[2011] Susan Bodnar Deren ALL RIGHTS RESERVED

[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]

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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

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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.

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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.

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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

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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

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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

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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

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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,

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“. . . 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

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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

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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

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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

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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


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)

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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).



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

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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).

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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.





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

http://depts.washington.edu/bioethx/topics/adcare.html%20on%208/10/2010

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

http://www.census.gov/prod/1/pop/p25-1130/p251130a.pdf

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.

http://aspe.hhs.gov/daltcp/reports/2008/ADCongRpt.pdf

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).


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


attorney for health care (DPAHC), appoints a health care surrogate to make medical



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).



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,


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.


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).

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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.

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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.

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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.





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.



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.




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.



Fisher, Robert. J. 1993. Social desirability bias and the validity of indirect questioning.

Journal of Consumer Research, 20, 303-315.




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.


Determination Act and the Uniform Health Care Decisions Act. Health and Social

Work. 23:275-281.




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.





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.


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.


60



633.



Leventhal, Howard and Meyer, D. 1980. The common sense representation of illness


Press. II: 7-30.






of self-regulation of health and Illness. In L. D. Cameron & H. Leventhal, (Eds.),


Francis Group.








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





28(1): 100-116.


61


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.




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.


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.




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



http://depts.washington.edu/bioethx/topics/adcare.html%20on%208/10/2010

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




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.





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.,



63

Medicine. 16(1): 32-40.





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.





160:2454-2460.

Wilson, Keith G., Curran, D. and McPherson, C. J. 2005. “A Burden to Others: A


34: 115-23.






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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


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

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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?

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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

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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,

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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

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(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

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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

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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

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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-

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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.

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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

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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

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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

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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-

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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



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.







88






face-to-face, structured interview with a trained graduate student interviewer. The survey


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.

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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.

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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.

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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.

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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

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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).

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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


construct the scale. The mean of the scale was 1.33.

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(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.

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Almost 46% of respondents had 2-3 health conditions and had a mean CES-D score of

1.339.


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

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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

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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-

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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

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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.

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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

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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

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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?

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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-

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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

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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;

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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.



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.

http://www.bmj.com.proxy.libraries.rutgers.edu/content/333/7574/886.full.pdf

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.


behavioral, and societal influences, 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


Press. II: 7-30.

Leventhal, Howard, Leventhal, E.A., and Cameron, L. 2001. Representations,




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.),


Francis Group.

Leventhal, Howard, Weinman, J., Leventhal, E.A., and Phillips L.A. 2008. Health







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


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



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.





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-

http://www.informaworld.com/smpp/title~db=all~content=t713648133

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‟


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.


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





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.



160:2454-2460.

Wilson, Keith G., Curran, D. and Christine J. McPherson. 2005. “A Burden to Others: A


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.

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Figure 3.1. Perceived Illness Burden

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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.

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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

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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


0.861



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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).


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


attorney for health care (DPAHC), appointing a health care surrogate to make medical


138


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).



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,


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

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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

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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

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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

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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

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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.


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

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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

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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

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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

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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

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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.

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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,

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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



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,

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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.












face-to-face structured interview with a trained graduate student interviewer. The survey


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

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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.

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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


construct the scale.

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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.

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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

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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

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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.

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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.


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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.

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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,

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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

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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.


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

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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.


Level of function/burden predicting odds of having a Living Will

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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],

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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.


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

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(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).


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

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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

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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

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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.

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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

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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.

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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

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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

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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).

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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.


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.




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


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., 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.



Foster, Tom. 2003. Suicide Note Themes and Suicide Prevention. The International

http://research.aarp.org/

http://www.ama-assn.org/ethic/epec/download/plenary_3.pdf%20on%207/9/2010

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.





Determination Act and the Uniform Health Care Decisions Act. Health and Social

Work. 23:275-281.




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.





127:1-12.


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.


behavioral, and societal influences, 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.




633.

Larson, Dale, G. and Tobin, D. R. 2000. End-of-Life Conversations: Evolving Practice

and Theory. JAMA. 284(12): 1573-1578.



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.),


Francis Group.




190





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




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.



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.

http://www.informaworld.com/smpp/title~db=all~content=t713648133

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.





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.



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.





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


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



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‟



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




Steinhauser, Karen E., Christakis, N. A., Clipp, E. C., McNeilly, M., McIntyre, L. and




Planning: Preparing for End-of-Life Decision Making. Annals of Internal

Medicine. 153(4): 256-261



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





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.,



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.





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.





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


160:2454-2460.

Wilson, Keith G., Curran, D. and Christine J. McPherson. 2005. “A Burden to Others: A


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.




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.

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Figure 4.1. Perceived Illness Burden

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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.

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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


0.927



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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


0.861



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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

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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

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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

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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

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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).


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

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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?

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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

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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.


Latinos prepare for the inevitable? Omega: The J Death & Dying 63(1): 1-20.


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.




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.


behavioral and societal influences, Washington, DC: National Academy Press.



http://www.bmj.com.proxy.libraries.rutgers.edu/content/333/7574/886.full.pdf

234


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.


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.


Psychology: The Search for Pathways between Behavior and Health. Annual Review of

Psychology. 59:477-505.



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.


End-of-Life Health Care Preferences. Dissertations submitted in partial fulfillment of the

Doctor of Philosophy (Sociology) at the University of Wisconsin-Madison


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.


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., Martin, D. K., Kelner, M. 1999. Quality of end-of-life care: patients‟



Planning: Preparing for End-of-Life Decision Making. Annals of Internal Medicine.

153(4): 256-261





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.


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.

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