Economic Impact of Advanced Pediatric Cancer on Families

Abstract

Context

Despite emerging evidence of substantial financial distress in families of children with complex illness, little is known about economic hardship in families of children with advanced cancer.

Objectives

To describe perceived financial hardship, work disruptions, income losses and associated economic impact in families of children with advanced cancer stratified by federal poverty level (FPL).

Methods

This is a cross-sectional survey of 86 parents of children with progressive, recurrent or non-responsive cancer at three children’s hospitals. Seventy-one families with complete income data (82%) are included in this analysis.

Results

Parental work disruptions were prevalent across all income levels, with 67 (94%) families reporting some disruption. At least one parent quit a job because of the child’s illness in 29 (42%) families. Nineteen (27%) families described their child’s illness as a great economic hardship. Income losses due to work disruptions were substantial for all families; families at or below 200% FPL, however, were disproportionately affected. Six (50%) of the poorest families lost more than 40% of their annual income as compared with two (5%) of the wealthiest families (P=0.006). As a result of income losses, nine (15%) previously non-poor families fell from above to below the 200% FPL.

Conclusion

The economic impact of pediatric advanced cancer on families is significant at all income levels, although poorer families suffer disproportionate losses. Development of ameliorative intervention strategies is warranted.

Introduction

Approximately 12,000 U.S. children will be diagnosed with cancer this year and 17% will die of their disease.^1–3 At any one time, thousands of children are living with advanced cancer. Recent data are elucidating the economic impact of pediatric complex chronic illness on families in broad strokes, yet none have focused on the experience of financial burden in families of children with advanced cancer.^4–6

Pediatric palliative care seeks to enhance quality of life and minimize suffering through the provision of “competent, compassionate, and consistent care to children with chronic, complex, and/or life-threatening conditions and their families.”⁷ As efforts to expand access to pediatric palliative care have grown, increasing attention is being paid to the diverse cohort of children with special health care needs (CSHCN)—within which children with cancer represent an important subset—who account for a significant proportion of those cared for by pediatric palliative care services.⁸ These children, who by definition suffer from “chronic physical, developmental, behavioral or emotional” conditions that necessitate health and related services of a type or amount beyond that required by children in general, present the unique challenge of chronic illness management in addition to pain and symptom management.^7–9 As pediatric palliative care providers aim to ameliorate suffering in this population, emerging evidence suggests a need for increased attention to a non-physical source of distress in these families—economic hardship.

National efforts to better understand patterns of utilization, and identify families at highest risk for financial burden, have been bolstered by policy initiatives—including Healthy People 2010—focused on optimizing care for CSHCN.¹⁰ Studies have reported that up to 40% of CSHCN families experience financial burden secondary to their child’s illness, and 25% experience work disruptions, including 13% who need to quit a job.^11,12 Child characteristics, including younger age, condition instability, and increased functional limitations, have been identified as risk factors for financial hardship.^11,12 Families of CSHCN with lower socioeconomic status (SES) or without health insurance are at higher risk of financial hardship, highlighting disparities in the distribution of burden.^11–14

Pediatric cancer patients have been identified as children who utilize a disproportionate amount of health resources.^8,15,16 In 2009, hospitalizations for children with cancer were eight days longer and cost five times more than hospitalizations for other pediatric conditions.¹⁷ Cross-sectional and retrospective studies of pediatric cancer families recapitulate risk factors and financial outcomes for less complicated CSHCN families. New diagnosis, receipt of care far from home, and prolonged hospitalizations have been identified as risk factors for financial burden, and poorer pediatric oncology families suffer disproportionate burden.^5,6,18–20 Financial impact far in excess of the general CSHCN population has been described, including 68–74% of pediatric oncology families with financial burden secondary to illness,^5,20 and a full 64–85% with work disruptions, including 35–45% needing to quit a job.^4–6

Limitations of this emerging literature include inadequate data on economic consequences to families of children with advanced cancer, which we hypothesize would be even greater given the prolonged and intensive illness experience. In this study, we aimed to describe patterns of financial impact experienced by families of children living with advanced cancer, as reported by parents during their child’s illness. We describe economic outcomes—perceived financial hardship, work disruptions, and income losses secondary to a child’s illness—stratified by family poverty level at three pediatric hospitals.

Methods

Data for this analysis come from the cross-sectional baseline Survey about Caring for Children with Cancer (SCCC), part of the Pediatric Quality of Life and Evaluation of Symptoms Technology (PediQUEST) Study, a prospective, randomized, controlled trial conducted in a population of children living with advanced cancer, which was approved by the Dana-Farber Cancer Institute Institutional Review Board. This study assessed the effects of providing families and providers with child-reported symptoms and quality of life collected using a computerized system. Detailed descriptions of the SCCC methods have been published previously.^21,22

Study Population

Children participating in the PediQUEST Study were at least two years of age, had at least a two-week history of progressive, recurrent, or non-responsive cancer, and were receiving cancer care at one of three centers: Dana-Farber Children’s Hospital Cancer Center (DFCHCC), Children’s Hospital of Philadelphia (CHOP), or Seattle Children’s Hospital (SCH). Eligible parents/legal guardians had a written command of English and the ability to complete self-administered surveys. All parents/legal guardians of enrolled children were mailed or handed the SCCC at the time of child enrollment with a self-addressed, stamped return envelope. Parents who did not respond received two bi-weekly reminders, either by phone or face-to-face. From December 2004 to June 2009, the study enrolled 104 children, and 86 parents completed the SCCC (71% consent rate). Of these, 71 families (82%) had complete income data and were included in this analysis.

Survey Instrument

The SCCC evaluates parental perceptions about a child’s illness across multiple domains. Parents report on current treatment (perceived prognosis, treatment goals, and suffering from treatment or illness); care received (type of care, quality and sensitivity of care, team work); and emotional and financial impact of illness. The SCCC additionally collects parent-reported sociodemographic information. Child’s age, gender, and diagnosis were abstracted from medical records.

This analysis focused on domains of family financial impact secondary to child illness including the following survey items: 1) “Which of these categories best describes your total combined family income for the past 12 months?” Eleven response options were allowed: less than $5000, $5000 through $11,999; $12,000 through $15,999; $16,000 through $24,999; $25,000 through $34,999; $35,000 through $49,999; $50,000 through $74,999; $75,000 through $99,999; $100,000 and greater; Don’t Know; No Response. 2) “How much of an economic or financial hardship has your child’s illness been for you and your family?” Ordinal four-category responses were allowed (great, moderate, little, or no economic hardship). 3) “During the course of your child’s illness, has anyone in the family had to consume benefits, cut back on work/quit work, or forego overtime to provide personal care for your child? Please specify who and in what way?” 4) “About how much yearly income has your family lost by quitting or cutting back on work?” Eight response options were allowed: none; less than $1000; $1000 to almost $5000; $5000 to almost $10,000; $10,000 to almost $20,000; $20,000 to almost $30,000; $30,000 to almost $50,000; and more than $50,000. 5) “Have you or another person in the family had to forego making a big purchase like furniture or a car to help pay for your child’s medical care?” 6) “Have you or another person in the family had to sell personal property like a house or car, take out a loan or mortgage, or incur credit card debt to pay for your child’s medical care? If so, please specify all that apply.” 7) “Have there been any fundraising efforts on your child’s behalf?” Pertinent family sociodemographic information and child disease information collected via survey and chart review were utilized for analysis.

Operational Definition of Variables

Family Economic Variables

To allow calculations with annual family income and income loss, we converted categorical ranges into discrete values by assigning each category to its midpoint value. To limit the weight of the highest category, we conservatively set maximum limits of $101,000 for income and $51,000 for income loss. These discrete income values were used in all subsequent analyses.

1. Adjusted annual family income as a percent of federal poverty level (FPL). Given concern that families may have a priori deducted income loss secondary to work disruptions when reporting their income, we chose to conservatively adjust family income by adding annual income loss to reported income, with a maximum limit of $152,000. This adjusted income was used for all subsequent analyses. Adjusted income was transformed into a percentage of FPL for the year the survey was administered and stratified into three levels (≤200%, 200–400%, >400%). Year specific FPLs were based on the Department of Health and Human Services Poverty Guidelines.²³

2. Percent annual income lost was estimated by dividing income loss by adjusted income and stratified as follows: ≤10%, 11% to 40%, and >40%. These cut points were determined prior to data analysis in accord with previous publications⁶ and correspond to levels of income consumed by health expenditures that are likely to push households into poverty, namely catastrophic expenditures.^24,25

3. Family financial hardship. This 4-item ordinal scale, previously published in the pediatric oncology and palliative care literature, was collapsed into three categories (great, moderate, and no or a little economic hardship).⁶

Child and Family Sociodemographic Variables

Parent education was dichotomized to “high school education or less” versus all others. Marital status was dichotomized to “two-parent households” versus “single-parent households.”

Statistical Methods

To test whether the economic impact of cancer therapy or financial coping strategies varied across income levels, we used Fisher’s exact test for categorical variables, and analysis of variance (ANOVA) or the Kruskal-Wallis test for continuous variables. Analyses were performed using SAS v.9.2 for Windows statistical software (SAS Institute Inc., Cary, NC).

Results

Family, Child and Parent Characteristics

Main characteristics of the 71 families with complete financial data are presented in Table 1. Families were predominantly English-speaking, two-parent households. The median household size was four. Parents averaged 43 years of age and were well-educated. Patients were evenly divided by gender (56% female), had a mean age of 11.7 years, and were predominantly non-Hispanic white based on parent reports. Brain tumors were underrepresented in our cohort (10%) as compared with an expected 20% for children less than 15 years of age with cancer.²⁶ Eighty-nine percent of children were receiving cancer-directed therapy at the time of the survey. Children in this cohort spent a median of 10 days in the hospital and 12 days in clinic over the preceding three months. The 15 families excluded from the analysis for missing income data were similar to the 71 analyzed in all characteristics except for age (mean±SD child age of the excluded group was 14.1±6 years), which did not reach statistical significance.

Table 1.

Family, Parent, and Child Characteristics by Federal Poverty Level

	Adjusted Annual Family Income as %FPLa,b			Total Nc=71 (%)	P-value
	≤200 Nc=12 n (%)	201–400 Nc=19 n (%)	>400 Nc=40 n (%)
Family Characteristics
English as primary language	11 (92)	18 (95)	40 (100)	69 (97)	0.19 d
Two-parent households (n=70)	8/11 (73)	18 (95)	35 (88)	61/70 (87)	0.21 d
Household size, median (p25, p75)	4.5 (3, 6)	5 (4, 5)	4 (3.5, 5)	4 (3,5)	0.35 e
Parental Characteristics
Age in years, mean±SD	40.5±11.6	45.5±8.7	43.1±5.2	43.4±7.5	0.21 f
High school education or less (n=66)	5/9 (56)	11 (58)	6/38 (16)	22/66 (33)	0.002 d
Child Characteristics
Female	5 (42)	10 (53)	25 (63)	40 (56)	0.41 g
Age, mean±SD	10.7±4.5	11.6±6.9	12.1±5.4	11.7±5.7	0.75 f
Race/Ethnicity
White, non-Hispanic	7 (58)	18 (95)	35/39 (90)	60/70 (86)
Black, non-Hispanic	4 (33)	0 (0)	2/39 (5)	6/70 (9)	0.02 d
Other	1 (8)	1 (5)	2/39 (5)	4/70 (6)
Disease and Care Characteristics
Diagnosis
Hematologic	5 (42)	5 (26)	12 (30)	22 (31)	0.83 d
Solid tumor	6 (50)	13 (68)	23 (56)	42 (59)
Brain tumor	1 (8)	1 (5)	5 (13)	7 (10)
Days since diagnosis, median (p25, p75)	697 (295, 1080)	624 (270, 955)	604 (365, 1057)	624 (336, 991)	0.88 e
Receiving cancer-directed therapy	10 (83)	18 (95)	35 (88)	63 (89)	0.59 d
Hospital days in last 3 months, median (p25, p75)	6 (0, 20)	10 (0, 20)	10 (1, 26)	10 (0,20)	0.82 e
Clinic visits in last 3 months, median (p25, p75)	12 (10, 12)	6.5 (5, 12)	12 (8, 15)	12 (7,14)	0.05 e

SD=standard deviation; FPL=Federal Poverty Level; p25=25^th percentile; p75=75^th percentile.

^aReported income was conservatively adjusted by calculating the midpoint for each category and adding the midpoint of reported lost income to derive adjusted baseline income. See Methods.

^bAdjusted annual family income was divided by the appropriate poverty guideline for household size and multiplied by 100 to achieve percentage of Federal Poverty Level.

^cDenominator indicated if warranted.

^dFisher’s exact test.

^eKruskal-Wallis test.

^fANOVA.

^gChi-squared test.

Forty (56%) families reported incomes >400% FPL (equivalent to a household income of $88,200 for a family of four in 2009), which is an overrepresentation compared with the expected 34–38% in their states of origin.²⁷ Congruent with this finding, poor families were underrepresented, with 12 (17%) families reporting baseline incomes at or below 200% FPL (equivalent to a household income of $44,100 for a family of four in 2009) compared with the expected 32–36% for their states of origin.²⁷

The distribution of sociodemographic and disease factors stratified by family income is also presented in Table 1. Congruent with U.S. national trends, children of minority race/ethnicity were more likely to be poor (P=0.02), and poorer parents were less educated (P=0.008).²⁸ There were no other significant differences in child, disease, or family characteristics across poverty levels. The distribution of family poverty levels did not vary by site of enrollment.

Financial Impact and Coping Strategies by Poverty Level

More than a quarter (28%) of parents reported their child’s illness has been a great economic hardship for the family (Table 2). Poor families were more likely to report great hardship (50%) than wealthy families (21%), although this did not reach significance (P=0.21). Work disruptions were ubiquitous across all poverty levels, with 94% of parents reporting they either cut back on hours, quit a job, or sacrificed overtime. In 42% of families, one or both parents quit a job because of their child’s illness (Table 2).

Table 2.

Financial Impact of Child’s Illness by Family Poverty Level

	Adjusted Annual Family Income as %FPL a,b			Total Nc=71 n (%)	P-value e
	≤200 Nc=12 n (%)	201–400 Nc =19 n (%)	>400 Nc =40 n (%)
Family Financial Outcomes (N=71)
Financial hardship					0.21
None or A little	1 (8)	5 (26)	15/38 (39)	21/69 (30)
Moderate	5 (42)	9 (47)	15/38 (39)	29/69 (42)
A great deal	6 (50)	5 (26)	8/38 (21)	19/69 (28)
Work Disruptions
Any caregiver work disruptiond	10 (83)	18 (95)	39 (98)	67 (94)	0.13
One or more parent quit a job	7/11 (64)	6/18 (33)	16 (40)	29/69 (42)	0.25
Mom alone quit job	5/11 (45)	5/18 (28)	13 (33)	23/69 (33)
Dad alone quit job	1/11 (9)	0/18 (0)	3 (8)	4/69 (6)
Both parents quit a job	1/11 (9)	1/18 (6)	0 (0)	2/69 (3)

FPL=Federal Poverty Level.

^aReported income was conservatively adjusted by calculating the midpoint for each category and adding the midpoint of reported lost income to derive adjusted baseline income. See Methods.

^bAdjusted annual family income was divided by the appropriate poverty guideline for household size and multiplied by 100 to achieve percentage of Federal Poverty Level.

^cDenominator indicated if warranted.

^dAny caregiver work disruptions include: cut back on work hours, quit a job, forgo overtime.

^eFisher’s exact test.

Median income loss in the cohort was 20%. Fourteen percent of families lost more than 40% of their annual income secondary to work disruptions, and poor families were most likely to suffer this impact (P=0.006) as illustrated in Fig. 1. After accounting for income losses resulting form work disruptions, nine previously non-poor families (15%) dropped from above to at or below 200% FPL. Notably, no family with a baseline household income >400% FPL fell below 200% of poverty.

Figure 1.

Level of family income loss stratified by family Federal Poverty Level (FPL). Adjusted annual family income was divided by the appropriate poverty guideline for household size and multiplied by 100 to achieve percentage of FPL. Comparison of level of family income loss across FPL, P=0.006 by Fisher’s exact test.

Complete data on coping strategies were available for 67 families (Table 3). Families from all poverty levels utilized similar coping strategies to pay for medical care and these included 40% who reduced expenses by avoiding big purchases, 40% who incurred debt (27% credit card, 10% personal loan, 3% additional mortgage), and 4% who lost capital by selling property. Fifty-one percent of families utilized fundraising to pay for medical care. Although not significant, poorer families tended to use this coping strategy less frequently.

Table 3.

Coping Strategies to Pay for Child’s Medical Care Stratified by Federal Poverty Level

	Adjusted Annual Family Income as %FPL a,b			Total Nc=71 n (%)	P-value d
	≤200 Nc=12 n (%)	201–400 Nc =19 n (%)	>400 Nc =40 n (%)
Coping Strategies
Forgo making a big purchase	4/11 (36)	9 (47)	14/37 (38)	27/67 (40)	0.79
Sold property	0/10 (0)	1 (5)	2/38 (5)	3/67 (4)	1
Took additional mortgage	1/10 (10)	1 (5)	0/38 (0)	2/67 (3)	0.18
Incurred credit card debt	2/10 (20)	5 (26)	11/38 (29)	18/67 (27)	0.93
Took out a loan	1/10 (10)	2 (11)	4/38 (11)	7/67 (10)	1
Fundraising	4/11 (36)	11 (58)	20/38 (52)	35/68 (51)	0.57

FPL=Federal Poverty Level

^aReported income was conservatively adjusted by calculating the midpoint for each category and adding the midpoint of reported lost income to derive adjusted baseline income. See Methods.

^bAdjusted annual family income was divided by the appropriate poverty guideline for household size and multiplied by 100 to achieve percentage of Federal Poverty Level.

^cDenominator indicated if warranted.

^dFisher’s exact test.

Discussion

This study builds upon previously published data demonstrating that the economic impact of pediatric cancer on families is significant, and provides a first dedicated look at the distribution of impact across family poverty levels.^4–6,20 Further, this study does so in a cohort of children with advanced cancer, a population underrepresented in the literature. Our findings are striking. The level of burden experienced by all families of children with advanced cancer—regardless of baseline family finances—is extraordinary. Families already struggling with their child’s progressive or relapsed cancer are bearing an economic burden above and beyond what most Americans would deem manageable. Despite the high prevalence of financial impact, the distribution of the burden is inequitable, with poorer families reporting disproportionate income losses. Our data suggest an urgent need for increased attention to economic outcomes in the palliative care setting as a potential targetable source of suffering.

Although economic burden may be a universal truth in complex chronic illness, our families reported financial burdens two to three times greater than those reported in the CSHCN literature.^11,12,14 Two-thirds of families reported a moderate or great amount of economic hardship. A remarkable 94% of families reported work disruptions secondary to a child’s illness. This finding is nearly four times greater than that reported in the U.S. National Survey of CSHCN.¹⁰ Even when compared with the most severely affected CSHCN (that is, those families with children who are usually or always affected by their condition, or those who rank the severity of their child’s condition as 10 out of 10), families of children with advanced cancer reported a 20–30% higher impact.¹¹ The existing data do not provide a clear reason for this greater impact. Advanced cancer may impose on families a longer duration of sustained, high-intensity treatment than other chronic illness with consequent higher income losses. The cyclic acuity of cancer therapy—including initial diagnosis and intensive treatment followed by potential return to health before relapse—may increase parental work disruptions and decrease parental employability. Alternatively, differential parental success in accessing economic resource supports could explain the increased financial impact. Families of children with complex chronic illness diagnosed at birth may be guided to enroll in supportive services (such as Supplemental Security Income) early in their child’s life in anticipation of the life-long need for financial supports, whereas oncology families may attempt to weather the storm of therapy without such supports secondary to anticipated cure and return to normalcy. Finally, greater emotional and psychological burdens (such as depression or anxiety) because of the likely fatality of advanced cancer may adversely affect parental ability to maintain employment or cope with economic challenges. Elucidation of the mechanistic pathway underlying this increased burden is necessary. Further, these data suggest a need to devise unique intervention strategies for pediatric oncology families within the broader population of children with life-threatening illness.

Whereas financial burden in our sample population was widespread, with equivalent amounts of work disruption and job loss across income levels, poor families suffered income losses far in excess of their wealthier peers. Half of poor families reported catastrophic income losses. These findings echo data in the CSHCN literature, and may reflect realities of less flexible working conditions.^10,13,14 Our data do not allow us to examine whether these most vulnerable families are accessing potential safety-nets. Eligibility for governmental supports with the potential to limit out-of-pocket expenses (such as food stamps, discounted utilities, or Medicaid) vary significantly from state to state, with most including families at or below 133% FPL. Although some of the poorest families in our cohort may have been eligible for these resources, whether they attempted to access them or were successful in doing so is unclear. Regardless, the existence of these economic safety-nets does not appear to be sufficient.

Families used a variety of financial coping strategies. The fact that as many as four of 10 families incurred debt to pay for their child’s care is striking. No clear differential patterns of coping by income level emerge from our data. Poorer families were no more likely to implement fundraising than their wealthier peers, although their disproportionate income losses would suggest they might benefit more from this coping mechanism. Whether the economic burdens described by our data impact child health outcomes in the pediatric cancer population is unknown. One can readily imagine that families who lose half of their income during a child’s treatment may face cost-shifting choices—for example, the need to prioritize food over home heating, or eschew medical care for other family members—with potential implications for child health. A more granular understanding of financial trade-offs made by pediatric oncology families is needed to elucidate potential pathways of impact on child suffering.

Our study has a number of limitations. Although this is the largest study among children with advanced cancer, our numbers likely limited our ability to detect statistical significance for some variables. That poor families were underrepresented in our sample may be secondary to exclusion of non-English speaking families. Further, that racial and ethnic minorities were underrepresented in our sample as compared with the U.S. population limits our ability to examine the role of race as a covariate in this study. Our data are cross-sectional in nature and do not allow us to draw conclusions about financial impact over time. Additionally, although this survey was conducted during the course of children’s illnesses, we cannot exclude the possibility of parental recall bias or intentional skewing of income reports. This said, prior studies of self-reported survey income have demonstrated very low (2–6%) rates of inaccurate wage and income reporting, generally leading to small (1%) net underreporting.²⁹

Our analyses are limited by the inherent imprecision in the collected data, namely family income reported in ranges. Although our decision to convert these range incomes to discrete midpoint values allows for data analysis, it results in imprecision in reporting of all outcomes including family income as percent of FPL and percent annual income lost. This limitation must be taken into consideration when interpreting our results; however, the value of broadly describing financial impact in this under-researched population remains. Finally, in our effort to conservatively account for potential parental reporting bias (i.e., a priori deduction of income loss when reporting baseline income), we chose to utilize adjusted annual income (lost income plus annual income) for all analyses. Sensitivity analyses conducted utilizing unadjusted income data suggest that we may have underestimated the percent income lost as well as the percent of families in poverty. Our data include information on work disruptions and income losses, but no information on the cost-shifting outcomes that result from these losses. Our analysis is based on family reported income but would have been bolstered by insurance data, which were not available. Similarly, distance from hospital has been identified as a predictor of financial hardship in other publications; however, we did not have access to this variable and thus were unable to assess its impact in our study population. That our patient sample was drawn from three tertiary care pediatric centers with multidisciplinary care teams limits the generalizability of our results. Pediatric cancer is a rare diagnosis, however, and the majority of children receive care at referral centers.

Conclusion

Advanced childhood cancer takes a disproportionate toll on family economics when compared with other children with complex chronic conditions. The extreme financial burden experienced by these families underscores the urgent need for prospective research into the longitudinal patterns of financial impact and the mechanisms underlying adverse economic outcomes. More important, clinical and policy interventions to ameliorate this devastating economic impact need to be developed.