Rising costs of cancer care have led to increased concerns about medical financial hardship for cancer survivors and their families in the United States. In this commentary, we provide an overview of research describing medical financial hardship and introduce a conceptual framework for identifying risk factors and research gaps at the patient and family, provider and care team, health care system, employer, and state and national policy levels. We then use this framework to highlight measurement and data infrastructure gaps related to hardship, summarize existing interventions to minimize hardship, and identify opportunities for future intervention efforts.

In 2010, there were approximately 13.8 million cancer survivors in the United States (1). The prevalence of cancer survivorship is projected to increase by more than 31% by 2020, reflecting an aging and growing population and earlier detection of cancer and improved treatments, resulting in longer survival following diagnosis. Based only on increased prevalence of cancer survivorship, the costs associated with cancer are projected to increase 27% between 2010 and 2020, from $124.6 billion to $157.8 billion (1). However, in recent years, the costs of cancer treatment have increased dramatically (2–4), and many treatments now have price tags of $100,000 or more annually (5, 6). Treatment intensity has also increased, and cancer patients are now treated with more agents and for longer periods of time (7, 8). Thus, earlier projections likely understate the health care costs associated with cancer for payers as well as for patients and their families.

Historically, cancer survivors have higher out-of-pocket (OOP) health care spending than similar individuals without a cancer history, even many years after diagnosis or the completion of treatment (9–11). They have also been more likely than individuals without a cancer history to report inability to work, more days lost from work, and more days spent in bed (9, 11). Cancer survivors may experience lost income from these productivity limitations and reduced access to employer-sponsored health insurance. Other recent trends, including increasing patient cost-sharing, with higher deductibles, copayments, and coinsurance rates (12, 13), and increased use of oral anticancer therapies (2, 3), for which patient OOP costs may be higher than for infused anticancer therapies, can increase patient burdens. Thus, cancer patients and their families are increasingly experiencing medical financial hardship associated with cancer, including problems paying medical bills, financial distress, and delaying or forgoing medical care due to costs (14–17).

In this commentary, we provide an overview of research describing medical financial hardship in cancer survivors and introduce a conceptual framework for identifying risk factors and research gaps. We then use this framework to highlight measurement and data infrastructure gaps related to hardship; summarize existing interventions to minimize hardship; and identify opportunities for future intervention efforts.

In the past several years, published research addressing medical financial hardship has increased substantially (14–16). These studies generally address one or more domains of hardship including material conditions, psychologic response, and coping behaviors (14). Material conditions are typically measured as OOP expenses for medical costs, productivity losses, reduction in income and assets, medical debt, trouble paying medical bills, and bankruptcy (14). The majority of studies of financial hardship in cancer survivors conducted to date evaluate material measures (14–16). As many as half of adult cancer survivors report some form of material financial hardship (14). Psychological response is typically measured as stress, distress, and worry resulting from paying medical bills or concerns about wages and wage loss associated with cancer. As many as 64% of cancer survivors report some form of psychologic financial hardship (14). Coping behaviors that survivors adopt in the face of increased OOP medical expenditures and distress are generally measured as delaying or forgoing medical care due to cost, and cost-related nonadherence to prescription medications for cancer and other conditions. Up to 45% of cancer survivors report some forms of behavioral financial hardship (14).

In published reviews, estimates of the prevalence of these domains of financial hardship vary widely and likely reflect underlying differences in study populations’ sociodemographic characteristics, including age, gender, race/ethnicity, educational attainment, marital status, income, geographic location, and health insurance coverage; clinical characteristics, including cancer type, stage of disease at diagnosis, comorbidity, treatment(s), time since diagnosis, and/or most recent treatment; methods of identifying cancer survivors; and measures of medical financial hardship evaluated (14–16).

In this section, we describe a conceptual framework for summarizing risk factors associated with medical financial hardship and identifying gaps in the current descriptive research literature that are needed to inform development of interventions to minimize hardship (Fig. 1). The framework has multiple hierarchical levels starting with cancer survivors and their families and caregivers in the center, surrounded by provider and provider care teams, health care systems, and state and national policy levels. We also include an employer level in the framework, because health insurance is mainly employer-based for the working age population, and paid and unpaid sick leave policies vary by employer. The employer level also intersects with all other levels in our conceptual framework because many large employers self-insure or select health insurance plans and provider networks, which are regulated by state and national policies.

Figure 1.

Factors at multiple levels associated with medical financial hardship. This figure contains a conceptual framework with multiple hierarchical levels starting with cancer survivors and their families in the center, surrounded by provider and provider care teams, health care systems, and state and national policy levels. An employer level is also in the framework and intersects with all other levels.

Figure 1.

Factors at multiple levels associated with medical financial hardship. This figure contains a conceptual framework with multiple hierarchical levels starting with cancer survivors and their families in the center, surrounded by provider and provider care teams, health care systems, and state and national policy levels. An employer level is also in the framework and intersects with all other levels.

Close modal

Patient and family-level risk factors for medical financial hardship

At the patient and family level, socioeconomic characteristics associated with greater risk of financial hardship include lower household income and/or educational attainment (18–21), minority race/ethnicity (18, 19, 22–25), rural residence (26), and being unmarried or female in some populations (18, 23, 24, 27). Cancer and its treatment can limit employment (9, 11, 28, 29), resulting in lost wages and loss of employment-based health insurance, which can also increase the risk of financial hardship (18, 27). Among cancer survivors, younger age is associated with greater risk of hardship (18, 22, 27, 30, 31), with large differences between those ages 18 to 64 years and those ages 65 years and older who are age-eligible for Medicare coverage. In the younger group, uninsured cancer survivors report greater levels of financial hardship than their counterparts with private or public health insurance (18, 19, 27). Health insurance literacy—understanding of and ability to use information about premiums, deductibles, copayments, coinsurance, and provider networks in decision-making—is associated with selection and enrollment in health insurance plans (32), and so, also may be associated with financial hardship.

Clinical factors associated with hardship include cancer site (23, 33, 34) and type of treatment, with higher levels of hardship among survivors who received chemotherapy or radiotherapy (19, 23). More recent treatment and/or diagnosis is also associated with greater hardship (14–16). Other clinical factors associated with hardship include presence of comorbidity (18, 19) and more advanced stage at diagnosis (19, 33).

Although research addressing risk factors for financial hardship at the patient level is accumulating (14–16), there are a number of research gaps. Better understanding of the relative importance of risk factors for financial hardship, including socioeconomic characteristics, employer accommodations, health insurance coverage and benefit design, health insurance literacy, and clinical factors, is an important research gap at the patient and family level (Table 1). Informal caregivers, including spouses and partners, sibling, parents, and children, also experience productivity losses and financial hardship (35), but little research has addressed risk factors for hardship in caregivers. Caregiver burden is especially relevant for pediatric cancer patients and survivors (36). In addition, cancer patients and their families may face other nonmedical aspects of financial hardship, including food and housing insecurity.

Table 1.

Examples of research questions addressing gaps in understanding of medical financial hardship in cancer survivors

Level of frameworkExample questions
Patient and family What are the strongest risk factors for financial hardship in patients and caregivers (e.g., socioeconomic characteristics, employer accommodations, health insurance coverage and benefit design, health insurance literacy, and clinical factors)? Which risk factors for financial hardship are potentially modifiable and thus could be addressed through intervention? What are the relationships between medical financial hardship and nonmedical aspects of financial hardship, such as food and housing insecurity? What are the associations between medical and nonmedical hardship and health outcomes? What is the trajectory of financial hardship following diagnosis for patients and caregivers? Does the trajectory vary for material, psychologic, and behavioral hardship? 
Provider and care team What are key provider and care team factors associated with higher prevalence of patient financial hardship (e.g., age, year of medical school graduation, training and specialty, geographic region, and practice setting)? Is EHR integration and use of system supports in practice associated with financial hardship? What are key barriers to full discussions of treatment costs, benefits, and effects on ability to maintain employment? Does perception of importance of financial hardship and referral to resources differ by provider specialty, training, patient volume, and other factors? Who is the best member of the provider team to discuss financial hardship? Is having a designated team member address treatment costs in practice associated with reduced prevalence of hardship? 
Health care system What are key health care system features associated with higher prevalence of patient financial hardship (e.g., breadth and depth of provider networks, benefit design, routine use of EHR functionality in quality improvement, and financial assistance infrastructure)? What aspects of health insurance benefit design are most important? What is the role of transparency of treatment cost and benefit in minimizing financial hardship? Is financial assistance infrastructure associated with reduced prevalence of hardship? 
Employer How does the prevalence of financial hardship among cancer survivors vary by employer features (e.g., health insurance coverage, paid and unpaid sick leave, workplace accommodations)? What is the role of benefit design for self-insured employers in development of financial hardship? 
State and national policy How do state-level policies, such as Medicaid eligibility threshold and expansion status, affect financial hardship? How do policies related to generic substitution, cost transparency, and oral parity affect financial hardship? Do policies related to availability of Marketplace coverage, essential health benefits, or elimination pre-existing coverage exclusions affect hardship? 
Level of frameworkExample questions
Patient and family What are the strongest risk factors for financial hardship in patients and caregivers (e.g., socioeconomic characteristics, employer accommodations, health insurance coverage and benefit design, health insurance literacy, and clinical factors)? Which risk factors for financial hardship are potentially modifiable and thus could be addressed through intervention? What are the relationships between medical financial hardship and nonmedical aspects of financial hardship, such as food and housing insecurity? What are the associations between medical and nonmedical hardship and health outcomes? What is the trajectory of financial hardship following diagnosis for patients and caregivers? Does the trajectory vary for material, psychologic, and behavioral hardship? 
Provider and care team What are key provider and care team factors associated with higher prevalence of patient financial hardship (e.g., age, year of medical school graduation, training and specialty, geographic region, and practice setting)? Is EHR integration and use of system supports in practice associated with financial hardship? What are key barriers to full discussions of treatment costs, benefits, and effects on ability to maintain employment? Does perception of importance of financial hardship and referral to resources differ by provider specialty, training, patient volume, and other factors? Who is the best member of the provider team to discuss financial hardship? Is having a designated team member address treatment costs in practice associated with reduced prevalence of hardship? 
Health care system What are key health care system features associated with higher prevalence of patient financial hardship (e.g., breadth and depth of provider networks, benefit design, routine use of EHR functionality in quality improvement, and financial assistance infrastructure)? What aspects of health insurance benefit design are most important? What is the role of transparency of treatment cost and benefit in minimizing financial hardship? Is financial assistance infrastructure associated with reduced prevalence of hardship? 
Employer How does the prevalence of financial hardship among cancer survivors vary by employer features (e.g., health insurance coverage, paid and unpaid sick leave, workplace accommodations)? What is the role of benefit design for self-insured employers in development of financial hardship? 
State and national policy How do state-level policies, such as Medicaid eligibility threshold and expansion status, affect financial hardship? How do policies related to generic substitution, cost transparency, and oral parity affect financial hardship? Do policies related to availability of Marketplace coverage, essential health benefits, or elimination pre-existing coverage exclusions affect hardship? 

Most published research addressing factors associated with financial hardship at the patient and family level is cross-sectional, and few longitudinal studies have addressed the trajectory of financial hardship and health outcomes following diagnosis for patients and caregivers or whether these trajectories vary for material, psychologic, and behavioral domains of hardship. In one of the few longitudinal studies, cancer survivors who filed for bankruptcy were at increased risk of mortality compared with similar cancer survivors who did not file for bankruptcy (33). However, little is known about the relationship between biological and behavioral risk factors and patient and caregiver outcomes, including health-related quality of life and survival. Finally, longitudinal observational research may lead to better understanding of not only which risk factors are most important and potentially modifiable, but also where in the cancer care continuum they may be most amenable to intervention, and the most feasible and efficient intervention approach.

Provider and care team risk factors for medical financial hardship

Little research evaluating provider factors associated with financial hardship has been conducted, and as a result, many research gaps exist. Other health services research has shown that provider characteristics, such as age (37, 38), year of medical school graduation (39, 40), training and specialty (37, 41), and practice characteristics, such as geographic region (42) and practice setting (41), are associated with treatment recommendations and patient outcomes. Presence of electronic health records (EHR) and systems strategies in a practice, including reminders and decision support, are associated with guideline-consistent care in other settings (43). Research evaluating whether these provider and practice characteristics, EHR and systems strategies, and perceptions are associated with recommendation of high-cost treatment and/or addressing financial hardship is a critical research gap. The Institute of Medicine and the American Society of Clinical Oncology have highlighted the important role of oncologists in discussions about the OOP costs of cancer care (44, 45). Although oncologists generally agree about their responsibility for these discussions (46), they are rare (46) and many oncologists feel uncomfortable engaging in them (46, 47). When asked about their attitudes regarding discussions of costs, more than 50% of patients desired discussions, but only about 33% actually had them (46). Insufficient physician time has been identified as a barrier to recommended care in other settings (48). Lack of cost transparency and provider financial knowledge may be additional barriers to cost discussions. Descriptive research on important and modifiable factors for incomplete discussions, such as identifying the member of the care team best suited for these discussions, if not the oncologist, will inform future intervention research.

Health care system–level risk factors for medical financial hardship

Little research on risk factors for financial hardship has been conducted at the organizational levels of health care systems. Research addressing other aspects of health systems and insurers has shown that benefit design at this level, such as the use of prescription drug formularies, step therapy, and patient cost-sharing, can affect receipt of treatment and treatment adherence (49–54). It is likely that health care system features, such as breadth and depth of provider networks, benefit design, routine use of EHR functionality in quality improvement, and financial assistance infrastructure, are associated with patient financial hardship, and these areas are important research gaps. Because many cancer survivors can be affected by relatively small changes at the health system and insurer level, these are especially important areas for future descriptive research not only to inform effective and cost-effective intervention strategies within systems, but also for broader dissemination and implementation of effective strategies at the state and national policy levels.

Employer-level risk factors for medical financial hardship

Employer-level features, such as whether they offer health insurance coverage to employees and the types of coverage offerings, availability of paid and unpaid sick leave, and workplace accommodations, potentially play a large role in medical financial hardship for employed cancer survivors. These same employer features, including health insurance offerings, sick leave, and accommodations, also affect employed family members and other informal caregivers (55), in their ability to support cancer survivors and provide health insurance coverage and income for the family. Some retirees also receive supplemental health insurance coverage and pension benefits from their former employers. Better understanding of employer features and their role in the development of hardship represent important research gaps. Because many working age and retired cancer survivors and their informal caregivers can be affected by aspects of employer-based insurance coverage (both primary and supplemental Medicare coverage) and sick leave policies, the employer level is an especially important area for future descriptive research. Development and evaluation of policies at the employer level may also inform dissemination and implementation of effective intervention strategies at the broader state and national policy levels.

State and national policy–level risk factors for medical financial hardship

The state and national levels are especially relevant for research evaluating medical financial hardship because employment policies and health insurance policies related to the Medicaid and Medicare programs are enacted in states or nationally. The federal Family and Medical Leave Act (FMLA) entitles eligible employees working at a location where the employer has at least 50 employees within 75 miles with up to 12 weeks of unpaid leave for their own serious health condition or to provide care for a spouse, child, or parent with a serious health condition (56). The FMLA allows states to require additional, more generous provisions. For example, in 2018, four states (California, New Jersey, New York, and Rhode Island) require some employers to offer paid family and medical leave, and ten states and Washington, DC, require some employers to offer paid sick leave (57). Evaluation of the effects of these state-level FMLA policies will be important for future research.

The most recent significant changes in state and national health policy are related to the Affordable Care Act (ACA), including introduction of the Marketplace coverage and essential health benefit standards nationally; elimination of pre-existing condition exclusions and life-time and annual coverage limits; dependent coverage expansion allowing young adults to remain covered under a parents’ health insurance up until age 26 years; and expansion of Medicaid eligibility in some states. Although little research has explicitly evaluated Marketplace coverage, essential health benefits, or elimination of pre-existing coverage exclusions for cancer survivors, dependent coverage expansion as part of the ACA in 2010 was associated with improved access to some preventive services and earlier stage at diagnosis in young adults (58–61).

Several studies have demonstrated positive effects of state-level Medicaid expansions to 138% of the federal poverty level for low-income adults with and without children (62–65). Following expansion in 2014, cancer patients and survivors in expansion states were more likely to be insured, have access to care or diagnosed at an earlier stage of disease than those in nonexpansion states (62–65). Exploring the effects of Medicaid expansions or other aspects of the ACA on financial hardship in cancer survivors will be an important area for additional research.

Outside of Medicaid expansions, there is tremendous heterogeneity in how Medicaid programs are operated by states, including differences in managed care penetration, physician reimbursement, and use and level of patient cost-sharing (66). In addition, some state Medicaid programs restrict the number of covered physician visits (67) or prescriptions per month (68), and others have instituted work requirements for eligible beneficiaries (69).

Other state-level policies, such as generic substitution laws, can restrict if and how pharmacists can offer generic substitution when patients are prescribed a branded drug (70). More recent cost transparency laws require pharmaceutical manufacturers to provide information about cost increases (70, 71). Oral parity laws have passed in 43 states and the District of Columbia to make patient OOP costs equivalent for oral and infusion antineoplastic therapies for patients with private health insurance (72). In one of the few evaluations, patients in health plans subject to oral parity laws had increased use of oral agents and modest protection against high OOP costs for oral medications compared with patients in plans not subject to oral parity laws (73). These state- and national-level employment and health policies may affect underlying risk of medical financial hardship and warrant additional research to better understand their impact on cancer survivors.

In this section, we use the conceptual framework to describe existing standardized measures of financial hardship and data infrastructure for descriptive and intervention research. To date, most research has not evaluated financial hardship with standardized self-reported measures, limiting comparison across patient populations and studies for prevalence and severity of hardship. Among the few validated measures are the InCharge Financial Distress/Financial Well-Being Scale (74) and the Comprehensive Score for Financial Toxicity measure (75, 76). Nationally representative surveys such as the National Health Interview Survey (NHIS; ref. 77), the Medical Expenditure Panel Survey (MEPS; ref. 78), and the Behavioral Risk Factor Surveillance System (BRFSS; ref. 79) contain items on medical financial hardship within material, psychologic, and behavioral domains that can be used in comparisons of individuals with and without a cancer history (80). In 2011 and 2016–2017, the MEPS Experiences with Cancer survey was fielded in cancer survivors with medical financial hardship items specific to cancer diagnosis, its treatment, and the lasting effects of treatment (81). Standardized measures and/or items from the publicly available NHIS, MEPS, and BRFSS can be used in studies with primary data collection to allow meaningful comparisons with other national or state populations.

Administrative claims data may contain information about patient cost-sharing, which can be used to estimate patient OOP costs. However, these data do not contain any information on services that are not covered by the insurer (where patients may pay 100% of the charges for services) and likely understate OOP costs. In the case of Medicare claims data, beneficiaries frequently have additional private supplemental coverage, and amounts listed as patient responsibility can include both patient OOP and contributions from other payers, although these amounts are not listed separately. Information about additional supplemental Medicare coverage is not available from claims. As a result, patient responsibility amounts from Medicare are not accurate representations of patient OOP. Finally, administrative claims do not contain information on income, wealth, assets, or other measures that might be used to inform understanding of material, psychologic, or behavioral domains of hardship.

As described in Table 2, there are a number of additional measurement and data infrastructure gaps impeding the assessment of medical financial hardship. Because of limited comparability across studies and few longitudinal studies of associations between financial hardship and health outcomes, little is known about the key measures of material, psychologic, and behavioral hardship and whether these measures should differ by type of cancer, time since diagnosis, or treatment for patients and caregivers. In addition, because patient-level data are generally not linked to longer-term outcomes, the associations between financial hardship and health outcomes, such as health-related quality of life and survival, are unclear. Further development of data linkages will inform this longitudinal research and help address other key questions, such as the optimal timing of measurement of hardship by domain or the best mechanism to collect and record sensitive financial data from patients (e.g., tablet in waiting room, in-person, online, phone).

Table 2.

Key measurement and data infrastructure questions to assess financial hardship

Level of frameworkExample questions
Patient and family What are key measures of material, psychologic, and behavioral hardship? How do measures of hardship differ by type of cancer, time since diagnosis, treatment, for patients and caregivers? How is health insurance literacy best measured? What is the best mechanism and timing to collect and record financial hardship data (e.g., tablet in waiting room, online)? What is the association between financial hardship and health outcomes, such as health-related quality of life and survival? 
Provider and care team How can provider and/or provider team assessment of patient costs of care and financial hardship be measured systematically? What is appropriate time period for measurement of financial hardship? How are patient responses about financial hardship best integrated with clinical workflow? 
Health care system What existing data sources and linkages can be used to assess financial hardship within a health system? Can measures of financial hardship be available in real time to inform decision-making and referrals? How can actions to address hardship be measured and their effects quantified? Is discussion of financial hardship a dimension of quality of care? Can data systems be used to prompt provider team about patient financial hardship (e.g., part of prior authorization for patients with health insurance coverage)? 
Employer How can employment trajectory following cancer diagnosis be measured? What are data sources for assessing the role of employer features (e.g., health insurance coverage, workplace accommodations, paid and unpaid sick leave) when evaluating financial hardship? 
State and national policy What existing data sources and linkages can be used to assess financial hardship? Which data sources and linkages can be used to evaluate the association between financial hardship and health outcomes, such as health-related quality of life and survival? Is discussion of financial hardship and action to minimize financial hardship a dimension of quality of care? Can quality measures be developed to compare providers and plans? How can existing surveys and research cohorts be enhanced to assess financial hardship? 
Level of frameworkExample questions
Patient and family What are key measures of material, psychologic, and behavioral hardship? How do measures of hardship differ by type of cancer, time since diagnosis, treatment, for patients and caregivers? How is health insurance literacy best measured? What is the best mechanism and timing to collect and record financial hardship data (e.g., tablet in waiting room, online)? What is the association between financial hardship and health outcomes, such as health-related quality of life and survival? 
Provider and care team How can provider and/or provider team assessment of patient costs of care and financial hardship be measured systematically? What is appropriate time period for measurement of financial hardship? How are patient responses about financial hardship best integrated with clinical workflow? 
Health care system What existing data sources and linkages can be used to assess financial hardship within a health system? Can measures of financial hardship be available in real time to inform decision-making and referrals? How can actions to address hardship be measured and their effects quantified? Is discussion of financial hardship a dimension of quality of care? Can data systems be used to prompt provider team about patient financial hardship (e.g., part of prior authorization for patients with health insurance coverage)? 
Employer How can employment trajectory following cancer diagnosis be measured? What are data sources for assessing the role of employer features (e.g., health insurance coverage, workplace accommodations, paid and unpaid sick leave) when evaluating financial hardship? 
State and national policy What existing data sources and linkages can be used to assess financial hardship? Which data sources and linkages can be used to evaluate the association between financial hardship and health outcomes, such as health-related quality of life and survival? Is discussion of financial hardship and action to minimize financial hardship a dimension of quality of care? Can quality measures be developed to compare providers and plans? How can existing surveys and research cohorts be enhanced to assess financial hardship? 

We did not identify any standard measures of if and how medical financial hardship is ascertained and addressed at the provider, health care system, employer, or state and national levels. As a result, data may not be available in real time to inform care. Research at each of these levels is limited by lack of measures and data infrastructure. As health care payers are increasingly considering patients’ experience of care in quality measurement and payment, this is an important research gap. Although assessment of financial hardship and documenting actions to address it is not currently considered a quality of care measure, standardized measurement and evaluation by payers at multiple levels could incentivize the discussions of costs of cancer care, as recommended by professional organizations (44). Limitations in standardized measurement and data infrastructure at multiple levels underlie the many challenges facing the development of effective and cost-effective interventions to minimize medical financial hardship.

In this section, we use the conceptual framework to summarize intervention research addressing financial hardship and identify key research gaps and leverage points for intervention at each level. At the patient level, patient assistance programs, individual drug coupons, pharmacy-specific purchasing agreements, and savings card programs are increasingly common as means to reduce patient OOP cost and increase access to prescription drugs (82–84). Other patient assistance programs offer cancer survivors assistance with housing costs, medical bills, transportation to and from care, and temporary lodging for those who travel for care (85, 86). Many of these programs have limits on eligibility and restrictions on amount and duration of support, however. Data about the use of any of these programs are not systematically collected, and evaluations of the effectiveness of these programs in reducing financial hardship or improving health outcomes have not been published (Table 3). Patient health insurance and financial literacy is sometimes cited as an important barrier to informed discussions of costs and benefits of different treatment options (32, 87), and improving it is a potential leverage point for intervention.

Table 3.

Examples of research questions for developing and informing interventions to minimize financial hardship in cancer survivors

Level of frameworkExample questions
Patient and family Does enrollment in patient assistance programs reduce hardship? Improve treatment adherence? Does offering detailed survivorship care planning minimize financial hardship and improve health outcomes? Can decision aids help patients understand aspects of health insurance and risk of financial hardship? What is the best timing for introduction of decision aids? Do they reduce hardship and improve health outcomes? What is the effectiveness and cost-effectiveness? 
Provider and care team How can providers and care team best discuss financial hardship with patients and their families? Are EHR reminders or prompts effective in improving communication about costs and benefits of treatment? What is the effectiveness and cost-effectiveness of financial navigators for a practice? 
Health care system What is the effectiveness and cost-effectiveness of financial navigators for a health system? What are the best models of care to minimize financial hardship and how can they be best integrated into routine healthcare? 
Employer How can employers offer accommodations to support cancer survivors and their caregivers to minimize hardship? What is the effectiveness and cost-effectiveness of offering accommodations from employer perspective? 
State and national policy What is the effectiveness and cost-effectiveness associated with interventions to reduce financial hardship from state perspective? Do effectiveness and cost-effectiveness vary from national perspective? 
Level of frameworkExample questions
Patient and family Does enrollment in patient assistance programs reduce hardship? Improve treatment adherence? Does offering detailed survivorship care planning minimize financial hardship and improve health outcomes? Can decision aids help patients understand aspects of health insurance and risk of financial hardship? What is the best timing for introduction of decision aids? Do they reduce hardship and improve health outcomes? What is the effectiveness and cost-effectiveness? 
Provider and care team How can providers and care team best discuss financial hardship with patients and their families? Are EHR reminders or prompts effective in improving communication about costs and benefits of treatment? What is the effectiveness and cost-effectiveness of financial navigators for a practice? 
Health care system What is the effectiveness and cost-effectiveness of financial navigators for a health system? What are the best models of care to minimize financial hardship and how can they be best integrated into routine healthcare? 
Employer How can employers offer accommodations to support cancer survivors and their caregivers to minimize hardship? What is the effectiveness and cost-effectiveness of offering accommodations from employer perspective? 
State and national policy What is the effectiveness and cost-effectiveness associated with interventions to reduce financial hardship from state perspective? Do effectiveness and cost-effectiveness vary from national perspective? 

Several studies are exploring the role of financial navigators in minimizing financial hardship (88, 89), and practices are increasingly including components of financial navigation for their patients. Evaluations of financial navigation are ongoing. State Medicaid programs and insurers have implemented and evaluated value-based care models focusing on the quality rather than quantity of care and improving patient outcomes, including medical homes in oncology and oncology pathways (90–94). In 2016, the Oncology Care Model (OCM), a multipayer value-based payment model developed by the Centers for Medicare and Medicaid Services Innovation Center was implemented in approximately 200 practices across the United States to improve the quality of cancer care for Medicare beneficiaries (95, 96). The OCM includes a navigation component, and it may be possible to assess the association between financial navigation and other components of quality of care in participating practices (96).

Few interventions have been developed to specifically minimize medical financial hardship, although, as described above, state and national health policies can affect medical financial hardship. Similarly, given the broad support for discussion of the costs of cancer care by professional societies (44) and other organizations (45), inclusion of data elements addressing whether a conversation with the care team took place might be an important first step in assessing patient financial hardship.

As interventions are developed and implemented, it will be important to evaluate their effectiveness and cost-effectiveness from the perspective of each level; this information will inform how payers, employers, and others make decisions about how best to minimize patient medical financial hardship and improve health outcomes. Information on comparative effectiveness and cost-effectiveness will also inform dissemination and implementation of interventions in other settings.

In this commentary, we described emerging research on medical financial hardship in cancer survivors and highlighted the numerous research gaps at all levels, including patient, provider and care team, health care system, employer, and state and national policy levels. To date, the vast majority of research on medical financial hardship has been conducted at the patient level (14–16). Foundational descriptive research and surveillance at other levels will be important, especially as standardized measures and data infrastructure are developed. With trends toward increasing costs of cancer treatment (2–4) and greater patient cost-sharing, especially for oral medications (3, 97), risks of medical financial hardship associated with cancer will likely increase in the future.

Although research on risk factors for financial hardship is limited, even at the patient level, findings across these studies are consistent—medical financial hardship is more common among populations that have historically experienced disparities in access to care and health outcomes, including racial/ethnic minorities (18, 19, 22–25), the poor (27), and the underinsured and uninsured (18, 19, 27). Identifying additional risk factors and monitoring hardship in these populations will be critical to minimize the potential for widening disparities in health outcomes in the future.

We identified very few interventions to minimize financial hardship at any level. Using our multilevel conceptual framework, we summarized published studies and identified critical gaps in medical financial hardship research and intervention developments. Our framework illustrates the importance of considering the interrelationship of multiple levels. For example, limited patient health insurance and financial literacy might be an important and potentially modifiable risk factor for developing medical financial hardship. A decision aid might help patients develop sufficient financial and health insurance literacy to have an informed discussion with the provider team and make informed treatment decisions about their care. The success of such an intervention requires (i) understanding which member of the provider team is best suited for discussion about the costs and benefits of treatments, including expected cost-sharing; (ii) training provider team member(s) to have these discussions; and (iii) data infrastructure at the practice and plan level to support this discussion and record patient preferences. The success of the intervention also requires that the patient and/or family member are able to maintain employer-based health insurance coverage and have sufficient sick leave for patient and caregiver (paid and unpaid) to successfully complete the selected treatment(s). Policies at the health system and insurer, employer, state and national levels related to health insurance benefit design, and health care delivery are also potential levers for intervention.

As described in this commentary, foundational descriptive research at multiple levels is critical to inform understanding of risk factors for medical financial hardship and to develop, implement, and evaluate interventions to reduce financial hardship and improve patient health outcomes. Addressing measurement and data infrastructure gaps that may limit development of effective interventions is necessary for these efforts. Evaluation of the effectiveness and cost-effectiveness of interventions from the perspectives of the provider and care team, health system and insurer, employer, and state and national levels will inform decisions about how best to address the growing problem of medical financial hardship for cancer survivors in the United States.

No potential conflicts of interest were disclosed.

1.
Mariotto
AB
,
Yabroff
KR
,
Shao
Y
,
Feuer
EJ
,
Brown
ML
. 
Projections of the cost of cancer care in the United States: 2010-2020
.
J Natl Cancer Inst
2011
;
103
:
117
28
.
2.
Conti
RM
,
Fein
AJ
,
Bhatta
SS
. 
National trends in spending on and use of oral oncologics, first quarter 2006 through third quarter 2011
.
Health Aff
2014
;
33
:
1721
7
.
3.
Shih
YT
,
Xu
Y
,
Liu
L
,
Smieliauskas
F
. 
Rising prices of targeted oral anticancer medications and associated financial burden on medicare beneficiaries
.
J Clin Oncol
2017
;
35
:
2482
9
.
4.
Lee
JA
,
Roehrig
CS
,
Butto
ED
. 
Cancer care cost trends in the United States: 1998 to 2012
.
Cancer
2016
;
122
:
1078
84
.
5.
Bach
PB
. 
Limits on Medicare's ability to control rising spending on cancer drugs
.
N Engl J Med
2009
;
360
:
626
33
.
6.
Memorial Sloan Kettering Cancer Center.
Price and value of cancer drug
.
Available from:
https://www.mskcc.org/research-areas/programs-centers/health-policy-outcomes/cost-drugs.
7.
Bradley
CJ
,
Yabroff
KR
,
Mariotto
AB
,
Zeruto
C
,
Tran
Q
,
Warren
JL
. 
Antineoplastic treatment of advanced-stage non-small-cell lung cancer: treatment, survival, and spending (2000 to 2011)
.
J Clin Oncol
2017
;
35
:
529
35
.
8.
Bradley
CJ
,
Yabroff
KR
,
Warren
JL
,
Zeruto
C
,
Chawla
N
,
Lamont
EB
. 
Trends in the treatment of metastatic colon and rectal cancer in elderly patients
.
Med Care
2016
;
54
:
490
7
.
9.
Zheng
Z
,
Yabroff
KR
,
Guy
GP
 Jr.
,
Han
X
,
Li
C
,
Banegas
MP
, et al
Annual medical expenditure and productivity loss among colorectal, female breast, and prostate cancer survivors in the United States
.
J Natl Cancer Inst
2016
;
108
.
10.
Davidoff
AJ
,
Erten
M
,
Shaffer
T
,
Shoemaker
JS
,
Zuckerman
IH
,
Pandya
N
, et al
Out-of-pocket health care expenditure burden for Medicare beneficiaries with cancer
.
Cancer
2013
;
119
:
1257
65
.
11.
Guy
GP
 Jr.
,
Ekwueme
DU
,
Yabroff
KR
,
Dowling
EC
,
Li
C
,
Rodriguez
JL
, et al
Economic burden of cancer survivorship among adults in the United States
.
J Clin Oncol
2013
;
31
:
3749
57
.
12.
Shankaran
V
,
Ramsey
S
. 
Addressing the financial burden of cancer treatment: from copay to can't pay
.
JAMA Oncol
2015
;
1
:
273
4
.
13.
Henry
J
.
Kaiser Family Foundation
.
Payments for cost sharing increasing rapidly over time 2017
.
Available from
: https://www.kff.org/health-costs/issue-brief/payments-for-cost-sharing-increasing-rapidly-over-time/.
14.
Altice
CK
,
Banegas
MP
,
Tucker-Seeley
RD
,
Yabroff
KR
. 
Financial hardships experienced by cancer survivors: a systematic review
.
J Natl Cancer Inst
2017
;
109
.
15.
Carrera
PM
,
Kantarjian
HM
,
Blinder
VS
. 
The financial burden and distress of patients with cancer: understanding and stepping-up action on the financial toxicity of cancer treatment
.
CA Cancer J Clin
2018
;
68
:
153
65
.
16.
Gordon
LG
,
Merollini
KMD
,
Lowe
A
,
Chan
RJ
. 
A systematic review of financial toxicity among cancer survivors: we can't pay the co-pay
.
Patient
2017
;
10
:
295
309
.
17.
Zafar
SY
,
Peppercorn
JM
,
Schrag
D
,
Taylor
DH
,
Goetzinger
AM
,
Zhong
X
, et al
The financial toxicity of cancer treatment: a pilot study assessing out-of-pocket expenses and the insured cancer patient's experience
.
Oncologist
2013
;
18
:
381
90
.
18.
Rim
SH
,
Guy
GP
 Jr.
,
Yabroff
KR
,
McGraw
KA
,
Ekwueme
DU
. 
The impact of chronic conditions on the economic burden of cancer survivorship: a systematic review
.
Exp Rev Pharmacoecon Outcomes Res
2016
;
16
:
579
89
.
19.
Wheeler
SB
,
Spencer
JC
,
Pinheiro
LC
,
Carey
LA
,
Olshan
AF
,
Reeder-Hayes
KE
. 
Financial impact of breast cancer in black versus white women
.
J Clin Oncol
2018
:
Jco2017776310
.
20.
Nipp
RD
,
Kirchhoff
AC
,
Fair
D
,
Rabin
J
,
Hyland
KA
,
Kuhlthau
K
, et al
Financial burden in survivors of childhood cancer: a report from the childhood cancer survivor study
.
J Clin Oncol
2017
;
35
:
3474
81
.
21.
Nipp
RD
,
Zullig
LL
,
Samsa
G
,
Peppercorn
JM
,
Schrag
D
,
Taylor
DH
 Jr
, et al
Identifying cancer patients who alter care or lifestyle due to treatment-related financial distress
.
Psychooncology
2016
;
25
:
719
25
.
22.
Weaver
KE
,
Rowland
JH
,
Bellizzi
KM
,
Aziz
NM
. 
Forgoing medical care because of cost: assessing disparities in healthcare access among cancer survivors living in the United States
.
Cancer
2010
;
116
:
3493
504
.
23.
Kent
EE
,
Forsythe
LP
,
Yabroff
KR
,
Weaver
KE
,
de Moor
JS
,
Rodriguez
JL
, et al
Are survivors who report cancer-related financial problems more likely to forgo or delay medical care?
Cancer
2013
;
119
:
3710
7
.
24.
Pisu
M
,
Kenzik
KM
,
Oster
RA
,
Drentea
P
,
Ashing
KT
,
Fouad
M
, et al
Economic hardship of minority and non-minority cancer survivors 1 year after diagnosis: another long-term effect of cancer?
Cancer
2015
;
121
:
1257
64
.
25.
Jagsi
R
,
Pottow
JA
,
Griffith
KA
,
Bradley
C
,
Hamilton
AS
,
Graff
J
, et al
Long-term financial burden of breast cancer: experiences of a diverse cohort of survivors identified through population-based registries
.
J Clin Oncol
2014
;
32
:
1269
76
.
26.
Palmer
NR
,
Geiger
AM
,
Lu
L
,
Case
LD
,
Weaver
KE
. 
Impact of rural residence on forgoing healthcare after cancer because of cost
.
Cancer Epidemiol Biomarkers Prev
2013
;
22
:
1668
76
.
27.
Banegas
MP
,
Guy
GP
 Jr.
,
de Moor
JS
,
Ekwueme
DU
,
Virgo
KS
,
Kent
EE
, et al
For working-age cancer survivors, medical debt and bankruptcy create financial hardships
.
Health Aff
2016
;
35
:
54
61
.
28.
Yabroff
KR
,
Lawrence
WF
,
Clauser
S
,
Davis
WW
,
Brown
ML
. 
Burden of illness in cancer survivors: findings from a population-based national sample
.
J Natl Cancer Inst
2004
;
96
:
1322
30
.
29.
Mehnert
A
,
de Boer
A
,
Feuerstein
M
. 
Employment challenges for cancer survivors
.
Cancer
2013
;
119
:
2151
9
.
30.
Ramsey
S
,
Blough
D
,
Kirchhoff
A
,
Kreizenbeck
K
,
Fedorenko
C
,
Snell
K
, et al
Washington State cancer patients found to be at greater risk for bankruptcy than people without a cancer diagnosis
.
Health Aff
2013
;
32
:
1143
52
.
31.
Weaver
KE
,
Rowland
JH
,
Alfano
CM
,
McNeel
TS
. 
Parental cancer and the family: a population-based estimate of the number of US cancer survivors residing with their minor children
.
Cancer
2010
;
116
:
4395
401
.
32.
Levitt
L
. 
Why health insurance literacy matters
.
JAMA
2015
;
313
:
555
6
.
33.
Ramsey
SD
,
Bansal
A
,
Fedorenko
CR
,
Blough
DK
,
Overstreet
KA
,
Shankaran
V
, et al
Financial insolvency as a risk factor for early mortality among patients with cancer
.
J Clin Oncol
2016
;
34
:
980
6
.
34.
Nekhlyudov
L
,
Walker
R
,
Ziebell
R
,
Rabin
B
,
Nutt
S
,
Chubak
J
. 
Cancer survivors' experiences with insurance, finances, and employment: results from a multisite study
.
J Cancer Surviv
2016
;
10
:
1104
11
.
35.
Van Houtven
CH
,
Ramsey
SD
,
Hornbrook
MC
,
Atienza
AA
,
van Ryn
M
. 
Economic burden for informal caregivers of lung and colorectal cancer patients
.
Oncologist
2010
;
15
:
883
93
.
36.
Nathan PC
HT
,
Kirchhoff
AC
,
Park
ER
,
Yabroff
KR
. 
Financial hardship and the economic impact of childhood cancer survivorship
.
J Clin Oncol
2018
.
In press
.
37.
Yabroff
KR
,
Saraiya
M
,
Meissner
HI
,
Haggstrom
DA
,
Wideroff
L
,
Yuan
G
, et al
Specialty differences in primary care physician reports of papanicolaou test screening practices: a national survey, 2006 to 2007
.
Ann Intern Med
2009
;
151
:
602
11
.
38.
Tsugawa
Y
,
Newhouse
JP
,
Zaslavsky
AM
,
Blumenthal
DM
,
Jena
AB
. 
Physician age and outcomes in elderly patients in hospital in the US: observational study
.
BMJ
2017
;
357
:
j1797
.
39.
Hershman
DL
,
Buono
D
,
McBride
RB
,
Tsai
WY
,
Neugut
AI
. 
Influence of private practice setting and physician characteristics on the use of breast cancer adjuvant chemotherapy for elderly women
.
Cancer
2009
;
115
:
3848
57
.
40.
Wright
JD
,
Neugut
AI
,
Wilde
ET
,
Buono
DL
,
Malin
J
,
Tsai
WY
, et al
Physician characteristics and variability of erythropoiesis-stimulating agent use among Medicare patients with cancer
.
J Clin Oncol
2011
;
29
:
3408
18
.
41.
Wilson
LE
,
Pollack
CE
,
Greiner
MA
,
Dinan
MA
. 
Association between physician characteristics and the use of 21-gene recurrence score genomic testing among Medicare beneficiaries with early-stage breast cancer, 2008–2011
.
Breast Cancer Res Treat
2018
;
170
:
361
71
.
42.
Kimmick
GG
,
Camacho
F
,
Mackley
HB
,
Kern
T
,
Yao
N
,
Matthews
SA
, et al
Individual, area, and provider characteristics associated with care received for stages I to III breast cancer in a multistate region of appalachia
.
J Oncol Pract
2015
;
11
:
e9
e18
.
43.
Yabroff
KR
,
Zapka
J
,
Klabunde
CN
,
Yuan
G
,
Buckman
DW
,
Haggstrom
D
, et al
Systems strategies to support cancer screening in U.S. primary care practice
.
Cancer Epidemiol Biomarkers Prev
2011
;
20
:
2471
9
.
44.
Meropol
NJ
,
Schrag
D
,
Smith
TJ
,
Mulvey
TM
,
Langdon
RM
 Jr
,
Blum
D
, et al
American society of clinical oncology guidance statement: the cost of cancer care
.
J Clin Oncol
2009
;
27
:
3868
74
.
45.
Institute of Medicine
. 
Delivering high-quality cancer care: charting a new course for a system in crisis
.
Washington, DC
:
The National Academies Press
; 
2013
.
46.
Shih
YT
,
Chien
CR
. 
A review of cost communication in oncology: Patient attitude, provider acceptance, and outcome assessment
.
Cancer
2017
;
123
:
928
39
.
47.
Wollins
DS
,
Zafar
SY
. 
A touchy subject: can physicians improve value by discussing costs and clinical benefits with patients?
Oncologist
2016
;
21
:
1157
60
.
48.
Yarnall
KS
,
Pollak
KI
,
Ostbye
T
,
Krause
KM
,
Michener
JL
. 
Primary care: is there enough time for prevention?
Am J Public Health
2003
;
93
:
635
41
.
49.
Osterberg
L
,
Blaschke
T
. 
Adherence to medication
.
N Engl J Med
2005
;
353
:
487
97
.
50.
Choudhry
NK
,
Avorn
J
,
Glynn
RJ
,
Antman
EM
,
Schneeweiss
S
,
Toscano
M
, et al
Full coverage for preventive medications after myocardial infarction
.
N Engl J Med
2011
;
365
:
2088
97
.
51.
Park
Y
,
Raza
S
,
George
A
,
Agrawal
R
,
Ko
J
. 
The effect of formulary restrictions on patient and payer outcomes: a systematic literature review
.
J Manag Care Special Pharm
2017
;
23
:
893
901
.
52.
Dusetzina
SB
,
Winn
AN
,
Abel
GA
,
Huskamp
HA
,
Keating
NL
. 
Cost sharing and adherence to tyrosine kinase inhibitors for patients with chronic myeloid leukemia
.
J Clin Oncol
2014
;
32
:
306
11
.
53.
Winn
AN
,
Keating
NL
,
Dusetzina
SB
. 
Factors associated with tyrosine kinase inhibitor initiation and adherence among medicare beneficiaries with chronic myeloid leukemia
.
J Clin Oncol
2016
;
34
:
4323
8
.
54.
Hershman
DL
,
Kushi
LH
,
Shao
T
,
Buono
D
,
Kershenbaum
A
,
Tsai
WY
, et al
Early discontinuation and nonadherence to adjuvant hormonal therapy in a cohort of 8,769 early-stage breast cancer patients
.
J Clin Oncol
2010
;
28
:
4120
8
.
55.
de Moor
JS
,
Dowling
EC
,
Ekwueme
DU
,
Guy
GP
 Jr
,
Rodriguez
J
,
Virgo
KS
, et al
Employment implications of informal cancer caregiving
.
J Cancer Surviv
2017
;
11
:
48
57
.
56.
US
Department of Labor
. 
Fact sheet #28: the family and medical leave act
.
Available from
: https://www.dol.gov/whd/regs/compliance/whdfs28.pdf.
57.
National Conference of State Legislatures. State Family and Medical Leave Laws.
Available from
: http://www.ncsl.org/research/labor-and-employment/state-family-and-medical-leave-laws.aspx.
58.
Han
X
,
Yabroff
KR
,
Robbins
AS
,
Zheng
Z
,
Jemal
A
. 
Dependent coverage and use of preventive care under the Affordable Care Act
.
N Engl J Med
2014
;
371
:
2341
2
.
59.
Robbins
AS
,
Han
X
,
Ward
EM
,
Simard
EP
,
Zheng
Z
,
Jemal
A
. 
Association between the affordable care act dependent coverage expansion and cervical cancer stage and treatment in young women
.
JAMA
2015
;
314
:
2189
91
.
60.
Wallace
J
,
Sommers
BD
. 
Effect of dependent coverage expansion of the Affordable Care Act on health and access to care for young adults
.
JAMA Pediatr
2015
;
169
:
495
7
.
61.
Han
X
,
Zang Xiong
K
,
Kramer
MR
,
Jemal
A
. 
The affordable care act and cancer stage at diagnosis among young adults
.
J Natl Cancer Inst
2016
;
108
.
62.
Jemal
A
,
Lin
CC
,
Davidoff
AJ
,
Han
X
. 
Changes in insurance coverage and stage at diagnosis among nonelderly patients with cancer after the affordable care act
.
J Clin Oncol
2017
;
35
:
3906
15
.
63.
Davidoff
AJ
,
Guy
GP
 Jr
,
Hu
X
,
Gonzales
F
,
Han
X
,
Zheng
Z
, et al
Changes in health insurance coverage associated with the affordable care act among adults with and without a cancer history: population-based national estimates
.
Med Care
2018
;
56
:
220
7
.
64.
Soni
A
,
Simon
K
,
Cawley
J
,
Sabik
L
. 
Effect of medicaid expansions of 2014 on overall and early-stage cancer diagnoses
.
Am J Public Health
2018
;
108
:
216
8
.
65.
Soni
A
,
Sabik
LM
,
Simon
K
,
Sommers
BD
. 
Changes in insurance coverage among cancer patients under the affordable care act
.
JAMA Oncol
2018
;
4
:
122
4
.
66.
Brooks
T
,
Wagnerman
K
,
Artiga
S
,
Cornachione
E
. 
Medicaid and CHIP eligibility, enrollment, renewal, and cost sharing policies as of January 2018: findings from a 50-state survey. Available from
: https://www.kff.org/medicaid/report/medicaid-and-chip-eligibility-enrollment-renewal-and-cost-sharing-policies-as-of-january-2018-findings-from-a-50-state-survey/.
67.
68.
Henry J Kaiser Family Foundation
.
Medicaid benefits: prescription drugs
.
69.
Henry J
Kaiser Family Foundation
.
What are states proposing for work requirements in Medicaid?
2018
.
70.
National
Academy for State Health Policy
.
State legislative action on pharmaceutical prices. 2018
. 
Available from:
https://nashp.org/state-legislative-action-on-pharmaceutical-prices/.
71.
Sarpatwari
A
,
Avorn
J
,
Kesselheim
AS
. 
State initiatives to control medication costs–can transparency legislation help?
N Engl J Med
2016
;
374
:
2301
4
.
72.
Printz
C
. 
Drug parity legislation: states, organizations seek to make oral cancer drugs more affordable
.
Cancer
2014
;
120
:
313
4
.
73.
Dusetzina
SB
,
Huskamp
HA
,
Winn
AN
,
Basch
E
,
Keating
NL
. 
Out-of-pocket and health care spending changes for patients using orally administered anticancer therapy after adoption of state parity laws
.
JAMA Oncol
2018
;
4
:
e173598
.
74.
Prawitz
AD
,
Garman
ET
,
Sorhaindo
B
,
O’Neill
B
,
Kim
J
,
Drentea
P
. 
The incharge financial distress/financial well-being scale: establishing validity and reliability
.
Fin Counsel Plan
2006
;
17
:
34
50
.
75.
de Souza
JA
,
Yap
BJ
,
Hlubocky
FJ
,
Wroblewski
K
,
Ratain
MJ
,
Cella
D
, et al
The development of a financial toxicity patient-reported outcome in cancer: The COST measure
.
Cancer
2014
;
120
:
3245
53
.
76.
de Souza
JA
,
Yap
BJ
,
Wroblewski
K
,
Blinder
V
,
Araújo
FS
,
Hlubocky
FJ
, et al
Measuring financial toxicity as a clinically relevant patient-reported outcome: The validation of the COmprehensive Score for financial Toxicity (COST)
.
Cancer
2017
;
123
:
476
84
.
77.
National
Center for Health Statistics
.
National health interview survey
.
Available from:
https://www.cdc.gov/nchs/nhis/index.htm.
78.
Agency for Healthcare Research and Quality
.
Medical expenditure panel survey
.
Available from
: https://meps.ahrq.gov/mepsweb/.
79.
Centers for Disease Control and Prevention
.
Behavioral risk factor surveillance system
.
Available from
: https://www.cdc.gov/brfss/index.html.
80.
Zheng
Z
,
Han
X
,
Guy
GP
 Jr
,
Davidoff
AJ
,
Li
C
,
Banegas
MP
, et al
Do cancer survivors change their prescription drug use for financial reasons? Findings from a nationally representative sample in the United States
.
Cancer
2017
;
123
:
1453
63
.
81.
Yabroff
KR
,
Dowling
E
,
Rodriguez
J
,
Ekwueme
DU
,
Meissner
H
,
Soni
A
, et al
The Medical Expenditure Panel Survey (MEPS) experiences with cancer survivorship supplement
.
J Cancer Surviv
2012
;
6
:
407
19
.
82.
Ross
JS
,
Kesselheim
AS
. 
Prescription-drug coupons–no such thing as a free lunch
.
N Engl J Med
2013
;
369
:
1188
9
.
83.
Howard
DH
. 
Drug companies' patient-assistance programs–helping patients or profits?
N Engl J Med
2014
;
371
:
97
9
.
84.
Zullig
LL
,
Wolf
S
,
Vlastelica
L
,
Shankaran
V
,
Zafar
SY
. 
The role of patient financial assistance programs in reducing costs for cancer patients
.
J Manag Care Spec Pharm
2017
;
23
:
407
11
.
85.
Landwehr
MS
,
Watson
SE
,
Macpherson
CF
,
Novak
KA
,
Johnson
RH
. 
The cost of cancer: a retrospective analysis of the financial impact of cancer on young adults
.
Cancer medicine
2016
;
5
:
863
70
.
87.
Zafar
SY
,
Ubel
PA
,
Tulsky
JA
,
Pollak
KI
. 
Cost-related health literacy: a key component of high-quality cancer care
.
J Oncol Pract
2015
;
11
:
171
3
.
88.
Shankaran
V
,
Leahy
T
,
Steelquist
J
,
Watabayashi
K
,
Linden
H
,
Ramsey
S
, et al
Pilot feasibility study of an Oncology Financial Navigation Program
.
J Oncol Pract
2018
;
14
:
e122
e129
.
89.
Spencer
JC
,
Samuel
CA
,
Rosenstein
DL
,
Reeder-Hayes
KE
,
Manning
ML
,
Sellers
JB
, et al
Oncology navigators' perceptions of cancer-related financial burden and financial assistance resources
.
Supp Care Cancer
2018
;
26
:
1315
21
.
90.
Page
RD
,
Newcomer
LN
,
Sprandio
JD
,
McAneny
BL
. 
The patient-centered medical home in oncology: from concept to reality
.
Am Soc Clin Oncol Educ Book
2015
:
e82
9
.
91.
Newcomer
LN
,
Malin
JL
. 
Payer view of high-quality clinical pathways for cancer
.
J Oncol Pract
2017
;
13
:
148
50
.
92.
Colligan
EM
,
Ewald
E
,
Ruiz
S
,
Spafford
M
,
Cross-Barnet
C
,
Parashuram
S
. 
Innovative oncology care models improve end-of-life quality, reduce utilization and spending
.
Health Aff
2017
;
36
:
433
40
.
93.
Kohler
RE
,
Goyal
RK
,
Lich
KH
,
Domino
ME
,
Wheeler
SB
. 
Association between medical home enrollment and health care utilization and costs among breast cancer patients in a state Medicaid program
.
Cancer
2015
;
121
:
3975
81
.
94.
Wheeler
SB
,
Kohler
RE
,
Goyal
RK
,
Lich
KH
,
Lin
CC
,
Moore
A
, et al
Is medical home enrollment associated with receipt of guideline-concordant follow-up care among low-income breast cancer survivors?
Med Care
2013
;
51
:
494
502
.
95.
Kline
RM
,
Bazell
C
,
Smith
E
,
Schumacher
H
,
Rajkumar
R
,
Conway
PH
. 
Centers for Medicare and Medicaid services: using an episode-based payment model to improve oncology care
.
J Oncol Pract
2015
;
11
:
114
6
.
96.
Kline
R
,
Adelson
K
,
Kirshner
JJ
,
Strawbridge
LM
,
Devita
M
,
Sinanis
N
, et al
The oncology care model: perspectives from the centers for Medicare & Medicaid services and participating oncology practices in academia and the community
.
Am Soc Clin Oncol Educ Book
2017
;
37
:
460
6
.
97.
Dusetzina
SB
. 
Drug pricing trends for orally administered anticancer medications reimbursed by Commercial Health Plans, 2000-2014
.
JAMA Oncol
2016
;
2
:
960
1
.