for Future Research………………………………224
References……………………………………………………………………...228
1
Chapter 1: Introduction
“I am enjoying myself and I really feel that my life, you know, is worth living, but the day that I
can‟t go to the bathroom by myself, the day I can‟t take a shower, the day I can‟t get up without
help. When I‟m lying in bed and someone has to take care of me constantly, I just don‟t want
that. I just don‟t want wires and plugs and everything to keep me going.”
- Male (age 76): Participant in New Jersey End-of-Life (NJEOL Study, 2006)
Health care practitioners and policy makers consider end-of-life (EOL)
communication and advance care planning to be important elements of quality health
care. Advance care planning (ACP) is best conceptualized as a process that involves
three steps. First, an individual considers their beliefs, values, goals of care and
preferences to decide what treatments s/he would or would not want upon diagnosis with
a terminal illness. Second, those preferences are communicated and discussed with
family, friends and health care providers. Finally, those preferences are formally
documented through an advance directive (AD) or do-not-resuscitate order in the medical
record or (Detering et al., 2010; Pearlman, 2010, Levi et al., 2010).
Advance directives are legally binding, written directions that outline the type of
medical care an individual would want or not want in the event s/he becomes unable to
make decisions for him/herself (NJ Bioethics Commission, 1991). They are formally
represented as: 1) living wills (LW), an instructive directive about the types of EOL care
and medical interventions patients desire (Smucker et al., 1993); 2) durable power of
attorney for health care (DPAHC), appoints a health care surrogate to make medical
decisions at the end of life (Ditto et al., 2001); and 3) a combined directive, a document in
2
which an individual has selected a health care representative and discussed these
preferences with them (NJ Bioethics Commission, 1991).
This project is motivated by the fact that, even though a substantial body of work
has examined ACP and the use of advance directives, questions remain about how to
increase rates of ACP and what theoretical factors influence those who have (or have not)
engaged the ACP process. To answer these questions, recent analyses of ACP have called
for researchers, policymakers, and practitioners to examine ACP as a health behavior
(Pearlman et al., 1995; Fried et al., 2009; Sudore and Fried, 2010), based on patient
perspectives that motivate such behavior (Leventhal, 2011; Carr, 2003).
The Common Sense Model of Self-Regulation (CSM) (Leventhal et al., 2003;
2011) is a widely used health behavior model asserting that an individual‟s health
preferences and behaviors are affected by both their actual condition and their
perceptions about their health condition (Leventhal et al, 1980; 2003; Carr and Moorman,
2009). The likelihood that an individual prepares for end of life through the process of
ACP may reflect their illness representations or beliefs about the duration, severity,
consequence and controllability of their health condition. Pursuant to the CSM, health
representations trigger a health behavior which, in this case, is ACP. Therefore, the CSM
may be useful in helping to explain why individuals engage in the processes involved in
end-of-life planning (Leventhal 2003; 2010; Carr, 2003).
Aims
I have two aims for this project. First, guided by the CSM and approaching
advance care planning as a health behavior motivated by illness representations, I begin
my dissertation with an analysis of patient narratives obtained from focus group data to
3
investigate what patient perspectives of illness representations motivate them to plan for
the end of life. Second, based on those findings, I explore if the thematic perceptions
identified in the qualitative analysis affect the likelihood of ACP in the larger survey
sample. By systematically analyzing patient perspectives and their effect on ACP
behaviors, I hope to contribute to a better understanding of how and for whom specific
illness representations affect end-of-life planning behaviors. To do this, I will focus on
one specific illness representation which emerged from the focus group data, which
captured how patients perceived their illnesses and any associated functional decline to
be burdensome to the self and to others. I label this perceived illness burden (PIB).
As one of the facilitators of the number NJEOL focus groups, I was struck by
strong and persistent themes; respondents did not want to be a burden (emotionally,
physically and financially) to current and/or future end-of-life caretakers. Patients
reported intricate and subtle interactions between physical and functional decline and
existential concerns - which could not be separated or compartmentalized - such as the
loss of sense of self and burden to others.
The patients I spoke with about their future plans - through dyadic interviews,
survey questionnaires and focus groups - informed me that the end of life is inherently
complicated. It was best articulated by a 70 year old woman who considered herself
“generally healthy,” but had engaged in ACP to protect her family members from future
burdens. When prompted by the focus group facilitator to explain why she had done
planning, she replied,
4
“. . . I‟ll finish this very quickly because I think it‟s important. See I don‟t think
they (the reasons for planning) are separated - if you want to (understand) end of
life planning you have understand how adults feel about end of life…”
She went on to state that for older adults, like younger adults, independence is important
– being able to take care of oneself, to do what is important for each person without
burdening others with your care or the emotional tolls of having to make difficult
decisions. She stated clearly that the issues around the end of life were complicated; she
did not think they were “separated.”
Based on the information I obtained as a focus group facilitator, I checked
whether the same themes were present in all of the NJEOL patient focus groups, and
further examined whether functional limitations leading to burden to the self and others
affected the odds of advance care planning in the larger NJEOL sample. This type of
mixed methods analysis is often referred to as sequential transformative analysis
(Creswell et al., 2002). In this approach, there are two distinct and sequential analysis
phases; they are rooted in a CSM theoretical lens. The first stage is qualitative (the
gathering of focus group data) and informs the analysis of the second stage which, for
this project, is quantitative (analysis of survey data). The theoretical lens is introduced at
the start of the project and shapes a directional research question aimed at exploring a
problem. For this dissertation, I use focus group and survey data from the NJEOL study -
which was designed to explore illness representations and EOL planning - guided by the
Common Sense Model.
Background
Advance Care Planning
5
Changes in the age of the U.S. population and causes of death make ACP
increasingly important. By 2030, 20% of the population will be over age 65 (U.S.
Census, 2011). Chronic conditions such as dementia-related disorders, cancer, and heart
disease have replaced sudden death from acute and infectious diseases as the primary
causes of death (Omran, 1971); death is now a process of old age (Caldwell, 2010). As
the life expectancy of the chronically and terminally ill has increased, so has the time
from diagnosis to death due to advances in technology, diagnosis and treatment. Persons
who are cognitively limited or those who have failed to make end-of-life plans often
endure unwanted costly medical interventions (Field and Cassel, 1997; Kaufman, 2000;
SUPPORT, 1995; Moorman, 2007) or, conversely, may have desired treatments
withdrawn or withheld (Lambert et al, 2005; Carr and Khodyakov, 2007).
Given these demographic transitions and the financial and emotional costs
associated with unwanted or contested end-of-life care, both federal and state
governments have instituted policies to provide patients an opportunity to engage in the
completion of ADs (Galambos, 1998). Federal and state statutes regulate the use of
advance directives. The 1990 Federal Patient Self-Determination Act (PSDA) mandates
that all health facilities receiving Medicare and Medicaid funds notify patients in writing
of their treatment options, right-to-die information, and their rights to put into place and
implement advance directives. This act assumes that patients will have an understanding
of advance directives and that this knowledge will bring about discussions between
patients, caregivers, and health care providers. In addition, the Uniform Health Care
Decision Act was passed in 1993 to provide consistency in implementation and
6
state/local adherence to a minimum level of standards (Uniform Law Commissioner,
1994).
Empirical studies report psychosocial, economic and quality-of-life benefits to
engaging in ACP. For example, discussions with physicians focusing on ACP result in
better understanding of future treatment options and reductions in patients‟ fears and
anxieties (Smucker et al., 1993; Ditto et al., 2001). Patients who have completed advance
directives report fewer concerns about communication with practitioners and family and
greater satisfaction with care; they also are more likely to make use of hospice care
(Tierney et al., 2001; Teno, et al., 2007; Ditto, et al., 2001; Smucker et al., 1993).
Patients and families also report improved quality-of-life and more positive mood
if they engaged in early discussions and set explicit goals for palliative care; these
discussions and goals were associated with increased duration of survival (Temel et al,
2010). Contrary to the rhetoric used by some in the debate over the passage of the Patient
Protection and Affordable Coverage Act (2009) and assertions of “death panels,” many
older adults are willing to discuss end-of-life plans and complete advance directives
(Morrison and Meier, 2004).
Yet, rates of advance care planning remain relatively low, with between one-third
to one-half of all U.S. adults having completed an advance directive (Moorman et al.,
2011; Hopp, 2000; Later and King, 2007; U.S. Department of Health and Human
Services, 2008). The modest prevalence of ACP is due to multiple factors. For example,
socioeconomic variables are primary predictors of ACP; white, well-educated, and well-
to-do individuals are more likely to engage in advance care planning, ethnic minorities
7
are less likely (Ditto et al., 2001; Carr, 2011; Waters, 2000; Hopp and Duffy, 2000;
Degenholtz et al., 2002).
Patient perceptions and health behaviors
Experiential and biopsychosocial factors also influence ACP behaviors. Health
care professionals often focus on concrete, objective concepts (e.g., physical functioning)
and biomarkers (e.g., lab results) when considering factors that are important at the end
of life. Concrete, experiential consequences of illness (e.g., changes in function) are also
an important indicator for patients. Many studies have found that functional limitations
are associated with patients having had discussed preferences for resuscitation with
family members and clinicians (Hoffman et al., 1997; Ziven et al, 2007) and completing
an advance directive (Schwartz et al, 2004). However, other studies have failed to
confirm this association (Wenger et al, 1995; SUPPORT Principal Investigators, 1995;
Pfeiffer et al., 2003). These inconsistencies can be explained by variations in patients‟
illness representations or beliefs/perceptions about the duration, severity, and
consequences resulting from their current health condition. Most patients‟ illness
representations are based in actual physical symptoms. Few studies have explored how
patients‟ illness representations serve to facilitate or impede ACP.
Clinicians have historically relied on how concrete, objective measures factor into
ACP; patients and family members, however, place greater emphasis on psychosocial
factors such as values, beliefs and perceptions of how their illness may affect those
around them (Garrido et al, forthcoming; Steinhauser et al, 2000). Beliefs about the
salience of individual autonomy and who should control EOL decisions have been found
to affect the likelihood and content of ACP. There is evidence that those who value
8
individual autonomy and control over major life decisions are more likely to engage in
ACP (Levi et al., 2010; Carr and Khodyakov, 2007; Moorman, 2011; Garrido et al,
forthcoming). Perceiving one‟s illness and related treatments as burdensome to the self
and others is another patient perspective that researchers believe may motivate ACP
(Wilson 2000; 2005; McPherson et al., 2007; Levi et al., 2010).
But do these beliefs and perceptions motivate behaviors such as ACP in a way
that is the same for everyone, and do perceptions and symptoms converge in similar ways
for all people? Understanding how patients‟ illness representations contribute to ACP
coincides with the recommendations put forth by the Institute of Medicine (IOM, 2001).
The IOM recommended that researchers and clinicians look at health and behavior
“biopsychosocially,” as interplay between biological, behavioral, and societal influences.
They also called for a reconceptualization of care that is patient-centered, including an
explicit understanding of how patients‟ beliefs and perceptions affect health behaviors.
Conceptual Framework – The Common Sense Model of Self-Regulation
The Common Sense Model of Self-Regulation (CSM) (Leventhal et al., 2003;
2008; 2011) is a health behavior model compatible with the IOM‟s (2001)
recommendations. It is both patient-centered and biopsychosocial. The CSM
presupposes that individuals are active problem solvers who attempt to assign meaning to
their somatic (biological) experiences. These assignments or beliefs form the individual‟s
“illness representations” (psychological). Illness representations are comprised of five
main features: (1) identity – the symptoms and illness label for the condition; (2) timeline
– the expected duration of the symptoms and condition; (3) consequences – the
anticipated impact of illness, associated symptoms, and treatment for self and others; (4)
9
control – the perception that the illness and outcomes can be controlled; and (5) cause –
the antecedent conditions believed to cause the illness (Leventhal, Leventhal, and
Cameron, 2001).
The Common Sense Model of Self-Regulation (CSM) (Leventhal et al., 2003;
2011) is a widely used health behavior model asserting that an individual‟s health
preferences and behaviors are not only affected by their actual condition, but also by their
perceptions about their health condition (Leventhal et al, 1980; Leventhal et al, 2003;
Carr and Moorman, 2009). The likelihood that an individual prepares for end of life
through the process of ACP may reflect their illness representations or beliefs about the
duration, severity, consequence and controllability of their health condition. Pursuant to
the CSM, health representations may trigger a health behavior which, in this case, is ACP.
Therefore, this analysis will use the CSM to help explain why individuals engage in the
processes involved in ACP (Leventhal 2003; 2010; Carr, 2003).
Outline of the Dissertation
Chapter 2 is entitled, Understanding Patients’ Perspectives on Advance Care
Planning: A Qualitative Study of the Perceptions of Older Adults at the End of Life. In
this chapter, using the CSM to ground my analysis theoretically, I explore how and what
illness representations affect advance care planning using patient narratives of those who
have (or have not) done planning. Two questions, in particular, are driving this analysis:
“What motivated you to plan for the end of life, or what things were you thinking about
when planning for the end of life? If no end-of-life plans have been made then what
things have prevented or discouraged you from end of life planning?” One predominant
theme that emerged from the focus group data was the salience of perceived illness
10
burden among many of the respondents in the NJEOL study. Chapter 3 is entitled,
Perceived Illness Burden and its associated correlates: A measure of objective and
subjective consequence at the end of life. Based on the themes extracted in Chapter 2,
perceived illness burden is a measure of patient appraisals that captures both functional
limitations and perceived burden to the self and others. I use a categorical construct of
perceived illness burden to answer the questions: Do the objective indicators of physical
health align with subjective perceptions of being a burden for all individuals? If there is
not agreement between the objective and subjective, are there specific characteristics of
individuals for whom there is not alignment? Chapter 4 (Using the Common Sense Model
to Understand the Relationship between Perceived Illness Burden and the Likelihood of
Advance Care Planning) builds on Chapter 3 and uses the four perceived illness burden
categories to examine the extent to which patients‟ perceived illness burden is associated
with ACP behaviors (end of life discussions, living will and durable power of attorney for
health care).
11
References
Caldwell, John C. 2001. Population health in transition. Bulletin of the World Health
Organization. 79(2):159-160.
Carr, Deborah. 2003. A „good death‟ for whom? Quality of spouse‟s death and
psychological distress among older widowed persons. Journal of Health and
Social Behavior, 44, 217-334.
Carr, Deborah. 2003. Illness Representations and End-of-Life Planning – R01-071403.
Unpublished manuscript.
Carr Deborah. and Moorman, S. M. 2009. End-of-Life treatment preferences among the
young old: An Assessment of psychosocial influences. Sociological Forum.
24(4): 754-778.
Carr, Deborah and Khodyakov, D. 2007. Health Care Proxies: Whom Do Young Old
Adults Choose and Why? Journal of Health and Social Behavior. 48:180-194.
Creswell, John, W., Clark Plano, V. L., Gutmann, M. L., and Hanson, W. E. 2002.
Advance Mixed Methods Research Design. In Tashakkori, A. And Teddlie, C. B.
(Eds.) Handbook of Mixed Methods in Social and Behavioral Science Research.
Thousand Oaks, CA: Sage Publications.
Degenholtz, Howard B., Meisel, Arnold, R., and Lave, J. 2002. Persistence of racial
disparities in advanced care plan documents among nursing home residents.
Journal of the American Geriatric Society. 50:378-381.
Detering, Karen M., Hancock, A. D., Reade, M. C., and Silvester, W. 2010. The impact
of advance care planning on end of life care in elderly patients: randomized
controlled trial. BMJ. 340:c1345, 1-9.
Ditto, Peter H., Danks, J. H., Smucker, W. D., Bookwala, J., Coppola, K. M., and
Dresser, R. 2001. Advance Directives as acts of communication: A randomized
controlled trial. Archives of Internal Medicine. 161: 421-430.
Field, Marilyn J., and Cassel, C. K. (Eds.). 1997. Approaching death: Improving care at
the end of life. Washington, DC: National Academy Press.
Fried, Terri R., Bullock, K., Iannone, L. and O'Leary, J. R. 2009. Understanding Advance
Care Planning as a Process of Health Behavior Change. Journal of the American
Geriatric Society. 57:1547-1555.
Galambos, Coleen M. 1998. Preserving end-of-life autonomy: The Patient Self
Determination Act and the Uniform Health Care Decisions Act. Health and
Social Work. 23:275-281.
12
Garrido, Melissa M., Idler, E., Leventhal H., and Carr, D. in process. Advance Care
Planning: The Role of End-of-Life Values and Beliefs about Control over the
Length of Life Submitted for consideration to the Journal of the American
Geriatrics Society.
Hoffman, James, C., Wenger, N. S., Davis, R. B., Teno, J., Connors, A. F., Desbiens, N.
for the SUPPORT Investigators. 1997. Patient Preferences for Communication
with Physicians about End-of-life decisions. Study to Understand Prognoses and
Preferences for Outcomes and Risks of Treatment. Annals of Internal Medicine.
127:1-12.
Hopp, Faith. P. 2000. Preferences for surrogate decision makers, informal
communication and advance directives among community-dwelling elders:
Results from a national study. The Gerontologist, 40, 4, 449-57.
Hopp, Faith P. and Duffy, S. A. 2000. Racial Variations in End-of-Life Care. Journal of
the American Geriatrics Society 48(6) 658-63
Kaufman, Sharon 2005. …And a Time To Die – How American Hospitals Shape the End
of Life. Scribner: New York.
Lambert, Heather, C., McColl, M. A., Gilbert, J., Wong, J., Murray, G. and Shortt, S. E.
D. 2005. Factors Affecting Long-Term-Care Residents‟ Decision-Making
Processes as They Formulate Advance Directives. The Gerontologist. 45(5): 626-
633.
Later, Elizabeth B. and King, D. 2007. Advance Directives: Results of a Community
Education Symposium. Critical Care Nurse. 27(6): 31-35
Leventhal, Howard and Meyer, D. 1980. The common sense representation of illness
danger. Contributions to medical psychology. S. Rachman. New York, Pergamon
Press. II: 7-30.
Leventhal, Howard, Leventhal, E. A., and Cameron, L. 2001. Representations,
Procedures, and Affect in Illness Self-Regulation: A Perceptual-Cognitive Model.
In A.Baum, T.a. Revenson, and J.E. Singer (Eds.), Handbook of Health
Psychology. NJ: Lawrence Erlbaum Associates.
Leventhal, Howard, Brissette, I., and Leventhal, E. A. 2003. The common sense models
of self-regulation of health and Illness. In L. D. Cameron & H. Leventhal, (Eds.),
The self regulation of health and illness behavior. London: Routledge Taylor &
Francis Group.
Leventhal, Howard, Weinman, J., Leventhal, E. A., and Phillips L. A. 2008. Health
13
Psychology: The Search for Pathways between Behavior and Health. Annual
Review of Psychology. 59:477-505.
Leventhal, Howard, Leventhal, E. A., Cameron, L., Bodnar-Deren, S., Breland, J., Hash-
Converse, J. and Phillips, L. A. 2011. Modeling Health and Illness Behavior: The
Approach of the Common Sense Model (CSM). In A. Baum (Ed.) Handbook of
Health Psychology, Second Edition. New York: Routledge.
Leventhal, Howard, Leventhal, E. A., and Cameron, L. 2001. Representations,
Procedures, and Affect in Illness Self-Regulation: A Perceptual-Cognitive Model.
In A.Baum, T.a. Revenson, and J.E. Singer (Eds.), Handbook of Health
Psychology. NJ: Lawrence Erlbaum Associates.
Levi, Benjamin H., Dellasega, C., Whitehead, M., and Green, M. J. 2010. What
Influences Individuals to Engage in Advance Care Planning? American Journal of
Hospice & Palliative Medicine. 27(5): 306-312.
McPherson, Christine J., Keith G. Wilson, and Murray, M. A. 2007. “Feeling Like A
Burden: Exploring the Perspectives of Patients at the End-of-Life.” Social
Science and Medicine 64: 417-27.
Moorman, Sara. M. 2011. The importance of feeling understood in marital conversations
about End-of-life health care. Journal of Social and Personal Relationships.
28(1): 100-116.
Morrison, Rolfe, S. and Meier, D. E. 2004. High Rates of Advance Care Planning in New
York City‟s Elderly Population. Archives on Internal Medicine. 164: 2421-2427.
New Jersey Bioethics Commission. 1991. Advance Directives for Health Care: Planning
Ahead for Important Health Care Decisions. Trenton, NJ: State of New Jersey
Commission of Legal and Ethical Problems in the Delivery of Health Care.
Omran, Abdel R. 1971. The epidemiologic transition: a theory of the epidemiology of
population change. Millbank Memorial Fund Quarterly. 29:509-538.
Pearlman, Robert .A. 2010. Bioethics at the end of life, Advance Care Planning.
Retrieved from
http://depts.washington.edu/bioethx/topics/adcare.html on 8/10/2010.
Pearlman, Robert. A., Cole, W. G., Patrick, D. L., Starks H. E. and Cain, K. C. 1995.
Advance Care Planning: Eliciting Patient Preferences for Life-Sustaining
Treatment. Patient Education and Counseling. 26: 353-361.
Pearlman, Robert A. and Starks, H. 2004. Why Do People Seek Physician-Assisted
14
Death? In Quill, T. and Battin, M.P. (Eds.) Physician-Assisted Dying: The Case
for Palliative Care and Patient Choice. Baltimore, MD: The John Hopkins
University Press. pp. 91-101.
Pfeifer, M.P., Mitchell, C.K., & Chamberlain, L. 2003. The value of disease severity in
predicting patient readiness to address end-of-life issues. Archives of Internal
Medicine, 163 (March 10), 609-612.
Schwartz, Charles. E., Merriman, M. P.; Reed, G. W., and Hammes, B. J. 2004.
Measuring Patient treatment preferences in end-of-life care research: applications
for advance care planning interventions and response shift research. Journal of
Palliative Medicine. 7(2):233-45.
Smucker, William. D., Ditto, P. H., Moore, K. A., Druley, J. A., Danks, J. H., and
Townsend, A. 1993. Elderly outpatients respond favorably to a physician-initiated
advance directive discussion. Journal of the American Board of Family
Practitioners. 6(5): 473-482.
Sudore, R. L. and Fried, T. R. 2010. Redefining the “Planning” in Advance Care
Planning: Preparing for End-of-Life Decision Making. Annals of Internal
Medicine. 153(4): 256-261
SUPPORT Principal Investigators. 1995. A Controlled Trial to Improve Care for
Seriously Ill Hospitalized Patients. JAMA. 274:1591-1598.
Temel, Jennifer S., Greer, J. A., Alona Muzikansky, M. A., Gallagher, E. R., Sonal
Admane, M. B., Jackson, V. A., Dahlin, C. M., Blinderman, C. D., Jacobsen, J.,
Pirl, W. F., Billings, J. A. and Lynch, T. J. 2010. Early Palliative Care for Patients
with Metastatic Non-Small Cell Lung Cancer. New England Journal of Medicine.
363:733-742.
Teno, Joan, Lynn, J., and Connors, A. F. 1997. The illusion of end-of-life resource
savings with advance directives. Journal of American Geriatric Society, 45, 513-
8.
Tierney, William, M., Dexter, P. R., Gramelspacher, G. P., Perkins, A. J., Zhou, X. H.,
and Wolinsky, F. D. 2001. The Effect of Discussions about Advance Directives
on Patients‟ Satisfaction with Primary Care. Journal of General Internal
Medicine. 16(1): 32-40.
U.S. Census. 2011. Aging Boomers Will Increase Dependency Ratio, Census Bureau
Project – Older American Population to Become More Diverse. Retrieved on
April 16, 2011 from: http://www.census.gov/prod/1/pop/p25-1130/p251130a.pdf
U.S. Department of Health and Human Services. 2008. Advance directives and advance
care planning: Report to Congress [online report]. Retrieved from
15
http://aspe.hhs.gov/daltcp/reports/2008/ADCongRpt.pdf on March 28, 2011.
U.S. Congress 2009. Patient Protection and Affordable Coverage Act
United States, Department of Health and Human Services. 2008. Federal Register, Vol.
73, No. 15, pp. 3971–3972
Wenger, Neil. S., Pearson, M. L., Desmond, K. A., Harrison, E. R., Rubenstein, L.V.,
Rogers, W. H., and Kahn, K. L. 1995. Epidemiology of do-not-resuscitate orders.
Disparity by age, diagnosis, gender, race and functional impairment. Archives of
Internal Medicine. 155:2056-2060.
Wilson, Keith. G, Scott, J. F., and Graham, I. D. 2000. Attitudes of terminally ill patients
toward euthanasia and physician-assisted suicide. Archives of Internal Medicine.
160:2454-2460.
Wilson, Keith G., Curran, D. and McPherson, C. J. 2005. “A Burden to Others: A
Common Source of Distress for the Terminally Ill.” Cognitive Behaviour Therapy
34: 115-23.
Ziven Bambauer, Kara. and Gillick, M. R. 2007. The Effect of Underlying Health Status
on Patient or Surrogate Preferences for End-of-Life Care: A Pilot Study.
American Journal of Hospice and Palliative Medicine. 24(3) 185-190.
16
Chapter 2: Understanding Patients’ Perspectives on Advance Care Planning: A
Qualitative Study of the Perceptions of Older Adults at the End of Life.
ABSTRACT
Objective. I use focus group data from the New Jersey End-of-Life (NJEOL) study
(2006-2008) to explore and describe how patients‟ perceptions affect their advance care
planning (ACP) behavior. Using the Common Sense Model of Illness Representations to
ground my analysis theoretically, two questions guided this analysis: “What motivated
you to plan for the end of life? If no end-of-life plans have been made then what things
have prevented or discouraged you from end-of-life planning?”
Methods. Patient narratives were elicited from eight focus groups conducted in English
and Spanish (stratified by gender and disease) designed specifically to explore illness
representations and how patients interpret, understand, and discuss their illnesses and the
implications that these interpretations have on advance care planning. Forty-six
respondents (aged 55-90) participated.
Results. Content analysis revealed three major themes (illness representations): (1)
Control and self-direction (autonomy) - being able to control/direct their care at the end
of life. (2) Consequences – Perceived burden was a major factor in advance care
planning, one that worked in a number of ways – as a catalyst to planning and as a reason
to avoid planning. (3) Past experiences with the deaths of others with whom they were
close. The results showed that illness representations served as motivators for ACP
behaviors.
17
Discussion. These themes broaden what we know about ACP, serve to guide health care
professionals, and suggest that by eliciting patient perceptions about illness impact,
practitioners may be able to facilitate increased levels of ACP.
Introduction
Health care practitioners and policy makers consider end-of-life (EOL)
communication and advance care planning to be important elements of quality health
care. Advance care planning (ACP) is best conceptualized as a process that involves
three steps. First, an individual considers their beliefs, values, goals of care and
preferences to decide what treatments s/he would or would not want upon diagnosis with
a terminal illness. Second, those preferences are communicated and discussed with
family, friends and health care providers. Finally, those preferences are formally
documented through an Advance Directive (AD) or do-not-resuscitate order in the
medical record or (Detering et al., 2010; Pearlman, 2010, Levi et al., 2010).
Advance directives are legally binding, written directions that outline the type of
medical care an individual would want in the event s/he becomes unable to make
decisions for him/herself (NJ Bioethics Commission, 1991). They are formally
represented as: 1) living wills (LW), an instructive directive about the types of EOL care
and medical interventions patients desire (Smucker et al., 1993); 2) durable power of
attorney for health care (DPAHC), appoints a health care surrogate to make medical
decisions at the end of life (Ditto et al., 2001); and 3) a combined directive, a document in
which an individual has selected a health care representative and discussed these
preferences with (NJ Bioethics Commission, 1991).
18
A substantial body of work has examined ACP and the use of advance directives.
This includes: the benefits of ACP (Drought and Kewnig, 2002; Sulmasy, 2002); the low
rates of and barriers to ACP (Moorman et al, 2011; Emanuel et al., 1995; Ditto et al.,
2001); the types of treatments patients are willing to accept (Berry and Singer, 1998;
SUPPORT , 1995; Fried, Bradley, & Towle, 2002; Rodriguez & Young, 2006); the
efficacy of ACP (Smucker et al., 1993; Teno, et al, 2007; Prendergast, 2001; Perkins,
2007); and characteristics of those who engage in planning (Carr and Khodyakov, 2007).
However, the process of ACP is still underutilized and not fully understood (Moorman et
al, 2011; Fried, 2009; Jackson, 2009). Questions remain about how to increase rates of
ACP and what factors influence those who have (or have not) engaged the ACP process.
To answer these questions, recent analyses of ACP have called for researchers,
policymakers, and practitioners to examine ACP as a health behavior (Pearlman et al.,
1995; Fried et al., 2009; Sudore and Fried, 2010), based on patient perspectives that
motivate such behavior (Leventhal, 2010; Carr, 2003).
The Common Sense Model of Self-Regulation (CSM) (Leventhal et al., 2003;
2011) is a widely used health behavior model asserting that an individual‟s health
preferences and behaviors are affected not only by their actual condition, but also by their
perceptions about their health condition (Leventhal et al, 1980; Leventhal et al, 2003;
Carr and Moorman, 2009). The likelihood that an individual prepares for end of life
through the process of ACP may reflect their illness representations or beliefs about the
duration, severity, consequence and controllability of their health condition. Pursuant to
the CSM, health representations may trigger a health behavior which, in this case, is
19
ACP. Therefore, the CSM may be useful in helping to explain why individuals engage in
the processes involved in ACP (Leventhal 2003; 2011; Carr, 2003).
The aim of this study is to use focus group data from the NJEOL study (2006-
2008) to investigate what patient perspectives or illness representations motivated them
to plan for the end of life. Conversely, if no plans had been made, I am interested in
ascertaining what perspective(s) prevented or discouraged participants from engaging in
the process of advance care planning.
Background
Advance care planning
Changes in demographics and causes of death make ACP increasingly important.
By 2030, 20% of the U.S. population will be over age 65 (U.S. Census, 2011). Deaths
from chronic conditions such as dementia-related disorders, cancer, and heart disease
have replaced deaths from acute and infectious diseases as the primary causes of death
(Omran, 1971); death is now a process of old age (Caldwell, 2010). As the life
expectancy of the chronically and terminally ill has increased, so has the time from
diagnosis to death due to advances in technology, diagnosis and treatment. Persons who
are cognitively limited or those who have failed to make end-of-life plans often endure
unwanted costly medical interventions (Field and Cassel, 1997; Kaufman, 2000;
SUPPORT, 1995) or may have desired treatments withdrawn or withheld (Lambert et al,
2005; Carr and Khodyakov, 2007).
Moreover, empirical studies report psychosocial, economic and quality-of-life
benefits to engaging in ACP. For example, discussions with physicians focusing on ACP
result in better understanding of future treatment options and reductions in patients‟ fears
20
and anxieties (Smucker et al., 1993; Ditto et al., 2001). Patients who have completed
advance directives report fewer concerns about communication with practitioners and
family and greater satisfaction with care; they also are more likely to make use of
hospice, i.e., treatment to reduce pain and maximize function (Tierney et al., 2001; Teno,
et al., 2007; Ditto, et al., 2001; Smucker et al., 1993).
Patients and families also reported improved quality-of-life and more positive
mood if they engaged in early discussions and set explicit goals for palliative care; these
discussions and goals were associated with increased duration of survival (Temel et al,
2010). Contrary to the rhetoric used by some in the debate over the passage of the Patient
Protection and Affordable Coverage Act (2009) and assertions of “death panels,” many
older adults are willing to discuss end-of-life plans and complete advance directives
(Morrison and Meier, 2004).
Given the demographic changes, the financial and emotional costs associated with
unwanted or contested end-of-life care (and the potential benefits of ACP) both federal
and state governments have instituted policies to provide patients the opportunity to
engage in the completion of ADs (Galambos, 1998). The Patient Self-Determination Act
(US Congress, 1990) requires all federally-funded health facilities to maintain written
policies and procedures guaranteeing that every adult receiving medical care will be
provided with written information about, or the opportunity to complete, an advance
directive.
Yet, rates of advance care planning remain relatively low, with between one-third
to one-half of all U.S. adults having completed an advance directive (Moorman et al.,
2011; Hopp, 2000; Later and King, 2007; U.S. Department of Health and Human
21
Services, 2008). The modest prevalence of ACP is due to multiple factors. For example,
socioeconomic variables are primary predictors of ACP; white, well-educated, and well-
to-do individuals are more likely to engage in advance care planning, ethnic minorities
are less likely (Ditto et al., 2001; Carr, 2011; Waters, 2000; Hopp and Duffy, 2000;
Degenholtz et al., 2002).
Even when formalized end-of-life plans exist, problems with implementation
sometimes occur. Individuals may not have had discussions with family and health care
providers regarding the contents of these documents, or the advance directive may be
unavailable to those who need to access it. For instance, it may be locked away in a safe
deposit box or lawyer‟s office file (Carr, 2010). In one study of end-of-life planning,
Cloud (2000) found that of those individuals who had been named health care proxy, only
70% of designees knew they had been selected. Often the formulation of ADs is done in
conjunction with the development of a signed and witnessed will regarding property and
assets. In some cases, the document is filed away with estate planning documents and is
not even discussed between spouses (Perkins, 2007). Understanding patients‟ views about
advance care planning includes understanding the possible limitations of ACP.
Understanding those limitations would help policymakers and practitioners to improve
current approaches to facilitating planning, as well as developing tools to assure that
planning is accessible and meaningful for patients and families.
Patient Perceptions and health behaviors
In addition to sociodemographics, experiential and biopsychosocial factors also
influence ACP behaviors. Health care professionals often focus on concrete, objective
concepts (e.g., physical functioning) and biomarkers (e.g., lab results) when considering
22
factors that are important at the end of life. Concrete, experiential consequences of
illness, such as changes in function are also an important indicator for patients. Many
studies have found that functional limitations are associated with patients having had
discussed preferences for resuscitation with family members and clinicians (Hoffman et
al., 1997; Ziven et al, 2007) and completing an advance directive (Schwartz et al, 2004).
However, other studies have failed to confirm this association (Wenger et al, 1995;
SUPPORT Principal Investigators, 1995; Pfeiffer et al., 2003). These inconsistencies
may be explained by variations in patients‟ illness representations or beliefs/perceptions
about the duration, severity, and consequences resulting from their current health
condition. Few studies have explored how patients‟ illness representations serve to
facilitate or impede ACP.
Clinicians have historically relied on how concrete, objective measures factor into
ACP; patients and family members, however, place greater emphasis on psychosocial
factors such as values, beliefs and perceptions of how their illness may affect those
around them (Garrido et al, forthcoming; Steinhauser et al, 2000). Beliefs about the
salience of individual autonomy and who should control EOL decisions have been found
to affect the likelihood and content of ACP. There is evidence that those who value
individual autonomy and control over major life decisions are more likely to engage in
ACP (Levi et al., 2010; Carr and Khodyakov, 2007; Moorman, 2011; Garrido et al,
forthcoming). Perceiving one‟s illness and related treatments as burdensome to the self
and others is another patient perspective that researchers believe may motivate ACP
(Wilson 2000; 2005; McPherson et al., 2007; Levi et al., 2010).
23
But do these beliefs and perceptions motivate behaviors such as ACP in a way
that is the same for everyone? Understanding how patients‟ illness representations
contribute to ACP coincides with the recommendations put forth by the Institute of
Medicine (IOM, 2001). The IOM recommended that researchers and clinicians look at
health and behavior “biopsychosocially,” as interplay between biological, behavioral, and
societal influences. They also called for a reconceptualization of care that is patient-
centered, one in which there needs to be an explicit understanding of how patients‟ beliefs
and perceptions affect health behaviors.
The Common Sense Model of Self-Regulation (CSM) (Leventhal et al., 2003;
2008; 2011) is a health behavior model compatible with the IOM‟s (2001)
recommendations. It is both patient-centered and biopsychosocial. The CSM
presupposes that individuals are active problem solvers who attempt to assign meaning to
their somatic (biological) experiences. These assignments or beliefs form the individual‟s
“illness representations” (psychological). Illness representations are comprised of five
main features: (1) identity – the symptoms and illness label for the condition; (2) timeline
– the expected duration of the symptoms and condition; (3) consequences – the
anticipated impact of illness, associated symptoms, and treatment for self and others; (4)
control – the perception that the illness and outcomes can be controlled; and (5) cause –
the antecedent conditions believed to cause the illness (Leventhal, Leventhal, and
Cameron, 2001).
People‟s appraisals of these five domains are based upon underlying prototypes
(psychological) from their prior experiences with illness, observations of others, and
general knowledge (social) (Kaptein et al., 2003) and, in the case of ACP, beliefs about
24
what the end of life entails. An illness representation may change as the illness or
experience with the illness progresses. Someone newly diagnosed with a condition may
characterize their illness one way at the point of initial diagnosis, yet s/he may have a
different, perhaps more realistic, understanding of their health trajectory as the illness
progresses (Leventhal et al., 2001; 2008; 2011).
Current Project
Using the CSM to ground my analysis theoretically, I will explore how and what
illness representations affect advance care planning using patient narratives of those who
have (or have not) done planning. These patient narratives were elicited in eight focus
groups (stratified by gender and disease) designed specifically to explore illness
representations and how patients interpret, understand, and discuss their illnesses and the
implications that these interpretations have on advance care planning (Carr, 2003). Focus
groups are an efficient and convenient way to collect data from several people
simultaneously. They are especially valuable for examining what people think, but also
how and why they think that way (Kitzinger, 1995). Two questions, in particular, are
driving this analysis: “What motivated you to plan for the end of life, or what things were
you thinking about when planning for the end of life? If no end of life plans have been
made then what things have prevented or discouraged you from end-of-life planning?”
Identifying whether and how specific illness cognitions affect advance care
planning is a basic yet important aim. Understanding exactly which health beliefs matter
to whom and how these beliefs work for patients are critical. For example, do perceptions
of burden (i.e., consequence) increase the likelihood of planning, as asserted by Wilson
and colleagues (205), or do some patients believe that planning or talking about end-of-
25
life issues is burdensome to others, compelling them to avoid EOL discussions and
planning? The themes derived from focus group data about what patients deem to be
important in their decisions whether or not to engage in ACP may provide the basis for
guiding discussions about the end of life and advance care planning.
Methods
This analysis is part of a larger NJEOL Study conducted between 2006 and 2008,
a project that used multiple methods of data collection and analysis. The NJEOL project
was a series of three studies, each informed by results from the preceding study. Initially,
a closed-ended, structured and in-person interview with 305 persons age 55+ was
administered. The sample included individuals with colorectal cancer, diabetes,
congestive heart failure (CHF), and controls. The survey data allowed us to investigate
statistical linkages between specific illness cognitions and end-of-life planning behaviors,
within and across the illness groups1. We then conducted eight focus group discussions
with a subset of the original 305 person sample. We conducted six English language
patient focus groups and two Spanish language groups, each of which consisted of a mix
of patients in terms of illness category and advance care planning behaviors. The primary
goals of the focus groups were exploratory; to identify the factors that shape ACP.
Finally, open-ended interviews with 10 patient-family member dyads were conducted as
part of the full study2.
1 Chapters three and four of this dissertation focus on analyses of the quantitative survey
data collected as part of the NJEOL study.
2 The family / caregiver focus groups and dyadic interviews that were also conducted as
part of the full NJEOL study are not part of this dissertation.
26
Qualitative methods are especially important to health research because they give
voice to individuals and patients, characterizing subjects in a full and complex fashion.
Focus groups are an efficient and convenient way to collect data from several people
simultaneously. They explicitly use group interaction as part of the method. The method
is particularly useful for exploring peoples‟ knowledge and experiences. They can be
used to examine what people think as well as how and why they think that way
(Kitzinger, 1995).
Focus groups are particularly useful for fostering discussion of sensitive issues. In
particular, focus groups can be used to address EOL issues, the role of one‟s physician in
planning for the end of life and the difficulties of caring for an ill loved one. A focus
group environment also permits participants to discuss what issues are important to them.
Moreover, researchers can utilize the interactions among participants to identify the
issues that are most difficult to discuss (Carr, 2003). Group participants are also able to
support one another as they articulate feelings prevalent in the group, even when those
feelings are not typical of the population at large. Focus groups, unlike structured
interviews, also have the potential to prompt important criticisms. This unique quality
aids the realization of critical discussion and consideration of various solutions, both of
which are instrumental to research oriented toward the improvement of health services
(Watts et al., 1987).
Sample - NJEOL Study
The New Jersey End-of-Life (NJEOL) study sample consists of data from 305
non-institutionalized older adults in New Jersey (NJ), 55 years of age and older. Patients
were recruited to participate if they were either English- or Spanish-speaking, had no
27
cognitive limitations, and had one or more of the following health conditions: cancer,
Type II diabetes, or congestive heart failure (CHF). A group of patients who did not have
any of the target illnesses were also recruited as a “healthy control” group. However,
many of these participants had one or more other health conditions, thus the label
“healthy” is largely a misnomer. Recruitment was conducted over the telephone from
two large university hospitals and one comprehensive cancer center in NJ.
The initial sampling frame consisted of 1,146 patients who were identified as
potential participants for the study through the general internal medicine department at
the University of Medicine and Dentistry of New Jersey (UMDNJ). Of this group, 575
respondents met the criteria for inclusion in the initial sampling pool. Reasons for non-
inclusion in the sampling pool included: invalid contact information/inability to locate
individuals; death of indentified possible participants; cognitive and physical limitation
precluding participation; and not meeting sampling frame characteristics (i.e. being too
young). Three- hundred-five participants consented to participate in the study,
representing 53% of the eligible sampling frame. Reasons for non-participation included
a general reluctance for patients at the end of life to participate in such a study and time
constraints (participants being too busy). The interview process consisted of a 1.5 hour
face-to-face structured interview with a trained graduate student interviewer. The survey
included questions regarding sociodemographics, health status and behaviors, EOL
planning, and attitudes toward treatments, religion/spirituality, and social supports (Carr,
2011).
Focus Group Participants and Recruitment
28
Patient focus group members were purposively (Patton, 2002) recruited from
persons who participated in the structured interview. Each focus group was stratified on
the basis of illness and gender. In each patient group, there was an attempt to have a mix
of patients who had done advance care planning and preparations and those who had not.
To assure and accommodate the ethnically diverse patient population, patient focus
groups were conducted in English and Spanish. Each focus group participant received a
stipend of $25. Consistent with standard focus group methodology, small groups were
selected to optimize interactions and encourage open communication (Krueger, 1988).
Forty-six participants consented to and participated in eight focus groups.
Procedures
Each focus group discussion was facilitated by either one of the project‟s
principal investigators (PhDs, Sociology) or one of two doctoral students (Sociology,
Public Health) who were part of a study team trained in focus group facilitation. In
addition, there was a trained note taker at all focus groups. Each focus group lasted
approximately 90–120 minutes, including time for the informed consent process. At the
beginning of each focus group, the facilitator explained the purpose of the study and
obtained consent from each participant.
Group interviews were structured by a set of semi-structured guiding questions
and probes (see Table 1.1) to ensure that patients discussed a variety of possible factors
involved in the EOL planning process. The guiding questions were designed by the
research team (two sociologists, a psychologist, and a geriatrician) to focus on: how
patients think about the symptoms, course, and consequences of the patient‟s illness; the
value and consequences of advance care planning; and the ways that their plans and
29
thoughts about the future reflect their illness representations. Due to the sensitive nature
of the discussions, a list of clinical psychologists and support staff was provided in case a
participant experienced emotional distress (none did). Before field notes were added to
the transcriptions, each focus group discussion was audio-taped, transcribed verbatim,
and checked for accuracy by the study coordinator.
Analytic Strategy
Four research study personnel (one PhD in psychology, two doctoral students in
psychology, and a graduate assistant in health law) and I, using a content analysis
approach, reviewed the transcripts independently, identified and assigned codes to
emergent themes related to the study questions: “What motivated you to plan for the end
of life, or what things were you thinking about when planning for the end of life? If no
end-of-life plans have been made then what things have prevented or discouraged you
from end-of-life planning?” The interview transcripts were initially read by each of the
coders who grouped participants‟ responses to the study questions by emerging themes
(Patton, 2002, p. 381). The thematic units of text were underlined and descriptive notes
were written in the margins of the transcripts, a process referred to as coding (Tashakkorv
& Teddie, 1994).
Using an iterative process, two study personnel and I then independently coded
the transcripts from each focus group based on the previously identified themes. Coded
units were labeled as specific EOL issues. Many were not mutually exclusive, but issues
that were conceptually different were given different descriptive labels. Labeled issues
were then compared between interviews. Similar issues were grouped together under one
overarching domain label and the data were re-coded by domain. As per qualitative data
30
analysis standards (Atkinson & Hammersley, 1994; Krueger, 1988; Levi et al., 2010), we
then compared our initial findings, identified and reconciled differences. Differences
were reconciled by discussion about the meaning of the code until agreement was
reached or created a new code that captured the content of the statement. Findings were
discussed with the other team members who provided a critique, addressing clarity,
consistency, and exhaustiveness. An iterative process of rereading and recoding passages
was conducted until a final consensus was reached (Atkinson & Hammersley, 1994;
Braun et al., 2010; Levi et al., 2010). The prevalence of each domain was recorded and
descriptive statements about each were developed using the patient‟s words. Quotes that
were selected for presentation in this paper were good illustrations of the domain and
provided data from the various patients (Singer, Martin, and Kelner, 1999). Inter-rater
reliability was high (kappa = 0.93). In order to increase reliability and trustworthiness,
ATLAS.ti (Scientific Software Development GmbH, Berlin) was used to create a coded
electronic data set and supported data management.
Results
Table 1.2 presents the characteristics of the 46 individuals who participated in the
NJEOL study patient focus groups. The respondents were older adults with a mean age of
70 (range 55-90); slightly over half were women (51%). The majority were married or
living in a marriage-like relationship (62%) and had an average of three or more living
children. Fifty-two percent self-identified as non-Hispanic white, 11% as non-Hispanic
black, over 28% as Hispanic, and 9% other. Forty-four percent of participants had a HS
degree or some college, while 21% had not finished high school. Slightly over half of the
participants had annual incomes of $39,999 or less, 31% of whom had incomes under
31
$14,999. Fifty-one percent of participants had a living will or advance directive, 44% had
named a Durable Power of Attorney for Health Care, and 64% had EOL discussions with
others. However, none of the five non-Hispanic black participants had done any formal
planning and only two reported having discussions with others about the end of life.
Among Hispanics, only thirty-one percent (4 out of 13) had EOL discussions and under
one-quarter (23%) had appointed a DPAHC. Eighty percent of focus group participants
had two or more health conditions (31% reporting four or more comorbidities).
[Table 1.2 about here]
Interview Analysis
Almost three-quarters of respondents who consented to participate in patient focus
groups had done some kind of advance care planning. Three major themes (illness/end-
of-life representations) that emerged as patients discussed if they had done any advance
care planning, such as having discussions with others, making a living will or naming a
health care proxy, included: (1) Control and self-direction (autonomy) - being able to
control/direct their care at the end of life. (2) Consequences – Perceived burden was a
major factor in advance care planning, one that worked in a number of ways – as a
catalyst to planning and as a reason to avoid planning. (3) Past experiences with the
illnesses or deaths of others with whom they were close.
[Figure 1.1 about here]
1. Control
This broad theme encompasses two separate elements. First, patients spoke about
being able to control or self-direct their care at the end of life. Patients often spoke about
32
their end-of-life plans and desires in terms of concrete plans, such as funeral
arrangements (pre-paid and/or pre-planned) and their desire to control or select the place
of death. The second element of control could be found in respondents‟ desires to
exercise control to assure consensus or avoidance of conflict among others, especially
adult children.
1.1 Self-direction/autonomy.
The desire to avoid institutional (nursing home) placement was a major concern
for respondents. Almost two-thirds stated that dying at home was at the top of their plans,
and that this desire had been communicated to partners and children. As articulated by
one respondent who had engaged in all types of ACP (discussions with others, combined
advance directive [LW and DPAHC], prepaid funeral trust, estate planning, and long-
term care insurance), the need to be independent and the ability to control the place where
he expects to spend the rest of his life was why he was so prepared for the end of life.
The problem is we have, we‟ve been in the same house for 42 years. Big
old house, we have a lot of room, and uh, we‟re really comfortable there.
My wife is picking flowers all day and tending to things and we‟ve got a
bridge club we‟ve been a member of for fifty years, and it‟s a small town
and we know a lot of people. And we wouldn‟t want to be
institutionalized. We wouldn‟t want to lose the, our independence. (Male,
non-Hispanic white, age 79 with cardio-vascular disease (CVD).
Another woman who, as a result of a diabetic coma, found herself in a nursing
home for rehabilitation emphasized that it was her desire to make sure she never ended
up in institutional care. She stated that this was why she formalized her advance care
plans, especially her residential plans:
33
I mean, there may be some nursing homes that are good and, sure, you
find people to talk to everyday and possibly play cards with or whatever
but my preference, I think, I would rather die at home, someplace where I
am comfortable. (Female, Japanese American, age 61 with diabetes).
She asserted further that her desire to be in control of how and where she spends her
remaining years has also motivated her to carefully monitor her diabetes and adhere to
treatment – medication, diet and exercise.
Another respondent spoke about wanting to be able to self-direct her end of life.
Although she had not formalized her plans with an AD, she had discussions with her
children. She felt that it was important that her children know what she wanted:
Dying at home, not hooked up, wherever that home may be. Um and
having an opportunity to say goodbye to each of my children and my
loved people… to, uh, not have everyone pretend I wasn‟t dying. (Female,
non-Hispanic black, age 74 with CVD).
These issues were important, even among those respondents who had not done
any EOL planning in terms of discussions or formal planning; one respondent had a
residency plan and another had a pre-paid funeral trust.
Control extended beyond wanting to determine the place of end of life, but also to
being able to self-direct care to spare children from having to do so. A woman - who had
no major health conditions, but had engaged in all three forms of advance care planning
(discussions, living will, and appointing a DPAHC) - asserted:
Well I am not going to be flippant and say „oh yeah, dying is part of
life…and I am looking forward to it,” I am not. Frankly, I am not looking
forward to it. I would like to live as long as possible. However, having
34
said that, I know that I am going to die and even though I am saying it, I
really don‟t want to believe it (but) I do know it. So, knowing that, I took
care of it (ACP). I didn‟t want our children and my husband to make any
decisions for me, they are all mine. (Female, non-Hispanic white, age 70,
no health conditions).
Another woman in her mid-seventies had a very similar perspective in terms of
control and her ability to self-direct.
Every day is a gift. Okay, so that‟s what I meant and why I took care of it
(advance care plans) because I am an obsessive compulsive person who
has to have everything in order and I don‟t want our children to have to, to
have to have anymore suffering than they will have because when my
husband dies and I die, our children will suffer – because we have a very
close relation to all three of them and our grandchildren – so I don‟t want
that to happen to them. They are going to be aggravated enough – I want
to take that away from them. (Female, non-Hispanic white, age 76 with
diabetes).
One respondent said that he did not want to be kept alive artificially. He decided
he would take control of his care so that his children would not have to.
If there‟s no quality of life and you are hooked up to a machine that is no
good. They (your children) should not be put through having to see you
lay there. They should not have to make the decisions. I will control that.
If the machine is keeping you alive, if nothing functions, and if they say
the brain is dead now you‟re just wasting your time! (People agree) ahah!
That‟s a torture for a child to do that. If my brain is gone, I‟m gone. Is
that the thing I need? No! To me that‟s torture to a child to have to make
that decision, and they don‟t have to make that because that one, I‟m
going to make clear to them, no, don‟t, don‟t even go open that. I already,
I already got it on paper. (Male, Hispanic, age 74 with CVD).
35
1.2 The ability to assert control in an attempt to assure familial consensus
The desire to have control was evident, not just in terms of the need to be
autonomous but also to avoid any possible conflict among children, a sentiment
articulated by one woman. Her desire to have control would, hopefully, alleviate the
burden of her children having to reach a consensus regarding her end of life.
I don‟t make decisions lightly, so the exploration of my own feelings. I
mean, just the set up with my kids are and everything, um, the way I run
my life I am very much in charge and I am very independent and nobody
tells me. You know, I‟m really in control and it is absolutely wonderful.
So, it (ACP) is very confronting. How do I design this? And, I need to
take control. My kids are wonderful. Um. Who do I put the burden on of
the decisions and pulling the plug and who will work well together of the
three (children). So I made the decisions. (Female, non-Hispanic white,
age 75 with CVD and hypertension)
Similarly, the possibility of a life that is functionally limited and dependent on
others overlaps with the ability to self-direct. In this example, the patient‟s attempt to take
control through ACP overlaps with a desire to prevent conflict among children.
… If I got my wits about me, I want to be alive. If I don‟t have my wits
about I‟m gone. I named my son to be my DPAHC, my daughter would
never „pull the plug‟ but he would in a minute. This way there will be no
fights. He knows what I want and will do it. He‟s realistic to understand
that there‟s no life when there is no fun. (Male, non-Hispanic white, age
74 with cancer and CVD).
One participant described the fact that while she had not “formalized” her plans,
she did have explicit discussions with her children. She has also written up her plans (but
36
not with an attorney, nor are they notarized) and provided copies of her wishes to her
children so that here will be no trouble between them stated:
We‟ve, we‟ve, oh I can‟t say we. I‟ve, my husband does not like to
participate in all of these such things (LW, DPAHC, etc.). What he has
done though, is written down things that we want to happen, should
something happen to us. Our daughter is the person who is really gutsy
and she would be able to do whatever, not the others. The others would
hopefully support her. So things that we‟ve written down, they (children)
all have copies, our daughter has one that she keeps in her safe. This way
there will be not troubles between them.” (Female, non-Hispanic White,
age 73 with CVD).
The fact that many of the themes were not mutually exclusive3 is explicitly
articulated by one respondent when she says that many of the issues discussed go hand-
in-hand with one another. This respondent talks about control and autonomy, her desire
to retain as much dignity as possible for herself, but also to spare her children the burden
of having to see her in a compromised position.
I want to take the burden off of everybody and it goes hand in hand. I also
want to have to, yeah again, to take the burden off, but I want to die with
as much dignity as possible. I want our children to remember me as the
person that I think they respected and knew not die without any dignity. I
cannot fathom that. That would be horrible for me, so that‟s another
important reason (for doing ACP). (Female, non-Hispanic white, age 72
with hypertension).
2. Perceived Consequences for Families
3 For purposes of this analysis (using ATLAS ti), quotes were only assigned one category.
37
Patients‟ cognitive representations of their illness – especially their beliefs about
the consequences and likelihood of disability and death resulting from their health
conditions -were a major consideration for many respondents in terms of EOL planning.
For example, burden was conceptualized by patients in relation to the self - no longer
being able to be independent in terms of daily activities and in concern for burdening
others. Notions of burden to self and others were rarely mutually exclusive. Perceived
illness burden included the loss of independence due to functional limitations from
disease, resulting in a sense of burden to the self and the possibility of burdening others.
2.1 Burden – instrumental and emotional tolls of caregiving and EOL decisions
Eighty percent of non-Hispanic white respondents who had engaged in ACP,
cited not wanting to burden others as being a main factor for why they had done
planning. The salience of relationships with family members was often at the forefront of
these discussions - in terms of an obligation to protect children from being burdened. In
one focus group, this type of burden to others was the main reason for all respondents (8
out of 8 non-Hispanic white participants). One woman - who described herself as being
in generally good health and had completed both a living will and DPAHC - summed up
this feeling succinctly:
We have to die quick. That‟s all we have to do. We don‟t want to be a
burden on anyone. I hope a plane crashes and takes both of us out
(husband and wife) at the same time. Instant, no pain, and NO BURDEN
(emphasis added by author) to others. (Female, non-Hispanic white, age
82).
38
This was a predominant theme among those who engaged in EOL planning. Many
respondents wanted to shelter their children from the burden of having to make EOL
decisions -in addition to instrumental activities of caregiving. One participant stated:
I don‟t want to be a burden. I came from a big family, my family is small,
I do not want to burden them. There are not enough of them (children) to
share the work of caring for us and the heavy burden of making these
decisions. I don‟t want them (children) to have that aggravation and
burden at the end of life, to say well – should we or shouldn‟t we – they
know exactly how I feel period. And that is what I want (…) It will save
them aggravation in the long run hopefully (…) I don‟t want them to have
that burden. (Female, non-Hispanic white, age 78 with cancer).
While perceived illness burden was a factor for some respondents as to why they
had done advance care planning, the majority of those who subscribed to this construct of
burden were non-Hispanic white. Burden to others was also discussed, mainly by
Hispanic and Asian respondents, as a primary reason for not having done end-of-life
planning, including having discussions with others (especially conversations with adult
children). These individuals clearly articulated their belief that the discussions themselves
would cause undue distress for their families.
One man, a seventy-three year old Indian immigrant with multiple comorbid
conditions (including six bypass surgeries) and congestive heart failure stated that he has
not really discussed EOL outside of this study, not even with his cardiologist. He has not
had any discussions with his children, even with his son (a physician) who is involved in
his care.
It is a very sensitive issue, but it is alright to talk about. In fact, I have
tried to initiate it, but my children and all, did not like it very much. They
39
just were not ready to. Even my wife is not ready yet, she doesn‟t even if
I talk. She is scared of the talk you know (…) it is probably fear. You
know, we were brought up like that, you know, because of the cultural,
this kind of planning, you don‟t do it. In India I think most of the families,
they were so knit together, there probably was not a necessity of all this
because the elderly people, they are still respected by the children. (…)
Many (elderly in India) still do not have life insurance, health insurance,
not much. They (Indian elderly) feel when the children go they are their
health insurances. They will take care of them (children will take care of
elders). (Male, Indian-American, age 73 with CHF, cancer, diabetes, and
CVD).
For some, discussing end of life was thought to burdensome or troublesome to
others, especially their children. Respondents also discussed how their children often did
not want to discuss the end of life, as it created distress and was emotionally burdensome.
One woman discussed how she has tried to have EOL discussions with her children, but
they do not want to hear about it. She explains that it is too emotional for them and that
the discussions represent a burden to them.
Because they (my children) don‟t really know about me (referring to her
physical condition and difficulty functioning) because they see me moving
and carry all, they have no idea, how I‟ll be feeling. As a result of how I
am feeling, I‟d be TRYING, and I tell them I need to have a meeting (to
discuss EOL plans). I tell them, and they get scared, but ah, they‟re
supposed to be coming on Thanksgiving and I‟m going to work it in there
somehow! (People laugh) I keep trying; you gotta try to tell them. I‟m not
going to be functional, they don‟t wanna hear that. I want to talk to them
about my future health concerns and what I would like them (to do), but I
can‟t with my daughter, cause she cracks. You know – I don‟t wanna hear
about it, no, - she does not want to be burdened with knowing it will
40
happen. (Female, Hispanic, age 64, with CVD, hypertension, and
diabetes).
Another respondent discussed her attempts to make plans and communicate them
with her children. She ends her discussion with focus group members, asserting that she
may disregard her children‟s desire not to plan for the end of life and put her plans in
writing.
My son don‟t want to hear that - talking about the end. No, every time I
discuss it with him I say I would like to let you know what‟s going on and
what‟s to be done and he says, “Why are you talking like that?” I say,
“Well, I want you to get it down on paper and if it has to get notarized, I
want that done.” “Oh, we‟ll talk about that later. You ain‟t leaving here
yet.” And he don‟t want to talk about it. So, what I‟m going to do is I‟m
going to go behind their backs… (Female, Hispanic, age 72 with diabetes
and arthritis).
Another participant, speaking about having EOL discussions with her children
and grandchildren, asserted that while she has had discussions with family, she has not
made a living will or appointed a proxy. She believed that there was no need. It would
not be one child‟s decision, but a collective decision made by all of her children.
Charging only one child with having to make decisions would be disproportionately
burdensome to that child.
And, we were in a car coming back (from a friend‟s funeral) my daughter
and my goddaughter, and, my goddaughter said, she said, oh God we can‟t
imagine nothing happening to you, I said but I‟m not here forever. I said
prepare yourself. I told them not to worry; I‟m not going away right now.
My goddaughter then said, if something happens, I will have to come to
your house and stay with you, sleep in your room, because your daughter
wouldn‟t be no good if something happens to you. My daughter who is
41
sitting next to me she said, that‟s the absolute truth and I‟m glad
everybody knows it cause I‟ll be crazy (people laugh). You‟ll have to
come get me and lock me up in, in University hospital in the mental ward.
And it was funny the way she said it, but, she‟s serious! She‟s serious; she
wouldn‟t be no good to anyone. Now, my son…he‟s stronger then, we
give him credit for. He‟s quiet. He‟s real quiet, but, I know in my heart
that he loves me, with no doubt (people: ahh) and he would be have to be
the one, to keep my daughter together. However, when it comes to making
decisions, it wouldn‟t be just him, it would be all my children and my
husband‟s daughter but she‟s my daughter. It would be everyone.
(Female, Hispanic, age 69 with CVD and diabetes.)
2.2 Perceived Illness Burden
Burden was often phrased in terms of the self (no longer being able to fully take
care of oneself due to poor health, functional limitations, and disability) which would
then lead to being a burden on others. Functional limitations, current or projected, were
often cited by respondents as being a primary concern by patients. These functional
limitations in turn were seen by patients to affect the sense of self and independence, with
the possibility that these losses (functional and sense of self) would render patients
dependent upon others. As stated by this patient:
I am enjoying myself and I really feel like my life, you know, is worth
living, but the day that I can‟t go to the bathroom by myself, the day I
can‟t take a shower, the day I can‟t get up without help. When I‟m lying
in bed and someone has to take care of me constantly, I just don‟t want
that. I just don‟t want wires and plugs and everything to keep me going!”
(Male, non-Hispanic white, age 76 with CVD and hypertension).
Another focus group participant discussed how he never wants to burden his
children with having to care for him. He asserted that his illness impacts his daily life
42
and that his limitations may possibly burden his children if he and his wife can no longer
care for themselves - these were the reasons he formalized his EOL plans:
I‟ve got two steel hips and a steel knee, and pains throughout the rest of
me, ah, and I had my bypass in ‟97. I got my life back really; it was all I
could do to breathe (…) I could not do the things I needed to (…) and I
was thinking, what‟s going on here. Since then I had a couple of
angioplasties, ah, a couple of rocky spots, and we, my wife and I both
gone through losing parents (…) We know that WE need to take care of
something, if we can‟t care for ourselves. WE need to take care of
something, rather than throw the burden on somebody who is unprepared,
and undocumented. OK? (Male, non-Hispanic white, age 71 diagnosed
with severe arthritis and CVD).
One man who made formal plans (LW and DPAHC) right before he went into
surgery, spoke strongly about not wanting to be functionally limited (“a vegetable”). He
did not want to burden anyone, especially his children and grandchildren:
Well, I have a living will. I put my son in charge of it because he will see
that I‟ve gotten everything I should have and knows that I don‟t want to be
a vegetable and I don‟t want to be ahh, a burden on any of my kids. I
don‟t want to be a burden on any of my grandchildren. I don‟t want to be
a burden. (Male, non-Hispanic white, age 75, cancer survivor with
hypertension).
3. Past Experiences
The final theme that emerged from the patient narratives was that of past
experiences with the end of life. The first theme that emerged amongst respondents was
having had a past experience with advance care planning, both positive and negative
experiences. The second was experience with the deaths of others, these experiences were
mostly negative and revolved around the witnessing the suffering of others.
43
3.1 Previous experience with advance care planning
Previous experiences with both own and others‟ advance care planning could be
positive or negative. One male participant described the experience he had with his
mother with whom he had an EOL discussion shortly before her death. Following the
discussion, he was called upon to make a decision on his mother‟s behalf and, had they
not had and EOL discussion, he asserts the process would have been more difficult for
him. It was this experience that facilitated his planning.
Want it all laid out for them (children) (…) I was with my mother the day
she died, she came back from her oncologist, and he said it‟s not working
anymore. So, I‟ll give you 10 days to 2 weeks. At that point she told me,
we did not have anything written or anything then, she told me that, do not
resuscitate. (…) She didn‟t make the 10 days; she did not make the ten
hours, alright? Now, I know what I went through, and I did not have a
decision to make (…) we had not (slows down and begins to cry) talked
about it before (…) I would not have been able to make that decision (…)
with a clear conscience. And I have done that with my kids. (…) My kids
are enlightened (to what I want), whatever term you want to use, they
know exactly what I want. When they are asked, they in clear conscience
will be able to say, pull the plug (…) because they know exactly what I
want. (Male, non-Hispanic white, age 73 with CVD)
In contrast, one woman, responding to a conversation about the need to be precise
and exact with the language in the living will, discussed the decisions she made for her
husband and, because the directions were vague, had to make decisions she was not
prepared to make. This experience is why she has both a living will and a DPAHC, and
the directives are specific.
44
I am exactly that person you have described (previous respondent talked
about problems associated with being vague in the language of the LW).
… I had a living will and we did not abide by the living will because there
was a flaw in it that we could not overcome (with regards to vague
language)…The doctor said to me, we can put him on a ventilator and
prolong his life. Well, the living will said no ventilator, but being there,
they asked me. They did not do as the LW said, they asked me instead,
and it was my choice not to let him die at that exact moment. And I did
this for a period of weeks – twice and by the third time…I chose not to
(extend his life) and it was my decision to allow. I stood there and
allowed him to die. I would not wish that on anybody… (Discussing her
plans), I have made the decisions so my children would not have to, would
not have to take this into their own lifestyle. (Female, non-Hispanic white,
age 76 with diabetes).
3.2 Witnessing or being part of another’s end-of-life experience
A number of respondents discussed stories about witnessing the suffering of
others as a result of lack of planning, communication, or problems with the
implementation of advance directives.
My brother passed away from a stroke, sister (in-law) waited six hours to
call EMS, they did all of the heroic measures, feeding tube, catheter, etc.
He had to live like that for another three years… I do not want that to
happen to me!” (Male, non-Hispanic white, age 68 diagnosed with CVD).
Another respondent conveyed another experience in which the advance directive,
in this case a DNR was not found and thus not followed in an emergency situation. His
father‟s experience prompted him to not only plan formally for the end of life, but to
assure that his physician, lawyer, wife, and children all had copies of his directive.
45
You need to give it to them (the AD to medical personnel). I saw my
father. I don‟t know if he had any ability to reason or not. But when they
put an IV in his arm, they had to restrain him – he would get his mouth
over to rip it out of his arm… Was he doing this because he did have a
DNR? He had a stroke. We put the DNR in the refrigerator, which they
tell you to do. They had to break into his house because we had not heard
from him. They found him having a stroke. They just ignored the DNR.
By the time we got to the hospital, they had enough tubes in him to choke
a horse…I did not have the guts at the time to pull those tubes… I don‟t
want my son and his family now to be put in that situation. (Male, non-
Hispanic white, age 76, diagnosed with diabetes).
Discussion
This qualitative study explored how older adults, identified by their physicians to
be close to the end of life, think about advance care planning. I used the Common Sense
Model of Self-Regulation (CSM) (Leventhal et al., 2003, 2010), a widely used health
behavior model that asserts that a person‟s health behaviors, in this case advance care
planning, are not only motivated by their actual condition, but also their perceptions
about their health condition (Leventhal, 1980; Leventhal et al., 2003; Carr and Moorman,
2009). I was specifically interested in exploring how patients‟ end-of-life plans and
thoughts about the future reflect their illness representations – or perceptions and beliefs
about the time line, controllability, and consequences of their health conditions.
To do this, I analyzed transcripts from eight focus groups, designed specifically to
examine illness representations and their influence on ACP. Forty-six individuals
participated in the focus groups which were held in 2006 and 2007. I found that
participants broadly discussed end-of-life planning in terms of control, consequences and
past experiences. Pursuant to the CSM, beliefs about controllability and the consequences
46
presented by an illness threat are two core illness representations that can trigger a health
behavior. Experience is another important facet of the CSM. Specifically, patients‟
illness perceptions are shaped by their own prior experiences, observation of the
experiences of others who have had to face similar situations, or general knowledge
(Kaptein et al., 2003).
The influence of patient perceptions on advance care planning - control
The first theme that emerged from the data was patient‟s desire to exert some
type of control over the end of life. Studies on patients in the United States have
illuminated the desire by many to be able to continue to have control in decision making
as being necessary to achieving a “good” death (Singer et al., 1999; Steinhauser et al.,
2000). From the American-European model of advance care planning, autonomy is at the
core of EOL decision making. Patient autonomy emphasizes individuals‟ rights and
desire to make decisions about the types of treatments s/he is willing to accept or reject at
the end of life (Kagawa-Singer and Blackhall, 2001).
Ideas about control influenced ACP in a number of ways. First, one thing that
motivated participants to engage in planning – from discussions to the formal planning -
was the desire to control or self-direct where they will live at their end of life. Patients
often believed that while they might not be able to control the progression of their illness,
or aging in general, they could assert control over some areas of their lives. When asked
about what EOL planning they had done, respondents regularly cited concrete plans such
as funeral arrangements (pre-paid and/or pre-planned) and the fact that they had
articulated and/or formalized plans that outlined where (the place) they wanted to spend
the end of their lives. An overwhelming majority of respondents adamantly voiced their
47
desire to avoid institutionalization and die at home, this desire was reflected in their
advance care plans. This is contradictory to Steinhauser and colleagues‟ (2000) findings
that found patients ranked being able to die at home at the bottom of things important to
patients at the end of life. But my results do coincide with national data that show that
three-quarters of older Americans want to die at home, although few do. Fifty-six
percent die in hospitals and nineteen percent in nursing homes (National Center for
Health Statistics, 2001; Cassel and Demel, 2001). This could be because the patients in
the NJEOL study focus groups, while selected by their physicians as being at the end of
life, all resided in the community and were relatively independent.
The desire to exercise some control at the end of life was also evidenced by
patients‟ assertion that by taking control and being autonomous, they could self-direct
any care that they would need at the end of life. This element of control has been
identified as an important factor associated with quality end-of-life care (Singer et al.,
1999; Murray et al., 2004). Research has also found that individuals who value self-
direction, autonomy, and control are more likely than those who do not to engage in
formal ACP (Garrido et al, forthcoming; Moorman, 2011). The need to be independent
and able to control the care received and the place where one expects to spend the rest of
his/her life was why many focus group participants had engaged in EOL planning. This
was very much driven by patients‟ desires to remain as independent as possible in
addition to sparing their children from the instrumental and emotional costs often
associated with caregiving.
The final element of control was evidenced by the theme of planning as a way to
control (or an attempt to control) relationships among one‟s adult children. A finding that
48
confirms family conflict research pertaining to caregiving at the end of life (Gentry,
2001) and recent EOL research that found that pressure from family often served as a
catalyst for ACP (Levi et al., 2010). Participants often asserted that one of the things that
motivated them to plan for the end of life was their desire to assert some control over any
future disagreements that might come up between adult children. Advance care planning
as a means to avoid decisional conflict is one way that patients can assert control over the
end of life (Murray et al., 2004; O‟Connor, 1997).
This desire to protect children is present in both domains of control and
consequence, and as a factor for why some patients engaged in ACP and others did not.
The importance of the family in health decisions is well documented (Born et al., 2004;
Hauser et al., 1997; Morrison et al., 2004; Gutheil and Heyman, 2006). In fact, many of
the themes that emerged as to why participants opted in or out of advance care planning
were not mutually exclusive. End-of-life planning and the decisions that are made when
one thinks about the end of life involve a complex interaction between informational and
emotional factors that involve an interplay between patients, family, and health care
providers (Dales et al., 1999).
The end-of-life decision-making process is complex. Many of the conceptually
discrete beliefs thought to motivate behaviors among individuals who are relatively
young and healthy, come together for individuals for whom death is proximal. For
instance, perceptions of burden to self and concerns for burdening others may be separate
constructs for someone who is relatively functionally independent. However, someone
who is infirm, disabled or close to death may conceptualize these two seemingly separate
concepts as one. For focus group participants, and perhaps others near the end of life,
49
these fused cognitions are driven by the fact that the end of life is both more threatening
and temporally proximate.
The influence of patient perceptions on advance care planning – consequences
Burden to others
End-of-life planning differs in several ways from other common health behaviors,
such as medication adherence and treatment-seeking. ACP is a proactive or preventative
health behavior. While many health behaviors are done for the benefit of the individual,
to spare him/her from somatic distress, it is commonly believed that ACP is often done to
spare family members and loved ones from distress in the long-term (Carr, 2003) and to
spare oneself from a bad death, which may encompass futile or unwanted treatments. The
need to avoid the distress of others was a common theme that emerged among focus
group participants. The negative consequences of encumbering or burdening others -
emotionally, physically and financially - was an important issue that many respondents
mentioned when discussing health, aging, and advance care planning. This coincides
with Glaser and Strauss‟ (1965) depiction of the “dying role” in which the patient is
expected to avoid distressing others above everything else, and patients‟ concern about
burdening others (Moorman, 2009; McPherson et al., 2007).
Over three-quarters of respondents in this study discussed EOL planning in terms
of burden, a finding consistent with the research that has found the desire to minimize
burden to be important to patients at the end of life (Steinhauser et al., 2000; Cousineau
et al., 2003; Levi et al., 2010). Additional research has found that the fear of burden is
associated with increased rates of ACP among white, middle-class respondents (Carr,
2011; Carr and Khodyakov, 2007; Wilson, 2000; Seymour et al., 2004; McPherson et al.,
50
2007). While the majority of respondents in this analysis stated that their desire not to
burden others was very important, there was a strong cultural influence as to how patients
conceptualized and acted upon perceptions of burden. Non-Hispanic white respondents
regularly cited that it was their desire not to burden others as a main reason for engaging
in ACP, especially formally documenting their plans. However, few non-white subjects
were similarly motivated by burden to engage in ACP. On the contrary, the desire to
buffer children from burden was cited as a reason for not doing EOL planning, including
having conversations about the end of life. This is a finding that is in agreement with a
number of studies on ethnicity and end-of-life planning (Morrison et al., 2004; Heyman
and Gutheil, 2010; Carr, 2011). This desire to shield others from the having EOL
discussions coincides with Glaser and Strauss‟ (1964, 1965) work on EOL
communication patterns, where they described two types of contexts in which EOL
communication can occur. The “closed context” exists when one party tries to hide
information from others (either the patient hiding the fact s/he is dying from loved ones,
or family members and others try to shield the patient from information regarding the end
of life). The “open awareness context” occurs when patient‟s terminal status is known
and shared by all involved (Levitz and Twerski, 2005).
Additionally, if planning did occur for patients in the Spanish language focus
groups, they were more likely to have had EOL discussions with others as opposed to
formally documenting their plans. This is especially evident for appointing a DPAHC;
only one of the 13 Hispanic focus group participants had named a health care proxy.
There was agreement among Spanish language focus group participants that these
decisions should not be shouldered by only one child. This finding is in agreement with
51
the research that asserts that Latinos may prioritize interdependence among family
members more so than individual autonomy (Carr, 2011; Gutheil and Heyman, 2006;
Blackhall et al., 1995), and as such see EOL decisions as something to be shared and
discussed with all family members. This appeared to be a cultural value as opposed to
something indicative of a specific nation-state or area in Latin American. The
respondents in the Spanish language focus groups came from diverse backgrounds: forty-
four percent were born in the Caribbean states (Puerto Rico and the Dominican
Republic), forty-four percent were born in the United States, and twelve percent came
from Mexico and Latin America.
These findings are important and have implications for EOL practice and policy.
The findings provide additional evidence that a one-size-fits-all approach to advance care
planning may be one reason for the low rates of planning among racialized minority
group members. The statements made by the participants in the NJEOL study focus
groups suggest cultural differences around the issue of burden. As suggested by Gutheil
and Heyman (2006), special care should be taken by those who work with elders of
various ethnic groups. Practitioners and other concerned parties should acknowledge the
burden associated with making life decisions for another, but this burden is especially
high if decision makers do not have a clear understanding of what a person wants at the
end of life. For that reason, both parents and children should be encouraged to have the
necessary discussions in a culturally relevant and sensitive way.
Perceived Illness Burden
An additional theme related to consequences which emerged from focus group
conversations was a conceptualization of burden that focused on burden to the self as
52
well as burden to others. Consequences in the CSM are defined as the anticipated
outcomes of illness, symptoms, and treatment for self and others (Leventhal et al, 2003).
The CSM asserts that patients are adept at and continuously monitoring their somatic
experiences, function, and the associated impact these limitations have on their
conception of the self, as well as how these limitations affect others (Leventhal et al.,
2003). When asked to discuss end-of-life planning, patients often contextualized their
EOL plans around the belief that functional decline leads to the prospect of no longer
being able to take care of one‟s self. This burden to the self was then followed by their
desire to protect others from the burdens of caregiving. This mind-body connection was
evident in many of the patient narratives. For focus group participants, the two types of
burden – self and others, were intertwined, they did not appear to differentiate between
the two at all. Patients described both current and projected functional decline, leading to
perceptions of burden which motivated their advance care panning behaviors.
An understanding of the mind-body connection articulated by focus group
respondents suggests that the inconsistencies in the previous research on the salience of
functional limitations as a predictor of EOL planning (such as: SUPPORT, 1995; Pfeiffer
et al, 2005; and Hoffman et al., 1997; Ziven et al., 2007) may be explained by the
variations in patients‟ representations. The SUPPORT study (1995) found no impact of
health conditions on ACP, but their measures were purely objective, with no reference to
how functional limitation may impede the sense of self or burden others. It is not
functional decline alone that affects whether one engages in ACP, but how these
functional declines are perceived by patients as having the potential to affect
independence, the sense of self as independent and autonomous, and present a burden to
53
others. This will vary from patient to patient, as each individual will have specific levels
of function that s/he considers to be burdensome. Physicians and other health
practitioners who have an understanding of the patients‟ perceptions and illness
representations can better frame the important discussions that are the foundation of
authentic and meaningful advance care plans.
Past experiences as an influence on patient perceptions and advance care planning.
Prior experience with advance care planning and the death of a loved one
emerged as a consistent theme in this analysis regarding why individuals engaged in
advance care planning. It is a finding present in a growing body of research on ACP.
Research on the correlates of ACP has found that past experience with the painful death
of a parent or spouse increased the likelihood that someone engaged in all elements of
ACP (Carr and Khodyakov, 2007), although patients did not appear to recognize that
their end of life may be different from that of their parents, who were of a different
generation. Levi and colleagues (2010) found that both experiences, making decisions
for others and witnessing the suffering of others at the end of life, influenced end-of-life
planning. The importance of experience is also a major factor in the formation of patient
perceptions.
Pursuant to the CSM, illness representations may be based on one‟s own or
someone else‟s illness experiences (Leventhal et al., 2003; 2007). Experience with
another person who engaged in ACP and its associated outcomes appear to influence end-
of-life planning. The success of ACP was articulated by a few of the NJEOL participants
as a driver of their decision to do end-of-life planning. Likewise, the shortcomings of
ACP that have been documented in the literature (Perkins, 2007) also influenced patient‟s
54
decisions to engage in EOL planning; a few respondents were present at deaths in which
the advance directive was vague or could not be located. These negative experiences
urged more careful and precise planning among focus group participants. Patients
discussed witnessing painful deaths marked by suffering in the absence of an advance
directive, leading many to engage in planning for themselves.
Finally, past experiences, including witnessing other appeared to be where some
individuals developed their ideas and expectations about being burdensome. However, I
was unable to find evidence that someone with a newly diagnosed condition perceives
their illness differently than someone who has experience with the illness. Experience of
this sort was not mentioned by patients as being influential in their advance care planning
behaviors.
Limitation and Future Directions
A number of limitations in this analysis should be acknowledged. The study
population consisted of 46 participants in eight focus groups. The generalizability of the
findings to other populations is limited because of the small sample size, non-probability
sampling technique, and the use of a single medical group to provide patients, even
though one was served mainly an ethnically diverse urban population, while the other
served a predominantly homogenous suburban patient population. Two additional
limitations that should be considered, common in interview-based research, is social
desirability bias and observer dependency. Social desirability bias occurs when
respondents respond in a way that is believed to be socially acceptable and desirable
(Fisher, 1993). Similarly, observer dependency, in which the results are influenced by the
focus group facilitator, may raise questions of validity, as can the ability of one subject to
55
influence other subjects. The appearance of agreement and conformity of opinion within
a focus group may be a result of group dynamics and the desire to conform, as opposed to
an aggregation of the views held by individual participants (Crabtree et al., 1993).
It is also important to note the strengths associated with the use of focus group
data. While not designed for statistically significant generalizations, focus groups are
well-suited to explore and clarify participants‟ concerns, motivations, and values (Basch,
1987; Levi et al., 2010). They also lend themselves to discussions of issues that are
sensitive in nature because of the supportive environment in which conversations occur.
Another strength of this study is that the NJEOL focus group sample was diverse in terms
of gender, race, ethnicity, and illness groups. This analysis is exploratory. Additional
study is warranted, both of advance care planning as a health behavior and the CSM as a
theoretical framework through which to examine ACP. Further research should also
include the collection of survey data to allow for quantitative analysis and the collection
of longitudinal data for analysis.
Conclusions
The findings from this study suggest that awareness of patient perceptions and
illness representations are helpful for gaining a better understanding of advance care
planning. The findings also suggest that the Common Sense Model is well suited to
examination of end-of-life decision making and advance care planning among older
adults. Illness representations, especially the domains of control and consequences, were
discussed by patients as to why they had or had not engaged in ACP. However, the
resultant behaviors motivated by the representations (and past experiences) vary,
especially between various cultural groups. For instance, some respondents engaged in
56
formal planning because they did not want to burden others. Other respondents cited fear
of burden to others as motivating their decision not to engage in formal planning. Health
practitioners, social service professionals and other concerned stakeholders working with
older adults at the end of life should be aware of these subgroup differences so that they
can approach EOL discussions in a way that is meaningful for patients. This is in keeping
with best practice recommendations that clinical encounters require negotiations between
a variety of cultural views held by clinicians, patients and families. This awareness can
better facilitate mutually agreed upon goals, outcomes, and help assure that satisfactory
care is provided to patients and their families (Kagawa-Singer and Blackhall, 2001).
A final implication of this study is that discussions about the end of life between
patients, families, and health care providers should focus on patients‟ perceptions about
their health condition in addition to specific diagnoses and medical interventions.
Focused attention on the ways in which patients make sense of their health sets the stage
for EOL discussions between practitioners, patients, and families that are patient centered
and meaningful. This is an approach to patient care that is advocated by the IOM (2001)
and supported by the use of the CSM as a means to understand advance care planning.
57
References
Atkinson, Paul and Hammersley, M. 1994. Ethnography and Participant Observation. In
Hammersley, M. Handbook of Qualitative Research. New York: Taylor and
Francis Pp. 248-261.
Berry, Scott. R. and Singer, P. A. 1998. The cancer specific advance directive. Cancer.
82: 1570-1577.
Braun, Ursula K., Ford, M. E., Beyth, R. J., and McCullough, L. B. 2010. The
physician‟s professional role in end-of-life decision-making: Voices of racially
and ethnically diverse physicians. Patient Education and Counseling. 80: 3-9.
Caldwell, John C. 2001. Population health in transition. Bulletin of the World Health
Organization. 79(2):159-160.
Carr, Deborah and Khodyakov, D. 2007. Health Care Proxies: Whom Do Young Old
Adults Choose and Why? Journal of Health and Social Behavior. 48:180-194.
Carr, Deborah. 2003. “A „good death‟ for whom? Quality of spouse‟s death and
psychological distress among older widowed persons.” Journal of Health and
Social Behavior, 44, 217-334.
Carr, Deborah. 2003. Illness Representations and End-of-Life Planning – R01-071403.
Unpublished manuscript.
Cassel, Christine, K. and Demel, B. 2001. Remembering death: public policy in the USA.
Journal of the Royal Society of Medicine. 94(9): 433-326.
Crabtree, Benjamin F., Yanoshik, M. K., Miller, W. L., and O‟Connor, P. J. (1993).
Selecting individual or group interviews. In D. L. Morgan Ed., Successful Focus
Groups: Advancing the State of the Art. Sage: Newbury Park, California. pp. 137-
149.
Curtis, J. Randall., Patrick, D. L., Caldwell, E. S., and Collier, A. C. 2000. Why don‟t
Patients and physicians talk about end-of-life care? Barriers to communications
for patients with acquired immunodeficiency syndrome and their primary care
clinicians. Archives of Internal Medicine. 160(11): 1690-1696.
Dales, Robert, O‟Connor, A., Herbert, P., Sullivan, K., and McKim, D. 1999. Intubation
and mechanical ventilation on COPD development of an instrument to elicit
patient preferences. Chest. 116(3): 792-800.
Degenholtz, Howard B., Meisel, Arnold, R., and Lave, J. 2002. Persistence of racial
disparities in advanced care plan documents among nursing home residents.
Journal of the American Geriatric Society. 50:378-381.
58
Detering, Karen M., Hancock, A. D., Reade, M. C., and Silvester, W. 2010. The impact
of advance care planning on end of life care in elderly patients: randomized
controlled trial. BMJ. 340:c1345, 1-9.
Ditto, Peter H., Danks, J. H., Smucker, W. D., Bookwala, J., Coppola, K. M., and
Dresser, R. 2001. Advance Directives as acts of communication: A randomized
controlled trial. Archives of Internal Medicine. 161: 421-430.
Drought, Theresa. S. and Koenig, B. 2002. “Choice” in end-of-life decision making:
Research fact or fiction? The Gerontologist 42 (special issue), 114-28.
Emanuel, Linda, L., Danis, M., Pearlman, R. A., and Singer, P. A. 1995. Advance Care
Planning as a Process: Structuring the Discussions in Practice. Journal of the
American Geriatric Society. 43: 440-446.
Field, Marilyn J., and Cassel, C. K. (Eds.). 1997. Approaching death: Improving care at
the end of life. Washington, DC: National Academy Press.
Fisher, Robert. J. 1993. Social desirability bias and the validity of indirect questioning.
Journal of Consumer Research, 20, 303-315.
Fried, Terri R., Bullock, K., Iannone, L. and O'Leary, J. R. 2009. Understanding Advance
Care Planning as a Process of Health Behavior Change. Journal of the American
Geriatric Society. 57:1547-1555.
Fried, Terri. R., Byers, A. L., Gallo, W. T., van Ness, P. H., Towle, V. R., and O'Leary, J.
R. 2006. Prospective Study of Health Status Preferences and Changes over Time
in Older Adults. Archives of Internal Medicine. 166: 890-895.
Galambos, Coleen M. 1998. Preserving end-of-life autonomy: The Patient Self
Determination Act and the Uniform Health Care Decisions Act. Health and Social
Work. 23:275-281.
Garrido, Melissa M., Idler, E., Leventhal H., and Carr, D. in process. Advance Care
Planning: The Role of End-of-Life Values and Beliefs about Control over the
Length of Life Submitted for consideration to the Journal of the American
Geriatrics Society.
Gentry, Deborah B. 2001. Resolving middle-age sibling conflict regarding parent care.
Conflict Resolution Quarterly. 19(1): 31-47.
Glaser, Barney. G., and Strauss, A. L. 1964. Awareness Contexts and Social Interactions.
American Sociological Review. 29: 669-679.
Glaser, Barney. G. and Strauss, A. L. 1965. Awareness of Dying. Chicago: Aldine.
59
Heyman, Janna C. and Gutheil, I. A. 2010. Older Latinos‟ Attitudes toward and Comfort
with End-of-Life Planning. Health and Social Work. 35(1): 17-26.
Hoffman, James, C., Wenger, N. S., Davis, R. B., Teno, J., Connors, A. F., Desbiens, N.
for the SUPPORT Investigators. 1997. Patient Preferences for Communication
with Physicians about End-of-life decisions. Study to Understand Prognoses and
Preferences for Outcomes and Risks of Treatment. Annals of Internal Medicine.
127:1-12.
Hopp, Faith. P. 2000. Preferences for surrogate decision makers, informal
communication and advance directives among community-dwelling elders:
Results from a national study. The Gerontologist, 40, 4, 449-57.
Hopp, Faith P. and Duffy, S. A. 2000. Racial Variations in End-of-Life Care. Journal of
the American Geriatrics Society 48(6) 658-63.
Institute of Medicine (IOM). 2001. Health and behavior: The interplay of biological,
behavioral and societal influences, Washington, DC: National Academy Press.
Institute of Medicine (IOM). 2001. Crossing the Quality Chasm: A New Health System
for the 21st Century. Washington, DC: National Academy Press.
Jackson, Jody, Rolnick, S. and Asche, S. 2009. Knowledge, attitudes and preferences
Regarding advance directives among patients of a managed care organization.
American Journal of Managed Care. 15(3): 177-186.
Kaptein, Adrian. A., Scharloo, M., Helder, D. I., Kleijn, W. C., van Korlaar, I. M., and
Woertman, M. 2003. Representations of Chronic Illness. In L.D. Cameron and H.
Leventhal (Eds.), The Self-Regulation of Health and Illness Behaviour. London:
Routledge.
Kaufman, Sharon 2005. …And a Time To Die – How American Hospitals Shape the End
of Life. Scribner: New York.
Kitzinger, Jenny. 1995. Qualitative research: Introducing focus groups. British Medical
Journal, 311, July 29, 299-302.
Kreuger, Richard. A. 1988. Focus Groups: A Practical Guide for Applied Research.
London: Sage Publishing.
Kagawa-Singer, Marjorie and Blackhall, L. J. 2001. Negotiating Cross-Cultural Issues at
the End of Life. “You Got to Go Where He Lives”. JAMA. 286(23): 2993-3001.
Lambert, Heather, C., McColl, M. A., Gilbert, J., Wong, J., Murray, G. and Shortt, S. E.
60
D. 2005. Factors Affecting Long-Term-Care Residents‟ Decision-Making
Processes as They Formulate Advance Directives. The Gerontologist. 45(5): 626-
633.
Later, Elizabeth B. and King, D. 2007. Advance Directives: Results of a Community
Education Symposium. Critical Care Nurse. 27(6): 31-35
Leventhal, Howard and Meyer, D. 1980. The common sense representation of illness
danger. Contributions to medical psychology. S. Rachman. New York, Pergamon
Press. II: 7-30.
Leventhal, Howard, Leventhal, E. A., and Cameron, L. 2001. Representations,
Procedures, and Affect in Illness Self-Regulation: A Perceptual-Cognitive Model.
In A.Baum, T.a. Revenson, and J.E. Singer (Eds.), Handbook of Health
Psychology. NJ: Lawrence Erlbaum Associates.
Leventhal, Howard, Brissette, I., and Leventhal, E. A. 2003. The common sense models
of self-regulation of health and Illness. In L. D. Cameron & H. Leventhal, (Eds.),
The self regulation of health and illness behavior. London: Routledge Taylor &
Francis Group.
Leventhal, Howard, Weinman, J., Leventhal, E. A., and Phillips L. A. 2008. Health
Psychology: The Search for Pathways between Behavior and Health. Annual
Review of Psychology. 59:477-505.
Leventhal, Howard, Leventhal, E. A., Cameron, L., Bodnar-Deren, S., Breland, J., Hash-
Converse, J. and Phillips, L. A. 2011. Modeling Health and Illness Behavior: The
Approach of the Common Sense Model (CSM). In A. Baum (Ed.) Handbook of
Health Psychology, Second Edition. New York: Routledge.
Levi, Benjamin H., Dellasega, C., Whitehead, M., and Green, M. J. 2010. What
Influences Individuals to Engage in Advance Care Planning? American Journal of
Hospice & Palliative Medicine. 27(5): 306-312.
Levitz, Yisrael and Twerski, A. J. 2005. Communication Patterns. A Practical Guide to
Rabbinic Counseling. Jerusalem, Israel: Feldheim Publishers.
McPherson, Christine J., Keith G. Wilson, and Murray, M. A. 2007. “Feeling Like A
Burden: Exploring the Perspectives of Patients at the End-of-Life.” Social
Science and Medicine 64: 417-27.
Moorman, Sara. M. 2011. The importance of feeling understood in marital conversations
about End-of-life health care. Journal of Social and Personal Relationships.
28(1): 100-116.
Morrison, Rolfe, S. and Meier, D. E. 2004. High Rates of Advance Care Planning in New
61
York City‟s Elderly Population. Archives on Internal Medicine. 164: 2421-2427.
Murray, Mary Ann, Miller, T., Fiset, V., O‟Connor, A, M., and Jacobsen, M. J. 2004.
Decision support: helping patients and families to find a balance at the end of life.
Journal of Palliative Nursing. 10(6): 270-278.
New Jersey Bioethics Commission. 1991. Advance Directives for Health Care: Planning
Ahead for Important Health Care Decisions. Trenton, NJ: State of New Jersey
Commission of Legal and Ethical Problems in the Delivery of Health Care.
O‟Conner, Annette, M. 1997. Decisional conflict. In McFarland, G. K., and McFarlane,
E. A. eds. Nursing Diagnoses and Intervention. 3rd
Edition. The C. V. Mosby
Company: Toronto: 486-496.
Omran, Abdel R. 1971. The epidemiologic transition: a theory of the epidemiology of
population change. Millbank Memorial Fund Quarterly. 29:509-538.
Rodriguez, Keri L. and Young, A. J. 2006. Perceptions of patients on the utility or
futility of end-of-life treatment. Journal of Medical Ethics. 32:444-449.
Patient Self-Determination Act of 1990, 42 U.S.C.A. 1395 (West 1995).
Patton, Michael Q., 2002. Handbook of Qualitative Research, 2nd
Edition. Thousand
Oaks: Sage Publications.
Pearlman, Robert .A. 2010. Bioethics at the end of life, Advance Care Planning.
Retrieved from
http://depts.washington.edu/bioethx/topics/adcare.html on 8/10/2010.
Pearlman, Robert. A., Cole, W. G., Patrick, D. L., Starks H. E. and Cain, K. C. 1995.
Advance Care Planning: Eliciting Patient Preferences for Life-Sustaining
Treatment. Patient Education and Counseling. 26: 353-361.
Perkins, Henry S. 2007. Controlling Death: The False Promise of Advance Directives.
Annals of Internal Medicine. 147(1):51-57.
Pfeifer, M.P., Mitchell, C.K., & Chamberlain, L. 2003. The value of disease severity in
predicting patient readiness to address end-of-life issues. Archives of Internal
Medicine, 163 (March 10), 609-612.
Prendergast, Thomas J. 2001. Advance care planning: Pitfalls, progress, promise. Critical
Care Medicine. 29(2): N34-N39.
Schwartz, Charles. E., Merriman, M. P.; Reed, G. W., and Hammes, B. J. 2004.
Measuring Patient treatment preferences in end-of-life care research: applications
for advance care planning interventions and response shift research. Journal of
Palliative Medicine. 7(2):233-45.
62
Scientific Software Development GmbH. 2005. ATLAS.ti Visual Qualitative Data
Analysis. Version: 5.0.66. Scientific Software Development Inc: Berlin,
Germany.
Seymour, Jane, Gott, B., Bellamy, G., Ahmedzai, S. H., and Clark, D. 2004. Planning for
the end of life: the views of older people about advance care statements. Social
Science and Medicine. 59(1): 57-68.
Singer, Peter A., Martin, D. K., Kelner, M. 1999. Quality of end-of-life care: patients‟
perspectives. JAMA. 281(2): 163-168.
Singer, Peter A., Thiel, E. C., and Naylor, D. C. 1995. Life-sustaining treatment
preferences of hemodialysis patients: implications for advanced directives.
Journal of the American Society of Nephrology. 6:1410-1417.
Singer, Peter A. 2001. Recent Advances, Medical Ethics. BMJ. 321:282
Smucker, William. D., Ditto, P. H., Moore, K. A., Druley, J. A., Danks, J. H., and
Townsend, A. 1993. Elderly outpatients respond favorably to a physician-initiated
advance directive discussion. Journal of the American Board of Family
Practitioners. 6(5): 473-482.
Steinhauser, Karen. E., Christakis, N. A., Clipp, E. C., McNeilly, M., McIntyre, L., and
Tulsky, J. A. 2000. Factors considered important at the end of life by patients,
family, physicians, and other care providers. JAMA. 284: 2476-2482.
Sulmasy, Daniel, P. 2002. A biopsychosocial-spiritual model for the care of patients at
the end of life. The Gerontologist, 42, 3, 24-33.
Tashakkori, Abbas and Teddlie, C. 1994. Handbook of Mixed Methods in Social and
Behavioral Research. Sage Publications: Thousand Oaks, CA.
Temel, Jennifer S., Greer, J. A., Alona Muzikansky, M. A., Gallagher, E. R., Sonal
Admane, M. B., Jackson, V. A., Dahlin, C. M., Blinderman, C. D., Jacobsen, J.,
Pirl, W. F., Billings, J. A. and Lynch, T. J. 2010. Early Palliative Care for Patients
with Metastatic Non-Small Cell Lung Cancer. New England Journal of Medicine.
363:733-742.
Teno, Joan, Lynn, J., and Connors, A. F. 1997. The illusion of end-of-life resource
savings with advance directives. Journal of American Geriatric Society, 45, 513-
8.
Tierney, William, M., Dexter, P. R., Gramelspacher, G. P., Perkins, A. J., Zhou, X. H.,
and Wolinsky, F. D. 2001. The Effect of Discussions about Advance Directives
on Patients‟ Satisfaction with Primary Care. Journal of General Internal
63
Medicine. 16(1): 32-40.
U.S. Census. 2011. Aging Boomers Will Increase Dependency Ratio, Census Bureau
Project – Older American Population to Become More Diverse. Retrieved on
April 16, 2011 from: http://www.census.gov/prod/1/pop/p25-1130/p251130a.pdf
U.S. Department of Health and Human Services. 2008. Advance directives and advance
care planning: Report to Congress [online report]. Retrieved from Retrieved on
March 28, 2011 from: http://aspe.hhs.gov/daltcp/reports/2008/ADCongRpt.pdf
U.S. Congress 2009. Patient Protection and Affordable Coverage Act
Waters, Catherine M. 2001. Understanding and Supporting African Americans‟
Perspectives of End-of-Life Care Planning and Decision Making. Qualitative
Health Research. 11: 385-400.
Watts, Mike and Ebutt, D. 1987. More than the sum of the parts: Research methods in
group interviewing. British Educational Research Journal, 13, 25-34.
Wenger, Neil. S., Pearson, M. L., Desmond, K. A., Harrison, E. R., Rubenstein, L.V.,
Rogers, W. H., and Kahn, K. L. 1995. Epidemiology of do-not-resuscitate orders.
Disparity by age, diagnosis, gender, race and functional impairment. Archives of
Internal Medicine. 155:2056-2060.
Wilson, Keith. G, Scott, J. F., and Graham, I. D. 2000. Attitudes of terminally ill patients
toward euthanasia and physician-assisted suicide. Archives of Internal Medicine.
160:2454-2460.
Wilson, Keith G., Curran, D. and McPherson, C. J. 2005. “A Burden to Others: A
Common Source of Distress for the Terminally Ill.” Cognitive Behaviour Therapy
34: 115-23.
Ziven Bambauer, Kara. and Gillick, M. R. 2007. The Effect of Underlying Health Status
on Patient or Surrogate Preferences for End-of-Life Care: A Pilot Study.
American Journal of Hospice and Palliative Medicine. 24(3) 185-190.
64
Table 1.1 Guiding Questions
Guiding questions Probes
Have you yourself done any end-
of-life planning, such as talking
with your family or friends about
your wishes, or making a living
will, or naming a durable power of
attorney for health care?
What types of end-of-life plans have you
made? Why or Why not?
a. Discussions with family or friends:
What were the circumstances
surrounding your discussions with
family members and/or friends?
Who first initiated the discussions
about end-of-life planning?
b. Living Will: A living will specifies
any intervention that you wish to
have if you are terminally ill and in
the hospital. What types of
interventions did you specify in
your living will? Why?
c. Durable power of attorney for health
care: Who did you choose as your
durable power of attorney for health
care? Why?
What things were you thinking
about when planning for the end
of life?
Media?
Age?
Religious influence:
a. How do your specific end-of-life
plans reflect you religious
teachings?
b. End-of-life planning of family or
friends?
Health experience of family or friends?
Your own health experience:
a. We all have different beliefs about
our own health, even if we happen
to have similar medical conditions:
(i) Thinking about your own health
as it now, what would you say are
the major factors that produced the
health problems you have? (ii)
When you think about the future,
are you expecting your health
condition to stay the same, get
worse, or get better? Why?
We get information about health from a
number of different sources. If you were to
list them, who or what would you name as
65
the sources, people of experiences that have
been most informative or helpful in dealing
with your medical condition?
If no end-of-life plans have been made then
what things have prevented or discouraged
you from end-of-life planning? Lack of
time; Good health; Family and/or friends
know preferences; Have not thought about
it.
Why is end-of-life planning or not
important to you?
What does being healthy mean to
you?
Absence of illness; Quality of life;
Independence; Mental and physical well-
being; Maintaining ones health through
proper diet and exercise?
What would you consider a “good
death” for yourself?
What things make it a “good death”?
What can you do to help ensure that you
have a “good death”?
Do you know of anyone who had what you
consider a “good death”? What specifically
made it a “good death”?
What actions would you take to
encourage an older adult, such as
a friend or family member, to plan
for their future end-of-life needs?
What if the older adult had: Congestive
Heart Failure? Diabetes? Colorectal
Cancer?
Do you have any comments or
questions about our focus group
today?
Are there any other questions you think
should have been included or not included
during our discussion?
66
Table 1.2 Characteristic of patient focus group participants
Source: NJEOL Focus Groups, 2006-2007. N=8 Focus Groups;
46 participants
Proportion/
Mean
N/SD
Type of Advance Care Planning
End-of-Life Discussions 0.641 29
Living Will 0.513 23
Durable Power of Attorney for Health Care 0.436 20
No advance care plans 0.282 13
Race
Non-Hispanic White 0.521 24
Non-Hispanic Black 0.109 5
Hispanic 0.282 13
Other 0.088 4
Sex
Male 0.487 22
Female 0.513 24
Age (in years) 70.00 9.02
Marital Status
Married 0.615 28
Widowed 0.128 6
Divorced/Separated/Never Married 0.256 12
Number of Living Children 3.49 2.48
Education
Less than High School Diploma 0.205 9
High School/Some College 0.436 20
Bachelors Degree + 0.359 17
Annual Income
$0 - 14,999 0.308 14
$15,000 - 39,999 0.205 9
$40,000 - 84,999 0.231 11
$85,000 + 0.179 8
Income missing/not answered 0.077 4
67
Number of Health Conditions
0 or 1 health condition 0.205 9
2 or 3 health conditions 0.487 22
4 + health conditions 0.308 14
Most important health condition for advance
care planning
Cancer 0.109 5
Cardio Vascular Disease 0.283 13
Diabetes 0.217 10
Hypertension 0.087 4
Other 0.261 12
None 0.043 2
68
Figure 1. Influences on advance care planning
69
Chapter 3: Perceived Illness Burden and its associated correlates: A measure of
objective and subjective consequence at the end of life.
ABSTRACT
Objective. I used data from the New Jersey End-of-Life (NJEOL) study (N=293) (2006-
2008), an ethnically-diverse sample of non-institutionalized older adults (≥ age 55) to
investigate perceived illness burden (PIB) and its correlates. PIB is a measure of patient
appraisals that captures both functional limitations and perceived burden to the self and
others. Two questions guide the analysis: Do the objective indicators of physical health
align with subjective perceptions of being a burden? If there is no agreement between the
objective and subjective, are there specific characteristics of individuals for whom there
is not alignment?
Methods. Post-hoc comparisons between PIB categories (high-disability/high-burden;
high-disability/low-burden; low-disability/high-burden; low-disability/low-burden) were
conducted using analysis of variance (ANOVA). Multinomial logistic regression was
used to examine the correlates of PIB.
Results. Of the sample, 28% of respondents were in the high-disability/high-burden
category, 18% in the high-disability/low-burden category, 12% in the low-disability/high-
burden category, and 41% in the low-disability/low-burden category. Race/ethnicity, age,
number of children, income, number of health conditions, and level of depressive
symptoms were all significant correlates of PIB category.
Discussion. An understanding of how perceived illness burden works for patients may
lead to end-of-life discussions and interventions that better meet the needs and goals of
patients.
70
Introduction
Considerable research has studied the factors deemed important at the end of life.
The desire to minimize the burden that one‟s illness poses to others has been identified as
important to patients (Moorman, 2009; Singer et al., 1999). For example, Steinhauser et
al., (2000) examined the factors patients believed to be important at the end of life and
found that not being a burden on family (89%) or society (81%) were very important to
respondents. Similarly, a recent American Association of Retired Persons (AARP) study
examined attitudes toward aging and advance care planning, found that eighty-four
percent of participants expressed concern that they did not want to become a burden to
others (Dinger, 2005).
The extent to which a care recipient believes they are a burden has been found to
affect a number of end-of-life health behaviors, both positive and negative (Moorman,
2009). Perceived burden has been negatively associated with treatment adherence
(Zweibel and Cassel, 1989; Cohen-Mansfield et al., 1992; McPherson et al, 2007) and the
use of end-of-life medical interventions (Chochinov et al., 2007). Patients‟ perceptions of
burdensomeness are positively correlated with suicidal ideation among older adults
(Foster, 2003), desiring to die quickly (Schroepfer, 2008), and requesting physician-
assisted suicide (Wilson et al., 2000). Moorman (2009) posits that perceptions of burden
are a catalyst for some patients to engage in positive end-of-life health behaviors such as
advance care planning, attending support groups (Ussher et al., 2006) or attempting to
strengthen and maintain close relations at the end of life (Singer, 1999).
Perceived burden has been defined as the “empathetic concern engendered from
the impact on others of one‟s illness and care needs, resulting in guilt, distress, feeling of
71
responsibility, and diminished sense of self” (McPherson, Wilson and Murray, 2007;
425). Perceived burden is generally regarded as a psychological construct. However, it is
possible that perceived burden may be detecting the effect of functional decline that
individuals often experience at the end of life or, as McPherson et al. (2007) states, the
“impact on others of one‟s illness and care needs.” People are generally good at rating
their physical health. Self-rated health (SRH) is another patient appraisal that has been
shown to be a better predictor of mortality than is physician assessment (Idler and
Benyamin, 1997; Ferraro and Kelley-Moore; 2001). Patient appraisal is not merely
psychological – or “in their head”; individuals are responding to real decrements in their
function. Similar to SRH, perceived burden as it has been defined in the literature may be
picking up the effect of functional decline.
This conception of perceived burden coincides with Leventhal‟s (1987) Common
Sense Model of Self-Regulation asserting that an individual‟s health beliefs/perceptions
(“illness representations”) are a result of their continual monitoring of their somatic
experiences, functional limitations and the associated impact these limitations have on
their conception of the self, as well as how these limitations affect others (Leventhal et
al., 2003; 2010; 2011). Perceived illness burden (PIB) is the belief that one‟s illness and
associated functional limitations/disability are burdensome to the self and others. I
believe that for many people, feelings of burdensomeness may be one accurate indication
of the impact of one‟s symptoms on daily life.
If PIB reflects the realities of actual health conditions and also contains an
important subjective component, it is important to consider the possible combinations of
burden and function to fully understand the correlates and consequences of PIB. Do the
72
objective indicators of physical health align with subjective perceptions of being a burden
for all individuals? Additionally, if there is not agreement between the objective and
subjective, are there specific characteristics of individuals for whom there is not
alignment? Understanding how perceived illness burden works for patients, may allow
for clinicians and others to tailor discussions and interventions that better meet the needs
and goals of patients. The goal of this paper is to examine the correlates of PIB by
looking at the characteristics of patients who comprise four possible PIB combinations:
1) high disability/ high burden; 2) high-disability/low-burden; 3) low-disability/high-
burden; and 4) low-disability/low-burden. I will use data from the New Jersey End-of-
Life (NJEOL) study (N=293), an ethnically-diverse sample of terminally ill, older adults
to conduct my analysis. The cross-sectional data were collected between 2005 and 2008.
Background
Perceived Illness Burden
Caregiver burden is an all-encompassing term used to describe the personal
energy, time restrictions, financial and physical strains, and/or psychological frustrations
associated with assisting persons with long-term care needs (George, 1986; Zarit, 1980).
The concept has been studied extensively from the perspective of the caregiver with
considerably less research focusing on the patients‟ or care recipients‟ perception of
being a burden on others. A high perceived burden of care may adversely affect the
caregiver – recipient relationship. It may contribute to an increased risk of depressive
symptoms and other adverse outcomes for both the patient and caregiver.
Evidence presented in the few studies that have looked a patients‟ perceived
burden indicates that patients‟ concern for burdening others is a significant factor
73
confronted by both terminally and chronically ill individuals at the end of life
(McPherson et al, 2007). Wilson et al (2005) observed that approximately 39 percent of
terminally ill patients reported mild levels of self-perceived burden, an additional 38
percent of respondents stated that they experienced moderate to extreme distress at the
idea of being a burden to others at the end of life. Additionally, perceived burden has
been associated with patient depression (Wilson et al., 2005) and diminished will to live
(McPherson et al., 2007; Chochinov et al., 2005). Similarly, Schroepfer (2008), in her
qualitative analysis of 96 patients at the end of life, found that perceived burden led to a
desire by some respondents to hasten death.
It is important to note that the studies that have looked at perceived burden have
all been cross-sectional; consequently, causality could not be established. For example, it
is possible that the presence of depressive symptoms led to perceptions of being
burdensome in Wilson and colleagues study (2005), as opposed to burden leading to
depression. In addition to the need for longitudinal analysis, additional examination of the
concept is needed so that we can begin to understand how perceived illness burden
works. For instance, is it driven by somatic sensations experienced by patients (in their
bodies) or is it a psychological factor such as depression (in their heads/minds)? If burden
is a “mind-body” construct, by looking at the correlates of the various categories of
perceived illness burden (high-disability/high-burden; high-disability/low-burden; low-
disability/high-burden; low-disability/low-burden) and the characteristics of those people
who occupy these categories; we can gain insight into whether it is PIB that matters for
the outcomes with which it has been associated, or is it social selection into the various
categories that matters?
74
Wilson et al (2005) identified three types of perceived burden: 1) concern for
others, 2) implications for self, and 3) minimizing burden or general distress for others.
The second type warrants additional analysis because individuals‟ beliefs about
burdensomeness to others occur as interplay between the self and the other. Much of the
research on perceived burden has used Cousineau et al.‟s (2003) construct of burden or a
similar paradigm of how an individual perceives their condition is troublesome to others.
They defined perceived burden as “a multi-dimensional construct arising from the care-
recipient‟s feelings of dependence and their resulting frustration and worry, which then
leads to negative feelings of guilt at being responsible for the caregiver‟s hardship.” (p.
111). Implicit in Cousineau‟s definition, is the acknowledgement that others may need to
provide help with tasks that the care recipient can no longer do for themselves.
Dependency on others implies that their illness/treatment has major consequences for
their daily lives.
Caregiving/receipt is a dyadic relationship; the care recipient may perceive their
condition and resulting functional limitations (present and future) through the eyes of the
caregivers providing this care. This self-perception can be partially explained by
Cooley‟s (1902) notion of the “looking glass self”. In other words, individuals do not
perceive themselves only in terms of the difficulties that their condition causes for those
around them; they are continually in a process of self-assessment of their functionality
and how they believe others perceive their abilities and limitations. For some care-
recipients, there may be observable cues from the caregiver that may reveal the burden
they are experiencing. Care-recipients could be responding to indicators of distress from
caregivers; some care providers may explicitly complain, while others may look
75
exhausted and strained. It may not be solely perceived feeling of being a burden; it may
be detected in more concrete ways as well. While I cannot explicitly model this, a
composite measure of perceived illness burden that includes physical limitations and
appraisals of burden to self and others, may give us a better understanding of what the
patient/care-recipient is experiencing.
Burden is not rooted solely in terms of the difficulties illness and associated
treatments cause to others, but also how health limitations have consequences for an
individual‟s sense of self as independent and autonomous. This is illustrated by
Moorman‟s (2009) finding that concerns about autonomy were correlated with feelings of
being burdensome, more so than norms of reciprocity, such as marital concerns and
caregiver availability. She suggests that “feeling like a burden may have more to do with
losing one‟s own functional independence than with infringing upon the independence of
one‟s caregiver” (Moorman, 2009, 147). These findings may call into question
McPherson and colleagues‟ assertion that feeling burdensome is predominantly rooted in
“empathetic concern for others.”
Past research on end-of-life decision making has found that patients report
intricate and subtle interactions between physical and functional decline and existential
concerns such as loss of sense of self and burden to others that could not be separated or
compartmentalized (Pearlman and Starks, 2004). Henceforth, when considering the
concept of perceived burden, it is important to include measures of functional limitations
and perceived burden to both the self and others. This analysis will extend the definition
of perceived burden to include these factors. This perspective, which I label “perceived
illness burden” (PIB) is also driven by the findings of my qualitative analysis of the New
76
Jersey End-of-Life (2006-2007) focus group data which examined what patients believe
to important when considering the health decisions they must make at the end of life (see
Chapter 2 of Bodnar-Deren dissertation, 2011). When discussing their motivations for
engaging in certain advance care planning behaviors, patients regularly discussed their
health and health behaviors from a “biopsychosocial” perspective, as interplay between
biological, psychological, and societal influences. First, they framed their discussions in
terms of illness and functional decline (biological). This was followed by a statement of
how that decline provides difficulties for their sense of self and independence
(psychological) which then leads to dependence on others (social). This conception of
perceived illness burden is illustrated in Figure 1.
[Insert Figure 1 about here]
Patients‟ perspectives directly link with the recommendations put forth by the
Institute of Medicine (IOM) (2001) which called for clinicians and researchers to look at
health and behavior “biopsychosocially”. They also called for a reconceptualization of
care that is patient-centered, one in which there is an explicit understanding of how
patients‟ beliefs and perceptions (illness representations) affect their health. The
Common Sense Model of Self-Regulation (CSM) (Leventhal et al., 2003; 2011) is a
model of understanding health and health behaviors that is both patient centered and
biopsychosocial.
Conceptual Framework
Most of the research to date on perceived burden has primarily focused on the
psychological aspects of burden and how patients‟ beliefs about burdening others are
associated with psychological factors such as depression (Wilson et al., 2005). However,
77
while the focus on feelings such as depression, dependency, frustration, and worry as
markers for perceived burden is appropriate, it is equally important to recognize the role
of functional limitations as a prior, if not the pivotal, antecedent of perceived burden. I
propose that for many respondents, feelings of burdensomeness may be an accurate
reflection of the impact of one‟s symptoms on daily life, including the lives of the patient
and their support network. The Common-Sense Model of self-regulation (CSM) provides
the theoretical basis for this assertion.
A core proposition of the CSM is that awareness of symptoms, functional decline,
and medical diagnoses are critical factors for the activation of illness representations and
changing views of the self that create the motivation for engaging in health behaviors
(Leventhal et al., 2003; 2008; 2010). The activation of representations creates an array of
expectations with respect to future somatic and functional experiences, the consequences
and causes of these changes and the possibilities for control by treatment (Leventhal et
al., 2003). In essence, perceptions (including burden) are anchored in concrete physical
experiences which then work together to inform behavior. For example, the experienced
level of function and the perceptions of functional decline are powerful drivers of self
assessments of health and predictors of mortality in community samples (Mora, 2009)
and terminally ill cancer patients (Shadbolt et al., 2002). Thus, the experience of current
levels of function and declines in function are critical antecedents of optimism or
pessimism about treatment and survival and are hypothesized to be major drivers of
perceived illness burden.
In this paper, I explore the “mind-body” relationship between disability and
burden by looking at the various categorical combinations of perceived illness burden
78
(high-disability/high-burden, high-disability/low-burden, low-disability/high-burden, and
low-disability/low-burden), I will be able to see whether and for whom perceptions are an
concordant reflection of reality (patients in the high/high and low/low categories);
whether and for whom PIB is mainly a psychological concept (high/low and low/high
categories). However, the extent to which one‟s perceptions actually reflect one‟s
physical realities may vary widely based on personal and sociodemographic traits such as
age, gender, race/ethnicity, family status and health status.
Correlates of perceived illness burden
Race/ethnicity. Research has consistently found that racial/ethnic and SES
disparities in health are pervasive and enduring across multiple health outcomes
(Williams et al., 2009, 2010). At every level in the process, from disease to disability
(Verbrugge & Jette, 1994), non-Hispanic black individuals are at a considerable
disadvantage compared to non-Hispanic whites (Ferraro & Farmer, 2004; Fuller-
Thompson et al., 2009). A higher proportion of African American, Native American,
Latino and Pacific Islanders are in poor or fair health compared to whites at nearly every
income level (although recent Latino immigrants report better health) (WHO, 2008).
Minorities are also more likely to have more functional limitations and higher rates of
disability because they have more health problems over the life course than do whites
(Williams et al., 2009; 2010).
Research also has shown that racial minority group members are less likely to
receive high-quality health care over the life course and at the end of life than are non-
Hispanic whites (Smedley, Stith & Nelson, 2003; Rhodes and Teno, 2009). Non-Hispanic
blacks have worse physical health than non-Hispanic whites, a disparity that holds in
79
some studies even after controlling for SES (Mouzon, 2010; Williams et al, 2009).
Conversely, Fuller-Thompson and colleagues (2009) recently found that over 75% of the
black-white difference in disability rates could be accounted for by SES.
Similar differences have been noted in perceptions of burden. Carr (2011) notes
that Blacks and Latinos may be less likely to feel burdensome even if they have
functional limitations because they prioritize family interdependence rather than
individual autonomy. In her analysis of the NJEOL data, Carr (2011) found that fear of
burden was significantly more acute for non-Hispanic whites than for Hispanics.
Therefore, it is possible for members of racialized minority groups to have high levels of
disability yet not feel burdensome to others in their social network. Mexican-Americans,
according to Blackhall and colleagues (1995), emphasize that family members,
particularly adult children, should aim to minimize the burdens on the dying patient as
opposed to the other way around.
In this analysis, I will examine if there are race/ethnicity differences in the
experiences of perceived illness burden among non-Hispanic whites, non-Hispanic
blacks, and Hispanic sample members. Minorities are more likely to have functional
limitations, as they have more health problems over the life course than whites, but
because of cultural differences in how people feel about family support and integration, I
hypothesize that both non-Hispanic blacks and Hispanics will be more likely to be in the
high-disability/low-burden category than are non-Hispanic whites.
Socioeconomic Status: A large body of research suggests that higher levels of
educational attainment and income are associated with better health over the life course
(Currie, 2009, Cutler and Lleras-Muney, 2010). The health-wealth gradient is among the
80
most robust findings in social sciences (Mouzon, 2010; Carr, 2011). Disability,
morbidity, and mortality are inversely correlated with income, wealth, and educational
attainment in virtually every study conducted on the relationship between SES and health
(WHO, 2008; Deaton, 2003). Poorer people die younger and are sicker with higher rates
of functional limitations and disability than rich people (Deaton, 2002).
Members of different SES groups, in terms of education and income, may also
experience illness burden differently. High SES individuals may be functionally
compromised yet not feel particularly burdensome to others. Economic resources may
enable individuals to purchase formal care or secure adaptive equipment. High SES
individuals also have better access to health care and formal care networks (home health
agencies, respite care, case management service) and may be at ease articulating with
medical and home care professionals (Carr, 2011b). This may allow them to reduce their
dependency on friends and relatives. The ability to self-direct or manage their own care
may buffer them from feeling burdensome to others, even if they are functionally
compromised. Similarly, individuals with low SES may not have the resources to adapt to
functional limitations, especially in terms of purchasing formal supports. Consequently,
even relatively low levels of functional limitations may force them to rely on family
members and friends for assistance, thus elevating feelings of burdensomeness.
I will examine how SES, operationalized as both educational attainment and
income, are correlated with the four categories of perceived illness burden. As a result of
the health-wealth gradient, I hypothesize that high-income and highly educated
participants will be more likely to be in the low-disability/low-burden category than those
with lower incomes and lower levels of educational attainment, due to the fact that they
81
are just more likely to have better health. I also expect that individuals with low income
may be more likely than those with high incomes to be in the low-disability/high-burden
category because they may not have the means to which to adapt to even small levels of
functional decline.
Age, Gender, and Family Identity. Older respondents are more likely to have
functional limitations due to declines normally associated with aging. A certain amount
of functional decline is inevitable with advancing age; over the life span the human body
functions less efficiently (Hooyman, N.R. and Kiyak, H.A., 2008). So while it is likely
that older respondents will be in both of the high disability categories, younger
individuals who are less physically impaired may perceive themselves to be burdensome,
due to their belief that because they are younger they should not have any physical
limitations and even minimal impairment will make them feel like a burden to others. As
a result, I hypothesize the younger respondents in my study may be more likely to be in
the low-disability/high-burden category than are their older counterparts. Additionally,
because older people may have had more time to adjust to functional limitations, they
may be more likely than younger respondents to be in the high-disability/low-burden
category.
Gender. Demographic characteristics such as gender, age, marital status and
parental status are other possible variables that may affect a person‟s membership in one
of the perceived illness burden categories. Moorman (2009) and Arber et al. (2008) found
that women report greater concern about burdening others than do men. However, other
studies have found no difference in perceived burden between men and women (Wilson
et al., 2005). Though women live longer than men, they also regularly report more co-
82
occurring conditions and functional limitations (Crimmins, 2004). This reality may affect
what perceived illness burden category they are members of. Due to higher morbidity and
levels of functional limitations, women may feel more burdensome than men because
they require more extensive/intensive care. Accordingly, it is possible that women are
more likely to be in the high/high category than the other three PIB categories as
compared to men. Whom men and women turn to for care also differs. Men are
overwhelmingly reliant on wives; women are more likely to rely on others for care,
mainly adult children (Antonucci and Akiyama, 1987), because women are more likely to
outlive their spouse (Federal Interagency Forum, 2010).
Moorman (2009) also observes that men may have lower standards for quality of
care and therefore, may expect care that is less burdensome. In one study of patients with
osteo-arthritis requiring physical assistance with activities of daily living, women were
more likely than men to be unhappy with the way their spouses provided care (Martire et
al., 2003). However, it is also possible that men, due to traditional Western constructions
of masculinity based on independence and self-sufficiency (Gerschick and Miller, 1995;
Smith et al., 2007), may feel more burdensome if they need even minimal assistance from
others. So it is possible that men will be over represented in the low-disability/high-
burden category than are women. However, women are socialized to be caregivers, not to
be dependent on others. Consequently, they may have a higher likelihood of being in the
low-disability/high-burden category because even a relatively small amount of disability
and reliance on others, especially husbands and children, will be perceived as
burdensome.
83
Family status. Research has found that marriage is protective for both men and
women in terms of morbidity and mortality; the benefits are greater for men (Lillard and
Panis, 1996). While these protections may be partially explained by selection, they are
present and it is possible that I will find that married men and women are both more
likely to be in both of the low disability categories. Cantor‟s (1975) hierarchical
compensatory model proposes that caregiving preferences of older adults are based on
social relationships; care is provided by the closest, most available and accessible
individual. This is the spouse/partner in most cases, followed by children, other family
members or friends. Formal caregivers provide caregiving when no other options are
available.
Pursuant to Horwitz (1985), there is a preferred hierarchy to the caregiving
arrangement. Spousal care makes sense, as spouses are most likely physically present –
they are on the scene, and the spouse is most familiar with the care recipient‟s
preferences and needs in most cases. This preference is evidenced by traditional western
marriage vows “in sickness and in health, until death do us part”. Spousal caregiving is
expected and may not be perceived as being as burdensome as having to rely on adult
children, other relatives or friends. Thus, marital status and the presence of children will
be explored as correlates in this analysis. If there is a difference in PIB category
membership based on marital status and the presence of children, I hypothesize that
married individuals will be more likely than those who are widowed or never married to
be in the low-disability/low-burden category than in the high/high, high/low or low/high
categories, a result of having better health and the care expectations associated with
marriage. Due to the hierarchy in caregiving, it is possible that single participants or
84
widowed respondents may feel burdensome, even with lower levels of disability, as a
result of having to turn to children or others to provide care. Similarly, individuals
without children may be more likely to be members of the low-disability/high-burden
categories than are married respondents with children because they may have to rely on
friends or more distant relatives for care.
Finally, quality of familial relationships may affect the category of perceived
illness burden an individual is in. Booth and Johnson (1994) found that declining health
and increased functional limitations decreased reported marital quality. Additionally,
those who are in conflictual marriage report distress over receiving spousal help
(Newsom and Schulz, 1998). Schroepfer (2008) found that poor social support from
caregivers contributed to terminally ill older adults‟ desire a hastened death. Moorman
(2009), however, did not find a significant effect of marital quality on perceived burden.
Parent/adult child relationship quality has also been found to positively correlate with
parent‟s health (Umberson, 1992). It is possible that those respondents that have good
family relationship quality, even if they are functionally limited, may perceive fewer
burdens on others. Likewise, individuals with strained family relationships may be more
likely to be in the high/high category, due to compromised health and higher perceptions
of burden.
Health Variables – Number of health conditions and depressive symptoms
I posit that both the number of health conditions a patient has and their level of
depressive symptoms will be important correlates of which category they are in. First, a
core aspect of the CSM is that patients‟ current and projected levels of function, disability
and somatic experiences are important antecedents of their perceptions about treatment
85
and survival (Leventhal, 2007). These perceptions then may inform perceived illness
burden. I believe that most patients will be adept at assessing their current health and
assigning concordant assessments. Individuals with many health conditions because they
have a higher burden of disease, may just simply feel more limited and burdensome
(members of the high-disability/high-burden category) while those with 0-1 health
conditions will experience lower perceived burden (low/low category).
In an analysis of another self-appraisal of health, self-assessed health (SAH) (also
referred to as self-rated health [SRH]), Mora and colleagues (2009) examined how trait
affect (cheerfulness, anxiety, and depression) and function affected self-assessed health,
in five cross-sectional panels and over time. They found that in each of the cross-
sectional analyses, SAH judgments were moderately correlated with trait affect but
strongly associated with level of function; however only functional limitations were
related to changes in SAH over time. In cross-sectional studies of burden, the effects
were reversed. Wilson and colleagues (2005) found affect (depression and anxiety) to be
highly correlated with burden, but physical symptoms were only insignificantly to
moderately correlated.
In their review of the burden literature, McPherson and colleagues (2007b) found
that only four of the fifteen quantitative articles examining the correlates of burden
included measures for both function or disability and psychological factors such as
depression (See McPherson et al., (2007b) for the complete analysis). In a study of 100
end-stage renal patients, functional status was correlated with burden (r=.026), but not as
highly as depression (r=0.39) (Cousineau, et al., 2003). Similar findings have been found
86
among ALS patients, with functional limitations (r=0.35) and burden (r=0.64) correlated
with SPB.
All of the studies on burden have been cross-sectional, so it is difficult to
determine if depression causes burden or burden causes depression. It is also impossible
to determine how changes in function affect burden, therefore due to the fact that my
analysis is cross-sectional; I expect that depressive symptoms will affect PIB group
membership. Research has found that physical illness and functional limitations are
directly associated with levels of depression (Aneshensel, Frerichs, and Huba, 1984;
Turner and Nah, 1988; Yang et al., 2002). Based on these findings, and the cross-
sectional design of my data, I believe that those respondents with high levels of
depressive symptoms will be more likely to be in both of the high burden categories –
high-disability/high-burden and low-disability/low-burden. Though many respondents
will be expert in assessing function and burden levels that are in agreement, depression
may make people think they are a burden even if their health is good.
Current Study
In this study, I ask: Do the objective indicators of physical health align with
subjective appraisals of being a burden for all individuals – does perceived illness burden
operate the same for all individuals? Additionally, if there is not agreement between the
objective and subjective, are there specific characteristics of individuals for whom there
is not alignment? To do so, I use multinomial logistic regression and explore the
correlates associated with membership in one of four categories of perceived illness
burden. I evaluate four categories of the dependent variable designed to capture different
combinations of subjective and objective perceptions of perceived illness burden: a) high-
87
disability/high-burden; b) high-disability/low-burden; c) low-disability/high-burden; d)
low-disability/low-burden. I assess the effects of sociodemographic characteristics
(race/ethnicity, age, gender, socioeconomic status, family roles, and physical and
psychological health status), as these factors may influence how respondents think about
and experience their health and subsequent illness representations such as perceived
illness burden.
Methods
Sample
The New Jersey End-of-Life (NJEOL) study sample includes 305 non-
institutionalized older adults in New Jersey (NJ), 55 years of age and older. Patients
were recruited to participate if they were either English- or Spanish-speaking, had no
cognitive limitations, and had one or more of the following health conditions: cancer,
Type II diabetes, or congestive heart failure (CHF). Patients who did not have any of the
target illnesses were also recruited as a “healthy control” group; however, many of these
participants had one or more other health conditions, thus the label “healthy” is largely a
misnomer. Recruitment was conducted over the telephone from two university hospitals
and one comprehensive cancer center in NJ.
The initial sampling frame consisted of 1,146 patients who were identified as
potential participants for the study through the general internal medicine department at
the University of Medicine and Dentistry of New Jersey (UMDNJ). Of this group, 575
respondents met the criteria for inclusion in the initial sampling pool. Reasons for non-
inclusion in the sampling pool included: invalid contact information/inability to locate
individuals; death of indentified possible participants; cognitive and physical limitation
88
precluding participation; and not meeting sampling frame characteristics (i.e. being too
young). Three- hundred-five participants consented to participate in the study,
representing 53% of the eligible sampling frame. Reasons for non-participation included
a general reluctance for patients at the end of life to participate in such a study and time
constraints (participants being too busy). The interview process consisted of a 1.5 hour
face-to-face, structured interview with a trained graduate student interviewer. The survey
included questions regarding sociodemographics, health status and behaviors, EOL
planning, and attitudes toward treatments, religion/spirituality, and social supports (Carr,
2011).
Measures
Dependent Variable – Perceived Illness Burden Category
Level of perceived illness burden. A composite variable that measures
participants‟ perceived level of functional limitation and burden is the key dependent
variable in these analyses. I constructed this variable based on participants‟ responses to
two questions assessing their current level of physical functioning and perceptions about
being a burden to self and others. 4
4 In the preliminary analyses, to evaluate whether all variables used in the multinomial
logistic regression also predicted perceived burden and function on their own, I estimated
the effects of all variables on SPB and on function. These OLS regression models are
shown in the appendix – Table 3D (OLS Regression predicting perceived burden by
functional limitations) and appendix – Table 3E (OLS regression predicting level of
functional limitations by perceived burden). The descriptive statistics and factor analyses
for the functional limitation scale and SPB scale variables can be found in the appendix,
tables 3A-C.
89
First, a functional limitations scale was constructed using Activities of Daily
Living (ADLs) and Independent Activities of Daily Living (IADLs) from the SF-12. The
SF-12 is a highly validated multipurpose short form survey selected from the SF-36
Health Survey (Ware, Kosinski, and Keller, 1995; 1996). Factor analysis revealed a
single factor encompassing nine items from the SF-12, (“How much does your current
health limit you in doing each of the following activities: Bathing or dressing yourself;
Bending, kneeling, or stooping”); and IADLs (“How much does your current health limit
you in doing each of the following activities: Lifting or carrying groceries; Climbing
several flights of stairs; Walking more than a mile; Walking several blocks; Walking one
block; Vigorous exercise (e.g. running, lifting heavy objects); moderate activity (e.g.
bowling, vacuuming)”). Responses were rated on a scale from 1 (lowest) to 5 (highest),
the Cronbach‟s alpha for this scale was .9275.
Perceived Burden. Next, a perceived burden (PB) scale was created. The four
survey questions that comprise the PB scale (rated on a scale from 1-5) were designed to
capture information that characterizes patients‟ attitudes about being a burden to self,
their friends, or their families, and to others. The items included in this scale were
constructed from the consequence items in the Illness Perception Questionnaire Revised
(IPQ-R), a validated measure of illness perceptions used in the CSM (Moss-Morris et al.,
2002; Weinman et al., 1996). The variables in the SPB scale were as follows: (1) “My
illness has major consequences for my daily life.” (2) “My illness causes difficulties for
those who are close to me.” (3) “The treatment for my illness has major consequences for
5 One respondent did not answer the questions in this part of the survey; as such, mean
imputation was used to address the missing information from the variables used to
construct the scale.
90
my daily life.” (4) “The treatment for my illness causes difficulties for those who are
close to me.” Responses to these statements were coded as: (1) strongly disagree, (2)
disagree, (3) neither disagree nor agree, (4) agree, (5) strongly agree. Using these four
variables, a scale was created by taking the mean. Factor analysis revealed a single-factor
construct, and the interitem reliability was evaluated using Cronbach‟s alpha (α=0.861).
Further justification for combining these measures into a composite score was based on
confirming that the correlations (r=0.441; p<0.001) between them were sufficiently high
to suggest a common underlying construct (Nunnally, 1978). Two hundred sixty-seven
respondents (88%) completed this part of the questionnaire. Thirty-one respondents were
not asked or did not answer the questions in this part of the survey; as such, mean
imputation was used to address the missing information from the four variables used to
construct the scale6. Responses ranged from 1, indicating a low perceived burden of care,
to 5 indicating high perceived burden of care.
Perceived Illness Burden. A categorical composite variable that captured both
facets of functional perceptions was then created using both the functional limitation and
SPB high/low dichotomous variables. Zero-level correlations revealed that while the
continuous measures of SPB and functional limitations were correlated significantly
though modestly (r = .0.441; p<0.001), there was considerable heterogeneity in the
sample. This suggests that while many individuals are skilled at making accurate
appraisals (if their health is bad, they feel like a burden) there are those for whom
6 In the models run looking at SPB and functional limitations, a dummy variable was
created for the thirty-one respondents who were not asked or did not answer the questions
in the survey. This variable was entered into the analysis to ascertain whether those 31
non-respondents altered the findings. As shown in table 3E (appendix), they did not.
91
physical symptoms and burden are not correlated. I also believe that it is a person‟s
membership in the various categories that may turn out to be the triggers for various
behaviors (positive and negative) attached to them7.
For the purpose of creating the categorical variable that captured both functional
limitations and self perceived burden, the functional limitation scale variable was split at
the median (2.78) to create a dichotomous variable, with 1 indicating a high level of
functional limitation. For ease of understanding and conciseness, I term level of
functional limitations “disability”. As with the functional limitation scale, SPB was
dichotomized into two categories split at the median (3.01). This variable was coded 1 for
high SPB and 0 for low SPB.
Based on the dichotomized SPB and functional limitation variables, I created four
categories to measure perceived illness burden: high-disability/high-burden; high-
disability/low-burden; low-disability/high-burden; and low-disability/low-burden. For the
purpose of regression, since they were the healthiest and largest group, participants in the
low functional limitation/low SPB were omitted as the PIB reference category.
Covariates
Demographic Variables.
Race/ethnicity was coded into three categorical dummy variables: Non-Hispanic
white, Non-Hispanic black, and Hispanic. For the purposes of regression, white
participants were omitted as the reference category for the race/ethnicity variable. Age. I
used participants‟ ages, in years, at the time of the survey. Gender. I used a dichotomous
variable where 1 represented “female” and 0 represented “male.” 7 An examination of how membership in the various PIB categories increases the
likelihood of Advance Care Planning is covered in Chapter 4 of this dissertation.
92
Family Roles. Marital status was coded into three categorical dummy variables:
married [reference category], widowed, or divorced/never married. Parental status was
also coded into dummy variables: no children (reference category), one child, two
children, and three or more children.)
Quality of familial relationships. The presence and quality of familial relations are
an important mechanism by which disability and burden may affect the various forms of
ACP. Four continuous variables were included in the analyses to look at the strength and
quality of the familial relationships. To assess the spousal/partner relationship,
respondents were asked two questions “How much is your spouse/partner critical of what
you do?” and “How much is your spouse/partner willing to listen to you when you need
to talk about your worries or problems? I considered both of these questions separately.
For those respondents who were not married or partnered, missing responses were
replaced by the sample average for each item (2.6 and 3.6 respectively). Seventy-four
respondents were currently not married or partnered and did not answer the question; as
such, mean imputation was used to address the missing information. The marital status
variable divorced or never married was previously entered to capture the non-
respondents. These questions were repeated to capture the relationship quality with
children. Respondents were asked “How much are your c children critical of what you
do?” and “How much are your children willing to listen to you when you need to talk
about your worries and problems?” Again, I considered both of these questions
separately. The responses to these questions were also coded on a four point Likert scale
(1 [never]-4 [always]). Forty respondents did not have any children and did not answer
these questions; as such, mean imputation (2.1 and 3.44 respectively) was used to address
93
the missing information. The variable having no children was entered into the analysis to
capture any differences in those without children.
Socioeconomic Status (SES).
Education. Educational attainment refers to years of completed schooling.
Respondents were asked “What is your highest degree from school?” Responses ranged
from (less than high school, high school/some college [reference category], or college
degree [BA/BS+]). Income. Respondents were asked to report their combined family
household income from all sources. Responses were coded into five categories: ($0-
14,999 [reference category], 15,000-39,999, 40,000-84,999, 85,000+, or income
missing/not answered).
Health variables.
Number of health conditions. A potentially important covariate used in this
analysis was the number of health conditions experienced by patients. Each subject was
read a list of health conditions, and asked whether a doctor had ever told them that they
have such a condition, or whether they were taking medicine for such a condition. The
conditions were as follows: asthma, lung problems, diabetes, cancer, ulcer(s), heart
disease, high blood pressure, heart attack, seizures, hepatitis, kidney problems,
tuberculosis, and depression/anxiety. The number of health conditions reported ranged
from 0 to 9 health conditions. Based on the variables‟ distribution, three dichotomous
variables were constructed for bivariate analysis to categorize the number of health
conditions each respondent reported (0-1 health conditions, 2-3 health conditions, and 4
or more health conditions).
94
Level of depressive symptoms. The items included in this scale were constructed
from 5 items from the Center for Epidemiological Studies Depression Scale (CES-D)
survey (Radloff, 1977). Using the 9-item scale, factorial analysis revealed one construct
consisting of the following five items from the scale: “How many days during the last
week did you (1) “feel lonely.” (2) “feel sad.” (3) “feel depressed.” (4) “feel everything
you did was an effort.” (5) “feel you could not get going.” As per the original citation, I
summed the items to create a composite score8. The interitem reliability was evaluated
using Cronbach‟s alpha (α=0.829).
Analytic Approach
Descriptive statistics for all measures are presented in Table 3.1. Descriptive
analyses were stratified by PIB group: high-disability/high-burden; high-disability/low-
burden; low-disability/high-burden; low-disability/low-burden. Post-hoc comparisons
were conducted using analysis of variance (ANOVA) to investigate significant subgroup
differences.
I then estimated stepwise multinomial logistic regression models (Tables 3.2 and
3.3) to examine the correlates of perceived illness burden. I used a stepwise modeling
method to identify the effect of each of the correlates: race/ethnicity, demographics, and
family status, SES, and health variables. I assessed the likelihood of PIB using
multinomial logistic regressions comparing those who were perceived themselves to be
both highly functionally disabled and burdensome (high-disability/high-burden); those
who reported high functional limitations but low levels of perceived burdensomeness
8 Three respondents did not answer the questions in this part of the survey; as such, mean
imputation was used to address the missing information from the variables used to
construct the scale. The mean of the scale was 1.33.
95
(high-disability/low-burden); those who were not functionally compromised although
perceived that they were a burden (low-disability/high-burden); and those who self-
reported as neither functionally impaired or burdensome. Due to the lack of previous
research on how race/ethnicity and other demographic variables affect perceived burden,
the first model I estimated looked at the likelihood of being in one of the PIB categories
by race/ethnicity, age, and sex. I estimated a second model that looked at family identity
including measures for marital and parental status and the quality of those relationships.
Table 3.2a shows the findings for models 1 and 2. Models three and four (Table 3.2b)
control for socio-economic status and health variables (physical and mental) respectively.
Results
Descriptive and Bivariate Analysis
Descriptive statistics for all variables used in the analysis are presented in Table
3.1. Twenty-eight percent of respondents self-reported that they had both a high level of
disability and a high level of perceived burden, 18% high-disability/low-burden, 11%
low-disability/high-burden, and 41% low-disability/low-burden. Fifty-six percent self-
identified as non-Hispanic white, 25% as non-Hispanic black, and 19% as Hispanic. The
respondents were older adults with a mean age of 69; a majority were women (64%).
More than half were married or living in a marriage-like relationship (50.5%) and 86%
had children, with over half of all respondents having 3 or more children. In terms of the
quality of familial relationships, respondents reported that on average, both spouses and
children were willing to listen when they needed to talk about their worries and problems.
Seventy-eight percent had a HS degree or higher and 42% earned over $40,000 per year.
96
Almost 46% of respondents had 2-3 health conditions and had a mean CES-D score of
1.339.
[Table 3.1 about here]
Table 3.1 also presents the descriptive statistics by PIB category. The data reveal
a significant association between race and perceived illness burden. Twice as many white
subjects were in the low-disability/low-burden category relative to the high-
disability/high-burden category; and while not as dramatic, the same effect was observed
when comparing to the high-disability/low-burden and low-disability/high-burden
groups. Hispanic respondents also report significant subgroup differences; almost 40%
were in the high levels of disability but low levels of perceived burden group with just
over 27% of Hispanic individuals in both the high-disability/high-burden and 25% in the
low-disability/low-burden categories. Results from ANOVA analyses reveal that SES
was also a significant factor in perceived illness burden. The majority (64%) of high
earners ($85,000+) has a low level of disability and burden; by contrast just 11% of the
lowest earners were in this category. Over one-third of those earning under $14,999 and
41% of those earning between $15,000-39,999 reported high-disability/high-burden.
There were also significant subgroup differences for education by level of
disability/burden. Sixty-one percent of those with a bachelor‟s degree or higher are in the
low-disability/low-burden category with only 19% reporting high-disability/high-burden.
The number of health conditions and level of depressive symptoms also illustrate
significant PIB category differences. The proportion of persons in the high-disability/high
burden category with four or more health conditions is considerably higher than those in
the low-disability/low-burden category. Additionally, individuals with one or no health
97
conditions report both low levels of disability and perceived burden. There were also PIB
category differences with regards to level of depressive symptoms. Individuals in the
high/high PIB category had significantly higher level of depressive symptoms (2.13) than
did patients in the three other categories (low/low, high/low and low/high). Additionally,
respondents with high disability but low burden had a mean level of depressive symptom
score (1.47) that was significantly higher than the mean depressive symptom score of
subjects in the low/low category (0.75).
Multinomial Logistic Regression Analyses- What are the correlates of perceived illness
burden?
These results are presented in Tables 3.2a and 3.2b. I compare participants in four
categories: those who are in the high-disability/high-burden category (reference
category), those who report high-disability but low-burden, individuals in the low-
disability/high-burden category, and those who report both low levels of disability and
perceived burden. Since little work has been done to explore the demographic correlates
of burden/function combinations, I first look solely at the effect of the demographic
variables (race/ethnicity, age, and gender). In the first model (Table 3.2/Model 1) both
non-Hispanic black and Hispanic respondents are significantly less likely than whites to
be in the low-disability/low-burden category, relative the high/high PIB category.
Hispanics are 40% less likely (p<.05) and non-Hispanic blacks almost one-third less
likely (p<.01) than non-Hispanic whites to be in the low-low category, relative to the
high-disability/high-burden category. Hispanics are over twice as likely as whites to be in
the high-disability/low-burden category, relative to the high/high PIB category (β=2.338;
p<.10), however I do not find a significant difference for black respondents. With each
98
one-year increase in age, the odds are significantly decreased (β=0.923) that a person is
in the low-disability/high-burden category relative to being in the high/high PIB category.
[Table 3.2a about here]
In model 2 (Table 3.2b), I add controls for marital/parental status and quality of
family relations. The race effect remains essentially unchanged with the exception that
the significant effect of being Hispanic relative to white with regards to the odds of rating
oneself as part of the low-disability/low-burden category (relative to high/high) is no
longer significant. Blacks respondents remain significantly less likely than whites to be
in the low/low category, relative to having both high levels of disability and burden
(β=0.304; p<.01). Marital status is not significantly correlated with the odds of being in
one of the four PIB categories, however the number of children is. Respondents with no
children are less likely to have high levels of disability but low perceived burden than are
those who have high levels of disability and high burden (β=0.266; p<.0.10), this effect is
moderately significant in models 2 and 3 (controlling for SES), however in the full model
(controlling for SES and Health Status), the effect no longer approaches significance.
Individuals with two children are also more likely to be in the high-disability/low burden
category than are those with three or more children, relative to being in the high /high
category (β=0.222; p<0.01).
Also moderately significant is one of the family relations variables. The more
critical one‟s spouse, the less likely a person is to be in either of the low burden
categories. For each one unit increase in the reported level that their spouse/partner are
critical of what they do respondents have 43 percent lower odds (β=0.569) of being in the
low-disability/high-burden category and 39% lower odds of being in the low-
99
disability/low-burden category (β=0.610) relative to the high-disability-high burden PIB
category (p<0.10). This effect however is no longer statistically significant in the full
model net of SES and health variables.
Table 3.2b represents the likelihood of PIB category membership controlling for
SES (model 4) and health variables (model 5). Educational attainment does not
significantly affect the odds of being in any of the PIB categories, however annual
income does. Compared to those who make more than $85,000 per year, respondents who
have incomes between $15,000 and $39,999 have 87 percent lower odds of being in the
high-disability/low burden category and 79 percent lower odds of being in the low-
disability/low-burden category relative to being in the high/high category (p<0.01).
Compared to those who make over $85,000 per year, the relative odds for those who
make less than $14,999 are 0.12 (p<.0.01) for being in the low/low PIB category. Persons
making between $40,000 and $84,999, they have a 63% lower odds of being in the low-
disability/low-burden category, relative to being in the high-disability/high-burden
category.
[Table 3.2b about here]
In the final model, I entered the number of health conditions reported by
participants and level of depressive symptoms. Both were highly significant when
considering PIB group membership. Respondents who have three or less health
conditions are significantly more likely than those with four or more comorbid conditions
to be in the low disability and burden categories relative to being in the high/high PIB
group. People with zero to one health conditions are almost seven times more likely to
have both low levels of disability and burden (β=6.81; p<0.000); and those with two or
100
three health conditions six times more likely (β=6.20; p<0.000). Additionally, compared
to those with four plus health conditions, respondents with two or three health conditions
are three times more likely to be in low-disability/high-burden category relative to the
high/high category (p<0.05).
Depressive symptoms also significantly impact the perceived illness burden
category a respondent is in. Depressive symptoms and PIB are negatively correlated with
one another. Respondents who exhibit more depressive symptoms are less likely to be in
the low-disability/low-burden group. For each one unit increase in level of depressive
symptoms, patients are 42% less likely to be in the low/low group as they are to be in the
high/high PIB category (β=0.584; p<0.000). Additionally, participants who had more
depressive symptoms are more likely to be in the high-burden category, even with low
levels of disability. For each one unit increase in depressive symptoms, patients are 30%
less likely to be the high-disability/low-burden category as in the high-disability/high-
burden group (β=0.714; p=.009).
In the final model, after controlling for demographics, family status, SES and
health, the regression analysis reveals that race/ethnicity is a significant predictor of
being in the high-disability/low-burden category versus being in the high disability and
burden category. Hispanics are over five times more likely as white respondents to report
high-disability but low perceived burden (p<0.05), however I do not find a significant
difference for black participants after SES is controlled. The race/ethnicity effect is no
longer significant across any of the categories for non-Hispanic black respondents. The
effect of being black disappears when SES is controlled.
101
As respondents age, the odds of being in the low-disability/high-burden category
relative to the high/high category significantly decreases. With each one-year increase in
age the odds are significantly decreased (β=0.918) that a person is in the low-
disability/high-burden category relative to being in the high/high PIB category. This
effect remained significant across all models (p<0.01). The only family variable that
remains significant in the full model is number of children. Compared to respondents
with three or more children, those with two children have a relative odds of 0.23 (p<0.05)
of being in the high/low category relative to being in the high/high PIB category. The
only income relationship that remains significant in the fully adjusted model is for those
who make between $15,000 and $39,999. Compared to those who make over $85,000,
middle income respondents are significantly less likely (β=0.162, p<0.05) to report low-
disability but high-burden relative to those how are in the high/high PIB group.
Discussion
In this analysis I extended the conceptualization of perceived burden to include
measures of burden to the self, others and a measure of functional limitations experienced
by a patient at the end of life. This composite variable was labeled perceived illness
burden and was theoretically based on the Common Sense Model of Illness
Representations (CSM) (Leventhal et al., 2003; 2007; 2011). The CSM is a framework
that has examines the ways that patients interpret, understand and respond to their own
health symptoms and the functional changes associated with those systems (Leventhal et
al, 2003). This conception of PIB was also motivated by the narratives of those who
participated in the focus groups conducted as part of the NJEOL study (2006-2008)
(findings not presented here – see chapter 2 of the dissertation) in which patients often
102
discussed burden explicitly fusing how functional limitations compromised their sense of
self causing them to perceive that they were burdensome to others, and that it was this
combination that facilitated their advance care planning behavior. An explicit mind-body
connection was present in their narratives; this variable was designed to capture it.
Two-thirds of participants in this study did appear to make apparently concordant
appraisals, if their health was bad, they felt like a burden. Conversely, if their health was
relatively good, they did not feel like a burden. These findings are consistent with the
CSM which suggests that negative functional changes inform the conception of the self
as a burden, and this combination is what ultimately translates into perceived illness
burden. The findings from this research also revealed that nearly one-third of the
respondents were “off-diagonal” cases, in which the objective health indicators (function)
did not correspond closely with the subjective measures (burden). Taken together this
suggests that perceived illness burden is not all “in one‟s head” (i.e., psychological), it is
also in their body, but appraisals may reflect both physical and psychological factors.
To explore this further, I examined the correlates of PIB, to see specifically who
were members of the various categories – those in which the subjective and the objective
were in agreement (high-disability/high-burden; low-disability/low-burden) and those in
which there was not (high-disability/low-burden; low-disability/high-burden). In other
words, does PIB work the same for all people?
How does perceived illness burden work and for whom? The sociodemographic
correlates of perceived illness burden - Race/Ethnicity
To my knowledge, this analysis is one of the first studies to explore race/ethnic
variations in perceived burden/function levels. This is important because of the well
103
documented racial/ethnic and SES disparities in health (Williams et al., 2009, 2010;
Verbage & Jette, 1994; Ferraro & Farmer, 2004; Crimmins & Saito, 2001; Fuller-
Thompson et al., 2010). Bivariate analysis finds that the majority of non-Hispanic white
sample members but just 18% of blacks and 11% of Hispanics, respectively, reported
both low levels of disability and burden. By comparison, over one-third of non-Hispanic
black respondents, but just 24% of whites were in the high-disability/high-burden
category, this finding is consistent with the health disparity literature expanding it to
conceptions of burden.
In the unadjusted model, non-Hispanic blacks were significantly less likely to
report low levels of disability and burden than were non-Hispanic whites. This effect was
fully accounted for by SES, however; black respondents were no more or less likely to be
in a specific PIB category than were whites. This finding is very much in line with recent
findings that over 75% of the black-white differences in disability rates could be
accounted for by SES (Fuller-Thompson et al., 2009); similarly, rates of burden seem to
also be attenuated by SES.
One finding in particular, that expands the literature on health disparities and their
effects is that the majority of Hispanic respondents in this study reported high-disability
but low-perceived burden. This confirms the assertion by Carr (2011) that Latinos may be
less likely to feel burdensome even if they have functional limitations. Hispanics in this
study have poor health, but do not feel that they are a burden - perhaps due to their beliefs
about familism and filial relations. Hispanics may prioritize interdependence among
family members rather than individual autonomy (Carr, 2011; Blackhall et al., 1995).
The health-wealth gradient, is it present in perceived illness burden?
104
Similar to those studies that have looked at SES as a possible correlate of
perceived illness burden (Moorman, 2009; Cousineau et al., 2003; Wilson et al., 2005), I
did not find an educational attainment effect in the regression analyses which could be
due to the fact that education is so highly correlated with race in the NJEOL sample
(Carr, 2011). In the analysis of variance (ANOVA), however some interesting effects
were present. The majority of respondents with four-year college degrees or higher were
in the low-disability/low-burden category. Similarly, the majority of the highest earners
in my sample were in the low/low category. Post-Hoc analysis indicates that both of these
subgroup differences were statistically significant. This is in accordance with the large
body of research that suggests that higher levels of education attainment drive higher
levels of health over the life course (Currie, 2009; Cutler and Lleras-Muney, 2010).
In both the bivariate and regression analyses, annual income emerges as a
significant correlate with PIB category. Comparisons show that on both ends of the
income scale, there are significant sub-group differences. As expected, the majority of
high earners are in the low/low PIB category, while one-third of low earners are in the
high-disability/high-burden group. A slightly higher proportion of those earning below
$14,999 are in the low-disability/high-burden category. This may indeed be due to the
fact that low SES individuals have less access to the means by which to adapt to
disability, such as quality health care, formal supports and adaptive equipment (Carr,
2011), as such a lower level of self-reported functional limitations may present a greater
perception of burden.
Interestingly in the final regression model, controlling for demographics, family
relations, SES and health status, the significance of SES was reduced. The only
105
categorical difference that remained was for individuals with incomes between $15,000 –
39,999 per year. For individuals with high disability, these respondents were less likely to
have low burden as opposed to high burden. This may be due to the fact that their annual
income puts them right above the federal poverty level in 2006 - 20089 and ineligible for
many local, state, and federal programs that would provide instrumental assistance for the
frail/elderly (see the Americans Act and Aging network for more information). Their
income however, makes it difficult to privately purchase home based care, this might
make them have to rely more on informal care from family members or in-kind help from
others, both of which are perceived as more intrusive (Horwitz, 1985).
Gender, Age, and Family Roles
Unlike previous research on gender and perceived burden (Moorman, 2009; Arber
et al., 2008), I did not find a significant difference for gender in either of my analyses.
The gender effect in Moorman‟s (2009) study was suppressed until she controlled for
religiosity. I did not include any measures of religiosity in this analysis, and as such the
effect may similarly be suppressed. It may also be possible that because women are more
likely than men to have higher levels of disability over the life course (Crimmins, 2004),
they may have adjusted to the reality of receiving care and as such, are less likely to
perceive it as burdensome. Additionally due to norms of intergenerational reciprocity and
deferred strategies involved in social exchange theory (Bengston et al., 1997); women, as
predominant caregivers to others, have banked a lifetime of caregiving credit that is
stored up against the more burdensome needs that accompany old age. Older women may
9 The federal poverty level for 2006 was $13,200; in 2007 it was $13,690; and in 2008 -
$14,000.
106
feel that assistance is due them given that they have provided care to others for most of
their lives.
While marital status and quality of family relationships were not significantly
related to PIB category in the fully adjusted model, the number of living children was.
Surprisingly, those with two children were significantly less likely to be in the low
burden category (than in the high/high PIB category). Having children may buffer
feelings of being burdensome, even if functionally limited. Respondents with two
children are however, surprisingly less likely to be in the high/low category relative to the
high/high category. It is possible that when there are two children disputes occur over
which child is responsible for various caregiving duties, disputes that would not occur if a
respondent has only one child. Having three or more children may allow the siblings to
distribute caregiving duties such that no one child is shouldering the entire burden.
Health variables
Both the number of health conditions and level of depressive symptoms were
highly correlated with PIB category. The study findings show that most people are skilled
at assessing how their physical health and disabilities correspond with perceptions of
burden. Those with more co-morbid conditions are more likely to be disabled and feel
burdensome. Participants with 0-1 health conditions were seven times more likely and
those with 2-3 conditions over six times more likely to be in the low-disability/low-
burden than in the high-disability/high-burden category. This makes sense according to
the CSM.
Interestingly, compared to people with four or more co-occurring conditions,
individuals with 2-3 health conditions are more likely to be in the low-disability/high-
107
burden category relative to the high/high category. This too makes sense. Hypertension
was the most common co-occurring illness in our sample. It was present in 86% of the
respondents with 2-3 conditions in the full sample, co-occurring with diabetes in over
50% of the sample. I can only speculate as to the effect, however hypertension is often
referred to as a “silent” illness (Zusman, 2011) because the symptoms are not readily
accessible by patients; diabetes too has been considered quiet, especially non-insulin
dependent diabetes (Aloozer, 2000; Lowe et al., 2009). It is possible that respondents did
not feel a day-to-day impact on their daily activities, but the notion of co-occurring
conditions alone may be burdensome to patients. Additionally, both diabetes and
hypertension require changes in behavior, diet, exercising, medication management and,
for some patients, monitoring. All of which may be perceived as being a burden, even
without functional limitations, especially if their symptoms are well controlled.
The well-established relationship between depression and perceptions of burden
present in the literature (Wilson et al., 2005; Cousineau et al., 2003; Chio et al., 2006)
was present in both the ANOVA and multinomial logistic regression models. Individuals
who exhibited more depressive symptoms were more likely to be in the high/high group
than in the low/low group. Similarly, depression predicted the “off-diagonal”- individuals
who have more depressive symptoms are also more likely to be in the low-
disability/high-burden group compared to being in the high/high group. This is evidence
that, for some, disability and burden do not always go hand-in-hand; this is especially
true for those with depressive symptoms. If a person is depressed, he or she may feel
burdensome even if they have relatively few functional limitations. The data are cross
sectional, so causality cannot be determined, but depression and PIB are highly
108
correlated. If you have low depression you are more likely to be healthy and free of
feelings that you are a burden. It is also possible that if you are healthy and not
burdensome you will have lower levels of depressive symptoms.
Limitations and Future Directions
This examination is a first step and considerable additional analysis is warranted.
A number of limitations in this analysis should also be acknowledged. The sample size,
while larger than many of the previous examinations of burden, is still quite modest
(n=293). Missing data were handled through mean imputation which biases the odds ratio
estimates towards the null value of one. However, since this yielded more conservative
estimates, it only acts to further substantiate the present findings. Because the sample was
drawn from a community that contains a major research university, respondents had
elevated educational levels and consequent socioeconomic status that may not be
generalizable to the broader population of older, chronically ill adults. However, this
sample is well-represented in terms of race/ethnicity. Furthermore, despite their chronic
illness diagnoses, respondents may also be positively selected on health status, given that
they were able to participate in the study. The data from this study are cross-sectional and
thus cannot be used to determine causal ordering.
Further research should look at how perceptions of illness burden change over
time, especially as one‟s health changes. Other correlates to PIB should also be
examined, such as religiosity correlates, attitudes towards death and dying, and past
caregiving experiences to identify a few. Further research should examine the
relationship between various representation categories and their links to specific health
behaviors. Since illness representations are comprised of both the subjective and
109
objective it would be useful to examine how the various combination categories affect
different types of behavior. For example, do concurrent appraisals work as a catalyst for
behaviors such as advance care planning or treatment adherence? Similarly, does
membership in various PIB categories correlate with any maladaptive behaviors?
Conclusions
It is important that we have a clear and precise understanding of what individuals
feel is important at the end of life and why individuals make the decisions that they do
regarding their end-of-life care. Past research has indicated that burden is an important
concern at the end of life, and this paper attempted to advance the research on burden and
examine the specific categories of perceived illness burden addressing the question, does
perceived illness burden work the same for all people? I found that there were significant
subgroup differences in terms of race/ethnicity, age, number of children, and income.
Especially relevant is that both the number of health conditions and level of depressive
symptoms were related to membership in a PIB category. Taken together this suggests
that perceived illness burden is not all in one‟s head, it is also in one‟s body, but
appraisals may reflect both physical and psychological factors. Something that clinicians
and other stakeholders consider when working with individuals at the end of life.
Knowledge about how and for whom PIB works, will allow physicians, social
workers, and other practitioners who work with patients at the end of life, anchor their
discussions with patients accordingly. This has important implications for interventions
and policies, if health behaviors such as advance care planning and medication adherence
are triggered by not only an individual‟s actual condition but also their perceptions about
their conditions (Carr and Moorman, 2009; Fried, 2009; Leventhal et al., 2003; 2007;
110
2011), then we need to have a clear understanding of how both the objective and
subjective work for various individuals and members of population subgroups. We
cannot assume that one size fits all in terms of patient perception and that there will be
congruence between the subjective and objective. Awareness of what factors are
important at the end of life is exceedingly important; knowing for whom different
elements of illness representations are salient is critical if we are to have meaningful
discussions with those at the end of life.
111
References
Aloozer, Francesca. 2000. Secondary Analysis of Perceptions and Meanings of Type 2
Diabetes among Mexican American Women. The Diabetes Educator. 26(5): 785-
795.
Antonucci, Toni C. and Hiroko Akiyama. 1987. “An Examination of Sex Differences in
Social Support among Older Men and Women.” Sex Roles 17: 737-49.
Arber, Sara, Tasha Vandrevala, Tom Daly, and Sarah Hampson. 2008. “Understanding
Gender Differences in Older People‟s Attitudes Towards Life-Prolonging
Medical Technologies.” Journal of Aging Studies 22: 366-75.
Atchley, Robert C. 1989. The continuity theory of normal aging. Gerontologist.
29:183-190.
Bengston, Vern, L., Burgess, E. O., and Parrott, T. M. 1997. Theory, Explanation and a
Third Generation Theoretical Development in Social Gerontology. Journal of
Gerontology: SOCIAL SCIENCES. 52B (2): S72-S88.
Berry, Jack W., Worthington, E. L. 2001. Forgivingness, relationship quality, stress while
imagining relationship events, and physical and mental health. Journal of
Counseling Psychology. 48(4): 447-455.
Blackwell, Leslie. J., Murphy, S. T., Frank, G., Michel, V., Palmer, J. M., and Azen, S.
1995. Ethnicity and attitudes toward patient autonomy. JAMA. 274 (10): 820-825.
Booth, Alan and Johnson, D. 1994. Declining Health and Marital Quality. Journal of
Marriage and Family. 56(1): 218-223
Cantor, Marjorie H. 1975) Life space and the social support system of the inner city
elderly of New York. Gerontologist 15, 23-27.
Carr, Deborah. 2011. Racial differences in end-of-life planning: Why don‟t blacks and
Latinos prepare for the inevitable? Omega: The J Death & Dying 63(1): 1-20.
Carr, Deborah. 2011b. If Nothing Is Certain But Death and Taxes, Why Don‟t Older
Adults Prepare for the End? The Social Stratification of Older Adults‟
Preparations for the End of Life. Living in a High Inequality Regime Conference.
University of Wisconsin-Madison, Center for Demography and Ecology. May,
2011.
Carr Deborah. and Moorman, S. M. 2009. End-of-Life treatment preferences among the
young old: An Assessment of psychosocial influences. Sociological Forum.
24(4): 754-778.
112
Chiò, A., A Gauthier, A. Calvo, P. Ghiglione, and R. Mutani. 2005. “Caregiver Burden
and Patients‟ Perception of Being a Burden in ALS.” Neurology 64: 1780-2.
Chochinov, Harvey M. 2002. Dignity-Conserving Care – A New Model for Palliative
Care: Helping the patient feel valued. JAMA. 287:2253-2260.
Chochinov, Harvey M., Hack, T., Hassard, T., Kristjanson, L. J., McClement, S., and
Harlos, M. 2005. Understanding the Will to Live in Patients Nearing Death.
Psychosomatics 46: 7-10.
Chochinov, Harvey M., Kristjanson, L. K., Hack, T. F., Hassard, T., McClement, S. and
Harlos, M. 2007. Burden To Others and The Terminally Ill. Journal of Pain and
Symptom Management 34: 463-71.
Cohen-Mansfield Jiska, Rabinovich B.A., Lipson S, Fein A, Gerber B, Weis-man S,.
1992. The decision to execute a durable power of attorney for health care and
preferences regarding the utilization of life-sustaining treatments in nursing home
residents. Archives of Internal Medicine. 151:289-94.
Cohen, Sheldon and Syme, S.L. 1985. Social support and health. San Francisco:
Academic Press.
Cohen, Sheldon and Leis, A. 2002. What determines the quality of life of terminally ill
Cancer patients from their own perspective? Journal of Palliative Care.
18: 48-58.
Cooley, Charles H. 1902. Human Nature and the Social Order. New York: Scribner‟s.
Cousineau, Natalie, McDowell, I., Hotz, S. and Hebert, . 2003 Measuring Chronic
Patients‟ Feelings of Being a Burden to their Caregivers – Development and
Preliminary Validation of a Scale. Medical Care 41(1):110-118.
Crimmins, Eileen. 2004. Trends in the Health of the Elderly. Annual Review of Public
Health. 25: 79-98.
Crimmins, Eileen and Saito, Y. 2001. Trends in healthy life expectancy in the United
States, 1970-1990: Gender, racial, and educational differences. Social Science &
Medicine. 52: 1629-1641.
Currie, Janet. 2009. Healthy, Wealthy, and Wise: Socioeconomic Status, Poor Health in
Childhood, and Human Capital Development. Journal of Economic Literature.
47(1): 87-122.
Cutler, David M. and Lleras-Muney, A. 2010. Understanding differences in health
behaviors by education. Journal of Health Economics. 29(1): 1-28.
113
Davison, Kathryn P. and Pennebaker, J. W. 1997.Virtual Narratives: Illness
Representations in Online Support Groups. In. K. J. Petrie and J. A. Weinman
(Eds.), Perceptions of Health and Illness. (pp. 463-484). Amsterdam: Harwood
Academic Publishers.
Davison, Sara N. 2006. Facilitating Advance Care Planning for Patients with End-Stage
Renal Disease: The Patient Perspective. Clinical Journal of the American Society
of Nephrology. 1: 1023-1028.
Davison, Sara N. and Simpson, C. 2009. Hope and advance care planning in patients with
end stage renal disease: qualitative interview study. BMJ online first. Retrieved on
Sep. 20, 2010 from
http://www.bmj.com.proxy.libraries.rutgers.edu/content/333/7574/886.full.pdf.
de Faye, Barbara J., Keith G. Wilson, Susan Chater, Raymond A. Viola, and Pippa Hall.
2006. “Stress and Coping with Advanced Cancer.” Palliative and Supportive
Care 4: 239-49.
Dinger, Erica. J. 2005. Death and Dying. AARP Massachusetts End of Life Survey
Research Report, August 2005. AARP Knowledge Management: Washington
DC. Retrieved on May 6, 2009 from http://research.aarp.org
Federal Interagency Forum on Aging-Related Statistics. 2010. Older Americans update
2008: Key indicators of well-being. Hyattsville, MD.
Ferraro, Kenneth F. and Kelley-Moore, J. A. 2001. Self-Rated Health and Mortality
Among Black and White Adults: Examining the Dynamic Evaluation Thesis.
Journal of Gerontology: SOCIAL SCIENCES. 56B (4): S195-S205.
Ferraro, Kenneth F. and Farmer, M. 2004. Double jeopardy to health hypothesis for
African Americans: Analysis and critique. Journal of Health and Social Behavior.
38(1): 38-54.
Foster, Tom. 2003. Suicide Note Themes and Suicide Prevention. The International
Journal of Psychiatry in Medicine. 33(4): 323-331.
Fuller-Thompson, Esme, Nuru-Jeter, A., Minkler, M., Guralnik, J. M. 2009. Black –
White Disparities in Disability among Older Americans. Further Untangling the
Role of Race and Socioeconomic Status. Journal of Aging and Health.. 21(5):
677-698.
Ganzin, Linda, Johnston, W.S., and Hoffman, W.F. 1999. Correlates of suffering in
amyotrophic lateral sclerosis. Neurology. 52:1434-1440.
114
Gauthier, A., A. Vignola, A. Calvo, E. Cavallo, C. Moglia, L. Sellitti, R. Mutani, and A.
Chiò. 2007. “A Longitudinal Study on Quality of Life and Depression in ALS
Patient-Caregiver Couples.” Neurology 68: 923-6.
George, Linda. 1996. “Social Factors and Illness.” Pp. 229-68 in Handbook of Aging and
the Social Sciences, edited by R. Binstock and L. George. San Diego, CA:
Academic Press.
Gerschick, Thomas J. and Miller, A. S. 1995. Coming to terms: Masculinity and physical
disability. In: D. Sabo and D. Gordon (Eds.) Men’s Health and illness: Gender,
power and the body. Sage Publications: Thousand Oaks, CA. pp. 183-204.
Gist, Yvonne and Velkoff, V. 1997. Gender and Aging: Demographic Dimensions.
Washington D.C.: U.S. Department of Commerce, Bureau of the Census.
Horwitz, Amy. 1985. Sons and Daughters as Caregivers to Older Parents: Differences in
Role Performance and Consequences. The Gerontologist. 25(6): 612-617.
Hooyman, N.R. and Kiyak, H.A. (2008). Social Gerontology. Boston, MA: Allyn and
Bacon.
Idler, Ellen L. and Benyamini, Y. 1997. Self-Rated Health and Mortality: A Review of
Twenty-Seven Communities Studies. Journal of Health and Social Behavior.
38: 21-37.
Institute of Medicine (IOM). 2001. Health and behavior: The interplay of biological,
behavioral, and societal influences, Washington, DC: National Academy Press.
Institute of Medicine (IOM). 2001. Crossing the Quality Chasm: A New Health System
for the 21st Century. Washington, DC: National Academy Press.
Johnson, Julia O., Daniel P. Sulmasy, and Marie T. Nolan. 2007. “Patients‟ Experiences
of Being a Burden on Family in Terminal Illness.” Journal of Hospice and
Palliative Nursing 9: 264-9.
Kehl, Karen, A. 2006. Moving Toward Peace: An Analysis of the Concept of a Good
Death. American Journal of Hospice and Palliative Medicine. 23(4): 277-286.
Leventhal, Howard, D. Meyer, et al. 1980. The common sense representation of illness
danger. Contributions to medical psychology. S. Rachman. New York, Pergamon
Press. II: 7-30.
Leventhal, Howard, Leventhal, E.A., and Cameron, L. 2001. Representations,
Procedures, and Affect in Illness Self-Regulation: A Perceptual-Cognitive Model.
In A.Baum, T.a. Revenson, and J.E. Singer (Eds.), Handbook of Health
Psychology. NJ: Lawrence Erlbaum Associates.
115
Leventhal, Howard, Brissette, I., and Leventhal, E.A. 2003. The common sense models
of self-regulation of health and Illness. In L.D. Cameron & H. Leventhal, (Eds.),
The self regulation of health and illness behavior. London: Routledge Taylor &
Francis Group.
Leventhal, Howard, Weinman, J., Leventhal, E.A., and Phillips L.A. 2008. Health
Psychology: The Search for Pathways between Behavior and Health. Annual
Review of Psychology. 59:477-505.
Leventhal, Howard, Leventhal, E. A., Cameron, L., Bodnar-Deren, S., Breland, J., Hash-
Converse, J. and Phillips, L. A. 2011. Modeling Health and Illness Behavior: The
Approach of the Common Sense Model (CSM). In A. Baum (Ed.) Handbook of
Health Psychology, Second Edition. New York: Routledge.
Lillard, Lee A. and Panis, C. W. A. 1996. Marital Status and Mortality: The Role of
Health. Demography, 33(3):313-327.
Loewe, Ronald, Schwartzman, J., Freeman, J., Quinn, L., and Zuckerman, S. 2009.
Doctor talk and diabetes: towards an analysis of the construction of chronic
illness. Social Science & Medicine. 47(9): 1267-1276.
Martire, Lynn M., Richard Schulz, Carsten Wrosch, and Jason T. Newsom. 2003.
“Perceptions and Implications of Received Spousal Care: Evidence from the
Caregiver Health Effects Study.” Psychology and Aging 18: 593-601.
McPherson, Christine J., Keith G. Wilson, and Mary Ann Murray. 2007a. “Feeling Like
A Burden: Exploring the Perspectives of Patients at the End-of-Life.” Social
Science and Medicine 64: 417-27.
McPherson, Christine J., Keith G. Wilson, and Mary Ann Murray. 2007b. “Feeling Like a
Burden to Others: A Systematic Review Focusing on the End-of-Life.” Palliative
Medicine 21: 115-28.
Moorman, Sara M. 2009. Facing End-of-Life Together: Marital Relationship Quality and
End-of-Life Health Care Preferences. Dissertations submitted in partial
fulfillment of the Doctor of Philosophy (Sociology) at the University of
Wisconsin-Madison
Moorman, Sara. M. 2011. The importance of feeling understood in marital conversations
about End-of-life health care. Journal of Social and Personal Relationships.
28(1): 100-116.
Mora, Pablo. A., DiBonaventura, M.D., Idler, E., Leventhal, E.A., and Leventhal, H.
116
2009. Psychological Factors Influencing Self-Assessments of Health: Toward an
Understanding of the Mechanisms Underlying How People Rate Their Own
Health. Annals of Behavioral Medicine. 36(3): 292-303.
Morita, Tatsuya, Sakagushi, Y., Hirai, K., Tsuneto, S., and Shima, Y. 2004. Desire for
death and requests to hasten death of Japanese terminally ill cancer patients
receiving specialized inpatient palliative care. Journal of Pain and Symptom
Management, 27:44-52.
Moss-Morris, R., Weinman, J., Petrie, K., Horne, R, Cameron, L., Buick, D. 2002. The
Revised Illness Perception Questionnaire (IPQ-R). Psychology & Health, 17(1):
1-16.
Mouzon, Dawne. 2010. Can Social Relationships Explain The Race Paradox in Mental
Health? Dissertation submitted in partial fulfillment of the Doctor of Philosophy
(Sociology) at the Graduate School – New Brunswick. Rutgers, The State
University of New Jersey.
Murray, M. A., O‟Connor, A., Fiset, V. and Viola R. 2003. Women‟s decision making
needs regarding place of care at end of life. Journal of Palliative Care. 19: 176-
184.
Newsom, Jason T. and Schulz, R. 1998. “Caregiving from the Recipient‟s Perspective:
Negative Reactions to Being Helped.” Health Psychology 17: 172-181.
Nunnally, Jum C. 1978. Psychometric Theory. New York: McGraw-Hill.
Pearlman, Robert A. and Starks, H. 2004. Why Do People Seek Physician-Assisted
Death? In Quill, T. and Battin, M.P. (Eds.) Physician-Assisted Dying: The Case
for Palliative Care and Patient Choice. Baltimore, MD: The John Hopkins
University Press. pp. 91-101.
Radloff, Lenore Sawyer. 1977. “The CES-D Scale: A Self-Report Depression Scale for
Research in the General Population.” Applied Psychological Measurement 1:
385-401.
Rhode, Romana L. and Teno, J. M. 2009. What‟s race got to do with it? Journal of
Clinical Oncology. 24: 5496-5498.
Rosenfeld, Kenneth. E., Wenger, N. S., and Kagawa-Singer, M. 2000. End-of-Life
Decision Making: A Qualitative Study of Elderly Individuals. Journal of General
Internal Medicine. 15(9): 620-626.
Rowe, John W. and Kahn, R. L. 1998. Successful Aging. New York: Pantheon Books.
Rowe, John W. and Kahn, R. L. 1997. Successful Aging. The Gerontologist 37(4): 433-
117
440.
Schroepfer, Tracy A. 2007. Critical Events in the Dying Process: The Potential for
Physical and Psychosocial Suffering. Journal of Palliative Medicine. 10(1): 136-
147.
Schroepfer, Tracy A. 2008. “Social Relationships and Their Role in the Consideration to
Hasten Death.” The Gerontologist 48: 612-21.
Shadbolt, Bruce, Barresi, J., and Craft, P. 2002. Self-Rated Health as a Predictor of
Survival among Patients with Advanced Cancer. Journal of Clinical Oncology.
20(10): 2514-2519.
Singer, Peter A., Martin, D.K., Kelner, M. 1999. Quality of end-of-life care: patients‟
perspectives. JAMA. 281(2): 163-168.
Singer, Peter A., Thiel, E.C., and Naylor, D.C. 1995. Life-sustaining treatment
preferences of hemodialysis patients: implications for advanced directives.
Journal of the American Society of Nephrology. 6:1410-1417.
Singer, Peter A. 2001. Recent Advances, Medical Ethics. BMJ. 321:282
Skelton, James. A. and Croyle, R. T. 1991. Mental representation, health and illness. In
J. A. Skelton & R. T. Croyle (Eds.), Mental representations in health and illness.
(pp. 1-6). New York: Springer-Verlag.
Smedley, Brian, Stith, A., and Nelson, 2003. Unequal treatment: confronting racial and
ethnic disparities in health care. Washington DC: National Academy Press.
Smith, James A., Braunack-Mayer, A., Wittert, G., and Warin, M. 2007. “I‟ve been
independent for so damn long!” Independence, masculinity and aging in a help
seeking context. Journal of Aging Studies. 21(4):325-335.
Steinhauser, Karen. E., Christakis, N. A., Clipp, E. C., McNeilly, M., McIntyre, L., and
Tulsky, J. A. 2000. Factors considered important at the end of life by patients,
family, physicians, and other care providers. JAMA. 284: 2476-2482.
SUPPORT Principal Investigators. 1995. A Controlled Trial to Improve Care for
Seriously Ill Hospitalized Patients. JAMA. 274:1591-1598.
Tang, Siew Tzuh. 2003. “When Death Is Imminent: Where Terminally Ill Patients with
Cancer Prefer to Die and Why.” Cancer Nursing 26: 245-51.
Tong, Elizabeth, Sarah A. McGraw, Edward Dobihal, Rosemary Baggish, Emily Cherlin,
and Elizabeth H. Bradley. 2003. “What Is A Good Death? Minority and
Nonminority Perspectives.” Journal of Palliative Care 19: 168-75.
118
Umberson, Debra. 1992. Relationships between Adult Children and Their parents:
Psychological Consequences for Both Generations. Journal of Marriage and
Family. 54(3): 664-674.
United States, Department of Health and Human Services. 2008. Federal Register, Vol.
73, No. 15, pp. 3971–3972
Ussher, Jane, Kirsten, L., Butow, P. and Sandoval, M. 2006. “What Do Cancer Support
Groups Provide Which Other Supportive Relationships Do Not? The Experience
of Peer Support Groups for People with Cancer.” Social Science & Medicine 62:
2565-76.
Valente, T. W. 2002. Evaluating health promotion programs. New York.
Verbrugge, Lois M. and Jette, A. M. 1994. The disablement process. Social Science &
Medicine. 38: 1-14.
Ware John E., Kosinski M., Keller S. D. 1995. A 12-item short-form health survey.
Construction of scales and preliminary tests of reliability and validity. Med Care.
34: 220-233.
Weinman, John., Petrie, K.J., Moss-Morris, R., and Horne, R. 1996. The Illness
Perceptions questionnaire: A new method for assessing the cognitive
representation of illness. Psychology and Health. 11(3): 431-445.
World Health Organization. 2008. Closing the gap in a generation: health equity through
Action on the social determinants of health. Final Report of the Commission on
Social Determinants of Health. Geneva, World Health Organization.
Williams, David R. and Mohammed. S. A. 2009. Discrimination and Racial Disparities
in Health: Evidence and Needed Research. Journal of Behavioral Medicine,
32(1): 20–47.
Williams, David R., Mohammed, S. A. , Leavell, J. and C. Collins 2010. Race,
Socioeconomic Status, and Health: Complexities, Ongoing Challenges, and
Research Opportunities. Annals of the New York Academy of Sciences, 1186: 69–
101.
Wilson, Keith. G, Scott, J. F., and Graham, I. D. 2000. Attitudes of terminally ill patients
toward euthanasia and physician-assisted suicide. Archives of Internal Medicine.
160:2454-2460.
Wilson, Keith G., Curran, D. and Christine J. McPherson. 2005. “A Burden to Others: A
Common Source of Distress for the Terminally Ill.” Cognitive Behaviour Therapy
34: 115-23.
119
Zarit, Steven. H., Reever, K. E. and Bach-Peterson, J. 1980. Relatives of the Impaired
Elderly: Correlates of Feelings of Burden. The Gerontologist. 20(6): 649-655.
Ziven Bambauer, K. and Gillick, M.R. 2007. The Effect of Underlying Health Status on
Patient or Surrogate Preferences for End-of-Life Care: A Pilot Study. American
Journal of Hospice and Palliative Medicine. 24(3) 185-190.
Zusman, Randall. 2011. Hypertension: Controlling the “silent killer”. Harvard Health
Publications: Cambridge Mass.
Zweibel, N. R. and Cassle, C. K. 1989. Treatment choices at the end of life: a comparison
of decisions by older patients and their physician-selected proxies. Gerontologist.
29:615-621.
120
Figure 3.1. Perceived Illness Burden
121
122
123
124
125
126
127
Appendix – Chapter 3
Table 3A - Descriptive Statistics for Variables Used in Disability/Burden Category Construction
Mean
Percent
Standard
Deviation
Perceived burden (SPB) scale (range 1-5) 2.99 1.023
SPB - missing data (dichotomous)
(1=respondent did not answer SPB questions) 10.4
Functional limitation Scale (ADL/IADL) (range 1-5) 3.07 9.934
Source: NJEOL Study (2006-2008). N=293. Note: Means are presented for continuous variables; proportions are presented
for categorical variables.
128
Appendix - Table 3B - Functional Limitation Scale
Factor Loadings
How much does your current health limit you in
Factor
Loading
Bathing or dressing yourself
0.743
Bending, kneeling, or stooping
0.765
Lifting or carrying groceries
0.783
Climbing several flights of stairs
0.831
Walking more than a mile
0.866
Walking several blocks
0.89
Walking one block
0.815
Vigorous Exercise (lifting heavy objects)
0.735
Moderate activity (bowling, vacuuming)
0.814
Cronbach's alpha for scale
0.927
(Only one component extracted)
Source: NJEOL Study (2006-2008). N=293
129
Appendix - Table 3C - Perceived Burden Scale Factor Loadings
Illness perception question
Factor
Loading
My illness has major consequences for my daily life
0.799
My illness causes difficulties for those who are close
to me
0.884
The treatment for my illness has major consequences
for my daily life
0.829
The treatment for my illness causes difficulties for
those who are close to me
0.85
Cronbach's alpha for scale
0.861
(Only one component extracted)
Source: NJEOL Study (2006-2008). N=293
130
131
132
133
134
135
136
Chapter 4: Using the Common Sense Model to Understand the Relationship
between Perceived Illness Burden and the Likelihood of Advance Care Planning.
ABSTRACT:
Objective. Despite nationwide enactments of laws encouraging advance care planning
(ACP), rates of completion are low. Prior research has focused on objective factors and
given far less attention to how patients‟ perceptions influence ACP. I use data from the
New Jersey End-of-Life (NJEOL) study (N=293) (2006-2008), an ethnically diverse
sample of non-institutionalized older adults (≥ age 55) to examine the extent to which
patients‟ perceived illness burden (PIB) is associated with ACP behaviors (end-of-life
discussions, living will, and durable power of attorney for health care (DPAHC)). PIB is a
measure of patient appraisals that captures both functional limitation and perceived
burden to the self and others.
Methods. Post-hoc comparisons between PIB categories (high-disability/high-burden;
high-disability/low-burden; low-disability/high-burden; low-disability/low-burden) were
conducted using analysis of variance (ANOVA) and multinomial logistic regressions
were used to examine the odds of engaging in ACP.
Results. Multinomial logistic regression, controlling for health and sociodemographic
variables revealed that respondents in the high-disability/high-burden category were
significantly more likely to engage in all types of ACP. Individuals in the high-
disability/low-burden category were significantly more likely than those in the low/low
category to have appointed a health care proxy.
137
Discussion. These findings suggest that functional impairments and perceptions of
burden (perceived illness burden) are important factors in ACP; eliciting patient
perceptions about the consequences of their illness may facilitate increased levels of ACP.
Introduction
The debate over and passage of the Patient Protection and Affordable Coverage
Act (2009) has elevated advance care planning (ACP) to the forefront of national
consciousness. From the Terri Schaivo decision in 2005 to the “death panels” rhetoric
surrounding the 2010 Health Care Reform debate, ACP is misunderstood and frequently
misrepresented by the media. Advance care planning is best conceptualized as a process
that involves three steps. First, an individual considers his or her values and preferences
and decides what medical treatments s/he would or would not want upon diagnosis with a
terminal illness. Next, an individual would talk about these values and preferences with
loved ones and physicians. Finally, these preferences would be formally documented
through an Advance Directive (AD) or do-not-resuscitate (DNR) order in the medical
record (Pearlman, 2010, Levi et al., 2010).
Advance directives are legally binding, written directions that outline the type of
medical care an individual would want in the event s/he becomes unable to make
decisions for him/herself (NJ Bioethics Commission, 1991). ADs are formally
represented as: 1) living wills (LW), an instructive directive about the type of EOL care
and medical interventions patients desire (Smucker et al., 1993); 2) durable power of
attorney for health care (DPAHC), appointing a health care surrogate to make medical
decisions at the end of life (Ditto et al., 2001); and 3) a combined directive, a document in
138
which an individual has selected a health care representative and discussed these
preferences with others (NJ Bioethics Commission, 1991).
A substantial body of work has examined ACP and the use of advance directives.
This includes: the benefits of ACP (Drought and Kewnig, 2002; Sulmasy, 2002); the low
rates of and barriers to ACP (Moorman et al, 2011; Emanuel et al., 1995; Ditto et al.,
2001); the types of treatments patients are willing to accept (Berry and Singer, 1998;
Fried, et al., 2002); the efficacy of ACP (Smucker et al., 1993; Teno, et al., 2007;
Prendergast, 2001; Perkins, 2007); and characteristics of those who engage in planning
(Carr and Khodyakov, 2007). However, the process of ACP is still underutilized and
poorly understood (Moorman et al, 2011; Fried, 2009). Questions remain about how to
increase rates of ACP and what factors influence those who have (or have not) engaged
the ACP process. To answer these questions, recent analyses of ACP have called for
researchers, policymakers, and practitioners to examine ACP as a health behavior
(Pearlman et al., 1995; Fried et al., 2009), based on patient perspectives that motivate
such behavior (Leventhal, 2010; Carr, 2003).
The Common Sense Model of Self-Regulation (CSM) (Leventhal et al., 2003;
2010) is a widely used health behavior model asserting that an individual‟s health
preferences and behaviors are not only affected by their actual condition, but also by their
perceptions about their health condition (Leventhal et al, 1980; 2003; Carr and Moorman,
2009). The likelihood that an individual prepares for end of life through the process of
ACP may reflect their illness representations or beliefs about the duration, severity,
consequence and controllability of their health condition. Pursuant to the CSM, health
representations trigger a health behavior which, in this case, is ACP. Therefore, the CSM
139
may be useful in helping to explain why individuals engage in the processes involved in
end-of-life planning (Leventhal 2003; 2010; Carr, 2005).
The aim of this study is to explore how patients‟ appraisals of illness burden
(operationalized in a variable that measures both functional limitations and perceived
burden) affect the likelihood that they will engage in the ACP process. To do this, I use
data from the New Jersey End-of-Life study (N=293), an ethnically-diverse sample of
terminally ill, older adults. I will assess the effect of perceived illness burden (PIB) on
two types of ACP - discussions with others and the formal documentation of their
preferences through three types of ADs: 1) preparation of a living will, 2) appointing a
DPAHC, and 3) combined directives (the appointment of a DPAHC and discussion of
these preferences with others).
Background
Changes in demographics and causes of death make ACP increasingly important.
By 2030, 20% of the population will be over age 65 (U.S. Census, 2011). Chronic
conditions such as dementia-related disorders, cancer, and heart disease have replaced
sudden death from acute and infectious diseases as the primary causes of death (Omran,
1971); death is now a process of old age (Caldwell, 2010). As the life expectancy of the
chronically and terminally ill has increased, so too, has the time from diagnosis to death
due to advances in technology, diagnosis and treatment. Persons who are cognitively
limited or those who have failed to make end-of-life plans often endure unwanted costly
medical interventions (Field and Cassel, 1997; Kaufman, 2000; SUPPORT, 1995;
Moorman, 2007) or, conversely, may have desired treatments withdrawn or withheld
(Lambert et al, 2005; Carr and Khodyakov, 2007).
140
Given these demographic transitions and the financial and emotional costs
associated with unwanted or contested end-of-life care, both federal and state
governments have instituted policies to provide patients the opportunity to engage in the
completion of ADs (Galambos, 1998). Federal and state statutes regulate the use of
advance directives. The 1990 Federal Patient Self-Determination Act (PSDA) mandates
that all health facilities that receive Medicare and Medicaid funds notify patients in
writing of their treatment options, right-to-die information, and their rights to put into
place and implement advance directives. This act assumes that patients will have an
understanding of advance directives and that this knowledge will bring about discussions
between patients, caregivers, and health care providers. In addition, the Uniform Health
Care Decision Act was passed in 1993 to provide consistency in implementation and
state/local adherence to a minimum level of standards (Uniform Law Commissioner,
1994).
Advance Care Planning
Empirical studies report psychosocial, economic and quality-of-life benefits of
engaging in ACP. For example, discussions with physicians focusing on ACP result in
better understanding of future treatment options and reductions in patients‟ fears and
anxieties (Smucker et al., 1993; Ditto et al., 2001). Patients who have completed advance
directives report fewer concerns about communication with practitioners and family,
greater satisfaction with care, and are more likely to make use of treatment to reduce pain
and maximize function (i.e., hospice) (Tierney et al., 2001; Teno et al., 2007; Ditto et al.,
2001; Smucker et al., 1993). Patients and families also reported improved quality of life
and more positive mood if they engaged in early discussions and set explicit goals for
141
palliative care; these discussions and goals were associated with increased duration of
survival (Temel et al., 2010). Contrary to rhetoric surrounding “death panels,” most
individuals are willing to discuss end-of-life plans and complete advance directives if
given the opportunity (Morrison and Meier, 2004).
Despite potential benefits and legislative incentives, rates of advance care
planning remain relatively low, with between one-third and one-half of all U.S. adults
having completed an advance directive (Moorman et al., 2011; Hopp, 2000; Later and
King, 2007; U.S. Department of Health and Human Services, 2008). In their analysis of
ACP, Kass-Bartelmes and Hughes (2003) found that fewer than 50% of critically or
terminally ill patients had completed an advance directive. However, recent studies find
that ACP completion rates may range from 50% to 70% among older adults in declining
health, especially highly educated adults (Carr & Khodyakov, 2007; Silveira et al., 2010;
Teno et al., 2007).
The modest prevalence of ACP is due to multiple factors. For example,
socioeconomic variables are primary predictors of ACP; white, well-educated, and well-
to-do individuals are more likely to engage in advance care planning, while minorities are
less likely to do so (Ditto et al., 2001; Carr, 2011; Waters, 2000; Hopp and Duffy, 2000;
Degenholtz et al., 2002). African Americans, in particular, are less likely than whites to
discuss ACP with family members (Carr, 2011; Hopp and Duffy, 2000) and less likely to
know about ACP at all (Waters, 2000). In their 2007 analysis of EOL planning among
respondents who participated in the Wisconsin Longitudinal Study (WLS), Carr and
Khodyakov found that rates of ACP were especially high (e.g., exceeding 70%, among
well-educated white individuals). Socioeconomic status (SES) is also positively
142
correlated with ACP; people with high SES are much more likely than lower SES
individuals to engage in all types of ACP (Carr, 2011).
Psychosocial and experiential factors also influence ACP significantly. Experience
with the death of a loved one and one‟s own recent hospitalization were associated with
higher odds of engaging in ACP; the belief that physicians should control the time of
death and measure of death avoidance (the desire to avoid thinking about death) both
decreased the odds of ACP (Carr 2010; Carr and Khodyakov, 2007). Studies also provide
suggestive evidence that ACP may be motivated by an additional psychosocial factor:
perceiving one‟s illness and related treatments as burdensome to self and others. For
example, older adults asked about their reasons for engaging in ACP often express
concerns about being a physical (84%) or financial burden (62%) to family and friends
(Dinger, 2005). However, I am not aware of any studies that have directly assessed
whether perceptions of burden predict specific aspects of ACP, nor have any studies
looked at how functional limitations work in conjunction with PB to affect formal ACP.
Function
Physical functioning is among the factors that patients consider important when
faced with a serious or terminal illness, specifically in terms of the ability to continue to
feel like one‟s normal self (Steinhauser et al., 2000). Function is frequently measured in
terms of an individual‟s ability to carry out their activities of daily living (ADLs, e.g.,
bathing, eating, dressing) or instrumental activities of daily living (IADLs, e.g., grocery
shopping, house cleaning) (Federal Interagency Forum on Age Related Statistics, 2010).
Function is not only one‟s health and physical abilities (disabilities), but how health
impedes daily life, as illustrated by the use ADLs/IADLs as measures of functional
143
limitation. A number of studies indicate that patients are less likely to opt for aggressive
treatment as their perceived level of future cognitive and physical function and quality of
life declines (Gerety et al., 1993; Murphy et al., 1994; Cohen-Mansfield et al., 1991;
Elpern et al., 1992; Schneiderman, et al., 1992). Research has also found that
characteristics indicative of a worsening prognosis, including functional decline, have
been independently associated with patients having discussed preferences for
resuscitation with family members and clinicians (Hoffman et al., 1997).
In a more recent study examining the effect of underlying health status on
palliative care patient preferences (N=86) at the end of life, Ziven and colleagues (2007)
analyzed how functional status and burden of disease shaped the preferences of patients.
Function was assessed by physicians and nurses using the Palliative Performance Scale
(Anderson, 1996), which measures functional decline in palliative care patients. Scores
were then correlated with three potential goals of care: Prolonging life, maintaining
function, and maximizing comfort. After controlling for race, gender, and marital status,
they found that patients who focused on function (rather than maintaining comfort or
prolonging life) were significantly more likely to have a DNR order in their medical
record than those who did not focus on function. Patients with greater functional
limitations preferred palliative care rather than aggressive treatment.
Based on these findings, the authors suggest that when working with patients at
the end of life, it may be beneficial for clinicians to place greater emphasis on the nature
and likely course of an advanced illness, in terms of elements such as functional
limitations, rather than focusing narrowly on prognostic indicators such as the amount of
time a patient has to live. They conclude that such an emphasis may lead to more
144
meaningful discussions about ACP (Ziven, Bambauer, and Gillick, 2007).
My analysis will expand Ziven‟s and colleagues‟ work from a cancer population
to a general patient population and focus on patients‟ own assessments of functional
limitation in combination with a measure of how they assess that their limitation affects
them and others. Self-assessment and the subjective components of health (i.e., how one
feels or believes oneself to be) have been found to be a powerful health cognition
(Winter, Lawton, and Langston, 2007). Research in other areas of health has found that
patient assessments of health are strong predictors of patient outcomes and behaviors. For
instance, Idler and Benyamini identified more than 45 studies linking self-ratings of
health (SRH) to mortality (Idler and Benyamini, 1997; Benyamini and Idler, 1999). In a
number of studies, SRH actually predicted mortality (Mossey and Shapiro, 1982;
Schoenfeld et al., 1994) and hospital utilization (Wolinsky and Fitzgerald, 1994) better
than physician assessment did.
The relationship between function and ACP is complex, as illustrated by a
number of large, nationally representative studies (e.g., the Study to Understand
Prognoses and Preferences for Outcomes Risks of Treatments, or SUPPORT). It is
important to note that these studies considered only objective indicators of functional
limitations and did not include measures of patients‟ beliefs about whether and how their
functional limitations intruded upon and burdened those around them. The SUPPORT
study found no association between disease severity or functional capacity and whether
or not a seriously ill hospitalized patient had discussed end-of-life issues with family or
physician (SUPPORT Principal Investigators, 1995). I posit that, when considering ACP,
both objective and subjective measurements matter. People seem to know something
145
about their health that physicians do not - from somatic experiences to beliefs about the
impact that their health has on them and others. Citing Stenback (1964), “the whole is
more than the sum of the parts”; Idler and Benyamini (1997) point out that clinicians and
researchers are often “measuring parts”, while patients “have access to the whole”.
Patients assess their health in terms of impacts that are personally meaningful, for
instance how their health affects their sense of self and impedes upon others. Clinicians
and researchers do not have similar access to patients.
Measures of functional limitations in terms of ADLs/IADLs do not capture all of
the ways that functional status impacts an individual‟s life or health trajectory.
ADLs/IADLs focus only on the physical toll to self, but not the emotional toll to self and
the possible effects limitations may have on others. This point is illustrated in follow-up
analyses of the SUPPORT data (Wu et al., 1995; Covinsky et al., 2000; McCarthy,
Phillips, and Zhong, 2000). Wu and colleagues (1995) found that, although more than
one-third of patients in the SUPPORT study met the researchers‟ definition of severe
limitations, these patients still rated their quality of life as "good" or “better” (Wu et al.,
1995). This suggests that some patients are satisfied to be alive even though they are
disabled, while others might find lesser degrees of disability to be unacceptable (Bergner
et al., 1981).
The goal of Wu and colleagues‟ work was to develop a model estimating the
probability of functional limitations two months following hospitalization – a departure
from most probability models which focus solely on the likelihood of survival. Their
analysis of the SUPPORT data corroborated the idea that models predicting future
functioning may help decision-makers assess the choice between an aggressive course of
146
care and optimizing comfort, suggesting that patients can make more realistic plans for
support services if prognosis for functional status is available (Wu et al., 1995). Data
from this analysis led the SUPPORT researchers to advocate the consideration of
functional outcomes more explicitly in their discussions with family members and
prognostication (Wu et al., 1995).
It is important to note that their findings also highlight that function is a relative
measure that falls upon a continuum (Wu et al., 1995). Each individual will have a
different threshold of functional impairment that will be perceived as potentially
problematic or burdensome based upon a number of factors. They include: the
availability of means by which they can adapt to functional limitations (e.g.,
psychologically, socially, or financially); the importance of the domains in which
limitations are located (e.g., someone who is not an avid exerciser may not be burdened
by their inability to walk two or more blocks); their social support system (presence and
quality); and their goals and aspirations. The goal of this study is to build on the analyses
of function and advance care planning and to make sense of the inconsistent findings
regarding the association between functional limitations and ACP. I will evaluate if and
how functional limitations, in combination with a measure of burden that captures the
relative component of function operationalized as burden to the self and others, predicts
the likelihood of ACP.
This conception of function working together with burden to motivate behavior
coincides with Leventhal‟s (1987) Common Sense Model of Self-Regulation (CSM). The
CSM asserts that an individual‟s health beliefs/perceptions (aka “illness representations”)
are a result of their continual monitoring of their somatic experiences, functional
147
limitations and the associated impact these limitations have on their self-concept, as well
as how these limitations affect others (Leventhal et al., 2003; 2010; 2011). Perceived
illness burden (PIB) is the belief that one‟s illness and associated functional
limitations/disability are burdensome to the self and others. I believe that for many
people, perceived illness burden is an accurate reflection of the impact of one‟s
symptoms on daily life and a strong motivator to engage in advance care planning.
However, not all respondents will have an appraisal of perceived illness burden in which
both the objective (functional limitations) and the subjective (burden) are in alignment. It
is important to examine how PIB affects the planning behaviors of those individuals
whom appraise their functional limitations as low, but their perceived burden as high, and
vice versa.
Perceived Illness Burden
Some of the research on function at the end of life has been responsive to the need
to take a more nuanced and relative look at function. For example, Rosenfeld and
colleagues (2000) conducted a qualitative study of aged individuals at the end of life,
finding that, contrary to clinicians‟ emphasis on identifying patient preferences for
specific life-sustaining treatments, participants instead discussed EOL planning in terms
of the outcomes of treatments. The patients were concerned about how treatments would
affect their physical and cognitive functioning. They did not want treatments or
interventions that would compromise their ability to engage in self-care, productivity, or
treatments that would burden caregivers. Function further factored into their study when
considering patients‟ willingness to assign decision-making authority to physicians and
family. When the prognosis included functional decline and little hope of functional
148
recovery, participants transferred decision-making authority from doctors to family
members. By contrast, patients reported reluctance to intrude on and burden loved ones
when illness prognoses were poor.
This reluctance to burden others – physically, emotionally, or financially – has
been found to factor into EOL decision-making (Rosenfeld et al., 2000). The extent to
which a care recipient believes they are a burden has been found to affect a number of
end-of-life health behaviors, both positive and negative (Moorman, 2009). Perceived
burden has been negatively associated with treatment adherence (Zweibel and Cassel,
1989; Cohen-Mansfield et al., 1992; McPherson et al, 2007) and the use of end-of-life
medical interventions (Chochinov et al., 2007). Patients‟ perceptions of burdensomeness
are positively correlated with suicidal ideation among older adults (Foster, 2003),
desiring to die quickly (Schroepfer, 2008), and requesting physician-assisted suicide
(Wilson et al., 2000). Moorman (2009) posits that perceptions of burden are a catalyst
for some patients to engage in positive end-of-life health behaviors such as advance care
planning (Moorman, 2009), attending support groups (Ussher et al., 2006) or attempting
to strengthen and maintain close relations at the end of life (Singer, 1999).
Perceived burden is generally regarded as a psychological construct. However, it
is possible that perceived burden may be detecting the effect of functional decline that
individuals often experience at the end of life or, as McPherson et al. (2007, 425) states,
the “impact on others of one‟s illness and care needs.” People are generally good at rating
their physical health; self-rated health (SRH) is another patient appraisal that has been
shown to be a better predictor of mortality than is physician assessment (Idler and
Benyamin, 1997; Ferraro and Kelley-Moore; 2001). Patient appraisal is not merely
149
psychological; individuals are responding to real decrements in their function. As noted
earlier, it is possible that there is not an alignment between objective and subjective
health for all individuals. By assessing categorical measures of PIB, my goal is to better
understand how the objective and subjective work together to facilitate or impede
advance care planning.
Burden is not rooted solely in terms of the difficulties illness and associated
treatments cause to others, but also how health limitations have consequences for an
individual‟s sense of self as independent and autonomous. This is illustrated by
Moorman‟s (2009) finding that concerns about autonomy were correlated with feelings of
being burdensome, more so than norms of reciprocity such as marital concerns and
caregiver availability. She suggests that “feeling like a burden may have more to do with
losing one‟s own functional independence than with infringing upon the independence of
one‟s caregiver” (Moorman, 2009, 147). These findings may call into question
McPherson and colleagues‟ assertion that feeling burdensome is predominantly rooted in
“empathetic concern for others.”
Past research on end-of-life decision making has found that patients report
intricate and subtle interactions between physical and functional decline and existential
concerns - which could not be separated or compartmentalized - such as loss of sense of
self and burden to others (Pearlman and Starks, 2004). Henceforth, when considering the
concept of perceived burden, it is important to include measures of functional limitations
and perceived burden to both the self and others. This perspective coincides with the
findings of my qualitative analysis of the New Jersey End-of-Life (NJEOL) focus group
data which examined what patients believe to important when considering the health
150
decisions they must make at the end of life (see Chapter 2 of Bodnar-Deren dissertation,
2011).
In the analysis of the qualitative data from the NJEOL focus groups, when
discussing their motivations for engaging in certain advance care planning behaviors,
patients regularly discussed their health and health behaviors from a “biopsychosocial”
perspective, as an interplay between biological, psychological, and societal influences.
First, they framed their discussions in terms of illness and functional decline (biological).
This was followed by a statement of how that decline provides difficulties for their sense
of self and independence (psychological) which then leads to dependence on others
(social). This conception of perceived illness burden is illustrated in Figure 1.
[Insert Figure 1 about here]
Patients‟ perspectives link directly with the recommendations put forth by the
Institute of Medicine (IOM) (2001), which called for clinicians and researchers to look
“biopsychosocially” at health and health behaviors such as ACP. They also called for a
reconceptualization of care that is patient-centered, in which there is an explicit
understanding of how patients‟ beliefs and perceptions (illness representations) affect
their health. The Common Sense Model of Self-Regulation (CSM) (Leventhal et al.,
2003; 2011) is a model of understanding health and health behaviors that is both patient
centered and biopsychosocial.
Conceptual Framework
Most of the research to date on perceived burden has primarily focused on the
psychological aspects of burden and how patients‟ beliefs about burdening others are
associated with psychological factors such as depression (Wilson et al., 2005). The focus
151
on feelings such as depression, dependency, frustration, and worry as markers for
perceived burden is appropriate. However, it is equally important to recognize the role of
functional limitations as a prior, if not pivotal, antecedent of perceived burden. I propose
that for many respondents, feelings of burdensomeness may be an accurate reflection of
the impact of one‟s symptoms on daily life, including the lives of the patient and their
support network. The Common-Sense Model of self-regulation (CSM) provides the
theoretical basis for this assertion.
A core proposition of the CSM is that awareness of symptoms, functional decline,
and medical diagnoses are critical factors for the activation of illness representations and
changing views of the self that create the motivation for engaging in health behaviors
(Leventhal et al., 2003; 2008; 2010). The activation of representations creates an array of
expectations with respect to future somatic and functional experiences, the consequences
and causes of these changes and the possibilities for control by treatment (Leventhal et
al., 2003).
In essence, perceptions (including burden) are anchored in concrete physical
experiences which work together to inform behavior. For example, the experienced level
of function and the perceptions of functional decline are powerful drivers of self
assessments of health and predictors of mortality in community samples (Mora, 2009)
and terminally ill cancer patients (Shadbolt et al., 2002). Thus, the experience of current
levels of function and declines in function are critical antecedents of optimism or
pessimism about treatment and survival; they are hypothesized to be major drivers of
perceived illness burden. I posit that concrete functional limitations are used by patients
to evaluate their sense of perceived burden; function and perceived burden then become
152
part of the illness or health representations held by the patient. According to the CSM,
health representations trigger a health behavior, which in this case is ACP.
Current Study
The current study examines how an individual‟s level of perceived illness burden
affects the likelihood that they have engaged in advance care planning. To fully
understand how each element of PIB affects ACP, it is important to examine how various
combinations of disability and function affect each of the ACP behaviors. Therefore, I
will look at how four PIB categories [1) high-disability/high-burden; 2) high-
disability/low-burden; 3) low-disability/high-burden; 4) low-disability/low-burden] affect
the odds of advance care planning. I will be looking at four specific types of advance care
planning: 1) Having end-of-life discussions with others; 2) the formation of instructive
directives or living will; 3) proxy directives – the naming of a durable power of attorney
for health care; and 4) pursuant to the recommendations of the Institute of Medicine
(Kass et al., 2005), that both informal and formal planning happen together, I will be
looking at combined directives - having both a discussion with others and naming a
DPAHC.
I argue that functional limitations alone and perceptions of burden alone are
insufficient to motivate the health behavior of ACP; it is their combined effect that is the
most potent driver of ACP. The CSM asserts that negative functional changes inform the
conception of the self as a burden; it is this combination that ultimately translates into a
health behavior (Leventhal, 2003). Therefore, I believe that functional limitations will
work together with conceptualizations of burden; individuals who experience high levels
of both may wish to alleviate any potential impact that their illness has on others or
153
assure themselves that their own burdens will not be prolonged. I hypothesize that
respondents in the high-disability/high-burden category will be most likely to engage in
all four types of advance care planning.
I also hypothesize that individuals in the “off-diagonal” categories (high-
disability/low-burden and low-disability/high-burden) will be somewhat more likely to
plan than those in the low-low category, but the magnitude of the effect and significance
will not be as large. This is based on earlier analysis of this data, in which I examined
perceived burden and functional limitations separately as antecedents to APC (see
Appendix, Table 3D and 3E). I found separate yet different effects between functional
limitations and perceived burden and ACP, in terms of significance and magnitude.
I also believe that some of the underlying correlates of membership in various
PIB categories may factor into the odds of ACP behavior. In a previous analysis of PIB
category membership (see dissertation chapter 3), race/ethnicity was a significant
predictor of being in the high-disability/low-burden category of PIB. Hispanic
respondents were much more likely to be in the high/low category than were non-
Hispanic whites. Race/ethnicity was also highly correlated with ACP, with minority
group members engaging in lower levels of all types of planning (Carr, 2011; Waters,
2000; Ditto et al., 2001). Since Hispanics are more likely to be in the high/low category
and are less likely to plan, I hypothesize that those in the high/low category may be less
likely to plan than those in the high/high category. However, I still believe they will be
more likely to plan than those in the low/low category because the high-disability/low-
burden category is heterogeneous on other measures.
154
Depression has also been found to be correlated with burden (Wilson, 2005).
Research on depression has found that depression has adverse effects on adaptive health
behaviors such as smoking, diet, over eating and sedentary lifestyle (Katon, 2003); those
who plan across the life course are less depressed (Lachman, 1993). Depression was also
found to be strongly associated with being in the high burden categories (increases in
depressive symptoms significantly decreased the odds that respondents were in the low-
disability/low burden category and high disability/low burden category) in my analysis of
the correlates of PIB categories. Although burden has been associated with end-of-life
attitudes and behaviors (e.g., a desire for a hastened death and the desire for physician
assisted suicide), I hypothesize that those in the low-disability/high-burden group will be
no more likely than those in the omitted category (low-low) to engage in ACP because
ACP is an adaptive EOL behavior (Moorman, 2009).
Other influences on advance care planning.
A number of other factors have been found to be associated with both ACP and
PIB; they will be controlled for in this analysis. A large body of research suggests that
higher levels of educational attainment and income are associated with better health over
the lifecourse (Cutler and Lleras-Muney, 2010) and higher levels of ACP (Carr, 2011;
Carr and Khodyakov, 2007). Race and ethnicity have been found to inversely correlate
with ACP (Carr, 2011; Teno, 2007; Degenholtz et al, 2002) and health and disability
(Williams et al., 2009; 2010). Similarly, racial/ethnic differences have been noted in
perceptions of burden. Carr (2011) notes that blacks and Latinos may be less likely to
feel burdensome even if they have functional limitations because they prioritize family
interdependence rather than individual autonomy. Family factors such as marital status,
155
presence of children and quality of familial relationships have also been found to affect
ACP (Moorman, 2009; Carr and Khodyakov, 2007; Hopp, 2000). Schroepfer (2008)
found that poor social support from caregivers contributed to terminally ill older adults‟
desire to hasten death. These variables have also been found to influence function and
burden. Women have poorer health than men over the life course; however, they have
longer life expectancies and are more likely to outlive their spouses (Crimmins, 2004)
and rely on children for care. Therefore, I will include controls for marital status, children
and quality of family relationships in my models, in addition to controlling for
race/ethnicity, educational attainment, and annual income.
Research on the end of life and perceived burden have found that physical
limitations and depression were both positively correlated with burden (McPherson et al,
2007; Wilson et al, 2005). Finally, prior research has found that individuals in poor health
and older persons tend to prefer surrogate or proxy decision making (Flynn et al., 2006;
Sulmasy et al., 2007). Each has been associated with functional limitations and burden.
Therefore, my models adjust for number of health conditions, depression, and age.
Methods
Sample
The New Jersey End-of-Life (NJEOL) study sample consists of data from 305
non-institutionalized older adults in New Jersey (NJ), 55 years of age and older. Patients
were recruited to participate if they were either English- or Spanish-speaking, had no
cognitive limitations, and had one or more of the following health conditions: cancer,
Type II diabetes, or congestive heart failure (CHF). A group of patients who did not have
any of the target illnesses were also recruited as a “healthy control” group; however,
156
many of these participants had one or more other health conditions, thus the label
“healthy” is largely a misnomer. Recruitment was conducted over the telephone from two
large university hospitals and one comprehensive cancer center in NJ.
The initial sampling frame consisted of 1,146 patients who were identified as
potential participants for the study through the general internal medicine department at
the University of Medicine and Dentistry of New Jersey (UMDNJ). Of this group, 575
respondents met the criteria for inclusion in the initial sampling pool. Reasons for non-
inclusion in the sampling pool included: invalid contact information/inability to locate
individuals; death of indentified possible participants; cognitive and physical limitation
precluding participation; and not meeting sampling frame characteristics (i.e. being too
young). Three- hundred-five participants consented to participate in the study,
representing 53% of the eligible sampling frame. Reasons for non-participation included
a general reluctance for patients at the end of life to participate in such a study and time
constraints (participants being too busy). The interview process consisted of a 1.5 hour
face-to-face structured interview with a trained graduate student interviewer. The survey
included questions regarding sociodemographics, health status and behaviors, EOL
planning, attitudes toward treatments, religion/spirituality, and social supports (Carr,
2011).
Measures
Dependent Variables
The dependent variables in these analyses are dichotomous variables measuring
both informal and formal advance care planning. For both dependent variables, a code of
1 indicates the presence of ACP and a score of zero indicates that no planning had been
157
done. Informal ACP was operationalized as having had a discussion about end-of-life
plans with someone. Two questions addressed the presence of formal end-of-life
planning. Respondents were instructed to answer “yes” or “no” to each of the following
questions: (1) “Do you have a living will (or an advance directive)? This is a set of
written instructions about the type of medical treatment you would want to receive if you
were unconscious or somehow unable to communicate.” (2) “Have you made any legal
arrangements for someone to make decisions for you about your medical care, if you
become unable to make those decisions for yourself? This person is sometimes called a
Durable Power of Attorney for Health Care.” I will also examine if participants have
formulated a combined directive, --both an EOL discussion and the naming of a health
care proxy. For each dependent variable, a code of 1 indicates the presence of ACP and a
score of zero indicates that no planning had been done.
Independent Variables
Level of functional limitation/burden. A composite variable that measures
participants perceived level of functional limitation and burden is the key independent
variable in these analyses. I constructed this variable based on participants‟ responses to
two questions assessing their current level of physical functioning and perceptions about
being a burden to self and others. 10
10
The descriptive statistics and factor analyses for the functional limitation scale and PB
scale variables can be found in the appendix, tables 4A-C. In preliminary analyses, I
considered functional limitation and perceived burden in separate analyses, both of which
significantly increased the odds of ACP (appendix 4D, 4E). An OLS regression analysis
was run to examine how function informed PB and PB affected function. Findings from
this analysis can be found in the Appendix, table 4F and 4G.
158
First, a functional limitations scale was constructed using Activities of Daily
Living (ADLs) and Independent Activities of Daily Living (IADLs) from the SF-12. The
SF-12 is a highly validated, multipurpose short form survey selected from the SF-36
Health Survey (Ware, Kosinski, and Keller, 1995; 1996). Factor analysis revealed a
single factor encompassing nine items from the SF-12, (“How much does your current
health limit you in doing each of the following activities: Bathing or dressing yourself;
Bending, kneeling, or stooping”); and IADLs (“How much does your current health limit
you in doing each of the following activities: Lifting or carrying groceries; Climbing
several flights of stairs; Walking more than a mile; Walking several blocks; Walking one
block; Vigorous exercise (e.g. running, lifting heavy objects); Moderate activity (e.g.
bowling, vacuuming)”). Responses were rated on a scale from 1 (lowest) to 5 (highest),
the Cronbach‟s alpha for this scale was .92711
.
Perceived burden. Next, a “perceived burden” (PB) scale was created. The four
survey questions that comprise the PB scale (rated on a scale from 1-5) were designed to
capture information that characterizes patients‟ attitudes about being a burden to self,
their friends, their families, and to others. The items included in this scale were
constructed from the consequence items in the Illness Perception Questionnaire Revised
(IPQ-R), a validated measure of illness perceptions used in the CSM (Moss-Morris et al.,
2002; Weinman et al., 1996). The variables in the PB scale were as follows: (1) “My
illness has major consequences for my daily life.” (2) “My illness causes difficulties for
those who are close to me.” (3) “The treatment for my illness has major consequences for
11
One respondent did not answer the questions in this part of the survey; mean
imputation was used to address the missing information from the variables used to
construct the scale.
159
my daily life.” (4) “The treatment for my illness causes difficulties for those who are
close to me.” Responses to these statements were coded as: (1) strongly disagree, (2)
disagree, (3) neither disagree nor agree, (4) agree, (5) strongly agree. Using these four
variables, a scale was created by taking the mean. Factor analysis revealed a single-factor
construct, and the interitem reliability was evaluated using Cronbach‟s alpha (α=0.861).
Two-hundred-sixty-seven respondents (88%) completed this part of the questionnaire.
Thirty-one respondents were not asked or did not answer the questions in this part of the
survey; as such, mean imputation was used to address the missing information from the
four variables used to construct the scale12
. Responses ranged from 1, indicating a low
perceived burden of care, to 5 indicating high perceived burden of care.
Perceived Illness Burden. A categorical composite variable that captured both
facets of functional perceptions was created using both the functional limitation and PB
high/low dichotomous variables. Zero-level correlations revealed that while the
continuous measures of PB and functional limitations were correlated (r = 0.441;
p=0,000), there was considerable heterogeneity in the sample. This suggests that while
many individuals are skilled at making accurate appraisals (e.g., they feel burdensome if
their health is poor); there are those for whom physical symptoms and burden are not
correlated. I also believe that a person‟s membership in the various categories may trigger
various behaviors (positive and negative) attached to them.
12
In the models run looking at PB and ACP, a dummy variable was created for the thirty-
one respondents who were not asked or did not answer the questions in the survey. This
variable was entered into the analysis to ascertain whether those 31 non-respondents
altered the findings. As shown in table 4E (appendix), they did not.
160
For the purpose of creating the categorical variable that captured both functional
limitations and perceived burden, the functional limitation scale variable was split at the
median (2.78) to create a dichotomous variable, with 1 indicating a high level of
functional limitation. For ease of understanding and conciseness, functional limitations
are termed “disability”. As with the functional limitation scale, PB was dichotomized into
two categories split at the median (3.01). This variable was coded 1 for high PB and 0 for
low PB.
Based on the dichotomized PB and functional limitation variables, I created four
categories to measure perceived illness burden: high-disability/high-burden; high-
disability/low-burden; low-disability/high-burden; and low-disability/low-burden. For the
purpose of regression, participants in the low-disability/low-burden category were
omitted as the PIB reference category because they were the healthiest and largest group.
Control Variables
Sociodemographic characteristics. Race/ethnicity was coded into three
categorical dummy variables: non-Hispanic white, non-Hispanic black, and Hispanic. For
the purposes of regression, white participants were omitted as the reference category for
the race/ethnicity variable. Other variables controlled in this analysis included: age (in
years), gender (1=female), education (less than high school, high school/some college
[reference category], or college degree [BA/BS+]), income ($0-14,999 [reference
category], 15,000-39,999, 40,000-84,999, 85,000+, or income missing/not answered).
The presence and quality of familial relations are an important mechanism by
which disability and burden may affect the various forms of ACP. Marital status was
coded into three categorical dummy variables: married (reference category), widowed, or
161
divorced/never married. Parental status was also coded into dummy variables: no children
(reference category), one child, two children, and three or more children.
Four continuous variables were included in the analyses to look at the strength
and quality of the familial relationships. To assess the spousal/partner relationship,
respondents were asked “How much is your spouse/partner critical of what you do?” and
“How much is your spouse/partner willing to listen to you when you need to talk about
your worries or problems?” For those respondents who were not married or partnered,
missing responses were replaced by the sample average for each item (2.6 and 3.6
respectively). Seventy-four respondents were currently not married or partnered and did
not answer the question; as such, mean imputation was used to address the missing
information. The marital status variable divorced or never married was previously
entered to capture the non-respondents. These questions were repeated to capture the
relationship quality with children. Respondents were asked “How much are your children
critical of what you do?” and “How much are your children willing to listen to you when
you need to talk about your worries and problems?” The responses to these questions
were also coded on a four point scale (1-4). Forty respondents did not have any children
and did not answer these questions; as such, mean imputation (2.1 and 3.44 respectively)
was used to address the missing information. The variable having no children was
entered into the analysis to capture any differences among those without children.
Number of health conditions. A potentially important covariate used in this
analysis was the number of health conditions experienced by patients. Each subject was
read a list of health conditions and asked whether a doctor had ever told them that they
have such a condition or whether they were taking medicine for such a condition. The
162
conditions were as follows: asthma, lung problems, diabetes, cancer, ulcer(s), heart
disease, high blood pressure, heart attack, seizures, hepatitis, kidney problems,
tuberculosis, and depression/anxiety. The number of health conditions reported ranged
from 0 to 9 health conditions. Based on the variables distribution, three dichotomous
variables were constructed for bivariate analysis to categorize the number of health
conditions each respondent reported (0-1 health conditions, 2-3 health conditions, and 4
or more health conditions).
Level of depressive symptoms. The items included in this scale were constructed
from 5 items from the Center for Epidemiological Studies Depression Scale (CES-D)
survey (Radloff, 1977). Using the 9-item scale, factorial analysis revealed one construct
consisting of the following five items from the scale: “How many days during the last
week did you (1) “feel lonely.” (2) “feel sad.” (3) “feel depressed.” (4) “feel everything
you did was an effort.” (5) “feel you could not get going.” As per the original citation, I
summed the items to create a composite score13
. The interitem reliability was evaluated
using Cronbach‟s alpha (α=0.829).
Analytic Approach
Stepwise binary logistic regression models were estimated to examine how level
of perceived illness burden affected the odds of engaging in EOL discussions and formal
ACP. A stepwise model was chosen to see if the effect of the key independent variable,
PIB category, changes with the addition of control variables (race/ethnicity,
demographics, family status, SES, and health variables) and to ascertain how these
13
Three respondents did not answer the questions in this part of the survey; as such,
mean imputation was used to address the missing information from the variables used to
construct the scale. The mean of the scale was 1.33.
163
variables affect the likelihood of engaging in ACP. Race was entered first because of the
strong relationship between race and ACP in the literature. The remaining independent
variables were entered based on the order that they are commonly present in a person‟s
life.
Results
Descriptive and Bivariate Analysis
Table 4.1 presents descriptive statistics (i.e., means and standard deviation for
continuous measures; proportions for categorical measures) by perceived illness burden
category. Forty-six percent of participants had a living will or advance directive, 41% had
named a Durable Power of Attorney for Health Care, and 69% had EOL discussions with
others. Slightly less than 40% of respondents had both discussed their EOL plans with
another and named a DPAHC. Twenty-eight percent of respondents self-reported that
they had both a high level of disability and a high level of perceived burden, 18% high-
disability/low-burden, 11% low-disability/high-burden, and 41% low-disability/low-
burden. Fifty-six percent self-identified as non-Hispanic white, 25% as non-Hispanic
black, and 19% as Hispanic. The respondents were older adults with a mean age of 69; a
majority were women (64%). More than half were married or living in a marriage-like
relationship (50.5%) and 86% had children, over half of all respondents had 3 or more
children. In terms of the quality of familial relationships, respondents reported that, on
average, both spouses and children were willing to listen when they needed to talk about
their worries and problems. Seventy-eight percent had a HS degree or higher and 42%
earned over $40,000 per year. Almost 46% of respondents had 2-3 health conditions.
[Table 4.1 about here]
164
Table 4.1 also presents the descriptive statistics by PIB category. The data did not
reveal significant subgroup differences in terms of perceived illness burden category for
any of the types of planning. The data did reveal a significant association between race
and perceived illness burden. Twice as many non-Hispanic white subjects were in the
low-disability/low-burden category relative to the high-disability/high-burden category
and, while not as dramatic, the same effect was observed when compared to the high-
disability/low-burden and low-disability/high-burden groups. Hispanic respondents also
reported significant subgroup differences; almost 40% were in the high-disability/high-
burden group with just over 27% of Hispanic individuals in both the high-disability/high-
burden and 25% in the low-disability/low-burden categories.
Results from ANOVA analyses reveal that SES was also a significant factor in
perceived illness burden. The majority (64%) of high earners ($85,000+) had a low level
of disability and burden; by contrast, just 11% of the lowest earners were in this category.
Over one-third of those earning under $14,999 and 41% of those earning between
$15,000-39,999 reported high-disability/high-burden. There were also significant
subgroup differences of education by level of perceived illness burden. Sixty-one percent
of those with a bachelor‟s degree or higher were in the low-disability/low-burden
category and only 19% reported high-disability/high-burden.
The number of health conditions and level of depressive symptoms also illustrate
significant PIB category differences. The proportion of persons in the high-
disability/high-burden category with four or more health conditions is considerably
higher than those in the low-disability/low-burden category. Additionally, individuals
with one or no health conditions report low levels of disability and perceived burden.
165
There were also PIB category differences with regards to level of depressive symptoms.
Individuals in the high/high PIB category had significantly higher level of depressive
symptoms (2.13) than did patients in the three other categories (low/low, high/low and
low/high). Moreover, respondents in the high/low category had a mean level of
depressive symptom score (1.47) that was significantly higher than the mean depressive
symptom score of subjects in the low/low category (0.75).
Logistic Regression Analyses
Four logistic regression analyses were conducted, predicting the odds of: 1)
having an EOL discussion with others; 2) having a living will; 3) naming a DPAHC; and
4) having both a discussion and naming a DPAHC. Each model was comprised of the
following six blocks, in which each block includes the variables of preceding blocks: 1)
main independent variable; 2) race/ethnicity; 3) age and gender; 4) Family relations
(presence and quality of); 5) SES (income/education); and 6) number of health
conditions.
Level of perceived illness burden predicting odds of having EOL discussions
The first model (Table 4.2) examines how the level of PIB predicts the odds of
having an EOL discussion with others. In this and subsequent models, a suppression
effect is evident. PIB level is not a significant predictor of discussions in the initial block;
however, after controlling for race, a significant effect emerged. A suppression effect
occurs when two independent variables have a positive relationship between one another
but opposite relationships with the dependent variable (MacKinnon, Krull, and
Lockwood, 2000), and the size of the correlation between an independent variable and
the dependent variable increases when a third variable is included (Tzelgov and Henik,
166
1991). Race is positively correlated with PIB (as illustrated in table 2.1) and negatively
correlated with the dependent variable, ACP. Once race is controlled for, the significant
relationship emerges between levels of perceived illness burden. A failure to consider
race will cause a failure to fully understand how level of perceived illness burden affects
ACP.
Block 2 illustrates how the level of PIB affects the likelihood that a respondent
has had EOL discussions after controlling for race/ethnicity. The model shows that
participants who have high levels of perceived illness burden are over 2.5 times more
likely to discuss EOL plans than individuals with low levels of burden/disability
(OR=2.60 CI=[1.24-5.43], p=.011). Similarly, respondents in the high-disability/low-
burden category are twice as likely as low/low respondents to have had discussions with
others. This effect was marginally significant. However, respondents in the low-
disability/high-burden group were no more likely to have had discussed their EOL plans,
suggesting that it may be disability (functional limitations) that motivates discussions
with others about the end of life. Both non-Hispanic black and Hispanic respondents were
significantly less likely to have had EOL discussions with others; non-Hispanic blacks
were almost 80% (OR=0.19, CI= [0.099-0.38], p=.000) less likely than non-Hispanic
whites, and Hispanic participants were over 90% (OR=0.07, CI= [0.03-0.16], p=.000)
less likely to have had EOL discussions with others. This trend continues across all
blocks of the model. In this model, patients‟ level of PIB account for 26% of the variance
in the likelihood of having informal advance care plans.
The effect of being in the high/high PIB category decreased slightly with the
addition of controls for sex and age, although neither variable was significantly related to
167
the likelihood of engaging in an ACP discussion. The model was then expanded to
control for additional demographic variables (gender and age) and familial
status/relationship quality (block 4). The odds of having an EOL discussion increased
slightly to almost 2.6 (OR=2.63, CI= [1.23-5.65], p=0.013) for those in the high-
disability/high-burden category relative to the low-disability/low-burden category.
Gender, age, marital status, and number of living children did not have a significant
impact on the probability of EOL planning discussions; however, the quality of the
familial relationship did.
In block 4, I controlled for family status. Being in the high/high category
increased the odds of having an EOL discussion; those who had both high disability and
burden were 2.6 times more likely to have discussions than those with low disability and
burden. Only one of the family variables was associated with EOL discussions. The more
critical the spouse/partner, the more likely it was that a respondent had an EOL
discussion. Respondents were 80% more likely to have an EOL discussion with others
(OR=1.77, CI=[1.07-2.95], p=0.028) with each one unit increase in how a respondent
rated their spouse/partner in terms of their being critical of what they do. The level of
children being critical did not significantly affect the odds of having an EOL discussion,
nor did having a spouse/partner and children who listens to you. The model fit increased
3% after controlling for demographics, family status, and familial relationship quality,
explaining 30% of the variance in the odds of having an EOL discussion.
[Table 4.2 about here]
In block 5, I controlled for SES (i.e., level of education and income). Participants
who had high levels of disability and burden were almost three times more likely to
168
discuss EOL plans than are individuals with low levels of burden/disability (OR=2.98,
CI=[1.35-6.58], p=0.007). The effect of income on the odds of having an EOL discussion
approached significance. Individuals who made over $85,000 annually were almost three
times more likely than those who had incomes under $14,999 to have had an EOL
discussion (OR=2.919, CI=[0.85-10.06), p= 0.090). The addition of SES caused a model
fit increase of four percent from 30% to 34% (R2
=0.34).
In the final block, the effect of PIB is partially explained by the number of health
conditions a respondent had, although it remained significant. This may be due to the fact
that the number of health conditions is significantly related to PIB category membership.
In a separate analysis of the correlates of PIB category membership, individuals with
three or less health conditions were six times more likely to be in the low/low category
relative to the high/high category (see Bodnar-Deren dissertation chapter 3 for full
analysis). Individuals who were in the high-disability/high-burden category were three
times more likely to have had EOL discussions than those in the low-disability/low-
burden category (OR=2.95, CI= [1.25-6.94, p=0.014). Also, the respondents in the high-
disability/low-burden group were no longer significantly more likely to have had EOL
discussions than those in the low/low category after controlling for race, gender, age,
familial status/relationship quality, educational attainment, income and number of health
conditions. Almost 35% of the variance in the odds of having an EOL discussion is
explained by a patient‟s reported level of perceived illness burden.
[Table 4.3 about here]
Level of function/burden predicting odds of having a Living Will
169
Table 4.3 represents the results from the second logistic regression analysis, level
of PIB predicting the odds of having a living will. As above, block 1 reveals no
significant effect from any of the PIB levels on the likelihood of having a living will
(instructive directive), although the effect is approaching significance. Perceived illness
burden does not change the odds of having formalized a living will. This trend continues
until SES is controlled for in block 5. Block 2 reveals the significant impact of
race/ethnicity on the likelihood of completing a living will. Non-Hispanic black and
Hispanic respondents were significantly less likely (OR=0.17 and 0.05, p=.000)
compared to non-Hispanic white participants to have an instructive directive (CI= [0.09-
0.32]; [0.02-0.12]), respectively.
In block 6, after expanding the baseline models to control for additional
demographic, family, and SES variables, the effect of PIB significantly affected the
likelihood of having a living will. Individuals in the high-disability/high-burden category
were 2.4 times more likely than those in the low-disability/low-burden category
(OR=2.37, CI=[1.13-4.99], p=.023) to have a living will. The effect stays significant after
controlling for the number of health conditions (OR=2.24, CI= [1.03-4.88], p=0.043). In
the full model, after controlling for sociodemographic, family, and health variables,
model fit was increased to 37% (R2
=0.37).
Level of function/burden predicting odds of Naming a DPAHC
Table 4.4 shows the logistic regression analysis for level of perceived illness
burden predicting the odds of having named a Durable Power of Attorney for Health
Care. Block 1 reveals no significant effect on the likelihood of naming a health care
proxy of any PIB category; high-disability/high-burden (OR=1.26, CI=[0.72-2.21],
170
p=0.423); high-disability/low-burden (OR=0.71, CI=[0.36-1.39], p=0.318); or low-
disability/high-burden (OR=0.74, CI=[0.34-1.63], p=0.455), relative to being in the low-
disability/low-burden category. As with the previous models, we see a suppression effect
for the impact of PIB category on naming a DPAHC; it is not until we control for race
(among the strongest predictors of ACP) that we see the significance of burden emerge.
Race is positively correlated with PIB category and negatively correlated with having a
proxy directive. Patients who were in the high-disability/high-burden category were over
two times more likely than those in the low/low category to have appointed a DPAHC
(OR=2.13, CI= [1.10-4.13], p=0.024). In this model, patients‟ perception of being both
functionally limited and burdensome to others account for 29% of the variance in the
likelihood of naming a DPAHC.
[Table 4.4 about here]
The addition of SES in block 5 significantly increased the effect that being in the
high-disability/high-burden category had on the likelihood of naming a DPAHC. There
was a 60% increase in the effect of being in the high/high category. Respondents with
high disability and burden were four times more likely to have appointed a proxy. Having
a college degree nearly doubled a participant‟s odds of appointing a proxy, this effect was
very close to being significant at the 0.05 level (OR=1.94, CI=[1.00-3.77], p=0.051).
Also approaching significance was annual income. Respondents who made more than
$85,000 annually were over three times more likely to have a proxy (OR=2.84, CI=[0.94-
9.90],p= 0.064).
In block 6, net of all controls, respondents who were in the high-disability/high-
burden category were over 4.5 times more likely to have completed a proxy directive
171
(OR=4.39, CI= [1.84-10.46], p=.001). Additionally, after controlling for demographics,
family status, SES and number of health conditions, participants in the high-
disability/low-burden category were also significantly more likely to have named a
DPAHC (OR=2.64, CI=[1.02-6.87], p=0.046). In this final block, 39% of the variance in
the odds of appointing a health care proxy was explained (R2
= 0.39).
Level of function/burden predicting odds of having both EOL discussions and naming a
DPAHC
A general pattern is present in all of the models due to the high overlap in discrete
planning tools, 38% of respondents had both an EOL conversation and DPAHC.
However, in my final model I will look at how PIB affects the odds of having a combined
directive because it is important that individuals who appoint a health care proxy
communicate their health plans and preferences directly to others (Kass, 2005). Table 4.5
shows the logistic regression predicting the odds of having both an EOL discussion and
appointing a DPAHC. Controlling for race/ethnicity, the model shows that participants in
the high-disability/high-burden category are over twice as likely as those in low-
disability/low-category to have both named a proxy and also had a discussion about their
EOL preferences and plans (OR=2.15, CI=[1.11-4.19], p=0,024).
[Table 4.5 about here]
The addition of family variables in block 4 increases the model fit from 30% in
block 2 to over 33% in block 4. Those with one child are over 3 times more likely to have
a DPAHC, compared to participants with no children (OR=3.31, CI= [1.06-10.35],
p=0.039). Block 5 introduces controls for SES and block 6 the number of health
conditions. In the final model, the key independent variable – level of PIB - significantly
172
affects the odds of having both an EOL discussion and a designated a proxy. Individuals
in the high-disability/high-burden (OR=4.73, CI= [1.95-11.48], p=0.001) and high-
disability/low-burden categories (OR=2.94, CI= [1.12-7.76), p=0.029) are more likely to
engage in both formal and informal ACP compared with those with low-disability/low-
burden.
In this last block, race/ethnicity is shown to significantly affect the odds of having
both types of plans. Non-Hispanic black participants and Hispanic participants are less
likely than non-Hispanic white participants to have discussions and name a proxy
[(OR=0.32, CI= [0.14-0.75], p=0.008); (OR=0.06, CI= [0.01-0.24], p=0.000)
respectively]. A number of control variables were moderately related to naming a proxy
for health care and discussing plans with others. Individuals who had one living child
were 3 times more likely than those with no children to name a health care proxy
(OR=3.13, CI=[0.93-10.48], p=0.065). SES was also related to the odds of having a
DPAHC; individuals with a college degree or higher were almost twice as likely to
designate a proxy, and those who made over $85,000 per year were over 3 times more
likely (OR=1.86, CI= [0.95-3.64], p=0.072) and (OR=3.31, CI= [0.98-11.18], p=0.053).
In this final model, over 40% of the variance in the likelihood of engaging in both
formal and informal planning (R2
= 0.40) was explained. After controlling for
demographics, family status and relationship quality, SES and number of health
variables, respondents who perceived themselves as both highly disabled and
burdensome were almost 5 times more likely to engage in both formal and informal
planning. Those in the high-disability/low-burden category were 3 times more likely than
173
those in the low-disability/low-burden category to have had both an EOL discussion and
appointed a DPAHC.
Discussion
This study documented how appraisals of disability and perceived burden
combine to affect advance care planning. I examined how four perceived illness burden
categories (high-disability/high-burden; high-disability/low-burden; low-disability/high-
burden; low-disability/low-burden) affected four types of ACP (discussions, living will,
DPAHC, and combined directive). The study also revealed that functional decline and
perceived burden work in tandem to affect the likelihood of future health planning. My
analysis yielded four findings that have implications for health care practice and policy.
First, pursuant to the Common Sense Model of Illness Representations
(Leventhal, 2003, 2010), function and burden work together to inform ACP, but only
when respondents were high in both function and burden was there a robust increase in
the odds of planning (compared to the reference group – low/low) across all outcomes.
Respondents in the high/high category were significantly more likely to do all types of
ACP; however, the effect was strongest for appointing a proxy for health care and the
combination of discussion/proxy. In other words, the effect was strongest for those who
experienced compromised function AND who perceive this compromised function to
impair daily life.
Those in the high-disability/low-burden category14
(individuals who were less
depressed, lower-middle incomes) only differed from the low/low respondents for the
14
In previous analysis of the correlates of PIB (see Bodnar-Deren dissertation chapter 3),
Hispanic respondents were more likely than non-Hispanic white respondents to be in the
high-disability/low-burden category relative to the high/high category. Lower – lower
174
two proxy directives, suggesting that functional decline/disability is an important factor
for proxy planning. Research on end-of-life planning has focused primarily on objective
health measures used by physicians or patients in EOL planning or on patients‟
demographic characteristics; there has been considerably less focus on subjective
measures such as patient self-appraisal and perception. In my review of the literature, no
study to date has focused simultaneously on objective or concrete health measures such
as functional limitation and patient perceptions (e.g., perceived burden).
This study has focused on two elements of patient self-appraisal: self-assessed
function (ADL/IADLs) and respondents‟ perceptions of how their functional limitations
affect both the self and others (PB). While many studies have looked at both aspects of
function, they have been studied in isolation. For example, several studies (e.g.,
SUPPORT 1995; Davison, 2006, 2009; Ziven, et al., 2007) have examined how
functional limitations (objectively measured by physicians in terms of ADL/IADLs)
affect the likelihood and content of ACP; the results have been mixed. Similarly, other
studies have found positive correlations between patients‟ self-perception of burden and
the presence and content of end-of-life plans (Wilson, et al., 2005, 2007; McPherson, et
al., 2007; Singer, et al., 1999). Two aspects of PIB level of disability and perceived
burden were significantly correlated, but the correlation was modest (r=0.441; p<0.001).
For 70% of the respondents, level of disability and burden were aligned (28% high-
middle income ($15,000-39,999) respondents were more likely to be in this category than
were high earners ($85,000+) relative to being in the high/high category. Individuals
with in this category reported higher levels of depressive symptoms than those in the
low/low category. Less depressed individuals were less likely to be in this category than
in the high/high category.
175
disability/high-burden and 41% low-disability/low burden); however, 30% of the sample
did not align in terms of the objective and subjective (18% high/low and 12% low/high).
Examining PIB and the likelihood of ACP using these categorical measures gives us
some insight about what facilitates ACP for different people.
This study is also unique because it explores both aspects of function and
examines their impact on both formal ACP and informal planning (discussions with
others). I posit that it is important to consider both aspects of planning because they are
inextricably linked to one another and to health behaviors. Furthermore, consideration of
both formal and informal planning for the end of life has the potential to provide insight
into barriers to or catalysts for translating informal discussions into formal planning
documents. Although there may be considerable overlap between these health behaviors,
it is not always the case that individuals progress from one to the other. While almost
70% of respondents in the study reported having an EOL discussion with others, only
41% named a proxy, and just 39% did both.
Functional limitations work together with burden; individuals who experience
high levels of both may wish to alleviate any potential and future impact that their
illnesses have on others, or assure themselves that their own burdens will not be
prolonged.15
Practitioners, social service professionals, family, and friends can better
15
As supplementary indicate (appendix 4F), compromised daily function (i.e.,
ADL/IADLs) is associated with enhanced perceptions of burden to both self and to
others. While both functional limitations and perceived burden are independently
associate with the likelihood of engaging in advance care planning (appendix 4D and 4E),
it is this concurrent construct that is most robustly associated with ACP. These findings
are consistent with the Common Sense Model (Leventhal et al., 2003). They suggest that
negative functional changes inform the conception of the self as a burden; this
176
meet the needs of individuals at the end of life and help patients formalize their advance
care plans by eliciting patient perceptions about illness impact. ACP is one of the surest
ways to assure that EOL preferences are honored.
Perceived illness burden and informal planning
Second, function and burden significantly affect the likelihood that individuals
will engage in informal planning, a very critical step in the ACP process. Current
conceptualizations of end-of-life planning envision ACP as a health behavior (Fried,
2009; Sudore and Fried, 2010) and process (Larson and Tobin, 2000) that begins with
thinking about preferences, followed by discussing these preferences with others, and,
finally, documenting these desires in an AD or DNR order in the medical record
(Pearlman, 2010). This study provides an important preliminary step for illuminating how
that process happens. Functional decline/disability emerged as important in the high/high
and high/low categories; members of both were more likely to have discussions.
However, in the fully adjusted block, it was only the combination of high disability and
burden that increased the odds of having a discussion. This signifies that functional
decline may trigger discussions, but it is the combination of being high in both function
and burden that crystallizes the behavior.
I looked at function and burden separately as predictors of having an EOL
discussion in preliminary analysis; it was function and not burden that increased the odds
of having had a conversation with others (see Appendix – tables 4D and 4E for full
comparisons). However, the magnitude of the effect is 60% greater in the combined
model (PIB). This suggests that the combination of disability and burden is most critical.
combination of burdensomeness is what ultimately translates into formal planning for the
end of life.
177
Self-assessed functional limitations combined with an understanding that the
consequences of functional decline impact the self and others are important steps in the
process of ACP. This conceptualization of ACP as a process and health behavior also
validates the use of the CSM (Leventhal et al, 2003) as paradigm through which to better
understand ACP and provide the foundation for interventions to increase rates of ACP, in
terms of both formal planning and discussion.
These findings also have clinical implications; a more thorough understanding of
the ACP process and the subjective/objective appraisals that inform each step of the
process enables clinicians to structure conversations with patients to more effectively
uncover the most relevant issues. Although functional decline may be a point of entry
into these difficult discussions, it is important for clinicians to understand that function is
not only an objective measure, but also something embedded in social relations – this is
the most meaningful and powerful aspect of function.
Perceived illness burden and formal advance care planning
Third, PIB significantly predicts the odds that one will engage in formal planning.
However, in comparison to the odds of completing a living will, being in the high/high
category was a far stronger correlate of having appointed a DPAHC. Individuals in both
of the high disability categories (high burden and low burden) were more likely to have
named a proxy. However, being in the high-disability/high-burden category was the most
robust indicator by far. Those in the high disability categories are more likely to
designate a health care proxy. Yet the effect is 1.7 times higher for those in the high-
disability/high-burden category than for those in the high-disability/low-burden category.
This coincides with Wu‟s assertion (1995) that function is a relative measure that falls on
178
a continuum with each person considering an array of factors, including the means by
which they can adapt to functional limitations. It also supports the range of literature
illustrating that the fear or perception of burdening others, as a result of functional
decline, increases patients‟ desire to engage in ACP (Rosenfeld et al., 2000; Singer, 1999;
Schroepfer, 2008).
As with discussions, those individuals who had low-disability/high-burden did not
differ from those with low-disability/low-burden in terms of having a living will or
naming a DPAHC; and individuals in the high-disability/low-burden category did not
differ from the low/low group in terms of completing a living will. It was however, truly
the combination that mattered, as indicated previously, in preliminary analysis for this
study, the effect of function on ACP was modeled and the effect size was modest in
comparison to the combined PIB variable (see Appendix Tables 4D and 4E). By using
the categorical measures, we get a precise picture of how the mind/body connection
works in terms of ACP. Individuals in the high-disability/high-burden category were
more likely to do all types of planning; individuals in the high-disability/low-burden
categories were also more likely to do proxy directives (relative to those in the low/low
category).
Individuals in the low-disability/high-burden did not differ from those in the
low/low category for all types of planning. It is the combination of being in the high-
disability/high-burden category that led to increased odds of advance care planning,
especially naming a proxy. Preliminary analysis showed (Appendix 4D, 4E) that
functional limitations were significant correlates in terms of having discussions, but not a
living will; burden was significant in terms of having a living will, but not discussions. It
179
is possible that some respondents view naming a proxy as being burdensome to the
designated individual. Therefore, both functional limitations and burden need to be
present in order for a proxy to be named.16
More information is needed to gain a more
nuanced, patient-centered perspective on the full effects of the various PIB categories;
qualitative research would be very helpful in this domain.
Finally, perceived illness burden emerged as a critical factor in predicting the
odds that a participant would have both a conversation about EOL plans and name a
health care proxy. Respondents in the high-disability/high-burden category were almost
five times more likely to have both had an EOL discussion and appointed a DPAHC after
controlling for race/ethnicity, other demographic, family, SES, and health variables. This
is important because, for the proxy directive to be meaningful, the preferences and wishes
of the patient must be communicated to those in the position to carry them out if they
become unable to do so themselves. Unfortunately, problems with implementation are
common even when formalized end-of-life plans exist. Individuals may not have had
discussions with family and health care providers regarding the contents of these
documents. The advance directive may also be unavailable to those who need to access it
when required; it may be locked away in a safe deposit box or lawyer‟s office file. Cloud
(2000) found that of those individuals who had been named health care proxy, only 70%
of designees knew they had been selected.
Race/ethnicity and sociodemographic correlates: level of perceived illness burden and
ACP
16
Preliminary and current analyses show that there are different effects for the different
categories (including many n.s. effects) suggesting that the main effect is not lost and it is
not only function (or high function so severe that is exerting an overpowering effect).
180
Fourth, this analysis provides some insight into the strong racial disparities in
advance care planning. My findings are in agreement with the existing literature on EOL
planning (Teno et al., 2007; Later and King, 2007; Carr, 2011; Waters, 2000; Degenholtz,
2002), race/ethnicity significantly affected the likelihood of formal end-of-life health
planning. Relative to non-Hispanic white patients, non-Hispanic black and Hispanic
patients were much less likely to have engaged in formal EOL planning. While the way
in which race and ethnicity affect EOL planning has been thoroughly examined, the
manner by which self-perception is affected by race/ethnicity warrants further analysis.
Self-perception, in concert with race/ethnicity, may be operating as a significant
motivator to older patients when considering whether or not to formalize EOL plans; it
should be discussed with patients and older adults. A separate analysis of the NJEOL data
(see Bodnar-Deren chapter 3 Doctoral Dissertation for full explanation) fully explored
the construct of perceived illness burden and illustrated a strong race effect - multinomial
regression analysis revealed that Hispanic respondents were almost 4 times more likely
than non-Hispanic respondents to be in the high-disability/low-burden category (relative
to the high/high category), the respondents in this category are the most likely to plan.
Another sociodemographic variable of interest is the role that family relations
played in the likelihood of engaging in ACP, specifically EOL discussions. Only one of
the family relationship variables was significantly correlated with the odds that a
participant had an EOL discussion; the more critical a respondent rated their spouse or
partner, the more likely it was that they had had EOL discussions. One possible
explanation may be that individuals in high quality marriages trust their spouse to make
their health care decisions (Moorman, 2011). If an individual feels that there is less
181
chance that their partner may be supportive or in agreement with their decisions, it is
possible that they will be more deliberate in assuring that their preferences are
communicated; or it is possible that the EOL discussions themselves were eliciting
criticism. It is also possible that marital/partner relationship quality is positive, even if
one feels their partner is critical (Skolnick, 2011); and finally, it could be that a critical
spouse is more likely to pressure their husband/wife to have EOL discussions, rather than
being passive about the end of life. However, this warrants further investigation, as the
level of spousal criticism was not significant in those models which examined formal
planning. Qualitative research would be very helpful in ascertaining how family quality
affects the presence and content of ACP.
The additions of controls for demographic and family status variables did not
significantly change the effect that PIB category membership had on any of the ACP
behaviors. However, the addition of socioeconomic variables did. For discussions,
controlling for annual income and level of educational attainment increased the odds that
those in the high-disability/high-burden category had engaged in EOL discussions by
13%. Introduction of SES to the model predicting the odds of having a living will
increased those odds by 28%. Controlling for SES drastically altered the effect on ACP
of perceived illness burden. The effect of being in the high/high category on having
named a health care proxy increased by 74% and combined directives by 80%. The
introduction of SES as a control also increased the likelihood that those in the high-
disability/low-burden category had done a combined directive when compared to those in
the low/low category. The model fit in all of the models was also increased. This is in
182
agreement with much of the literature on ACP, which consistently shows a strong
positive relationship between SES and all forms of end-of-life planning (Carr, 2011).
Unlike much of the existing literature on EOL planning, gender, marital status,
and the presence of children did not affect participants‟ likelihood to have had an EOL
discussion or formal advance directives. This result confirms the findings of Carr and
Khodyakov (2007) that neither marital status nor the presence of children was
significantly associated with EOL planning. This suggests that self-perception; more so
than demographics and even objective health indicators, operates as a significant
motivator to older patients when considering whether or not to formalize ACP and should
be discussed with patients and older adults. Individuals with both high disability and high
burden may want to alleviate potentially harmful impacts of illness on their significant
others.
Limitations and Future Directions
This examination is a first step and considerable additional analysis is warranted.
To fully ascertain the total effect that function and perceived burden have on advance
care planning, we need to further examine how they factor into the content and
particulars of any planning that has occurred, as well as any reasons why planning had
not occurred. Other factors affecting perceptions of burden could be religious group
membership and beliefs about who controls death or perceived needs for in-home or
institutional long-term care.
A number of limitations in this analysis should also be acknowledged. The sample
size, while larger than many of the previous examinations of PB, is still quite small
(n=305). Missing data were handled through mean imputation which biases the odds ratio
183
estimates towards the null value of one. However, since this yielded more conservative
estimates, it acts to further substantiate the present findings. Respondents had elevated
educational levels because the sample was drawn from a community that contains a
major research university. Consequently, socioeconomic status may not be generalizable
to the broader population of older, chronically ill adults. However, this sample is well-
represented in terms of race/ethnicity. Furthermore, despite their chronic illness
diagnoses, respondents may also be positively selected on health status, given that they
were able to participate in the study. The data from this study are cross-sectional and thus
cannot be used to determine causal ordering.
Regardless of these limitations, this analysis is an important start to the
examination of how function and burden may factor into end-of-life planning. Continued
analysis would be beneficial to the recipients of end-of-life care and their family, friends,
and other stakeholders. Further study should include longitudinal study to see how
changes over time, in terms of how burden and function affect the relationships between
PIB and ACP. Longitudinal data would also be useful to see if changes in function
affected changes in individuals existing advance care plans. Further work should also
determine and examine what criteria individuals use to gauge burden and how this is
shaped by culture, past history or caregiving and relationships. Qualitative research
would be especially useful in this domain.
Conclusions
The findings of this study suggest that health care practitioners, social service
professionals, family members, and friends should discuss the reality of functional
decline and perceived burden. A pro-active approach to end-of-life planning should be
184
part of the strategies used to alleviate some of the distress associated with caregiving and
the receipt of care. The growing literature on burden indicates that such intervention is
beneficial to both caregiver and care recipient (Lawrence et al., 1998); the opportunities
presented by discussing function and PB, such as the possibility of increasing advance
care planning, benefit all involved in EOL care.
Physicians, social workers, and other practitioners who work with patients at the
end of life, should root their discussions in terms of function AND values because (as per
the CSM) it is this mind – body connection that is most meaningful to patients, in terms
of illness representations, which are the catalyst to health behaviors such as advance care
planning. This approach is also patient-centered because the meaning, especially in terms
of burden and intrusiveness to self and others, is not fixed to a particular threshold. It is
relative. Each person will have different levels of disability to which they can adapt,
based on their capacity to affect meaningful activities and notions of the self. Discussing
both functional limitations (disability) and the meaning that these limitations have on
each person‟s individual reality (burden), will better lead to authentic and personally
meaningful EOL discussions between practitioner and patient, family members and
patient, and ultimately authentic ACPs.
The findings from this study empirically reinforce the recommendations put forth
by Saraiya et al. (2008) who assert that informed EOL planning will be ineffective in the
absence of discussion between patients, families, and practitioners. Among the goals of
palliative care is to allow patients to experience quality end-of-life care, in keeping with
his or her specified and meaningful goals. Singer (2001) identified a number of domains
185
essential for quality end-of-life care, including achieving a sense of control, relieving
burden, and strengthening relations with family and friends.
Discussing notions of disability and burden with patients addresses a patient-
centered perspective and allows the patient to exercise control through the formulation of
meaningful ACP. From a clinical perspective, EOL planning and decision making should
be a shared process. Therefore, it requires awareness and an open recognition of the
patient‟s perspective by the physician. Patients‟ appraisals of perceived burden appear to
increase the likelihood of formal planning; discussion of this perception may be the
gateway that clinicians and other stakeholders can use in approaching the benefits that
advance care planning presents to patients and their families. Perceived illness burden is
modifiable; discussions and planning can lead to a reduction in feeling of
burdensomeness. For those patients who may be disabled, but not necessarily feeling like
a burden, the discussions around the topic of perceived illness burden should still be
viewed as fruitful because they can open the door to conversations about other patient
perspectives that have meaning for the individual and possibly patient care in general.
186
References
Anderson, Fraser, Downing, G. M. and Hill, J. 1996. Palliative Performance Scale (PPS):
A new tool. Journal of Palliative Care. 12:5-11.
Benyamini, Y. and Idler, E. L. 1999. Community Studies Reporting Association between
Self-Rated Health and Mortality. Additional Studies, 1995 to 1998. Research on
Aging. 21(3): 392-401.
Bergner, Marilyn, Bobbitt, R. A., Carter, W. B. and Gilson, B. S. 1981. The Sickness
Impact Profile: development and final revision of a health status measure.
Medical Care. 19:787-805.
Caldwell, John C. 2001. Population health in transition. Bulletin of the World Health
Organization. 79(2):159-160.
Carr, Deborah. 2011. Racial differences in end-of-life planning: Why don‟t blacks and
Latinos prepare for the inevitable? Omega: The J Death & Dying. 63(1): 1-20.
Carr, Deborah. 2003. Illness Representations and End-of-Life Planning – R01-071403.
Unpublished manuscript.
Carr, Deborah and Khodyakov, D. 2007. Health Care Proxies: Whom Do Young Old
Adults Choose and Why? Journal of Health and Social Behavior. 48:180-194.
Carr, Deborah and Moorman, S. M. 2009. End-of-Life treatment preferences among the
young old: An Assessment of psychosocial influences. Sociological Forum.
24(4): 754-778.
Chochinov, Harvey M., Kristjanson, L. K., Hack, T. F., Hassard, T., McClement, S. and
Harlos, M. 2007. Burden To Others and The Terminally Ill. Journal of Pain and
Symptom Management 34: 463-71
Cloud, John. 2000. Dying on our own terms. Time. September 18, 60-67.
Cohen-Mansfield Jiska, Rabinovich B. A., Lipson S., Fein A., Gerber B. and Weisman S.
1992. The decision to execute a durable power of attorney for health care and
preferences regarding the utilization of life-sustaining treatments in nursing home
residents. Archives of Internal Medicine. 151:289-294.
Cooley, Charles, H. 1902. Human Nature and the Social Order. New York: Scribner's.
Cousineau, Natalie, McDowell, I., Hotz, S., and Hebert, P. 2003. Measuring Chronic
Patients‟ Feelings of Being a Burden to their Caregivers – Development and
Preliminary Validation of a Scale. Medical Care 41(1):110-118.
187
Covinsky, Kenneth E., Fuller, J. D., Johnston, C. B., Hamel, M. B., Teno, J. M. and
Phillips, R. 2000. Communication and decision making in seriously ill patients:
Findings of the SUPPORT project. Journal of the American Geriatrics Society.
48(5): 187-193.
Crimmins, Eileen. 2004. Trends in the Health of the Elderly. Annual Review of Public
Health. 25: 79-98.
Cutler, David M. and Lleras-Muney, A. 2010. Understanding differences in health
behaviors by education. Journal of Health Economics. 29(1): 1-28.
Degenholtz, Howard, Meisel, A. R. and Lave, J. 2002. Persistence of racial disparities in
advanced care plan documents among nursing home residents. Journal of the
American Geriatric Society. 50:378-381.
Dinger, Erica J. 2005. Death and Dying - AARP Massachusetts End of Life Survey
Research Report, August 2005. AARP Knowledge Management: Washington DC.
http://research.aarp.org.
Ditto, Peter H., Danks, J. H., Smucker, W. D., Bookwala, J., Coppola, K. M., and
Dresser, R. 2001. Advance Directives as acts of communication: A randomized
controlled trial. Archives of Internal Medicine. 161: 421-430.
Ditto, Peter H., Hawkins, N. A., and Pizarro, D. A. 2005. Imagining the end-of-life: On
the psychology of advance medical decision-making. Motivation and Emotion.
29: 475-496.
Elpern, Ellen H., Patterson P.A., Gloskey D. and Bone R. C. 1992. Patients' preferences
for intensive care. Critical Care Medicine. 20:43-7.
Emanuel Linda L., von Gunten C. F. and Ferris F. D. 1999. Plenary 3 Elements and
Models of End of-life Care. The Education for Physicians on End-of-life Care
(EPEC) Curriculum. The Robert Wood Johnson Foundation. Retrieved from
http://www.ama-assn.org/ethic/epec/download/plenary_3.pdf on 7/9/2010
Federal Interagency Forum on Aging-Related Statistics. 2010. Older Americans update
2008: Key indicators of well-being. Hyattsville, MD.
Ferraro, Kenneth F. and Kelley-Moore, J. A. 2001. Self-Rated Health and Mortality
Among Black and White Adults: Examining the Dynamic Evaluation Thesis.
Journal of Gerontology: SOCIAL SCIENCES. 56B (4): S195-S205.
Field, Marilyn J., and Cassel, C. K. (Eds.). 1997. Approaching death: Improving care at
the end of life. Washington, DC: National Academy Press.
Foster, Tom. 2003. Suicide Note Themes and Suicide Prevention. The International
188
Journal of Psychiatry in Medicine. 33(4): 323-331.
Flynn, Kathryn, E., Smith, M. A., and Vanness, D. 2006. A Typology of Preferences for
Participation in Healthcare Decision-Making. Social Science and Medicine. 63:
1158-1169.
Fried, Terri R., Bullock, K., Iannone, L. and O'Leary, J. R. 2009. Understanding Advance
Care Planning as a Process of Health Behavior Change. Journal of the American
Geriatric Society. 57:1547-1555.
Galambos, Coleen M. 1998. Preserving end-of-life autonomy: The Patient Self
Determination Act and the Uniform Health Care Decisions Act. Health and Social
Work. 23:275-281.
Garrido, Melissa M., Idler, E., Leventhal H., and Carr, D. in process. Advance Care
Planning: The Role of End-of-Life Values and Beliefs about Control over the
Length of Life Submitted for consideration to the Journal of the American
Geriatrics Society.
Gerety, Meghan B., Chiodo, L. K., Kanten, D. N., Tuley, M. R. and Cornell, J. E. 1993.
Medical treatment preferences of nursing home residents: relationship to function
and concordance with surrogate decision-makers. Journal of the American
Geriatric Society. 41: 953-60.
Hoffman, James, C., Wenger, N. S., Davis, R. B., Teno, J., Connors, A. F., Desbiens, N.
for the SUPPORT Investigators. 1997. Patient Preferences for Communication
with Physicians about End-of-life decisions. Study to Understand Prognoses and
Preferences for Outcomes and Risks of Treatment. Annals of Internal Medicine.
127:1-12.
Hopp, Faith P. and Duffy, S. A. 2000. Racial Variations in End-of-Life Care. Journal of
the American Geriatrics Society. 48(6) 658-63.
Idler, Ellen L. and Benyamini, Y. 1997. Self-Rated Health and Mortality: A Review of
Twenty-Seven Communities Studies. Journal of Health and Social Behavior. 38:
21-37.
Institute of Medicine (IOM). 2001. Health and behavior: The interplay of biological,
behavioral, and societal influences, Washington, DC: National Academy Press.
Institute of Medicine (IOM). 2001. Crossing the Quality Chasm: A New Health System
for the 21st Century. Washington, DC: National Academy Press.
Jackson, Jody, Rolnick, S. and Asche, S. 2009. Knowledge, attitudes and preferences
Regarding advance directives among patients of a managed care organization.
American Journal of Managed Care. 15(3): 177-186.
189
Kass-Bartelmes, Barbara and Hughes, R. 2003. Advanced care planning: Preferences for
care at the end of life. Research in action. Washington, DC: Agency for
Healthcare Research and Quality.
Koss, Leon. 2005. Reflections on Public Bioethics: A View from the Trenches. Kennedy
Institute of Ethics Journal. 15(3): 221-250.
Katon, W. J. 2003. Clinical health services relationships between major depression,
depressive symptoms and general medical illness. Behavioral Psychiatry. 54(3):
216-226.
Kaufman, Sharon. 2005. …And a Time To Die – How American Hospitals Shape the
End of Life. Scribner: New York.
Lachman, M. E. and Burack, O. R. 1993. Planning and Control Processes Across the Life
Span: An Overview. International Journal of Behavioral Development. 16(2):
131-143.
Lambert, Heather, C., McColl, M. A., Gilbert, J., Wong, J., Murray, G. and Shortt, S. E.
D. 2005. Factors Affecting Long-Term-Care Residents‟ Decision-Making
Processes as They Formulate Advance Directives. The Gerontologist. 45(5): 626-
633.
Larson, Dale, G. and Tobin, D. R. 2000. End-of-Life Conversations: Evolving Practice
and Theory. JAMA. 284(12): 1573-1578.
Later, Elizabeth B. and King, D. 2007. Advance Directives: Results of a Community
Education Symposium. Critical Care Nurse. 27(6): 31-35
Lawrence, Renee, H., Tennstedt, S. L., and Assmann, S. F. 1998. Quality of the
caregiver-care recipient relationship: Does it offset negative consequences of
caregiving for family caregivers? Psychology and Aging. 13(1): 150-158.
Leventhal, Howard, and Meyer, D. 1980. The common sense representation of illness
danger. Contributions to medical psychology. S. Rachman. New York: Pergamon
Press. II: 7-30.
Leventhal, Howard, Brisette, I., and Leventhal, E. A. 2003. The common sense models of
self-regulation of health and Illness. In L. D. Cameron & H. Leventhal, (Eds.),
The self regulation of health and illness behavior. London: Routledge Taylor &
Francis Group.
Leventhal, Howard, Weinman, J., Leventhal, E. A., and Phillips L. A. 2008. Health
Psychology: The Search for Pathways between Behavior and Health. Annual
Review of Psychology. 59:477-505.
190
Leventhal, Howard, Leventhal, E. A., Cameron, L., Bodnar-Deren, S., Breland, J., Hash-
Converse, J. and Phillips, L. A. 2011. Modeling Health and Illness Behavior: The
Approach of the Common Sense Model (CSM). In A. Baum (Ed.) Handbook of
Health Psychology, Second Edition. New York: Routledge.
Levi, Benjamin, Dellasega, C., Whitehead, M. and Green, M. J. 2010. What Influences
Individuals to Engage in Advance Care Planning? American Journal of Hospice
and Palliative Medicine. 27(5): 306-312.
MacKinnon, D. P., Krull, J. L., and Lockwood, C. M. 2000. Equivalence of the
Mediation, Confounding and Suppression Effect. Prevention Science. 1(4): 173-
181.
McPherson, Christine J., Keith G. Wilson, and Mary Ann Murray. 2007. “Feeling Like A
Burden: Exploring the Perspectives of Patients at the End-of-Life.” Social
Science and Medicine 64: 417-27.
Moorman, Sara M. 2009. Facing End-of-Life Together: Marital Relationship Quality and
End-of-Life Health Care Preferences. Dissertations submitted in partial
fulfillment of the Doctor of Philosophy (Sociology) at the University of
Wisconsin-Madison
Moorman, Sara. M. and Carr, D. 2008. Spouses‟ Effectiveness and End-of life Health
Care Surrogates: Accuracy, Uncertainty, and Errors of Overtreatment or
Undertreatment. The Gerontologist 48(6): 811-189.
Moorman, Sara, M., Carr, D., Kirchoff, K. T. and Hammes-Gundersen, B. J. 2011. An
Assessment of Social Diffusion in the Respecting Choices® Advance Care
Planning Program.
Moorman, Sara, M. 2011. The importance of feeling understood in marital conversations
about end-of-life health care. Journal of Social and Personal Relationships. 28(1):
100-116.
Mora, Pablo, A., DiBonaventura, M. D., Idler, E., Leventhal, E. A., and Leventhal, H.
2009. Psychological Factors Influencing Self-Assessments of Health: Toward an
Understanding of the Mechanisms Underlying How People Rate Their Own
Health. Annals of Behavioral Medicine. 36(3): 292-303.
Morrison, Rolfe, S. and Meier, D. E. 2004. High Rates of Advance Care Planning in New
York City‟s Elderly Population. Archives on Internal Medicine. 164: 2421-2427.
Moss-Morris, Rona, Weinman, J., Petrie, K., Horne, R., Cameron, L. and Buick, D. 2002.
The Revised Illness Perception Questionnaire (IPQ-R). Psychology & Health.
17(1): 1-16.
191
Mossey, Jana M. and Shapiro, E. 1982. Self-rated health: a predictor of mortality among
The elderly. American Journal of Public Health. 72(8): 800-808.
Murphy Donald J., Burrows D., Santilli S., Kemp A. W., Tenner S. and Kreling B. 1994.
The influence of the probability of survival on patients' preferences regarding
cardiopulmonary resuscitation. New England Journal of Medicine. 330:545-549.
New Jersey Bioethics Commission. 1991. Advance Directives for Health Care: Planning
Ahead for Important Health Care Decisions. Trenton, NJ: State of New Jersey
Commission of Legal and Ethical Problems in the Delivery of Health Care.
Omran, Abdel R. 1971. The epidemiologic transition: a theory of the epidemiology of
Population change. Milbank Memorial Fund Quarterly. 29:509-538.
Pearlman, Robert A., Cole, W. G., Patrick, D. L., Starks H. E. and Cain, K. C. 1995.
Advance Care Planning: Eliciting Patient Preferences for Life-Sustaining
Treatment. Patient Education and Counseling. 26: 353-361.
Pearlman, Robert A. 2010. Bioethics at the end of life, Advance Care Planning.
Retrieved from on August 10, 2010 from:
http://depts.washington.edu/bioethx/topics/adcare.html.
Pearlman, Robert A. and Starks, H. 2004. Why Do People Seek Physician-Assisted
Death? In Quill, T. and Battin, M.P. (Eds.) Physician-Assisted Dying: The Case
for Palliative Care and Patient Choice. Baltimore, MD: The John Hopkins
University Press. pp. 91-101.
Rosenfeld, Kenneth E., Wenger, N. S. and Kagawa-Singer, M. 2000. End-of-Life
Decision Making: A Qualitative Study of Elderly Individuals. Journal of General
Internal Medicine. 15(9): 620-626.
Schroepfer, Tracy A. 2008. “Social Relationships and Their Role in the Consideration to
Hasten Death.” The Gerontologist 48: 612-21.
Silveira, Maria J., Kim, S. Y. and Langa, K. M. 2010. Advance directives and outcomes
of surrogate decision making before death. The New England Journal of
Medicine. 362(13): 1211-1218.
Saraiya, Biren, Bodnar-Deren, S., Leventhal, H. and Levental, E. 2008. End of Life
Planning: Is it Relative for Patients and Oncologists. Decisions in Choosing
Cancer Therapy And Palliative Care. The Journal Cancer. 4:293-281.
Schoenfeld, D. E., Malmrose, L. C., Blazer, D., Gold, D. T. and Seeman, T. E. 1994.
192
Self-Rated Health and Mortality in the High-Functioning Elderly – a Closer Look
at Healthy Individuals: MacArthur Field Study of Successful Aging. The Journal
of Gerontology. 49(3): M109-M115.
Schneiderman Lawrence, Pearlman R. A., Kaplan R. M., Anderson J. P. and Rosenberg
E. M. 1992. Relationship of general advance directive instructions to specific life-
sustaining treatment preferences in patients with serious illness. Archives of
Internal Medicine. 152:2114-22.
Schwartz, Charles, E., Merriman, M. P., Reed, G. W. and Hammes, B. J. 2004.
Measuring patient treatment preferences in end-of-life care research: applications
for advance care planning interventions and response shift research. Journal of
Palliative Medicine. 7(2):233-45.
Shadbolt, Bruce, Barresi, J. and Craft, P. 2002. Self-Rated Health as a Predictor of
Survival Among Patients with Advanced Cancer. Journal of Clinical Oncology.
20(10): 2514-519.
Singer, Peter A., Martin, D. K. and Kelner, M. 1999. Quality of end-of-life care: patients‟
perspectives. JAMA. 281(2): 163-168.
Singer, Peter A. 2001. Recent Advances, Medical Ethics. BMJ. 321:282
Skolnick, Arlene. 2011. Grounds for Marriage: How Relationships Succeed or Fail. In
Skolnick and Skolnick (Eds.) Family in Transition. New York: Allyn & Bacon.
Smucker, William D., Ditto, P. H., Moore, K. A., Druley, J. A., Danks, J. H. and
Townsend, A. 1993. Elderly outpatients respond favorably to a physician-initiated
advance directive discussion. Journal of the American Board of Family
Practitioners. 6(5): 473-482.
Steinhauser, Karen E., Christakis, N. A., Clipp, E. C., McNeilly, M., McIntyre, L. and
Tulsky, J. A. 2000. Factors considered important at the end of life by patients,
family, physicians, and other care providers. JAMA. 284: 2476-2482.
Sudore, R. L. and Fried, T. R. 2010. Redefining the “Planning” in Advance Care
Planning: Preparing for End-of-Life Decision Making. Annals of Internal
Medicine. 153(4): 256-261
SUPPORT Principal Investigators. 1995. A Controlled Trial to Improve Care for
Seriously Ill Hospitalized Patients. JAMA. 274:1591-1598.
Sulmasy, D. P., Hughes, M., Thompson, R. E., Astrow, A. B., Terry, P. B., Kub, J. and
Nolan, T. 2007. How Would Terminally Ill Patients Have Others Make Decisions
for Them in the Event of Decisional Incapacity? A Longitudinal Study. Journal of
the American Geriatrics Society. 55: 1981-1988.
193
Temel, Jennifer S., Greer, J. A., Alona Muzikansky, M. A., Gallagher, E. R., Sonal
Admane, M. B., Jackson, V. A., Dahlin, C. M., Blinderman, C. D., Jacobsen, J.,
Pirl, W. F., Billings, J. A. and Lynch, T. J. 2010. Early Palliative Care for Patients
with Metastatic Non-Small Cell Lung Cancer. New England Journal of Medicine.
363:733-742.
Teno, Joan M., Gruneir, A., Schwartz, Z., Nanda, A. and Wetle, T. 2007. Association
Between advance directives and quality of end-of-life care: A national study.
Journal of the American Geriatrics Society 55(2): 189-194.
Tierney, William M., Dexter, P. R., Gramelspacher, G. P., Perkins, A. J., Zhou, X. H.,
and Wolinsky, F. D. 2001. The Effect of Discussions about Advance Directives
on Patients‟ Satisfaction with Primary Care. Journal of General Internal
Medicine. 16(1): 32-40.
Tzelgov, Joseph and Henik, A. 1991. Suppression situations in psychological research:
Definitions, implications, and applications. Psychological Bulletin. 109(3): 524-
536.
U.S. Census. 2011. Aging Boomers Will Increase Dependency Ratio, Census Bureau
Project – Older American Population to Become More Diverse. Retrieved on
April 16, 2011 from: http://www.census.gov/prod/1/pop/p25-1130/p251130a.pdf
U.S. Department of Health and Human Services. 2008. Advance directives and advance
care planning: Report to Congress [online report]. Retrieved on March 28, 2011
from http://aspe.hhs.gov/daltcp/reports/2008/ADCongRpt.pdf.
Ussher, Jane, Kirsten, L., Butow, P. and Sandoval, M. 2006. “What Do Cancer Support
Groups Provide Which Other Supportive Relationships Do Not? The Experience
of Peer Support Groups for People with Cancer.” Social Science & Medicine 62:
2565-76.
Ware John E., Kosinski, M. Keller, S. D. 1996. A 12-item short-form health survey.
Construction of scales and preliminary tests of reliability and validity. Medical
Care. 34: 220-233.
Waters, Catherine M. 2001. Understanding and Supporting African Americans‟
Perspectives of End-of-Life Care Planning and Decision Making. Qualitative
Health Research. 11: 385-400.
Wenger, Neil S., Pearson, M. L., Desmond, K. A., Harrison, E. R., Rubenstein, L. V.,
Rogers, W. H. and Kahn, K. L. 1995. Epidemiology of do-not-resuscitate orders.
Disparity by age, diagnosis, gender, race and functional impairment. Archives of
Internal Medicine. 155:2056-2060.
194
Weinman, John, Petrie, K. J., Moss-Morris, R. and Horne, R. 1996. The Illness
perceptions questionnaire: A new method for assessing the cognitive
representation of illness. Psychology and Health. 11(3): 431-445.
Williams, David R. and Mohammed. S. A. 2009. Discrimination and Racial Disparities
in Health: Evidence and Needed Research. Journal of Behavioral Medicine,
32(1): 20–47.
Williams, David R., Mohammed, S. A. , Leavell, J. and C. Collins 2010. Race,
Socioeconomic Status, and Health: Complexities, Ongoing Challenges, and
Research Opportunities. Annals of the New York Academy of Sciences, 1186: 69–
101
Wilson, Keith G., Scott, J. F., and Graham, I. D., 2000. Attitudes of terminally ill patients
toward euthanasia and physician-assisted suicide. Archives of Internal Medicine.
160:2454-2460.
Wilson, Keith G., Curran, D. and Christine J. McPherson. 2005. “A Burden to Others: A
Common Source of Distress for the Terminally Ill.” Cognitive Behaviour Therapy
34: 115-23.
Winter, Laraine, A., Lawton, M. P., and Langston, C. A. 2007. Symptoms, affects and
self-rated health. Evidence for a subjective trajectory of health. Journal of Aging
and Health. 19(3): 453-469.
Wolinsky, Frederic. D, and Fitzgerald, J. F. 1994. Subsequent Hip Fracture Among Older
Adults. American Journal of Public Health. 84(8): 1316-1318.
Wu, Albert W., Damiano, A. M., Lynn, J., Alzola, C., Teno, J., Landefeld, C. S.,
Desbiens, N., Tsevat, J., Mayer-Oaites, A., Harell, F. E. and Knaus, W. A. 1995.
Predicting Future Functional Status for Seriously Ill Hospitalized Adults - The
SUPPORT Prognostic Model. Annals of Internal Medicine. 122:342-350.
Ziven Bambauer, Kara. and Gillick, M. R. 2007. The Effect of Underlying Health Status
on Patient or Surrogate Preferences for End-of-Life Care: A Pilot Study.
American Journal of Hospice and Palliative Medicine. 24(3) 185-190.
Zweibel, Nancy R. and Cassle, C. K. 1989. Treatment choices at the end of life: a
comparison of decisions by older patients and their physician-selected proxies.
Gerontologist. 29:615-621.
195
Figure 4.1. Perceived Illness Burden
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
Appendix – Chapter 4
Table 4A - Descriptive Statistics for Variables Used in Perceived Illness Burden Category Construction
Mean Percent
Standard
Deviation Valid N
Perceived burden (PB) scale (range 1-5) 2.99 1.023
PB - missing data (dichotomous)
(1=respondent did not answer PPB questions) 10.4
Functional limitation Scale (ADL/IADL) (range 1-5) 3.07 9.934
Source: NJEOL Study (2006-2008). N=293. Note: Means are presented for continuous variables; proportions are presented for
categorical variables.
212
Appendix - Table 4B - Functional Limitation Scale
Factor Loadings
How much does your current health limit you in
Factor
Loading
Bathing or dressing yourself
0.743
Bending, kneeling, or stooping
0.765
Lifting or carrying groceries
0.783
Climbing several flights of stairs
0.831
Walking more than a mile
0.866
Walking several blocks
0.89
Walking one block
0.815
Vigorous Exercise (lifting heavy objects)
0.735
Moderate activity (bowling, vacuuming)
0.814
Cronbach's alpha for scale
0.927
(Only one component extracted)
Source: NJEOL Study (2006-2008). N=293
213
Appendix - Table 4C - Perceived Burden Scale Factor Loadings
Illness perception question
Factor
Loading
My illness has major consequences for my daily life
0.799
My illness causes difficulties for those who are close to me
0.884
The treatment for my illness has major consequences for
my daily life
0.829
The treatment for my illness causes difficulties for those
who are close to me
0.85
Cronbach's alpha for scale
0.861
(Only one component extracted)
Source: NJEOL Study (2006-2008). N=293
214
215
216
217
218
219
220
221
222
CHAPTER 5 – DISCUSSION AND CONCLUSION
In this project, I have set out to examine advance care planning as a health behavior
based on the patient perspectives (i.e., illness representations that motivate such behaviors). This
is an approach that has been advocated by a number of aging and end-of-life scholars who have
called for researchers, policymakers, and practitioners to rethink end-of-life planning and to
examine it as a health behavior rooted in the patients‟ perspective (Pearlman et al., 1995; Fried et
al., 2009; Sudore and Fried, 2010). These recommendations have also been put forth by the
Institute of Medicine (IOM) (2001) which has urged clinicians and researchers to look at health
and behavior “biopsychosocially,” from a perspective that is patient-centered - in which there is
an explicit understanding of how patients‟ beliefs and perceptions (illness representations) affect
their health. The Common Sense Model of Self of Illness Regulation (CSM) (Leventhal et al.,
2003; 2011) is a model of understanding health and health behaviors that is both patient-centered
and biopsychosocial.
Structure of the Dissertation and Key Findings
The CSM is a widely used health behavior model based on the proposition that an
individual‟s likelihood of preparing for end-of-life care may be motivated by their illness
representations or beliefs about the identity, timeline, controllability, causes and consequences of
their illness and treatment. If, how, and to what end older adults prepare for the end of life will
be linked to their beliefs about how their current health condition will progress and affect the
daily well-being of themselves and those around them (Leventhal, 2003; 2011).
Simply, an individual‟s health preferences and behaviors are affected not only by their
actual condition, but also by perceptions of their health condition (Carr and Moorman, 2009;
Leventhal et al., 2003; 2011). A core proposition of the CSM is that awareness of symptoms,
223
functional decline, and medical diagnoses are critical factors for the activation of illness
representations and changing views of the self that create the motivation for engaging in health
behaviors (Leventhal et al., 2003; 2008; 2010). In this project, I explore the “mind-body”
relationship between physical function and patient‟s perceptions of feeling burdensome to the
self and others, and then explore how this construct affects the likelihood that one will engage in
advance care planning. I label this construct perceived illness burden (PIB).
In the second chapter of this dissertation, using patient narratives generated during focus
group discussion, I explored which patients‟ illness representations motivated them to plan for
the end of life. Conversely, if no plans had been made, I examined what perspective(s) prevented
or discouraged them from engaging in the process of advance care planning. Three major
themes (illness representations) emerged from the focus groups: (1) Control and self-direction
(autonomy) – patients commonly discussed advance care planning in terms of being able to
control/direct their care at the end of life; (2) Consequences, specifically perceived burden,
emerged as a major factor in advance care planning, working as both a catalyst to planning or a
reason to avoid planning; (3) Patients discussed their past experiences with the deaths of others
with whom they were close as a motivator for end-of-life planning. All three of the themes
articulated by respondents illustrated that illness representations served as motivators for ACP
behaviors.
Based on the themes that were present among focus group participants, I decided to
further investigate the illness representations identified by patients as being important by
conducting a quantitative analysis of the NJEOL study sample (N=293). The domain of
consequences was particularly salient to patients, working as both a catalyst for and barrier to
224
ACP. I felt that burden was an emergent theme that should be further investigated because it was
exceptionally complex in terms of how patients operationalized it.
Burden was not rooted solely in terms of the difficulties illness and associated treatments
cause to others, but also how health limitations had consequences for an individual‟s sense of
self as independent and autonomous. Over seventy-five percent of focus group respondents
discussed burden from a “biopsychosocial” perspective, as interplay between biological,
behavioral, and societal influences. First, they framed their discussions in terms of illness and
functional decline (biological). This was followed by a statement of how that decline provides
difficulties for their sense of self and independence (psychological) which then leads to
dependence on others (social).
This interplay between function (body) and burden to self and others (mind) corresponds
with past research on end-of-life decision making; it has been found that patients report intricate
and subtle interactions - that could not be separated or compartmentalized - between physical and
functional decline and existential concerns such as loss of sense of self and burden to others
(Pearlman and Starks, 2004). Moorman‟s (2009) study on healthy adults‟ concerns about
burdening others at the end of life, further illustrates this with her assertion that “feeling like a
burden may have more to do with losing one‟s own functional independence than with infringing
upon the independence of one‟s caregiver,” (Moorman, 2009, 147).
Therefore, in Chapter 3, I explore the “mind-body” relationship between disability and
burden based on patient‟s narrative and perspectives and a growing body of literature that
suggests that beliefs about burden at the end of life are complex. I did this by looking at the
various categorical combinations of perceived illness burden (high-disability/high-burden; high-
disability/low-burden; low-disability/high-burden; low-disability/low-burden). By creating
225
discrete PIB categories, I was able to see whether and for whom perceptions are an accurate
reflection of reality (patients in the high/high and low/low categories); and if and for whom PIB
is more so a psychological concept (those in the high/low and low/high categories).
Two-thirds of participants in this study appeared to make relatively accurate appraisals: if
their health was bad, they felt like a burden. Conversely, if their health was relatively good, they
did not feel like a burden. These findings are consistent with the CSM which suggests that
negative functional changes inform the conception of the self as a burden; this combination is
what ultimately translates into perceived illness burden. The findings from this research also
revealed that nearly one-third of the respondents were “off-diagonal” cases, in which the
objective health indicators (function) did not correspond closely with the subjective measures
(burden). Taken together, this suggests that perceived illness burden is not all “in one‟s head”
(i.e., psychological); it is also in one‟s body, but appraisals may reflect both physical and
psychological factors.
Race/ethnicity, age, number of children, income, number of health conditions, and level
of depressive symptoms were all significant correlates of being in an “off-diagonal” PIB
categories. Compared to non-Hispanic whites, Hispanic respondents were much more likely to
be in the high-disability/low-burden category as opposed to the low/low category. This
corresponded with my focus group data analysis, in which Hispanic respondents spoke about
burden; however, they conceptualized it very differently. The majority of Hispanic participants
cited their desire not to burden their children as the primary reason that they did not engage in
advance care planning behaviors.
This finding is consistent with a number of studies of ethnicity and end-of-life planning
(Morrison et al., 1998; Gutheil and Heyman, 2010; Carr, 2011). This desire to shield others from
226
having EOL discussions coincides with Glaser and Strauss‟ (1964, 1965) work on EOL
communication patterns, wherein they described two types of contexts in which EOL
communication can occur. The “closed context” exists when one party tries to hide information
from others (either the patient hiding the fact s/he is dying from loved ones, or family members
and others trying to shield the patient from information regarding the end of life). The “open
awareness context” occurs when patient‟s terminal status is known and shared by all involved
(Levitz and Twerski, 2005).
Level of depressive symptoms also predicted membership in the “off-diagonal”
categories - individuals who had more depressive symptoms were also more likely to be in the
low-disability/high-burden group compared with the high/high group. This is evidence that for
some, disability and burden do not always go hand-in-hand; this is especially true for those with
depressive symptoms. If a person is depressed, he or she may feel burdensome even if they have
relatively few functional limitations.
Interestingly, compared to people with four or more co-occurring conditions, individuals
with 2-3 health conditions are more likely to be in the low-disability/high-burden category
relative to the high/high category. This too makes sense. Hypertension was the most common co-
occurring illness of those in the low/high category. However, I can only speculate as to the
effect. Hypertension is often referred to as a “silent” illness (Zusman, 2011) because the
symptoms are not readily accessible by patients; diabetes has also been considered quiet,
especially non-insulin dependent diabetes (Aloozer, 2000; Lowe et al., 2009). It is possible that
respondents did not feel a day-to-day impact on their daily activities, but the notion of co-
occurring conditions alone may be burdensome to patients. Additionally, both diabetes and
hypertension require changes in behavior, diet, exercising, medication management, and for
227
some patients - monitoring. All of this may be perceived as being burdensome, even without
functional limitations, especially if their symptoms are well controlled.
In Chapter 4, after a close examination of who comprised perceived illness burden
categories, I considered how membership in the perceived illness burden categories (high-
disability/high-burden, high-disability/low-burden, low-disability/high-burden, low-
disability/low-burden) predicted the likelihood of various advance care planning behaviors (EOL
discussions, living will, DPAHC, and combined directives in which patients had both had a
discussion and named a proxy). The study also revealed that functional decline and perceived
burden work in tandem to affect the likelihood of future health planning. Multinomial logistic
regression, controlling for health and sociodemographic variables, revealed that respondents in
the high-disability/high-burden category were significantly more likely to engage in all types of
ACP. Individuals in the high-disability/low-burden category were significantly more likely than
those in the low/low category to have appointed a health care proxy.
According to the Common Sense Model of Illness Representations (Leventhal, 2003;
2011), function and burden work together to inform ACP, but only when respondents were high
in both function and burden was there a robust increase in the odds of planning (compared to the
reference group – low/low) across all outcomes. Respondents in the high/high category were
significantly more likely to do all types of ACP; however, the effect was strongest for appointing
a proxy for health care and the combination of discussion/proxy. In other words, the effect was
strongest for those who experienced compromised function AND perceived compromised
function as impairments to daily life. Those in the high-disability/low-burden category
(individuals who were less depressed, lower-middle incomes) only differed from the low/low
228
respondents for the two proxy directives, suggesting that functional decline/disability is an
important factor for proxy planning.
This study has focused on two elements of patient self-appraisal: self-assessed function
(ADLs/IADLs) and respondents‟ perceptions of how their functional limitations affect both the
self and others (PIB). While many studies have looked at both aspects of function and burden,
they have been studied in isolation. For example, several studies (e.g., SUPPORT 1995;
Davison, 2006, 2009; Ziven, et al., 2007) have examined how functional limitations (objectively
measured by physicians in terms of ADLs/IADLs) affect the likelihood and content of ACP; the
results have been mixed. Similarly, other studies have found positive correlations between
patients‟ self-perception of burden and the presence and content of end-of-life plans (Wilson, et
al., 2005, 2007; McPherson, et al., 2006; Singer, et al., 1999). Further research should be
conducted regarding the combinations of physical and psychological factors that stimulate health
behaviors and outcomes.
Research on end-of-life planning has focused primarily on objective health measures
used by physicians or patients in EOL planning or on patients‟ demographic characteristics; there
has been considerably less focus on subjective measures such as patient self-appraisal and
perception. In my review of the literature, no study to date has focused simultaneously on
objective or concrete health measures such as functional limitation and patient perceptions (e.g.,
perceived burden).
Limitations and Future Directions
A number of limitations in this analysis should be acknowledged. In chapter one, the
study population consisted of 46 participants in eight focus groups. The generalizability of the
findings to other populations is limited because of the small sample size, non-probability
229
sampling technique, and the use of a single medical group to provide patients, even though one
served mainly an ethnically diverse urban population, while the other served a predominantly
homogenous suburban patient population. Two additional limitations that should be considered,
common in interview-based research, is social desirability bias and observer dependency. Social
desirability bias occurs when respondents respond in a way that is believed to be socially
acceptable and desirable (Fisher, 1993). Similarly, observer dependency, wherein the results are
influenced by the focus group facilitator, may raise questions of validity, as can the ability of one
subject to influence other subjects. The appearance of agreement and conformity of opinion
within a focus group may be a result of group dynamics and the desire to conform, as opposed to
an aggregation of the views held by individual participants (Crabtree et al., 1993). However,
although not generalizable due to methodology and small sample size, the NJEOL focus group
sample was diverse in terms of gender, race, ethnicity, and illness groups; this is a considerable
strength of the NJEOL data.
A number of limitations were present in Chapters 3 and 4 as well. The sample size, while
larger than many of the previous examinations of burden, is still quite modest (n=293). Missing
data were handled through mean imputation which biases the odds ratio estimates towards the
null value of one. However, since this yielded more conservative estimates, it only acts to further
substantiate the present findings. Because the sample was drawn from a community that contains
a major research university, respondents had elevated educational levels and consequent
socioeconomic status that may not be generalizable to the broader population of older,
chronically ill adults. However, this sample is well-represented in terms of race/ethnicity.
Furthermore, despite their chronic illness diagnoses, respondents may also be positively selected
230
on health status, given that they were able to participate in the study. The data from this study are
cross-sectional and thus cannot be used to determine causal ordering.
Directions for Future Research and Policy Implications
Regardless of these limitations, this analysis is an important start to the examination of
how function and burden may factor into end-of-life planning. Continued analysis would be
beneficial to the recipients of end-of-life care and their family, friends, and other stakeholders.
Further study should include collection of longitudinal data to see how changes over time, in
terms of how burden and function, affect the relationships between PIB and ACP. Longitudinal
data would also be useful to evaluate if changes in function affected changes in individuals
existing advance care plans. Further work should also determine and examine what criteria
individuals use to gauge burden and how this is shaped by culture, past history or caregiving and
relationships.
Additional research should look at how perceptions of illness burden change over time,
especially as one‟s health changes. Other correlates to PIB should be examined as well, such as
religiosity correlates, attitudes towards death and dying, and past caregiving experiences to
identify a few. Further research should examine the relationship between various representation
categories and their links to specific health behaviors other than ACP. It is possible that a similar
construct, measuring both the subjective and the objective, could be helpful in analyses of other
health behaviors. Since illness representations are comprised of both the subjective and
objective, it would be useful to examine how the various combination categories affect different
types of behavior. For example, do concurrent appraisals work as a catalyst for behaviors such as
advance care planning or treatment adherence? Similarly, does membership in various PIB
categories correlate with any maladaptive behaviors?
231
Functional limitations work together with burden; individuals who experience high levels
of both may wish to alleviate any potential and future impact that their illnesses have on others
or assure themselves that their own burdens will not be prolonged. Practitioners, social service
professionals, family, and friends can better meet the needs of individuals at the end of life and
help patients formalize their advance care plans by eliciting patient perceptions about illness
impact. ACP is one of the surest ways to assure that EOL preferences are honored.
The findings of this study also suggest that health care practitioners, social service
professionals, family members, and friends should discuss the reality of functional decline and
perceived burden. A pro-active approach to end-of-life planning should be part of the strategies
used to alleviate some of the distress associated with caregiving and the receipt of care. The
growing literature on burden indicates that such intervention is beneficial to both caregiver and
care recipient (Lawrence et al., 1998); the opportunities presented by discussing function and PB,
such as the possibility of increasing advance care planning, benefit all involved in EOL care.
Physicians, social workers, and other practitioners who work with patients at the end of
life should root their discussions in terms of function AND values because (as per the CSM) it is
this mind – body connection that is most meaningful to patients, in terms of illness
representations, which are the catalyst to health behaviors such as advance care planning. This
approach is also patient-centered because the meaning, especially in terms of burden and
intrusiveness to self and others, is not fixed to a particular threshold. It is relative. Each person
will have different levels of disability to which they can adapt, based on their capacity to affect
meaningful activities and notions of the self. Discussing both functional limitations (disability)
and the meaning that these limitations have on each person‟s individual reality (burden) will
232
better lead to authentic and personally meaningful EOL discussions between practitioner and
patient, family members and patient, and ultimately formalized ACPs.
The findings from this study empirically reinforce the recommendations put forth by
Saraiya et al. (2008) who assert that informed EOL planning will be ineffective in the absence of
discussion between patients, families, and practitioners. Among the goals of palliative care is to
allow patients to experience quality end-of-life care, in keeping with his or her specified and
meaningful goals. Singer (2002) identified a number of domains essential for quality end-of-life
care, including achieving a sense of control, relieving burden, and strengthening relations with
family and friends.
Discussing notions of disability and burden with patients is a patient-centered perspective
that allows the patient to exercise control through the formulation of meaningful ACP. From a
clinical perspective, EOL planning and decision-making should be a shared process. Therefore, it
requires awareness and an open recognition of the patient‟s perspective by the physician.
Patients‟ appraisals of perceived burden appear to increase the likelihood of formal planning;
discussion of this perception may be the gateway that clinicians and other stakeholders can use in
achieving the benefits that advance care planning presents to patients and their families.
Perceived illness burden is modifiable; discussions and planning can lead to a reduction in
feeling of burdensomeness. For those patients who may be disabled, but not necessarily feeling
like a burden, the discussions around the topic of perceived illness burden should still be viewed
as fruitful because they can open the door to conversations about other patient perspectives that
have meaning for the individual and, possibly, patient care in general.
233
References
Aloozer, Francesca. 2000. Secondary Analysis of Perceptions and Meanings of Type 2 Diabetes
among Mexican American Women. The Diabetes Educator. 26(5): 785-795.
Carr, Deborah. 2011. Racial differences in end-of-life planning: Why don‟t blacks and
Latinos prepare for the inevitable? Omega: The J Death & Dying 63(1): 1-20.
Carr Deborah. and Moorman, S. M. 2009. End-of-Life treatment preferences among the
young old: An Assessment of psychosocial influences. Sociological Forum. 24(4): 754-
778.
Crabtree, Benjamin F., Yanoshik, M. K., Miller, W. L., and O‟Connor, P. J. (1993).
Selecting individual or group interviews. In D. L. Morgan Ed., Successful Focus Groups:
Advancing the State of the Art. Sage: Newbury Park, California. pp. 137-149.
Davison, Sara N. 2006. Facilitating Advance Care Planning for Patients with End-Stage
Renal Disease: The Patient Perspective. Clinical Journal of the American Society of
Nephrology. 1: 1023-1028.
Davison, Sara N. and Simpson, C. 2009. Hope and advance care planning in patients with
end stage renal disease: qualitative interview study. BMJ online first. Retrieved on Sep.
20, 2010 from
http://www.bmj.com.proxy.libraries.rutgers.edu/content/333/7574/886.full.pdf.
Fisher, Robert. J. 1993. Social desirability bias and the validity of indirect questioning.
Journal of Consumer Research, 20, 303-315.
Fried, Terri R., Bullock, K., Iannone, L. and O'Leary, J. R. 2009. Understanding Advance
Care Planning as a Process of Health Behavior Change. Journal of the American
Geriatric Society. 57:1547-1555.
Glaser, Barney. G., and Strauss, A. L. 1964. Awareness Contexts and Social Interactions.
American Sociological Review. 29: 669-679.
Glaser, Barney. G. and Strauss, A. L. 1965. Awareness of Dying. Chicago: Aldine.
Heyman, Janna C. and Gutheil, I. A. 2010. Older Latinos‟ Attitudes toward and Comfort
with End-of-Life Planning. Health and Social Work. 35(1): 17-26.
Institute of Medicine (IOM). 2001. Health and behavior: The interplay of biological,
behavioral and societal influences, Washington, DC: National Academy Press.
Institute of Medicine (IOM). 2001. Crossing the Quality Chasm: A New Health System
for the 21st Century. Washington, DC: National Academy Press.
234
Leventhal, Howard, Leventhal, E. A., and Cameron, L. 2001. Representations,
Procedures, and Affect in Illness Self-Regulation: A Perceptual-Cognitive Model. In
A.Baum, T.A. Revenson, and J.E. Singer (Eds.), Handbook of Health Psychology. NJ:
Lawrence Erlbaum Associates.
Leventhal, Howard, Brissette, I., and Leventhal, E. A. 2003. The common sense models
of self-regulation of health and Illness. In L. D. Cameron & H. Leventhal, (Eds.), The self
regulation of health and illness behavior. London: Routledge Taylor & Francis Group.
Leventhal, Howard, Weinman, J., Leventhal, E. A., and Phillips L. A. 2008. Health
Psychology: The Search for Pathways between Behavior and Health. Annual Review of
Psychology. 59:477-505.
Leventhal, Howard, Leventhal, E. A., Cameron, L., Bodnar-Deren, S., Breland, J., Hash-
Converse, J. and Phillips, L. A. 2011. Modeling Health and Illness Behavior: The
Approach of the Common Sense Model (CSM). In A. Baum (Ed.) Handbook of Health
Psychology, Second Edition. New York: Routledge.
Levitz, Yisrael and Twerski, A. J. 2005. Communication Patterns. A Practical Guide to
Rabbinic Counseling. Jerusalem, Israel: Feldheim Publishers.
Loewe, Ronald, Schwartzman, J., Freeman, J., Quinn, L., and Zuckerman, S. 2009
Doctor talk and diabetes: towards an analysis of the construction of chronic illness. Social
Science & Medicine. 47(9): 1267-1276.
McPherson, Christine J., Keith G. Wilson, and Murray, M. A. 2007. “Feeling Like A Burden:
Exploring the Perspectives of Patients at the End-of-Life.” Social Science and Medicine
64: 417-27.
Moorman, Sara M. 2009. Facing End-of-Life Together: Marital Relationship Quality and
End-of-Life Health Care Preferences. Dissertations submitted in partial fulfillment of the
Doctor of Philosophy (Sociology) at the University of Wisconsin-Madison
Morrison, Rolfe, S. and Meier, D. E. 2004. High Rates of Advance Care Planning in New
York City‟s Elderly Population. Archives of Internal Medicine. 164: 2421-2427.
Pearlman, Robert A., Cole, W. G., Patrick, D. L., Starks H. E. and Cain, K. C. 1995.
Advance Care Planning: Eliciting Patient Preferences for Life-Sustaining Treatment.
Patient Education and Counseling. 26: 353-361.
Pearlman, Robert. A., Cole, W. G., Patrick, D. L., Starks H. E. and Cain, K. C. 1995.
Advance Care Planning: Eliciting Patient Preferences for Life-Sustaining Treatment.
Patient Education and Counseling. 26: 353-361.
Saraiya, Biren, Bodnar-Deren, S., Leventhal, H. and Levental, E. 2008. End of Life
Planning: Is it Relative for Patients and Oncologists. Decisions in Choosing
235
Cancer Therapy And Palliative Care. The Journal Cancer. 4:293-281.
Singer, Peter A. 2001. Recent Advances, Medical Ethics. BMJ. 321:282
Singer, Peter A., Martin, D. K., Kelner, M. 1999. Quality of end-of-life care: patients‟
perspectives. JAMA. 281(2): 163-168.
Sudore, R. L. and Fried, T. R. 2010. Redefining the “Planning” in Advance Care
Planning: Preparing for End-of-Life Decision Making. Annals of Internal Medicine.
153(4): 256-261
SUPPORT Principal Investigators. 1995. A Controlled Trial to Improve Care for
Seriously Ill Hospitalized Patients. JAMA. 274:1591-1598.
Wilson, Keith. G, Scott, J. F., and Graham, I. D. 2000. Attitudes of terminally ill patients
toward euthanasia and physician-assisted suicide. Archives of Internal Medicine.
160:2454-2460.
Wilson, Keith G., Curran, D. and McPherson, C. J. 2005. “A Burden to Others: A Common
Source of Distress for the Terminally Ill.” Cognitive Behaviour Therapy 34: 115-23.
Ziven Bambauer, Kara. and Gillick, M. R. 2007. The Effect of Underlying Health Status
on Patient or Surrogate Preferences for End-of-Life Care: A Pilot Study. American
Journal of Hospice and Palliative Medicine. 24(3) 185-190.
Zusman, Randall. 2011. Hypertension: Controlling the “silent killer”. Harvard Health
Publications: Cambridge Mass.