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Cancer Survivorship and Supportive Care Economics Research: Current Challenges and Next Steps

Cancer Survivorship and Supportive Care Economics Research: Current Challenges and Next Steps Abstract Background Rapid growth in the number of cancer survivors raises numerous questions about health and economic outcomes among survivors along with their families, caregivers, and employers. Health economics theory and methods can contribute to many open questions to improve survivorship. Methods In this paper, we review key areas where more research is needed and describe strategies for improving data infrastructure, research funding, and capacity building to strengthen survivorship health economics research. Conclusions Health economics has broadened an understanding of key supply- and demand-side factors that promote cancer survivorship. To ensure necessary research in survivorship health economics moving forward, we recommend dedicated funding, inclusion of health economics outcomes in primary data collection, and investments in secondary data sets. The number of cancer survivors in the United States is predicted to nearly double between 2016 and 2040, when more than 26 million Americans will be living with a history of cancer (1). This increase—attributed to substantial improvements in cancer treatment as well as the aging of the population and more cancer diagnoses—raises a number of questions about health and economic outcomes among survivors along with their families, caregivers, and employers. Health economics research has already helped us understand the financial burden of cancer, which manifests through out-of-pocket spending and employment effects; the role of health insurance coverage and delivery systems in prevention, early detection, and treatment; and spillover effects of cancer care to other health and nonhealth domains (2-4). However, these studies are just scratching the surface of areas that could benefit from application of health economics theory and methods. In this manuscript, we highlight key areas for survivorship health economics research, strengths, and weaknesses of our existing data infrastructure and identify areas for future growth and investment. The survivorship period, which runs from initial diagnosis and treatment to end-of-life care, can span decades of a patient’s life. Because other papers in this volume focus on cancer treatment, we consider survivorship care for patients not actively undergoing cancer-directed therapy other than endocrine therapies for early stage breast and prostate cancers. Although our paper is far from an exhaustive review, we summarized key results from a subset of influential papers prior to discussing gaps in the literature. Demand-Side Factors Health economics methods and models are well suited to understanding demand-side factors originating with patients and families that ultimately influence length and quality of life for survivors and their support networks. Employment, Income, and Insurance Cancer diagnosis and treatment have negative consequences for employment (5). These consequences include employment loss, reduced hours worked, forgone promotions, work-related disability (6,7), discrimination (8), reduced income, and loss of employment-based health insurance (9). Some survivors increase weekly hours worked following treatment, possibly to maintain health insurance and to replenish assets spent on treatment (9,10). Few employer- or provider-based interventions have been tested and implemented to mitigate these economic consequences and represent areas for future research. There is a need to determine whether policies such as paid sick leave, protections for employees at small businesses and self-employed workers, Medicaid work restrictions, and health insurance outside the employer-based system improve economic and health outcomes for cancer survivors. Limited health protections for workers may increase risk of recurrence or delayed treatment. Low-income workers may be more likely to quit cancer treatment to keep their jobs because their employers may be less likely to offer paid and unpaid sick leave and workplace accommodation. Ultimately, the health toll on individuals who remain working because of the incentives posed by employer-based health insurance and need for income to pay for ongoing treatment could potentially undermine treatment adherence and contribute to health inequities. Long-Term and Late Health and Financial Effects of Initial Treatment Thanks to breakthrough treatments, cancer has become a chronic condition for many survivors. However, survivorship comes with physical, financial, and social costs. Late effects of cancer and its treatment include organ toxicities, chronic toxicities, and recurrent and new malignancies (11,12). Some treatments continue for many years, and their adverse effects accumulate over time throughout the survivorship period (12). Another long-term and late effect of cancer and its treatment is financial toxicity (13), but few interventions and policy initiatives have been developed to alleviate its consequences. Cancer survivors spend more out of pocket for medical care than their counterparts without a cancer history, even after initial cancer treatment. This financial burden of cancer continues to grow as the costs of cancer care increases and treatment extends well into the survivorship period and for some forms of care. Spending on cancer drugs in the United States has grown 64% from 2013 to 2018, and the median annual list price of newly approved cancer drugs is more than $150 000 even for therapeutics that do not significantly increase survival time. As employer-based health insurance plans shift costs of care to patients through higher deductibles and copayments, patient out-of-pocket spending is increasing (14,15). Financial stressors leave patients and their families with considerable debt and possible bankruptcy and exacerbate negative emotional and physical effects associated with cancer treatment. Caregiving Cancer survivors commonly face a variety of functional limitations associated with renewed or chronic cancer treatment or late and long-term effects of the disease and prior therapy. These limitations may generate a need for caregiver support, with the level of support and type of provider changing over time. As cancer is a disease of aging, the caregiving needs associated with cancer are often complicated by the presence of comorbid conditions that may contribute to functional limitations. Caregiving may either be provided by a formal provider, such as a home health agency, long-term care facility, or hospice, or by an informal caregiver. Most caregiving is informal, and for older cancer survivors, it is often unpaid and provided by a spouse or adult daughter (16,17). One economic issue is substitution between formal and informal caregiving. The economic focus is the burden on informal caregivers associated with the loss of income and other opportunity costs, including lower quality of life, associated with unpaid caregiving time (18). In addition, informal caregivers may experience physical and mental distress and may incur incremental health-care utilization and costs to manage those health problems. However, informal care can be the only option for patients whose insurance does not cover the necessary services. Supply-Side Factors Survivorship relies on a number of supply-side factors such as access to health-care providers and insurance that reimburses their services. In this section, we highlight some of the understudied dimensions of survivorship care and emerging payment models that should be tracked over time. Palliative Care Early integration of palliative care for newly diagnosed patients with cancer is increasingly recommended, especially for patients diagnosed with metastatic disease (19). Palliative care services may include professional services, specific medications, durable medical equipment, and supplies, as well as alternative services such as massage, yoga, or guided meditation to alleviate pain and improve functioning. In the United States, both the organization and financing of palliative care services is driven by insurance coverage, with Medicare taking a leading role, and private insurers typically adopting and/or adapting Medicare policy. Although most insurers will cover professional services, home health care, skilled nursing, and hospice, many place barriers such as prior authorization, documentation of homebound status, prior hospitalization, or imminent death to qualify. Further, the traditional Medicare benefit does not include prescription medications commonly needed for symptom management, instead patients must have supplemental prescription drug coverage (Part D or otherwise) or be enrolled in hospice. Economic research is needed to understand the tradeoffs between different models of palliative care delivery, the costs and benefits of services, and their impacts on health and work. In traditional Medicare, comprehensive palliative care has traditionally been provided through the hospice benefit reserved for patients estimated to be in the last 6 months of life. This has tended to discourage early and regular palliative care services during survivorship. However, results from several recent studies suggest that integration of palliative care during early periods of active cancer therapy reduces costs of care and may extend survival. End-of-Life Care The end-of-life (EOL) phase of cancer survivorship draws on both palliative care and formal and informal caregiving services, and all issues associated with access, spending, and outcomes are relevant during EOL. Specific to EOL is the issue of care intensity, the extent to which invasive, uncomfortable, and generally futile services are provided that may be inconsistent with patient preferences as well as financially costly. Key supply-side issues include provider incentives related to timing of hospice enrollment or receipt of futile cancer treatments near the EOL. Emerging Payment and Coverage Models With the growth in the number of cancer survivors and particularly those who have complex care needs, it is increasingly recognized that new strategies for delivering cancer survivorship care are needed. The Centers for Medicare and Medicaid Services (CMS) are currently testing several ways of meeting these goals in the Medicare population (discussed below), but these models may represent better ways of caring for younger survivors as well. We need a better understanding of whether the same models that perform well for older, largely retired adults in Medicare also provide positive outcomes for younger survivors, and we need to understand the intergenerational spillovers of various policy efforts. If Medicare spending is lowered because an adult daughter reduces hours worked to provide informal caregiving, the net social harms may outweigh savings in Medicare. Oncology Care Model In 2016, CMS launched the Oncology Care Model (OCM), a multipayer alternative payment model for oncology physician practices providing chemotherapy to patients with cancer. Under OCM, approximately 200 oncology practices entered payment agreements for 6-month episodes of care for Medicare beneficiaries with cancer-receiving chemotherapy. Practices bill using fee-for-service billing rules and may also bill Medicare $160 per patient per month to support care coordination and improved care delivery. Practices are eligible for performance-based payments if they meet goals for quality and total costs of care. OCM focuses primarily on patients in active cancer treatment; however, there are several requirements that have potential to influence care for cancer survivors. Notably, practices are required to provide beneficiaries with a treatment summary and survivorship care plan when they make the transition from active treatment to posttreatment survivorship care. Additionally, practices must use electronic records and provide additional enhanced services including patient navigation, care consistent with guidelines, and a care plan that includes information such as prognosis, goals of treatment, expected response to treatment, expected out-of-pocket costs, and advance care plans. More transparency about expected costs of treatment may lead to better financial counseling and strategies to limit financial toxicity, which may provide benefits to patients beyond their primary treatment. In addition, OCM requires practices to report quality measures related to screening and management of pain and depression for OCM participants. This may prompt practices to develop strategies for improving these symptoms among patients receiving chemotherapy that may produce benefits into survivorship care. Oncology Medical Homes Oncology medical homes aim to deliver high-quality, patient-centered care for individuals with cancer building on the patient-centered medical home model of primary care delivery (20). These models emphasize team-based care. Some preliminary evidence suggests that these models may help lower use of emergency department visits and hospitalizations (21,22); however, additional research is needed to understand the impact of these models on care and outcomes for cancer survivors. Primary Care Plus The Comprehensive Primary Care Plus model is a national advanced primary care medical home model that seeks to strengthen primary care through regionally based payment reform and care delivery transformation (23). This model combines quarterly care management payments with prospectively paid and retrospectively reconciled performance-based incentive determined by patient experience, quality, and utilization measures. Practices can choose between traditional fee-for-service billing or second track shifts providing quarterly, lump sum comprehensive primary care payments. These payments could allow practices to increase the comprehensiveness of care delivered and ultimately lead to larger payments than under fee-for service care while also taking over some care that was historically provided by specialists. Risk-Stratified Model Although cancer patients report that they prefer to continue seeing their oncologist for their survivorship care (24), this is likely to become increasingly challenging because of the growing number of cancer survivors and a relative workforce shortage (25). The risk-stratified model classifies survivors based on multiple factors including prognosis, risk of recurrence, severity of chronic adverse effects of treatment, risk of late effects and additional cancers, functional status, personal resources and capacity to self-manage aspects of their care, and ability to navigate the health-care system (26). In such “risk stratified” models of care, patients with minimal ongoing problems who have low risk for recurrence and late effects are followed in primary care and those with higher risk may be followed in shared care models that include oncology and primary care clinicians. Such models have been tested in England with promising results (27). Research is needed to develop and test these new care models in the United States. Medicare Advantage In 2020, 24 million individuals, 40% of Medicare beneficiaries overall, were enrolled in Medicare Advantage (MA) plans (28). In contrast to traditional Medicare, MA plans receive capitated payments from CMS and employer groups, for “bundled” coverage for provision of medical care services normally paid directly to providers under fee-for-service payments under parts A, B, and usually D and often offer lower cost sharing. However, many MA plans require preauthorization for cancer treatment, and lower patient cost-sharing may come at the expense of a limited provider network. Although much of the of literature comparing MA relative to traditional Medicare is mixed with respect to variation in survivorship cancer care delivery and outcomes, few papers have used econometrics to account for nonrandom selection into plans (29,30). Diffusion of Effective Survivorship Models Ensuring adoption of effective survivorship care models will be challenging given the difficulty of identifying which models are effective in the first place and the variety of payers influencing care. Detecting meaningful effects will probably require long-term, multicenter trials. The heterogeneity of cancer care delivery also poses an obstacle. Some patients are treated in multispecialty cancer centers that take ownership off all aspects of patient care and are housed in the same health system as patients’ primary care providers. Other patients receive oncology care from 2 or more physician groups and primary care from another, unaffiliated practice. Unlike most new oncology drugs, which fit into the existing drug prescribing, dispensing, and delivery system, survivorship care needs to be tailored to the capabilities and reach of specific oncology and primary care providers and matched to patient needs. Although survivorship care, which can include a diverse bundle of services depending on patients’ needs following the acute cancer treatment, is generally not well reimbursed and may not be considered part of an initial disease episode, cancer providers may feel the need to adopt some form of survivorship care to respond to payment incentives and certification requirements. In the absence of strong evidence and evaluation, there is a risk that ineffective survivorship care practices may persist. Continual evaluation of existing survivorship care practices can help avoid wasteful care. Plans should be scrutinized to balance the risk of an undetected recurrence against the harms of excessive screening and surveillance. Currently, many cancer survivors are screened for additional tumors beyond required guidelines. Improving Research With Existing Data Resources Although there are many survivorship areas that would benefit from health economics research, limitations with currently available datasets pose challenges for analysts. In this section, we describe investments in research data that are important for the next phase of health economics survivorship research. Table 1 summarizes current limitations and strategies for improving the major categories of data, which are discussed below. Table 1. Opportunities to improve existing data resources for health economics survivorship research Data type . Current limitations . Improvement strategies . Population-based cancer registry Missing detailed treatment information Longer-term follow-up information needed Lack financial outcomes (employment, financial toxicity, etc) Incorporate claims data, especially Medicare Advantage and all-payer claims when available Link to administrative economic data (tax returns, credit reports, Social Security benefits) Longitudinal survey data Health-care claims Typically lack stage at diagnosis, functional status Challenging to identify palliative care Miss care reimbursed outside the system Difficult to ascertain death vs change in insurance Continue to develop all-payer claims and pooled insurer data to track patients across insurance providers Link to other survey and administrative datasets to recover additional details and outcomes Methods work using electronic medical records to develop strategies to detect palliative care, advance directive information, and so forth, in claims data Health-care delivery system research databases Limited to patients in specific health-care systems Data are difficult to access, especially for researchers outside participating institutions Expand access to data and training for outside researchers Surveys National samples typically include too few survivors to study specific cancers or treatments Costly data collection Long lag time between data collection and release Fund modules or sample supplements building on existing cohorts Support a centralized survey data repository on survivorship Data type . Current limitations . Improvement strategies . Population-based cancer registry Missing detailed treatment information Longer-term follow-up information needed Lack financial outcomes (employment, financial toxicity, etc) Incorporate claims data, especially Medicare Advantage and all-payer claims when available Link to administrative economic data (tax returns, credit reports, Social Security benefits) Longitudinal survey data Health-care claims Typically lack stage at diagnosis, functional status Challenging to identify palliative care Miss care reimbursed outside the system Difficult to ascertain death vs change in insurance Continue to develop all-payer claims and pooled insurer data to track patients across insurance providers Link to other survey and administrative datasets to recover additional details and outcomes Methods work using electronic medical records to develop strategies to detect palliative care, advance directive information, and so forth, in claims data Health-care delivery system research databases Limited to patients in specific health-care systems Data are difficult to access, especially for researchers outside participating institutions Expand access to data and training for outside researchers Surveys National samples typically include too few survivors to study specific cancers or treatments Costly data collection Long lag time between data collection and release Fund modules or sample supplements building on existing cohorts Support a centralized survey data repository on survivorship Open in new tab Table 1. Opportunities to improve existing data resources for health economics survivorship research Data type . Current limitations . Improvement strategies . Population-based cancer registry Missing detailed treatment information Longer-term follow-up information needed Lack financial outcomes (employment, financial toxicity, etc) Incorporate claims data, especially Medicare Advantage and all-payer claims when available Link to administrative economic data (tax returns, credit reports, Social Security benefits) Longitudinal survey data Health-care claims Typically lack stage at diagnosis, functional status Challenging to identify palliative care Miss care reimbursed outside the system Difficult to ascertain death vs change in insurance Continue to develop all-payer claims and pooled insurer data to track patients across insurance providers Link to other survey and administrative datasets to recover additional details and outcomes Methods work using electronic medical records to develop strategies to detect palliative care, advance directive information, and so forth, in claims data Health-care delivery system research databases Limited to patients in specific health-care systems Data are difficult to access, especially for researchers outside participating institutions Expand access to data and training for outside researchers Surveys National samples typically include too few survivors to study specific cancers or treatments Costly data collection Long lag time between data collection and release Fund modules or sample supplements building on existing cohorts Support a centralized survey data repository on survivorship Data type . Current limitations . Improvement strategies . Population-based cancer registry Missing detailed treatment information Longer-term follow-up information needed Lack financial outcomes (employment, financial toxicity, etc) Incorporate claims data, especially Medicare Advantage and all-payer claims when available Link to administrative economic data (tax returns, credit reports, Social Security benefits) Longitudinal survey data Health-care claims Typically lack stage at diagnosis, functional status Challenging to identify palliative care Miss care reimbursed outside the system Difficult to ascertain death vs change in insurance Continue to develop all-payer claims and pooled insurer data to track patients across insurance providers Link to other survey and administrative datasets to recover additional details and outcomes Methods work using electronic medical records to develop strategies to detect palliative care, advance directive information, and so forth, in claims data Health-care delivery system research databases Limited to patients in specific health-care systems Data are difficult to access, especially for researchers outside participating institutions Expand access to data and training for outside researchers Surveys National samples typically include too few survivors to study specific cancers or treatments Costly data collection Long lag time between data collection and release Fund modules or sample supplements building on existing cohorts Support a centralized survey data repository on survivorship Open in new tab Population-Based Cancer Registry Data Population-based state cancer incidence registry data, most notably the Surveillance, Epidemiology, and End Results Program, provide information about large numbers of newly diagnosed patients with cancer across payers and settings of care. Registries typically collect clinical details about cancer diagnoses (eg, site, histology, stage), some information about the first course of treatment, and vital status. We see several areas where data investments could improve the utility of these data for health economics. Longer-term follow-up data are necessary and could come from survey or administrative data or a combination of sources. Key domains for expansion include detailed systemic treatment information (eg, chemotherapy, targeted agents), adverse events, late effects, recurrence, and financial outcomes including employment and financial toxicity. Claims data represent the most promising source for adding medical variables to Surveillance, Epidemiology, and End Results and other registries but require tracking and harmonization across a number of datasets. Promising areas of development include incorporating Medicare Advantage encounter data to include the nearly 40% of Medicare beneficiaries who no longer participate in fee-for-service Medicare (28); all-payer data from states that collect it; claims from other large insurers or insurer groups, although data privacy rules can make linkages challenging, and the time required for linking data can limit the timeliness of research. Although claims data can help identify survivors with high out-of-pocket spending, most economic outcomes need to come from other sources. Administrative sources of economic outcomes that could be linked include consumer credit reports, IRS tax data, and Social Security benefit claiming and/or earnings data (31-33). Survey data could also capture this information, potentially in greater detail, although systematic collection of such data can be costly to implement and maintain. Claims Data As noted above, health-care claims data are an excellent source of information on specific treatments and services that have billable diagnostic or procedure codes. However, claims data generally lack critical details like cancer stage at diagnosis and outcomes that are not necessary for billing. Palliative care services can be difficult to identify in claims or hospital discharge datasets, especially when the same codes may be used to describe services like chemotherapy that can be given with palliative or curative intent. When patients are not observed in a dataset, it can be impossible to determine whether this reflects an unmet need for care, death, change in health insurance, or access to care in another system such as Veterans’ Affairs Administration care. Currently, the frequent insurance transitions of the population aged younger than 65 years implies that available panel datasets have selected samples if they can be assembled at all. The increasing availability of pooled private insurer data resources like the Health Care Cost Institute may create new opportunities for the use of claims data in survivorship research. Similarly, state all-payer claims datasets may allow researchers to see care for all patients, even if they change from one insurer to another. However, state regulations may limit how such data are used, and such datasets often lack information on patients enrolled in self-insured plans, which can be a substantial proportion of privately insured individuals in many states. Foundational research describing the populations that can and cannot be observed over time in these datasets will be important, along with efforts to link these data to registries, economic, and quality-of-life outcomes. Additional methods work is needed, potentially linking electronic health record text, to improve identification of key variables and constructs such as palliative care services and patient preferences and code status including do not resuscitate orders and advance directive information. Health-care Delivery System Research Databases Economic evaluations that address cancer survivorship care delivery, outcomes, and costs often require cross-sectional and longitudinal data across health-care systems, the age continuum (eg, aged younger than 65 years and 65 years or older), and the phase of care continuum—including diagnosis, treatment, surveillance, and end-of-life phase of care (34,35). For more than 2 decades, data infrastructures consistent with those built and maintained via the National Cancer Institute–funded Cancer Research Network (CRN) and the Population-based Research to Optimize the Screening Process have collected and curated electronic health records, claims, and tumor data on patients of all ages, for long periods prior to, during, and after a cancer diagnosis. These data include detailed information on patients enrolled in employer-sponsored plans, MA plans, Medicaid and dual-eligible plans, and self-pay Affordable Care Act–compliant plans but are limited to patients in specific health-care systems. These data allow for economic and comparative effectiveness research specific to survivorship outcomes including treatment, recurrence, and costs (36-38). Although access to these proprietary data sources often require collaboration with researchers embedded within these health-care systems, to better enhance the depth and breadth of research that can be conducted, the NCI and research consortium leaders are committed to leveraging these research resources for expanded access by the extramural community. These data also have limited information about economic outcomes. Survey Data Survey data offer the opportunity to fill in gaps that are missed by administrative data sources and to connect information from multiple domains of respondents’ lives. Datasets like the National Health Interview Survey, the Medical Expenditure Panel Survey, and the Health and Retirement Study have created opportunities to understand diverse topics such as employment after cancer diagnosis and financial burden in a nationally representative sample, however, these strengths must be balanced against limitations. The samples of cancer survivors in nationally representative datasets are often too small to study specific cancers or treatments. Self-report data limit the ability to include detailed treatment information if linked claims data are not available. Finally, there is frequently a multi-year gap between data collection and availability. Strategies to increase the use of survey data in survivorship research could include funding modules or additional sample supplements in existing nationally representative cohorts and centralizing deposit of survey data that could be used for survivorship research. As internet panels increasingly allow for low-cost collection of survey data through resources like Prolific (www.prolific.co) and the Understanding America Study (uas.usc.edu), rapid, low-cost survey data collection on cancer survivors should become more common. Strategies to Increase Survivorship Research and Capacity Funders can target strategic investments to increase the quality and volume of our health economics survivorship research and improve the lives of millions of survivors. Dedicated funding for health economics research and training, including the medical and social aspects for economists and social scientists and theory and methods for clinical researchers, are important steps in this direction. In particular, we believe that investments in new data linkages combining diverse sets of longitudinal and health outcomes are critical to grow this field. Specific strategies that funding agencies can adopt to facilitate this growth are summarized in Table 2 and include the following: Table 2. Strategies to increase survivorship research and capacitya Gap to address . Strategy . Key activities . Improve quality and volume of health economics cancer survivorship research Increase dedicated funding for health economics Specific requests for applications with dedicated funding Priority funding lines Training grants, conferences, educational opportunities Limited availability of economics-relevant outcomes in primary data collection Require a standard set of economics outcomes in all sponsored data collections Convene expert panel to devise measure set Limited follow-up periods for clinical trials and other studies Facilitate access to patient identifiers for future linkage to survey or administrative data Devise standard consent language to allow future linkages to be performed Develop protocol for interested researchers to obtain initial trial data and participant identifiers Lack of datasets with diverse longitudinal health and economic outcomes Support new data linkages Expand SEER linkage to Medicare Advantage Assess the feasibility of linking financial data Fund methods work on topics such as fuzzy matching and algorithms to identify specific services and constructs with incomplete data Gap to address . Strategy . Key activities . Improve quality and volume of health economics cancer survivorship research Increase dedicated funding for health economics Specific requests for applications with dedicated funding Priority funding lines Training grants, conferences, educational opportunities Limited availability of economics-relevant outcomes in primary data collection Require a standard set of economics outcomes in all sponsored data collections Convene expert panel to devise measure set Limited follow-up periods for clinical trials and other studies Facilitate access to patient identifiers for future linkage to survey or administrative data Devise standard consent language to allow future linkages to be performed Develop protocol for interested researchers to obtain initial trial data and participant identifiers Lack of datasets with diverse longitudinal health and economic outcomes Support new data linkages Expand SEER linkage to Medicare Advantage Assess the feasibility of linking financial data Fund methods work on topics such as fuzzy matching and algorithms to identify specific services and constructs with incomplete data a SEER = Surveillance, Epidemiology, and End Results Program. Open in new tab Table 2. Strategies to increase survivorship research and capacitya Gap to address . Strategy . Key activities . Improve quality and volume of health economics cancer survivorship research Increase dedicated funding for health economics Specific requests for applications with dedicated funding Priority funding lines Training grants, conferences, educational opportunities Limited availability of economics-relevant outcomes in primary data collection Require a standard set of economics outcomes in all sponsored data collections Convene expert panel to devise measure set Limited follow-up periods for clinical trials and other studies Facilitate access to patient identifiers for future linkage to survey or administrative data Devise standard consent language to allow future linkages to be performed Develop protocol for interested researchers to obtain initial trial data and participant identifiers Lack of datasets with diverse longitudinal health and economic outcomes Support new data linkages Expand SEER linkage to Medicare Advantage Assess the feasibility of linking financial data Fund methods work on topics such as fuzzy matching and algorithms to identify specific services and constructs with incomplete data Gap to address . Strategy . Key activities . Improve quality and volume of health economics cancer survivorship research Increase dedicated funding for health economics Specific requests for applications with dedicated funding Priority funding lines Training grants, conferences, educational opportunities Limited availability of economics-relevant outcomes in primary data collection Require a standard set of economics outcomes in all sponsored data collections Convene expert panel to devise measure set Limited follow-up periods for clinical trials and other studies Facilitate access to patient identifiers for future linkage to survey or administrative data Devise standard consent language to allow future linkages to be performed Develop protocol for interested researchers to obtain initial trial data and participant identifiers Lack of datasets with diverse longitudinal health and economic outcomes Support new data linkages Expand SEER linkage to Medicare Advantage Assess the feasibility of linking financial data Fund methods work on topics such as fuzzy matching and algorithms to identify specific services and constructs with incomplete data a SEER = Surveillance, Epidemiology, and End Results Program. Open in new tab Increase dedicated funding for health economics. This can include specific requests for applications with dedicated funding streams or priority funding lines and decisions. To ensure that the workforce will be large enough to conduct necessary studies in health economics, funding for training grants, conferences, and other educational opportunities should be prioritized. Require economics-relevant outcomes in sponsored primary data collection. An expert panel could recommend a standard set of outcomes such as measures of labor supply and financial burden that would allow consistency across studies and pooling of samples, similar to the National Institutes of Health efforts to collate and standardize COVID-19 survey measures. Facilitate access to patient identifiers such as Social Security numbers to enable longitudinal outcomes to sponsored clinical trials and other studies. This would help build the infrastructure to add additional administrative and survey outcomes and increase the utility of existing trial data beyond the initially funded research questions. Support new data linkages with diverse longitudinal health and economic outcomes. Although there are currently limitations with many of our available administrative data sources, connecting these data to survey and registry data remains one of our most powerful tools for survivorship research. Because this work can develop in parallel with public and private sector investments in data quality and availability, we recommend focusing on areas that leverage existing resources, for example, adding Medicare Advantage Encounter data to the SEER-Medicare linkage so that treatment data are available for all Medicare beneficiaries. New approaches to funding and facilitating survivorship health economics research can pay dividends in the coming years to support cancer survivors. Funding LHN and CJB received in-kind support from the Population Health Shared Resource of the University of Colorado Cancer Center. Notes Role of the funder: The funder did not influence this this publication or review previous drafts. Disclosures: Nothing to disclose. Author contributions: Conceptualization: LHN, AJD, DHH, NLK, DPR, KRY, CJB,; Writing- initial outline and assignments: LHN and CJB; Writing-initial draft and revisions: LHN, AJD, DHH, NLK, DPR, KRY, CJB. Prior presentation: National Cancer Institute Future of Cancer Health Economics conference, December 2020. References 1 Bluethmann S , Mariotto A, Rowland J. Anticipating the “Silver Tsunami”: prevalence trajectories and comorbidity burden among older cancer survivors in the United States . Cancer Epidemiol Biomarkers Prev . 2016 ; 25 ( 7 ): 1029 – 1036 . Google Scholar Crossref Search ADS PubMed WorldCat 2 Yabroff KR , Han X, Song W, Zhao J, Jemal A, Zheng Z. 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Integration of palliative care into standard oncology care: American Society of Clinical Oncology clinical practice guideline update . J Clin Oncol . 2017 ; 35 ( 1 ): 96 – 112 . Google Scholar Crossref Search ADS PubMed WorldCat 20 National Committee for Quality Assurance. Oncology medical home recognition: program retirement; 2021 . https://www.ncqa.org/programs/health-care-providers-practices/oncology-medical-home/oncology-medical-home-recognition-program-retirement/. Accessed May 26, 2021. 21 Colligan EM , Ewald E, Keating NL, et al. Two innovative cancer care programs have potential to reduce utilization and spending . Medical Care . 2017 ; 55 ( 10 ): 873 – 878 . Google Scholar Crossref Search ADS PubMed WorldCat 22 Kuntz G , Tozer JM, Snegosky J, Fox J, Neumann K. Michigan oncology medical home demonstration project: first-year results . J Oncol Pract . 2014 ; 10 ( 5 ): 294 – 297 . Google Scholar Crossref Search ADS PubMed WorldCat 23 Centers for Medicare & Medicaid Services. Comprehensive Primary Care Plus; 2021 . https://innovation.cms.gov/innovation-models/comprehensive-primary-care-plus. Accessed May 26, 2021. 24 Hudson SV , Miller SM, Hemler J, et al. Adult cancer survivors discuss follow-up in primary care: “not what I want, but maybe what I need .” Ann Fam Med . 2012 ; 10 ( 5 ): 418 – 427 . Google Scholar Crossref Search ADS PubMed WorldCat 25 Erikson C , Salsberg E, Forte G, Bruinooge S, Goldstein M. Future supply and demand for oncologists: challenges to assuring access to oncology services . J Oncol Pract . 2007 ; 3 ( 2 ): 79 – 86 . Google Scholar Crossref Search ADS PubMed WorldCat 26 Mayer DK , Alfano CM. Personalized risk-stratified cancer follow-up care: its potential for healthier survivors, happier clinicians, and lower costs . J Natl Cancer Inst . 2019 ; 111 ( 5 ): 442 – 448 . Google Scholar Crossref Search ADS PubMed WorldCat 27 Jefford M , Rowland J, Grunfeld E, Richards M, Maher J, Glaser A. 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For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) © The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JNCI Monographs Oxford University Press

Cancer Survivorship and Supportive Care Economics Research: Current Challenges and Next Steps

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Oxford University Press
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Copyright © 2022 Oxford University Press
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1052-6773
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1745-6614
DOI
10.1093/jncimonographs/lgac004
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Abstract

Abstract Background Rapid growth in the number of cancer survivors raises numerous questions about health and economic outcomes among survivors along with their families, caregivers, and employers. Health economics theory and methods can contribute to many open questions to improve survivorship. Methods In this paper, we review key areas where more research is needed and describe strategies for improving data infrastructure, research funding, and capacity building to strengthen survivorship health economics research. Conclusions Health economics has broadened an understanding of key supply- and demand-side factors that promote cancer survivorship. To ensure necessary research in survivorship health economics moving forward, we recommend dedicated funding, inclusion of health economics outcomes in primary data collection, and investments in secondary data sets. The number of cancer survivors in the United States is predicted to nearly double between 2016 and 2040, when more than 26 million Americans will be living with a history of cancer (1). This increase—attributed to substantial improvements in cancer treatment as well as the aging of the population and more cancer diagnoses—raises a number of questions about health and economic outcomes among survivors along with their families, caregivers, and employers. Health economics research has already helped us understand the financial burden of cancer, which manifests through out-of-pocket spending and employment effects; the role of health insurance coverage and delivery systems in prevention, early detection, and treatment; and spillover effects of cancer care to other health and nonhealth domains (2-4). However, these studies are just scratching the surface of areas that could benefit from application of health economics theory and methods. In this manuscript, we highlight key areas for survivorship health economics research, strengths, and weaknesses of our existing data infrastructure and identify areas for future growth and investment. The survivorship period, which runs from initial diagnosis and treatment to end-of-life care, can span decades of a patient’s life. Because other papers in this volume focus on cancer treatment, we consider survivorship care for patients not actively undergoing cancer-directed therapy other than endocrine therapies for early stage breast and prostate cancers. Although our paper is far from an exhaustive review, we summarized key results from a subset of influential papers prior to discussing gaps in the literature. Demand-Side Factors Health economics methods and models are well suited to understanding demand-side factors originating with patients and families that ultimately influence length and quality of life for survivors and their support networks. Employment, Income, and Insurance Cancer diagnosis and treatment have negative consequences for employment (5). These consequences include employment loss, reduced hours worked, forgone promotions, work-related disability (6,7), discrimination (8), reduced income, and loss of employment-based health insurance (9). Some survivors increase weekly hours worked following treatment, possibly to maintain health insurance and to replenish assets spent on treatment (9,10). Few employer- or provider-based interventions have been tested and implemented to mitigate these economic consequences and represent areas for future research. There is a need to determine whether policies such as paid sick leave, protections for employees at small businesses and self-employed workers, Medicaid work restrictions, and health insurance outside the employer-based system improve economic and health outcomes for cancer survivors. Limited health protections for workers may increase risk of recurrence or delayed treatment. Low-income workers may be more likely to quit cancer treatment to keep their jobs because their employers may be less likely to offer paid and unpaid sick leave and workplace accommodation. Ultimately, the health toll on individuals who remain working because of the incentives posed by employer-based health insurance and need for income to pay for ongoing treatment could potentially undermine treatment adherence and contribute to health inequities. Long-Term and Late Health and Financial Effects of Initial Treatment Thanks to breakthrough treatments, cancer has become a chronic condition for many survivors. However, survivorship comes with physical, financial, and social costs. Late effects of cancer and its treatment include organ toxicities, chronic toxicities, and recurrent and new malignancies (11,12). Some treatments continue for many years, and their adverse effects accumulate over time throughout the survivorship period (12). Another long-term and late effect of cancer and its treatment is financial toxicity (13), but few interventions and policy initiatives have been developed to alleviate its consequences. Cancer survivors spend more out of pocket for medical care than their counterparts without a cancer history, even after initial cancer treatment. This financial burden of cancer continues to grow as the costs of cancer care increases and treatment extends well into the survivorship period and for some forms of care. Spending on cancer drugs in the United States has grown 64% from 2013 to 2018, and the median annual list price of newly approved cancer drugs is more than $150 000 even for therapeutics that do not significantly increase survival time. As employer-based health insurance plans shift costs of care to patients through higher deductibles and copayments, patient out-of-pocket spending is increasing (14,15). Financial stressors leave patients and their families with considerable debt and possible bankruptcy and exacerbate negative emotional and physical effects associated with cancer treatment. Caregiving Cancer survivors commonly face a variety of functional limitations associated with renewed or chronic cancer treatment or late and long-term effects of the disease and prior therapy. These limitations may generate a need for caregiver support, with the level of support and type of provider changing over time. As cancer is a disease of aging, the caregiving needs associated with cancer are often complicated by the presence of comorbid conditions that may contribute to functional limitations. Caregiving may either be provided by a formal provider, such as a home health agency, long-term care facility, or hospice, or by an informal caregiver. Most caregiving is informal, and for older cancer survivors, it is often unpaid and provided by a spouse or adult daughter (16,17). One economic issue is substitution between formal and informal caregiving. The economic focus is the burden on informal caregivers associated with the loss of income and other opportunity costs, including lower quality of life, associated with unpaid caregiving time (18). In addition, informal caregivers may experience physical and mental distress and may incur incremental health-care utilization and costs to manage those health problems. However, informal care can be the only option for patients whose insurance does not cover the necessary services. Supply-Side Factors Survivorship relies on a number of supply-side factors such as access to health-care providers and insurance that reimburses their services. In this section, we highlight some of the understudied dimensions of survivorship care and emerging payment models that should be tracked over time. Palliative Care Early integration of palliative care for newly diagnosed patients with cancer is increasingly recommended, especially for patients diagnosed with metastatic disease (19). Palliative care services may include professional services, specific medications, durable medical equipment, and supplies, as well as alternative services such as massage, yoga, or guided meditation to alleviate pain and improve functioning. In the United States, both the organization and financing of palliative care services is driven by insurance coverage, with Medicare taking a leading role, and private insurers typically adopting and/or adapting Medicare policy. Although most insurers will cover professional services, home health care, skilled nursing, and hospice, many place barriers such as prior authorization, documentation of homebound status, prior hospitalization, or imminent death to qualify. Further, the traditional Medicare benefit does not include prescription medications commonly needed for symptom management, instead patients must have supplemental prescription drug coverage (Part D or otherwise) or be enrolled in hospice. Economic research is needed to understand the tradeoffs between different models of palliative care delivery, the costs and benefits of services, and their impacts on health and work. In traditional Medicare, comprehensive palliative care has traditionally been provided through the hospice benefit reserved for patients estimated to be in the last 6 months of life. This has tended to discourage early and regular palliative care services during survivorship. However, results from several recent studies suggest that integration of palliative care during early periods of active cancer therapy reduces costs of care and may extend survival. End-of-Life Care The end-of-life (EOL) phase of cancer survivorship draws on both palliative care and formal and informal caregiving services, and all issues associated with access, spending, and outcomes are relevant during EOL. Specific to EOL is the issue of care intensity, the extent to which invasive, uncomfortable, and generally futile services are provided that may be inconsistent with patient preferences as well as financially costly. Key supply-side issues include provider incentives related to timing of hospice enrollment or receipt of futile cancer treatments near the EOL. Emerging Payment and Coverage Models With the growth in the number of cancer survivors and particularly those who have complex care needs, it is increasingly recognized that new strategies for delivering cancer survivorship care are needed. The Centers for Medicare and Medicaid Services (CMS) are currently testing several ways of meeting these goals in the Medicare population (discussed below), but these models may represent better ways of caring for younger survivors as well. We need a better understanding of whether the same models that perform well for older, largely retired adults in Medicare also provide positive outcomes for younger survivors, and we need to understand the intergenerational spillovers of various policy efforts. If Medicare spending is lowered because an adult daughter reduces hours worked to provide informal caregiving, the net social harms may outweigh savings in Medicare. Oncology Care Model In 2016, CMS launched the Oncology Care Model (OCM), a multipayer alternative payment model for oncology physician practices providing chemotherapy to patients with cancer. Under OCM, approximately 200 oncology practices entered payment agreements for 6-month episodes of care for Medicare beneficiaries with cancer-receiving chemotherapy. Practices bill using fee-for-service billing rules and may also bill Medicare $160 per patient per month to support care coordination and improved care delivery. Practices are eligible for performance-based payments if they meet goals for quality and total costs of care. OCM focuses primarily on patients in active cancer treatment; however, there are several requirements that have potential to influence care for cancer survivors. Notably, practices are required to provide beneficiaries with a treatment summary and survivorship care plan when they make the transition from active treatment to posttreatment survivorship care. Additionally, practices must use electronic records and provide additional enhanced services including patient navigation, care consistent with guidelines, and a care plan that includes information such as prognosis, goals of treatment, expected response to treatment, expected out-of-pocket costs, and advance care plans. More transparency about expected costs of treatment may lead to better financial counseling and strategies to limit financial toxicity, which may provide benefits to patients beyond their primary treatment. In addition, OCM requires practices to report quality measures related to screening and management of pain and depression for OCM participants. This may prompt practices to develop strategies for improving these symptoms among patients receiving chemotherapy that may produce benefits into survivorship care. Oncology Medical Homes Oncology medical homes aim to deliver high-quality, patient-centered care for individuals with cancer building on the patient-centered medical home model of primary care delivery (20). These models emphasize team-based care. Some preliminary evidence suggests that these models may help lower use of emergency department visits and hospitalizations (21,22); however, additional research is needed to understand the impact of these models on care and outcomes for cancer survivors. Primary Care Plus The Comprehensive Primary Care Plus model is a national advanced primary care medical home model that seeks to strengthen primary care through regionally based payment reform and care delivery transformation (23). This model combines quarterly care management payments with prospectively paid and retrospectively reconciled performance-based incentive determined by patient experience, quality, and utilization measures. Practices can choose between traditional fee-for-service billing or second track shifts providing quarterly, lump sum comprehensive primary care payments. These payments could allow practices to increase the comprehensiveness of care delivered and ultimately lead to larger payments than under fee-for service care while also taking over some care that was historically provided by specialists. Risk-Stratified Model Although cancer patients report that they prefer to continue seeing their oncologist for their survivorship care (24), this is likely to become increasingly challenging because of the growing number of cancer survivors and a relative workforce shortage (25). The risk-stratified model classifies survivors based on multiple factors including prognosis, risk of recurrence, severity of chronic adverse effects of treatment, risk of late effects and additional cancers, functional status, personal resources and capacity to self-manage aspects of their care, and ability to navigate the health-care system (26). In such “risk stratified” models of care, patients with minimal ongoing problems who have low risk for recurrence and late effects are followed in primary care and those with higher risk may be followed in shared care models that include oncology and primary care clinicians. Such models have been tested in England with promising results (27). Research is needed to develop and test these new care models in the United States. Medicare Advantage In 2020, 24 million individuals, 40% of Medicare beneficiaries overall, were enrolled in Medicare Advantage (MA) plans (28). In contrast to traditional Medicare, MA plans receive capitated payments from CMS and employer groups, for “bundled” coverage for provision of medical care services normally paid directly to providers under fee-for-service payments under parts A, B, and usually D and often offer lower cost sharing. However, many MA plans require preauthorization for cancer treatment, and lower patient cost-sharing may come at the expense of a limited provider network. Although much of the of literature comparing MA relative to traditional Medicare is mixed with respect to variation in survivorship cancer care delivery and outcomes, few papers have used econometrics to account for nonrandom selection into plans (29,30). Diffusion of Effective Survivorship Models Ensuring adoption of effective survivorship care models will be challenging given the difficulty of identifying which models are effective in the first place and the variety of payers influencing care. Detecting meaningful effects will probably require long-term, multicenter trials. The heterogeneity of cancer care delivery also poses an obstacle. Some patients are treated in multispecialty cancer centers that take ownership off all aspects of patient care and are housed in the same health system as patients’ primary care providers. Other patients receive oncology care from 2 or more physician groups and primary care from another, unaffiliated practice. Unlike most new oncology drugs, which fit into the existing drug prescribing, dispensing, and delivery system, survivorship care needs to be tailored to the capabilities and reach of specific oncology and primary care providers and matched to patient needs. Although survivorship care, which can include a diverse bundle of services depending on patients’ needs following the acute cancer treatment, is generally not well reimbursed and may not be considered part of an initial disease episode, cancer providers may feel the need to adopt some form of survivorship care to respond to payment incentives and certification requirements. In the absence of strong evidence and evaluation, there is a risk that ineffective survivorship care practices may persist. Continual evaluation of existing survivorship care practices can help avoid wasteful care. Plans should be scrutinized to balance the risk of an undetected recurrence against the harms of excessive screening and surveillance. Currently, many cancer survivors are screened for additional tumors beyond required guidelines. Improving Research With Existing Data Resources Although there are many survivorship areas that would benefit from health economics research, limitations with currently available datasets pose challenges for analysts. In this section, we describe investments in research data that are important for the next phase of health economics survivorship research. Table 1 summarizes current limitations and strategies for improving the major categories of data, which are discussed below. Table 1. Opportunities to improve existing data resources for health economics survivorship research Data type . Current limitations . Improvement strategies . Population-based cancer registry Missing detailed treatment information Longer-term follow-up information needed Lack financial outcomes (employment, financial toxicity, etc) Incorporate claims data, especially Medicare Advantage and all-payer claims when available Link to administrative economic data (tax returns, credit reports, Social Security benefits) Longitudinal survey data Health-care claims Typically lack stage at diagnosis, functional status Challenging to identify palliative care Miss care reimbursed outside the system Difficult to ascertain death vs change in insurance Continue to develop all-payer claims and pooled insurer data to track patients across insurance providers Link to other survey and administrative datasets to recover additional details and outcomes Methods work using electronic medical records to develop strategies to detect palliative care, advance directive information, and so forth, in claims data Health-care delivery system research databases Limited to patients in specific health-care systems Data are difficult to access, especially for researchers outside participating institutions Expand access to data and training for outside researchers Surveys National samples typically include too few survivors to study specific cancers or treatments Costly data collection Long lag time between data collection and release Fund modules or sample supplements building on existing cohorts Support a centralized survey data repository on survivorship Data type . Current limitations . Improvement strategies . Population-based cancer registry Missing detailed treatment information Longer-term follow-up information needed Lack financial outcomes (employment, financial toxicity, etc) Incorporate claims data, especially Medicare Advantage and all-payer claims when available Link to administrative economic data (tax returns, credit reports, Social Security benefits) Longitudinal survey data Health-care claims Typically lack stage at diagnosis, functional status Challenging to identify palliative care Miss care reimbursed outside the system Difficult to ascertain death vs change in insurance Continue to develop all-payer claims and pooled insurer data to track patients across insurance providers Link to other survey and administrative datasets to recover additional details and outcomes Methods work using electronic medical records to develop strategies to detect palliative care, advance directive information, and so forth, in claims data Health-care delivery system research databases Limited to patients in specific health-care systems Data are difficult to access, especially for researchers outside participating institutions Expand access to data and training for outside researchers Surveys National samples typically include too few survivors to study specific cancers or treatments Costly data collection Long lag time between data collection and release Fund modules or sample supplements building on existing cohorts Support a centralized survey data repository on survivorship Open in new tab Table 1. Opportunities to improve existing data resources for health economics survivorship research Data type . Current limitations . Improvement strategies . Population-based cancer registry Missing detailed treatment information Longer-term follow-up information needed Lack financial outcomes (employment, financial toxicity, etc) Incorporate claims data, especially Medicare Advantage and all-payer claims when available Link to administrative economic data (tax returns, credit reports, Social Security benefits) Longitudinal survey data Health-care claims Typically lack stage at diagnosis, functional status Challenging to identify palliative care Miss care reimbursed outside the system Difficult to ascertain death vs change in insurance Continue to develop all-payer claims and pooled insurer data to track patients across insurance providers Link to other survey and administrative datasets to recover additional details and outcomes Methods work using electronic medical records to develop strategies to detect palliative care, advance directive information, and so forth, in claims data Health-care delivery system research databases Limited to patients in specific health-care systems Data are difficult to access, especially for researchers outside participating institutions Expand access to data and training for outside researchers Surveys National samples typically include too few survivors to study specific cancers or treatments Costly data collection Long lag time between data collection and release Fund modules or sample supplements building on existing cohorts Support a centralized survey data repository on survivorship Data type . Current limitations . Improvement strategies . Population-based cancer registry Missing detailed treatment information Longer-term follow-up information needed Lack financial outcomes (employment, financial toxicity, etc) Incorporate claims data, especially Medicare Advantage and all-payer claims when available Link to administrative economic data (tax returns, credit reports, Social Security benefits) Longitudinal survey data Health-care claims Typically lack stage at diagnosis, functional status Challenging to identify palliative care Miss care reimbursed outside the system Difficult to ascertain death vs change in insurance Continue to develop all-payer claims and pooled insurer data to track patients across insurance providers Link to other survey and administrative datasets to recover additional details and outcomes Methods work using electronic medical records to develop strategies to detect palliative care, advance directive information, and so forth, in claims data Health-care delivery system research databases Limited to patients in specific health-care systems Data are difficult to access, especially for researchers outside participating institutions Expand access to data and training for outside researchers Surveys National samples typically include too few survivors to study specific cancers or treatments Costly data collection Long lag time between data collection and release Fund modules or sample supplements building on existing cohorts Support a centralized survey data repository on survivorship Open in new tab Population-Based Cancer Registry Data Population-based state cancer incidence registry data, most notably the Surveillance, Epidemiology, and End Results Program, provide information about large numbers of newly diagnosed patients with cancer across payers and settings of care. Registries typically collect clinical details about cancer diagnoses (eg, site, histology, stage), some information about the first course of treatment, and vital status. We see several areas where data investments could improve the utility of these data for health economics. Longer-term follow-up data are necessary and could come from survey or administrative data or a combination of sources. Key domains for expansion include detailed systemic treatment information (eg, chemotherapy, targeted agents), adverse events, late effects, recurrence, and financial outcomes including employment and financial toxicity. Claims data represent the most promising source for adding medical variables to Surveillance, Epidemiology, and End Results and other registries but require tracking and harmonization across a number of datasets. Promising areas of development include incorporating Medicare Advantage encounter data to include the nearly 40% of Medicare beneficiaries who no longer participate in fee-for-service Medicare (28); all-payer data from states that collect it; claims from other large insurers or insurer groups, although data privacy rules can make linkages challenging, and the time required for linking data can limit the timeliness of research. Although claims data can help identify survivors with high out-of-pocket spending, most economic outcomes need to come from other sources. Administrative sources of economic outcomes that could be linked include consumer credit reports, IRS tax data, and Social Security benefit claiming and/or earnings data (31-33). Survey data could also capture this information, potentially in greater detail, although systematic collection of such data can be costly to implement and maintain. Claims Data As noted above, health-care claims data are an excellent source of information on specific treatments and services that have billable diagnostic or procedure codes. However, claims data generally lack critical details like cancer stage at diagnosis and outcomes that are not necessary for billing. Palliative care services can be difficult to identify in claims or hospital discharge datasets, especially when the same codes may be used to describe services like chemotherapy that can be given with palliative or curative intent. When patients are not observed in a dataset, it can be impossible to determine whether this reflects an unmet need for care, death, change in health insurance, or access to care in another system such as Veterans’ Affairs Administration care. Currently, the frequent insurance transitions of the population aged younger than 65 years implies that available panel datasets have selected samples if they can be assembled at all. The increasing availability of pooled private insurer data resources like the Health Care Cost Institute may create new opportunities for the use of claims data in survivorship research. Similarly, state all-payer claims datasets may allow researchers to see care for all patients, even if they change from one insurer to another. However, state regulations may limit how such data are used, and such datasets often lack information on patients enrolled in self-insured plans, which can be a substantial proportion of privately insured individuals in many states. Foundational research describing the populations that can and cannot be observed over time in these datasets will be important, along with efforts to link these data to registries, economic, and quality-of-life outcomes. Additional methods work is needed, potentially linking electronic health record text, to improve identification of key variables and constructs such as palliative care services and patient preferences and code status including do not resuscitate orders and advance directive information. Health-care Delivery System Research Databases Economic evaluations that address cancer survivorship care delivery, outcomes, and costs often require cross-sectional and longitudinal data across health-care systems, the age continuum (eg, aged younger than 65 years and 65 years or older), and the phase of care continuum—including diagnosis, treatment, surveillance, and end-of-life phase of care (34,35). For more than 2 decades, data infrastructures consistent with those built and maintained via the National Cancer Institute–funded Cancer Research Network (CRN) and the Population-based Research to Optimize the Screening Process have collected and curated electronic health records, claims, and tumor data on patients of all ages, for long periods prior to, during, and after a cancer diagnosis. These data include detailed information on patients enrolled in employer-sponsored plans, MA plans, Medicaid and dual-eligible plans, and self-pay Affordable Care Act–compliant plans but are limited to patients in specific health-care systems. These data allow for economic and comparative effectiveness research specific to survivorship outcomes including treatment, recurrence, and costs (36-38). Although access to these proprietary data sources often require collaboration with researchers embedded within these health-care systems, to better enhance the depth and breadth of research that can be conducted, the NCI and research consortium leaders are committed to leveraging these research resources for expanded access by the extramural community. These data also have limited information about economic outcomes. Survey Data Survey data offer the opportunity to fill in gaps that are missed by administrative data sources and to connect information from multiple domains of respondents’ lives. Datasets like the National Health Interview Survey, the Medical Expenditure Panel Survey, and the Health and Retirement Study have created opportunities to understand diverse topics such as employment after cancer diagnosis and financial burden in a nationally representative sample, however, these strengths must be balanced against limitations. The samples of cancer survivors in nationally representative datasets are often too small to study specific cancers or treatments. Self-report data limit the ability to include detailed treatment information if linked claims data are not available. Finally, there is frequently a multi-year gap between data collection and availability. Strategies to increase the use of survey data in survivorship research could include funding modules or additional sample supplements in existing nationally representative cohorts and centralizing deposit of survey data that could be used for survivorship research. As internet panels increasingly allow for low-cost collection of survey data through resources like Prolific (www.prolific.co) and the Understanding America Study (uas.usc.edu), rapid, low-cost survey data collection on cancer survivors should become more common. Strategies to Increase Survivorship Research and Capacity Funders can target strategic investments to increase the quality and volume of our health economics survivorship research and improve the lives of millions of survivors. Dedicated funding for health economics research and training, including the medical and social aspects for economists and social scientists and theory and methods for clinical researchers, are important steps in this direction. In particular, we believe that investments in new data linkages combining diverse sets of longitudinal and health outcomes are critical to grow this field. Specific strategies that funding agencies can adopt to facilitate this growth are summarized in Table 2 and include the following: Table 2. Strategies to increase survivorship research and capacitya Gap to address . Strategy . Key activities . Improve quality and volume of health economics cancer survivorship research Increase dedicated funding for health economics Specific requests for applications with dedicated funding Priority funding lines Training grants, conferences, educational opportunities Limited availability of economics-relevant outcomes in primary data collection Require a standard set of economics outcomes in all sponsored data collections Convene expert panel to devise measure set Limited follow-up periods for clinical trials and other studies Facilitate access to patient identifiers for future linkage to survey or administrative data Devise standard consent language to allow future linkages to be performed Develop protocol for interested researchers to obtain initial trial data and participant identifiers Lack of datasets with diverse longitudinal health and economic outcomes Support new data linkages Expand SEER linkage to Medicare Advantage Assess the feasibility of linking financial data Fund methods work on topics such as fuzzy matching and algorithms to identify specific services and constructs with incomplete data Gap to address . Strategy . Key activities . Improve quality and volume of health economics cancer survivorship research Increase dedicated funding for health economics Specific requests for applications with dedicated funding Priority funding lines Training grants, conferences, educational opportunities Limited availability of economics-relevant outcomes in primary data collection Require a standard set of economics outcomes in all sponsored data collections Convene expert panel to devise measure set Limited follow-up periods for clinical trials and other studies Facilitate access to patient identifiers for future linkage to survey or administrative data Devise standard consent language to allow future linkages to be performed Develop protocol for interested researchers to obtain initial trial data and participant identifiers Lack of datasets with diverse longitudinal health and economic outcomes Support new data linkages Expand SEER linkage to Medicare Advantage Assess the feasibility of linking financial data Fund methods work on topics such as fuzzy matching and algorithms to identify specific services and constructs with incomplete data a SEER = Surveillance, Epidemiology, and End Results Program. Open in new tab Table 2. Strategies to increase survivorship research and capacitya Gap to address . Strategy . Key activities . Improve quality and volume of health economics cancer survivorship research Increase dedicated funding for health economics Specific requests for applications with dedicated funding Priority funding lines Training grants, conferences, educational opportunities Limited availability of economics-relevant outcomes in primary data collection Require a standard set of economics outcomes in all sponsored data collections Convene expert panel to devise measure set Limited follow-up periods for clinical trials and other studies Facilitate access to patient identifiers for future linkage to survey or administrative data Devise standard consent language to allow future linkages to be performed Develop protocol for interested researchers to obtain initial trial data and participant identifiers Lack of datasets with diverse longitudinal health and economic outcomes Support new data linkages Expand SEER linkage to Medicare Advantage Assess the feasibility of linking financial data Fund methods work on topics such as fuzzy matching and algorithms to identify specific services and constructs with incomplete data Gap to address . Strategy . Key activities . Improve quality and volume of health economics cancer survivorship research Increase dedicated funding for health economics Specific requests for applications with dedicated funding Priority funding lines Training grants, conferences, educational opportunities Limited availability of economics-relevant outcomes in primary data collection Require a standard set of economics outcomes in all sponsored data collections Convene expert panel to devise measure set Limited follow-up periods for clinical trials and other studies Facilitate access to patient identifiers for future linkage to survey or administrative data Devise standard consent language to allow future linkages to be performed Develop protocol for interested researchers to obtain initial trial data and participant identifiers Lack of datasets with diverse longitudinal health and economic outcomes Support new data linkages Expand SEER linkage to Medicare Advantage Assess the feasibility of linking financial data Fund methods work on topics such as fuzzy matching and algorithms to identify specific services and constructs with incomplete data a SEER = Surveillance, Epidemiology, and End Results Program. Open in new tab Increase dedicated funding for health economics. This can include specific requests for applications with dedicated funding streams or priority funding lines and decisions. To ensure that the workforce will be large enough to conduct necessary studies in health economics, funding for training grants, conferences, and other educational opportunities should be prioritized. Require economics-relevant outcomes in sponsored primary data collection. An expert panel could recommend a standard set of outcomes such as measures of labor supply and financial burden that would allow consistency across studies and pooling of samples, similar to the National Institutes of Health efforts to collate and standardize COVID-19 survey measures. Facilitate access to patient identifiers such as Social Security numbers to enable longitudinal outcomes to sponsored clinical trials and other studies. This would help build the infrastructure to add additional administrative and survey outcomes and increase the utility of existing trial data beyond the initially funded research questions. Support new data linkages with diverse longitudinal health and economic outcomes. Although there are currently limitations with many of our available administrative data sources, connecting these data to survey and registry data remains one of our most powerful tools for survivorship research. Because this work can develop in parallel with public and private sector investments in data quality and availability, we recommend focusing on areas that leverage existing resources, for example, adding Medicare Advantage Encounter data to the SEER-Medicare linkage so that treatment data are available for all Medicare beneficiaries. New approaches to funding and facilitating survivorship health economics research can pay dividends in the coming years to support cancer survivors. 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Published: Jul 5, 2022

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