Julia Thornton Snider, PhD

 To date, breakthrough chimeric antigen receptor (CAR) T-cell therapies, such as tisagenlecleucel, indicated for pediatric acute lymphoblastic leukemia (pALL) and diffuse large B-cell lymphoma (DLBCL), and axicabtagene ciloleucel, indicated for DLBCL, although clinically effective, have been limited by treatment delays. Our study measured the social value of CAR T-cell therapy (CAR T) for relapsed or refractory pALL and DLBCL in the United States and quantified social value lost due to treatment delays.

 We used an economic framework for therapy valuation, measuring social value as the sum of consumer surplus and manufacturer profit. Consumer surplus is the difference between the value of health gains from a therapy and its incremental cost, while accounting for indirect costs and benefits to patients.

 For 20 incident cohorts of pALL (n = 20 × 400 = 8000) and DLBCL (n = 20 × 5902 = 118,040), we quantified patient value, calculated as the value of additional quality-adjusted life-years gained with CAR T, minus the incremental cost of CAR T compared with standard of care (SOC). We calculated manufacturer profits using a range of production costs given uncertainties in the production process. Patient value and manufacturer profits were summed to obtain total social value. We measured social value lost from treatment delays, assuming that patients received the SOC while awaiting CAR T-cell treatment.

 Depending on production costs, as much as $6.5 billion and $34.8 billion in social value was generated for patients with pALL and DLBCL, respectively. However, with 1, 2, or 6 months of treatment delay (assuming $200,000 production costs), the pALL population lost 9.8%, 36.2%, and 67.3% of social value, respectively, whereas the DLBCL population lost 4.2%, 11.5%, and 46.0%, relative to no delay.

 The social value of CAR T is significantly limited by treatment delays. Efficient payment mechanisms, adequate capital, and payment policy reform are urgently needed to increase patient access and maximize the value of CAR T.

Chimeric antigen receptor (CAR) T-cell therapies can provide significant benefit to patients with relapsed/refractory pediatric acute lymphoblastic leukemia (pALL) and diffuse large B-cell lymphoma (DLBCL) and to US society, generating up to $6.5 billion and $34.8 billion of social value for patients with pALL and DLBCL, respectively.

However, with 1, 2, or 6 months of treatment delay, patients with pALL lost 9.8%, 36.2%, and 67.3% of social value, respectively; patients with DLBCL lost 4.2%, 11.5%, and 46.0% of social value, respectively.

The magnitude of CAR T-cell therapy’s value depends on timely patient access. Efficient payment mechanisms, adequate physical and human capital, and payment policy reform could help reduce treatment delays.

Although there have been major advances in treatments for hematologic cancers such as pediatric acute lymphoblastic leukemia (pALL) and diffuse large B-cell lymphoma (DLBCL),

 efficacious treatments have historically remained limited for the population with relapsed or refractory disease.

 However, chimeric antigen receptor (CAR) T-cell therapies, such as tisagenlecleucel and axicabtagene ciloleucel, offer a possible cure for these patients.

 A recent review by the Institute for Clinical and Economic Review (ICER)

 concluded that tisagenlecleucel for pALL and axicabtagene ciloleucel for DLBCL are cost-effective treatments with incremental costs per quality-adjusted life-year (QALY) of $45,971 and $136,078, respectively.

Despite the recent approval of breakthrough therapies using CAR T cells in the United States, patients have faced barriers to treatment, including manufacturing challenges and a lack of formal coverage policies for CAR T-cell therapy (CAR T) in an inpatient setting,

 Given the aggressive nature of relapsed/refractory disease, patients eligible for CAR T may have to settle for less efficacious third- or fourth-line therapies

 or even die while waiting for CAR T reimbursement approval.

Cost-effectiveness analyses, like the ICER report, are useful for informing how resources may be allocated to treatments with the greatest QALY gains; however, stakeholders must consider the trade-off between treatment access today and incentivizing future treatment innovation. Social value analyses can complement cost-effectiveness analyses by shedding light on the access/innovation trade-off. Both types of analyses can inform coverage decisions, but they provide insight into different trade-offs that decision makers must weigh.

In this study, we measured the social value of treating pALL and DLBCL with CAR T in the United States and the social value lost from treatment delays as reported in the media.

 Social value analyses are used to quantify a therapy’s economic value from a societal perspective

 and determine the share of that value accruing to the manufacturer and patients. Expanded patient access and greater health benefits increase social value, whereas a greater requirement of society’s resources to produce the therapy (ie, higher production costs) reduces it. The higher the share of social value accruing to the manufacturer, the stronger the incentives for innovation. However, when treatment is delayed, social value is lost for both patients and manufacturers: Patients lose access to health gains from the treatment, and manufacturer profit is reduced.

An economic framework for therapy valuation was used. Specifically, we measured social value as the sum of consumer surplus and manufacturer profit.

 In the health context, consumer surplus measures the difference between the value of the health gains from a therapy and its incremental cost to the patient. It also accounts for indirect costs and benefits to patients. We calculated the economic benefit of tisagenlecleucel for pALL relative to standard of care (SOC), clofarabine monotherapy, and of axicabtagene ciloleucel for DLBCL relative to salvage chemotherapy.

 In each case, social value was estimated for 20 incident cohorts in the United States over a lifetime horizon. In each year, a new incident cohort entered the model and the existing prevalent cohorts aged an additional year. Each cohort’s survival followed that of the average patient for each treatment. We explain our calculations using tisagenlecleucel as an example; calculations for axicabtagene ciloleucel were similar, unless otherwise noted.

We obtained clinical and cost parameters from the literature and ICER’s assessment of CAR T (

 ICER reported that 400 incident cases of relapsed or refractory pALL occur annually and estimated that the average patient with pALL treated with CAR T would gain 7.9 discounted life-years (12.1 undiscounted) and 7.2 discounted QALYs (10.9 undiscounted) over SOC. Costs to the patient of tisagenlecleucel and SOC were obtained from the literature.

 Other treatment-associated costs were obtained from ICER’s report and can be found in Table 1.

We measured patient value, also known as consumer surplus, which is the difference between how much a consumer is willing to pay for a good or service and its price. We first estimated the health value that patients obtained, calculated as the value of QALYs gained with tisagenlecleucel compared with SOC, valuing each QALY gained at $150,000 (a midrange value from the literature).

 To obtain patient value, the incremental cost of tisagenlecleucel relative to SOC was subtracted from the health value.

 Next, we estimated productivity gains from tisagenlecleucel. Because the value of QALYs gained to patients includes the value of the labor and leisure they afford, some of the health value from tisagenlecleucel is attributable to productivity gains. Nationally representative data on employment and wages by age and sex from the Bureau of Labor Statistics and the US Census Bureau were used to calculate productivity gains.

The manufacturer profits were calculated next. We considered a range of production costs from $100,000 to $300,000 to reflect uncertainty and likely changes in the production process over time. The midpoint value, $200,000, was taken as the base-case value for sensitivity analysis. Given uncertainty about the future price of tisagenlecleucel with loss of exclusivity and competitor entry, we simplistically assumed a 30% price reduction in 2030 based on estimates from the literature.

 Finally, total social value was calculated by summing the patient value and manufacturer profit.

We calculated social value lost from treatment delays for the first pALL cohort. We examined the first cohort rather than all 20 cohorts given uncertainty around the extent of treatment delays in the future. We assumed that patients would take the SOC treatment while waiting for tisagenlecleucel and would initiate tisagenlecleucel treatment if they survived long enough to receive it. Survival of patients treated with clofarabine monotherapy was obtained from clinical trial data.

 For the first cohort, life-years, QALYs, and productivity were calculated conditional on patients receiving treatment after 1, 2, and 6 months of delay. Incremental value lost was calculated as the difference between the value obtained by patients who were treated immediately (0-month delay) and those who experienced delays.

The steps above were repeated to measure the value of axicabtagene ciloleucel for the treatment of 20 incident cohorts of patients with DLBCL, using a clinical trial for salvage chemotherapy

 as the SOC comparator in the treatment delays analysis. Table 1

 contains the parameters used in the calculations for patients with DLBCL. All health and monetary values were discounted at a rate of 3.0% and costs were inflated to 2017 US dollars. Additional detail is available in the 

We ran sensitivity analyses to test how consumer surplus, manufacturer profit, and social value changed by varying key model inputs. In 1-way sensitivity analyses, we adjusted the number of patients eligible for treatment, economic value of a QALY, price of CAR T, production costs, and future reduction in CAR T price individually from minimum to maximum values. We also varied life-year and QALY gains concurrently by ±50% and potential patient income by ±20%. In multiway sensitivity analyses, we conducted 1000 Monte Carlo simulations to vary each of the above parameters concurrently by selecting values of each parameter from its distribution, which measured the sensitivity of social value, manufacturer profit, and consumer surplus to the model assumptions. Each parameter was assumed to follow a beta distribution.

In the population with pALL (n = 20 × 400 = 8000), considering production costs of $100,000, $200,000, and $300,000 and a price of $475,000, we found that the total social values of tisagenlecleucel at each production cost were $6.5 billion, $5.8 billion, and $5.2 billion, respectively (

). The value accruing to patients was $4.4 billion regardless of production costs, representing 68.9%, 76.1%, and 85.0% of total social value, respectively. This translates to 48,485 life-years, 44,010 QALYs (worth $6.6 billion), and $352.0 million in productivity (worth 5.3% of QALY gains). The remaining 15.0% to 31.1% of total social value accrued to manufacturers.

Assuming no treatment delays, patients with pALL in the first cohort gained 2872 total QALYs. The value of those QALY gains totaled $430.8 million, of which $23.0 million (5.3%) was attributable to added patient productivity from employment gains. Accounting for the cost of acquiring CAR T, the total patient value was $271.2 million and the total social value was $381.2 million. This translates to 7.2 QALYs (worth $1.1 million), $57,423 in added productivity, and a social value of $952,991 per patient.

However, with 1, 2, or 6 months of treatment delay (assuming $200,000 production costs), the first pALL cohort lost 9.8%, 36.2%, and 67.3% of social value, respectively, relative to no treatment delays. Contributing to this were losses of 311, 1146, and 2128 total life-years; 282, 1040, and 1932 total QALYs; and $2.3 million, $8.3 million, and $15.4 million in total productivity, respectively. Each patient lost 0.8, 2.9, and 5.3 life-years; 0.7, 2.6, and 4.8 QALYs; $5638, $20,796, and $38,622 in productivity (

); and $93,560, $345,133, and $640,967 in social value, respectively. The loss of social value stems primarily from a high mortality rate in patients receiving SOC while awaiting treatment with tisagenlecleucel.

In the population with DLBCL (n = 20 × 5902 = 118,040), given production costs of $100,000, $200,000, and $300,000 and a price of $373,000, the total social values of axicabtagene ciloleucel were $34.8 billion, $25.8 billion, and $16.7 billion, respectively (

). The value accruing to patients was $13.5 billion regardless of production costs, which represents 38.7%, 52.2%, and 80.5% of total social value, respectively. This translates to gains of 372,617 life-years, 306,595 QALYs (worth $46.0 billion), and $12.5 billion in productivity (worth 27.3% of QALY gains). The remaining 19.5% to 61.3% of total social value accrued to manufacturers.

The first cohort of patients with DLBCL gained 20,008 total QALYs, assuming no treatment delays. The value of those QALY gains totaled $3.0 billion, of which $818.9 million (27.3%) was attributable to added patient productivity from employment gains. Accounting for the cost of acquiring CAR T, the total patient value was $659.5 million, and the total social value was $1.68 billion. This translates to 3.39 QALYs (worth $508,500), $138,742 in added productivity, and a social value of $284,743 per patient.

However, with 1, 2, or 6 months of treatment delay (assuming $200,000 production costs), the first DLBCL cohort lost 4.2%, 11.5%, and 46.0% of social value, respectively, relative to no treatment delays. Contributing to this were losses of 1021, 2796, and 11,185 total life-years; 840, 2301, and 9204 total QALYs; and $34.4 million, $94.2 million, and $376.7 million in total productivity, respectively. Each patient lost 0.2, 0.5, and 1.9 life-years; 0.1, 0.4, and 1.6 QALYs; and $5827, $15,955, and $63,821 in productivity (Figure 2

), resulting in losses of $11,959, $32,745, and $130,982 in social value, respectively.

In the pALL analysis, results of 1-way sensitivity analyses showed that social value was most sensitive to the discount rate, value of a QALY, and survival gains (

). When key parameter assumptions were varied simultaneously to test the sensitivity of the model to those inputs, the social value and patient value results were most sensitive to the discount rate, value of a QALY, and survival gains (

), and the manufacturer profits were most sensitive to the production costs, discount rate, and number of patients eligible for tisagenlecleucel (

In the DLBCL analysis, results of 1-way sensitivity analyses indicated that social value was most sensitive to the survival gains, value of a QALY, and production costs of axicabtagene ciloleucel (Table 2). These findings are similar in the multiway sensitivity analysis of social value (

). Meanwhile, multiway sensitivity analyses indicated that patient value was most sensitive to survival gains, value of a QALY, and discount rate, whereas manufacturer profits were most sensitive to production costs, number of patients eligible for axicabtagene ciloleucel, and discount rate (

Because the total social value of CAR T is determined by the survival gains and the production costs, it is expected that social value is most sensitive to the aforementioned parameters. Meanwhile, the price of CAR T, future reduction in its price, and patient income had no effect on total social value because the former 2 parameters affect only the patients’ and manufacturers’ shares of social value, whereas the latter affects only the amount of patient value attributable to productivity.

CAR T has provided the hope of a cure to patients who otherwise have limited treatment options and poor prognoses.

 Patients receiving CAR T are expected to experience meaningful improvements in life expectancy and QALYs, enabling them to contribute to overall productivity and generate social value. In both pALL and DLBCL, patients lost a substantial share of social value with treatment delays.

Various reasons have been reported for the treatment delays.

 One-time curative treatments such as tisagenlecleucel and axicabtagene ciloleucel present a challenge to existing payment systems because their costs accrue up front, whereas benefits accrue over a lifetime, in contrast with other cancer therapies that are administered over an extended time period. To address this challenge, novel financing mechanisms, such as an outcomes-based approach to reimbursement for tisagenlecleucel, are currently being discussed.

 One reimbursement approach under consideration would allow participating payers to pay for tisagenlecleucel only when patients respond within 1 month of treatment,

 allowing payers and manufacturers to share the financial risk. Additionally, aspects of the US healthcare system present challenges to outcomes-based contracts for curative therapies like CAR T. Because the average American changes health insurers every few years,

 the payers that pay the up-front costs of treatment with CAR T may not be the same ones that cover the cured individual years down the line. Thus, the payer may benefit from only a fraction of the savings, which reduces the incentive to invest in curative therapies.

 Creative solutions have been proposed to combat this “free-rider” problem.

 Although an outcomes-based contract developed for CAR T may help reduce payers’ risk of paying for nonresponse, the issue of up-front costs disincentivizing innovation remains.

 Further, the development of formal policies to cover CAR T has been slow. Currently, reimbursement is frequently done on an individual basis,

 with hospitals facing high financial risk to treat patients with CAR T without a guarantee of payment from insurers.

 Although larger health plans are often better equipped than smaller regional plans to handle such requests, reviewing each case individually lengthens the authorization process.

 In some cases, waiting for CAR T reimbursement approval may take up to 90 days, which may be longer than a patient’s survival.

 Some payers, such as Medicare, have had success securing coverage of CAR T in the outpatient setting

; however, challenges remain to provide sufficient reimbursement to hospitals to administer the treatment in an inpatient setting. Even with the recent approval of the new technology add-on payment of up to $186,500 per patient,

 intended to mitigate the additional costs of treatment, the reimbursement promised may fall short of the additional costs. When faced with high financial risk in the event that the costs of treating patients with CAR T exceed this payment cap, hospitals face disincentives for CAR T adoption. Thus, such policies may limit access for patients.

Additionally, CAR T is produced through a complex and individualized process

 that may be challenging to scale quickly. Efforts are currently under way to minimize delays caused by inefficiencies in production.

 Timely administration also necessitates that treatment centers be equipped with the proper equipment and human capital. Educating community oncologists is especially important in maximizing the efficacy and safety of CAR T, as patients are usually referred to their local oncologists for follow-up care after receiving treatment at the specified transplant centers.

 Professional organizations, such as the American Society of Hematology and the Foundation for the Accreditation of Cellular Therapy, are in the process of developing guidelines on CAR T.

Our social value analysis indicates that facilitating timely patient access is a key consideration in determining an optimal financing approach. For patients with rapidly progressing cancer and high mortality rates,

 delaying treatment comes at a high cost. The case of CAR T provides a lesson to payers, policy makers, and innovators for incentivizing innovation and providing access to other curative therapies. In particular, therapies providing large QALY gains, such as curative therapies, bring large social value to society. Allowing innovators to share in that value incentivizes the development of future cures. However, stakeholders must work together to facilitate prompt patient access to such therapies. Efficient payment mechanisms, sufficient technological capabilities, adequate capital and human capital, and payment policy reform are required to minimize treatment delays for patients. Others have also argued that the price of CAR T should be lowered.

 These considerations are particularly important given other new or curative therapies in the pipeline, such as voretigene neparvovec-rzyl for mutation-associated retinal dystrophy,

 and LentiGlobin BB305 for sickle cell disease and beta-thalassemia.

Our study is based upon the overall experience of patients with pALL and DLBCL and does not account for heterogeneity in patient experiences. We excluded caregiver burden from this analysis, but a reduction could be expected using CAR T-cell therapies, as they may offer patients a possible remission with fewer treatments and adverse events. Additionally, our study examined the impact of treatment delays of various lengths in only the first cohort of patients. It is uncertain how treatment delays may change in the future. Because of a lack of clinical data, we were also unable to account in our analysis for potential reductions in CAR T efficacy due to treatment delays. To the extent that delayed treatment reduces CAR T efficacy, our estimates of the social value lost because of treatment delays are conservative.

Moreover, the total cost of treatment with CAR T is not yet clear

 and may change over time. The average total costs of tisagenlecleucel used in our analysis ($736,265; obtained from the ICER report

) included the average costs required by patients with pALL over the course of their treatment history (costs of CAR T, chemotherapy treatment, palliative chemotherapy, pretreatment, stem cell transplantation, adverse events, administration and monitoring, future healthcare, and end-of-life costs). This estimate substantially exceeded the average cost of treatment in the literature, which considered physician costs for leukapheresis and administration of lymphodepletion therapy, facilities, CAR T, drugs other than CAR T, facility fees for hospitalizations for cytokine release syndrome, and physician costs. These estimates ranged from $432,131 to $510,963 with the outcomes-based pricing arrangement.

The average total cost of axicabtagene ciloleucel used in our analysis ($551,642) included costs accrued by patients over their treatment history (described above) and exceeded the $402,647 estimate reported by Hernandez et al in 2018.

CAR T-cell therapies have the potential to provide significant benefit to patients with pALL and DLBCL and to society in the United States, particularly through gains in survival and productivity. However, the magnitude of benefit depends upon the ability of patients to access these treatments promptly.

 Precision Health Economics (JTS, MB, RK, KB, DPG), Oakland, CA; Finance Department, Carlson School of Management, University of Minnesota (PK-M), Minneapolis, MN; Novartis Pharmaceuticals Corporation (JZ), East Hanover, NJ.

 Dr Thornton Snider is an employee of and holds equity in Precision Health Economics, which received payment from Novartis to conduct this research. Ms Brauer was employed by Precision Health Economics at the time that this research was conducted. Ms Kee is employed by Precision Health Economics. Dr Batt reports data interpretation for Novartis and consultancy for Precision Health Economics and has received honoraria from Novartis. Dr Karaca-Mandic provides consulting services to Precision Health Economics and received payment from Precision Health Economics for her involvement in the preparation of this manuscript. Dr Zhang is an employee of Novartis and owns Novartis stock. Dr Goldman served as a consultant to Precision Health Economics during the conduct of the study; owns equity (<1%) in Precision Health Economics’ parent company, Precision Medicine Group; has received payment from ACADIA Pharmaceuticals for service on a policy advisory board; was paid a fee for a lecture on cost-effectiveness at Amgen; and is the director of the Leonard D. Schaeffer Center for Health Policy & Economics, which is supported by gifts and grants from individuals, corporations, and associations; by government grants and contracts; and by private foundations (specific information about funding sources is available at healthpolicy.usc.edu).

 Concept and design (JTS, PK-M, JZ, DPG); acquisition of data (JTS, MB, RK, JZ); analysis and interpretation of data (JTS, MB, RK, KB, PK-M, JZ, DPG); drafting of the manuscript (JTS, MB, RK, KB, PK-M, JZ); critical revision of the manuscript for important intellectual content (JTS, MB, RK, KB, PK-M, JZ, DPG); statistical analysis (JTS, MB, RK); obtaining funding (JTS, DPG); administrative, technical, or logistic support (MB, RK); and supervision (JTS, KB, DPG).

 Rebecca Kee, BA, Precision Health Economics, 1999 Harrison St, Ste 1420, Oakland, CA 94612. Email: rebecca.kee@precisionxtract.com.

1. Cancer in children and adolescents. National Cancer Institute website. cancer.gov/types/childhood-cancers/child-adolescent-cancers-fact-sheet. Updated October 8, 2018. Accessed October 12, 2018.

2. Pui CH, Evans WE. A 50-year journey to cure childhood acute lymphoblastic leukemia. 

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Articles

Treatment delays limit the social value generated by chimeric antigen receptor (CAR) T-cell therapy for the treatment of pediatric acute lymphoblastic leukemia and diffuse large B-cell lymphoma.

The Potential Impact of CAR T-Cell Treatment Delays on Society

 To compare the well-being of long-term cancer survivors with that of US residents of similar age and demographic characteristics, patients recently diagnosed with cancer, and individuals with chronic illness.

 Using the Health and Retirement Study, a survey of US residents older than 50 years, we defined 4 cohorts: long-term cancer survivors (>4 years post diagnosis), individuals recently diagnosed with cancer (≤4 years post diagnosis), individuals with chronic illness, and US residents older than 50 years (“nationally representative cohort”). Well-being measures included self-reported health, utility, happiness, medical utilization and spending, employment, and earnings, and these measures were compared across cohorts, adjusting for survey year, demographic characteristics, smoking, and number of comorbidities. We imputed medical spending using the Medical Expenditure Panel Survey and the Medicare Current Beneficiary Survey. 

 Long-term cancer survivors fared significantly better than those recently diagnosed with cancer, those with chronic illness, and individuals in the nationally representative cohort in the majority of well-being measures (

<.05), including fewer doctor visits, hospitalizations, and hospital nights; better utility and self-reported health; and greater likelihood of employment. Long-term cancer survivors had lower healthcare spending than those recently diagnosed with cancer (

<.01) and significantly greater happiness than the nationally representative cohort and those with chronic illness (

 Although patients with cancer experience diminished well-being in the short term across a variety of measures, in the long term, cancer survivors do as well as or better than US residents of similar age and demographic characteristics. This finding is striking given that one might expect long-term cancer survivors to do worse than similar individuals without a history of cancer.

Although patients with cancer experience diminished well-being in the short term across a variety of measures, in the long term, cancer survivors do as well as or better than US residents of similar age and demographic characteristics.

We found that long-term cancer survival has an effect on well-being comparable with that of many chronic conditions. This is notable given that 70% of US residents older than 50 years have at least 1 comorbidity.

Advances in cancer care that offer a chance of long-term survival may enable patients to experience a high quality of life.

Cancer care is changing rapidly. Over the past 60 years in the United States, population aging and declining mortality from heart disease have led to an increase in the total number of deaths from cancer.

 However, this increase in the total number of deaths masks substantial improvement in the cancer mortality risk faced by individuals. Adjusting for population aging, cancer mortality rates in the United States have declined by 23% from 1990 to 2012, from 214.95 to 166.40 per 100,000 individuals.

 Reasons for this decline include advances in early detection and treatment and decreased smoking.

 Although the debate continues about the efficacy of the “war on cancer” and further progress is needed,

 the trend of improving mortality is clear. Studies examining many tumor types have found that advances in care have led to significant mortality declines in recent decades.

 These reductions in mortality have increased the number of cancer survivors.

However, surviving cancer does not guarantee a high quality of life. Adverse effects of cancer treatment are well documented. Cardiac toxicities are a known complication of specific chemotherapeutics used in breast and other solid tumors, leaving many cancer survivors with degrees of heart failure.

Radiotherapy in patients with breast cancer has been linked to an increased risk of coronary events.

 Impairment of renal function is a complication of certain cancer treatment regimens, often resulting in acute damage and chronic kidney disease.

Patients with cancer also face an increased risk of second primary malignancies.

 For example, a meta-analysis found that patients treated for Hodgkin lymphoma are at increased risk of developing second primary lung cancer.

 A study of testicular cancer survivors found that, compared with surgery only, radiotherapy below the diaphragm significantly increased the risk of second primary cancers or cardiovascular diseases.

Because long-term cancer survivors face the risk of second primary malignancies and may live with complications from treatment toxicities, one might expect that they would experience a low quality of life. However, research to date has tended to focus on clinical and short-term outcomes, while comparatively little is known about the well-being of long-term cancer survivors. As improvements in treatment have made long-term survival a possibility for some patients with advanced cancers, understanding the well-being of long-term survivors is of increasing importance. 

In this study, we measured the well-being of long-term cancer survivors and compared their well-being with that of individuals recently diagnosed with cancer, individuals with chronic illness, and US residents of similar age and demographic characteristics. According to the CDC, “There is no consensus around a single definition of well-being.”

 Therefore, we studied a variety of well-being measures encompassing patients’ health, life satisfaction, and productivity.

This study compared well-being outcomes across 4 cohorts: 1) long-term cancer survivors, 2) patients recently diagnosed with cancer, 3) individuals with at least 1 nonterminal chronic illness, and 4) a nationally representative sample of US residents older than 50 years. Regression analysis was used to compare outcomes across the cohorts, adjusting for survey year, demographic characteristics, smoking, and number of comorbidities. A study schematic is presented in 

We used the Health and Retirement Study (HRS), a nationally representative biannual survey of US residents older than 50 years, to construct the 4 cohorts and follow their well-being outcomes over time. The HRS was designed to illuminate various aspects of US residents’ lives, including health, finances, and employment, as they approach and move into retirement.

The common definition of long-term survival in individuals who have had a cancer diagnosis is 5 years post diagnosis.

 Because the HRS data are only collected every 2 years, we chose 4 years as the threshold for separating short- from long-term cancer survivors (ie, long-term survivors were diagnosed at least 4 years ago, whereas short-term survivors were diagnosed less than 4 years ago). This definition enabled us to focus on long-term survivors beyond the acute treatment phase. Taking a long-term perspective was important because research suggests that the transition from patient to long-term survivor can be challenging,

 and we sought to understand survivors’ well-being after that transition has been undertaken.

The chronic illness cohort included individuals with a diagnosis of diabetes, hypertension, heart disease, lung disease, or prior stroke, but with no prior cancer diagnosis. The nationally representative cohort consisted of all individuals older than 50 years in the 2010 HRS data, as this was the most recent nationally representative wave in the HRS. The cohorts were analyzed over 2004 through 2012 at the person-year level, although we used HRS data from as far back as 1992 to identify whether patients were short-term or long-term cancer survivors. A patient could appear in multiple cohorts. 

Because most outcomes in the HRS data are self-reported, these data are not ideally suited for tracking medical expenditures. Therefore, we developed an algorithm to impute total medical expenditures, including medical and pharmacy costs, in the HRS based on 2 data sets: the Medicare Current Beneficiary Survey (MCBS) for those older than 65 years and the Medical Expenditure Panel Survey (MEPS) for those between ages 50 and 64 years.

Specifically, we used the MCBS Cost and Use files, which combine results of a nationally representative survey of Medicare beneficiaries with administrative data to obtain accurate estimates of Medicare program spending.

 Health conditions were identified using the Health Status and Functioning (Community) data file from the Cost and Use files. We used the MCBS and the HRS from 1992 to 2010 to develop this imputation and applied it to our 2004-2012 HRS sample.

To estimate the medical expenditures of individuals aged between 50 and 64 years, we used the MEPS years 2000 through 2010. Although MEPS is based on survey data, it is designed to measure healthcare cost and utilization.

 In addition, the MEPS data were used to estimate utility for everyone older than 50 years based on the EQ-5D-3L measure, a utility measure introduced by the EuroQol Group in 1990. All health conditions were identified via self-reports from the MEPS condition files and household consolidated files.

Because there is no single consensus definition of well-being,

 we selected a wide range of well-being outcomes, including medical service use, healthcare spending, employment status, earnings, self-reported health, utility, and happiness.

Medical service use measures included the numbers of doctor visits, hospital admissions, and hospital nights over the 2 years prior to the survey. Healthcare spending (medical and pharmacy) was measured annually in the year of the survey and inflated to 2014 dollars using the medical Consumer Price Index (CPI). We constructed a binary variable for whether an individual was working at the time of the survey. We identified individuals as working if they were working full-time, they were working part-time, or they were workingpart-time in retirement. Earnings were measured annually in the year of the survey, conditional on the individual working, and inflated using the CPI for all urban consumers. Earnings summarized the market value of an individual’s effort in the labor force and served as a proxy for productivity. 

For self-reported health, we constructed a binary variable: poor or fair health versus good, very good, or excellent health. Self-reported health was measured at the time of the survey.

Utility was measured using a regression of EQ-5D-3L score on health, demographics, and functional status over the year prior to the survey.

 For happiness, we constructed a binary variable: not at all, a little, or moderately happy versus quite a bit or very much happy. Happiness was measured over the 30 days prior to the survey. 

We looked at the differences in these well-being outcomes across the 4 cohorts controlling for the year of the survey (2004-2012), age group (51-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, ≥85 years), gender, race (white/Caucasian, black/African American, other), ethnicity (Hispanic, non-Hispanic), education (less than high school, general educational development certificate, high school graduate, some college, college and above), health insurance (insured, not insured), smoking history (ever smoked, never smoked), and comorbidity count. The latter included any prior diagnoses of cancer, diabetes, heart disease, hypertension, lung disease, or stroke. Prior diagnosis was determined by whether an individual indicated ever being diagnosed with any of the mentioned diseases, as determined in the year of eligibility for the cohort in question. 

We performed a descriptive analysis on the HRS data using standard weights to compare outcomes across cohorts at the person-year level. Simple 2-way 

tests were conducted to assess the significance of the differences in means across cohorts. 

We ran multivariable analyses for all outcomes to compare the well-being of long-term cancer survivors with that of the other 3 cohorts, adjusting for survey year, demographic characteristics, smoking, and number of comorbidities. Because cancer could plausibly affect the subsequent development of comorbidities,

 we also ran an alternative specification omitting number of comorbidities as a covariate. Models were selected to be appropriate for the given outcome. We used a Poisson model for medical service use, an ordinary least squares model for annual earnings conditional on being employed, a Tobit model for utility, and a generalized least squares model with a gamma distribution and log link for healthcare spending. Additionally, we used logistic models for the binary outcomes: self-reported health, happiness, and employment status. After conducting the analyses, we estimated the predicted values of all outcomes using the mean values of the covariates for the nationally representative cohort in 2010. 

In 2010, there were 1184 long-term cancer survivors; 676 individuals recently diagnosed with cancer; 12,583 individuals with chronic illness; and 22,034 US residents older than 50 years (“the nationally representative cohort”). Over the biennial survey waves from 2004 to 2012, there were 8817; 3374; 57,108; and 22,034 person-years in the cohorts, respectively. Cohort characteristics are shown in 

. Long-term cancer survivors were, on average, older compared with the other cohorts and more likely to be female, white, and non-Hispanic and to have health insurance. 

presents the results of the descriptive analysis. The means of medical service use, employment, and medical spending for individuals recently diagnosed with cancer, the chronic illness cohort, and the nationally representative cohort were all significantly different from those of long-term cancer survivors (

<.01). The means of utility and earnings conditional on being employed for the chronic illness and nationally representative cohorts were significantly different from those of long-term cancer survivors (

<.01), whereas those of the recently diagnosed cohort were not statistically different from those of long-term cancer survivors. Grouping self-reported health as excellent, very good, or good versus fair or poor, the recently diagnosed cohort had a significantly lowerlikelihood of being excellent/very good/good,and the nationally representative cohort had a significantly higher likelihood, than long-term cancer survivors (

<.01). Grouping happiness as very much or quite a bit versus moderately, a little, or not at all, the chronic illness cohort was significantly less likely to be very much/quite a bit than long-term cancer survivors (

The results of the multivariable analyses are presented in 

, which shows the predicted values of the different well-being measures in each cohort. Long-term cancer survivors fared better than the recently diagnosed cohort in terms of healthcare utilization, utility, healthcare spending, self-reported health, and employment (all 

<.01). Differences in other outcomes were not significant. Similarly, long-term cancer survivors fared better than individuals with chronic illness in terms of healthcare utilization, utility, self-reported health (all 

<.01), happiness, and employment status (both 

<.05). Differences in other outcomes were not significant. Compared with the nationally representative sample, long-term cancer survivors fared better in terms of healthcare utilization, utility, employment status (all 

<.01), self-reported health, and happiness (both 

<.05). Other outcomes were not significantly different. 

The multivariable results are presented graphically in 

, which shows how the well-being measures compared across the 4 cohorts. To display all outcomes on a common scale, well-being measures were normalized so that the value of the long-term cancer survivors cohort is 100%.

The results of the alternative specification in which number of comorbidities was omitted as a covariate are presented in the 

). As in the base case, long-term cancer survivors fared better than the recently diagnosed cohort in the majority of well-being outcomes, with the exceptions of utility, happiness, and earnings, which were not significantly different. Compared with individuals with chronic illness, long-term cancer survivors had moderately higher healthcare utilization and spending and lower self-reported health but greater utility. There were no significant differences in hospital nights, happiness, employment, and earnings between long-term cancer survivors and individuals with chronic illness. Compared with the nationally representative cohort, long-term cancer survivors had greater healthcare utilization and spending and lower self-reported health and employment. Differences between long-term cancer survivors and the nationally representative cohort in terms of utility, happiness, and earnings were not significant.

Our study results show a marked improvement in cancer survivors’ well-being in the long term compared with the first 4 years after diagnosis. Moreover, long-term cancer survivors fared at least as well as average US residents across a variety of well-being measures, controlling for demographics and the number of comorbidities. When comorbidities were not controlled for, long-term cancer survivors had modestly higher healthcare utilization and spending and modestly lower self-reported health and employment compared with average US residents. Taken together, these results suggest that when long-term cancer survivors fare worse than individuals without cancer, it is due to their comorbidity burden. These differences tended to be modest in size (eg, 1 extra doctor visit over 2 years). Moreover, even without controlling for comorbidity burden, there were no differences in happiness, utility, or earnings between long-term cancer survivors and average US residents.

Our analysis indicated that 70% of US residents older than 50 years have at least 1 comorbidity. Therefore, the fact that long-term cancer survivors fare comparably with others their age, many of whom are also dealing with health conditions, suggests that in the long term, cancer has an effect on well-being comparable with that of many chronic conditions.

Previous literature has compared the well-being of cancer survivors with that of individuals with chronic illness.

 Steel et al studied the difference in health-related quality of life (HRQoL) of individuals with hepatocellular carcinoma (HCC), individuals with chronic liver disease (CLD), and the general population.

 Those with HCC had statistically significantly poorer HRQoL than those with CLD and the general population. Those with HCC and CLD had better social and family well-being than the general population. 

In a similar study, Elliot et al compared the well-being of cancer survivors without other chronic conditions, cancer survivors with chronic conditions, and individuals with chronic conditions other than cancer with that of healthy individuals.

 They found that, generally, the well-being outcomes of cancer survivors without chronic conditions did not statistically differ from those of individuals with 1 chronic condition. 

Other studies have compared the well-being of cancer survivors with that of the general population. Using data from the American Cancer Society’s Study of Cancer Survivors, Zhou et al showed that ovarian cancer survivors had an HRQoL similar to that of the general population 1 year post diagnosis.

 Kunitake et al found that long-term colorectal cancer survivors had better overall physical and mental health compared with the general population.

 Similarly, Greenwald et al found that cervical cancer does not reduce the quality of life of long-term survivors,

 and Schmidt et al found that breast cancer survivors may experience long-term quality of life comparable with that of the general population.

 Finally, Thong et al found that although disease progression reduces cancer survivors’ quality of life, the effect diminishes over time.

Our study echoes the existing literature in finding that long-term cancer survivors fared as well as or sometimes better than the general population. The main contribution of our study is that it investigates short- and long-term cancer survivors’ well-being across a variety of measures in a large, nationally representative sample.

Our findings are important given that recent breakthroughs in cancer treatment have raised the prospect of long-term survival for increased numbers of cancer patients. For example, new immuno-oncology agents, including ipilimumab, pembrolizumab, and nivolumab, have offered significant survival gains to patients,

 and preliminary data suggest the prospect of long-term survival for some metastatic cancer patients.

Our study does have limitations. First, the HRS data do not distinguish between cancer types and, therefore, we were unable to perform separate analyses by tumor type. As such, our results represent the experience of the “average” cancer survivor and will overrepresent those with particularly common or less deadly cancers. 

Second, the most recent nationally representative cohort in the HRS was in 2010, which was a recession year. Because the nationally representative cohort was constructed from the 2010 survey year and all other cohorts were constructed from the 2004-2012 survey years, secular trends could affect the well-being outcomes. However, we controlled for this by including survey year indicators in our analysis.

Third, in studying the well-being of cancer survivors, we necessarily limited the analysis to those who survived. Thus, we cannot definitively determine whether the observed changes in cancer survivors’ well-being between the short- and long-term time horizons are due to cancer survivorship itself or to characteristics potentially common to individuals more likely to survive cancer. 

Finally, we focused mainly on objective measures of well-being rather than subjective measures. However, focusing on objective measures does come with the advantage that, in doing so, we avoided any biases that can result from the way that subjective well-being outcomes are measured, as these outcomes can be subject to self-reporting or measurement bias.

Our study results are striking, given concerns that patients with cancer may experience a low quality of life.

 Although we do find that quality of life is reduced in the short term, we find that it solidly rebounds in the years after diagnosis, becoming comparable with or even better than that of others of similar age and demographic characteristics. The high quality of life experienced by long-term cancer survivors is relevant in the midst of debate about the cost and value of cancer care. Although cost and other up-front considerations are important, it is also important to keep the patient’s perspective in mind. Prior research has shown that patients value therapies that give them a chance of long-term survival.

 Our study shows that the well-being of long-term cancer survivors eventually recovers, becoming similar to or even better than that of the general population. To the extent that long-term survivors enjoy a high quality of life, it makes interventions offering patients with cancer a chance of long-term survival more valuable.

Precision Health Economics (JS, JTS, EvE), Los Angeles, CA; Bristol-Myers Squibb (TO), Plainsboro, NJ; Wake Forest Baptist Medical Center (KB), Winston-Salem, NC; Georgetown University (TD), Washington, DC.

This study compares the well-being of long-term cancer survivors with that of US residents of similar age and demographic characteristics, patients recently diagnosed with cancer, and individuals with chronic illness.

The Well-Being of Long-Term Cancer Survivors

To develop a model of the option value a therapy provides by enabling patients to live to see subsequent innovations and to apply the model to the case of nivolumab in renal cell carcinoma (RCC) and non—small cell lung cancer (NSCLC).

A model of the option value of nivolumab in RCC and NSCLC was developed and estimated.

Data from the Surveillance, Epidemiology, and End Results (SEER) cancer registry and published clinical trial results were used to estimate survival curves for metastatic cancer patients with RCC, squamous NSCLC, or nonsquamous NSCLC. To estimate the conventional value of nivolumab, survival with the pre-nivolumab standard of care was compared with survival with nivolumab assuming no future innovation. To estimate the option value of nivolumab, long-term survival trends in RCC and squamous and nonsquamous NSCLC were measured in SEER to forecast mortality improvements that nivolumab patients may live to see.

Compared with the previous standard of care, nivolumab extended life expectancy by 6.3 months in RCC, 7.5 months in squamous NSCLC, and 4.5 months in nonsquamous NSCLC, according to conventional methods. Accounting for expected future mortality trends, nivolumab patients are likely to gain an additional 1.2 months in RCC, 0.4 months in squamous NSCLC, and 0.5 months in nonsquamous NSCLC. These option values correspond to 18%, 5%, and 10% of the conventional value of nivolumab, respectively.

Option value is important when valuing therapies like nivolumab that extend life in a rapidly evolving area of care. 

Therapies that enable patients to live to see further innovations in care have option value. 

Option value raises the conventionally estimated value of nivolumab to patients with metastatic cancer by 18% in renal cell carcinoma, 5% in squamous non—small cell lung cancer (NSCLC), and 10% in nonsquamous NSCLC. 

Option value is particularly important in disease areas where there is rapid innovation. In such areas, payers and providers should consider option value when gauging the value of new therapies.

Cancer is a leading cause of death in the United States,

 with rising prevalence. In 1995, there were 7.1 million individuals in the US who had ever been diagnosed with cancer (2.7% of the population)

; by 2012, there were 13.4 million such individuals (4.3%).

 Although cancer mortality rates are declining,

 prevalence is expected to increase because of population aging,

 advances in cardiovascular care, earlier cancer detection, and life-extending treatment.

Increasing cancer prevalence translates to greater demand for oncology care, with implications for payers. Given growing pressures on payers’ budgets and debate over the cost of cancer treatment,

 value is an increasingly important consideration in coverage and access decisions. Moreover, the pressures on payers will increase as cancer prevalence rises, further reinforcing the importance of value.

As value has grown in importance in assessing cancer care, organizations have offered recommendations on measuring value. For example, the National Comprehensive Cancer Network and the American Society of Clinical Oncology recently published frameworks for considering a therapy’s efficacy and cost to determine its value.

 The Institute of Clinical and Economic Review, an independent health technology assessment (HTA) organization, uses cost-effectiveness analysis (CEA) and budget impact analysis to measure therapies’ value,

Traditional methods of CEA measure the value of an innovation by comparing benefits such as survival gains and improved quality of life with costs, assuming no other health technology improvements besides the one in question.

Option value is the benefit a therapy provides patients by enabling them to survive to the next innovation.

Author | Julia Thornton Snider, PhD

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