death or debt? national estimates of financial...

Accepted Manuscript

DEATH OR DEBT? NATIONAL ESTIMATES OF FINANCIALTOXICITY IN PERSONS WITH NEWLY-DIAGNOSED CANCER

Adrienne M. Gilligan PhD , David S. Alberts MD ,Denise J. Roe DrPH , Grant H. Skrepnek PhD

PII: S0002-9343(18)30509-6DOI: 10.1016/j.amjmed.2018.05.020Reference: AJM 14693

To appear in: The American Journal of Medicine

Received date: 19 February 2018Revised date: 5 May 2018Accepted date: 23 May 2018

Please cite this article as: Adrienne M. Gilligan PhD , David S. Alberts MD , Denise J. Roe DrPH ,Grant H. Skrepnek PhD , DEATH OR DEBT? NATIONAL ESTIMATES OF FINANCIAL TOXICITY INPERSONS WITH NEWLY-DIAGNOSED CANCER, The American Journal of Medicine (2018), doi:10.1016/j.amjmed.2018.05.020

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https://doi.org/10.1016/j.amjmed.2018.05.020

https://doi.org/10.1016/j.amjmed.2018.05.020

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Clinical Research Study

DEATH OR DEBT? NATIONAL ESTIMATES OF FINANCIAL TOXICITY IN

PERSONS WITH NEWLY-DIAGNOSED CANCER

Adrienne M. Gilligan, PhD1,2

, David S. Alberts, MD3, Denise J. Roe, DrPH

4, and Grant H.

Skrepnek, PhD5,6

1Adjunct Assistant Professor, The University of North Texas Health Sciences Center, College of

Pharmacy, Fort Worth, TX. 2Researcher, Life Sciences, Truven Health Analytics, an IBM Company, Houston, TX.

3Director Emeritus, The University of Arizona, The University of Arizona Cancer Center, Tucson,

AZ. 4Professor, The University of Arizona, Mel and Enid Zuckerman College of Public Health,

Tucson, AZ. 5Associate Professor, The University of Oklahoma Health Sciences Center, College of Pharmacy,

Oklahoma City, OK. 6TSET Cancer Research Scholar, The University of Oklahoma Health Sciences Center, Peggy

and Charles Stephenson Cancer Center, Oklahoma City, OK.

Corresponding Author:

Grant H. Skrepnek, PhD

The University of Oklahoma Health Sciences Center

College of Pharmacy

1110 N. Stonewall Avenue

Oklahoma City, OK. 73117

Office: +1.405.271.6878 EXT. 47105

Fax: +1.405.271.6430

Email: [email protected]

KEY WORDS: cancer, net worth, debt, bankruptcy, Health and Retirement Study

This endeavor represents original work which has not been previously published or presented

and is not under consideration for publication elsewhere. All authors had access to the data and a

role in writing the manuscript.

Disclaimer: Adrienne Gilligan is an employee of Truven Health Analytics, an IBM Company in

Houston, TX. This work derives, in part, as an extension of Dr. Gilligan’s PhD dissertation,

completed at The University of Arizona. ABSTRACT

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Purpose: To evaluate the impact of cancer upon a patient’s depletion of net worth and incursion

of debt in the U.S.

Methods: This longitudinal study used the Health and Retirement Study (HRS) from 1998-2014.

Persons ≥50 years with newly-diagnosed malignancies were included, excluding minor skin

cancers. Multivariable generalized linear models were employed to assess changes in net worth

and debt (consumer, mortgage, home equity) at two-and four-years following diagnosis (Year+2,

Year+4) after controlling for demographic and clinically-related variables, cancer-specific

attributes, economic factors, and mortality. A two-year period prior to cancer diagnosis served

as an historical control.

Results: Across 9.5 million total estimated new diagnoses of cancer from 2000-2012,

individuals averaged 68.6±9.4 years with slight majorities being married (54.7%), not retired

(51.1%), and Medicare beneficiaries (56.6%). At Year+2, 42.4% depleted their entire life’s assets,

with higher adjusted odds associated with worsening cancer, requirement of continued treatment,

socio-economic factors (i.e., increasing age/income/household size, female sex), clinical

characteristics (i.e., current smoker, worse self-reported health, hypertension, diabetes, lung

disease), Medicaid, and uninsured (p<0.05); average losses were −$92,098. At Year+4, financial

insolvency extended to 38.2%, with several consistent socio-economic, cancer-related, and

clinical characteristics remaining significant predictors of complete asset depletion.

Conclusion: Using nationally-representative data, this investigation of an estimated 9.5 million

newly-diagnosed persons with cancer ≥50 years of age found a substantial proportion incurring

financial toxicity. As large financial burdens have been found to adversely affect access to care

and outcomes among cancer patients, the active development of approaches to mitigate these

effects among already vulnerable groups remain of key importance.

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CLINICAL SIGNIFICANCE Cancer’s financial burden frequently peaks during treatment and often increases with

improving prognosis.

Nationally, financial toxicity in cancer was substantial, with 42.4% of estimated 9.5 million

newly-diagnosed depleting their life assets within two years.

Several factors were independently associated with financial toxicity: worsening cancer;

requirement of continued treatment; socio-economic factors including advancing age; and

clinical characteristics.

More recent clinical recommendations support evaluations of financial and economic aspects

of cancer treatment.

INTRODUCTION

Approximately 15.5 million Americans have a history of cancer, with an estimated

1,688,780 new cases and 609,640 deaths annually.1 With 87% of diagnoses occurring in persons

≥50 years, cancer remains the second leading cause of death in the U.S.1 Cancer’s financial

burden is often substantial during treatment phases and often increases with improving

prognoses.2 Cancer’s direct medical costs in the U.S. exceed $80 billion, while indirect costs of

premature morbidity and mortality over $130 billion.1,3

With 6.5% of direct costs among the

non-elderly alone involving out-of-pocket payments, over half of all persons with cancer

experienced house repossession, bankruptcy, loss of independence, and relationship

breakdowns.2,4

Additionally, 40-85% of cancer patients stop working during initial treatment,

with absences ranging up to six months.5 Deductibles and copayments for treatment, supportive

care, and nonmedical or indirect costs (e.g., travel, caregiver time, lost productivity) may be

financially devastating even with healthcare coverage.6

“Financial toxicity” involves the unintended financial consequences of medical treatment,

including both objective and subjective attributes reflecting a patient’s financial burden.7,8

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Clinical recommendations now suggest the assessment of economic implications of treatment

algorithms and broader value assessments, whenever possible.9 As such, the purpose of this

investigation was to assess the financial impact of cancer among the newly-diagnosed in the U.S.

Changes in net worth and debt (i.e., consumer, mortgage, home equity debt) were analyzed

according to numerous socio-economic and clinical factors.

METHODS

This longitudinal study utilized the nationally-representative Health and Retirement

Study (HRS, RAND version) from 1998-2014.10,11

Sponsored by the National Institute of Aging

and Social Security Administration, the HRS uses complex sampling methodologies to collect

information biennially across the U.S. population of persons ≥50 years.10,11

All data are fully de-

identified and anonymized, hence exempt from human subject’s protection.10,11

Inclusion criteria involved newly-diagnosed cases of cancer based upon the HRS data

item, “Since the previous [interview], has a doctor ever told you that you have cancer or a

malignant tumor, excluding minor skin cancers?”11

An index date was defined from “In what

year was your (most recent) cancer diagnosed?”11

A baseline/pre-index period was used for

cases to serve as historical controls two-years prior to a cancer diagnosis.

The primary study outcome was the change in net worth from baseline ranging up to two-

and four-years following a new cancer diagnosis (designated Year+2 and Year+4), calculated as

the change in nominal net dollar value of total wealth (i.e., all assets and secondary residences

minus all debts and liabilities to include though not limited to consumer, mortgage, and home

equity debt) (eTable 1; eAppendix 1).10,11

The change in net worth was analyzed as: A) a

binary/dichotomous measure, reflecting if persons fully depleted their assets following a cancer

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diagnosis (yes/no); and B) a continuous measure to quantify the monetary change associated

with a new diagnosis. Additional outcomes included measures of debt categorized as: A)

consumer (i.e., the value of all liabilities including credit card and medical expenses excluding

home and mortgage debt); B) mortgage (i.e., the total value of all mortgages on the primary

residence); and C) home equity (i.e., the value of other loans on the primary residence). Inflation

adjustment utilized the U.S. Bureau of Labor Statistics Consumer Price Index (CPI) rate.12

This study empirically extends theoretical frameworks from economics (i.e., Modigliani

life-cycle hypothesis, which explains consumption and savings behaviors across one’s life span),

health service research (i.e., Andersen Healthcare Utilization Model), and social determinants of

health (e.g., political economy of health).13,14,15

Independent variables included demographic

factors (i.e., age, sex, race/ethnicity, marital status), human capital characteristics (i.e., education,

self-reported health, health problems, depression score), economic factors (i.e., household size,

retired status, geographic region, income, health insurance), cancer-related attributes (i.e.,

diagnoses, treatment, cancer status), clinical case-mix/comorbidities (i.e., health problems,

smoking status, alcohol consumption), mortality (i.e., confirmed and not due to missing data or

loss of follow-up), and a categorical variable designating the subprime mortgage and fiscal crisis

of 2008 (eTable 1; eAppendix 2).10,11

Generalized linear models were used to evaluate multivariable associations between

financial toxicity and independent variables (i.e., binomial/logistic with logit link for

dichotomous measures, Gaussian with log link for continuous measures).16

Residual diagnostics

and deviance assessments were conducted, and replicate bootstrap weights to explicitly control

for HRS’s complex survey design were used to obtain nationally-representative and robust

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standard estimates.10,11

Analyses were conducted using SAS 9.3 (Cary, NC) and STATA SE

13.0 (College Station, TX).

RESULTS

An estimated 9,527,522 total new diagnoses of cancer were observed from 2000-2012;

76.9% (n=7,330,580) and 68.5% (n=6,525,382) had follow-up at Year+2 and Year+4, respectively

(Figure 1). Survival estimates were 87.3% at Year+2 and 79.5% at Year+4. Individuals

averaged 68.6±9.4 years with a slight majority that were currently married (54.7%), not retired

(51.1%), and Medicare beneficiaries (56.6%). Predominant health problems included arthritis

(56.1%), high blood pressure (49.9%), and heart disease (24.7%). By Year+2, 55.6% noted an

improvement in their cancer status, with 94.1% having cancer status improving or remaining the

same by Year+4. Approximately one-third continued to require cancer treatment at Year+2

(36.3%) and Year+4 (30.1%). The average net worth at Year+2 decreased by −$92,098±1,945,627

from an initial average net worth of $644,031±2,183,014. Some 42.4% had their entire life’s

assets depleted at Year+2, with 38.2% at Year+4. Tables 1 and 2 present full demographics,

clinical, and financial outcome characteristics.

Multivariable Analysis of Complete Asset Depletion and Incurrence of Debt by Type

Multivariable results which assessed the depletion of net worth (and incurrence of various

forms of debt) following a cancer diagnosis appear in Table 3, controlling for associations

between numerous socioeconomic and clinical factors. Both at Year+2 and Year+4, a higher odds

of complete asset depletion were independently associated with females, advancing age, non-

white/non-black race, higher income, retired, increasing household size, current smoking,

diabetes, and arthritis (p<0.05). Notably, those ≥75 years were associated with a 173% and 66%

higher odds of complete asset depletion at Year+2 and Year+4, respectively (p<0.001). Worsening

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cancer status was associated with a 29% higher odds of total asset depletion, with the

requirement of continued cancer treatment (+7%), current smoker (+59%), hypertension

(+232%), and lung disease (+46%) also significant at Year+2 alone (p<0.05). At Year+4, Black

race was independently associated with a +24% higher odds of complete asset depletion

(p<0.001).

The 2008 fiscal crisis was associated with a decreased overall odds of asset depletion

(ORYear+2(adjusted)=0.48, ORYear+4(adjusted)=0.83), though with substantially higher odds of mortgage

debt (ORYear+2(adjusted)=2.47, ORYear+4(adjusted)=1.16) (p<0.001). While mixed results were

generally observed concerning specific debt types, significantly higher odds were associated with

increasing income, uninsured, current smoker, and lung disease (p<0.05) at Year+2 and Year+4.

In several instances, the incursion of any type of debt was associated with either a decrease or

insignificant odds of overall net worth depletion (e.g., new cancers, cancer treatment at Year+4,

Hispanic ethnicity, uninsured, certain chronic conditions, 2008 fiscal crisis).

Multivariable Analysis of Extent of Asset Depletion and Incurrence of Debt

The multivariable associations between monetary changes following cancer diagnoses

after controlling for numerous sociodemographic and clinical factors appears in Table 4. Large

and significant decreases in net worth were independently associated with worsening cancer

status (Year+2(adjusted)=−$221,082, Year+4(adjusted)=−$438,634) and with improving cancer status

(Year+2(adjusted)=−$137,174, Year+4(adjusted)=−$75,574) (p<0.001). Those ≥75 years were

associated with large decreases in overall net worth of over −$115,000 at Year+4 (p<0.001).

Female sex and Black race were both independently associated with large decreases of

approximately −$250,000 or more in overall net worth both at Year+2 and Year+4, with losses of

approximately −$150,000 with poorer self-reported health (p<0.001). While some chronic

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health problems were associated with significant decreases in net worth at Year+2 (i.e.,

hypertension, diabetes, lung disease, stroke), fewer were noted at Year+4 (i.e., lung disease)

(p<0.05). The 2008 fiscal crisis and psychiatric problems were associated with significant

monetary increases in net worth during both follow-up periods (p<0.001).

DISCUSSION

This investigation of an estimated 9.5 million newly-diagnosed persons ≥50 years

assessed the two- and four-year financial impact of new diagnoses occurring from 2000-2012

using the nationally-representative Health and Retirement Survey. This work extends previous

state-level and smaller samples by providing findings in a broader population- and policy-based

context, including adaptations of empirically-validated models for health service utilization,

social determinants of health, and the Modigliani life cycle hypothesis.13-15,17,18

Overall survival

exceeded 75%, some 42.4% of individuals depleted their life assets two years following

diagnoses (LossAverage,Year+2=−$92,098), and 38.2% incurred longer-term insolvency

(LossAverage,Year+4=−$51,882). Reflective of this study’s findings, cancer costs are consistently

found to be highest during treatment and the final months of life.19-23

This “U-shaped” cost

curve reaches its nadir between initial treatment and end-of-life, with the overall height and

width varying by cancer site, stage at diagnosis, and patient age.19-22,24-22

Following a drop in

costs after initial treatment, increases are generally observed between 24-48 months after

diagnosis.22

Collectively, poorer consumer credit has been reported to be associated with

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changing income, utilizing savings, borrowing money, and being unable to purchase health

services.25

At Year+2, financial toxicity was independently associated with worsening cancer,

demographic/socioeconomic factors and clinical characteristics. Additionally, a higher adjusted

odds of complete loss of net worth was associated with increasing age (especially ≥75 years),

Medicaid, the requirement of continued cancer treatment, and being retired (p<0.05). At both

Year+2 and Year+4, protection against financial toxicity was associated with private insurance,

being currently married, and the 2008 fiscal crisis (p<0.05). Year+4 findings generally paralleled

Year+2, although ≥75 years, current alcohol use, and Black race were independently associated

with greater monetary losses and odds of net worth depletion at Year+4 (p<0.05).

Numerous investigations document the clinical and economic burden of cancer.26

Barriers surrounding screening, prevention, and control often result in a substantial impact on

morbidity and mortality, extending other stakeholders and society overall. Tang et al. (2012)

reported that disability days alone equate to 20% of cancer-related expenditures among

employed persons.27

Often, fewer patients initiate cancer treatment as out-of-pocket

expenditures increase (i.e., suggesting a price elasticity of demand), while others have

discontinued care early.28,29

For some, high treatment expenditures are associated with

nonadherence and poorer clinical outcomes.23,30,31

Both providers and patients have reported a

reluctance to discuss cost-related issues due to a potential to bias treatment recommendations.32

The Kaiser Family Foundation reported that 8% of patients experience delays or omissions of

care due to expenses and 4% had chosen secondary treatments, while 13% borrowed money

from relatives, 11% sought aid of a charity or public assistance, and 7% obtained loans or took

out second mortgages.33,34

Guy et al. (2013) reported annual excess economic burdens of over

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$16,000 among cancer survivors, while Weaver et al. (2010) found that over two million cancer

survivors did not receive necessary medical services because of financial concerns.33,35

Himmelstein et al. (2009) observed that 46% of persons indicated illness and medical bills as a

primary reason for filing bankruptcy, and that 38% lost coverage prior to filing bankruptcy due

to illness or job loss.17

Ramsey et al. (2011) found a 2.7x higher odds of bankruptcy among

persons with cancer in Washington State, with bankruptcy also being associated with a greater

risk of mortality.18,36

Pool et al. (2018) also reported that negative wealth shocks (i.e., ≥75%

decreases in total net worth) were associated with an increased risk of mortality in the U.S.37

Disparities are known contributors to major inequities that often can either mitigated through

active interventions or prevented altogether.32

The present study found higher odds of short- and

long-run financial toxicity based on worsening cancer, numerous socio-economic factors, and

comorbidities.

The 2008 fiscal crisis was observed to incur less overall financial toxicity, potentially due

to an observed shift to increase mortgage debt, to lower initial financial assets during the crisis,

or to a broader implementation of safety net programs seeking to provide financial assistance

during that time period (e.g., Supplemental Nutrition Assistance Program (SNAP), Earned

Income Tax Credit (EITC), Unemployment Insurance (UI)).38

Raucher and Elliot (2016)

reported that both financial security and income stabilization following the 2008 financial events

was higher among those with greater initial wealth and higher income households in the U.S.39

Furthermore, Liao (2015) found that median net worth peaked in the U.S. in 2004 and 2007, and

significant decreases occurred irrespective of age following the financial events of 2008.40

Data

from 2012-2013 indicated that financial declines began to slow overall, although financial

recovery was present only among persons from 65-74 years of age.40

Overall, wealth

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inequalities have been observed to be lower among younger individuals, a finding also seen

internationally.39-41

However, Ramsey et al. (2013) reported that younger cancer patients had 2-

5x higher rates of bankruptcy than those ≥65 years and a survey of over 1,600 cancer survivors

found that patients <65 years reported 130% more financial difficulties including borrowing

money, incurring debt, and filing for bankruptcy.18,42

Though the current study found increasing

odds of financial toxicity by age, these data only include persons ≥50 years.

Private insurance was also found in the current study to protect against financial toxicity.

While older persons may have favorable indicators of financial status (i.e., higher assets, lower

debt-to-income ratios), it may be likely that an inherent ability to obtain private healthcare

coverage can be a mitigating factor. Narang et al. (2016) reported that Medicare beneficiaries

without supplemental insurance incurred substantially higher out-of-pocket expenditures

compared to beneficiaries with supplemental coverage and Taylor et al. (2014) reported a

relatively poor alignment of existing government-based benefits with patient preferences,

particularly involving concurrent palliative care, home-based long-term care, and unrestricted

cash.43,44

Though not explicitly investigated in the current work, the Patient Protection and

Affordable Care Act (ACA) of 2010 also sought to make healthcare more comprehensive,

affordable, and accessible both in cancer and across other conditions.45

Concerning expanded

coverage, previous evidence suggests that adult Medicaid beneficiaries with cancer have poor

clinical outcomes that parallel those with no insurance at all.46

In developing health policy,

careful balances between cost, quality, and access remain vital concerns when considering

advocacy for patient’s interests and preferences while considering the efficient use of resources.

The finding that psychiatric problems were associated with less financial toxicity

deserves further inquiry, as it may be indicative of successful psychiatric treatment interventions

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or as a proxy for better access to care. Routine screening for psychological distress is

recommended for all cancer patients, with the prevalence of adjustment or depressive disorders

in cancer ranging from 11% to 35-37%.47

Nationally, among all adults, Xiang et al. (2016)

reported that those with serious psychological distress increased their utilization of emergency

department visits in the U.S. from 2003-2014, additionally with a higher overall use of both

ambulatory and acute care services.48

Furthermore, a higher prevalence of cancer was also

observed among those with serious psychological distress, including an almost two-fold increase

in antidepressant use among cancer survivors in the U.S. occurring from 1999-2012.48,49

Certain inherent limitations should be considered in the current work. While the HRS

does capture selected risk factors, cancer stratification (by either type or stage), specific

treatment protocols, and comprehensive medical histories are not present.10,11

To explicitly

preserve the ability to drawn national inference via the HRS’ complex sampling methodology,

comparisons were drawn by utilizing historical self-controls with multivariable analyses that

controlled for numerous demographic, socio-economic, and clinical factors. Future

complementary work may seek to report comparative differences in outcomes including potential

employment trend or retirement differences between cancer and other disease states without

necessarily seeking to obtain national estimates. As Medicare supplemental insurance was not

independently assessed, results are limited to broader types of coverage only. Mortality in this

study differs from standard five-year survival rates obtained from national registries, in part,

because only confirmed deaths were analyzed, which may lead to potential survival bias.1

Caution should also be exerted in comparing current relative survival rates, as recent advances in

detection and treatment are intuitively not captured.1

CONCLUSION

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This investigation of an estimated 9.5 million newly-diagnosed persons with cancer ≥50

years of age found that 42.4% of individuals depleted their life assets two years following

diagnosis, extending to 38.2% Year+4. A higher odds of asset depletion at Year+2 was noted

among groups with known vulnerabilities including poorer cancer status (worsening cancer,

requirement of continued treatment), socio-economic factors (increasing age, female sex,

Medicaid, retired, increasing household size) and clinical characteristics (smoking, poorer self-

reported health, hypertension, diabetes, lung disease). Though varying associations were

observed at Year+4, several socio-economic, cancer-related, and clinical characteristics remained

significant predictors of complete asset depletion. As large financial burdens have been found to

adversely affect access to care and outcomes, the active development of approaches to mitigate

these effects among already vulnerable groups remain of key importance.

Acknowledgements: No financial or material support was received for this investigation.

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adults in the United States. J Clin Oncol 2013;31:3749-57

36. Ramsey SD, Bansal A, Fedorenko CR, et al. Financial insolvency as a risk factor for early

mortality among patients with cancer. J Clin Oncol 2016;34(9):980-6

37. Pool LR, Burgard SA, Needham BL, et al. Association of Negative Wealth Shock with All-

Cause Mortality in Middle-aged and Older Adults in the United States. JAMA

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38. Moffitt RA. The Great Recession and the Social Safety Net. Ann Am Acad Pol Soc Sci

2013;650(1):143-66

39. Raucher E, Elliot W. Wealth as security: Growth curve analyses of household income and

net worth during a recession. J Family Econ Issues 2016;37(1):29-41

40. Liao M. Changes in economic well-being of the elderly. New York: City University of New

York (CUNY) Academic Works, 2015

41. Brown S, Taylor K. Household debt and financial assets: Evidence from Germany, Great

Britain, and the USA. J Royal Stat Soc: Series A (Stat Soc) 2008;171(3):615-43

42. Whitney RL, Bell J, Reed S, et al. Work and financial disparities among adult cancer

survivors in the United States. J Clin Oncol 2014;32:31s

43. Narang AK and Nicholas LH. Out-of-Pocket Spending and Financial Burden Among

Medicare Beneficiaries with Cancer. JAMA Oncol 2017;3(6):757-65

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44. Taylor DH Jr, Danis M, Zafar SY, et al. There is a mismatch between the Medicare benefit

package and the preferences of patients with cancer and their caregivers. J Clin Oncol

2014;32:3163-68

45. Moy B, Abernethy AP, Peppercorn JM. Core elements of the Patient Protection and

Affordable Care Act and their relevance to the delivery of high-quality cancer care. Health

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46. Institute of Medicine and National Research Council. From Cancer Patient to Cancer

Survivor: Lost in Transition. Washington, DC: The National Academies Press, 2005

47. National Comprehensive Cancer Network. Distress management. Clinical practice guidelines.

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48. Xiang X, Larrison CR, Tabb KM. Trends in Health Care Utilization Among Adults With

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49. Xiang X, An R, Gehlert S. Trends in Antidepressant Use Among U.S. Cancer Survivors,

1999–2012. Psych Serv 2015;66:564

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Figure 1. Study Population Flow Chart for the Health and Retirement Study (Weighted)

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Table 1. Demographic and Clinical Characteristics of New Cancer Diagnoses from 2000-2012. (Overall nestimated=9,527,522) Percentage Percentage

Sex Region

Female 50.5% New England 7.2%

Male 49.5% Middle Atlantic 10.8%

Race East North Central 15.9%

White 87.7% West North Central 8.8%

Black 9.6% South Atlantic 26.2%

Other 2.7% East South Central 5.4%

Ethnicity West South Central 8.9%

Non-Hispanic 98.2% Mountain 4.5%

Hispanic 1.8% Pacific 12.3%

Marital Status Year of Initial Cancer Diagnosis

Married 54.7% 2000 12.9%

Never married 19.7% 2002 15.1%

Divorced/Widowed 25.6% 2004 11.7%

Primary Insurance 2006 12.7%

Medicare 56.6% 2008 11.2%

Medicaid 1.9% 2010 18.5%

Private plan 33.6% 2012 18.0%

Uninsured 7.9% Cancer Characteristics, 2-Years Post-Diagnosis

Self-Reported Health Better 55.6%

Poor (5) 11.3% Same 39.3%

Fair (4) 29.3% Worse 5.1%

Good (3) 31.4% New Cancer 4.4%

Very Good (2) 19.5% Cancer Treatment 36.3%

Excellent (1) 8.2% Cancer Characteristics, 4-Years Post-Diagnosis

Chronic Health Problems Better 48.8%

High blood pressure 49.9% Same 45.3%

Diabetes 16.7% Worse 5.9%

Lung disease 13.4% New Cancer 4.0%

Heart disease 24.7% Cancer Treatment 30.1%

Stroke 6.4% Mean ± SD

Arthritis 56.1% Age (years) 68.6 ± 9.4

Psychiatric Problems 15.7% Education (years) 12.7 ± 6.9

Retirement Status Income (USD, 2016) $72,224 ± 113,625

Retired 48.9% Household Size 1.9 ± 0.8

Not retired 51.1%

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Current Smoker (% yes) 17.4%

Alcohol Use (% yes) 55.9%

Confirmed Mortality (2-

years post-diagnosis) 12.7%

Confirmed Mortality (4-

years following diagnosis) 20.5%

Abbreviations: CESD = Center for Epidemiologic Studies Depression Scale, SD = standard deviation, USD = United States dollars

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Table 2. Outcome Characteristics of Newly-Diagnosed Cancer Patients.

2-Years Post-Diagnosis (n =

7,330,580)

4-Years Post-Diagnosis (n = 6,525,382)

Presence of Debt (Yes/No) Percentage Percentage (estimated frequency)

Depletion of Net Worth (% Yes) a 42.4% 38.2%

Incurrence of Consumer Debt (% Yes) a 34.2% 42.1%

Incurrence of Mortgage Debt (% Yes) a 28.9% 38.2%

Incurrence of Home Equity Debt (% Yes) a 43.9% 39.7%

Value of Net Worth or Debt (USD 2016) Mean ± Standard Deviation Mean ± Standard Deviation

Net Worth $644,031 ± 2,183,014 $611,780 ± 1,759,480

Consumer Debt $3,629 ± 19,738 $3,574 ± 22,887

Mortgage Debt $32,160 ± 76,620 $30,986 ± 77,655

Home Equity Debt $206,126 ± 714,452 $202,775 ± 765,775

Baseline Change in Net Worth or Debt Mean ± Standard Deviation Mean ± Standard Deviation

Change in Net Worth a −$92,098 ± 1,945,627 −$51,882 ± 1,461,119

Change in Consumer Debt +$1,264 ± 66,727 +$1,480 ± 51,230

Change in Mortgage Debt +$2,126 ± 68,904 +$4,369 ± 72,535

Change in Home Equity Debt −$49,084 ± 651,269 −$41,611 ± 714,220

USD = United States dollars

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Table 3. Multivariable Logistic Regression Analyses of Complete Net Worth Depletion and Incurrence of Debt by Type

2-Years Following Initial Cancer Diagnosis

Net Worth Depletion Incurrence of

Consumer Debt

Incurrence of

Mortgage Debt

Incurrence of Home

Equity Debt

Odds

Ratio

95%

Confidence

Interval

Odds

Ratio

95%

Confidence

Interval

Odds

Ratio

95%

Confidence

Interval

Odds

Ratio

95%

Confidence

Interval

Cancer Characteristics

Cancer Status

(Unchanged = reference)

Better 1.08a

(1.01,1.16) 0.89c

(0.82,0.96) 0.67c

(0.61,0.75) 1.16c

(1.08,1.24)

Worse 1.29b (1.08,1.56) 0.49

c (0.40,0.60) 0.09

c (0.05,0.16) 1.05 (0.87,1.27)

New Cancer 0.87 (0.73,1.03) 0.88

(0.74,1.05) 3.21c

(2.53,4.07) 1.32c

(1.13,1.54)

Cancer Treatment 1.07a (1.01,1.15) 1.61

c (1.48,1.75) 0.54

c (0.48,0.60) 1.06 (0.98,1.14)

Demographics

Age (per year)

(50-64 years = reference)

65-74 years 1.30c (1.12,1.50) 0.49

c (0.43,0.58) 0.17

c (0.14,0.21) 1.12 (0.97,1.30)

75+ years 2.73c (2.33,3.19) 0.25

c (0.21,0.30) 0.10

c (0.08,0.12) 2.61

c (2.21,3.07)

Female 1.34c (1.25,1.44) 1.29

c (1.18,1.40) 0.92 (0.84,1.02) 1.57

c (1.47,1.69)

Race

(White = reference)

Black 1.02 (092,1.14) 0.79c (0.70,0.89) 0.94 (0.79,1.11) 1.11 (0.98,1.25)

Other 1.60c (1.35,1.91) 1.30

a (1.01,1.68) 0.15c

(0.12,0.20) 0.98 (0.82,1.17)

Hispanic 1.01 (0.71,1.43) 1.79c

(1.31,2.47) 0.24c

(0.15,0.40) 0.86 (0.64,1.15)

Marital Status

(Never married = reference)

Married 0.88 (0.77,1.00) 0.81b (0.71,0.93) 3.77

c (3.01,4.71) 0.98 (0.87,1.10)

Divorced/widowed 0.99 (0.88,1.13) 0.59c

(0.51,0.67) 4.06c

(3.27,5.05) 1.39c

(1.23,1.57)

Current Alcohol Use 0.96 (0.90,1.03) 1.16c

(1.07,1.26) 0.87c

(0.79,0.97) 0.98 (0.92,1.06)

Current Smoker 1.59c (1.46,1.75) 1.60

c (1.44,1.79) 1.12

(0.99,1.27) 1.35

c (1.22,1.50)

Socioeconomic Indicators

Education (per year) 0.99 (0.99,1.00) 0.97c

(0.96,0.98) 1.09c

(1.07,1.11) 1.00 (0.99,1.01)

Income (per $100) 1.01c

(1.01,1.02) 1.01a

(1.01,1.02) 1.01c

(1.01,1.02) 1.01c

(1.01,1.02)

Primary Health Care Coverage

(Medicare = reference)

Medicaid 3.09c

(2.39,4.01) 0.62c

(0.49,0.77) 0.19c

(0.13,0.26) 4.61c

(3.46,6.14)

Private Insurance 1.03

(0.88,1.19) 0.76c (0.66,0.88) 0.49

c (0.42,0.59) 1.34

c (1.15,1.55)

Uninsured 1.22a

(1.02,1.47) 1.15

(0.95,1.40) 1.38c

(1.11,1.70) 1.61c

(1.32,1.95)

Retired 1.23c

(1.14,1.32) 0.65c

(0.60,0.71) 0.69c

(0.61,0.75) 1.02 (0.94,1.10)

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Household Size (per person) 1.48c

(1.39,1.57) 1.09b

(1.03,1.17) 1.01 (0.94,1.09) 1.29c

(1.21,1.38)

Chronic Health Problems

Self-reported Health d 1.11

c (1.07,1.15) 0.88

c (0.84,0.92) 1.08

c (1.03,1.13) 1.28

c (1.24,1.33)

Hypertension 1.22c

(1.15,1.31) 1.22c

(1.13,1.34) 0.59c

(0.53,0.65) 0.93

(0.87,1.00)

Diabetes 1.56c

(1.43,1.69) 3.16c

(2.88,3.46) 2.42c

(2.15,2.74) 0.86c

(0.78,0.94)

Lung Disease 1.46c

(1.31,1.62) 1.77c

(1.57,1.99) 1.88c

(1.64,2.15) 1.03 (0.92,1.15)

Heart Disease 0.93 (0.86,1.01) 1.23c

(1.11,1.36) 1.81c

(1.59,2.05) 1.27c

(1.17,1.38)

Stroke 1.05 (0.91,1.21) 3.44c

(2.99,3.96) 1.12 (0.91,1.38) 0.67c

(0.58,0.77)

Arthritis 0.95 (0.88,1.01) 1.06 (0.97,1.16) 0.87b

(0.79,0.96) 1.14c

(1.06,1.22)

Psychiatric Problems 0.73c

(0.67,0.81) 1.25c

(1.12,1.39) 1.39c

(1.22,1.58) 0.62c

(0.56,0.68)

CESD Depression Scale Symptoms 0.99 (0.97,1.02) 0.89c

(0.87,0.92) 0.86c

(0.84,0.89) 1.03c

(1.01,1.05)

2008 Fiscal Crisis

2-years post diagnosis 0.48c

(0.44,0.53) 0.71c

(0.64,0.80) 2.47c

(2.13,2.85) 0.39c

(0.35,0.43)


Net Worth Depletion Incurrence of

Consumer Debt

Incurrence of Mortgage

Debt

Incurrence of Home

Equity Debt

Odds

Ratio

95%

Confidence

Interval

Odds

Ratio

95%

Confidence

Interval

Odds

Ratio

95%

Confidence

Interval

Odds

Ratio

95%

Confidence

Interval


Cancer Status


Better 1.01 (0.94,1.08) 1.26c

(1.17,1.37) 1.24c

(1.14,1.35) 1.20c

(1.12,1.28)

Worse 1.68c

(1.35,2.10) 2.76c

(2.23,3.41) 0.52c

(0.39,0.70) 1.77c

(1.39,2.26)

New Cancer 0.93 (0.77,1.12) 1.07 (0.85,1.34) 0.53c

(0.41,0.69) 1.55c

(1.32,1.83)

Cancer Treatment 0.52a

(0.30,0.92) 0.30 (0.08,1.11) 1.76c

(1.65,1.93) 0.73 (0.52,1.07)

Demographics

Age (per year)


65-74 years 1.63c (1.42,1.88) 0.55

c (0.47,0.64) 0.34

c (0.29,0.40) 1.38

c (1.19,1.61)

75+ years 1.65c (1.42,1.91) 0.30

c (0.25,0.36) 0.33

c (0.27,0.40) 1.98

c (1.70,2.32)

Female 1.66c

(1.54,1.78) 1.06 (0.98,1.15) 0.98 (0.89,1.07) 1.70c

(1.58,1.83)

Race (White = reference)

Black 1.24c

(1.09,1.41) 1.03 (0.89,1.20) 1.04 (0.89,1.21) 1.47c

(1.28,1.69)

Other 3.13c

(2.56,3.81) 1.77c

(1.47,2.13) 0.41c

(0.29,0.58) 0.76b

(0.64,0.91)

Hispanic Ethnicity 0.77a

(0.59,0.98) 1.74c

(1.28,2.38) 0.89 (0.63,1.26) 1.09 (0.85,1.42)

Marital Status


Married 1.03 (0.92,1.16) 0.84b (0.74,0.96) 2.16

c (1.82,2.55) 1.66

c (1.48,1.86)

Divorced/Widowed 1.14a

(1.01,1.28) 0.87a (0.77,0.99) 1.72

c (1.46,2.03) 1.58

c (1.40,1.78)

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Multivariable general linear models (Binomial/Bernoulli distribution, logit-link), also adjusting for geographic region

Statistical significance at: a p<0.05; b p<0.01; c p<0.001 d Self-reported health: 1= Excellent to 5 = Poor

Current Alcohol Use 1.17c

(1.08,1.26) 0.89

(0.85,1.01) 0.98 (0.89,1.08) 1.04 (0.96,1.12)

Current Smoker 0.74c

(0.66,0.82) 0.85a

(0.76,0.96) 1.02 (0.91,1.15) 1.62c

(1.44,1.81)


Education (per year) 0.99c

(0.98,0.99) 1.02c

(1.01,1.02) 1.02c

(1.01,1.03) 1.02c

(1.01,1.02)

Income (per $100) 1.01c

(1.01,1.02) 0.99c

(0.99,0.99) 1.01a (1.01,1.02) 1.01

c (1.01,1.01)



Medicaid 0.75

(0.56,1.01) 0.40c

(0.30,0.54) 0.16c

(0.11,0.23) 2.09c

(1.51,2.88)

Private Insurance 1.10

(0.95,1.26) 0.82b

(0.71,0.95) 0.64c

(0.54,0.75) 0.97

(0.84,1.13)

Uninsured 1.28b

(1.07,1.54) 0.96 (0.79,1.16) 1.99c

(1.63,2.43) 1.78c

(1.49,2.15)

Retired 1.39c

(1.28,1.50) 0.73c

(0.66,0.79) 0.74c

(0.67,0.81) 1.21c

(1.11,1.32)

Household Size (per person) 1.24c

(1.17,1.31) 0.90c

(0.85,0.96) 0.99 (0.92,1.06) 1.06a

(1.01,1.13)


Self-Reported Health d 1.25

c (1.19,1.29) 0.95

a (0.90,0.99) 0.86

c (0.83,0.90) 1.24

c (1.19,1.28)

Hypertension 0.63c

(0.59,0.68) 1.29c

(1.19,1.40) 1.18c

(1.07,1.29) 0.71c

(0.66,0.77)

Diabetes 1.88c

(1.71,2.08) 1.94c

(1.75,2.15) 0.92 (0.83,1.03) 1.34c

(1.22,1.48)

Lung Disease 0.95 (0.84,1.07) 1.66c

(1.45,1.90) 2.30c

(2.00,2.65) 0.82b

(0.73,0.93)

Heart Disease 0.88c

(0.81,0.97) 0.74c

(0.67,0.81) 1.05 (0.94,1.16) 1.02 (0.94,1.11)

Stroke 1.15 (0.99,1.34) 1.97c

(1.70,2.29) 1.14

(0.96,1.35) 1.25b

(1.08,1.45)

Arthritis 1.27c

(1.18,1.37) 1.09a

(1.01,1.18) 1.23c

(1.12,1.36) 0.88c

(0.82,0.94)

Psychiatric Problems 0.58c

(0.53,0.65) 0.63c

(0.56,0.70) 1.49c

(1.33,1.68) 0.49c

(0.44,0.55)

CESD Depression Scale Symptoms 0.93c

(0.91,0.95) 1.06c

(1.03,1.08) 1.08c

(1.05,1.11) 0.96b

(0.94,0.99)

2008 Fiscal Crisis

4-years post-diagnosis 0.83c

(0.77,0.89) 0.75c

(0.69,0.82) 1.16b

(1.05,1.27) 0.71c

(0.66,0.76)

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Table 4. Multivariable Regression Analyses of Dollar Change in Total Net Worth and Debt at Two- and Four-Years Following Newly-

Diagnosed Cancer


Change in Total Net Worth Change in Consumer

Debt

Change in Mortgage Debt Change in Home Equity Debt

β estimate 95% Confidence

Interval

Β

estimate

95%

Confidence

Interval

Β estimate 95%

Confidence

Interval

Β

estimate

95% Confidence

Interval


Cancer Status


Better −137,174c

(−194221,−80127) −1,881b

(−3403,−359) −909 (−3542,1724) −6,775 (−19430,5880)

Worse −221,082c

(−282891,−159272) −1,759 (−4649,1132) -33,398c

(-40512,-26283) -62,047c

(-79010,-45084)

New Cancer −24,827 (−85376,35722) 10,522c

(6759,14286) 8,503b

(2815,14191) 54,111c

(38285,69937)

Cancer Treatment 47,420 (−22424,117264) 1,324b

(309,2338) −5,129c

(−7545,−2713) 63,747c

(44716,82777)

Demographics

Age (per year)


65-74 years 141,735c (81709,201760) 3,012

b (793,5231) -20,515

c (−24127,-16903) 52,037

c (28221,75853)

75+ years −27,169 (−74351,20013) 2,097 (−89,4284) -10,325c (−14065,-6584) −32,094

c (−47747,−16440)

Female −256,314c

(−324089,−188540) −3754c

(−4997,−2510) −2,768a (−5321,-214) −67,747

c (−78246,−57248)

Race

(White = reference)

Black −292,562c

(−369955,−215170) −7,315c

(−9119,−5511) −3,492 (−8084,1101) −29,656c

(−41262,−18049)

Other 118,165b

(39383,196946) 3,754a

(561,6948) −9,318c

(−14236,−4401) 10,589 (−6960,28138)

Hispanic 42,235 (−45418,129888) −8,545c

(−10551,−6539) −98,949c (−112930,−84969) 57,078

b (22148,92008)

Marital Status


Married 128,996b

(41757,216235) −1,505 (−3369,358) 2,754 (−2060,7567) −45,237b

(−74990,−15484)

Divorced/Widowed 8,287 (−62077,78651) 5,689c

(3159,8218) −4,261 (−8898,375) −92,380c

(−122590,−62170)

Current Alcohol Use −65,047a

(−117164,−12931) −5,077c

(−6461,−3693) −8,670c

(−11200,−6139) 63,319c

(49050,77589)

Current Smoker 294,574c

(161947,427201) 4,231c

(2558,5903) −7,803c

(−10991,−4614) −2,789 (−17624,12045)


Education (per year) 2,467 (−3337,8270) −291c

(−363,−219) 1,274c

(1047,1502) 273 (−1416,1962)

Income (per $100) −22c

(−25,−19) 1c

(1,2) −1

(−3,2) −71c

(−79,−62)



Medicaid 58,702 (−16623,134027) −38,508c

(−48132,−28883) 4,987 (−10903,929) 39,284c (17319,61249)

Private Insurance 290,494c

(210302,370686) 4,247c (1950,6545) −18,043

c (−22635,-13452) 18,807

b (4622,32993)

Uninsured 265,407c

(152294,378520) 20,563c

(14919,26206) 15,372c

(8331,22413) 107,352c

(65993,148710)

Retired −42766 (−122152,36619) 2,681c

(1221,4141) −3,867b

(−6644,−1090) −74,752c

(−92791,−56714)

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Household Size (per person) −66318b (−104641,−27996) 1,579

c (817,2341) −5,426

c (−7872,−2981) −49,751

c (−56561,−42941)


Self-reported Health d −164,036

c (−199984,−128089) −258 (−977,461) 1,168

(-48,2384) −2,065 (−11215,7085)

Hypertension −66,056b (−103992,−28121) 3,670

c (2741,4598) −9,718

c (−11969,−7466) −10,338

a (−19552,-1124)

Diabetes −147,545c (−195172,−99918) 16,704

c (14273,19135) 8,310

c (5428,11192) −15,427

b (−26233,−4622)

Lung Disease −203,070c (−248423,−157717) 4,433

c (3095,5771) 14,986

c (11924,18047) −28,735

b (−45634,−11835)

Heart Disease 41,118a (1747,80488) −1,614 (−3331,104) −5,859

c (−8836,−2883) 69,622

c (50274,88969)

Stroke −81,322c (−133488,−29156) 36,310

c (27775,44846) −6,285

c (−9773,−2797) −54,333

c (−71221,−37445)

Arthritis 341,935c (255332,428538) −8,057

c (−9843,−6271) 4,317

c (1645,6990) 28,559

c (16751,40368)

Psychiatric Problems 417,390c (315372,519408) −3,365

b (−5290,−1440) 1,638 (−1252,4528) 67,945

c (52970,82920)

CESD Depression Scale Symptoms −2,607 (−18524,13311) 231a (38,423) 2,239

c (1602,2876) −37,651

c (−45620,−29683)

2008 Fiscal Crisis

2-years post diagnosis 230,706c

(172584,288827) 657 (−534,1848) 6,060c

(3459,8662) 12,919

(-2480,28319)


Change in Total Net Worth Change in Consumer

Debt

Change in Mortgage Debt Change in Home Equity Debt

β estimate 95% Confidence

Interval

β

estimate

95%

Confidence

Interval

β

estimate

95%

Confidence

Interval

β

estimate

95% Confidence

Interval


Cancer Status

Better −75,574c

(−114896,−36252) −374 (−977,229) 7,817c

(4685,10950) 429 (−16319,17178)

Worse −438,634c

(−557353,−319915) 5,845c

(4409,7280) −20,344c

(−27134,−13553) −82,989c

(−107289,−58690)

New Cancer −112,739

(−247047,21570) −1,340

(−2865,185) 4,664 (−3131,12459) −57,831c

(−75846,−39816)

Cancer Treatment 41,288 (−123498,197660) 136 (−1891,2264) 2,645 (−10987,16347) 41,986 (−66534,150245)

Demographics

Age (per year)


65-74 years 78,204 (−5338,161746) 5,068c (3421,6714) -8,435

a (-15147,-1723) 117,342

c (86551,148133)

75+ years −118,560c (−180510,−56610) 4,087

c (2485,5690) 5,648 (-1202,12499) −31,299

b (−54318,−8280)

Female −393,390c

(−476749,−310032) 1,189c

(609,1768) −5,436b

(−8621,−2252) −45,957c

(−61020,−30893)

Race

(White = reference)

Black −303,852c

(−378936,−228767) −8,423c

(−10192,−6655) −3,040 (−9668,3588) −42,829c

(−63722,−21936)

Other −61,176 (−168692,46340) 3,060c

(1527,4593) −6,569a (−11971,-1166) 24,129 (−3489,51747)

Hispanic Ethnicity 444,327c

(315278,573376) −17,144c

(−20219,−14070) −174,424c (−189122,−159726) 75,290

c (42717,107862)

Marital Status


Married -29,183 (−101501,43136) −1,737b

(−2716,−758) 395 (−3943,4732) −91,230c

(−119925,−62535)

Divorced/Widowed 158,604c

(91245,225963) 1,802b

(736,2868) 20,896c

(16208,25585) -11,755 (−46985,23475)

Current Alcohol Use −23,600 (−59213,12013) −4,422c

(−5333,−3510) −16,353c

(−19940,−12765) 18,326 (−505,37156)

Current Smoker −118,630c

(−166286,−70974) 3,631c

(2654,4609) −101 (−4864,4662) −53,676c

(−66771,−40581)

ACCEPTED MANUSCRIPT

ACCEPTED MANUSCRIP

T

28

Multivariable general linear models (Gaussian distribution, log-link), also adjusting for geographic region

Statistical significance at: a p<0.05; b p<0.01; c p<0.001 d Self-reported health: 1= Excellent to 5 = Poor


Education (per year) 1,310a (54,2565) 9 (−18,35) 743

c (611,874) 963

c (542,1384)

Income (per $100) −65c

(−88,−43) −1c

(−1,−2) −1c

(−1,−2) −21c

(−29,−14)



Medicaid 99,291a

(19749,178833) −25,553c

(−35373,−15734) −11,355b

(−19576,−3135) 87,471c

(62457,112486)

Private Insurance 223,059c

(140116,306002) 7,063c

(5524,8602) −19,201c

(−26399,−12004) −29,619c

(−43135,−16103)

Uninsured 82,416a

(6117,158715) 1,378 (−406,3161) 13,102b

(4069,22135) −22,359b

(−40266,−4453)

Retired 160,814c

(84489,237139) −3,046c

(−3843,−2249) 1,174 (−2278,4626) 19,963b

(6719,33208)

Household size (per person) −101,731c

(−127796,−75666) 3,151c

(2418,3884) 895 (−1674,3464) −42,169c

(−50126,−34211)


Self-reported Health d −178,929

c (−227260,−130598) −1,845

c (−2205,−1485) −7,003

c (−8549,−5457) −17,189

c (−23361,−11018)

Hypertension 57,400a

(9773,105027) −1,370c

(−2010,−730) −1,990 (−5082,1102) -2,492 (−21185,16201)

Diabetes 15,495 (−23797,54788) 7,492c

(6265,8719) −21,749c

(−26155,−17343) 22,082c

(11120,33044)

Lung Disease −64,748b

(−108559,−20937) 8,084c

(6465,9703) 22,200c

(17645,26753) −23,377b

(−36904,−9849)

Heart Disease −27,078 (−65326,11169) 772 (−123,1667) −10,182c

(−13404,−6961) 8,223 (−8967,25413)

Stroke 17,748 (−39321,74817) 1,893c

(841,2946) −2,063 (−5352,1227) −63,509c

(−77356,−49662)

Arthritis 174,026c

(82184,265868) 763b

(191,1335) 11,434c

(8155,14713) 15,974 (−2266,3214)

Psychiatric Problems 928,117c

(728963,1127270) −6,701c

(−7707,−5693) 27,532c

(23068,31996) 90,375c

(67371,113379)

CESD Depression Scale Symptoms −46,097c

(−57345,−34849) −677c

(−873,−481) -48 (−955,859) −27,591c

(−33418,−21764)

2008 Fiscal Crisis

4-years post-diagnosis 87,425c

(41316,133534) 2,553c

(1970,3137) 10,222c

(7264,13180) 47,679c

(35541,59817)

death or debt? national estimates of financial...

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