The Pennsylvania State University

The Graduate School

College of Health and Human Development

THREE ESSAYS ON CANCER SURVIVORSHIP

AND LABOR SUPPLY

A Dissertation in

Health Policy and Administration

by

Michael P. Markowski

© 2010 Michael P. Markowski

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Doctor of Philosophy

December 2010

ii

The dissertation of Michael P. Markowski was reviewed and approved* by the following:

Pamela F. Short

Professor of Health Policy and Administration, Demography, and Public Health Sciences

Dissertation Adviser

Chair of Committee

John Moran

Assistant Professor of Health Policy and Administration

Christopher S. Hollenbeak

Associate Professor of Surgery and Public Health Sciences

Janice Penrod

Associate Professor of Nursing

Dennis Shea

Professor of Health Policy and Administration

Head of the Department of Health Policy and Administration

*Signatures are on file in the Graduate School

iii

ABSTRACT

These essays examine the effects of cancer on labor supply by cancer survivors in three

situations: patients deciding whether to continue working during treatment, spouses deciding

whether and how much to work in the years following treatment, and survivors deciding about the

timing of retirement. These choices affect individual and societal welfare. Work matters to

individual cancer survivors because it is a source of personal fulfillment, a measure of health and

vitality, income, and employer-sponsored health insurance benefits. Work matters to society. The

National Cancer Institute has estimated that cancer costs over $20 billion annually in work-loss

days. Efforts to support and accommodate work by cancer survivors would reduce the indirect

economic costs of cancer to society and would potentially improve the well-being of cancer

survivors and their families.

Although sixty percent of newly diagnosed cancer survivors decide to continue to work

during treatment, little is known about the factors that are associated with that decision.

Furthermore, spouses share in the cancer survivorship journey, yet the labor supply effect of

cancer on spouses in working couples is unknown. With the incidence of cancer increasing with

age, the labor supply decision of older cancer survivors increasingly becomes a decision about

retirement.

The studies use data from the Penn State Cancer Survivor Study (PSCSS) funded by the

National Cancer Institute, and supplemented with data from the Health and Retirement Study

(HRS), to produce estimates of the effects of cancer on work status and the usual hours of work

per week for cancer survivors and spouses at difference stages of survivorship. Logistic

regression methods were used to produce estimates of the effects of cancer on working or on

complete retirement. Tobit models were used to estimate the effect of cancer on hours of work.

The first study finds that the decision to work during treatment is mainly determined by

clinical considerations, such as cancer type and stage, although job-related health insurance of

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survivors and spouses is associated with a greater likelihood of working through treatment. The

second study finds that cancer has little long-term effect on the labor supply of the spouses of

survivors, at least in older couples or where both partners were working at diagnosis, the

situations that were the focus of this research. The third study finds that survivors of both

genders who remain cancer-free postpone retirement compared to other adults with no cancer

history, but female survivors with recurrences or new cancers retire sooner.

v

TABLE OF CONTENTS

LIST OF TABLES....……………………………………………………………………………..vii

LIST OF FIGURES....………………………………………………………………………….....ix

LIST OF CHARTS………………………………………………………………………………...x

ACKNOWLEDGEMENTS...….……………………………………………………………….....xi

CHAPTER ONE: Introduction…..……………………………………………………………1

REFERENCES………………………………………………………………….12

CHAPTER TWO: What Factors Affect the Decision to Keep Working During Treatment?..19

BACKGROUND………………………………………………………………..20

CONCEPTUAL MODEL…………………………………………………….…23

METHODS……………………………………………………………………...29

RESULTS……………………………………………………………………….33

DISCUSSION…………………………………………………………………...39

REFERENCES………………………………………………………………….46

CHAPTER THREE: What is the Effect of Cancer on the Labor Supply of Spouses of Cancer

Survivors?......…………………………………………………………..65

BACKGROUND…………………………………………………………..……66

CONCEPTUAL MODEL……………………………………………………….68

METHODS……………………………………………………………………...71

RESULTS……………………………………………………………………….79

DISCUSSION…………………………………………………………………...82

REFERENCES………………………………………………………………….85

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CHAPTER FOUR: What is the Effect of Cancer on Time-to-Retirement?.............................102

BACKGROUND………………………………………………………………103

CONCEPTUAL MODEL……………………………………………………...105

METHODS…………………………………………………………………….109

RESULTS……………………………………………………………………...116

DISCUSSION………………………………………………………………….118

REFERENCES………………………………………………………………...120

CHAPTER FIVE: Conclusions……………………………………………………………...130

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LIST OF TABLES

Table 1.1: Dissertation Design & Cancer Survivor Literature Contribution…………………....18

Table 2.1: Characteristics of Cancer Survivor Study Sample at Diagnosis by Gender………….51

Table 2.2: Percent of Survivors Working During Treatment by Cancer Site and Stage at

Diagnosis within Gender..…………………………………………………………...54

Table 2.3: Percent of Survivors Working During Treatment by Characteristic at Diagnosis

within Gender………………………………………………………………………..55

Table 2.4: Percentile Distribution of Treatment Period Durations by Cancer Type and

Cancer Survivor Gender (Months)…………………………………………………..57

Table 2.5: Adjusted Odds Ratios for Factors Associated with Working During the

Treatment Period, Male Survivors…………………………………………………..58

Table 2.6: Adjusted Odds Ratios for Factors Associated with Working During the

Treatment Period, Female Survivors………………………………………………...60

Table 2.7: Odds Ratios for Main Effects and Interaction of Own Health Insurance at

Diagnosis for Married Cancer Survivors, Where Cancer Survivor is Working at

Diagnosis……………………………………………………………………….……62

Table 3.1: Changes in Patterns of Married Couples’ Work Choices from Diagnosis to

Wave 2……………………………………………………………...………………..90

Table 3.2: Characteristics of Spouses of Cancer Survivors in Working Couples at

Diagnosis and the Association with Working at Wave 2...…………………...……..91

Table 3.3: Husbands: Mean Usual Hours of Work by Cancer Characteristic of Survivors in

Working Couples at Diagnosis……..………………………………………………..94

Table 3.4: Wives: Mean Usual Hours of Work by Cancer Characteristic of Survivors in

Working Couples at Diagnosis……..………………………………………………..95

Table 3.5: Results of Logit Models: All Spouses, Adjusted Odds Ratios of Likelihood of

Working at Wave 2 Interview, Given Cancer of Survivor and Both Working

at Diagnosis………………………………………………………………………….96

Table 3.6: Results of Tobit Models: Contribution to Hours Worked by Spouse of Cancer

Survivor at Wave 2 Interview, Given Both Working at Diagnosis………………….97

Table 3.7: Characteristics of CSS and HRS Spouses at Diagnosis in Working Couples..………98

Table 3.8: Results of Logit Models: Adjusted Odds Ratios for Husbands of Cancer

Survivors Compared to Husbands without Cancer in Married Couples with

Both Working at Diagnosis …………………………………………………………99

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Table 3.9: Results of Logit Models: Adjusted Odds Ratios for Wives of Cancer Survivors

Compared to Wives without Cancer in Married Couples with Both Working at

Diagnosis…………………………………………………………………………..100

Table 3.10: Results of Tobit Models: Effects of Cancer on Hours of Work: CSS vs. HRS,

Given Both Working at Time of Diagnosis………………………………………...101

Table 4.1: Characteristics of PSCSS and HRS Samples, Working at Diagnosis/Baseline……..125

Table 4.2: Attrition in Surveys by Gender, by Count and by Percentage Lost in Follow-up

for Any Reasons……………………………………………………………………127

Table 4.3: Adjusted Odds Ratios for Factors Associated with Time Until First Retirement,

Self-Reported Completely Retired; Excludes Partially Retired, Males: CSS

Survivors v. HRS Controls………………………………………………….……...128

Table 4.4: Adjusted Odds Ratios for Factors Associated with Time Until First Retirement,

Self-Reported Completely Retired; Excludes Partially Retired, Females: CSS

Survivors v. HRS Controls…………………………………………………….…...129

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LIST OF FIGURES

Figure 1.1: Stages of Cancer Survivorship (Mullan 1985)……………………………………....16

Figure 1.2: Key Employment Decisions Faced by Cancer Survivors: A Sequence of

Choices……...……………………………………………………………………….17

Figure 2.1: Conceptual Model of Factors Which Affect Time for Treatment and Ability to

Work in the Treatment Period……………………………………………………….50

Figure 3.1: Key Employment Decisions Faced by Spouses of Cancer Survivors: A

Sequence of Choices at Each Stage of the Joint Survivorship Experience……….…88

Figure 3.2: Conceptual Model of Factors Which Affect the Work Decision at Wave 2………...89

Figure 4.1: Conceptual Model of Factors Which Affect the Retirement Decision……………..123

Figure 4.2: From Survey to Sample in Study, 1936-1947 Birth Cohorts……………………....124

x

LIST OF CHARTS

Chart 1: Estimated Odds Ratios and 95% Confidence Intervals for Working During

Treatment by Cancer Site, Male Survivors (Colorectal Cancer Reference

Group)……………………………………………………………………….………….63

Chart 2: Estimated Odds Ratios and 95% Confidence Intervals for Working During

Treatment by Cancer Site, Female Survivors (Colorectal Cancer Reference

Group)……………………………………………………………………….………….64

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ACKNOWLEDGEMENTS

―It takes a village‖ might best describe the dissertation process. I am grateful to the

cancer survivors and families, who despite the challenges placed in their way by this disease,

generously gave their time and information to benefit others. My adviser, Pamela Farley Short,

helped immensely with her constant encouragement and frequent critical reviews of work in

process. Thanks to my committee members, John Moran, Christopher Hollenbeak, and Janice

Penrod, for their valuable time and suggestions. Besides my adviser, I was blessed with many

good teachers, whose gifts were included in this work: Rebecca Wells, Peter Kemper, Dennis

Shea, Tom Arnold, Jim Prichard, Neil Storms, the IHM community, and especially, Joe Vasey for

his SAS assistance. My cohort colleagues, Chandra Ganesh, Yu Bai, Tokunbo Oluwole, and

Harry Holt spent many hours in study group sessions, classes, and sharing time outside of class

with me. It was very difficult to spend the weekdays and nights on campus separated from my

wife, but they helped to lessen the pain by their kind words and thoughtfulness. Finally, Bev Fahr

helped with course registration, oral defense logistics, and completion of required documentation.

My wife, Pat, deserves special thanks for the gift of time – time to attend classes at a

distant campus and time spent in hours of programming and writing in the production of this

dissertation. Our son, Paul, and daughter-in-law, Marisa, provided much needed logistical

support, and our grandsons, Nolan and Shane, provided inspirational boosts when energy

wavered. Our son, Andrew, and daughter-in-law, Jenell, along with granddaughters Elyse and

Layne, offered encouragement via Skype and shared laughter to help us along the way. My

sisters, Ann and Kathy, provided timely encouragement. Last, but not least, without the sacrifices

of my mom, Mary Ann, and my dad, Joe ―the truck driver,‖ I would not have been in a position to

write this dissertation.

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CHAPTER ONE

INTRODUCTION

This dissertation is about cancer survivors and the decision to work at various stages of the

survivorship experience. These stages have been described by a physician and cancer survivor,

Fitzhugh Mullan, as ―seasons of survival‖ (Figure 1.1). Each survivor’s journey begins with

diagnosis and lasts a lifetime. The announcement of a cancer diagnosis forces a confrontation

with mortality in the acute stage which is coterminous with the treatment period. Decisions about

working in this stage are complicated by stress and side effects from radiation, chemotherapy or

surgery. Following treatment, a cancer survivor lives with the daily fear of recurrence and

lingering effects of treatment in the extended survival stage. When the fear of recurrence fades

into the background, a cancer survivor crosses a psychological boundary into the permanent

survival stage. However, some consequences of treatment, ―late effects,‖ may manifest

themselves long after the initial treatment. Late effects include fatigue, pain, diminished

cognitive function, and most significantly, the recurrence of cancer.

The treatment stage, or acute survival stage as characterized by Mullan, is dominated by

fear of dying. The cancer survivor must cope with the shock of the life-threatening diagnosis,

engage the medical system for assessments and treatments, and share the news with family,

friends, co-workers and employers. The cancer survivor has intense contact with healthcare

professionals and a support network. Cancer survivors must make choices under duress with

potential negative consequences for health outcomes, including longevity and quality-of-life.

Fatigue, difficulty focusing on mental tasks, intrusive thoughts, anxiety, depression, lymphedema,

and worries about finances are commonly reported by cancer survivors in treatment (Anderson,

& Hacker, 2008; Fallowfield, Ratcliffe, Jenkins, & Saul, 2001; Doyle-Lindrud, 2007; King et

2

al., 2008; Loscalzo, & Clark, 2007; Spelten, et al., 2003; Rodin, 2008; Towers, Carnevale, &

Baker, 2008).

Once treatment has been completed, the cancer survivor enters a transitional stage in

which the cancer survivor faces a major process of adjustment that continues with the progression

to the stage of permanent survival. The adjustment process involves physical, mental, spiritual,

social, emotional, and economic issues. Fear of mortality is replaced by fear of recurrence in the

extended survival stage. Cancer survivors engage in a heightened sense of self-monitoring for

new signs of cancer and many experience excessive worrying. Many have difficulty with

memory and problem-solving from chemotherapy. Pain and complications like increased levels

of fatigue may linger from treatment or only manifest themselves over time. Cancer survivors

report a lessening of support from families, friends, co-workers, and employers who assume that

successful treatment has restored a cancer survivor to his or her state of physical and mental

health prior to the diagnosis. With the end of treatment, cancer survivors lose the frequency and

intensity of contact they once held with healthcare professionals, who offered support and

reassurance. As Mullan notes, a cancer survivor’s perspective has changed forever (American

Cancer Society, 2010; Institute of Medicine, 2006; Main, Nowels, Cavender, Etschmaier, &

Steiner, 2005; Michaelson et al., 2008; Pryce, Munir, & Haslam, 2007).

In the permanent survival stage, a cancer survivor’s fears of recurrence gradually fade as

a daily concern and the adjustment process that began with diagnosis is largely achieved. The

fear of recurrence never leaves entirely, but its intensity and intrusiveness diminish significantly

(Mullens, McCaul, Erickson, & Sandgren, 2004). However, not all risks diminish. For example,

an increased risk of suicide remains (Rowland, 2006). For some cancer survivors, their journey

yields spiritual growth and infuses their lives with new meaning (Peuckmann et al., 2007;

Schroevers, Ranchor, & Sanderman, 2006). For others, simply returning to as much normalcy as

possible is sufficient. In this stage, a cancer survivor may endure some residual effects from

3

cancer treatment, such as fatigue or cognitive deficits, like memory issues or problem-solving

capacity (Lawenda, Mondry, & Johnstone, 2009).

Each stage of cancer brings a somewhat different set of set of work choices by cancer

survivors (Figure 1.2). Furthermore, decisions made at one stage in survivorship may reverberate

through subsequent stages. In the treatment stage, the time required for treatment and its side

effects like fatigue, pain, and difficulties with mental concentration and problem-solving may

influence the work decisions of cancer survivors (Reeves, 2008). While working may yield

positive psychological benefits by helping a survivor cope with their worries, working may be an

economic necessity for others (Main, Nowels, Cavender, Etschmaier, & Steiner, 2005). Cancer

survivors may not receive accommodating support from employers for time off for medical

treatments or with the burden of the side effects that make working more difficult (Pryce, Munir,

& Haslam, 2007). Co-workers may or may not be able to temporarily replace the work forgone

by cancer survivors (Hansson, Bostrom, & Harms-Ringdahl, 2006). Paid sick leave, which

allows taking time off for treatment may not be a job benefit. Wage losses may be significant

(Longo, Fitch, Deber, & Williams, 2006). Future career prospects or even continued

employment with associated job benefits like health insurance may be jeopardized (Main,

Nowels, Cavender, Etschmaier, & Steiner, 2005). Cancer survivors’ work decisions may be

influenced by these concerns since they may lose income or access to employer-sponsored health

insurance, when they need them the most.

Survivors may continue working during treatment, but this choice may affect their future

health status and ability to work at later stages. Some survivors may choose to stop working

altogether during treatment. Others may continue working but reduce hours of work or take some

time off for treatment. Survivors have reported foregoing treatment due to work considerations.

Failure to comply fully with treatment protocols may have effects on health and work choices in

subsequent stages (Bradley, Neumark, Luo, & Bednarek, 2007). In addition, the shock of a

4

serious illness may have changed their preferences for work or leisure (Bradley, Neumark, Luo,

& Bednarek, 2007; Coile, 2004; McClellan, 1998).

In the extended survival season, the transition stage, attention turns to issues surrounding

return-to-work for cancer survivors who stopped working during treatment. Job duties may

change because of lingering treatment effects like fatigue and its interaction with cognitive

impairment, including memory loss, inability to concentrate on complex tasks, and difficulty with

learning or multitasking (Bradley, Neumark, Luo, Bednarek, & Schenk, 2005; Mulrooney, 2008).

Other treatment consequences like pain, sexual dysfunction, and incontinence may diminish

quality of life and the ability to work (American Cancer Society, 2010; Shaw, 2008; Stilos,

Doyle, & Daines, 2008; Reeves, 2008; Jackson et al., 2008; Vadivelu, Schreck, Lopez,

Kodumudi, & Narayan, 2008). The number of work hours may be reduced as survivors cope

with the outcomes of treatment. The physical and mental burdens may induce some survivors

who worked through treatment to stop working. Alternatively, some survivors who stopped

working may have difficulty returning to work, due to the expectations of normalcy by employers

and co-workers or from lingering effects of treatment previously noted. Others who did return to

work may reduce hours or stop working again. Still others, who worked during treatment, may

stop working in the extended survival season as a consequence of effects of poor health outcomes

from treatment. The poor outcomes may occur for two reasons. The outcome may be due to

working instead of concentrating on recovery or sacrificing treatment in order to work.

In the permanent survival period, the last stage, cancer survivors have come to terms with

cancer as a chronic illness and lingering effects like pain (American Cancer Society, 2010; de

Ridder, Geenen, Kuijer, & van Middendorp, 2008; Hinton, 2008). Most of the adjustments in

return-to-work or the number of hours spent working have been made by this stage. Some

cancer survivors may continue working to recover income losses from earlier stages or to

maintain benefits to assist with future medical expenses. Alternatively, some survivors may retire

5

sooner for health reasons or because their preferences for leisure have changed in the course of

their personal journey (Bradley, Neumark, Luo, & Bednarek, 2007).

The literature on cancer survivorship and employment has largely focused on return-to-

work issues. The return-to-work studies have reported the proportion of cancer survivors

working in two different timeframes measured from diagnosis. The short-term timeframe spans

from 1 to 5 years after diagnosis. The long-term timeframe continues beyond the traditional cure

reference point of 5 years. These periods roughly correspond to the later stages of survival

proposed by Mullan (1985) and the National Cancer Institute’s classification of cancer patients

along a survivorship continuum that includes treatment, post-treatment, and continuing care

(Institute of Medicine, 2006).

The treatment stage ends within approximately 12 months for most survivors. For

example, one study of breast cancer survivors’ post-treatment return-to-work suggested that most

survivors had completed treatment prior to 12 months (Bouknight, Bradley, & Luo, 2006).

Another study has suggested that the return-to-work adjustment was largely finished by 12

months with 73% of cancer survivors returning to work. In the following 12 months, the

cumulative return rate increased slightly to 78% (Short, Vasey, & Tunceli, 2005). Consequently,

research on return-to-work often focuses on employment 12 months or more after diagnosis.

Studies of employment in the initial 12 months after diagnosis are often interpreted as

characterizing employment patterns during treatment (Lauzier et al., 2008). Significant time

costs have been found in the treatment of initial cancers in the first 12 months (Yabroff et al.,

2007).

Under the circumstances, rates of employment for cancer survivors reported in the

literature depend on the stage of survivorship or time from diagnosis. One study on return-to-

work reported that 71% of breast cancer survivors were working at 3 months (Satariano &

DeLorenze, 1996). Another study of prostate cancer survivors found that 72% were working at

six months (Bradley, Neumark, Luo, Bednarek, & Schenk, 2005). In the extended survival stage,

6

a study of breast cancer survivors reported that 82% were working at twelve months (Bouknight,

Bradley, & Luo, 2006). Another study found that 87% of cancer survivors were working at one

to five years (Short, Vasey, & Tunceli, 2005). In a study in the permanent survival stage, eighty-

six percent of testicular cancer survivors were working over 11 years later (Skaali et al., 2008).

The return-to-work studies largely ignore the issues surrounding the decision to work

during treatment. One survivor study found that cancer survivors were approximately evenly

divided on the decision to work or stop working during treatment (Short, Vasey, & Tunceli,

2005). In addition, most studies in the return-to-work literature are limited to survivors with a

single type of cancer, usually the most prevalent cancer such as breast, prostate, colon, and lung

(Bradley, & Bednarek, 2002; Bradley, Neumark, Luo, & Bednarek, 2007; Bouknight, Bradley, &

Luo, 2006; Drolet, Maunsell, Mondor, Brisson, C., Brisson, J., & Deschenes, 2005; Sanchez,

Richardson, & Mason, 2004), which makes it more difficult to compare employment patterns by

cancer site.

In making decisions about work, cancer survivors confront two forces that push in

opposite directions. The fatigue, pain, cognitive impairment, or psychosocial stresses that

accompany the disease may increase the value of taking time off to focus on health issues. On

the other hand, good jobs, pensions, and health insurance can provide social support, income, and

benefits in the face of uncertainty about future medical costs. Economic theory cannot predict

which of these twin forces will be dominant. Hence, the answers to questions about cancer’s

effect on labor supply require empirical studies.

The number of cancer survivors is large and growing. The National Cancer Institute

estimated that the number of people alive, who ever had a diagnosis of invasive cancer,

approached 11.4 million in 2006 (American Cancer Society, 2010). This population is expected

to double by 2020 as the incidence of cancer increases in an aging population and longevity is

increased through early detection and treatment (Edwards et al., 2002). The Institute of

7

Medicine provided a ―state of the cancer survivor‖ in its report, ―From Cancer Patient to

Survivor‖ (2006). The Institute noted that 61% of cancer survivors were 65 years of age or older

with an additional 38% in the prime working ages from 20 to 64 years. Breast, prostate and

colon cancers accounted for half of all cancer sites. Breast and prostate cancers were the most

prevalent sites respectively for women and men. Colon cancer affected men and women and

ranked third overall among cancers in prevalence.

The short-term and long-term effects of cancer on the employment of survivors add to the

economic costs of the disease. The total monetary burden of the disease was estimated by the

National Institutes of Health at $263.8 billion in 2010 (American Cancer Society, 2010).

Interestingly, the direct medical expense of $102.8 billion represents less than half of the total

costs (38%). Morbidity costs or the productivity lost due to the effects of the illness were $20.9

billion (7.9%). The morbidity cost or the productivity lost due to premature death was $140.1

(53.1%). Over 1.65 billion work days of a total of 2.5 billion work days were impaired by cancer

(Kessler, Greenberg, Mickelson, Meneades, & Wang, 2001).

The three essays comprising this dissertation aim at filling important gaps in the

literature on survivorship and employment (Table 1.1). All three studies use data from the Penn

State Cancer Survivor Survey (PSCSS). The Penn State Cancer Survivor Survey is a population-

based panel survey funded by the National Cancer Institute. The longitudinal design spans the

period 1997 through 2004. Cancer survivors were recruited from cancer registries at four medical

centers including The Johns Hopkins Hospital, Lehigh Valley Hospital and Health Network,

Geisinger Medical Center, and Milton S. Hershey Medical Center. Participants were included

based on these criteria: (1) between the ages of 25 and 62 at diagnosis, (2) with a first cancer

diagnosed between January 1997 and December 1999, and (3) with a prognosis that would permit

participation over four waves in the panel survey. The first of four annual interviews was

conducted from October 2000 through December 2001. The survey participants varied by wave

8

beginning with 1,761 in Wave 1 and ending with 1,232 in Wave 4. Retrospective questions were

asked about employment status and health at diagnosis. Change was measured at the annual

interviews. Survey questions mirrored the content of the Health and Retirement Survey. This

panel survey, which is managed by the University of Michigan, began in 1992. It includes 30,000

participants who are aged 51 or older.

Most previous studies of cancer survivorship and employment have focused on a single

cancer site, and often breast or prostate cancers (Bouknight, Bradley, & Luo, 2006; Bradley,

Bednarek, & Neumark, 2002; Bradley, Neumark, Luo, Bednarek, & Schenk, 2005; Drolet et al.,

2005; Maunsell et al., 2004). The PSCSS included multiple cancers, which will facilitate effect

due to clinical differences in cancer and expand the potential generalization of results to a wider

population of cancer survivors.

The need for better designed studies including population-based research and control

groups was noted in an assessment of the literature on cancer and labor supply studies (Steiner,

Cavender, Main, & Bradley, 2004), and two of the studies comprising this thesis make use of

non-cancer control groups drawn from the Health and Retirement Study (HRS). The use of more

rigorous methods and complementary research designs will add to the growing body of

knowledge about labor supply effects of cancer.

The first study examined cancer survivor labor supply decisions during the treatment

period. The treatment period has been included in studies with two different objectives. Two

studies estimated the economic costs of cancer within 18 months of diagnosis (Longo, Fitch,

Deber, & Williams, 2006; Lauzier et al., 2008). However, neither study estimated the labor

supply changes in work status or hours on subsequent financial consequences. Other studies have

included the treatment period in measuring return-to-work for cancer survivors who stopped

working during treatment (De Boer et al., 2008; Short, Vasey, & Tunceli, 2005). However, this

9

focus does not address the 60% of cancer survivors who continue to work during treatment

(Short, Vasey, & Tunceli, 2005). The decision to work during treatment may affect labor supply

choices in subsequent stages. The initial work decisions during treatment may ultimately affect

health outcomes or the survival trajectory or quality of life for long-term survivors. Here,

working during treatment is studied with retrospective questions about work status at diagnosis

and working during treatment asked in the first PSCSS interview, which occurred one to five

years after diagnosis. Baseline covariates for cancer, socio-demographic, and job-related

characteristics were tested for their association with the decision to keep working during

treatment.

The second study addressed the labor supply decisions of the spouses of cancer

survivors. Research sponsors, like the Office of Cancer Survivorship, have supported a broader

definition of cancer survivor that includes spouses, families, caregivers, and others affected by

cancer (Institute of Medicine, 2006). Bradley and Bednarek (2002) have encouraged studies of

spousal labor supply to provide a complete picture of the labor supply effects of cancer. Spouses

may add to the cost of cancer by reducing labor supply or mitigate the economic loss to society

by continuing to work. In the PSCSS, considering working age cancer survivors (25-64) two to

six years after diagnosis, two-thirds of the cancer survivors were married and both partners were

working at diagnosis in two-thirds of those survivor couples. Thus, the potential effects of cancer

on spouse employment could be quite significant. These effects are examined here by making

comparisons at two levels. The first set of comparisons is limited to a sample drawn only from

the Penn State Cancer Survivor Survey that is limited to married survivors and their spouses.

Studying employment differences among survivors’ spouses that are associated with differences

in cancer type, socio-demographic, and job-related characteristics may inform the design of

employer accommodation programs or psychosocial support offerings. At the second level, the

effect of cancer on spousal labor supply is estimated by comparing survivor spouses in PSCSS to

10

a non-cancer control group, married individuals in the Health and Retirement Study who are not

married to a cancer survivor. Labor supply outcomes are measured at the larger and extensive

margin of work status and at the smaller and intensive margin of usual hours of work per week.

The third study focused on providing a longitudinal perspective that would complement

the cross-sectional designs used in the study of retirement decisions by older cancer survivors.

Retirement is a process that occurs over time. Some people may ―retire‖ by changing to less

stressful jobs. Others may partially retire by working less than full-time. Still others completely

retire by leaving the work-for-pay labor market. This study used a time-to-event methodology

where the event was defined as time to first complete retirement. It compared cancer survivors

with persons without cancer from the HRS across four annual PSCSS interviews. Baseline

covariates were used to predict if cancer survivors were more or less likely to retire compared to

HRS individuals. The outcome measures were work status and the usual hours of work per week.

With over 60% of cancer survivors aged 65 or older (Institute of Medicine, 2006), cancer may

have significant welfare implications if it accelerates or constrains the timing of retirement. One

cross-sectional study with population-based methods and a control group from a nationally

representative survey concluded that cancer had little long-term effect on the employment of

older survivors who remained cancer-free (Short, Vasey, & Moran, 2008). Here that conclusion

is re-examined with different methods, but with data from essentially the same cancer and non-

cancer samples, drawn respectively, from PSCSS and HRS. Multiple methods may provide

more reliable evidence by including a time-to-event design that compensates for the observations

missing at some future measurement point by observing cancer survivors over an extended period

of time and not at a slice in time.

In summary, the purposes of this dissertation were to fill gaps in the cancer survivor

literature for labor supply effects of cancer related to three important types of decisions:

decisions about working during treatment made by cancer survivors, longer term decisions about

11

working made by spouses, and decisions about the timing of retirement made by older cancer

survivors. The information will provide a basis for more complete estimates of the economic cost

of cancer in general and for retirees, and help with the design of work accommodations based on

consideration of cancer survivor characteristics.

12

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Oncology Nursing, 12(3), 445-454.

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16

Figure 1.1

Stages of Cancer Survivorship (Mullan, 1985)

Dx Year 1

Acute Stage (Treatment Period)

Year 5

Extended Stage Permanent Stage

Features: Facing mortality,

emotional stress, side effects of

treatment including, cognitive

impairment, and pain from

radiation, chemotherapy, or

surgery. Begins with diagnosis

and lasts through the end of

initial treatment. It is

distinguished by concerns

about mortality.

Features: Lingering physical

and mental affects from

treatment, intrusive thoughts

and risk of anxiety or

depression. Need for

psychosocial support. Fear of

recurrence is a salient factor.

Passage may occur in less than

five years.

Features: Fear of recurrence

fades, attempt to regain life

much as before diagnosis,

spiritual growth, lingering

long-term physical and mental

effects of treatment such as

fatigue. Cancer treated as a

chronic condition with

survivorship as a life-long

experience.

Short-Term Long-Term

17

Figure 1.2

Key Employment Decisions Faced by Cancer Survivors: A Sequence of Choices

Work through

Treatment

Return to Work?

Quit during /

after Treatment?

Reduce Hours?

Not

Employed

Employed

Part Time

Employed

Full Time

No

Yes

No

Yes Yes

No

Yes

No

18

Table 1.1

Dissertation Design & Cancer Survivor Literature Contribution

Cancer Survivor Characteristics

Study 1

What Factors

Affect the

Decision to

Keep Working

During

Treatment?

Study 2

What is the

Effect of

Cancer on the

Labor Supply

of Spouses of

Cancer

Survivors?

Study 3 What is the

Effect of

Cancer on

Time-to-

Retirement?

Focus Cancer Survivor X X

Spouse X

Time Period

Short Term (Acute/Treatment)

X

Long Term (Extended/Permanent)

X X

Labor Supply

Outcome

Work Status X X X

Hours X

Age

Working Age (25-64)

X X

Older (50+)

X X

Cancer Site Single

Multiple X X X

Study Design

Cross-Sectional X X

Time-to-Event X

Control X X

19

CHAPTER TWO

WHAT FACTORS AFFECT THE DECISION TO KEEP WORKING DURING

TREATMENT?

Abstract

Background. About 60% of cancer survivors continue to work during treatment, but little is

known about the factors that are associated with this decision.

Data. A sample of survivors working at diagnosis was identified using data from Wave I of the

Penn State Cancer Survivor Study. Work status during treatment was identified from

retrospective questions asked at the Wave 1 interview.

Study design. Logistic regression was used to estimate the likelihood of working through

treatment with three types of covariates: cancer characteristics, socio-demographic

characteristics, and job-related characteristics. Separate models were estimated for male and

female survivors. A supplementary analysis focused on married survivors and the effects of

having one’s own job-related health insurance and a spouse with job-related insurance.

Principal findings. Type and stage of cancer were the most important predictors of the decision

to keep working during treatment. There were few significant differences associated with socio-

demographic variables, except that more highly educated women were more likely to continue

working. Survivors with insurance from their own jobs at diagnosis were more likely to continue

working, as were female survivors married to husbands with job-related insurance.

Conclusions. There is some evidence that decisions about working during treatment are

influenced by health insurance considerations (―job lock‖). However, these decisions mainly

depend on clinical considerations such as cancer type.

20

Background

Cancer survivors pass through stages in the survivorship experience. According to

Mullan (1985), a cancer diagnosis is a life-changing event with lasting consequences for cancer

survivors on their personal journey through three stages of survivorship or the ―seasons of

survival.‖ Each stage presents unique challenges to the cancer survivor for his or her health and

the consequences those decisions have for working. The announcement of a cancer diagnosis

forces a confrontation with mortality in the acute stage, which is coterminous with the treatment

period. Decisions about working in this stage are complicated by stress and side effects from

radiation, chemotherapy or surgery. Following treatment, a cancer survivor lives with the daily

fear of recurrence and lingering effects of treatment in the extended survival stage. As time since

diagnosis increases, a cancer survivor crosses a psychological boundary into the permanent

survival stage, as fear of recurrence fades into the background.

Studies of cancer survivors in the treatment stage have largely addressed the amount of

time taken off for treatment and its economic effects. One study conducted in the first six months

after diagnosis, found that female breast cancer survivors and male prostate cancer survivors lost

22 and 20 days respectively (Bradley, Oberst, & Shenk, 2006). Another found that a third of

cancer survivors experienced a loss of one third of their work days over a month of treatment,

which added to their financial distress (Longo, Fitch, Deber, & Williams, 2006). There has been

considerably less emphasis on whether people work or not during treatment.

The goal of this study is to address the omission of the decision to work during treatment

from the research on cancer treatment and employment, and to extend its focus on a few specific

cancer sites to all types of cancer. First, this study compares the likelihood of working across

cancer types. Second, this study examines factors associated with return-to-work in the previous

literature to assess their association with working through treatment. These factors include cancer

stage, recurrence, time since diagnosis, gender, age, education, physically-demanding occupation,

full-time work, self-employment, and job-related benefits including health insurance and pensions

21

at diagnosis (Spelten, Sprangers, & Verbeek, 2002; Short, Vasey, & Tunceli, 2005; Bradley,

Neumark, Luo, & Bednarek, 2007).

This study will inform the design of clinical support programs that can help cancer

survivors make informed choices about working during treatment. Clinicians will have more

information about factors that may compromise treatment protocols. The design of clinical

support programs may be improved with features that prioritize the recovery of cancer survivors

and minimize the consequences of their work choices during treatment. Furthermore, this study

will aid employers in improving the design of employee accommodation programs to ease the

burden of fatigue and diminished cognitive processing. Employers adjust accommodation

programs to reduce the tension cancer survivors experience in their decision to work during

treatment (Pryce, Munir, & Haslam, 2007). This tension arises from the conflict between

working for financial reasons and the desire to stop working and devote maximum effort to

treatment and recovery (Bradley, Neumark, Luo, & Bednarek, 2007).

Cancer survivorship is an important topic for study for several reasons. First, many

people will get cancer. The lifetime risk for cancer in males is 1 in 2 and 1 in 3 for females

(American Cancer Society, 2010). Second, cancer’s effects are felt through all three stages of

survivorship. Fears of recurrence may persist for extended periods (van den Beuken-van

Everdingen et al., 2008). Survivors experience increased self-monitoring behavior and have more

intrusive thoughts because of the fear of recurrence. Hewitt, Rowland, and Yancik (2003)

identified an extensive list of physical and mental effects of cancer including sexual dysfunction,

chronic pain from lymphedema, anxiety, sense of isolation, and economic hardship. Furthermore,

significant proportions of cancer survivors reported health problems that affected their ability to

work, including poor to fair health (30%), psychological problems (5%), difficulty with daily

living activities (11%), and functional limitations (58%).

The physical and mental effects of cancer and its treatment have consequences for

working during all stages of survivorship. Cancer has contributed to work-related disability and

22

increased the likelihood of quitting work in the extended survival stage. These effects have been

found to differ by cancer site, stage, and recurrence (Short, Vasey, & Tunceli, 2005).

Chemotherapy treatment has delayed return-to-work after stopping for treatment (Sanchez,

Richardson, & Mason, 2004) in the short-term. Seventeen percent of cancer survivors reported

that they were unable to work (Hewitt, Rowland, & Yancik, 2003).

Cancer survivorship is increasing. Improvements in prevention, screening, and treatment

programs have produced higher five-year survival rates. Overall, the five-year survival rate for

all stages of cancer has increased from an average of 50% for cancers diagnosed in 1974-1975 to

66% for cancers detected in 1996-2003. In 2006, the cancer survivor population was estimated

at 11.4 million survivors (American Cancer Society, 2010) or about 4% of the U.S. population.

By 2025, the cancer survivor population is projected to double (Edwards et al., 2002). More

people are surviving cancer, so they are experiencing the long-term effects of cancer and its

treatment. Consequently, decisions in the treatment period have greater significance since more

cancer survivors will reach the extended and permanent survival stages. The work decisions in

those stages may be influenced by the decision to work during the treatment stage.

The effects of cancer on employment add to the economic disease burden of cancer. The

disease burden includes direct medical expenditure for prevention and treatment, and indirect

productivity costs on society through lost production due to mortality (premature death) and

morbidity (effects of illness). The total monetary burden of the disease was estimated by the

National Institutes of Health at $264 billion in 2010 (American Cancer Society, 2010). Morbidity

and mortality cost respectively, $21 billion and $140 billion. Lost days of work contribute to the

indirect costs of cancer. Cancer accounts for two-thirds of the 2.5 billion work-impaired days lost

annually to chronic illness (Kessler, Greenberg, Mickelson, Meneades, & Wang, 2001). As the

number of cancer survivors increase and live into the extended and permanent survival stages, the

economic burden of cancer will increase for direct medical treatment and indirect productivity

23

losses. However, the increase in this burden may be mitigated over the stages of cancer by

improved clinical support programs and employer accommodations in the treatment stage.

Conceptual Model

Becker (1965) observed that time has alternative uses that give rise to associated

opportunity costs. Individuals allocate their time among many activities in a way that brings

them the maximum satisfaction from their combination of work and leisure time. Grossman

(1972) conceptualized an individual’s health as an asset that depreciates over time unless restored

by investments in health, which includes the consumption of medical services. The decision to

restore health involved consideration of the costs and benefits of seeking medical treatment to

restore productivity and its effects on future benefits including income. Coile (2004) describes a

serious illness, like cancer, as a ―health shock.‖ Shock implies reconsideration of work

preferences with new information about the perceived risk of mortality. Confronted with worries

about survival, cancer patients re-evaluate the costs and benefits of restoring health and their

original work-leisure preferences (Berger, 1983; Berger & Fleisher, 1984; Bradley, Neumark,

Luo, & Bednarek, 2007; Bradley, Bednarek, & Neumark, 2002; Killingsworth, 1983; McClellan,

1998). The key factors from the return-to-work literature, that influence the opportunity costs of

whether or not to work during treatment, are summarized in Figure 2.1. These factors formed

three general categories: cancer-related, personal characteristics, and job-related features

(Bouknight, Bradley, & Luo, 2006).

Restoring health typically requires consumption of medical care. Treatment requires time

for visits and recuperation (Bradley, Oberst, & Shenk, 2006; Drolet et al., 2005). This time must

be reallocated from leisure activities, household production or market work time. In making a

decision about taking time off for treatment, workers compare the benefits of continuing to work

with its cost (Killingsworth, 1983; Paringer, 1983).

24

Framing the decision to work or not during treatment as a cost-benefit decision by a

cancer survivor requires consideration of the costs and benefits of working or not during

treatment. On the cost side of working, a cancer survivor may jeopardize his or her survival

prospects in the treatment stage if working reduces time for appointments, treatment doses, or

recuperation. Furthermore, continuing to work may harm the long-term trajectory for health in

the extended or permanent survival stages by exacerbating treatment side effects like fatigue and

pain. A reduction in productivity from poor health outcomes may jeopardize promotional

opportunities or continued employment with associated benefits like health insurance, sick leave,

retirement plan, and income in the extended or permanent survival stages. Alternatively, working

during treatment minimizes lost income in the treatment stage and retains access in the short-term

to job-related benefits like health insurance and sick leave. It may also demonstrate a strong

desire to work which may be interpreted by an employer as a signal of work commitment. This

may reduce the risk of losing a job in later cancer survivorship stages.

Stopping work for treatment has its own costs and benefits. On the cost side, there may

be a loss of income without full sick leave benefits. Also, stopping work may increase the risk of

slower promotion or unemployment in the extended or permanent stages, especially under poor

economic conditions. This might cost access to health insurance, retirement plans, sick leave

benefits, and future income. However, the benefits may accrue in better survival odds in

treatment and subsequent stages, recuperation to improve health outcomes and minimize the side

effects of treatment on productivity in the extended and permanent survival stages.

Time represents the largest opportunity cost in this cost-benefit framework. Its impact is

two-fold. First, there are of hours of work and income that may be lost in the decision to stop

working during treatment. Second, continuing to work during treatment may be associated with

poorer health outcomes over the cancer patient’s survival trajectory.

In the short-term, the decision to work or not during treatment requires cancer survivors

to allocate their time to work or to non-work activities like treatment. Treatment requires time for

25

medical appointments and recuperation and, consequently, treatment time is a large part of the

cost of stopping work for treatment. The time required for treatment varies significantly by

cancer site and stage and, consequently, so does the opportunity cost in lost income. Skin and

thyroid cancers may require relatively less time than advanced stages of breast cancer. Stage I

skin melanoma may require one time surgery. More advanced stages (II, III) of skin melanoma

require removal of lymph nodes and additional chemotherapy, which increases treatment and

recuperation time. Breast cancer in stages II and III may be treated surgically by breast

conserving surgery or modified radical mastectomy accompanied by chemotherapy. Recovery

time increases with extensive surgery. One study estimated that the time spent for initial

treatment defined as the first 12 months of care, varied from 270 hours for skin cancer, less than

1000 hours for breast and prostate cancers, and over 5,000 hours for gastric and ovarian cancers

(Yabroff et al., 2007).

In addition to the time for treatment, side effects vary among treatments. Fatigue and

pain may reduce productivity (Institute of Medicine, 2006). Surgery for skin melanoma in stage I

may be less likely to require chemotherapy compared to advanced stages of breast cancer.

Chemotherapy has been associated with higher levels of fatigue which may affect the ability to

work (Bovbjerg, Montgomery, & Raptis, 2005; Nail, 2004; Whitmer, Tinari, & Barsevick, 2004).

Some one-third of colon cancer survivors delayed returning to work by up to three months

following treatment with chemotherapy (Sanchez, Richardson, & Mason, 2004). The effects of

fatigue on breast and prostate cancer survivors may be significant since they lost an average of

one working month in the first six months after diagnosis (Bradley, Oberst, and Shenk, 2006). If

the time required for treatment is substantial, some survivors may feel pressured to shorten the

duration of treatment by foregoing the full treatment protocol. Some breast cancer survivors have

reported cutting back on treatments due to work considerations (Bradley, Neumark, Luo, &

Bednarek, 2007). Stopping work may mean substantial income losses combined with an

26

increased burden of out-of-pocket expenses that result in significant financial distress (Lauzier et

al., 2008; Longo, Fitch, Deber, & Williams, 2006).

Longer term, the decision to stop working may jeopardize job security and benefits by

increasing the risk of being laid off (Main, Nowels, Cavender, Etschmaier, & Steiner, 2005).

Stopping work is a signal to an employer that the employee may significantly increase health

insurance costs for the employer or is at risk of becoming less productive. A survivor’s lifetime

income may be reduced if stopping work channels a survivor into a different promotion path

(Hesselius, 2007; Kennedy, Haslam, Munir, & Pryce, 2007). Continuing to work during

treatment may affect future health if treatment protocols are compromised by work demands on

their time and increased stress levels. New health issues may develop or late effects of cancer

may be accelerated if cancer survivors reduce treatments or allow insufficient recuperation to

meet work demands (Bradley, Neumark, Luo, & Bednarek, 2007).

Alternatively, in the short term, working during treatment may help survivors cope with

the psychological burden of the diagnosis and provide ―psychic benefits‖ (Becker, 1965;

Kennedy, Haslam, Munir, & Pryce, 2007; Main, Nowels, Cavender, Etschmaier, & Steiner, 2005;

Peteet, 2000). For some survivors, working during treatment is an economic necessity (Hansson,

Bostrom, & Harms-Ringdahl, 2006; Hesselius, 2007; Judiesch & Lyness, 1999; Kennedy,

Haslam, Munir, & Pryce, 2007). Working also avoids reduction in income in the absence of

sick leave benefits (Bradley, Neumark, Luo, & Bednarek, 2007; Longo, Fitch, Deber, &

Williams, 2006; Lauzier et al., 2008).

Personal characteristics may influence the decision to work during treatment. There are

differences in labor force participation by gender that suggest men would be more likely to

continue working during treatment than women. Since men on average earn more than women,

men incur a higher opportunity cost for stopping work during treatment. Consequently, male

cancer survivors may be more likely to keep working given the higher cost of stopping compared

to female cancer survivors. Furthermore, younger cancer survivors may be more able to cope

27

with the side effects of treatment and continue working compared to older survivors whose

productivity may diminish significantly. Likewise, younger cancer survivors may not have

access to accumulated savings or retirement plans that compensate for the lost wages from

stopping work during treatment. Persons with higher levels of education are more likely to

continue working, since education is associated with better job characteristics including working

conditions, health insurance, retirement, sick leave and wages. The opportunity cost of lost

income increases with educational levels and also more benefits are at risk in the longer term.

Race may matter because of cultural differences and the possibility of discrimination. Whites

earn more on average than African-Americans so the opportunity cost of stopping work would be

greater in comparison to black Americans. A married survivor may be more likely to continue

working because a spouse could minimize income losses by working, facilitate treatment

appointments, or take up additional household duties that allow the survivor to work. Cancer

patients with children may be more or less likely to keep working depending on their productivity

in market work or child-raising functions. Some survivors may stop working because they are

now more productive in child-rearing compared to working. Other survivors may keep on

working since they cannot sustain the lost income because there are few areas of child-raising

where consumption might be cut. Cancer survivors in less densely populated areas have longer

distances to travel, which increases the time away from work, and raises the opportunity cost of

stopping work. Some travel is unavoidable for treatment. However, cancer survivors may

continue working to minimize the total amount of time lost from work for treatment

appointments. Self-employed persons are more likely to keep working since they have more

flexibility to accommodate the time required for medical appointments. This flexibility in their

use of time can minimize the opportunity costs from lost wages from not working a fixed

schedule of hours. Cancer survivors working full-time may be more likely to continue working

since they have greater opportunity costs in lost income due to not working. However, full-time

workers may have less flexibility to accommodate appointments for medical treatment. Other

28

workers may be too dependent on the work of the cancer survivor. The expense of trying to

accommodate a cancer survivor might jeopardize his or her long-term employment.

Consequently, a cancer survivor may be forced to incur short-term losses in income to avoid

increasing the risk of losing a job in the longer term. The existence of other chronic medical

conditions may combine with cancer to reduce productivity and increase the likelihood that

cancer survivors will stop working in the treatment stage. Survivors with other sources of non-

labor income or assets may be less likely to continue working since they can replace income lost

from not working.

Job characteristics influence opportunity costs and the likelihood of working during

treatment. Cancer survivors with employer-contingent health insurance may be more likely to

work during treatment due to the large costs they would incur for unsubsidized medical prices in

treatment and the uncertain medical expenses in the extended and permanent survivorship stages.

This could result in ―job lock‖ where cancer survivors feel compelled to continue working during

treatment and not increase their risk of losing benefits when they are most needed. Likewise,

survivors with rich retirement benefits, such as defined benefit plans, may be more likely to keep

working since the benefits depend on longevity in the job. Cancer survivors with access to paid

sick leave incur lower opportunity costs for stopping work in the short-term because they may

lose little if any income. Many of the foregoing benefits are provided by large employers, so

cancer survivors may be more or less likely to quit working if employed by a large employer

compared to a small firm. Unlike small firms that offer no job protections for stopping work

during treatment, large employers are required by the Family Medical Leave Act to provide

unpaid time (US Department of Labor, 2007). This reduces the risk of losing employment due to

taking time off from work for treatment and consequently lowers the opportunity cost of not

working. Cancer survivors working in physically demanding jobs may be less likely to work

during treatment due to the added burden of side effects of treatment or recuperation from surgery

which reduces their productivity in their current job.

29

Methods

Sample

The cancer survivors in this study were identified in the first wave of the Penn State

Cancer Survivor Study (PSCSS), which has been described in detail by Short, Vasey, & Tunceli

(2005). This study used the same sample of cancer survivors, but focused on the responses to

questions about working during treatment. The PSCSS eligibility criteria included: (1) cancer

survivors between the ages of 25 and 62 at diagnosis, (2) with a first cancer diagnosed between

January 1997 and December 1999, and (3) with a prognosis that would permit participation over

four waves in the panel survey. Survivors were recruited from cancer registries at four medical

centers including The Johns Hopkins Hospital, Lehigh Valley Hospital and Health Network,

Geisinger Medical Center, and Milton S. Hershey Medical Center. The first of four annual

interviews was conducted from October 2000 through December 2001.

Of the 1,763 survivors interviewed at Wave 1, 1,393 (79%) were working for pay at the

time of their cancer diagnosis. Seventy-eight (5.6%) cancer survivors did not respond about

working during treatment. The final study sample included 1,315 cancer survivors, who were

working at the time of diagnosis and who responded to the question about working during

treatment. Of these, 794 (60.3%) continued to work during treatment while 521 survivors

(39.7%) stopped working entirely during treatment. Survivors who continued to work during

treatment were asked a follow-up question about how much time, if any, they had taken off

during treatment, while still working throughout the period.

The distribution of the sample characteristics is summarized in Table 2.1. Survivors with

breast cancer represented almost one-third of the sample (32%). Forty-two percent were

diagnosed with stage I cancer. Thirty-nine percent were treated at The Johns Hopkins Hospital.

At diagnosis, 82% were less than 57 years-old. Ninety-three percent were white. Eighty percent

were married at diagnosis. Sixty-five percent had some college education. Sixteen percent were

employed in physical occupations. Over 98% had health insurance at diagnosis, including fifty-

30

four insured through their own employer at the time of diagnosis. Thirty-seven percent of the

survivors had children under age 18. Seventy-eight percent were working full-time. Thirteen

percent were self-employed. Seventy percent had some type of retirement benefit either as a

defined benefit (traditional pension) or defined contribution (401k) or both. Seventy-five percent

lived in areas with a total population of less than 1 million. Fifty-six percent worked in locations

with fewer than 100 employees at the work location.

Variables

The primary outcome of interest is whether patients who were working at diagnosis

continued working during treatment. Working during treatment was operationalized by a survey

question: ―Did you stop working while you were being treated (for the cancer you had at that

time)?‖ In addition, interviewers were directed to code responses according to the following

instructions: if R (cancer survivor) stopped working for only part of the treatment, code ―no.‖ If

R (survivor) never went back to work, code ―yes.‖ While every attempt was made to accurately

determine the work status of cancer survivors through the treatment period, there was some

ambiguity present that may have affected the responses of cancer survivors. A cancer survivor

may have used sick leave for part of the treatment period and otherwise kept working. One

cancer survivor might have reported that choice as working through treatment (correct) while

another cancer survivor may have responded that they had stopped working (incorrect).

Consequently, this ambiguity introduces some degree of measurement error for the outcome

variable. From a statistical perspective, this adds to the variability in the data and diminishes the

likelihood of finding statistically significant differences.

All covariates were measured at the time of diagnosis. Some covariates were not

included in the models because of the small number of observations (e.g., race), or variables were

measured at interview, which is subsequent to the treatment period (e.g., chronic medical

conditions). Other variables like wages, salaries, sick leave, and working conditions are

31

measured by proxy using educational attainment or the size of employers, since these variables

are correlated with better paying jobs, benefits, and working conditions.

Cancer site and stage were abstracted from the cancer registries. Colorectal cancer serves

as the reference cancer site since it is highly prevalent and common to both genders. There is no

stage IV cancer per se since that stage was by definition included with blood cancers and by

design included in lymphoma stage IV. The remaining cancers include varying proportions of

stages I-III cancer.

The physical nature of the cancer survivor’s job was constructed by examination of the

survivor’s responses to a question about his or her work. ―What sort of work do you do?‖ The

responses were assigned to one of twenty-three Bureau of Labor Statistics Standard Occupational

Classification categories (2000). The 23 codes were collapsed into a dichotomous variable that

reflected the nature of the work based the BLS job descriptions for the standard occupation

categories.

Full-time work is defined by working 35 or more hours at diagnosis. Since the type of

retirement plan may have different consequences for labor market behavior, retirement benefits

are categorized by defined benefit or defined contribution (Gustman, Mitchell, & Steinmeier,

1994).

Cancer survivors living in rural locations must use more time for travel associated with

cancer treatment. Beale codes, which indicate the degree of rurality using population size, were

assigned to cancer survivors based on their residence at the time of diagnosis (Economic

Research Service, 2008). Cancer survivor locations were categorized as populations ―less than

250,000,‖ ―250,000 to less than one million,‖ and ―one million or more.‖

Since this is a sample of cancer survivors and not all cancer patients, healthy survivor

bias may be present. Consequently, time since diagnosis in yearly categories is used to control

for unobserved differences between the longest surviving patients and the newer survivors at the

first interview.

32

Statistical Analysis

Since the labor economics literature traditionally examines the labor supply separately for

men and women, analyses were conducted separately by gender. Statistical tests of the bivariate

relationship between working and the covariates were conducted using Chi-square tests.

Multivariate logistic regression models of the likelihood of working during treatment were

estimated for male and female survivors. Pair-wise comparisons of covariates with multiple

categories such as cancer site, stage, age, education, retirement benefits, and time since diagnosis

were conducted using Wald tests for the equality of the coefficients. SAS version 9.2 was used in

all statistical tests.

The sample includes both unmarried and married cancer survivors. Since married

survivors may have an alternative source of health insurance through a spouse, the effect of

having health insurance from a current employer may be accentuated or diminished by a spouse

with his or her own health insurance. A married survivor might be less likely to work during

treatment if the survivor believes that he or she has a potential alternative source for health

insurance. Alternatively, a survivor may be more likely to work if he or she views the health

insurance benefit as a potential replacement against a spouse’s risk of losing his or her own health

insurance. Consequently, an interaction term for survivor and spouse health insurance was tested

in a subset of married couples with 601 female and 408 male cancer survivors. There were 71

female (11.8%) and 51 male (12.5%) cancer survivors among the couples with both spouses as

policy holders.

33

Results

Bivariate Summary

The overall proportion of cancer survivors working during treatment (60%) varied by

cancer site, stage (Table 2.2), socio-demographic, and job characteristics (Table 2.3). Treatment

period durations varied by cancer site (Table 2.4) but averaged 4 and 6 months for male and

female cancer survivors respectively.

The proportion of male cancer survivors working during treatment was associated (chi-

square test, alpha level) with cancer site (.10), stage (.05), race (.10), education (.05), physical

occupation (.05), employer-contingent health insurance (.05), and employer retirement benefit

plans (.05) (Table 2.2). Male cancer survivors worked most frequently with the following

characteristics: lymphatic cancer in stages I-III (83%), stage I cancers (70%), white (60%), post-

graduate education (71%), non-physical occupation (64%), employer-contingent health benefits

(64%), and a combination of defined benefit and defined contribution retirement plans (89%)

(Table 2.3). For the most prevalent male cancers, prostate, colon, skin, and lung, the proportion

working during treatment was respectively 61, 61, 75, and 31 percent (Table 2.2).

Likewise, the proportion of female survivors who worked was associated (chi-square test,

alpha level) with cancer site (.01), education (.01), physical occupation (.01), employer-

contingent health insurance (.05), and time since diagnosis (.05). Female cancer survivors were

more likely to work with melanoma skin cancer (77%), post-graduate education (71%), a non-

physical occupation (63%), employer-contingent health insurance (64%), and 2 to 3 years since

diagnosis. For the most prevalent female cancers, breast, colon, skin, and lung, the proportion

working during treatment was respectively 68, 52, 77, and 39 percent (Table 2.2).

While the treatment period duration varied by cancer site which require different

protocols involving combinations of surgery, radiation, and chemotherapy in varying doses, 50%

of male cancer survivors’ treatment periods ended in less than 6 months and 75% finished within

7 months (Table 2.4). Similarly, half of the female cancer survivors completed treatment by 6

34

months and 75% in a little over one year (Table 2.4). Melanoma skin cancer was the shortest

duration for both male and female cancer survivors with half completing treatment in less than

two months. Alternatively, blood cancer treatment periods took more time for both male and

female cancer survivors with half of the survivors completing treatment within one year but many

required more than one year or longer. For the most prevalent cancers, half of male cancer

survivors completed prostate cancer treatment within 3 months with seventy-five percent

completion by a little more than half-a-year. Half of breast cancer survivors had treatment

durations of 8 months or less and seventy-five percent spent up to a little more than a year in

treatment.

Multivariate Results

The adjusted odds ratios (OR) from the multivariate regression models for male and

female cancer survivors are presented in Table 2.5 (male) and Table 2.6 (female). The models

include three specifications to illustrate the effects of adding blocks of variables for cancer

(Model I), person covariates (Model II), and job characteristics (Model III). The results are

reported as odds ratios with 95% confidence intervals. Male (Chart 1) and female (Chart 2)

comparisons of cancer sites are organized by adjusted odds ratios in descending order.

Male Cancer Survivors

Male survivors (Table 2.5) with blood cancer (OR = 0.38; .14, 1.05, p=.06) were less

likely to work through treatment compared to the colorectal cancer reference group. There were

additional differences among cancer sites based on pair-wise comparisons which are summarized

in Chart 1. Male survivors with lymph stages I-III were more likely to work than male survivors

with blood (p =.008), respiratory (p =.04), or other cancers (p =.04). Prostate cancer survivors

were more likely to work compared to male survivors with blood (p =.01), respiratory (p =.09), or

other cancers (p =.05). Male survivors with skin cancer were more likely to work compared to

35

male survivors with blood cancer (p =.03), but not compared to those with respiratory (p <.12) or

other cancer (p <.14). However, this result is likely related to the small number of cases in the

sample. On balance, the results indicate that male survivors with blood, respiratory, and other

cancers were less likely to work during treatment compared to male survivors with lymph cancer

in stages I-III, skin, or prostate cancer.

Male survivors with stage II (OR = 0.48; 0.27, 0.87, p =.04), stage III (OR = 0.50; 0.26,

96, p =.08) or unstaged (OR = 0.36; 0.12, 1.12, p =.07) cancers were less likely to work in the

treatment period compared to survivors with stage I cancer. Paired comparisons of stages found

no significant differences. There were no differences in the likelihood of working associated with

time since diagnosis for male survivors.

Among person-related covariates, male survivors aged 45-52 were less likely to work

compared to survivors aged 25-44 (OR=0.60; 0.34, 1.06, p =.08). There were no significant

effects for other age groups, education, race, marital status, the presence of children under age 18,

or the size of the metropolitan area.

For job-related factors, male survivors with current employer health insurance were more

likely to continue working during treatment compared to male survivors who were not dependent

on their employer for health insurance (OR = 1.73; 1.09, 2.73, p =.02). Male survivors with

defined benefit retirement plans (OR = 2.18; 1.03, 4.59, p =.02) or a combined defined benefit

and defined contribution plan (OR = 9.53; 1.82, 49.96, p =.008) were more likely to work during

treatment compared to male survivors with no retirement benefit plan. Furthermore, male

survivors with a combined defined benefit and contribution plan were more likely to keep

working than male survivors with only a defined benefit plan (p =.06). Male survivors in firms

with 100-499 employees were less likely to keep working during treatment compared to male

survivors in firms with fewer than 25 employees (OR = 0.41; 0.16, 1.05, p =.06). However, there

was no consistent pattern to this effect across employer size. There were no statistically

36

significant effects for physical occupations, full-time work, or self-employment in the

multivariate analysis.

Female Cancer Survivors

Female survivors (Table 2.6) with breast (OR = 2.20; 0.93, 5.20, p =.07), melanoma of

the skin (OR = 4.95; 1.33, 18.47, p =.02) or thyroid (OR = 2.50; 0.89, 7.0, p = 7.00) cancer were

more likely to work through treatment compared to the colorectal cancer reference group.

Additional differences were found in pair-wise comparisons of cancer sites (Chart 2). Female

survivors with skin cancer were more likely to work in treatment compared to female survivors

with central nervous system, blood (p =.004), head and neck (p =.03), lymph stages I-III (p

=.095), respiratory (p =.003), uterus (p =.007), and other cancers not individually identified (p

=.006). Female survivors with lymph stage IV were more likely to work compared to female

survivors with blood (p =.05), central nervous system (p =.03), or respiratory (p =.05) cancers,

but less likely to work compared with skin cancer survivors (p =.095). Female survivors with

thyroid cancer were more likely to work compared to survivors with blood (p =.01), central

nervous system (p =.008), respiratory (p =.01), uterus (p =.02), or other cancers not separately

listed (p =.02). Female survivors with breast cancer were more likely to work compared to

female survivors with central nervous system (p =.007), blood (p =.008), respiratory (p =.006),

uterine (p =.009), or other cancers (p =.004). On balance, female survivors with central nervous

system, blood, respiratory, colon, uterus, or other cancers were less likely to work during

treatment compared to female survivors with skin, lymph stage IV, thyroid, or breast cancer.

Stage was not statistically significant. Female survivors 3 to 4 years since diagnosis

(OR = 2.14; 1.19, 3.87, p =.01) were more likely to be working compared to female survivors

with less than one year since diagnosis.

For person-related variables, educational attainment categories were the covariate with

statistical significance. Neither age, race, marital status, the presence of children under age 18,

37

nor size of metropolitan area had a significant effect for female survivors on the likelihood of

working. Female survivors who completed high school (OR = 4.40; 1.51, 12.83, p =.007), some

college (OR = 4.63; 1.56, 13.75, p =.006), college (OR = 6.51; 2.12, 19.96, p =.0009), or post-

graduate work (OR = 6.68; 2.19, 20.42, p =.0008) were more likely to work during treatment than

female survivors with ―less than high school‖ education. Pair-wise comparisons found

differences among educational levels. Female survivors with college (p =.09) or post-graduate (p

=.08) education were more likely to work compared to high school graduates. Likewise, female

survivors with college (p =.03) or post-graduate (p =.02) education were more likely to work

compared to female survivors with other technical or vocational training.

There were no statistically significant results for job-related variables. No effects were

found in the multivariate analysis for physical occupation, full-time work, and self-employment,

health insurance with a current employer, retirement benefits, or firm size.

Married Cancer Survivors and Health Insurance through a Current Employer

Married cancer survivors may have another option for health insurance through their

spouse. Some cancer survivors may view this as an option or form of ―insurance hedge‖ against

losing their own health insurance if they stop working. Other cancer survivors may be married to

a spouse without his or her own health insurance and are more likely to work rather than take

time off and jeopardize the only source of health insurance. If a married cancer survivor and his

or her spouse have independent health insurance policies, the cancer survivor may be less likely

to work because if he or she does lose access to health insurance, his or her spouse may be able to

provide replacement coverage.

One widely used approach in the literature to test for the effect of job lock is by use of an

interaction term. A subset of married cancer survivors was selected from the cancer respondents.

The test for interaction of each spouse’s own employer-contingent health insurance availability

was measured by creating a counterfactual group which included non-working spouses at the time

38

of diagnosis. These spouses may have a prior employer-sponsored plan or the potential for

obtaining one from re-employment.

The direct effect of having employer-contingent health insurance increases the likelihood

of working for both male (OR = 2.1; 1.1, 4.1, p <.05) and female cancer survivors (OR = 2.1; 1.1,

4.1, p <.05). Having a spouse with employer health insurance also has a direct effect and

increases the likelihood of working by female cancer survivors (OR = 1.9; 1.0, 3.5, p <.05). One

reason for this effect may be that the female cancer survivor may not have access to coverage

through that policy and keeps working to minimize the impact of uncertain future medical

expenses.

There is no strong evidence for job lock among married male cancer survivors (Table 2.7,

row 3, column 1) since the odds ratio is not statistically significant (OR = 0.7; 0.2, 1.9) for the

interaction term. However, there is evidence of job lock among married female cancer survivors

(Table 2.7, row 3, column 4). The direct effect is diminished for female cancer survivors if both

have their own employer-contingent health insurance (OR = 0.4; 0.2, 0.9). The cancer survivor is

less likely to continue working if her husband or partner also has employer-sponsored health

insurance. Having a potential alternative to her employer-contingent health insurance decreases

the likelihood of working during treatment and weakens the ―job lock‖ effect of the employer-

contingent health insurance.

39

Discussion

The major finding in this paper is that the decision to work during treatment is

consistently associated with clinical variables like cancer site and stage. Few significant effects

were found among the personal attributes or job characteristics. Several variables that were

statistically significant in bivariate analyses were not significant in multivariate analyses.

Controlling for other covariates, the male survivor model found no significant differences for

respiratory cancer compared to colorectal cancer, high school education compared to less than

high school diploma, or physical occupation compared to occupations without physically

demanding work. The female model failed to find statistically significant differences for cancers

of blood, respiratory systems, or uterus compared to colorectal cancer, physical occupation

compared to occupations with less physical work, health insurance from an employer at diagnosis

compared to not holding a policy, and defined contribution retirement plans compared to no

retirement plan

Even in the multivariate models, however, male cancer survivors differed in the

likelihood of working by cancer site. While cancer on balance reduces labor supply for working

cancer survivors in the treatment period (Short, Vasey, Tunceli, 2005), the proportion of

survivors working during treatment varies by cancer site. This study found that male survivors

with prostate, skin, or lymph cancer (stages I-III) were more likely to work during treatment

compared to male survivors with blood, respiratory, and other cancers. In a similar study of

cancer survivors in the extended survivorship period, Short, Vasey, & Tunceli (2005) found

virtually the same result. In another study of cancer survivors from the extended survival season,

Schultz, Beck, Stava, & Sellin (2002) found approximately the same results. They reported that

cancer survivors with genitourinary, melanoma, and Hodgkin’s cancers were more likely to be

working compared to survivors with lung cancers. Since the incidence of new cases varies by

cancer site, estimates of the proportion of cancer survivors who work during treatment must

40

account for cancer sites. Prostate, lung, skin, and non-Hodgkin’s lymphoma cancers represent

half of all new cases in men (American Cancer Society, 2010).

Likewise, female cancer survivors differed in the likelihood of working during treatment

according to the type of cancer. Female survivors with breast, skin, thyroid, or lymph cancer

(stage IV) were more likely to work during treatment compared to survivors with central nervous

system, blood, respiratory, colon, uterine, or other cancers. Again, these differences by cancer

site are consistent with the return-to-work literature in the extended survivorship period (Schultz,

Beck, Stava, & Sellin, 2002; Short, Vasey, & Tunceli, 2005). Breast, lung, skin, thyroid, and

non-Hodgkin’s lymphoma cancers comprise about half of all new cases in women (American

Cancer Society, 2010).

Cancer stage affects the likelihood of working by male cancer survivors working during

treatment for stages I-III. Male survivors with localized (stage I) cancers were more likely to

work during treatment compared to survivors with more advanced cancer at diagnosis (stages II-

III or unstaged). While stage IV is not measured separately in this sample, it is included in

differences between blood cancers and lymphoma stage IV. Similar findings were reported by

Short, Vasey, & Tunceli (2005) for cancer survivors in the extended survivorship period.

However, closer examination of the most prevalent male cancer, prostate, found no differences by

stage. In a prospective study of prostate cancer survivors, Bradley, Neumark, Luo, Bednarek, and

Schenk (2005) found that any initial differences by stage in the likelihood of returning to work

had faded by the end of the treatment period. The lack of differences in the current study for

prostate cancer by stage may result from the nature of the sample which contains few stage I

cases.

Female cancer survivors’ likelihood of working during treatment is not associated with

cancer stage. In contrast, the literature suggests mixed results. One study of female breast cancer

survivors near the end of the treatment period (twelve months) found that survivors were less

likely to be working with stage III or IV cancers compared to stage 0 (Bouknight, Bradley, &

41

Luo, 2006). Since differences were not found for stage I or II cancers, this suggests that findings

for a staging effect may be strongly influenced by the selection of the reference group. Stage 0 or

in situ breast cancer is the most localized presentation and requires less extensive surgery or

doses of radiation or chemotherapy treatment. Consequently, any other stage in comparison with

stage 0 involves more extensive treatment which may affect the decision to work during

treatment. Using stage I as the reference group in this study, yielded no association between the

likelihood of working during treatment and cancer stage. Another study of married breast cancer

survivors in the extended survival period found that even with ―in situ‖ or stage 0 cancers as a

reference group, no effects were found for cancer stage (Bradley, Neumark, Luo, & Bednarek,

2007). A study of combined male and female cancer survivors did find that survivors with stage

II or stage III cancer were less likely to be working in the extended survival period compared to

survivors with stage I cancer at diagnosis (Short, Vasey, & Tunceli, 2005). However, this result

in a composite group may be influenced by an association between stage and the likelihood of

working by men as found in the present study. A separate analysis of female breast cancer

survivors in this study found no significant differences in the likelihood of working during

treatment for cancer stage. There may be other cancer sites with less prevalence where cancer

stage may have an effect on the likelihood of working during treatment. However, the evidence

from this study suggests that the decision to continue working during treatment by female cancer

survivors is driven more by cancer site than stage at diagnosis.

There was only one personal characteristic of significance. While female cancer

survivors were more likely to work with greater levels of education, no similar association was

found for men. Female cancer survivors with high school, some college, college, or post-

graduate study were more likely to work during treatment compared to survivors with a less than

high school education. The literature has reported mixed results for the effect of education. In a

similar multisite cancer study of cancer survivors regardless of gender, higher levels of education

increased the likelihood of working in the extended survival stage (Short, Vasey, & Tunceli,

42

2005). In studies of female breast cancer survivors, higher levels of educational attainment have

been associated with greater likelihood of working at the end of the treatment period and in the

extended survival stage (Bouknight, Bradley, & Luo, 2006) while another found no effect in the

extended survival stage (Drolet et al., 2005). A separate analysis of breast cancer survivors in

this study found an association between higher levels of education and a greater likelihood of

working during treatment. Furthermore, the lack of association between education and working

for male cancer survivors is consistent with one recent study of prostate cancer survivors at the

end of the12-month treatment period (Bradley, Neumark, Luo, Bednarek, & Schenk, 2005) which

found no strong pattern of evidence for education. A separate analysis of prostate cancer

survivors in this study sample yielded similar results to the Bradley study.

Employer-sponsored health insurance and retirement benefits were associated with an

increased likelihood of working during treatment for male cancer survivors. There are no reports

in the return-to-work literature for the impact of employer-sponsored benefits on the likelihood of

working by male cancer survivors. However, a study of married breast cancer survivors

(Bradley, Neumark, Luo, & Bednarek, 2007) suggested that employer-sponsored health insurance

increased the likelihood of working at six month intervals up to 18 months after diagnosis –

essentially during, at the end, and post-treatment stage. Other focus group studies support the

idea that job benefits, especially health insurance, are one of the reasons cancer survivors worked

during treatment (Main, Nowels, Cavender, Etschmaier & Steiner, 2005).

There is evidence of job lock among married female cancer survivors. A wife with

cancer and with her own employer-sponsored health insurance was less likely to work if her

husband also had his own health insurance from his employer. This finding is consistent with is

consistent with more general labor market studies on ―job lock‖ and health insurance.

The apparent difference in the importance of education versus employer-sponsored job

benefits by gender may be an artificial distinction. Since better education is often associated with

better jobs including rich benefit packages, there is a significant amount of multicollinearity

43

between education and job-related covariates. Larger samples with more power to discriminate

between the contributions of each covariate would provide more discriminating results. What

should not be overlooked is that to the extent that jobs and education imply a ―better job with

greater earnings,‖ this may act as a powerful incentive to continue working during treatments as

suggested in focus group studies (Main, Nowels, Cavender, Etschmaier & Steiner, 2005).

However, the financial pressure to keep working during treatment may have unintended

consequences for health outcomes and productivity in subsequent stages (Bradley, Neumark, Luo,

& Bednarek, 2007; Main, Nowels, Cavender, Etschmaier, & Steiner, 2005).

Two limitations of this study include the healthy survivor effect and the measurement of

the outcome variable, ―working during treatment.‖ The ideal counter-factual sample would

include all survivors at diagnosis with measurement of their treatment period work decisions – a

prospective study design. Since this study is retrospective, it only includes a subset of the ideal

sample, those cancer survivors who lived at least until the first interview. Consequently, this

sample does not include the sickest patients. This affects the study results since the sicker

patients would more likely have stopped working than continued working during treatment. The

likelihood of working during treatment is therefore, overstated to an unknown degree. The

results here may be improved by additional prospectively designed studies. The advantage to the

current study design is that it provides an economical approach without sacrificing material

accuracy about the size of effects on the likelihood of working during treatment.

The other limitation is the measurement of ―working during treatment‖ and the potential

errors by survivors who self-report work status during treatment. This increases the measurement

error of the outcome variable, which in turn increases the variability of the data and the

magnitude of the error term. Larger standard errors make it more difficult to declare differences

statistically significant. This may partly explain the lack of findings, especially for the personal

and job characteristics which suffer to some extent from multicollinearity. This is especially

relevant for the distinguishing between the effects of employer-sponsored benefits in contrast to

44

the effects of education on the likelihood of working, since greater education is itself associated

with better jobs that include benefits.

Clinicians can assist patients in making choices about working during treatment.

Clinicians can inform patients about types of cancer or stage that provide less of a barrier to

continue working during treatment. This information can allay patient fears about risking the loss

of livelihood or health insurance benefits, especially in the context of current labor market

conditions and long-term unemployment (U.S. Department of Labor, 2010). Since cancer is

associated with aging, many older cancer survivors, who stop working, may face the same fears

of older unemployed workers that they might never work again once they become unemployed

(Rich, 2010). However, while cancer survivors continue to work, little is known about the effects

of working on outcomes, including longevity and quality-of-life, compared to cancer survivors,

who are able to stop working during treatment. Future research may provide more information

about the health consequences of working or not during treatment for cancer.

Employers must weigh choices about accommodations for cancer survivors. Many cancer

survivors with specific types of cancer or stage are more likely to work during treatment.

Employers who can accommodate the need for time off for treatment with flexible work hours or

arrange for temporary coverage may find that this is a cost-effective way to retrain valuable

employees when the cost of searching, training, and replacing an employee may be substantial.

Average turnover costs per employee were $14,000 and ranged from $7,500 in leisure and

hospitality to almost $20,000 in information processing industries (O’Connell & Kung, 2007).

Beyond the financial costs to replace and train an employee, there are morale costs to other

employees. Other employees may evaluate the fairness and justice of accommodation for a

cancer survivor as a litmus test for their own health or caregiver issues. Work place justice and

equity have been linked to dissatisfaction with consequences for morale and organizational trust

(Forret & Love, 2008; Hubbell & Chory-Assad, 2005; Nichols, 2008).

45

The importance of employer-sponsored benefits either measured by health insurance,

retirement plans, or education associated with access to jobs with rich benefits suggests that there

may be an unintended consequence of employer-sponsored benefits designed to reduce turnover

and retain productive workers. Focus groups have suggested that a salient reason for working

during treatment when survivors would prefer not to work is the potential financial impact from

the loss of income and health insurance benefits when they are most needed in facing uncertain

future medical expenses.

The consistency between cancer types in the treatment period used in the present study

and the extended survivorship season in the literature has implications for investments in support

programs and employer accommodations. Since cancer survivors with breast or prostate cancer

are more likely to work during treatment and in the extended survival season, early use of support

programs and workplace accommodations may lead to further reductions in lost productivity.

The benefits also accrue over a longer period of time which will affect cost-benefit evaluations of

these programs and accommodations.

Finally, cancer survivors with more education and higher income jobs are not more

likely to take time off for treatment because they can afford that choice. In fact, they are more

likely to continue working during treatment because of the higher opportunity costs of lost

income and potential for jeopardizing access to health insurance if they take time off. On the

other hand, less educated cancer survivors, who are more likely to be working in lower-paying

jobs, that may also lack health insurance benefits, were more likely to quit because of lower

opportunity costs, lose income that may be needed for out-of-pocket expenses. The disparity in

educational opportunities exacerbates any existing healthcare disparities.

46

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50

Figure 2.1

Conceptual Model of Factors Which Affect Time for Treatment and Ability to Work in the Treatment Period

Cancer-Related Characteristics

- Site

- Stage

- Time since Diagnosis

Work Outcome during Treatment

- Keep Working

- Stop Working

Person-Related Characteristics

- Gender

- Age

- Education

- Race

- Marital Status

- Children

- Job-opportunities

- Self-employed

- Full-time status

Job-Related Characteristics

- Health Insurance Source

- Retirement Benefits

- Sick Leave

- Wages / Salaries

- Employer Size

51

Table 2.1

Characteristics of Cancer Survivor Study Sample at Diagnosis by Gender

Male Survivors Female Survivors Totals

Characteristic Number Column

Percent Number

Column

Percent Number

Column

Percent

Working During Treatment 483 100 832 100 1315 100

Cancer Site

Blood 35 7.3 30 3.6 65 4.9

Breast - - 415 49.9 415 31.6

Central Nervous System 19 3.9 23 2.8 42 3.2

Colorectal 51 10.6 31 3.7 82 6.2

Head & Neck 35 7.3 23 2.8 58 4.4

Lymphatic, Stages I-III 23 4.8 15 1.8 38 2.9

Lymphatic, Stage IV 19 3.9 12 1.4 31 2.4

Prostate 113 23.4 - - 113 8.6

Respiratory 16 3.3 31 3.7 47 3.6

Skin (Melanoma) 24 5.0 26 3.1 50 3.8

Thyroid 29 6.0 61 7.3 90 6.8

Uterus - - 71 8.5 71 5.4

Other cancers 119 24.6 94 11.3 213 16.2

Stage

I 145 30.0 408 49.0 553 42.1

II 178 36.9 259 31.1 437 33.2

III 88 18.2 108 13.0 196 14.9

IV (Blood and Lymphoma

Stage IV cancers)

54 11.2 42 5.1 96 7.3

Unstaged 18 3.7 15 1.8 33 2.5

Treatment Center

Geisinger Medical Center 100 20.7 132 15.9 232 17.6

The Johns Hopkins Hospital 158 32.7 357 42.9 515 39.2

Lehigh Valley Hospital 133 27.5 211 25.4 344 26.2

Milton S. Hershey Med. Ctr. 92 19.1 132 15.9 224 17.0

Time Since Diagnosis

1 Year or less 45 9.3 72 8.7 117 8.9

2 Years 165 34.2 288 34.6 453 34.4

3 Years 153 31.7 272 32.7 425 32.3

4 or more Years 120 24.8 200 24.0 320 24.3

52

Table 2.1 (cont.)

Characteristics of Cancer Survivor Study Sample at Diagnosis by Gender

Male Survivors Female Survivors Totals

Characteristic Number Column

Percent Number

Column

Percent Number

Column

Percent

Age Group (Years)

25-44 145 30.0 302 36.3 447 34.0

45-52 136 28.2 286 34.4 422 32.1

53-57 105 21.7 156 18.7 261 19.8

58-61 89 18.4 79 9.5 168 12.8

62+ 8 1.7 9 1.1 17 1.3

Race

White 457 94.6 765 91.9 1222 92.9

Non-White 25 5.2 65 7.8 90 6.8

Missing 1 <1 2 <1 3 <1

Total 483 100 832 100 1315 100

Married

Single 63 13.0 193 23.2 256 19.5

Married or Living with Partner 420 87.0 639 76.8 1059 80.5

Education

Less Than HS 23 4.8 28 3.4 51 3.9

High School 147 30.4 252 30.3 399 30.3

Some College 87 18.0 212 25.5 299 22.7

College 117 24.2 159 19.1 276 21.0

Post-graduate 105 21.7 167 20.1 272 20.7

Other Training 4 <1 13 1.6 17 1.3

Missing 0 0 1 <1 1 <1

Physical Occupation

No 339 70.2 759 91.2 1098 83.5

Yes 141 29.2 72 8.7 213 16.2

Total 483 100 832 100 1315 100

Current Employer-Contingent

Health Insurance

No 184 38.1 412 49.5 596 45.3

Yes 297 61.5 410 49.3 707 53.8

Missing 2 <1 10 1.2 12 <1

53

Table 2.1 (cont.)

Characteristics of Cancer Survivor Study Sample at Diagnosis by Gender

Male Survivors Female Survivors Totals

Characteristic Number Column

Percent Number

Column

Percent Number

Column

Percent

Children Under 18

No 312 64.6 523 62.9 835 63.5

Yes 171 35.4 309 37.1 480 36.5

Fulltime Employment

No 49 10.1 217 26.1 266 20.2

Yes 429 88.8 601 72.3 1030 78.3

Missing 5 1.1 14 1.6 19 1.4

Self-employed

No 402 83.2 738 88.7 1140 86.7

Yes 81 16.8 93 11.2 174 13.2

Missing 0 1 <1 1 <1

Company-wide

Employees

Less than 25 97 20.1 177 21.3 274 20.8

25-99 53 11.0 84 10.1 137 10.4

100-499 68 14.1 138 16.6 206 15.7

500 more 254 52.6 408 49.0 662 50.3

Missing 11 2.3 25 3.0 36 2.7

Any Retirement Benefit

Defined Benefit 150 31.1 241 29.0 391 29.7

Defined Contribution 181 37.5 285 34.3 466 35.4

Defined Benefit &

Defined

Contribution 18 3.7 23 2.8 41 3.1

No Benefit 128 26.5 255 30.6 383 29.1

Missing 6 1.2 28 3.4 34 2.6

Size of Metro Area

Population

Over 1 million 103 21.3 234 28.1 337 25.6

250k – 1million 249 51.6 387 46.5 636 48.4

Under 250k 131 27.1 211 25.4 342 26.0

54

Table 2.2

Percent of Survivors Working During Treatment

by Cancer Site and Stage at Diagnosis within Gender1

Male Survivors Female Survivors

Characteristic Number Percent Number Percent

Working During Treatment 483 59.4 832 60.9

Cancer Site * ***

Blood 35 48.6 30 40.0

Breast - 415 68.4

Central Nervous System 19 63.2 23 30.4

Colorectal 51 60.8 31 51.6

Head & Neck 35 57.1 23 56.5

Lymphatic, Stages I-III 23 82.6 15 66.7

Lymphatic, Stage IV 19 73.7 12 75.0

Prostate 113 61.1 -

Respiratory 16 31.3 31 38.7

Skin (Melanoma) 24 75.0 26 76.9

Thyroid 29 62.1 61 70.5

Uterus - 71 45.1

Other cancers 119 53.8 94 52.1

Stage **

I 145 69.7 408 61.8

II 178 56.2 259 62.5

III 88 54.5 108 59.3

IV (Blood and Lymphoma

Stage IV cancers)

54 57.4 42 50.0

Unstaged 18 38.9 15 53.3

Total 483 59.4 832 60.9 1Chi-square test for differences in the proportions working by category of variable within gender.

Alpha = *.10, **.05, ***.01

55

Table 2.3

Percent of Survivors Working During Treatment By Characteristics at Diagnosis within Gender

1

Male Survivors Female Survivors

Characteristic Number Percent Number Percent

Working During Treatment 483 59.4 832 60.9

Age Group at diagnosis

(Years)

25-44 145 64.1 302 58.6

45-52 136 54.4 286 64.0

53-57 105 59.1 156 57.7

58-61 89 58.4 79 64.6

62+ 8 75.0 9 66.7

Race *

White 457 60.4 765 61.3

Non-White 25 44.0 65 56.9

Missing 2 -

Married

Single 63 57.1 193 59.6

Married or Partner 420 59.8 639 61.4

Education ** ***

Less Than HS 23 43.5 28 21.4

High School 147 51.0 252 55.2

Some College 87 59.8 212 62.3

College 117 61.5 159 66.7

Post-graduate 105 71.4 167 71.3

Other Training 4 75.0 13 38.5

Missing 1 -

Physical Occupation ** ***

Yes 141 48.2 72 41.7

No 339 63.7 759 62.9

Missing 3 - -

Current Employer-Contingent

Health Insurance

** **

Yes 297 64.0 410 64.4

No 184 52.2 412 57.5

Missing 3 - 10 -

Size of Metro Area

Population (persons)

Over 1 million 103 62.1 234 63.7

250k – 1million 249 59.8 387 61.8

Under 250,000 131 56.5 211 56.4 1Chi-square test for differences in the proportions working by category of variable within gender.

Alpha = *.10, **.05, ***.01

56

Table 2.3 (cont.)

Percent of Survivors Working During Treatment By Characteristics at Diagnosis within Gender

1

Male Survivors Female Survivors

Characteristic Number Percent Number Percent

Children Under 18

Yes 145 58.6 257 60.7

No 338 59.8 575 61.0

Fulltime Employment Yes 429 59.4 601 60.7

No 49 61.2 217 61.3

Missing 5 - 14 -

Self-employed

Yes 77 63.0 86 58.1

No 402 58.7 738 61.4

Missing 4 - 8 -

Employees Company-Wide

Less than 25 102 62.8 185 56.2

25-99 57 59.7 89 69.7

100-499 69 47.8 142 65.5

500 more 254 61.0 409 59.7

Missing 1 - 7 -

Retirement Benefit **

No Retirement Benefit 128 52.3 255 56.1

Defined Benefit 150 62.7 241 61.0

Defined Contribution 181 58.6 285 66.3

Defined Benefit &

Defined Contribution

18 88.9 23 65.2

Missing 6 - 28 -

Time Since Diagnosis Until

1st Interview **

Less than 1 Year 45 51.1 72 50.0

1- 2 Years 165 57.6 288 60.1

2- 3 Years 153 62.1 272 68.0

3 to 4 or More Years 420 61.7 200 56.5 1Chi-square test for differences in the proportions working by category of variable within gender.

Alpha = *.10, **.05, ***.01

57

Table 2.4

Percentile Distribution of Treatment Period Durations

by Cancer Type and Cancer Survivor Gender1

(Months) Male Survivors

n=483

Female Survivors

n=832

Cancer Site 50

th

Percentile (95th CI)

75th

Percentile (95th CI)

50th

Percentile (95th CI)

75th

Percentile (95th CI)

Blood 12 (7-50)

50 (18-∞)

9 (6-35)

46 (13- ∞)

Breast NA NA 8 (7-9)

13 (11-15)

Central Nervous System 4 (3-14)

19 (4--∞)

6 (1- ∞)

NC

Colorectal 8 (6-10)

15 (10-29)

7 (3-8)

9 (8-11)

Head & Neck 3 (1-4)

5 (3-7)

4 (1-5)

6 (4-21)

Lymphatic, Stages I-III 6 (5-7)

17 (6-17)

10 (6-10)

17 (10- ∞)

Lymphatic, Stage IV 7 (5-15)

15 (7- ∞)

8 (3-12)

11.5 (7- ∞)

Prostate 3 (2-4)

7 (5-9)

NA NA

Respiratory 6 (1-7)

NC 4 (1-7)

8 (4-24)

Skin (Melanoma) 1.5 (1-4)

8 (2-∞)

1 (1-13)

15 (3-36)

Thyroid 4 (2-24)

NC 3

(2-4) 18

(5- ∞)

Uterus NA NA 1 (1-3)

4

(3-7)

Other cancers 4 (3-5)

7 (6-10)

6 (4-7)

16 (9-24)

All Survivors 5 (4-5)

10 (8-12)

7 (6-7)

12 (11-15)

1Notes:

The treatment period duration was calculated by using the difference between the date of diagnosis in the registry and the last date of treatment for the initial cancer and reported by the cancer survivor. This method does not include any additional recuperation time

from surgery or side effects from chemotherapy or radiation. Furthermore, treatment may still be ongoing for some survivors. The

Kaplan-Meier method was used in SAS Proc Lifetest to estimate the 50th and 75th percentiles and the 95% confidence intervals for the treatment duration measured in months since diagnosis. Some cancer survivors treatments were not observed as ending since 182

persons (14%) were still in treatment at the time of their Wave 1 interview. NA = not applicable; NC = cannot be computed.

58

Table 2.5

Adjusted Odds Ratios for Factors Associated with Working During the Treatment Period Male Survivors

1

(*** p<.01; **p<.05; *p<.10, n=469)

All Covariates

Measured at Diagnosis

(except area population)

Model I

Cancer-

Related (n=469)

Lower 95%

CI

Upper 95%

CI

Model II

Person-

Related (n=469)

Lower 95%

CI

Upper 95%

CI

Model III

Job-

Related (n=465)

Lower 95%

CI

Upper 95%

CI

Blood 0.383** 0.15 0.976 0.372** 0.141 0.985 0.381* 0.138 1.052

Central Nervous System 0.969 0.311 3.016 0.835 0.256 2.721 0.981 0.288 3.347

Head & Neck 0.89 0.364 2.173 1.046 0.413 2.647 0.965 0.365 2.553

Lymphatic,

Stages I, II, III 2.938* 0.842 10.255 2.857 0.797 10.238 2.555 0.684 9.548

Lymphatic, Stage IV 1.129 0.333 3.825 1.065 0.3 3.776 0.993 0.267 3.693

Prostate 1.324 0.633 2.768 1.338 0.618 2.899 1.399 0.62 3.157

Respiratory 0.283** 0.083 0.965 0.328* 0.092 1.175 0.474 0.126 1.783

Skin (Melanoma) 2.089 0.686 6.364 1.87 0.583 6 1.661 0.489 5.643

Thyroid 0.879 0.333 2.321 0.829 0.3 2.294 0.811 0.282 2.332

Other Cancers 0.812 0.408 1.616 0.756 0.372 1.538 0.711 0.337 1.498

Stage II 0.527** 0.311 0.894 0.517** 0.296 0.903 0.483** 0.269 0.867

Stage III 0.522** 0.292 0.931 0.477** 0.258 0.881 0.503** 0.264 0.956

Unstaged 0.265** 0.093 0.758 0.253** 0.085 0.751 0.364* 0.12 1.102

2 years since diagnosis 1.186 0.59 2.385 1.15 0.556 2.377 1.11 0.524 2.352

3 years since diagnosis 1.537 0.752 3.142 1.473 0.698 3.11 1.313 0.61 2.827

4 years since diagnosis 1.374 0.658 2.868 1.262 0.582 2.739 1.169 0.527 2.594

Age 45-52 0.744 0.433 1.28 0.595* 0.335 1.058

Age 53-57 0.88 0.471 1.645 0.685 0.353 1.327

Age 58-61 0.835 0.417 1.67 0.732 0.348 1.541

High School 1.277 0.472 3.455 1.145 0.405 3.242

Some College 1.636 0.581 4.604 1.38 0.458 4.155

College 1.962 0.716 5.375 1.477 0.48 4.549

Post-graduate 2.955** 1.044 8.366 2.183 0.692 6.882

Technical-Trade 2.387 0.169 33.652 2.11 0.129 34.418

Non-white 0.636 0.251 1.61 0.653 0.248 1.716

Married 1.124 0.599 2.11 1.131 0.583 2.195

Child Under 18 0.778 0.482 1.255 0.725 0.442 1.19

59

Table 2.5 (cont).

Adjusted Odds Ratios for Factors Associated with Working During the Treatment Period

Male Survivors1

(*** p<.01; **p<.05; *p<.10, n=469)

All Covariates

Measured at Diagnosis

(except area population)

Model I

Cancer-

Related

Lower 95%

CI

Upper 95%

CI

Model II

Person-

Related

Lower 95%

CI

Upper 95%

CI

Model III

Job-

Related

Lower 95%

CI

Upper 95%

CI

Mid-size Metro Area at

Interview 0.849 0.482 1.494 0.91 0.502 1.651

Small Metro Area at

Interview 0.757 0.401 1.426 0.763 0.392 1.483

Physical Occupation 0.773 0.441 1.354

Full-time 0.803 0.384 1.68

Self-employed 1.362 0.56 3.31

Own Health Insurance

from Employer 1.727** 1.093 2.729

Defined Benefit 2.178** 1.033 4.592

Defined Contribution 1.572 0.801 3.086

Defined Benefit &

Defined Contribution 9.526*** 1.816 49.959

Company-wide 25-99 0.803 0.316 2.043

Company-wide 100-499 0.409* 0.158 1.054

Company-wide 500 + 0.654 0.281 1.522

-2 Log Likelihood 602.1** 584.6** 557.5*** 1Note - Reference Group includes colorectal cancer, stage I, age 25-44 at diagnosis, less than high school education; white, single, no children under 18, large urban areas over 1 million in population;

job without physical demands; part-time work; working for someone else; health insurance from source other than employer at diagnosis, no employer-sponsored retirement benefit (defined benefit like pension or defined contribution like 401(k)), and companies with fewer than 25 total employees..

60

Table 2.6

Adjusted Odds Ratios for Factors Associated with Working During the Treatment Period

Female Survivors1

(*** p<.01; **p<.05; *p<.10, n=792

All Covariates Measured

at Diagnosis

(except area population)

Model I

Cancer-

Related (n=792)

Lower 95%

CI

Upper 95%

CI

Model II

Person-

Relate (n=792)

Lower 95%

CI

Upper 95%

CI

Model III

Job-

Related (n=774)

Lower 95%

CI

Upper 95%

CI

Blood 0.653 0.221 1.929 0.676 0.224 2.042 0.678 0.206 2.225

Breast 2.276** 1.016 5.095 2.207* 0.973 5.006 2.204* 0.933 5.204

Central Nervous System 0.369 0.107 1.271 0.391 0.111 1.382 0.502 0.134 1.888

Head & Neck 1.253 0.402 3.899 1.107 0.351 3.491 1.126 0.344 3.688

Lymphatic,

Stages I, II, III 1.638 0.414 6.474 1.387 0.341 5.647 1.317 0.31 5.598

Lymphatic, Stage IV 3.055 0.658 14.193 2.969 0.622 14.171 3.334 0.675 16.477

Respiratory 0.669 0.232 1.932 0.754 0.252 2.253 0.676 0.216 2.119

Skin (Melanoma) 3.981** 1.143 13.865 4.159** 1.171 14.777 4.948** 1.325 18.474

Thyroid 2.384* 0.918 6.193 2.547* 0.95 6.831 2.501* 0.893 6.999

Uterus 0.901 0.358 2.269 0.993 0.389 2.533 1.014 0.38 2.709

Other Cancers 1.052 0.439 2.519 1.043 0.43 2.529 1.035 0.407 2.63

Stage II 0.884 0.62 1.26 0.853 0.594 1.226 0.858 0.59 1.247

Stage III 0.882 0.544 1.429 0.815 0.497 1.337 0.867 0.516 1.456

Unstaged 0.684 0.193 2.431 0.719 0.199 2.602 0.797 0.202 3.134

2 years since diagnosis 1.638* 0.946 2.836 1.594 0.91 2.793 1.534 0.855 2.749

3 years since diagnosis 2.406*** 1.378 4.202 2.264*** 1.284 3.99 2.141** 1.186 3.865

4 years since diagnosis 1.471 0.828 2.611 1.401 0.777 2.527 1.327 0.718 2.454

Age 45-52 1.205 0.818 1.775 1.272 0.849 1.906

Age 53-57 0.984 0.618 1.566 1.078 0.664 1.749

Age 58-61 1.193 0.656 2.169 1.284 0.693 2.379

High School 4.367*** 1.52 12.551 4.397*** 1.507 12.827

Some College 5.122*** 1.767 14.846 4.628*** 1.557 13.754

College 6.592*** 2.232 19.469 6.509*** 2.123 19.958

Post-graduate 6.859*** 2.329 20.203 6.680*** 2.185 20.424

Technical-Trade 1.912 0.402 9.093 1.68 0.338 8.353

61

Table 2.6 (cont).

Adjusted Odds Ratios for Factors Associated with Working During the Treatment Period

Female Survivors1

(*** p<.01; **p<.05; *p<.10, n=792) All Covariates

Measured at Diagnosis

(except area population)

Model I

Cancer-

Related

Lower 95% CI

Upper 95% CI

Model II

Person-

Related

Lower 95% CI

Upper 95% CI

Model III

Job-

Related

Lower 95% CI

Upper 95% CI

Non-white 0.842 0.451 1.572 0.896 0.466 1.722

Married 1.04 0.715 1.514 1.004 0.675 1.494

Child Under 18 0.832 0.58 1.193 0.871 0.598 1.268

Mid-size Metro Area at

Interview 0.892 0.598 1.329 0.974 0.582 1.632

Small Metro Area at

Interview 0.756 0.483 1.183 0.814 0.482 1.374

Physical Occupation 1.152 0.736 1.804

Full-time 0.721 0.394 1.321

Self-employed 0.91 0.611 1.356

Own Health Insurance

from Employer 0.984 0.524 1.851

Defined Benefit 1.152 0.795 1.671

Defined Contribution 1.141 0.686 1.898

Defined Benefit &

Defined Contribution 1.448 0.897 2.337

Company-wide 25-99 2.331 0.814 6.673

Company-wide 100-499 1.732 0.894 3.359

Company-wide 500+ 1.462 0.803 2.66

-2 Log Likelihood 998.3*** 969.9*** 931.9*** 1Note - Reference Group includes colorectal cancer, stage I, age 25-44 at diagnosis, less than high school education; white, single, no children under 18, large urban areas over 1 million in population;

job without physical demands; part-time work; working for someone else; health insurance from source other than employer at diagnosis, no employer-sponsored retirement benefit (defined benefit like

pension or defined contribution like 401(k)), and companies with fewer than 25 total employees.

62

Table 2.7

Odds Ratios for Main Effects and Interaction of Own Health Insurance at Diagnosis for Married Cancer Survivors

Where Cancer Survivor is Working at Diagnosis1

Main Effects &

Interaction Term Married Male Cancer Survivors

(n=370) Married Female Cancer Survivors

(n=513) OR

1 Lower 95% CI Upper 95% CI OR1 Lower 95% CI Upper 95% CI

Main Effect - Survivor with

Health Insurance form

Current Employer

2.1** 1.1 4.1 2.1** 1.1 4.1

Main Effect - Spouse with Health Insurance from

Current Employer

1.4 0.6 3.1 1.9** 1.0 3.5

Interaction - Both Have Own Health Insurance with a

Current Employer

0.7 0.2 1.9 0.4** 0.2 0.9

1Notes: Survivor working, employer-sponsored health insurance *** p<.01; **p<.05; *p<.10

63

Chart 1

Estimated Odds Ratios and 95% Confidence Intervals

for Working During Treatment by Cancer Site

Male Survivors

(Colorectal Cancer Reference Group)

0.1 1 10

Blood

Respire

Other

Thyroid

CNS

HeadNeck

Colon

Lymph4

Prostate

Skin

Lymph1239

Notes: Lymph4 = Lymphatic Cancer, Stage IV; Lymph1239 = Lymphatic Cancer all

other stages; CNS=Central Nervous System; Scale is Logarithmic for symmetry in odds

ratio, meaning an OR = 2.0 is symmetrical in effect with OR = 0.5

64

Chart 2

Estimated Odds Ratios and 95% Confidence Intervals

for Working During Treatment by Cancer Site

Female Survivors

(Colorectal Cancer Reference Group)

0.1 1 10 10

0

CNSBlood

RespireColon

UterusOther

HeadNeckLymph1239

BreastThyroidLymph4

Skin

Notes: Lymph4 = Lymphatic Cancer, Stage IV; Lymph1239 = Lymphatic Cancer all

other stages; CNS=Central Nervous System; Scale is Logarithmic for symmetry in

odds ratio, meaning an OR = 2.0 is symmetrical in effect with OR = 0.

65

CHAPTER THREE

WHAT IS THE EFFECT OF CANCER ON THE LABOR SUPPLY OF SPOUSES OF

CANCER SURVIVORS?

Abstract

Background. Two-thirds of cancer survivors are married and two-thirds of married couples are

―working couples‖ where both husband and wife were working at the time of diagnosis. Cancer

may contribute to the variation in labor supply decisions among spouses as well as differentiate

them from spouses in couples without cancer.

Data. A sample of 739 working couples with a cancer survivor was identified from Wave 1 of

the Penn State Cancer Survivor Study (PSCSS) with diagnosis dates from 1997 through 1999.

Labor supply outcomes were measured at two to six years after diagnosis at Wave 2 (2002). A

subset of 286 PSCSS couples was combined with a sample of 1,026 working couples from the

Health and Retirement Study for ages 55 and over. Variables included clinical, socio-

demographic, and job-related characteristics.

Study design. Logistic regression was used to estimate the likelihood of working. Tobit

regression methods were used to estimate effects on hours of work. Separate labor supply models

were estimated by gender for spouses of cancer survivors. These models were also used to

compare differences between spouses in cancer and non-cancer couples.

Principal findings. There were few clinical effects for cancer on spousal labor supply in

working couples. Labor supply was associated with education and job benefits.

Conclusions. Cancer did not result in systematic labor supply adjustments by spouses in the

short-term period from two to six years after diagnosis. There may be some distributional

consequences around husbands in treatment or advanced cancer stage III.

66

Background

Cancer affects more than cancer survivors (National Cancer Institute, 2004; Twombly,

2004). In married couples, the diagnosis presents new challenges to the spouse of a cancer

survivor. These challenges include making adjustments in spousal labor supply through a

sequence of cancer survivorship stages beginning with conditions at diagnosis and continuing

through the long-term. This paper provides estimates of spousal labor supply two to six years

after diagnosis.

Cancer imposes significant economic costs. The disease burden includes direct medical

expenditure for prevention and treatment, and indirect productivity costs on society through lost

production due to mortality (premature death) and morbidity (effects of illness). The total

monetary burden of the disease was estimated by the National Institutes of Health at $263.8

billion in 2010 (American Cancer Society, 2010). Morbidity and mortality cost respectively,

$20.9 and $140 billion. In addition to the dollar costs of lost productivity, cancer accounted for

1.65 billion of the work-impaired days lost annually to chronic illness (Kessler, Greenberg,

Mickelson, Meneades, & Wang, 2001).

In addition to the economy-wide economic costs of cancer, there is a disproportionate

human toll on spouses and families. Fatigue from treatment may be severe and linger for cancer

survivors. Severe fatigue may increase the caregiving burden on a spouse which reduces his or

her work effort or makes work more difficult (Passick & Kirsch, 2005). In some cases, the

caregiving burden leads to loss of income and subsequent financial distress (Longo, Fitch, Deber,

& Williams, 2006).

Most cancer survivors are married. In 2001, two-thirds of adult cancer survivors were

married or living with a partner (Ibrahim, Short, & Tunceli, 2004). The data used in this paper

suggest that two-thirds of the married couples were ―working couples‖ at the time of diagnosis

67

(Table 3.1), where a ―working couple‖ is defined as a married couple where husband and wife

both work for pay.

Labor supply choices by married couples involve some degree of joint decision-making

(Chiappori, 1997; Killingsworth, 1983; Vermulen, 2005). The economic models of the

negotiated work choices in married couples range from unitary to collective decisions. In

elementary unitary models, each spouse independently maximizes his or her utility from a unique

combination of work and leisure hours. The total utility for the couple and labor supply is

simply the sum of the independent decisions about the hours of work. Furthermore, a spouse’s

utility function may or may not incorporate factors that affect the utility of his or her partner’s

satisfaction. Becker (1965) has referred to incorporation of the other person’s utility function as

altruistic behavior. Alternatively, collective models assume that labor supply decisions are

determined simultaneously by the spouses.

There are gaps in the cancer survivor literature for spousal labor supply. Bradley and

Bednarek (2002) reported on spouses working several years after diagnosis, but information on

baseline labor supply was inadequate for more extensive analysis. A small pilot study of a

convenience sample by Passick and Kirsch (2005) examined the spousal effects of caregiving for

survivors with significant fatigue. They found that some spouses reported fewer work hours

(28%), reduced job responsibilities (32%), or diminished effectiveness at work (32%). However,

significant limitations in the sample and covariates were a barrier to labor supply estimates. A

study of short-term cancer survivors by Longo, Fitch, Deber, and Williams (2006) reported that

some households experienced financial distress (20%) from reductions in spousal work hours

during the two-year period following diagnosis and treatment. The study, however, did not report

labor supply estimates. In summary, the current studies with spouses in the cancer survivor

literature are limited by the lack of power, dependence on bivariate frameworks, inadequate

quantitative measures of labor supply, or study designs that omit control groups.

68

The purpose of this paper is to fill a gap in the cancer survivor literature about spousal

labor supply. The paper addresses two related research questions. First, what factors explain,

especially cancer type and stage, differences in the labor supply adjustments of spouses of

working age cancer survivors (25-64)? Second, what is the effect of cancer on the labor supply of

spouses of older working cancer survivors (50-64)? Answers to these questions will provide

information to married couples, clinicians, social workers, employers, and public policymakers.

Married couples will have more information about how others have adjusted to cancer.

Clinicians and social workers will be able to improve the design of support programs for the

needs of married couples. Employers may implement or modify programs of accommodation for

spouses of cancer survivors. Finally, policymakers and supporting cost-effectiveness studies will

benefit from knowledge of spousal responses to cancer.

Conceptual Model

Serious illness, like cancer, disturbs the equilibrium in a married couple’s joint labor

supply decision. A cancer diagnosis may change preferences for work or leisure for one or both

marriage partners. Some spouses of cancer survivors may work more to compensate for lost

income or benefits, while other spouses may work less to provide caregiving or perform

household activities. A spouse’s decision to work more or less depends on the spouse’s utility

function and comparison of opportunity costs of working or not working (Berger, 1983; Coile,

2004; McClellan, 1998).

Spousal work choices may not be entirely independent of work decisions made by the

cancer survivor. Economic models of joint labor supply decisions in married couples provide

alternative decision-making frameworks across a continuum of ―jointness‖ (Blundell &

MaCurdy, 1999; Chiappori, 1999; Fortin, & Lacroix, 1997; Keeley, 1981; Killingsworth, 1983;

Vermulen, 2005). The models range from a simple or unitary perspective in which each spouse

makes independent adjustments in his or her labor supply to more complicated collective

69

decisions. In the simplest decision-making model, each spouse evaluates the opportunity cost of

working or not working and maximizes his or her own utility. The married couple’s joint utility

is simply the sum of the independent labor supply decisions. In more complicated models, the

labor supply decisions are correlated, given simultaneously determined decisions by each spouse

(Apps& Rees, 1997; Chiappori, 1997; Clain & Leppel, 1994; Duguet & Simonnet, 2003; Fortin

& Lacroix, 1997). Among older working couples, the labor supply decision may take the form of

a joint retirement decision (Blau, 1998; Coile, 2003, 2004; Gustman & Steinmeier, 2004).

Adjustments in labor supply may occur at the extensive or intensive margin. At the

extensive margin, the marginal adjustment is binary – to work or not work. An adjustment of this

magnitude involves greater risks for losing job-related benefits or large reductions in lifetime

income. On the intensive margin, the marginal adjustment is along a continuum of hours of work.

Here, a spouse may be able to make smaller changes and reduce the risk of loss of benefits or

income while providing caregiving or household production services.

Spouses share in the cancer survivorship experience. Mullan (1985) has described this

experience as progression through stages of survival. Collectively, Mullan named these stages

―seasons of survival.‖ The first stage is the acute stage, which is synonymous with the initial

treatment period (i.e., approximately one year or less). This stage is followed by the extended

survival stage or ―post-acute stage.‖ For many survivors, this stage may last from one to five

years after diagnosis while they deal with fears of recurrence. Once those fears recede into the

background of daily life, the survivor has transitioned into the third and final stage – permanent

survival. Traditionally, the five-year post-diagnosis has been the benchmark for long-term

survival. Not all survivors spend the same time in the extended survival stage. Some cancer

survivors may quickly resolve fears of recurrence and live life much in the same manner as before

the diagnosis. Mullan’s framework guides our conceptualization of the sequence and context of

decisions facing cancer survivors and provides a reference point for comparison with the

literature. The acute and extended survival stages approximate the short-term period (1 up to 5

70

years) and the permanent survival stage represents the long-term period (5 or more years).

Spouses and cancer survivors negotiate the joint labor supply decision in the short-run and long-

run periods. At each period, a spouse weighs the opportunity costs of working more or less and

the effect on his or her own utility (Becker, 1965; Gronau, 1997; Keeley, 1981).

Spouse work choices in either the short-run or long-run are constrained by the work

situation in the household at diagnosis (Figure 3.1). From this initial condition, a spouse makes

choices about work in a sequence of survivorship stages. Initial decisions made at one stage may

become permanent in subsequent stages. Spouses working at diagnosis face large and small

marginal decisions about working. At the extensive margin, a spouse must decide to keep

working or stop working. At the intensive margin, a spouse considers working the same, more, or

fewer hours. Continuing to work maintains the flow of work-related income and may provide

access to another source of health insurance. Stopping work may allow more time for caregiving

or taking on additional household production duties (e.g., child-rearing). Alternatively, the labor

supply decisions of non-working spouses are to continue not to work or start working at the

extensive margin.

The situation for working and non-working spouses at diagnosis is further constrained by

the work status of a cancer survivor at diagnosis (Table 3.1). The most frequent work status

combination at diagnosis is both spouses working at diagnosis. A spouse with a working cancer

survivor has more flexibility in work choices compared with a spouse who is also the sole worker

at diagnosis.

A spouse’s work decision involves three sets of factors which are categorized as cancer,

socio-demographic, and job-related (Figure 3.2). Cancer, stage, treatment, co-morbid conditions,

and physically demanding work may alter the productivity and the work preferences of a cancer

survivor. These changes may ―spillover‖ and influence the productivity and work-leisure

preferences of the spouse. Time-since diagnosis allows more time to make adjustments and make

decisions on their permanence. Spousal socio-demographic characteristics may increase or

71

decrease labor supply since labor force participation differs by gender. People are less likely to

work as they age and near retirement or have young children at home. Education is indirectly

associated with better paying jobs and working conditions. Employers may provide health

insurance or pension benefits that may increase or decrease labor supply. Other benefits may

depend on the size of an employer. Large employers are more likely to offer paid sick leave,

have labor capacity to offer flexible arrangements, and must comply with the Family Medical

Leave Act for unpaid leave.

Methods

Overview

Labor supply may vary due to differences in spousal characteristics broadly categorized

as cancer, socio-demographic, and job-related. The effect of cancer may be demonstrated in two

ways. First, cancer may have differential effects among spouses of cancer survivors. Second,

cancer may have different effects on spouses in married couples with and without cancer. Each

effect provides information that will inform the decisions of married couples, program managers,

and policymakers.

These effects were measured in two separate analyses. The first analysis measured

cancer’s effect on ―within group differences‖ in labor supply for spouses of cancer survivors.

Measurement of these differences will aid the design of employee accommodations, psychosocial

support programs, and cancer survivorship plans. Data for this analysis was provided by the Penn

State Cancer Survivor Survey (PSCSS), which was funded by the National Cancer Institute. The

second analysis measured cancer’s effect on the differences in labor supply ―between groups.‖

The magnitude of the differences in labor supply between spouses in married couples with and

without cancer will benefit cancer survivorship plans, employee accommodations, and

policymaking choices in allocating resources to mitigate indirect economic costs of cancer on

productivity due to morbidity. The Health and Retirement Study (HRS) provided data for the

72

comparison group and the PSCSS data in the first analysis were restricted to the age range in the

HRS. Consequently, the ―between group‖ differences were measured for spouses of older cancer

survivors.

Labor supply effects in each analysis were measured for two outcomes: work status and

usual hours of work per week. The extensive margin is the decision to work or not. Labor supply

effects were estimated at the extensive margin using logistic regression methods for the

likelihood of working at two to six years after diagnosis. The effects were reported as odds ratios

for the likelihood of working at two to six years after diagnosis. The intensive margin measures

changes in the hours of work. However, changes in the hours of work included zero hours of

work. The large number of zero observations would bias estimated coefficients for cancer and

other spousal characteristics. If one conceptualizes the number of work hours as the result of two

decisions, the decision to work at all (extensive margin) and given that decision, how many hours

to work (intensive margin), then the bias in the estimated effects can be reduced by using an

alternative to OLS (Ordinary Least Squares). Tobit regression methods were used to estimate the

effect of cancer for a combination of intensive and extensive margins – the decision to work and

if working, how many hours of work per week. Given large differences in labor force

participation between men and women, separate estimates of labor supply were made by gender.

Data

Overview of Penn State Cancer Survivor Survey (PSCSS)

The main data source was the Penn State Cancer Survivor Survey (PSCSS), which

collected data from survivors not only about their own work but also about their spouses’ work.

This survey, funded by the National Cancer Institute, gathered four waves of data in annual

interviews from late 2000 through 2004. Data for spouses of cancer survivors were selected from

the Wave 2 (2002) of the PSCSS survey which represented the time period two to six years after

diagnosis.

73

There were four waves of annual interviews from 2001 through 2004 (Short, 2001).

Three major criteria governed inclusion in the survey: (1) a first cancer diagnosis occurred from

1997 to 1999, (2) a high probability of survival over the four data collection waves (as

determined by staging and ICD-Oncology), and (3) ages 25 to 62 at the time of diagnosis.

Patients were selected from tumor registries at four hospitals (Geisinger Medical Center, Lehigh

Valley Hospital and Health Network, Milton S. Hershey Medical Center, and The Johns Hopkins

Hospital). The survey instrument included retrospective questions about work status, hours of

work, socio-demographic characteristics, employment-related benefits, and cancer attributes at

time of diagnosis (study baseline). Details about the PSCSS have been published elsewhere

(Short & Mallonee, 2006; Short, Vasey, & Tunceli, 2005).

The data in this study were provided by Wave 2 (2002) of the PSCSS. This wave

contained 1,107 married couples. A ―married couple‖ was defined as a husband and wife who

reported that they were married at diagnosis and remained so through the interview date at Wave

2. About seven percent of the responses to marital status questions were excluded because the

cancer survivor was living with a partner, in a same-sex relationship, or was not married to the

same person as at diagnosis.

The initial working status varied among married couples at diagnosis. Joint decisions

about working or not working made prior to diagnosis resulted in four combinations of work

status at diagnosis (Table 3.1). In most couples, a cancer survivor and spouse, a ―working

couple,‖ were both working at baseline (739, 67.4%). The remaining married couples had one or

nobody working at diagnosis: survivor-only (154, 14.1%), spouse-only (147, 13.4%) and nobody

(56, 5.1%). These combinations form the context for the contribution of spousal characteristics

to labor supply choices. Given the magnitude of the combination of both husband and wife

working at diagnosis (67.4%), the analyses of spousal labor supply focused on this combination.

Estimates of spousal labor supply at two to six years after diagnosis were conditional on a

―working couple‖ at diagnosis.

74

First Analysis: The PSCSS Data for Within Group Differences Among Spouses of Cancer

Survivors

The PSCSS data in the two analyses have common data sources. Data were collected at

diagnosis (baseline) by retrospective questions in the Wave 1 interview or from cancer registries.

Changes since baseline were captured in the subsequent interviews at Waves 1 and 2. The study

endpoint is the Wave 2 interview which occurs two to six years after diagnosis.

Spousal characteristics in ―working couples,‖ including the condition of the cancer

survivor at diagnosis, are described in Table 3.2. The spouse sample included 739 working

couples with 469 husbands (63.5%) and 270 wives (36.5%). The proportion of husbands aged 53

or older at diagnosis (31.3%) was larger than the proportion of wives in the same age groups

(26%). Post-graduate education was slightly higher among husbands (24.3%) than wives

(22.1%). More husbands (55%) had health insurance through a current employer than wives

(40.7%). The proportion of young children at home was greater for wives (7.8%) than husbands

(5.5%). Husbands worked more often in full-time jobs (88.7%) than wives (66.3%).

Second Analysis: PSCSS Data for Between Group Differences

The spouses of cancer survivors aged 55 or older in 2002 were selected from the working

couple data in Table 3.2 for comparison to a control group of similar ages. The data contained

286 working couples with 153 husbands and 135 wives among the spouses. Characteristics of the

sample are described in Table 3.7.

Second Analysis: HRS data for Between Group Differences

The data are from the Health and Retirement Study (HRS), Version H, Round 6, 2002.

The biennial survey began in 1992 and is managed by the University of Michigan (Leacock,

2006). Data are collected every two years on the socioeconomic, demographic, health, work, and

retirement characteristics for a nationally representative sample of persons aged 50 or older.

75

HRS version H had over 30,000 persons (RAND HRS, 2008). There were 1,026 working

couples aged 55 or older in 2002. Among the spouses in this group were 416 husbands and 610

wives. The sample data from the PSCSS and HRS are compared in Table 3.7. The main

differences between the samples are in educational attainment and employer-sponsored health

insurance (chi-square, alpha=.05).

Statistical Analysis

Given large differences in labor force participation between men and women, labor

supply has often been measured separately by gender. Consequently, the data are analyzed

separately for effects on a husband or a wife of a cancer survivor.

Bivariate associations between spousal characteristics at diagnosis and work status at

Wave 2 used chi-square tests. These methods were also applied to comparison of sample

differences between the PSCSS and HRS working couple data. Changes in mean hours of work

between diagnosis and Wave 2 were examined using t-tests and displayed as confidence intervals.

All statistical tests used an alpha=.05 criterion for statistical significance.

Work status model parameters were estimated via logistic regression methods, using

maximum likelihood methods and conditional on both spouse and cancer survivor working at the

time of diagnosis. Hour models were estimated using a Tobit method. The Tobit procedure

estimates coefficients when samples are censored with respect to observations of the outcome

variable – zero work hours. The Tobit procedure compensates for the unobserved work hours and

produces estimates of the latent variable that includes the effects of both large and small

adjustments in work hours by spouses. Tobit coefficients are not directly interpretable as OLS

coefficients. The coefficients require an adjustment procedure for comparison to OLS

coefficients. All statistical analyses were executed in SAS version 9.2.

76

Variable Specifications: First Analysis – Between Group Differences - PSCSS

With few exceptions, model outcome and explanatory variables were specified as binary

variables with the presence of a characteristic represented as 1, and 0 otherwise. Hours were

reported as a continuous variable with a lower bound of zero. Data for work status, and usual

hours of work per week, were based on questions in the PSCSS that were identical to wording in

the HRS survey. Work status was determined by the answer to the question: ―Are you working

for pay?‖ Hours of work were captured by the question: ―How many hours a week do you

usually work?‖ The usual hours of work variable was specified as continuous from a lower

boundary of zero. Full-time work status was determined by a response to survey questions about

full-time work, or, if that response was missing, by using working more than 35 hours in the usual

hours worked per week.

The effect of cancer on spousal labor supply was specified by the potential behavior of a

working cancer survivor and his or her likelihood of quitting work. A cancer survivor’s

likelihood of quitting work is associated with cancer sites. Short, Vasey, and Tunceli (2005)

found that with some cancer sites, cancer survivors were more or less likely to quit working. The

likelihood of quitting may be viewed as a proxy variable for the severity of cancer and its work

impairment. It may also signal an increasing demand on spouses for caregiving, assistance with

medical appointments, or taking on additional household activities. These demands could reduce

the spousal labor supply. On the other hand, a spouse faced with an increasing likelihood that a

cancer survivor will quit working, may respond by continuing to work to maintain access to

health insurance benefits and minimize income losses. Short, Vasey, and Tunceli (2005),

grouped cancer sites by differences in the log odds of quitting work compared to a cancer in

common among men and women – colon cancer. Cancer survivors with blood, central nervous

system, and head & neck cancers were most likely to stop working. They were followed by

survivors with respiratory, lymph, melanoma (skin), thyroid, and other cancers. Finally,

survivors who were least likely to stop working included those with uterine, female breast,

77

prostate, and thyroid cancers. The cancer variable was categorized by groups indicating a cancer

survivor’s likelihood of quitting work: high, medium, and low.

Several cancer and health-related variables may modify the effects of cancer sites. Stage

was categorized as I, II, or III. Stage IV cancers were limited to blood and lymphatic cancers so

their effect is included in the cancer group with the highest likelihood of quitting for a cancer

survivor. Treatment status was defined as: not in treatment, still in treatment with an active

cancer, or still in treatment with an inactive cancer where data were sufficient for this level of

detail. Otherwise, the treatment status was collapsed into a dichotomous variable to resolve

limitations of the sample data structure. Recurrence, which is a new cancer or the spread of the

original cancer to distant organs, is a binary variable Specific chronic medical conditions

including, asthma, heart disease, stroke, diabetes, and respiratory diseases were summarized as

the presence or absence of that condition in a binary covariate.

Socio-demographic, time since diagnosis, and job-related variables were largely

characterized as categorical variables. Age at diagnosis categories included ages less than 45, 45-

52, 53-57, 58-61, and 62 or more. Educational attainment was specified as less than high school,

high school, some college, college, and post-graduate. The presence of young children in the

home (under age 6) was indicated by a binary variable in estimation of the effect on the labor

supply of a wife. Too few cases and linearity in the data eliminated this variable from the

husband models. A physically-demanding job for a spouse was inferred from job descriptions in

the PSCSS survey responses. These job descriptions were Bureau of Labor Statistics

occupational category codes. The multiple codes were collapsed into a binary covariate to

indicate the physical nature of a spouse’s job. The time since diagnosis variable measured the

days between the date of diagnosis and the Wave 2 interview date. The time data were

summarized into categories for 2 years or less, 3 years, 4 years, and 5 or more years since

diagnosis. Having your own health insurance policy through a current employer, a job-dependent

benefit, was determined by response to survey questions about the policy holder and whether the

78

insurance was sponsored by a current employer. No racial or ethnic variables were specified

since the PSCSS survey instrument limited the question to cancer survivors.

Variable Specifications: Second Analysis – Between Group Differences

The previously defined variables were included here with the following exceptions. A

binary indicator for cancer replaces site detail, recurrence, stage, and treatment status. Age

groups reflect an older population, so the categories included less than 58, 58-61, and 62 and

over. In order to maintain equivalency with the HRS, the separate educational categories of

college and post-graduate education were collapsed into a single category.

Since the PSCSS survey used many of the same questions as the HRS, the construction of

corresponding variables from HRS data was similar with one exception: time since diagnosis.

Since there was no cancer diagnosis date for the HRS control group observations, a ―pseudo-

diagnosis date‖ was created for a baseline reference point by randomly assigning PSCSS cases to

HRS spouses (Short, Vasey, & Moran, 2008). Assignment was made separately by gender. The

diagnosis date in the PSCSS case became the start date for the corresponding HRS case. The

time since diagnosis was calculated as the elapsed time from this date until the Wave 2 interview

date. Differences were not statistically significant in the distribution of the time-since-diagnosis

data for the PSCSS and HRS spouses (chi-square, alpha = .05).

79

Results

First Analysis: Cancer Effects on Labor Supply Within a Group of Spouses of Cancer Survivors

Bivariate Results

Fewer married couples were ―working couples‖ 2 to 6 years after diagnosis. The number

of working couples decreased from 738 to 604 (-18.2%). Work status at Wave 2 was associated

with spousal characteristics at diagnosis (Table 3.2). For husbands, associations were found for

age-at-diagnosis, own employer-sponsored health insurance, breast and respiratory cancers, and a

wife’s chronic medical condition. A wife’s work status was also associated for age-at-diagnosis,

own employer health insurance, a young child at home, a full-time job, recurrence of cancer in

her husband and his treatment status. All associations were tested for significance via chi-square

tests (alpha = .05).

Cancer characteristics of survivors were not statistically significant in effect on the

change in hours between diagnosis and Wave 2. There were no statistically significant

differences between the mean hours of work in a usual week at diagnosis and years later at Wave

2 (Tables 3.3 and 3.4). No effects were found for cancer site, stage, treatment status, or

recurrence for husbands or wives who worked in both periods (t-test, alpha=.05).

Multivariate Results

Work status effects of cancer, socio-demographic, and job-related characteristics are

reported as odds ratios (OR) for married couples, conditioned on both spouse and cancer survivor

working at diagnosis (Table 3.5). The odds ratios measure the likelihood of working two to six

years after diagnosis. No cancer characteristics at diagnosis were associated with work status of

spouses. Current treatment status at the time of the interview was negatively associated with

working for wives (OR=0.1; 0.24, 0.406, p <.01). The presence of a young child (under age 6)

80

reduced the likelihood of working for wives (OR=0.027; 0.006, 0.118, p <.01). Both husbands

and wives were less likely to work with age (p <.01).

The hour effects of cancer characteristics and covariates are reported as Tobit coefficient

estimates (Table 3.6). These estimates account for spouses who were working at diagnosis but

later reported zero hours of work in a usual week. Treatment status effects were negatively

associated with hours worked by wives (-13.5, p =.005). Increasing age at diagnosis was

negatively associated with hours for husbands and wives (p <.01). Working full-time at

diagnosis was positively associated with the number of hours worked at Wave 2 for husbands

(5.6, p <.05) and wives (9.5, p <.01).

Second Analysis: Cancer Effect on the Labor Supply of Spouses Comparing Married Couples

with and without Cancer

Bivariate Results

The proportion of husbands and wives working two to six years after diagnosis decreased

for both married couples with and without a cancer survivor (Table 3.7). Significant covariates

for working at Wave 2 included educational attainment and own health insurance benefits that

were contingent on a current employer (chi-square, alpha=.05).

Multivariate Results

Work status differences at two to six years after diagnosis are reported as odds ratios for

husbands (Table 3.8) and wives (Table 3.9) in a comparison of working couples with and without

a cancer survivor. The odds ratios measure the marginal effect of a variable on the likelihood of a

spouse working at two to six years after diagnosis given the condition of working couple at

diagnosis. Models I, II, and III illustrate the initial effect, if any, of cancer and how that effect

changes in magnitude as other covariates are included for the full model (Model III) results which

follow.

81

Cancer was not associated with the likelihood of working. Other socio-demographic and

job-related variables accounted for differences in work status. For husbands, age was negatively

associated with the likelihood of working (p <.01). Positive associations were found for

husbands with college and postgraduate education (OR=2.012; 1.075, 3.763, p <.05) and working

full-time at diagnosis (OR=1.606; 0.942, 2.737, p <.10). Wives were less likely to work with age

(p <.01). Wives were more likely to work with some college education (OR=1.955; 1.093, 3.498,

p < .05), college and post-graduate education (OR=2.717; 1.472, 5.015, p <.01), and a job-

dependent health insurance benefit (OR=1.925; 1.290. 2.872, p <.01).

Results for cancer and the usual hours of work per week are reported as Tobit

coefficients in Table 3.10. No association was found for cancer and the usual hours of work per

week. For husbands, age was negatively associated with hours (p <.01), but a positive association

was found working full-time at diagnosis (13.53, p <.01). There was a negative association for

working with increasing age for wives (p <.01). Positive associations were found for high school

(4.35, p <.10), some college (6.59, p <.05), college and post-graduate education (10.13, p <.01),

working full-time at diagnosis (11.52, p <.01), and job-dependent health insurance (7.68, p <.01).

82

Discussion

This paper addressed the effects of cancer on spousal labor supply in married working

couples in the short-term after initial treatment. Time since diagnosis ranged from two to six

years. This paper incorporated two analyses of the effects of cancer on labor supply in working

couples: (1) differences within a group of spouses of cancer survivors of working age (25-64) and

(2) differences between cancer survivors (age 55 and up) and persons without a history of cancer.

The major finding is that there is little evidence for widespread systematic effects of

cancer on the labor supply of spouses in working married couples. While this is good news for

the overall picture of average effects, there are some distributional consequences for wives under

certain conditions.

A wife whose husband was in treatment was less likely to work. If her husband was in an

advanced stage of cancer (III), she reduced her usual hours of work per week. Since this new

round of treatment extends beyond an initial treatment period, it may imply that the health of the

cancer survivor has deteriorated, perhaps as a result of recurrence or other late effects. In

addition, married couples may experience new financial distress or a continuation of financial

distress from treatment shortly after diagnosis. The magnitude of wage losses suffered by

married couples in the first year after treatment is significant. Lauzier et al. (2008) found that

wage losses for Canadian breast cancer survivors in married couples averaged 27% in the year

after diagnosis due in part to working fewer hours. The average wage loss masked even larger

losses. Ten percent lost two-thirds or more of pay and another sixteen percent lost between one-

third and two-thirds of salary Wage losses were more likely among survivors who were less

educated, worked part-time or were self-employed, short job tenure, or had chemotherapy.

Longo, Fitch, Deber, and Williams (2006) reported significant financial stress on families in the

year following diagnosis. Seventeen percent of the families reported significant financial distress

which was associated with time off from work for caregivers, including spouses, and

chemotherapy. Wives who reduce work well after the initial treatment period could face similar

83

economic consequences. For some, this may represent a continuation of financial distress or a

new episode after a brief respite.

There are several limitations to this study related to measurement of potentially important

variables that may affect spousal labor supply. These include spouse’s own health, pensions, and

differentiation of cancer site effects. While more direct measures of spousal health and pension

benefits might improve coefficient estimates, some of the effects of these variables may have

been captured in other covariates. Age may act as proxy for chronic medical conditions since

there is some correlation between aging and chronic conditions. Likewise, education is often

associated with better paying jobs and benefits, like pensions, so these variables may also account

for some of the pension effects. Given the small numbers of some types of cancer, the cancer

sites were grouped into three broad categories of cancers. Although no effect was found for

cancer, in general, in the short-term, certain types of cancer may be associated with labor supply

effects in the long-run as late effects appear.

The findings suggest several policy implications. First, the good news is that in general,

once past treatment, on average, most spouses in working couples continue to work in the short-

run. Adjustments in labor supply during treatment were largely temporary. Second, given the

evidence in this study for treatment effects, some segment of married working couples may be at

risk for financial distress. How many and how deep that distress is should be explored in future

research. Third, spouses share in the cancer survivorship experience, so efforts by clinicians and

social workers that improve care for cancer survivors will yield additional benefits for spousal

labor supply and alleviation of financial distress. Efforts to mitigate chemotherapy side effects

should improve care plans. Social workers can be proactive in dealing with clients with risk

factors for financial distress. Fourth, employers can feel more confident that investments in

accommodation programs for spouses will be beneficial since most working spouses will

continue to work years after treatment. Lastly, government income assurance programs or health

84

insurance subsidies may improve the economic welfare of married couples where treatment

effects on spousal labor supply are significant burdens.

85

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88

Figure 3.1

Key Employment Decisions Faced by Spouses of Cancer Survivors:

A Sequence of Choices at Each Stage of the Joint Survivorship Experience

Work

No

Yes

Employed

Full Time

Employed

Part Time

No Yes

No

No

Yes Yes

Not

Employed

Reduce Hours? Quit in Stage

Return to Work?

89

Figure 1: Conceptual Model of Factors Which Affect the Retirement Decision

Figure 3.2

Conceptual Model of Factors Which Affect the Work Decision at Wave 2

Spouse Job-Related Characteristics

- Full-time status

- Own Health Insurance

- Size of Employer

Survivor Health and Accommodation

Characteristics

- Cancer & Site

- Stage

- Treatment Status

- Years since Diagnosis

- Recurrence

- Number of Chronic Conditions

- Own Health Insurance

- Paid Sick Leave

- Size of Employer

Work Outcome at Wave 2

- Keep or Stop Working

- Change in Usual Hours of Work

Per Week

Socio-Demographic Characteristics

- Gender

- Age

- Educational Attainment

- Marital Status

- Race

90

Table 3.1

Changes in Patterns of Married Couples’ Work Choices

from Diagnosis to Wave 2

Household

Work

Combinations

at Diagnosis

Household Work Combinations at Wave 2 (Changes) (n=1108)

Nobody

Working

Survivor

Only

Working

Spouse

Only

Working

Both

Working Total

Row

Percent

Nobody

Working 41 9 4 2 56 5.1

Survivor

Only

Working

34 84 6 30 154 14.1

Spouse Only

Working 26 2 81 38 147 13.4

Both

Working 37 55 112 534 739 67.4

Total 138 150 203 604 10961

Column

Percent 12.6 13.7 18.5 55.2 100.0

1Note: 12 observations (1%) of sample missing work status

Chi-Square 693; DF 9; p-value <.0001

91

Table 3.2

Characteristics of Spouses of Cancer Survivors in Working Couples

1 at Diagnosis and

the Association with Working at Wave 2

(*Chi-Square .05, Association of Covariate with working at Wave 2; H =Husband, W=Wife)

Husbands n=469

Wives n=270

Totals n=739

Characteristic Number Column

Percent Number

Column

Percent Number

Column

Percent

Spouse: Age in years at

Diagnosis H,W

25-44 166 35.4 105 38.9 271 36.7

45-52 156 33.3 95 35.2 251 34.0

53-57 69 14.7 49 18.2 118 16.0

58-61 52 11.1 20 7.4 72 9.7

62+ 26 5.5 1 0.4 27 3.7

Missing

Spouse: Education at

Interview

Less Than HS 20 4.3 11 4.1 31 4.2

High School 160 34.1 97 35.9 257 34.8

Some College 79 16.8 53 19.6 132 17.9

College 91 19.4 59 21.9 150 20.3

Post-graduate 114 24.3 49 18.2 163 22.1

Other 4 0.9 11 4.1 4 0.5

Missing 1 0.2 1 0.4 2 0.3

Spouse: Own Health

Insurance from Current

Employer at Diagnosis H,W

No 211 45.0 160 59.3 371 50.2

Yes 258 55.0 110 40.7 368 49.8

Spouse: Children Under 6

years at Interview W

No 443 94.5 249 92.2 692 93.6

Yes 26 5.5 21 7.8 47 6.4 1Note: Working couple is defined as both husband and wife working at diagnosis.

92

Table 3.2 (cont.)

Characteristics of Spouses of Cancer Survivors in Working Couples

1 at Diagnosis and

the Association with Working at Wave 2

(*Chi-Square .05, Association of Covariate with working at Wave 2; H =Husband, W=Wife)

Husbands n=469

Wives n=270

Totals n=739

Characteristic Number Column

Percent Number

Column

Percent Number

Column

Percent

Spouse: Fulltime Status at

Diagnosis W

Working Part-time 42 9.0 82 30.4 124 16.8

Working Full-time 416 88.7 179 66.3 595 80.5

Missing 11 2.3 9 3.3 20 2.7

Surviving Partner’s Cancer

Site

Blood 17 3.6 20 7.4 37 5.0

Breast H

261 55.7 - - 261 35.3

CNS 12 2.6 11 4.1 23 3.1

Colorectal 16 3.4 37 13.7 53 7.2

Head & Neck 8 1.7 23 8.5 31 4.2

Lymphatic

Stage I, II, III

10 2.1 13 4.8 23 3.1

Lymphatic

Stage IV

8 1.7 8 3.0 16 2.2

Prostate - - 61 22.6 61 8.3

Respiratory H

12 2.6 10 3.7 22 3.0

Skin 19 4.1 12 4.4 31 4.2

Thyroid 34 7.3 19 7.0 53 7.2

Uterus 39 8.3 - - 39 5.3

Other Cancer 33 7.0 56 20.7 89 12.0

Surviving Partner’s Stage of

Cancer

I 240 51.2 92 34.1 332 44.9

II 144 30.7 94 34.8 238 32.2

III 52 11.1 45 16.7 97 13.1

IV 25 5.3 28 10.4 53 7.2

Unstaged 8 1.7 11 4.1 19 2.6

Surviving Partner’s Cancer

Recurrence W

No 390 83.2 223 82.6 613 83.0

Yes 78 16.6 47 17.4 125 16.9

Missing 1 0.2 1 0.1 1Note: Working couple is defined as both husband and wife working at diagnosis.

93

Table 3.2 (cont.)

Characteristics of Spouses of Cancer Survivors in Working Couples

1 at Diagnosis and

the Association with Working at Wave 2

(*Chi-Square .05, Association of Covariate with working at Wave 2; H =Husband, W=Wife)

Husbands n=469

Wives n=270

Totals n=739

Characteristic Number Column

Percent Number

Column

Percent Number

Column

Percent

Surviving Partner’s Time

Since Diagnosis

2 years or less 35 7.5 17 6.3 52 7.0

3 years 160 34.1 90 33.3 250 33.8

4 years 156 33.3 83 30.7 239 32.3

5 + years 118 25.2 80 29.6 198 26.8

Surviving Partner’s Treatment

Status

Still in Treatment

Active Cancer, W

21 4.5 17 6.3 38 5.1

Still in Treatment

Non-Active Cancer

4 0.9 3 1.1 7 1.0

Not in Treatment W

444 94.7 250 92.6 694 93.9

Surviving Partner’s Chronic

Medical Condition Wave 2 H

No 264 56.3 140 51.9 404 54.7

Yes 205 43.7 130 48.2 335 45.3

Surviving Partner’s Physically

Demanding Job

No 432 92.1 191 70.7 623 84.3

Yes 37 7.9 78 28.9 115 15.6

Missing 1 0.4 1 0.1

Surviving Partner’s Cancer

Groups for Quitting Work

Lowest Likelihood 334 71.2 80 29.6 414 56.0

Middle Likelihood 98 20.9 136 50.4 234 31.7

Highest Likelihood 37 7.9 54 20.0 91 12.3 1Note: Working couple is defined as both husband and wife working at diagnosis.

94

Table 3.3

Husbands: Mean Usual Hours of Work by the Cancer Characteristic of Survivors in

Working Couples1 at Diagnosis

Husband Working Both at Diagnosis and Wave 2

Cancer Survivor

Characteristic Diagnosis Wave 2

n

(408)

Mean Lower

95%

CI

Upper

95%

CI

n

(408)

Mean Lower

95%

CI

Upper

95%

CI Site

Blood 16 39.7 34.2 45.2 16 38.1 31.1 45.1

Breast 217 43.8 42.4 45.2 217 44.6 43.1 46.0

CNS 11 40.5 33.7 47.2 11 47.3 41.2 53.3

Colorectal 14 43.6 39.8 47.5 14 42.1 39.0 45.3

Head & Neck 8 43.8 40.0 47.5 8 45.6 39.5 51.7

Lymphatic

Stage I, II, III

10 43.5 36.0 51.0 10 48.3 39.1 57.5

Lymphatic

Stage IV

7 45.0 37.9 52.1 7 44.0 38.6 49.4

Respiratory 8 47.5 41.6 53.4 8 50.0 38.2 61.8

Skin 19 49.1 43.9 54.3 19 46.5 41.2 51.7

Thyroid 31 44.1 38.5 49.6 31 44.4 41.0 47.8

Uterus 35 40.9 37.7 44.1 35 42.2 37.8 46.6

Other 32 42.1 37.8 46.3 32 42.8 39.8 45.7

Lower Probability Quitting Work 283 43.5 42.2 44.8 283 44.3 43.0 45.5

Same Probability Quitting Work 90 44.7 42.5 46.8 90 44.8 42.8 46.8

Higher Probability Quitting Work 35 40.9 37.7 44.0 35 42.7 38.8 46.6

Stage

I 209 43.1 41.4 44.7 209 44.9 43.4 46.4

II 123 44.5 42.9 46.1 123 43.9 42.2 45.6

III 45 45.2 43.0 47.4 45 44.7 41.6 47.8

IV 23 41.3 37.1 45.5 23 39.9 34.8 44.9

Unstaged 8 36.3 23.1 49.4 8 42.8 29.3 56.2

Treatment Status

Still in Treatment

Active Cancer

17 44.4 40.7 48.0 17 43.8 40.8 46.7

Still in Treatment

Non-Active Cancer

4 41.3 31.2 51.3 4 46.3 34.3 58.2

Not in Treatment 387 43.5 42.4 387 44.2 43.2 45.3 387

Any New Cancer Since Diagnosis

Yes 67 45.0 42.2 47.9 67 45.1 42.3 48.0

No 341 43.2 42.1 44.3 341 44.1 43.0 45.2

Total 408 43.5 42.5 44.6 408 44.2 43.2 45.3

1Note: Working couple is defined as both husband and wife working at diagnosis.

95

Table 3.4 Wives: Mean Usual Hours of Work by the Cancer Characteristic of Survivors in

Working Couples1 at Diagnosis

Wife Working Both at Diagnosis and Wave 2

Cancer Survivor

Characteristic Diagnosis Wave 2

n

(222)

Mean Lower

95%

CI

Upper

95%

CI

n

(222)

Mean Lower

95%

CI

Upper

95%

CI Site

Blood 16 36.5 31.6 41.4 16 35.1 29.4 40.7

CNS 11 27.1 17.8 36.4 11 32.9 25.3 40.5

Colorectal 33 40.2 37.1 43.4 33 37.5 33.8 41.1

Head & Neck 21 40.1 34.3 45.9 21 37.0 32.7 41.4

Lymphatic

Stage I, II, III

11 37.6 30.6 44.6 11 36.6 29.3 43.9

Lymphatic

Stage IV

8 38.3 33.2 43.3 8 37.8 29.4 46.1

Prostate 50 38.2 34.2 42.1 50 37.5 34.1 40.9

Respiratory 7 37.1 25.5 48.7 7 37.1 27.2 47.1

Skin 11 36.4 27.6 45.1 11 35.1 27.3 42.8

Thyroid 16 36.3 28.6 43.9 16 35.6 29.7 41.6

Other 38 36.4 32.6 40.2 38 41.0 38.3 43.7

Lower Probability Quitting Work 66 37.7 34.3 41.1 66 37.0 34.2 39.9

Same Probability Quitting Work 108 37.9 35.9 39.9 108 38.4 36.5 40.2

Higher Probability Quitting Work 48 35.9 32.2 39.6 48 35.4 32.5 38.4

Stage

I 77 37.3 34.5 40.0 77 37.6 35.4 39.9

II 80 36.9 33.9 39.9 80 36.7 34.3 39.1

III 34 40.8 37.5 44.1 34 39.8 35.8 43.8

IV 24 37.1 33.6 40.5 24 36.0 31.6 40.3

Unstaged 7 28.9 15.3 42.4 7 34.1 25.2 43.1

Treatment Status

Still in Treatment

Active Cancer

9 43.3 31.8 54.9 9 37.8 25.6 49.9

Still in Treatment

Non-Active Cancer

1 20 - - 1 15 - -

Not in Treatment 212 37.2 35.6 38.8 212 37.4 36.1 38.8

Any New Cancer Since Diagnosis

Yes 34 33.0 28.1 37.8 34 34.9 31.6 38.3

No 188 38.2 36.5 39.9 188 37.8 36.3 39.3

Total 222 37.4 35.8 39.0 222 37.3 36.0 38.7 1Note: Working couple is defined as both husband and wife working at diagnosis.

96

Table 3.5

Results of Logit Models: All Spouses

Adjusted Odds Ratios of Likelihood of Working at Wave 2 Interview, Given Cancer of

Survivor and Both Working at Diagnosis1

Variable Sp=Spouse; S=Survivor

Dx = at Diagnosis; W=Wave I

Interview

Husbands (n=456) Wives (n=264) OR 95% CI OR 95% CI

Sp_High School-W 0.712 0.186 0.712 0.416 0.048 3.606

Sp_Some College-W 0.639 0.152 0.639 0.208 0.021 2.026

Sp_College-W 0.702 0.154 0.702 0.454 0.046 4.451

Sp_Post-graduate-W 1.269 0.282 1.269 0.679 0.053 8.641

Sp_Age53_57-W 0.4 0.131 0.4 0.268** 0.075 0.948

Sp_Age58_61-W 0.164*** 0.056 0.164 0.135*** 0.039 0.462

Sp_Age62_over-W 0.072*** 0.028 0.072 0.031*** 0.006 0.153

Sp_Own Health Insurance-Dx 1.396 0.682 1.396 1.628 0.592 4.476

Sp_Child Under Age 6-Dx NA NA NA 0.027*** 0.006 0.118

S_Time Since Dx-3 years 1.356 0.367 5.01 0.372 0.037 3.732

S_Time Since Dx-4 years 1.226 0.34 4.421 0.369 0.036 3.776

S_Time Since Dx 5 + years 1.517 0.381 6.04 0.355 0.034 3.668

S_Stage II-Dx 0.885 0.424 1.845 1.07 0.334 3.433

S_Stage III Dx 1.103 0.32 3.795 0.614 0.192 1.963

S_Still in Treatment-W 0.58 0.134 2.51 0.1*** 0.024 0.406

S_Cancer Spread / Returned-W 1.294 0.509 3.286 0.946 0.299 2.988

S_Chronic Medical Cond.-W 0.848 0.407 1.764 0.899 0.374 2.16

S_Has Physically Demanding

Job-Dx

1.513 0.377 6.066 0.549 0.214 1.409

Sp_Working Full-time -Dx 1.042 0.379 2.868 1.701 0.662 4.37

S_Cancer Group – Same

Probability of Quitting Work as

Colon Cancer-Dx

1.926 0.705 5.256 0.562 0.187 1.69

S_Cancer Group – Higher

Probability of Quitting Work

Than Colon Cancer-Dx

1.888 0.392 9.092 2.582 0.575 11.592

1Note: Reference group includes cancer group with lowest likelihood of survivor quitting work, Stage I at diagnosis, time since

diagnosis less than two years, age less than 53, no children under age 6, less than high school education, job at diagnosis not

physically demanding, no chronic conditions.

*** p < .01; ** p < .05; * p <.10

97

Table 3.6

Results of Tobit Models:

Contribution to Hours Worked by Spouse of Cancer Survivor at Wave 2 Interview

Given Both Working at Diagnosis1

Variable Sp=Spouse; S=Survivor

Dx = at Diagnosis;

W=Wave 2 Interview

Husband of Survivor (n=452) Wife of Survivor (n=204)

Est. S.E. T p Est. S.E. t p

Intercept 41.80 5.37 7.78 .0001 21.87 9.01 2.43 0.015

Sp_High School-W -4.98 3.85 -1.29 0.196 3.98 6.59 0.60 0.546

Sp_Some College-W -4.25 4.09 -1.04 0.299 -6.17 6.81 -0.91 0.365

Sp_College-W -2.60 4.11 -0.63 0.527 0.52 6.78 0.08 0.939

Sp_Post-graduate-W -1.42 4.06 -0.35 0.727 4.91 7.04 0.70 0.485

Sp_Age53_57-W -6.48*** 2.13 -3.05 0.002 -3.34 3.18 -1.05 0.293

Sp_Age58_61-W -12.18*** 2.64 -4.62 .0001 -17.50*** 4.05 -4.32 .0001

Sp_Age62_over-W -20.38*** 2.49 -8.18 .0001 -19.69*** 6.90 -2.85 0.004

Sp_Own Health Insurance-

Dx

2.25 1.64 1.37 0.169 1.31 2.63 0.50 0.619

Sp_Child Under Age 6-Dx 0.78 3.20 0.24 0.807 -1.42 5.29 -0.27 0.788

S_Time Since Dx-3 years -1.55 3.22 -0.48 0.630 2.33 5.27 0.44 0.658

S_Time Since Dx-4 years 0.06 3.31 0.02 0.986 0.86 5.36 0.16 0.873

S_Time Since Dx 5 +

years

-1.51 1.79 -0.84 0.399 2.96 3.01 0.98 0.327

S_Stage II-Dx 0.12 2.66 0.05 0.963 0.46 3.42 0.13 0.894

S_Stage III Dx -3.27 3.69 -0.89 0.375 -13.54*** 4.78 -2.83 0.005

S_Still in Treatment-W 2.51 2.22 1.13 0.259 -2.93 3.26 -0.90 0.368

S_Cancer Spread /

Returned-W

-0.79 1.67 -0.47 0.638 -1.21 2.52 -0.48 0.632

S_Chronic Medical Cond.-

W

0.53 3.23 0.16 0.870 2.14 2.82 0.76 0.448

S_Has Physically

Demanding Job-Dx 5.58** 2.69 2.07 0.038 9.49*** 2.77 3.42 0.001

Sp_Working Full-time -Dx 3.51* 2.00 1.75 0.080 3.63 4.25 0.85 0.393

S_Cancer Group – Same

Probability of Quitting

Work as Colon Cancer-Dx

0.19 2.99 0.06 0.950 6.50 4.65 1.40 0.162

Sigma _ Model

Significance

16.40 0.60 27.51 .0001 16.24 0.92 17.64 <.0001

1Note: Reference group includes cancer group with lowest likelihood of survivor quitting work, Stage I at diagnosis, time since

diagnosis less than two years, age less than 53, no children under age 6, less than high school education, job at diagnosis not physically demanding, no chronic conditions.

*** p < .01; ** p < .05; * p <.10

98

Table 3.7

Characteristics of CSS and HRS Spouses at Diagnosis in Working Couples

1

(Columns Represent Percents; N=1312 ; CSSn=286; HRSn=1026)

(*Chi-Square .05, H=Difference in Husbands between Surveys; F = Difference in Wives between Surveys)

Husbands Wives

Characteristic CSS

(n=153)

HRS

(416)

CSS

(n=133)

HRS

(n=610)

Age Group at Diagnosis

25-52 18.3 16.1 50.3 46.4

53-57 34.0 28.6 33.8 36.6

58-61 31.4 31.3 15.0 15.3

62+ 16.3 24.0 0.8 1.6

Missing 0.2 0.2

Education HW

Less Than HS 8.5 19.5 14.1 19.9

High School 34.0 28.6 33.1 32.9

Some College 20.3 19.7 27.2 25.5

College & Post-grad 37.3 32.0 25.6 21.6

Missing 0.2 0.1

Own Employer-Contingent

Health Insurance HW

No 57.5 35.3 63.9 48.5

Yes 42.5 64.4 36.1 51.3

Missing 0.2 0.2

Fulltime Employment

No 15.0 15.1 33.8 27.2

Yes 80.4 84.6 64.7 72.6

Missing 4.6 0.2 1.5 0.1

Time Since Diagnosis to

Wave2

2 years or less 7.8 5.5 4.5 7.5

3 years 26.8 35.1 33.8 36.4

4 years 39.2 35.8 35.3 37.7

5 + years 26.1 23.6 26.3 18.4

Working at Wave 2

No 20.3

27.2 24.8 21.8

Yes

79.1 72.8 75.2 78.2

Missing 0.7

1Note: Working couple is defined as both husband and wife working at diagnosis.

99

Table 3.8

Results of Logit Models:

Adjusted Odds Ratios for Husbands of Cancer Survivors Compared to Husbands without Cancer in Married Couples with Both Working at Diagnosis

1

(*** p<.01; **p<.05; *p<.10)

Variables

Model I

Cancer-

Related (n=568)

Lower 95%

CI

Upper

95% CI

Model II

Person-

Related (n=567)

Lower

95% CI

Upper 95%

CI

Model III

Job-

Related (n=559)

Lower

95% CI

Upper

95% CI

Cancer 1.455 0.928 2.282 1.331 0.827 2.142 1.21 0.739 1.983

Time Since Dx-3 years 1.064 0.441 2.566 1.131 0.464 2.753

Time Since Dx-4 years 0.988 0.413 2.368 1.112 0.458 2.699

Time Since Dx 5 + years 0.968 0.39 2.403 1.085 0.431 2.733

Age at Inteview-58_61 0.361*** 0.223 0.585 0.379*** 0.233 0.616

Age at Inteview-62+ 0.246*** 0.147 0.412 0.25*** 0.147 0.424

High School 0.804 0.453 1.425 0.793 0.445 1.412

Some College 0.876 0.47 1.633 0.897 0.479 1.681

College & Postgraduate 1.984** 1.067 3.689 2.012** 1.075 3.763

Full-time 1.606* 0.942 2.737

Own Health Insurance via

Current Employer

0.853 0.548 1.328

1Note: Reference group: no cancer, time since diagnosis less than 2 years, age less than 58, less than high school education, working but not full-time at diagnosis, no health insurance from current employer.

*** p < .01; ** p < .05; * p <.10

100

Table 3.9

Results of Logit Models:

Adjusted Odds Ratios for Wives of Cancer Survivors Compared to Wives without Cancer in Married Couples with Both Working at Diagnosis

1

(*** p<.01; **p<.05; *p<.10, n=692)

Variables

Model I

Cancer-

Related (n=743)

Lower 95% CI

Upper 95% CI

Model II

Person-

Related (n=742)

Lower

95%

CI

Upper 95% CI

Model III

Job-

Related (n=739)

Lower 95% CI

Upper 95% CI

Cancer 0.845 0.545 1.309 0.797 0.501 1.269 0.951 0.587 1.543

Time Since Dx-3 years 0.625 0.26 1.5 0.647 0.268 1.564

Time Since Dx-4 years 0.496 0.208 1.185 0.527 0.219 1.267

Time Since Dx 5 + years 0.437 0.176 1.084 0.458 0.183 1.145

Age at Inteview-58_61 0.338*** 0.218 0.524 0.374*** 0.238 0.586

Age at Inteview-62+ 0.191*** 0.056 0.653 0.177*** 0.051 0.608

High School 1.557 0.913 2.653 1.486 0.866 2.55

Some College 2.034** 1.144 3.617 1.955** 1.093 3.498

College & Postgraduate 2.966*** 1.623 5.421 2.717*** 1.472 5.015

Full-time 1.169 0.776 1.763

Own Health Insurance via

Current Employer 1.925*** 1.29 2.872

1Note: Reference group: no cancer, time since diagnosis less than 2 years, age less than 58, less than high school education, working but not full-time at diagnosis, no health insurance from current

employer.

*** p < .01; ** p < .05; * p <.10

101

Table 3.10

Results of Tobit Models:

Effects of Cancer on Hours of Work: CSS vs. HRS

Given Both Working at Diagnosis1

Variable Sp=Spouse; S=Survivor

Dx = at Diagnosis;

I=Wave 2 Interview

Husband of Survivor (n=554; zero bound =142 )

Wife of Survivor (n=734; zero bound =164)

Est. S.E. T p Est. S.E. T p

Intercept 22.56 6.26 3.6 0.000 12.54 3.94 3.18 0.002

Cancer 1.13 2.60 0.44 0.663 0.93 2.07 0.45 0.656

Time Since Dx-3 years 2.70 4.90 0.55 0.581 -1.96 3.16 -0.62 0.536

Time Since Dx-4 years 0.63 4.86 0.13 0.897 -3.88 3.16 -1.23 0.219

Time Since Dx 5 + years 0.43 5.08 0.09 0.932 -3.26 3.40 -0.96 0.339

Age at Inteview-58_61 -11.53*** 2.59 -4.45 <.0001 -13.77*** 2.24 -6.14 <.0001

Age at Inteview-62+ -22.22*** 3.01 -7.39 <.0001 -24.94*** 6.92 -3.61 0.000

High School -4.50 3.42 -1.31 0.189 4.35* 2.61 1.67 0.095

Some College -0.67 3.71 -0.18 0.858 6.59** 2.72 2.42 0.015

College & Postgraduate 5.22 3.36 1.55 0.120 10.13*** 2.72 3.73 0.000

Full-time 13.53*** 3.23 4.18 <.0001 11.52*** 1.87 6.16 <.0001

Own Health Insurance via

Current Employer

-1.86 2.38 -0.78 0.435 7.68*** 1.67 4.6 <.0001

Sigma _ Model Significance 25.07 0.94 26.68 <.0001 20.47 0.65 31.62 <.0001 1Note: Reference group: no cancer, time since diagnosis less than 2 years, age less than 58, less than high school education, working but not full-time at diagnosis, no health insurance from current

employer.

*** p < .01; ** p < .05; * p <.10

102

CHAPTER FOUR

WHAT IS THE EFFECT OF CANCER ON TIME-TO-RETIREMENT?

Abstract

Background. Cross-sectional methods may not detect cancer effects on retirement. The selected

time period may occur after the effects have happened. Since retirement decisions unfold over

time, changes in the time-to-retirement carry economic and social consequences for individuals.

Data. A sample of older cancer survivors from the Penn State Cancer Survivor Survey was

identified from Wave 1 and compared to a sample with non-cancer individuals of similar ages

from the Health and Retirement Study in Round 5 (2000). Cancer, socio-demographic, and job-

related characteristics were included to measure effects on the time-to-complete-retirement.

Study design. A longitudinal approach used logistic regression to measure the likelihood of

completely retiring compared to a control group of persons without a history of cancer. Monthly

observations were created for cancer, recurrence, socio-demographic, and job-dependent benefits,

including health insurance and pensions. Data were tracked from diagnosis date until lost to

follow-up, or until the event, complete retirement, or the end of the study period in 2004.

Principal findings. Cancer-free men and women are less likely to retire completely and

continued to work compared to men and women with no history of cancer. Recurrence increases

the likelihood of retiring sooner for women with cancer compared to women with no history of

cancer.

Conclusions. Overcoming economic losses from cancer may require individuals to work longer

than they otherwise might if they had no history of cancer. Recurrence can pose a threat to

economic recovery or the sense of fulfillment from a job – a loss upon loss. Longitudinal

methods are necessary for decisions like retirement that unfold over time.

103

Background

The employment of cancer survivorship has been studied at different points in the

lifetime continuum or ―seasons of cancer‖ (Mullan, 1985). The studies have contributed to our

knowledge of employment patterns from the time of diagnosis through the treatment period up to

long-term survivors (Bouknight, Bradley, & Luo, 2006; Bradley, Neumark, Bednarek, & Schenk,

2005; Short, Vasey, & Tunceli, 2005). While a diagnosis of cancer may occur at any age, the

incidence of cancer in persons aged 50 or more at diagnosis is substantially higher than under the

age of 50. In 2005, the age-adjusted incidence rate for all cancers under age 50 was 95 cases per

100,000 persons compared to 1,382 cases per 100,000 persons for those 50 or older (National

Cancer Institute, 2005). As the incidence of cancer and its risk rise with age, the health shock

from cancer may increasingly occur in the context of a retirement decision.

Cancer in the period just before retirement has implications for both cancer survivor and

society. Does cancer for older survivors (50-64) accelerate or delay retirement compared to

cancer-free persons? There are important economic consequences to this decision. If cancer

survivors retire sooner, they may have less saved for the post-retirement period than they had

planned (Jefferson, 2007; Johnson, Mermin, & Murphy, 2007). In addition, society suffers the

loss of their economic contributions. However, if cancer survivors retire later, they may increase

their savings for retirement or continue working as a hedge against uncertain future medical costs.

The individual decisions to work longer until complete retirement contribute important economic

and social benefits to society (Butrica, Smith, & Steuerle, 2006).

While health has been linked to retirement decisions, the retirement literature has not

focused specifically on cancer. Health has been broadly defined by a variety of measures.

Measures of health have included self-reported health status, activities of daily living (ADL), and

the presence of any number of chronic diseases, but with no specific focus on cancer (Bound,

1989; Bound, Schoenbaum, Stinebrickner, & Waidmann, 1998; Coile, 2004; Costa, 1994;

Kerkhofs, Lindeboom, & Theeuwes, 1999; Smith, 2003; Larsen & Gupta, 2007). It is impossible

104

to answer questions about the effect of cancer on retirement given the limitations in the current

retirement literature.

Alternatively, the cancer survivor literature has addressed the work choices of older

survivors across the stages of cancer survivorship. Studies have spanned the treatment period

(Bouknight, Bradley, & Luo, 2006), longer-term cancer survivors in post treatment (Bradley &

Bednarek, 2002; Bradley, Bednarek, & Neumark, 2002a; Bradley, Bednarek, & Neumark, 2002b;

Bradley, Neumark, Bednarek, & Schenk, 2005; Bradley, Neumark, Luo, & Bednarek, 2005;

Drolet et al., 2005; Short, Vasey, & Tunceli, 2005), and older cancer survivors nearing retirement

(Bednarek & Bradley, 2005; Short, Vasey, & Moran, 2008).

A major limitation of the cancer survivor literature is the absence of studies that

emphasize retirement as the focal decision. The current body of research has focused mainly on

work status, not specifically on retirement, and has usually relied on cross-sectional instead of

longitudinal data and methods. Cross-sectional methods may miss effects over time because they

miss events that occurred before the designated study period. A thought experiment will illustrate

this. Imagine that most people retire at age 65. If a cross-section was taken at age 66, one would

find little difference in employment between cancer survivors and other individuals. Any

retirement because of cancer could have occurred before this age. It would have been lost in the

noise created by the large wave of retirements prior to age 65. One might conclude that cancer

had no employment effect. However, the employment effect of cancer can be measured by

whether it accelerates or delays retirement. A longitudinal approach tracks an individual over

multiple slices of time and is more likely to detect events.

The purpose of this paper is to answer a key question about the retirement behavior of

cancer survivors from the time of diagnosis through several years afterwards. Are older cancer

(aged 50-64) survivors more likely to ―retire completely,‖ sooner or later, than persons without

cancer? This paper contributes to the cancer survivor literature in two ways. First, it specifically

focuses on the impact of cancer on the time to complete retirement. Second, it provides a

105

complementary framework to existing cross-sectional study results. Multiple perspectives

increase the reliability of assessments of cancer survivor behavior. The information may be used

to help plan for retirement with a better understanding of the risks and consequences of cancer for

individual welfare. It will also inform public policy makers about growing needs and potential

work contributions of cancer survivors as they live longer with a chronic disease.

Conceptual Model

Older cancer survivors, like other individuals, allocate their time between work life and

retirement. From the perspective of the traditional labor supply model, modified to include the

effects of health, they must decide if the utility of retirement is greater than the utility of

continuing to work (McGarry, 2002). In making this decision, there are practical dimensions

including uncertainty over the adequacy of savings to supplement consumption in the retirement

period and the prospect of increasing medical expenditures as their health declines (Smith, 2003).

One of the prime events that may influence the ability to earn income and save for retirement is

the occurrence of cancer later in life. These years are often considered to be the prime earning

years. They are particularly important because they represent an opportunity to save for

retirement. Cancer may increase the uncertainty about future medical expenditures and alter the

value that individuals assign to leisure time. While the desire to increase savings from income

may influence individuals to continue working as a hedge against future consumption, individuals

may also substitute leisure for work if their work-leisure preferences have changed with the shock

of an illness like cancer (Becker, 1965; Berger, 1983; Berger & Pelkowski, 2004; Kalemli-Ozcan,

& Weil, 2002).

Traditional views of retirement as a singular and one-time event are challenged by

growing evidence of retirement as a process of many events and decisions (Figure 4.1).

Retirement may be an initial one-time choice or it may be the result of a gradual process of

slowly withdrawing from the labor force by reducing effort via changing jobs, working fewer

106

hours, or shifting to part-time work (Cahill, Giandrea, & Quinn, 2008; Taylor, 2007; De Vaus,

Wells, Kendig, & Quine, 2007; Moen, 2007). Subsequent to that initial choice, retirees may

return to the labor force in a reverse process of some degree of labor force attachment.

There is a first time for the complete retirement event (Gustman, Mitchell, & Steinmeier,

1995). If cancer accelerates the timing of that choice, cancer survivors may enter retirement with

inadequate savings and income when they face potentially greater medical expenses. Cancer may

delay the timing of retirement, if cancer survivors continue to work because they are worried

about losing benefits or income to pay medical and other bills.

Cancer effects on retirement may increase the likelihood of retiring sooner or later than

persons without cancer. A cancer survivor compares the utility from retirement to the utility of

continuing to work. If the value (utility) of his or her remaining leisure time increases, he or she

may choose to stop working and retire (Short, Vasey, & Moran, 2008). Alternatively, cancer

survivors may place less value on their remaining leisure time and continue working to provide

income in future periods or because working provides psychic benefits (Bradley, Bednarek, &

Neumark, 2002b).

In addition to cancer, other covariates affect retirement decisions. One event that is

unique to cancer survivors is recurrence, which is the spread of an existing cancer to distant sites,

or the occurrence of a new cancer. Recurrence may affect the timing of retirement. It may

accelerate the event if its affect on an individual’s life expectancy leads to increasing the value of

his or her remaining leisure time (Short, Vasey, & Moran, 2008). Alternatively, a cancer survivor

may delay retirement because he or she gains utility from a sense of ―making a contribution‖ with

his or her remaining time and health (Bradley, Bednarek, & Neumark, 2002b).

Increasing age may raise the likelihood of retiring because of the availability of financial

support from Social Security, defined benefit pension plans, and 401(k) plans. Alternatively,

increasing age may not increase the likelihood of complete retirement, if older workers preference

for work and leisure change with time and yield more utility from working instead of retiring.

107

Chronic diseases may affect the ability of cancer survivors to continue working and may

accelerate the decision to retire. The increasing burden and cumulative impact of several chronic

diseases may tip the comparison of utility from working to more utility from leisure due to the

effort required to work when in poor health (Becker, 1965; Short, Vasey, Tunceli, 2005).

Marital status may increase the likelihood of retiring because of resources like extra

income, pensions, or an alternative source of health insurance that is unavailable to single

persons. Alternatively, marriage may decrease the likelihood of retiring since an individual may

work to increase savings to support a spouse and pay for uncertain medical expenditures (Berger

& Pelkowski, 2004; Coile, 2003).

Effects of social and economic disparities may be captured in racial and ethnic variables.

In the case of discrimination or disparities, individuals may be forced to delay retirement to build

savings for the post-retirement period due to lower wages over their working lives. Alternatively,

Social Security and Medicare benefits may offer more incentive to retire, since they may reduce

some of the inequality in the post-retirement period.

Higher levels of education are associated with greater income, benefits, and perhaps less

physically-demanding jobs, which combined may increase the likelihood of delaying retirement.

However, the greater life-time income and benefits afforded by educational attainment may

provide income in the post-retirement period through savings made possible by the higher

education. This may accelerate retirement (Short, Vasey, Tunceli, 2005).

The greater physical demands of work may lead to earlier retirement. The amount of

effort to provide a marginal hour of labor may be relatively large for a cancer survivor who

suffers from the side effects of treatment or late effect of cancer. Consequently, leisure time

becomes more attractive (Short, Vasey, Tunceli, 2005).

Full-time work may lengthen the time to retirement since it provides more income,

benefits, and opportunities for socialization than part-time work. Alternatively, the accumulation

108

of savings made possible by the income and benefits of a full-time job may make earlier

retirement possible.

Self-employment may increase the time to retirement, since self-employed individuals

may not have the income or benefit package available to those who work for someone else. In

particular, self-employed individuals may have sustained significant income losses during

treatment and must continue to work to recoup that lost income for future retirement.

Individuals with health insurance based on employment may retire later, since this health

insurance is a protection against the risk of unexpected medical costs (Bradley, Bednarek,

Neumark, 2002a). Alternatively, if an employer’s health benefit package is available to retirees,

this may accelerate retirement decisions.

The availability of retirement benefits may reduce the length of time to retirement if the

benefit package offers accumulation of large sums before retirement. On the other hand,

retirement benefit packages that create a large portion of post-retirement income based on the last

few years of salary or wages provide incentives to delay retirement.

The time since diagnosis may affect retirement decisions. A cancer survivor may have

had more or less time to make adjustments at work, which now offers more or less utility than

retirement. Part of that adjustment process may include a change in preferences for work and

leisure.

109

Methods

In order to control for secular changes in factors and economic conditions which may

affect the time-to-first complete retirement outcome for older cancer survivors, this paper used a

control sample of cancer-free individuals taken from the Health and Retirement Study (HRS)

sample, RAND H, Round 6. The control sample is the reference for the effect of cancer on the

time to first complete retirement in the Penn State Cancer Survivor Study (PSCSS) sample. Since

this paper focuses on the timing of the first retirement choice, there are three selection criteria

common to both samples: the individual must be born in the years 1936 through 1947, working

at the time of diagnosis, and has not retired at any time prior to this diagnosis. The birth year

requirement ensures that both control and cancer samples include persons aged 50 through 64, the

year prior to the age for qualification for full Social Security benefits (Figure 4.2).

The ―time-to-complete retirement – the first time‖ outcome depends on survey responses

to questions for the PSCSS and HRS. For the PSCSS cancer survivors, complete retirement was

operationalized by responses to two questions. First, complete retirement candidates were

determined by responses to either of two questions: (1) ―Are you working for pay, temporarily

laid off or on leave, unemployed and looking for work, retired, disabled and unable to work, a

homemaker, a student or something else?‖ and (2) ―Are you doing any work for pay at the present

time?‖ Second, the survey responses from Waves 1 through 4 were examined for the ―first time a

cancer survivor reported that he or she had retired and were not doing any work for pay.‖ The

month and year of this event were recorded from responses to the question: ―In what month and

year did you retire?‖ For the HRS sample, complete retirement was indicated by the responses to

the question: ―At this time, do you consider yourself to be completely retired, partly retired, or

not retired at all?‖ Rounds in HRS were searched for the first response to this question

(SAYRET variable in the HRS data set), and the month and year were recorded by responses to

this question: ―In what month and year did you retire?‖

110

Samples

The cancer survivors in this study were originally identified in the first wave of the Penn

State Cancer Survivor Study (PSCSS) which has been described in detail by Short, Vasey, &

Tunceli (2005). The study eligibility criteria included: (1) cancer survivors between the ages of

25 and 62 at diagnosis, (2) with a first cancer diagnosed between January 1997 and December

1999, and (3) with a prognosis that would permit participation over four waves in the panel

survey. Survivors were recruited from cancer registries at The Johns Hopkins Hospital, Lehigh

Valley Hospital and Health Network, Geisinger Medical Center, and Milton S. Hershey Medical

Center. The first wave of interviews at annual intervals was conducted from October 2000 into

December 2001. Thereafter, panel participants were interviewed in subsequent annual waves

from 2002 through 2004.

The final PSCSS sample includes 584 cancer survivors aged 51-64 with 196 observed

retirement events (33.6%). Each person contributes information up to the point in time he or she

is no longer observed or reaches the end point for the survey, which is the Wave 4 interview date

in the PSCSS observation. The PSCSS cancer survivors represent a total of 30,085 person-months

in the study period.

The Health and Retirement Survey (HRS) is a panel survey that began in 1992 under the

auspices of the University of Michigan. It enrolls a sample of persons at age 51 and follows them

through time with interviews at two-year intervals. Questions about employment, retirement,

and health are framed as ―What has changed since the last interview?‖ This paper uses

information from HRS Waves 1 through 8 (1992-2006). HRS Waves prior to the study period

(2000-2004; Waves 1-4) and after the study period (Wave 8 - 2008) were reviewed for references

to cancer, last dates of employment, and the timing of retirement events. The objective of this

review was to select control cases that met the following criteria: no evidence of cancer,

employed at baseline, and the first retirement date after baseline. HRS Waves 5 through 7

(2000-2004) parallel Waves 1-4 in the PSCSS (2000-2004) over the study period.

111

Observations were selected from Wave 5 (2000) since this HRS wave approximates the

baseline period in Wave 1 PSCSS period (1997-1999). The time-to-event analysis requires the

same starting date between cancer and control cases. Since there is no date of diagnosis

(baseline) date in the HRS sample, one must be imputed. Therefore, a ―pseudo-baseline‖ date was

created using a method found in prior cancer survivorship studies using control groups (Bradley,

Bednarek, & Neumark, 2002b; Short, Vasey, & Moran, 2008). The diagnosis date for the PSCSS

cases is the starting point or ―baseline date‖ for linked HRS observations. To assign a baseline

date to HRS cases, comparable to the date of diagnosis in PSCSS, HRS cases were randomly

assigned the diagnosis date of a case in the PSCSS samples (with replacement and separately by

gender).Based on the previously specified inclusion criteria and, after cancer survivors were

excluded from the HRS, the final HRS sample included 2,447 persons. There were 1,093 first

retirement events over the study period, which was 44.7% of the initial sample. The observations

represent 135,698 person-months.

The sample characteristics for the PSCSS and HRS are described in Table 4.1 and

differences are noted at the Chi-square .05 level. While the retirement percentages are similar of

males (40.5 PSCSS; 42.8 HRS), there is a statistically significant difference in female retirement

rates between the cancer and control samples (29.0 PSCSS; 46.8 HRS). This may be due to the

age differences between the samples. The distribution of ages for females shows that almost half

(44.5%) of the PSCSS observations are less than 55 years of age, while about a quarter of the

HRS cases are in the same age group (27.8%). The two samples were statistically different in the

proportion of non-white observations, with the cancer group showing much less diversity in

percentage composition for males (3.9 PSCSS; 14.6 HRS) and females (8.0 PSCSS; 22.3 HRS).

The proportion of married individuals was greater in the PSCSS sample compared to the HRS.

However, in comparing differences by gender, the rate difference for males was not statistically

significant (88.4 PSCSS; 87.8 HRS), while the difference was significant for females (74.7

PSCSS; 62.9 HRS). Educational differences were significant for both males and females in the

112

PSCSS sample compared to the HRS. There is a higher proportion of males (39.7 PSCSS; 30.3

HRS) and females (33.5 PSCSS; 21.6) in the PSCSS sample who have completed college degrees

or post-graduate degrees. The type of job that implies greater physical requirements (―blue

collar‖) occurs in larger proportion among the HRS observations. Both males (28.9 PSCSS; 49.8

HRS) and females (11.4 PSCSS; 28.8 HRS) in HRS are found more often in ―blue collar‖ jobs.

HRS individuals were more likely to be working full-time for males (91.0 PSCSS; 93.4 HRS)

with statistically significant differences for females (71.0 PSCSS; 78.1 HRS). There are no

statistically significant differences for self-employed individuals between the samples for males

(19.8 PSCSS; 20.1 HRS) or females (14.5 PSCSS; 11.9 HRS). With regard to any retirement

benefit (defined benefit or defined contribution), a greater proportion of males in the PSCSS

sample (75.9%) have benefits compared to HRS individuals (67.2%) and this difference is

statistically significant. The difference between samples for females is not significant (64.2

PSCSS; 64.2 HRS). Given health insurance benefits from a current employer, the HRS sample

had greater proportions for males with that benefit (59.9 PSCSS; 65.4 HRS) and a statistically

significant greater proportion among females (46.0 PSCSS; 62.5 HRS).

While the attrition rates are similar between genders, they differ by survey (Table 4.2).

The attrition on a percentage basis is slightly greater for females than for males in the HRS

survey. The percentage of the original number of participants who were engaged in all four waves

was 64.1% for males and 62.3% for females. The rate of attrition was higher in the PSCSS

survey. Only 43.1% of males participated in all 4 waves compared to 49.4% for females. The

rates of attrition are lower for the HRS survey with 35.9% of males and 37.7% of females in the

HRS who were lost to follow-up over the four waves. The higher rates of attrition were in the

PSCSS survey with 56.9% of males and 50.6% of females lost in the study period. The attrition

rates included all cases lost to follow-up over the baseline interview for whatever cause. Reasons

vary from death, to inability to locate, and to simply refusal to participate.

113

Variables

The date of the first complete retirement is critical for the construction of the time to first

and complete retirement. Retirement events were recorded in response to questions on both the

PSCSS and HRS survey instruments that ask if the respondent has retired since the last wave

interview and, if so, when (month, year). Responses were recorded as ―retired first time‖ if the

associated date indicated that this event occurred after the baseline date (diagnosis date). For

PSCSS individuals, the baseline date is the date of cancer diagnosis, while linked HRS

observations use the same baseline date as a reference point to determine if HRS responses are

valid (i.e., retirement dates on or after the baseline date). The HRS date of first retirement was

constructed by finding the date of first retirement and cross-referencing to other variables about

whether retirement was complete or partial. Only cases that were recorded as complete were

retained for this study.

The response variable is the number of days between the two dates. The days were

converted to months for the statistical analysis. For those who retired during the study period, the

number of days was the first retirement date less the baseline date. Those who did not retire

during the study period were censored observations, either because we did not observe the

retirement event due to dropping out of the study or the event had not yet occurred by the end of

the study period. For censored observations, the number of days was measured by the last

observed date less the baseline date (which is the date of diagnosis for the PSCSS individuals and

the ―pseudo-date‖ of diagnosis for the HRS individuals). Each person contributed ―person-

months‖ according to the length of time in which they were observed in the study period.

Since each person was measured in terms of person-months until an event, we are

interested in the average length of time until an individual retires after a cancer diagnosis and

what factors might explain the length of time to the retirement event. How long individuals with

cancer continue working compared to a reference control group is an indication of the effect of

cancer on retiring sooner or later.

114

There are three sets of control variables: cancer-related, person, and job. In addition to

the direct effect of cancer on the elapsed time to first retirement, there may be an effect from a

cancer-related condition, recurrence – a new cancer or the spread of an existing cancer.

Recurrence may accelerate the retirement effect of the original cancer diagnosis as leisure time

becomes more valuable with the diminished expectations about life expectancy. On the other

hand, recurrence may delay retirement if an individual continues working to avoid the income

loss in the face of uncertain future medical expenses or consumption levels, which may affect the

individual or family members. Recurrence is a time-varying variable and was recorded in the

month since diagnosis in which it happens. Survey responses must indicate the date (month, year)

that a new cancer occurred or that the original cancer had spread. The month and year were

converted to a month since baseline in which this recurrence occurred. Once recurrence occurred,

the monthly binary variable was set equal to 1 to indicate the potential effects of recurrence over

time.

The person-related variables include time-varying age and chronic medical conditions,

and time-invariant variables like marital status, race, and the level of educational attainment.

Person-related time-invariant variables were measured at diagnosis. Time-varying variables were

measured at each wave. Age was incremented for each month. Other time-varying variables were

kept at a constant value in the months between waves and changed only at wave interview

intervals.

Age was calculated using birthdates from the registries for the PSCSS individuals and the

birth date supplied on the survey for HRS respondents. Age was grouped in the regression model

into categories as persons aged over the course of the study period: less than 60, 60-61, 62, 63-

64, and 65 and older. The lowest age group is the reference category. Marital status is a binary

variable with 1 indicating married and 0 not married. Information about race was dichotomized

as non-white =1 and white=0. The education variable measured the highest level attained, and it

115

was categorized into less than high school, high school, some college, and college or post-

graduate. Less than high school is the reference category.

The job context variables included the physical nature of the work, full-time, self-

employed, source of health insurance, and any retirement benefits. All are time-invariant

variables with initial values determined at baseline.

The physical nature of the work is represented by dichotomizing the occupation code

(BLS Occupational Codes) of the survivor into a physically-demanding type job (yes=1, no=0).

Full-time work was defined by working 35 or more hours at diagnosis for PSCSS respondents.

Respondents who reported weekly hours of 35 or more hours were coded as 1, otherwise, 0. For

HRS individuals, full-time work was a response to a survey question about working full-time or

part-time. HRS respondents were coded as 1 for a response of full-time work, otherwise 0. Self-

employment is a binary variable based on survey questions that ask if the respondent is working

for someone else (1= yes; 0= no). The source of health insurance was dichotomized by whether

or not the cancer survivor has his or her own coverage with a current employer at baseline. If a

person’s own health insurance benefit was contingent on working for a current employer, the own

health insurance was coded as 1, otherwise, 0. Any retirement benefit is a binary variable. It was

set equal to 1, if the survivor reports having a defined benefit pension plan, defined contribution

like a 401(k), or both at baseline. Otherwise, lacking a reported retirement benefit, it was coded

as 0. Time since diagnosis is a time-varying variable which was measured continuously as

months since diagnosis. Since the effect of the passage of time may be nonlinear, the concept of

time since diagnosis was captured by using two variables in the regression model – time since

diagnosis and years since diagnosis squared since this relationship may be non-linear.

116

Statistical Analysis

Time-to-event models with discrete measurements may be estimated using standard

logistic regression techniques (Allison, 1995; Singer & Willett, 2003). Since the retirement

literature commonly treats male and female retirement choices separately, this paper follows a

similar approach and estimates separate multivariate logistic regression models for the odds ratio

(OR) of the independent variables with results reported up to the .10 level. SAS version 9.3 was

used in all statistical tests. The full effect of cancer includes the cancer and the recurrence

variable. A Wald test was performed to determine if the combined coefficient was statistically

significant.

Results

Separate multiple logistic regression models are estimated for male and female cancer

survivors in Table 4.3 and Table 4.4. The models estimated the association between cancer and

covariates on the likelihood of retiring completely for the first time over the post-diagnosis

period, compared to a control without cancer in the study period. Cancer and its recurrence form

the base specification (Model I), cancer and person characteristics follow, (Model II), and the full

model specification includes cancer, person, and job characteristics (Model III).

Male Cancer Survivors

The Model III column in Table 4.3, the full specification form of the model, contains the

estimated parameters for the three groups of variables and their 95% confidence interval. Cancer-

free survivors were less likely to retire completely (OR=.529; 0.408, 0.686) and hence, retire later

than individuals without cancer. The marginal effect of recurrence alone on the log odds of the

likelihood of retiring were not significant (OR=0.965; 0.461, 2.021). However, the combination

of coefficients for cancer and recurrence was significant at the .10 level (p =0.065) and suggests

that cancer survivors with recurrence are more likely to retire, compared to cancer-free survivors

117

without recurrence and individuals without cancer. Cancer-free male cancer survivors retire later,

but recurrence leads to earlier retirement.

Aging increases the likelihood of retiring compared to individuals under age 60. Age

categories were consistently associated with retiring (p <.01). The odds ratios range ranged from

1.58 to 4.76. As time since diagnosis passed, a cancer survivor was more likely to retire

(OR=1.03; 1.015, 1.045). A cancer survivor working with a defined benefit plan at diagnosis was

more likely to retire (OR=1.409; 1.098, 1.807). Cancer survivors were less likely to retire with a

full-time job (OR=0.678; 0.503, 0.914), if self-employed (OR=0.56; 0.416, 0.753), or with job-

dependent insurance from a current employer (OR=0.651; 0.525, 0.806).

Female Cancer Survivors

The combined coefficient effect of cancer (OR=0.323; 0.247, 0.423) and recurrence

(OR=1.57; 0.906, 2.722) was significant (p =0.009). This suggests that the combined effect may

be small and its direction is unclear, since the magnitudes and signs of the resulting odds ratio are

opposites.

Cancer-free survivors were less likely to retire completely (OR=0.323; 0.247, 0.423), and

hence, retire later than individuals without cancer. The marginal effect of recurrence alone on

the log odds of the likelihood of retiring were not significant (OR=1.57; 0.906, 2.722).

However, the combination of coefficients for cancer and recurrence was significant at the .01

level (p =0.009) and suggests that cancer survivors with recurrence are more likely to retire,

compared to cancer-free survivors without recurrence and individuals without cancer. Cancer-

free female cancer survivors retire later, but recurrence leads to earlier retirement.

Cancer survivors were more likely to retire with age (p <.01). This effect was consistent

across the age categories with odds ratios from 1.525 to 3.246. Time since diagnosis (OR=1.014;

0.999, 1.028), and any type of pension plan like defined benefit (OR=1.676; 1.279, 2.195),

defined contribution (OR=1.325; 1.024, 1.714), or a combination of both (OR=1.9; 1.355, 2.664)

118

also made earlier retirement more likely. A cancer survivor was less likely to retire if he or she

had some college (OR=0.722; 0.544, 0.959), or his or her own health insurance policy through a

current employer (OR=0.643; 0.52, 0.794).

Discussion

Cancer-free cancer survivors are those who have survived since diagnosis without recurrence

(i.e., spread of the original cancer to a new site or return of an old cancer). Cancer-free survivors

are less likely to retire sooner than older individuals without cancer. The cancer-free survivors

continue to work and delay retirement. Some of the model factors that are significant offer clues

to working longer – the employer-sponsored health insurance for men and women. Good pension

programs were negatively associated with longer durations until retirement. Furthermore, the

cancer-free survivors may work longer to recover income lost during treatment or life-time

income lost because cancer altered a career path. Their preference for the use of time may have

changed as they found a new source of fulfillment in working. What is clear is that recurrence

changes this trajectory. Cancer survivors with recurrence retire sooner. They lose the

opportunity to continue working and accrue the income, health insurance, pension benefits, and

perhaps personal fulfillment from working.

If job lock affects workers at the younger working ages, it may be at work with older

cancer survivors. It may, however, be more than health insurance. It may also reflect trying to

recover from economic costs of cancer. Cancer survivors were more likely to retire with the

availability of a rich benefits package. That package may more than compensate for any wage

losses during the treatment period or additional out-of-pocket costs over time.

In a study of employed older cancer survivors, Short, Vasey, & Moran (2008) found that

female survivors were less likely to be working 7-10 years post-diagnosis, while there was no

statistically significant difference for men. However, Bradley, Bednarek, & Neumark (2002b)

found that female breast cancer survivors worked more hours after returning to work. Both

119

studies were cross-sectional snapshots at a point in time post-diagnosis. A time-to-event

perspective, such as the approach used in this paper, follows an individual and measures the

hazard of retiring over that period compared to a similar control group. If the analysis reveals

that cancer survivors are more likely to retire, then estimates at the end-point in the cross-

sectional studies may be biased and underestimate the consequence of cancer in labor supply

adjustments over time.

One limitation of this study lies in its longitudinal design. A longitudinal study exposes

findings to attrition bias and a healthy survivor effect. Some of the attrition is normal in the

course of longitudinal surveys. The danger is that over time, the surviving sample becomes less

representative of the population of cancer survivors. Another limitation is that this study

examined only one aspect of a very complex concept like retirement. Future studies of cancer

survivors and retirement can add missing pieces like decisions involving a choice between full-

time or part-time work or changing jobs.

120

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123

Next Retirement Decision

- Re-entry

o Full-time v Part-time

- Retire

o Complete

o Partial

- Keep repeating cycle

Figure 4.1

Conceptual Model of Factors Which Affect the Retirement Decision

Health-Related Characteristics Over Time

- Cancer

- Years since Diagnosis

- Recurrence

- Number of Chronic Conditions

First Retirement Decision

- Impetus

o Voluntary

o Involuntary

- First Retirement

o Complete

o Partial

- Keep Working

o Full-time v Part Time

o Change Careers v Jobs

Socio-Demographic Characteristics

- Gender

- Age

- Education

- Race

- Marital Status

Job-Related Characteristics

- Self-employed

- Full-time status

- Health Insurance Source

- Retirement Benefits

- Wages

124

PSCSS

All Survivors at Wave 1

Interview

1763

PSCSS

All Survivors Working at

Diagnosis and First Retirement is

After Diagnosis

1335

PSCSS

Study Sample

Birth Years 1936-1947

584

30085 Person-Months

196 Retirement events

Figure 4.2

From Survey to Sample in Study

1936-1947 Birth Cohorts

HRS

All Respondents in RAND-H

30405

HRS

Birth Years 1936-1947

7862

HRS

Study Sample

No Cancer, Working at

Diagnosis, First Retirement after

Diagnosis

2447

135698 Person-Months

1093 Retirement events

125

Table 4.1

Characteristics of PSCSS and HRS Samples

Working at Diagnosis/Baseline

(Column Percents)

(*Chi-Square .05, M =Difference in Males between Surveys; F = Difference in Females between Surveys)

Males Females

Characteristic CSS

(n=232)

HRS

(1313)

CSS

(n=352)

HRS

(n=1134)

Age Group at Diagnosis M,F

Less than 55 40.1 30.5 44.9 27.8

55-59 36.6 50.7 40.1 52.9

60-61 19.4 16.2 11.1 16.3

62+ 3.9 2.7 4.0 3.0

Total

100 100 100 100

Race M,F

White 93.5 85.4 90.1 77.7

Non-White 3.9 14.6 8.0 22.3

Missing 2.6 2.0

Total 100 100 100 100

Married F

Single 11.6 12.2 25.3 37.0

Married or Partner 88.4 87.8 74.7 62.9

Missing 0.2

Total 100 100 100 100

Education M,F

Less Than HS 31.5 29.3 35.2 33.2

High School 7.3 21.3 7.4 19.2

Some College 19.0 19.0 21.9 26.0

College & Post-grad 39.7 30.3 33.5 21.6

Missing 2.6 0.2 2.0

Total 100 100 100 100

Physical Occupation M,F

No 71.1 49.6 88.1 69.8

Yes 28.9 49.8 11.4 28.8

Missing 0.6 0.6 1.4

Total 100 100 100 100

126

Table 4.1 (cont.)

Characteristics of PSCSS and HRS Samples

Working at Diagnosis/Baseline

(Column Percents)

(*Chi-Square .05, M =Difference in Males between Surveys; F = Difference in Females between Surveys) Males Females

Characteristic CSS

(n=232)

HRS

(1313)

CSS

(n=352)

HRS

(n=1134)

Fulltime Employment F

No 6.9 6.6 26.7 21.8

Yes 91.0 93.4 71.0 78.1

Missing 2.2 0.1 2.3 0.1

Total 100 100 100 100

Self-employed

No 80.2 79.7 85.2 87.7

Yes 19.8 20.1 14.5 11.9

Missing 0.2 0.3 0.4

Total 100 100 100 100

Any Retirement Benefit M

No 23.7 32.1 34.9 34.8

Yes 75.9 67.2 64.2 64.2

Missing 0.4 0.8 0.9 1.0

Total 100 100 100 100

Own Employer-Contingent

Health Insurance F

No 38.4 34.4 52.0 37.4

Yes 59.9 65.4 46.0 62.5

Missing 1.7 0.2 2.0 0.1

Total 100 100 100 100

Time Since Diagnosis to Wave1

Lt 1 Year 9.5 7.7 6.3 7.3

1-2 Years 29.3 28.1 27.6 25.8

2-3 Years 34.9 35.6 30.4 32.6

4 or more Years 26.3 28.6 35.8 34.3

Total 100 100 100 100

Retire in Post Diagnosis Period

Through Wave4F

No 59.5 57.2 71.0 53.2

Yes 40.5 42.8 29.0 46.8

Total 100 100 100 100

127

Table 4.2

Attrition in Surveys by Gender

by Count and by Percentage Lost in Follow-up for Any Reasons1

Males Females

CSS HRS CSS HRS

Number of Initial Wave Observations and Remainder At Each Wave Wave 1 232 1313 352 1134

Wave 2 150 1073 252 897

Wave 3 117 949 205 797

Wave 4 100 841 174 706

Percentage at Initial Wave and Remaining Percent of Original Count At Each Wave

Wave 1 100 100 100 100

Wave 2 64.7 81.7 71.6 79.1

Wave 3 50.4 72.3 58.2 70.3

Wave 4 43.1 64.1 49.4 62.3 1Note: Reasons lost to follow-up include death, inability to locate, and simply refusal to participate.

128

Table 4.3

Adjusted Odds Ratios for Factors Associated with Time Until First Retirement

Self-Reported Completely Retired; Excludes Partially Retired Males: CSS Survivors v. HRS Controls

1

(*** p<.01; **p<.05; *p<.10, n=40,482 person-months)

Variables Model I

Cancer-

Related

Lower

95%

CI

Upper

95% CI Model II

Person-Related

Lower

95%

CI

Upper

95% CI Model III

Job-Related

Lower 95%

CI

Upper

95% CI

Cancer- Dx 0.426*** 0.334 0.426 0.518*** 0.403 0.667 0.529*** 0.408 0.686

Recurrence 1.032 0.496 1.032 0.93 0.445 1.944 0.965 0.461 2.021

Ages 60-61 1.581*** 1.272 1.966 1.577*** 1.267 1.964

Age 62 4.761*** 3.816 5.939 4.932*** 3.946 6.165

Ages63-64 1.905*** 1.441 2.518 1.987*** 1.498 2.635

Ages 65 and older 3.395*** 2.376 4.85 3.513*** 2.436 5.066

Nonwhite - Dx 1.155 0.905 1.474 1.099 0.86 1.405

Married - Dx 0.993 0.784 1.259 0.958 0.755 1.216

High school - Dx 1.062 0.845 1.336 1.069 0.849 1.347

Some College - Dx 0.961 0.742 1.245 0.97 0.74 1.271

College & More - Dx 1.026 0.809 1.302 1.093 0.825 1.447

Chronic Cond. (Count.) 1.073 0.979 1.176 1.073 0.979 1.176

Time since Dx (month) 1.028*** 1.014 1.043 1.03*** 1.015 1.045

Physically job - Dx 1.104 0.901 1.353

Fulltime Job - Dx 0.678** 0.503 0.914

Self-employed - Dx 0.56*** 0.416 0.753

Own Employer Health

Insurance – Dx

0.651*** 0.525 0.806

Defined Benefit - Dx 1.409*** 1.098 1.807

Defined Contribution - Dx 0.871 0.679 1.117

Both Benefit & Contribution 1.195 0.885 1.614

Test -2 Log L Chi-Sq P-value -2 Log L Chi-Sq P-value -2 Log L Chi-Sq P-value

Cancer + Recurrence 5.2663 0.022** 4.0852 0.043** 3.415 0.065*

Model Fit 6433.786 50.45 <.0001 6147.628 358.78 <.0001 6102.509 402.2387 <.0001 1Note: Reference group includes age less than 60, less than high school education, white, single, job without physical demands, part-time work, working for someone else, health insurance from source other than employer at diagnosis, no employer-sponsored retirement benefit (defined benefit like pension or defined contribution like 401(k)).

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Table 4.4

Adjusted Odds Ratios for Factors Associated with Time Until First Retirement

Self-Reported Completely Retired; Excludes Partially Retired Females: CSS Survivors v. HRS Controls

1

(*** p<.01; **p<.05; *p<.10, n=45,443 person-months)

Variables Model I

Cancer-

Related

Lower 95%

CI

Upper 95%

CI Model II

Person-

Related

Lower 95%

CI

Upper 95%

CI Model III

Job-Related

Lower 95%

CI

Upper 95%

CI

Cancer- Dx 0.253*** 0.196 0.325 0.318*** 0.244 0.414 0.323*** 0.247 0.423

Recurrence 1.79** 1.037 3.091 1.547 0.893 2.679 1.571 0.906 2.722

Ages 60-61 1.525*** 1.217 1.912 1.551*** 1.236 1.945

Age 62 3.214*** 2.514 4.108 3.302*** 2.58 4.227

Ages63-64 1.819*** 1.38 2.397 1.88*** 1.424 2.483

Ages 65 and older 3.246*** 2.268 4.646 3.385*** 2.354 4.868

Nonwhite – Dx 1.118 0.908 1.376 1.086 0.878 1.342

Married – Dx 0.997 0.839 1.186 0.953 0.799 1.137

High school - Dx 0.874 0.687 1.112 0.828 0.641 1.071

Some College - Dx 0.793* 0.614 1.023 0.722** 0.544 0.959

College & More -Dx 0.774* 0.594 1.008 0.694** 0.511 0.941

Chronic Cond. (Count.) 1.017 0.923 1.122 1.031 0.934 1.138

Time since Dx (month) 1.013* 0.998 1.027 1.014* 0.999 1.028

Physically job - Dx 1.015 0.812 1.268

Fulltime Job - Dx 0.96 0.776 1.189

Self-employed - Dx 0.875 0.643 1.192

Own Employer Health

Insurance - Dx

0.643*** 0.52 0.794

Defined Benefit 1.676*** 1.279 2.195

Defined Contribution 1.325** 1.024 1.714

Both Benefit & Contribution 1.9*** 1.355 2.664

Test -2 Log L Chi-Sq P-value -2 Log L Chi-Sq P-value -2 Log L Chi-Sq P-value

Cancer + Recurrence 9.6631 0.002*** 7.4633 0.006*** 6.712 0.009***

Model Fit 6127.967 120.1009 <.0001 5947.517 310.7295 <.0001 5920.521 335.791 <.0001 1Note: Reference group includes age less than 60, less than high school education, white, single, job without physical demands, part-time work, working for someone else, health insurance from source

other than employer at diagnosis, no employer-sponsored retirement benefit (defined benefit like pension or defined contribution like 401(k)).

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CHAPTER FIVE

CONCLUSIONS

This dissertation addressed three research questions about the effects of cancer

on labor supply in three different scenarios. The purpose of the studies was to fill gaps in niches

in the cancer survivor literature and inform our understanding for improvements in support

program design, employer accommodations, and public policies to enhance the welfare of cancer

survivors. This chapter summarizes the study findings and the policy relevance.

What is the Effect of Cancer on the Labor Supply of Spouses of Cancer Survivors?

The major finding is that with the exception of treatment status, there is little evidence for

systematic effects of cancer on the labor supply of spouses in working married couples. Yet

while this is a null finding with positive policy implications, treatment effects suggest that some

distributional issues may be masked by this average effect. A wife whose husband was in

treatment was less likely to work and reduced hours of labor supply. Since this occurs after the

initial treatment, some married couples may experience new financial distress or a continuation of

financial distress from treatment shortly after diagnosis. The magnitude of wage losses suffered

by married couples in the first year after treatment is significant. The literature included evidence

of mean wage losses of 27% for breast cancer survivors in the first year after diagnosis and

significant financial distress in 16.5% of families and married couples.

The findings suggest several policy implications. First, the good news is that in general,

once past treatment, on average, most spouses in working couples continue to work in the short-

run. Adjustments in labor supply during treatment were largely temporary. Second, given the

evidence in this study for treatment effects, some segment of married working couples may be at

risk for financial distress. How many and how deep that distress is should be explored in future

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research. Third, spouses share in the cancer survivorship experience, so efforts by clinicians and

social workers that improve care for cancer survivors will yield additional benefits for spousal

labor supply and alleviation of financial distress. Efforts to mitigate chemotherapy side effects

should improve cancer treatment and cancer survivorship care plans. Social workers can be

proactive in dealing with clients with risk factors for financial distress. Fourth, employers can

feel more confident that investments in accommodation programs for spouses will be beneficial,

since most working spouses will continue to work years after treatment. Lastly, government

income assurance programs or health insurance subsidies may improve the economic welfare of

married couples, where treatment effects on spousal labor supply are significant burdens.

What Factors Affect the Decision to Keep Working During Treatment?

While cancer survivors decide to work or not to work during treatment in about equal

proportions, this decision is driven by the type of cancer, and to some extent for men, the stage of

cancer. Certain cancers require shorter treatment protocols (e.g., skin melanoma) or short courses

of chemotherapy or radiation. Other cancers, like blood and lymphoma, may require

chemotherapy for some time with significant side-effects from the treatment, in addition to the

fatigue associated with cancer treatment. Male survivors with prostate, skin, or lymph cancer

(stages I-III) were more likely to work during treatment, compared to male survivors with blood,

respiratory, and other cancers. Likewise, female survivors with breast, skin, thyroid, or lymph

cancer (stage IV) were more likely to work during treatment, compared to survivors with central

nervous system, blood, respiratory, colon, uterus, or other cancers. Male survivors with localized

(stage I) cancers were more likely to work during treatment, compared to survivors with more

advanced cancer at diagnosis (stages II-III or unstaged). Female cancer survivors’ likelihood of

working during treatment is not associated with cancer stage. While female cancer survivors

were more likely to work with greater levels of education, no similar association was found for

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men. Employer-sponsored health insurance and retirement benefits were associated with an

increased likelihood of working during treatment for male cancer survivors. However, there is

some evidence of ―job lock‖ behavior by wives with cancer. They are more likely to stop

working if a husband has his own employer-sponsored health insurance. Wives may view this as

a ―hedge‖ against the risk of losing benefits by stopping work.

Several policy implications follow from these findings. First, the importance of

employer-sponsored health insurance and working in better paying jobs, that is often associated

with higher education, suggests that there may be unintended consequences for job-dependent

health insurance. Stopping work to focus on treatment or recovery may be a luxury for many,

who fear that this will jeopardize their access to health insurance benefits when it is most needed.

Two options are possible. Employers can increase efforts to allay worker fears of losing a job by

focusing on cancer treatment and recovery. Alternatively, gradually opening up high-risk

insurance pools with greater federal subsidies to ensure affordable access may provide the

security desired by cancer survivors. Second, the impact of treatment period decisions to work or

not have yet to be studied in terms of future outcomes or quality of life for cancer survivors.

Third, cancer survivors with breast or prostate cancer are more likely to work during treatment

compared to other cancers. Since these are among the most prevalent cancers, early use of

support programs and workplace accommodations may benefit employers’ retention of valuable

employees, but the programs may mitigate potentially significant wage losses. Finally, the

disparities in educational opportunities have consequences for healthcare disparities. Less

educated cancer survivors, who are more likely to be working in lower-paying jobs, often without

health insurance benefits, are more likely to quit because of lower opportunity costs. In the long-

run, these disparities can be addressed by reductions in educational disparities. In the short run,

identifying those cancer survivors who are at greater risk for quitting and losing income can help

employers and public policy makers tailor supportive interventions.

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What is the Effect of Cancer on Time-to-Retirement?

There is some evidence that cancer and recurrence increased the duration of time-to-

retirement for male cancer survivors and, consequently, they worked longer compared to persons

without cancer. However, the evidence for female cancer survivors contains inconsistent signals

over its direction and magnitude. Some of the factors that are significant offer clues to working

longer. Retirement was negatively associated with access to employer-sponsored health

insurance. The opportunity cost of retirement is greater with good health insurance benefits.

However, good pension programs had the opposite effect and were positively associated with

retirement. From a policy perspective, access to health insurance that is independent of an

employer, may facilitate retirement decisions and improve the well-being of older cancer

survivors, whose preferences have changed for more leisure.

CURRICULUM VITA

Michael Markowski, Ph.D.

EDUCATION

2010 Ph.D. The Pennsylvania State University

Health Policy and Administration University Park, PA

1977 M.A. Economics George Mason University

Fairfax, VA

1972 A.B. Economics King’s College

Wilkes-Barre, PA

PROFESSIONAL HEALTHCARE EXPERIENCE

PharMetrics, Boston, MA, 2001; Director, Health Economics. Designed and managed

pharmacoeconomics studies and production of analytic data files for pharmaceutical industry and

managed care clients.

Independence Blue Cross, Philadelphia, PA, 1998 – 2001; Director, Health Services Research.

Managed production of HEDIS reports for NCQA accreditation and utilization reports for medical

management.

Keystone Health Plan Central, Camp Hill, PA, 1994 – 1998; Director, Healthcare Research &

Analysis. Managed production of HEDIS and medical utilization reports and evaluated outcomes

of quality improvement interventions for managed care enrollees.

Hospital Association of Pennsylvania, Camp Hill, PA, 1992 – 1994; 1986 – 1989; Director, Policy

Analysis. Analyzed legislation for impacts and produced briefing papers on issues of interest to

hospital members.

Pennsylvania Health Care Cost Containment Council, Harrisburg, PA, 1989 – 1992; Director of

Operations. Managed production of hospital cost and outcome reports, including the first

statewide hospital financial report.

OTHER PROFESSIONAL EXPERIENCE

1973 – 1986: Worked in economic rural development, logistics management, and transportation

planning and economic modeling, mainly for federal agencies including USDA, DOT, and DOD.

TEACHING EXPERIENCE

Adjunct instructor since 1988 at several institutions: Pennsylvania State University (World Campus /

Harrisburg), Immaculata University, Delaware County Community College, and the University of St.

Francis. Taught a variety of undergraduate and graduate courses in economics, accounting, business,

and healthcare finance.