Factors contributing to financial distress in young adults with cancer: Material resources,health,and workplace

Abstract

BACKGROUND:

Financial distress is a primary concern for young adults with cancer.

OBJECTIVE:

The aim of this study was to identify material resources, physical and psychological health, and workplace variables that are associated with financial distress in young adult cancer survivors.

METHODS:

A cross-sectional study was conducted using the Cancer Survivor Employment Needs Survey. Participants were young adults (18–39 years of age) who lived in the United States and had a cancer diagnosis. Multivariable linear regression was used to model relations between financial distress and material resources, physical and psychological health, and workplace variables.

RESULTS:

Participants (N = 214) were mostly non-Hispanic White (78%), female (79%), and had a mean age of 31 years and 4.6 years post-diagnosis. Material resources, physical and psychological health, and workplace variables were all identified as contributing to study participants’ financial distress. Among the young adults surveyed, financial distress was prevalent, and an array of problems were associated with financial distress.

CONCLUSION:

Oncology and rehabilitation providers should openly discuss finances with YAs with cancer and guide them to resources that can address their financial, benefits, and vocational needs to ultimately improve quality of life.

Keywords

Young adults financial distress financial burden cancer oncology work

1 Introduction

Financial distress (FD) has been identified as a common problem for oncology patients throughout the lifespan, especially adolescent and young adult (AYA) cancer survivors [1]. Unlike adolescents, young adults (YA) with cancer may not have parental financial support during their cancer treatment and rehabilitation. Further, YAs are just emerging in the workplace and may not receive benefits associated with lengthy tenure in employment (such as accrued paid sick and personal leave) compared to older adults with cancer [2]. Symptom burden from cancer and its treatments affects two-thirds of the young adult (YA) patient population and oftentimes negatively interferes with work performance [1, 3]. YA survivors experience career interruptions, absenteeism, demotion, changes in job tasks, and job loss [1, 4]. The financial issues of YAs with cancer have been associated with several negative outcomes, including medication access barriers, medical treatment adherence, symptom burden, quality of life, emotional distress, and survival [1 , 5–9].

Twenty-eight percent of AYAs working before their cancer diagnosis had not returned to work 15 to 35 months post-diagnosis [10]. Yet, the functions of work, as well as the workplace environment and relationships, have the potential to promote wellness, grounding persons during cancer treatment and contributing to recovery following treatment [11]. Workplace benefits such as medical insurance and paid leave have been shown to mitigate the financial impact of cancer [2]. Less is known about the other aspects of the workplace, especially the informal aspects of the workplace and its culture, including relationships with coworkers and supervisors.

Little is known about the relationship between FD and the physical and psychological health of YA cancer survivors. Research has shown that financial burden resulting from cancer care costs is a strong predictor of poor quality of life among cancer survivors [2]. In YAs with cancer, there is an emotional impact from the financial burden of cancer and appropriate coping strategies are needed [12].

Conceptual models of financial burden among AYA cancer patients have been proposed and examined. Material, psychosocial, and behavioral domains have all been established as relevant [12]. Danhauer and colleagues have expanded the model, including the provider or care team, health care system and practice setting, and health policy environment (state/federal), as well as the AYA cancer patients’ developmental stage and cancer treatment phase [13].

For the purpose of the present study, the focal construct was FD. The definition applied was adopted from Salsman and colleagues: “a subjective measure of the impact of financial burden on patient well-being; captures the affective experience and reflects the extent of worry, anxiety, or anguish about financial burden; experienced or anticipated” [14]. Financial burden, a related but distinct construct invoked in the definition of FD, was defined by the same source as “a relatively objective measure of personal financial status, defined as the ratio of total out-of-pocket spending on health-related costs (medical and nonmedical expenses) to total household income” [14]. The aim of this study, was to explore material resources, health, and workplace variables associated with FD in YA cancer survivors to better understand and further define the multidimensional model of financial burden.

2 Methods

We conducted a cross-sectional study using an online survey of a convenience sample. The Institutional Review Boards at the involved academic institutions approved the study. The Committee for the Protection of Human Subjects at the University of Texas Health Science Center at Houston determined this project (HSC-GEN-15-0657 qualified for exempt status according to 45 CFR 46.101(b). This sample has been previously described by Scardaville and colleagues [15].

2.1 Participant recruitment

Study selection criteria were YAs (18–39 years old) who lived in the United States and had a cancer diagnosis other than nonmelanoma skin cancer. The upper age limit, 39 years, was selected in order to align with the National Cancer Institute’s YA definition. Participants were recruited in 2016 through online cancer advocacy and service organizations, social media, and organizational newsletters. Researchers also sent a letter and follow-up flyer to respondents to request their help sharing the survey. In addition, comprehensive cancer center patients were recruited in-person and directed to the online survey. Recruitment populations included outpatients in active treatment; hospitalized patients; posttreatment patients seen in primary and long-term follow-up clinics; patient members of the institution’s YA Advisory Council; and patients attending locally sponsored health education conferences, fundraising events, and social support events. Participants who completed the survey were offered a $20 gift card.

2.2 Cancer Survivor Employment Needs Survey (CSEN)

The revised 49-item YA Cancer Survivor Employment Needs (CSEN-YA) survey was used [15]. The CSEN was adapted from an existing British survey “Work and cancer: How cancer affects working lives,” to fit a US cohort of YA cancer patients and survivors [16, 17]. Modifications to the CSEN for a YA patient cohort allowed for the following to be reported: 1) employment with more than one employer; 2) student status; 3) financial dependence on parent(s); and 4) disease status, i.e., active, relapsed, chronic, remitted.

This self-report survey consists of six scales regarding physical and mental health side effects of cancer, the effects of cancer on work attendance and productivity, the effects of disclosing cancer at work, the provision of reasonable workplace accommodations, knowledge of available cancer-related resources, and knowledge of employment-related federal legislation and programs. The survey takes approximately 20 to 25 minutes to complete and includes skip logic so that respondents see only questions pertinent to their employment and disease status.

2.3 Analyses

For the analyses reported here, all survey items on four of the six CSEN-YA scales were utilized: Cancer Side Effect, Effect at Work, Effect at Disclosure, and Reasonable Accommodation (9, 11, 8, and 8 scale items; respectively). The survey findings pertaining to the other two scales (Knowledge of Available Resources and Knowledge about Employment-Related Federal Legislation or Program) were previously published and omitted from the analyses presented [15].

FD was assessed with the following survey item: “Please indicate to what extent you disagree or agree with the following statement: ‘Apart from concerns about my health, financial problems caused by my cancer have created more stress for me than anything else.”’ A 5-point Likert scale ranging from “strongly disagree” (1) to “strongly agree” (5) was used. Sociodemographic variables that might confound the associations between FD and material resources, health, and workplace variables were selected a priori on the basis of a literature review: gender, race, age, years since diagnosis, and education level [18 –23]. These variables were summarized as counts with percentages or means with standard deviations, and medians.

FD was treated as a continuous variable, with integer ratings between 1 and 5. Multivariable linear regression was used to separately model relationships between FD and responses among the 4 selected scales, each of which contained items that addressed material resources (personal income/expenses, employment benefits); physical and psychological health; and workplace (accommodations, disclosure/stigmatization) (with a separate model for each of these variables), while adjusting for potentially prognostic covariates (gender, race, age, time since diagnosis, and education). Normal quantile plots were used to verify the approximate normality of model residuals. Likert-scale variables were treated as continuous data, while other variables were discrete. For discrete variables, differences between categories were assessed by Tukey-adjusted contrasts. For continuous variables, model coefficients represented the linear association of the variable with FD and were illustrated with graphs. Additionally, associations between discrete responses to the FD item and discrete responses to each item assessing material resources (personal income/expenses, employment benefits); physical and psychological health; and workplace (accommodations, disclosure/stigmatization) were summarized in contingency tables that included counts and percentages.

Statistical analyses were performed using R statistical software (R Core Team, 2020, version 3.6.3) [24]. In all statistical tests, two-sided alpha = 0.05 was used to signify statistical significance. Assessment of differences among discrete variable levels in the regression models were estimated using the “emmeans” package [25].

3 Results

Survey responses were received from 257 participants. Due to the requirement for common non-missing data in modeling, responses declined per sequential exclusions to 222 for gender, 220 for education, 218 for FD, 217 for age at diagnosis, and 216 due to exclusion of 1 participant who provided an invalid age of 3 years. The final dropped to 214 owing to the requirement that the time since diagnosis (age at survey –age at diagnosis) be non-negative. Thereafter, counts within each model (material resources, physical and psychological health, workplace variables) depended upon the number of participants with non-missing data in the specific variable assessed in that model.

Regarding demographics, the racial distribution of the participants was 78% non-Hispanic White, 11% Hispanic, 5% Black, and 4% Asian. Participants were predominantly female (79%). Geographically, participants were from 39 states and 2 territories of the United States. Twenty-five percent were from Texas, 10% from California, and 5% each from both New York and New Jersey. The remaining participants were scattered around the country at rates of less than 5% per state. Most participants had completed at least an associate’s degree (69%). Age at diagnosis ranged from 1 to 39 years, with a mean of 26 (SD = 8.7) years. Age at the time of the survey ranged from 18 to 39 years, with a mean of 31 (SD = 6.3). Time since diagnosis ranged from 0 to 34 years, with a mean of 4.6 years (SD = 5.8).

Response frequencies to the item assessing FD can be found in Table 1. The majority of participants (n = 127, 59%) either agreed or strongly agreed with the statement: “Apart from concerns about my health, financial problems caused by my cancer have created more stress for me than anything else.” Demographic distribution according to the primary FD item is provided in Table 2, aiming to establish how distressed the sample was by specific subgroups.

Table 1
Response frequencies to the primary financial distress item

Apart from concerns about my health, financial problems caused by my cancer have created more stress for me than anything else

N %

Strongly disagree 17 7.9

Disagree 37 17.3

Neither agree nor disagree 33 15.4

Agree 81 37.9

Strongly agree 46 21.5

Apart from concerns about my health, financial problems caused by my cancer have created more stress for me than anything else
	N	%
Strongly disagree	17	7.9
Disagree	37	17.3
Neither agree nor disagree	33	15.4
Agree	81	37.9
Strongly agree	46	21.5

Table 2

Demographic breakdown by financial distress item

Apart from concerns about my health, financial problems caused by my cancer have created more stress for me than anything else
	Strongly disagree		Disagree		Neither		Agree		Strongly agree
Race/Ethnicity	N	%	N	%	N	%	N	%	N	%
White - non-Hispanic	12	70.6	34	91.9	21	63.6	60	74.1	39	84.8
Hispanic	1	5.9	1	2.7	8	24.2	11	13.6	3	6.5
Black or African American	2	11.8	0	0.0	1	3.0	5	6.2	3	6.5
Asian	1	5.9	2	5.4	2	6.1	3	3.7	1	2.2
Unknown	1	5.9	0	0.0	1	3.0	2	2.5	0	0.0
Sex
Female	16	94.1	31	83.8	25	75.8	57	70.4	39	84.8
Male	1	5.9	6	16.2	8	24.2	24	29.6	7	15.2
Education
Did not complete high school	0	0.0	1	2.7	2	6.1	1	1.2	0	0.0
Completed high school or equivalent	0	0.0	1	2.7	3	9.1	9	11.1	3	6.5
Completed some college	3	17.6	4	10.8	7	21.2	19	23.5	8	17.4
Finished technical/vocational school	1	5.9	0	0.0	0	0.0	3	3.7	1	2.2
Finished an Associate’s Degree	0	0.0	6	16.2	2	6.1	6	7.4	8	17.4
Finished a Bachelor’s Degree	6	35.3	9	24.3	12	36.4	22	27.2	13	28.3
Finished some or all of a graduate degree	7	41.2	16	43.2	7	21.2	21	25.9	13	28.3
Marital status
Never married; not living with a partner	4	23.5	7	18.9	10	30.3	36	44.4	15	32.6
Living with a partner	2	11.8	3	8.1	4	12.1	7	8.6	9	19.6
Married	9	52.9	24	64.9	14	42.4	36	44.4	18	39.1
Divorced	2	11.8	3	8.1	5	15.2	2	2.5	4	8.7
Income
Less than $20,000 per year	2	11.8	1	2.7	7	21.9	8	9.9	11	23.9
$20,000–$39,999 per year	3	17.6	4	10.8	7	21.9	13	16.0	10	21.7
$40,000–$59,999 per year	1	5.9	5	13.5	4	12.5	14	17.3	5	10.9
$60,000–$79,999 per year	3	17.6	7	18.9	8	25.0	14	17.3	8	17.4
$80,000–$99,999 per year	3	17.6	2	5.4	1	3.1	12	14.8	4	8.7
$100,000 per year or more	4	23.5	17	45.9	5	15.6	16	19.8	6	13.0
Do not know	1	5.9	1	2.7	0	0.0	4	4.9	2	4.3
Employment status
Never employed	0	0.0	2	5.4	1	3.0	10	12.5	0	0.0
Employed, full-time (not self-employed)	9	52.9	25	67.6	16	48.5	39	48.8	16	34.8
Employed, part-time (not self-employed)	1	5.9	4	10.8	8	24.2	10	12.5	8	17.4
Employed & on (un)paid medical leave	2	11.8	0	0.0	1	3.0	4	5.0	3	6.5
Self-employed	1	5.9	0	0.0	2	6.1	2	2.5	5	10.9
Unemployed, and actively looking for work	0	0.0	0	0.0	2	6.1	7	8.8	7	15.2
Unemployed, NOT looking to work, not retired	3	17.6	5	13.5	2	6.1	5	6.3	2	4.3
Retired AS A RESULT of cancer diagnosis	1	5.9	1	2.7	1	3.0	3	3.8	5	10.9

Note: Percentages are column percentages.

The following sections summarize the results from the multivariable regression models, which reflect adjustment for the potentially prognostic covariates of gender, race, age, time since diagnosis, and education. Table 3 reports the variables and whether trends or significant associations or with FD were detected.

Table 3

Material resources, health, & workplace variables associated with financial distress

Not significantly related (* denotes trending)	Slope	p-value	Associated with financial distress	Slope	p-value	(Tukey)
Personal Expenses
			Estimated out-of-pocket costs	0.1	0.041	0.025
Employment benefits
Paid leave	0	1	Unpaid leave	–1	0.009	0.025
FMLA leave	–0	0.24	Short-term disability leave	–1	0.01	0.026
Long-term disability leave	–0	0.8
Physical Health Status
Not as productive in my job because of cancer	–0	0.36	Concern about effects of dx on ability to work	0.2	0.032
Fatigue	0.2	0.16	Job demands became too stressful for me to handle	0.2	0.011
Loss of concentration	0	0.98	Job was too physically demanding for me to continue work	0.2	0.012
Nausea	–0	0.37	Pain	0.2	0.02
Physical changes (hair loss, assistive device)*	0.2	0.053	Mobility impairment (e.g., difficulty walking)	0.2	0.011
Other treatment-related sickness*	0.2	0.059
Psychological Health Status
Anxiety*	0.2	0.052	Depression	0.2	0.003
Workplace accommodations
Flexible work schedule*	–0	0.09
Reduced work hours	–0	0.19
Request for a change in job duties	–0	0.26
Telecommuting	0	1
Work time to access employee benefits	–0	0.28
Enhanced information technology accessibility*	–0	0.05
Physical modifications of the workspace	–0	0.5
Stigmatization Following Cancer Diagnosis Disclosure
Concerned about keeping dx confidential	0	0.57	Lack of emotional support at work regarding cancer dx	0.2	0.021
Comfortable disclosing cancer dx to employer	–0	0.55	Stigmatized at work, left out of work-related activities	0.3	0.002
Comfortable disclosing cancer dx to co-workers	–0	0.71	Reduced satisfaction in supervisor relationships	0.3	0.0003
Fired*	0.2	0.09	Reduced satisfaction in co- worker relationships, felt isolated	0.2	0.003
			Treated as incompetent, duties reassigned & workload reduced	0.2	0.003
			Received negative evaluations after having taken leave	0.3	0.0006
			Lost a promotion	0.2	0.003
			Passed up for promotions or projects	0.2	0.002
			Diminished career prospects, promotions, job transfers	0.2	0.022
			Did not explain my job performance for fear of losing job	0.3	0.0003
			Concerned employer could not hold my job, if on leave	0.3	0.0003
			Received subtle pressure to resign, take leave, retire	0.3	0.0006
			Employer asked me to resign	0.3	0.006

3.1 Material resources: Income and expenses

Household income was not significantly associated with FD (see Table 4), but FD increased significantly with increased estimated out-of-pocket expenses for diagnosis and treatment (p = 0.025); with each doubling in out-of-pocket costs, FD increased by 0.05 units.

Table 4
Relationship between financial distress, primary medical payor, & household income

Contrast Estimate SE p-value (Tukey)

Primary Medical Payor

Your student health insurance - Your employer-provided health insurance 0.2 0.9 0.86 1.00

Your student health insurance - Parent’s/legal guardian’s employer-provided health insurance 0.3 0.9 0.74 1.00

Your student health insurance - Individually purchased health insurance plan –0.1 0.9 0.92 1.00

Your student health insurance - Medicare 0.2 1.0 0.81 1.00

Your student health insurance - Medicaid –0.3 1.0 0.74 1.00

Your student health insurance - Community based donations (including crowdsourcing) –0.7 1.2 0.55 1.00

Your student health insurance - Out-of-pocket (you paid your bills) 1.5 1.6 0.34 0.99

Your student health insurance - Out-of-pocket (someone else paid your bills) 0.6 1.8 0.75 1.00

Your student health insurance - Other 0.6 1.0 0.52 1.00

Your employer-provided health insurance - Parent’s/legal guardian’s employer-provided health insurance 0.1 0.3 0.64 1.00

Your employer-provided health insurance - Individually purchased health insurance plan –0.3 0.3 0.41 1.00

Your employer-provided health insurance - Medicare 0.1 0.4 0.87 1.00

Your employer-provided health insurance - Medicaid –0.5 0.4 0.21 0.96

Your employer-provided health insurance - Community based donations (including crowdsourcing) –0.9 0.8 0.27 0.98

Your employer-provided health insurance - Out-of-pocket (you paid your bills) 1.3 1.3 0.30 0.99

Your employer-provided health insurance - Out-of-pocket (someone else paid your bills) 0.4 1.6 0.79 1.00

Your employer-provided health insurance - Other 0.5 0.4 0.22 0.97

Parent’s/legal guardian’s employer-provided health insurance - Individually purchased health insurance plan –0.4 0.4 0.32 0.99

Parent’s/legal guardian’s employer-provided health insurance - Medicare –0.1 0.4 0.85 1.00

Parent’s/legal guardian’s employer-provided health insurance - Medicaid –0.6 0.4 0.13 0.89

Parent’s/legal guardian’s employer-provided health insurance - Community based donations, crowdsourcing –1.0 0.8 0.20 0.96

Parent’s/legal guardian’s employer-provided health insurance - Out-of-pocket (you paid your bills) 1.2 1.3 0.36 1.00

Parent’s/legal guardian’s employer-provided health insurance - Out-of-pocket (someone else paid your bills) 0.3 1.6 0.86 1.00

Parent’s/legal guardian’s employer-provided health insurance - Other 0.3 0.4 0.48 1.00

Individually purchased health insurance plan - Medicare 0.3 0.5 0.48 1.00

Individually purchased health insurance plan - Medicaid –0.2 0.5 0.62 1.00

Individually purchased health insurance plan - Community based donations (including crowdsourcing) –0.6 0.8 0.45 1.00

Individually purchased health insurance plan - Out-of-pocket (you paid your bills) 1.6 1.3 0.22 0.97

Individually purchased health insurance plan - Out-of-pocket (someone else paid your bills) 0.7 1.6 0.66 1.00

Individually purchased health insurance plan - Other 0.7 0.5 0.12 0.86

Medicare - Medicaid –0.5 0.5 0.25 0.98

Medicare - Community based donations (including crowdsourcing) –0.9 0.8 0.26 0.98

Medicare - Out-of-pocket (you paid your bills) 1.3 1.3 0.34 0.99

Medicare - Out-of-pocket (someone else paid your bills) 0.4 1.6 0.82 1.00

Medicare - Other 0.4 0.5 0.44 1.00

Medicaid - Community based donations (including crowdsourcing) –0.4 0.8 0.62 1.00

Medicaid - Out-of-pocket (you paid your bills) 1.8 1.3 0.17 0.93

Medicaid - Out-of-pocket (someone else paid your bills) 0.9 1.6 0.57 1.00

Medicaid - Other 0.9 0.5 0.07 0.72

Community based donations (including crowdsourcing) - Out-of-pocket (you paid your bills) 2.2 1.5 0.14 0.90

Community based donations (including crowdsourcing) - Out-of-pocket (someone else paid your bills) 1.3 1.7 0.44 1.00

Community based donations (including crowdsourcing) - Other 1.3 0.9 0.13 0.88

Out-of-pocket (you paid your bills) - Out-of-pocket (someone else paid your bills) –0.9 2.0 0.66 1.00

Out-of-pocket (you paid your bills) - Other –0.9 1.3 0.51 1.00

Out-of-pocket (someone else paid your bills) - Other 0.0 1.6 0.98 1.00

Estimated Household Income

Less than $20,000 per year - $20,000–$39,999 per year 0.2 0.3 0.63 1.00

Less than $20,000 per year - $40,000–$59,999 per year 0.3 0.3 0.45 0.99

Less than $20,000 per year - $60,000–$79,999 per year 0.4 0.3 0.17 0.82

Less than $20,000 per year - $80,000–$99,999 per year 0.3 0.4 0.49 0.99

Less than $20,000 per year - $100,000 per year or more 0.7 0.3 0.04 0.36

Less than $20,000 per year - Do not know 0.3 0.5 0.57 1.00

$20,000–$39,999 per year - $40,000–$59,999 per year 0.1 0.3 0.74 1.00

$20,000–$39,999 per year - $60,000–$79,999 per year 0.3 0.3 0.34 0.96

$20,000–$39,999 per year - $80,000–$99,999 per year 0.1 0.4 0.76 1.00

$20,000–$39,999 per year - $100,000 per year or more 0.5 0.3 0.07 0.55

$20,000–$39,999 per year - Do not know 0.1 0.5 0.80 1.00

$40,000–$59,999 per year - $60,000–$79,999 per year 0.2 0.3 0.56 1.00

$40,000–$59,999 per year - $80,000–$99,999 per year 0.0 0.4 1.00 1.00

$40,000–$59,999 per year - $100,000 per year or more 0.4 0.3 0.16 0.80

$40,000–$59,999 per year - Do not know 0.0 0.5 0.96 1.00

$60,000–$79,999 per year - $80,000–$99,999 per year –0.2 0.3 0.60 1.00

$60,000–$79,999 per year - $100,000 per year or more 0.3 0.3 0.37 0.97

$60,000–$79,999 per year - Do not know –0.2 0.5 0.76 1.00

$80,000–$99,999 per year - $100,000 per year or more 0.4 0.3 0.19 0.84

$80,000–$99,999 per year - Do not know 0.0 0.6 0.97 1.00

$100,000 per year or more - Do not know –0.4 0.5 0.44 0.99

Contrast	Estimate	SE	p-value	(Tukey)
Primary Medical Payor
Your student health insurance - Your employer-provided health insurance	0.2	0.9	0.86	1.00
Your student health insurance - Parent’s/legal guardian’s employer-provided health insurance	0.3	0.9	0.74	1.00
Your student health insurance - Individually purchased health insurance plan	–0.1	0.9	0.92	1.00
Your student health insurance - Medicare	0.2	1.0	0.81	1.00
Your student health insurance - Medicaid	–0.3	1.0	0.74	1.00
Your student health insurance - Community based donations (including crowdsourcing)	–0.7	1.2	0.55	1.00
Your student health insurance - Out-of-pocket (you paid your bills)	1.5	1.6	0.34	0.99
Your student health insurance - Out-of-pocket (someone else paid your bills)	0.6	1.8	0.75	1.00
Your student health insurance - Other	0.6	1.0	0.52	1.00
Your employer-provided health insurance - Parent’s/legal guardian’s employer-provided health insurance	0.1	0.3	0.64	1.00
Your employer-provided health insurance - Individually purchased health insurance plan	–0.3	0.3	0.41	1.00
Your employer-provided health insurance - Medicare	0.1	0.4	0.87	1.00
Your employer-provided health insurance - Medicaid	–0.5	0.4	0.21	0.96
Your employer-provided health insurance - Community based donations (including crowdsourcing)	–0.9	0.8	0.27	0.98
Your employer-provided health insurance - Out-of-pocket (you paid your bills)	1.3	1.3	0.30	0.99
Your employer-provided health insurance - Out-of-pocket (someone else paid your bills)	0.4	1.6	0.79	1.00
Your employer-provided health insurance - Other	0.5	0.4	0.22	0.97
Parent’s/legal guardian’s employer-provided health insurance - Individually purchased health insurance plan	–0.4	0.4	0.32	0.99
Parent’s/legal guardian’s employer-provided health insurance - Medicare	–0.1	0.4	0.85	1.00
Parent’s/legal guardian’s employer-provided health insurance - Medicaid	–0.6	0.4	0.13	0.89
Parent’s/legal guardian’s employer-provided health insurance - Community based donations, crowdsourcing	–1.0	0.8	0.20	0.96
Parent’s/legal guardian’s employer-provided health insurance - Out-of-pocket (you paid your bills)	1.2	1.3	0.36	1.00
Parent’s/legal guardian’s employer-provided health insurance - Out-of-pocket (someone else paid your bills)	0.3	1.6	0.86	1.00
Parent’s/legal guardian’s employer-provided health insurance - Other	0.3	0.4	0.48	1.00
Individually purchased health insurance plan - Medicare	0.3	0.5	0.48	1.00
Individually purchased health insurance plan - Medicaid	–0.2	0.5	0.62	1.00
Individually purchased health insurance plan - Community based donations (including crowdsourcing)	–0.6	0.8	0.45	1.00
Individually purchased health insurance plan - Out-of-pocket (you paid your bills)	1.6	1.3	0.22	0.97
Individually purchased health insurance plan - Out-of-pocket (someone else paid your bills)	0.7	1.6	0.66	1.00
Individually purchased health insurance plan - Other	0.7	0.5	0.12	0.86
Medicare - Medicaid	–0.5	0.5	0.25	0.98
Medicare - Community based donations (including crowdsourcing)	–0.9	0.8	0.26	0.98
Medicare - Out-of-pocket (you paid your bills)	1.3	1.3	0.34	0.99
Medicare - Out-of-pocket (someone else paid your bills)	0.4	1.6	0.82	1.00
Medicare - Other	0.4	0.5	0.44	1.00
Medicaid - Community based donations (including crowdsourcing)	–0.4	0.8	0.62	1.00
Medicaid - Out-of-pocket (you paid your bills)	1.8	1.3	0.17	0.93
Medicaid - Out-of-pocket (someone else paid your bills)	0.9	1.6	0.57	1.00
Medicaid - Other	0.9	0.5	0.07	0.72
Community based donations (including crowdsourcing) - Out-of-pocket (you paid your bills)	2.2	1.5	0.14	0.90
Community based donations (including crowdsourcing) - Out-of-pocket (someone else paid your bills)	1.3	1.7	0.44	1.00
Community based donations (including crowdsourcing) - Other	1.3	0.9	0.13	0.88
Out-of-pocket (you paid your bills) - Out-of-pocket (someone else paid your bills)	–0.9	2.0	0.66	1.00
Out-of-pocket (you paid your bills) - Other	–0.9	1.3	0.51	1.00
Out-of-pocket (someone else paid your bills) - Other	0.0	1.6	0.98	1.00
Estimated Household Income
Less than $20,000 per year - $20,000–$39,999 per year	0.2	0.3	0.63	1.00
Less than $20,000 per year - $40,000–$59,999 per year	0.3	0.3	0.45	0.99
Less than $20,000 per year - $60,000–$79,999 per year	0.4	0.3	0.17	0.82
Less than $20,000 per year - $80,000–$99,999 per year	0.3	0.4	0.49	0.99
Less than $20,000 per year - $100,000 per year or more	0.7	0.3	0.04	0.36
Less than $20,000 per year - Do not know	0.3	0.5	0.57	1.00
$20,000–$39,999 per year - $40,000–$59,999 per year	0.1	0.3	0.74	1.00
$20,000–$39,999 per year - $60,000–$79,999 per year	0.3	0.3	0.34	0.96
$20,000–$39,999 per year - $80,000–$99,999 per year	0.1	0.4	0.76	1.00
$20,000–$39,999 per year - $100,000 per year or more	0.5	0.3	0.07	0.55
$20,000–$39,999 per year - Do not know	0.1	0.5	0.80	1.00
$40,000–$59,999 per year - $60,000–$79,999 per year	0.2	0.3	0.56	1.00
$40,000–$59,999 per year - $80,000–$99,999 per year	0.0	0.4	1.00	1.00
$40,000–$59,999 per year - $100,000 per year or more	0.4	0.3	0.16	0.80
$40,000–$59,999 per year - Do not know	0.0	0.5	0.96	1.00
$60,000–$79,999 per year - $80,000–$99,999 per year	–0.2	0.3	0.60	1.00
$60,000–$79,999 per year - $100,000 per year or more	0.3	0.3	0.37	0.97
$60,000–$79,999 per year - Do not know	–0.2	0.5	0.76	1.00
$80,000–$99,999 per year - $100,000 per year or more	0.4	0.3	0.19	0.84
$80,000–$99,999 per year - Do not know	0.0	0.6	0.97	1.00
$100,000 per year or more - Do not know	–0.4	0.5	0.44	0.99

3.2 Material resources: Employment benefits

Participants who had unpaid leave (p = 0.025) or short-term disability leave (p = 0.026) had significantly higher levels of FD. Paid leave, Family and Medical Leave Act (FMLA) leave, and long-term disability leave were not significantly associated with higher levels of FD. FD was also not significantly associated with the primary source of medical insurance premium payment (i.e., employer, self, or alternate; see Table 4).

3.3 Physical health status

At time of cancer diagnosis, concern about effects of diagnosis on ability to work was associated with FD (p = 0.032); each 1-unit increase in concern regarding anticipated disability after cancer diagnosis was associated with a 0.17-unit increase in FD. Having excessively stressful job demands and having excessively physical job demands were also significantly associated with FD (p = 0.011 and p = 0.012, respectively); each additional unit of excessive job demands was associated with a 0.19- and 0.18-unit increase in FD, respectively. Decreased productivity was not significantly associated with FD.

When either pain or mobility impairment negatively affected work, participants’ FD increased (p = 0.011 and p = 0.020, respectively); each additional unit of impairment was associated with a 0.19- and 0.22-unit increase in financial distress, respectively. Cancer treatment–related symptoms of fatigue, loss of concentration, and nausea were not significantly associated with FD. Physical changes (e.g., hair loss, need for cane/walker/wheelchair) and other symptoms tended to have higher FD (p = 0.053, p = 0.059, respectively), but this relationship did not reach significance.

3.4 Psychological health status

Participants who reported that depression had had a negative effect on their work had significantly higher levels of FD (p = 0.003); each additional unit of depression effect was associated with a 0.24-unit increase in FD. Participants with higher anxiety tended to have higher FD (p = 0.052), but this relationship did not reach significance.

3.5 Workplace: Accommodations

FD was not significantly associated with workplace accommodations. FD showed non-significant trends of reduction in association with flexible work schedules and the ability to work remotely through information technology. Reduced work hours, request for a change in job duties, telecommuting, worktime to access employee benefits, and physical modifications of the workspace were not associated with FD.

3.6 Workplace: Disclosure and stigmatization

Disclosure of the cancer diagnosis did not significantly raise concerns of FD among participants, but the implications of this disclosure on workplace relationships and job security was significantly positively associated with FD. FD was significantly associated with participants’ lack of emotional support at work regarding the cancer diagnosis and feeling stigmatized following disclosure of their cancer diagnosis ((p = 0.021 and p = 0.002, respectively); each additional unit of agreement was associated with a 0.18-unit and 0.27-unit increase in FD; respectively. FD was also significantly associated with participants’ perceptions that disclosure of their cancer diagnosis had reduced their satisfaction in their relationship with their supervisor (p = 0.0003) and in their general work relationships (p = 0.003); each additional level of reduced satisfaction was associated with 0.26 and 0.22 units of increased FD; respectively. Agreement with the statement that they had been treated as incompetent or received negative performance reviews following disclosure of their cancer diagnosis was associated with FD (p = 0.003 and p = 0.0006, respectively); each additional unit of agreement was associated with a 0.24- and 0.30-unit increase in FD; respectively.

Participants who were not successful with a promotion application and/or had been overlooked for projects following disclosure of their cancer status had significantly higher FD (p = 0.003 and p = 0.002); each additional unit of agreement was associated with a 0.24- and 0.23-unit increase in FD; respectively. Participants’ perceptions of diminished career prospects was also associated with FD (p = 0.022); each additional unit of agreement was associated with a 0.17-unit increase in FD.

FD was significantly associated with concern that disclosure of one’s cancer diagnosis could lead to job loss (p = 0.0003) and that one’s employer would not hold one’s job following a leave of absence (p = 0.0003). Each additional level of concern was associated with 0.27 and 0.26 units of increased FD, respectively. Having been either subtly pressured to stop working (p = 0.0006) or directly requested to resign (p = 0.006) following disclosure of cancer status was significantly associated with FD; each additional unit of agreement was associated with a 0.28- and 0.26-unit increase in FD, respectively. Having been fired following disclosure of cancer status tended to exacerbate FD, but this was not significant (p = 0.09); each additional unit of agreement was associated with a 0.15-unit increase in FD.

4 Discussion

Financial distress was prevalent among the YAs with cancer included in this analysis, 59% reported financial problems caused by cancer created the greatest amount of stress. In this sample, material resources, health, and workplace variables were significantly associated with FD. With regard to material resources (e.g., income, expenses, and employment benefits), household income was not associated with FD; however, FD did increase based on the estimated amount of out-of-pocket expenses for cancer treatment. The potential disruption to participants’ pre-cancer budget, regardless of income, may partially explain this source of FD. The cost of cancer diagnosis and treatment is a well-established source of financial hardship among YAs with cancer [12]. Given that increasing out-of-pocket expenses were positively associated with FD, it is not surprising that participants who had unpaid leave or short-term disability leave had significantly higher levels of FD than those with paid or long-term disability leave. Additionally, those who had taken FMLA did not have significant levels of FD, perhaps because the FMLA protects workers’ job upon return from medical leave and job security was less of an issue.

With regard to various workplace factors (e.g., accommodations, disclosure and stigmatization), several were associated with FD. While the effects of disclosure of cancer diagnosis to employers greatly increased FD, interestingly, job accommodations were not associated with reducing FD. To receive job accommodations, an employee must disclose the need for reasonable accommodations (i.e., cancer diagnosis and functional limitations). This is consistent with our finding that disclosure of the cancer diagnosis itself did not raise FD significantly, but also supports that disclosing does not inherently result in negative work outcomes. Rather, we speculate that disclosing and receiving reasonable accommodations may increase perceptions of job security by the recipient, maintain a level of work productivity, and/or may increase the employee and employers’ awareness that the recipient is an individual protected from discrimination under the Americans with Disabilities Act (ADA) [26]. Despite this, our findings revealed the implications of disclosure on workplace relationships and job security had a deleterious effect on FD. For instance, FD was associated with a lack of emotional support at work, reduced satisfaction with supervisor and co-worker relationships, feeling stigmatized, perceptions of being treated as incompetent or receipt of negative performance reviews following disclosure. Perceived diminished career prospects, pressure to resign, and reports of being passed up for promotion after disclosure were also associated with greater FD.

Finally, physical and psychological health status affected participants’ FD levels. Ability to work and concerns regarding disability after cancer diagnosis were associated with increased FD, as were stressful and physically demanding jobs. In particular, pain and mobility impairment were positively associated with FD. Additionally, higher levels of depression and anxiety increased FD; however, only depression had a significant effect. These findings are consistent with previous literature that demonstrate the effects of cancer on physical and mental work capacity were associated with financial burden from time off from work [3].

4.1 Practice implications

Within the healthcare system, opening communication regarding FD may prevent or minimize workplace problems and financial burden during and after cancer treatment. Oncology and rehabilitation providers are well suited to impart health information and workplace implications to patients and potentially employers. Such shared communication may serve to set health-related expectations at each treatment phase for the patient and their employer; identify relevant legislation and related workplace protections; and mobilize workplace benefits, accommodations, and support.

Financial advice and vocational support may improve financial security and quality of life following treatment among YA’s with cancer. The National Comprehensive Cancer Network’s guidelines [27] recognize financial and benefits counseling is an essential element of YA cancer care to address out-of-pocket medical expenses, avoid insurmountable medical debt, and address future financial needs. Referrals to State/Federal vocational rehabilitation agencies provide vocational services, postsecondary educational attainment support, and benefits counseling to people with disabilities may provide the specialized services and education YAs with cancer need at this financially distressing time in their lives. Since factors related job loss, diminished career opportunities, and stigmatization following disclosure of cancer to employers were strongly associated with financial distress, education on the protections afforded to cancer patients through the ADA Amendments Act of 2008 is needed. Young adults just emerging in the workforce may have a lack of awareness of their protections following onset of illness or disability, especially among those that were previously healthy.

Since material resources such as out-of-pocket expenses for cancer diagnosis and treatment, unpaid leave, and short-term disability were associated with FD, healthcare and rehabilitation providers may relieve FD by educating and referring YAs to the numerous not-for-profit and grant organizations providing financial support [28]. Such financial relief can alleviate distress associated with cancer-related financial problems and help survivors move forward with their lives.

Aspects of psychological and physical health were associated financial distress. Interventions that help YAs cope with the psychological and physical consequences of cancer may in turn ameliorate financial distress and improve occupational functioning. Rehabilitation professionals are particularly well equipped to support cancer patients with the psychological adjustment to disability and rehabilitation of physical functioning after cancer treatment. Referrals from healthcare providers to personal mental health counseling to improve coping after cancer diagnosis may be needed.

From a policy perspective, this research demonstrates financial distress is a significant concern among YAs with cancer and we submit labor and healthcare policy reform to address this need is warranted. For instance, those with paid leave were not significantly financially distressed, whereas, participants on unpaid leave or with short-term supplemental income were. Policies and work programs that protect the financial earnings of workers with cancer are needed. While healthcare coverage has increased among young adults in the United States since the Affordable Care Act (ACA), the cost for coverage continues to be a challenge to enrollment [29]. Post-ACA, young adults with cancer carry substantial medical debt and are more likely to be unemployed compared with cancer-free peers [30, 31]. This represents a critical risk to financial security and increases the financial burden of cancer treatment. Individuals with cancer who are uninsured are at risk for inconsistent cancer treatment and follow-up care, resulting in poor health and quality of life outcomes [2 , 33]. Given the substantial financial burden of cancer and improved survivorship rates, increasing access to health insurance and affordability for YA who may or may not be in the workforce is an urgent public health policy issue.

4.2 Limitations

The findings were based on participant self-reports, which may result in biases or inaccuracies. Participants were not randomly sampled; and thus, the responses of 214 conveniently accessible patients may not generalize to other YA cancer survivors. Labor policies vary across geographical jurisdictions. Study participants were from the United States, where various local and state laws, combined with federal laws, would have been relevant to participants’ work experiences. Public labor policy is all the more variable across different countries and may also limit generalizability of study results beyond the United States [34]. Participants were heterogeneous in regard to years of age represented (18 to 39), cancer type, and treatment phase. Sub-groups within the sample may have varying levels of risk for FD [35]. For example, emerging adults (age 18 to 25 years) and young adults (age 26 to 39 years) may have distinct financial, benefits, and vocational challenges. These analyses only showed associations; causation was not established.

A specific statistical limitation was that responses to individual items were analyzed independently, without adjustment for multiple testing. A simple Bonferroni adjustment to alpha as 0.05/46 = 0.0011 would be an improperly conservative compensation for multiple testing since it fails to account for the expected correlation among the responses to these questions [36]. Tukey-adjustment was utilized to compensate for multiple comparisons among levels of discrete variables for relevant questions. With alpha = 0.05, falsely significant results might be anticipated for approximately 2 of these questions (1 in 20), whereas significant associations were found for half of the questions analyzed.

5 Conclusion

YAs with cancer are especially vulnerable to FD, as their career trajectories are just beginning, and they have little accrued tenure or leverage in the wider labor market or in their specific workplaces. Among the YAs surveyed, FD was prevalent, and an array of variables were associated with FD. Material resources, psychological and physical health, and workplace variables were all significantly associated with FD. Oncology and rehabilitation providers should openly discuss finances with YAs with cancer and guide them to resources that can address their financial, benefits, and vocational needs to ultimately improve quality of life.

Ethics statement

This is an observational study. The Committee for the Protection of Human Subjects at the University of Texas Health Science Center at Houston has determined that this project (HSC-GEN-15-0657) qualifies for exempt status according to 45 CFR 46.101(b).

Informed consent

Informed consent was obtained from all individual participants included in the study.

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Footnotes

Acknowledgments

We thank Amy Ninetto, Scientific Editor, Research Medical Library, MD Anderson Cancer Center, for editing the manuscript and Rice University Neuroscience Undergraduate Research Assistant Andres Vasquez for reviewing the literature.

Funding

The contents of this article were developed under subcontract to SEDL and then the American Institutes for Research (AIR), from Southwest ADA Center (SWADA); SEDL merged with AIR in 2015. The SWADA is a program of Independent Living Research Utilization, at TIRR Memorial Hermann in Houston, Texas. The SWADA work was funded by grants (Nos. H133A060091 and H133A110027) from the Department of Education’s National Institute on Disability and Rehabilitation Research and then by grant numbers 90DP0022 and 90DP0092 from the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR). NIDILRR is a Center within the Administration for Community Living (ACL), Department of Health and Human Services (HHS). The contents of this presentation do not necessarily represent the policies of NIDILRR, ACL, and HHS, and you should not assume endorsement by the Federal Government. The statistical analysis work was supported in part by Cancer Center Support Grant P30 CA016672-44 from the National Institutes of Health/National Cancer Institute.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Author contributions

RR: conceptualization, writing. ACR: formal analysis and visualization (lead), writing – review & editing (equal). MKM: conceptualization, funding acquisition, methodology, investigation, project administration, software, supervision, writing – review & editing. GTA: conceptualization, writing, editing. SMC: methodology, data curation, investigation. MGSA: investigation, writing. NV: conceptualization (supporting), supervision (lead), writing – review & editing (supporting). SK: methodology, investigation. FLM: conceptualization (supporting), supervision (lead), writing – review & editing (supporting). All authors read and approved the final manuscript.

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