Dana P. Goldman, 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. 

. 2013;50(3):185-196. doi: 10.1053/j.seminhematol.2013.06.007.

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 study the association between cost sharing for diabetes medications, adherence, hospitalization rates, and healthcare costs, with relationship to patient risk.

A retrospective claims analysis of data from 35 large, private, self-insured employers (2004 to 2012).

We examined outcomes for 92,410 patients aged 18 to 64 years with a type 2 diabetes (T2D) diagnosis who filled at least 1 T2D prescription. First, we examined the relationship between adherence, measured as the proportion of days covered, and cost sharing, measured as the out-of-pocket cost to purchase a pre-specified bundle of T2D prescriptions. We then examined the association between adherence and hospital days. Simulations showed the effect of increased cost sharing on adherence and inpatient utilization.

A $10 increase in out-of-pocket cost was associated with a 1.9% reduction in adherence (

 <.01). In turn, a 10% reduction in adherence was associated with a 15% increase in per-patient hospital days (0.17 days; 

<.01). For the average plan, switching from low to high cost sharing reduced per-patient medication costs by $242 and increased per-patient hospitalization costs by $342, for a net increase of $100 in plan costs. Increases in per-patient costs were greater for high-risk patients, such as those with heart failure ($1328).

 Increased cost sharing for T2D medication was associated with reductions in pharmacy costs, but higher total costs for patients with T2D. This problem is particularly acute for patients with 1 or more cardiovascular comorbidities. The results suggest that increased diabetes cost sharing may hamper efforts to lower the total cost of diabetes care.

	This study investigated the association between cost sharing for type 2 diabetes (T2D) medications and patient outcomes, including medication adherence, hospitalizations, and healthcare costs to payers. Increasing patient out-of-pocket costs decreased patient medication adherence, and poorer medication adherence resulted in increased days spent in the hospital. The long-term hospitalization costs associated with lower adherence were greater than the health plan’s prescription drug cost-savings, particularly for high-risk patients. Decreasing out-of-pocket expenses may actually decrease long-term T2D-related costs for payers. 

		Higher levels of cost sharing are associated with lower levels of adherence to T2D medications. 

		Reduced adherence to T2D medications is associated with an increased number of days spent in hospital. 

		Although higher cost sharing reduces T2D drug costs, it raises hospitalization costs and, overall, increases costs to the plan. 

		High cost sharing has particularly adverse consequences in high-risk populations, such as those with heart failure or prior myocardial infarction, in terms of patient health and plan costs.

The last decade has seen a proliferation of payer-driven efforts to contain rising healthcare costs. Prescription drugs have increasingly been subject to utilization management policies, including tiered formularies and patient cost sharing for branded products. However, a large body of literature shows that increased cost sharing can have unintended consequences of patient noncompliance to prescribed medication regimens,

 and may delay treatment initiation in patients newly diagnosed with chronic illness, including diabetes, hypertension, high cholesterol, and multiple sclerosis.

 This has prompted concern that increased cost sharing may worsen outcomes for patients.

If high cost sharing worsens patient medication adherence, it could lead to costly complications that partially or completely offset payers’ pharmacy savings. For example, a study of patients with high cholesterol suggested that higher cost sharing reduced drug adherence and worsened outcomes for patients at high risk of complications, subsequently increasing overall costs.

 However, the relationships between cost sharing, medication adherence, and outcomes are sensitive to drug class and patient condition.

 More disease-specific estimates on the implications of cost sharing for patient outcomes and plan-spending are needed.

 In particular, in the case of diabetes, there have been a number of innovations over the past 10 years, and more information is needed on whether the relationship between cost sharing, adherence, and outcomes has changed as a result.

Disruptions to therapy caused by high cost sharing can be particularly harmful for patients with diabetes. Successful blood glucose control in patients with diabetes substantially decreases the risk of numerous complications, including cardiovascular disease (CVD), end-stage renal disease, stroke, retinopathy, and death.

 Diabetes affects 29.1 million Americans, costs the US healthcare system $245 billion annually (2012 data), and is projected to increase dramatically over the next 40 years.

 Type 2 diabetes (T2D), which represents 90% to 95% of adult diabetes cases,

 is a progressive disease, and requires most patients to eventually use drug therapy to achieve blood glucose control.

 Although antihyperglycemic drugs can effectively control blood glucose levels, research indicates that these drugs are underutilized, with studies finding the percent adherent to T2D medications ranges from 31% to 87%, with sizable groups of nonadherent patients in many populations.

Given the launch of new classes of antihyperglycemics in a healthcare system increasingly concerned with budget constraints, our study updates past work on cost sharing in diabetes

 in light of the new landscape. Moreover, given that past studies have found that increased cost sharing for diabetes medications may lead to complications which offset the pharmacy cost savings,

 one might wonder whether payers have changed benefit designs such that these offsets are no longer present. To answer these questions, we measured associations between cost sharing of T2D-related medications, patient adherence, and total costs for health plans. To do this, we used retrospective data on medication use, healthcare utilization, and spending among privately insured patients with T2D to simulate the per-patient healthcare spending associated with low-cost ($10 co-payment) and high-cost ($50 co-payment) pharmacy plans.

A retrospective cohort design was used to analyze the associations among cost sharing, medication adherence, and per-patient all-cause hospital days. Combining these analyses, it was possible to link cost sharing to hospitalization and estimate the impact of cost sharing on plan costs. Additional detail can be found in the 

The study sample was assembled using a longitudinal database of medical and pharmacy claims from 2004 to 2012 from 35 self-insured employers. The data set was constructed at the person-year level to reflect a person’s experience in a specific health plan.

Patients with T2D were identified for the study based on 1 or more inpatient or ambulatory visit with a T2D diagnosis (

International Classification of Diseases, Ninth Revision, Clinical Modificatio

n diagnosis codes 250.x0 or 250.x2). To create a working-age sample and exclude patients also covered by Medicare—for whom the claim history may be incomplete&mdash;the sample was restricted to patients aged 18 to 64 years at baseline. To ensure sufficient follow-up for analysis, patients were required to have had continuous health plan and pharmacy benefit enrollment during the first observed year with a T2D diagnosis, and for at least 1 year afterward.

Patients excluded from the study included those with less than 1 year of follow-up, those pregnant, and those with gestational diabetes. Additionally, patients using an insulin pump were excluded due to the difficulty in measuring adherence in claims data; however, patients using insulin, not on a pump device, were included. Because this study examined adherence to T2D medications, person-year data were retained only for years in which the patient filled 1 or more T2D prescriptions. Data from the first year with a T2D diagnosis were excluded because the patient may not have had T2D for the entire year. Finally, we excluded outlier patient data in terms of cost sharing and hospital days, as well as patients in small plans (<10 total enrollees with T2D). Additional details on outlier exclusions are available in the eAppendix.

Data were collected for the following covariates: patient age, gender, indicators for each of the Charlson comorbidities

 during the previous year, medical cost sharing faced by the patient in the given year, and indicators for each year in the study period.

 Medical cost sharing was defined as the average portion of medical (ie, inpatient, outpatient, and emergency department) services that individuals in a given plan paid out of pocket (OOP).

The key explanatory variable was cost sharing for T2D medications, measured as the average patient OOP cost of purchasing a T2D drug prescription in a given plan. Specifically, the OOP costs to obtain a fixed bundle of T2D prescriptions were averaged across the individuals in a plan, and the result was divided by the number of drugs in the bundle to obtain the average OOP cost per T2D prescription. Total T2D drug costs were defined as the total amount the plan and patient spent on T2D drugs in a given year.

The key outcome measures were patient medication adherence and number of hospital days. Adherence was measured as the proportion of days covered (PDC) (ie, the fraction of days in the year in which the patient had a supply of at least 1 T2D medication on hand). For example, a PDC of 0.75 indicates that a patient had T2D medications on hand for about 9 months of the year. PDC was selected over another common adherence measure—the medication possession ratio&mdash;which has a tendency to overestimate adherence. Hospital days were defined as the number of days an individual spent in hospital in a given year. Hospital stays in which a patient was admitted and discharged in a single day were counted as a full day.

Linear regression models were used to estimate the relationship between cost sharing, adherence and hospital days at the patient-year level. First, adherence was estimated as a function of drug cost sharing, then days in hospital were estimated as a function of adherence. Adjusted analyses controlled for age, gender, each of the Charlson comorbidities, and the year (to absorb time trends). Because patient adherence to T2D medications may also depend on OOP spending for other healthcare services, the analysis of adherence, as a function of T2D medication cost sharing, also controlled for medical cost sharing.

We used these estimates in a 2-step process to predict total plan costs (ie, plans’ drug plus hospitalization costs) at high and low levels of T2D drug cost sharing. Low cost sharing and high cost sharing were defined as an average OOP cost per T2D prescription of $10 and $50, which approximately corresponded to the 10th and 90th percentiles of cost sharing, respectively. Regression models were used to predict adherence and hospital days at the low and high cost-sharing levels, holding covariates at their mean values. Payer hospitalization cost was determined by multiplying the predicted number of days in hospital by the average payer cost of a hospital day. Costs were inflated to 2012 US dollars using the Consumer Price Index for All Urban Consumers. By combining the analyses linking cost sharing to adherence and adherence to days in hospital, we predicted the plan’s T2D drug costs and hospital costs at high and low cost-sharing levels, and estimated how total plan cost would vary with cost sharing.

These predictions were performed for several scenarios. The base case used the full study sample. Subsequent analyses were performed on subsamples defined by patient risk: specifically, the presence of CVD, congestive heart failure (CHF), prior myocardial infarction (MI), renal disease, or prior hospitalization. Additional subsamples included lower-risk patients, including those in none of the above risk groups, and those with no comorbidities. Finally, because our plan-level cost-sharing measure was potentially subject to measurement error in smaller plans, the analyses were repeated based on plan size subsamples. Our base-case analysis included all T2D patients in plans with 10 or more patients with T2D that year; subanalyses also examined plans with greater than 20, 50, 100, and 500 patients with T2D.

From more than 7 million covered lives in the database, 727,476 had at least 1 claim with a T2D diagnosis. After applying exclusion criteria, 92,410 patients from 1514 healthcare plans were eligible for study inclusion. Plans had an average of 8776 members and 144 patients with T2D in the study cohort. Table 1 shows the personal characteristics of patients included in the study overall, as well as those in low and high cost-sharing plans (defined by the 10th and 90th percentiles of cost sharing). The sample was mostly male (56.1%), aged between 50 to 64 years (71.4%), and had an average of 1.17 Charlson comorbidities at baseline.

The mean annual per patient OOP amount for T2D drugs was $242 (SD = $337) and the mean OOP amount per individual T2D prescription was $22.70 (SD = $16.70). Only 10% of patients had an average OOP per T2D prescription cost exceeding $54, and 10% had an average OOP cost below $11.

The average adherence to T2D medications, measured as PDC, was 67% (SD = 29.1%). Patients with T2D spent an average of 1.15 (SD = 7.70) days in hospital each year. In a given year, 1 in 6 (16.6%) patients with T2D was hospitalized. Adherence in the sample ranged from 0.27% to 100%. (No one had a zero PDC because individuals were required to fill ≥1 prescription for cohort inclusion.) Individuals in low cost-sharing plans had an average adherence of 69%, whereas in high cost-sharing plans, the average adherence was 63%.

Compared with their counterparts in low cost-sharing plans, individuals in high cost-sharing plans generally tended to be younger (average age of 50.8 vs 53.2 years) and healthier (eg, fewer comorbidities, fewer days in hospital), consistent with past research.

 This suggests that any negative effects of cost sharing could be understated in our analysis due to selection of healthier patients to less generous plans.

Analysis Figure 1 illustrates the negative association between cost sharing and adherence for T2D prescription drug therapy. The figure, which reports the unadjusted average co-payment per prescription to the average medication adherence per plan, shows that a $10 increase in the OOP cost of T2D drugs was associated with 1.6 percentage points lower medication adherence (

 <.01). The average PDC in the plan was 67%, which means a $10 increase in OOP costs is associated with a —2.4% change in adherence (or 1.6 percentage points out of 67).

 Although Figure 1 shows a negative relationship between adherence and cost sharing, it does not account for differences across patients within the plan. After adjusting for observed patient characteristics, the negative relationship between cost sharing for T2D prescriptions and adherence to T2D drug therapy persisted. Based on the regression, a $10 increase in the OOP cost of T2D drugs was associated with a lower PDC of 1.3 percentage points (

 <.01), or a —1.94% change in drug adherence for a plan with the average PDC of 67% (or 1.3 percentage points out of 67).

 Increased adherence was associated with reduced all-cause hospital days. Each 10% point increase in adherence was associated with a 0.17-day reduction in the number of days a patient was expected to spend in hospital annually (

 <.01) (Figure 2). For a plan with 100,000 members with T2D, this equates to a reduction of 17,000 patient-days in hospital annually. This represented a 14.8% decrease in the mean 1.15 hospital days per patient, per year.

Increased cost sharing for T2D drugs was associated with increased payer hospitalization costs. Raising the average OOP cost per T2D prescription from $10 to $50 was associated with a $242 reduction in per-person payer costs for T2D prescriptions and an increase of $342 per person of payer hospitalization costs, as shown in Figure 3.

Impact of increased cost sharing by risk group and plan size. 

As shown in Table 2, increased cost sharing was associated with higher total plan costs across a variety of plan sizes and patient populations. Increasing the average OOP payment per T2D prescription from $10 to $50 resulted in increased overall payer costs (combined drug and hospitalization costs) for all high-risk and 1 of 2 low-risk patient populations. The largest increase in plan costs was for patients with T2D and CHF ($1328 per patient, per year). The only patient risk group for which increased cost sharing did not increase overall plan costs was patients with no comorbidities other than diabetes.

Although plans of all sizes experienced increased overall payer costs, the plan impact of increased cost sharing was highest in the largest plans. Among plans with more than 500 members with T2D, increased cost sharing was associated with a $169 increase in per-person plan costs compared with $100 in plans with 10 or more members with T2D. This means that for a plan with 1 million covered lives and 150,000 members with T2D (assuming 15% prevalence), increasing cost sharing would raise plan costs by $25 million annually.

Efforts to contain healthcare costs have led payers to shift the disease management burden to patients through increased prescription drug cost sharing. In this study, we estimated how increased cost sharing affected medication adherence, hospital days, and total per-patient health plan costs for patients with T2D using claims data. We found that each $10 increase in patient OOP cost was associated with a 1.6 percentage point decrease in adherence (2.4%; 

 <.01); moreover, a 10% reduction in adherence was associated with 15% higher per-patient hospital days. Combining these results, we found that for the average plan, switching from low to high cost sharing would potentially reduce plan per-patient medication costs by $242 and increase plan per-patient hospitalization costs by $342, for a net increase in plan cost of $100. Thus, although higher cost sharing reduces T2D drug costs to the plan, it may raise hospitalization costs by a greater amount, more than offsetting the savings. For a plan with 150,000 members with T2D, this could amount to an increase of $25 million in annual plan costs.

High cost sharing has particularly adverse consequences for patient outcomes and plan costs in certain high-risk populations (eg, patients with various comorbidities and/or prior hospitalizations) in our model. In particular, high cost sharing among patients with CHF, CVD, and prior MI led to simulated large increases in per-patient, annual hospitalization costs, at $1794, $1220, and $632, respectively. These costs far outweighed any per-person savings associated with medication cost sharing, at $466, $272, and $149, respectively. Likewise, healthcare plans with more than 500 patients with T2D experienced the highest increase in per-person annual hospitalization costs, at $550—which was higher than per-person cost savings associated with medication cost sharing, at $381. On the other hand, for the healthiest patients with no comorbidities during the study period, cost sharing on diabetes medication resulted in modest cost savings. However, the relationship between cost sharing and adherence was consistent across the high-risk and low-risk groups, so it is unclear whether these savings would persist over longer time periods if patients failed to adhere to their medications and their diabetes worsened or led to complications. This should be considered in future work.

These findings are consistent with prior work demonstrating that cost sharing worsens patient medication adherence, resulting in costly complications. For example, previous studies have shown that patient cost sharing leads to decreased medication adherence and can compromise health in patients with T2D and other chronic conditions.

 Results such as these led the Congressional Budget Office to change its scoring system, such that every 10% increase in prescription medication use reduces estimated medical costs by 2%.

 This study confirms these findings and further demonstrates that decreasing cost sharing for patients with T2D may be advantageous for payers. Specifically, lower patient OOP expenses for T2D medications may improve access and adherence, leading to improved long-term patient outcomes and decreased overall costs.

 On the other hand, increased medication cost sharing may decrease adherence and associated total payer costs.

These findings demonstrate the importance of a comprehensive approach to benefit design incorporating costs, value, and patient outcomes. As T2D prevalence increases in the United States, the cost burden of T2D care will increase for payers,

 stimulating further efficiencies in healthcare delivery systems. As plans experiment with cost sharing and tiered prescription plan designs to lower payer costs, some patients may face substantial, and sometimes prohibitive, OOP costs.

 Plans may also sort newer T2D therapies into higher cost-sharing tiers, with possible consequences for drug initiation and adherence, as well as overall plan costs.

 Our findings suggest that, rather than imposing policies that limit or disrupt T2D care, payers may see greater benefit from policies that carefully consider cost sharing and adherence promotion. More generally, formulary policy, like all aspects of healthcare delivery, should be designed to encourage the use of services that foster quality, improve outcomes, and offer the most value to patients and society.

An analysis of claims from over 90,000 patients with type 2 diabetes (T2D) demonstrates that increased medication cost sharing is associated with higher rates of hospitalization and increased plan costs.

Impact of Type 2 Diabetes Medication Cost Sharing on Patient Outcomes and Health Plan Costs

Lipid-lowering therapy (LLT) is suboptimally used in patients with hyperlipidemia in the 2 highest statin benefit groups (SBGs), as categorized by the American College of Cardiology and the American Heart Association. This study estimated the social value of reducing low-density lipoprotein cholesterol (LDL-C) levels by 50% for patients in SBGs 1 and 2 who have been treated with standard LLT but have not reached LDL-C goal, as well as the potential value of PCSK9 inhibitors for patients in these groups.

We used National Health and Nutrition Examination Surveys (NHANES) and US Census data to project the population of SBGs 1 and 2 in the time period 2015 to 2035. We used insurance claims data to estimate incidence rates of major adverse cardiac events (MACEs), and NHANES with National Vital Statistics data to estimate cardiovascular disease mortality rates. Using established associations between LDL-C and MACE risk, we estimated the value of reducing LDL-C levels by 50%. We incorporated results from a meta-analysis to estimate the value of PSCK9 inhibitors.

Among those treated with LLT with LDL-C  >70 mg/dL in SBGs 1 and 2, the cumulative value of reducing LDL-C levels by 50% would be $2.9 trillion from 2015 to 2035, resulting primarily from 1.6 million deaths averted. The cumulative value of PCSK9 inhibitors would range from $3.4 trillion to $5.1 trillion (1.9-2.8 million deaths averted), or $12,000 to $17,000 per patient-year of treatment. 

 Lowering LDL-C in high-risk patients with hyperlipidemia has enormous potential social value. For patients in these high-risk groups, PCSK9 inhibitors may have considerable net value depending on the final prices payers ultimately select.

	Before PCSK9 inhibitors even reached market, their costs to the healthcare system were the subject of much scrutiny. We estimated the social value of PCSK9 inhibitors to place their costs in context with the value they generate for patients and society.

		Our study confirmed what we already knew: reducing lipid levels significantly in high-risk patients can have a considerable health impact. 

		Our study suggests that depending on the resulting confirmation of current estimates of effectiveness, PCSK9 inhibitors can provide considerable value when appropriately targeted, even at currently negotiated prices. 

		We also show that estimates of the likely budget impact are considerably off the mark when realistic uptake patterns are applied.

Cardiovascular disease (CVD) remains the leading cause of death, disability, and medical costs in the United States. Nearly 1 in 3 Americans dies of heart disease or stroke, and the annual cost of CVD in the United States exceeds $600 billion.

 Elevated low-density lipoprotein cholesterol (LDL-C) has been associated with increased risk for CVD events and death.

 Historically, statins have served as first-line lipid-lowering therapy (LLT) for LDL-C reduction, although recent guidelines have questioned the role of specific LDL-C goals in the primary and secondary prevention of CVD events.

Despite estimates of the costs of CVD in the United States, little is known about the economic value of reducing the hyperlipidemia burden among those at high risk for CVD who, despite use of standard LLT, do not achieve conventional LDL-C goals (eg, ≤70 mg/dL). The American College of Cardiology (ACC) and American Heart Association (AHA) classify patients as being in the highest-risk statin benefit groups (SBGs) with established atherosclerotic CVD (ASCVD) (SBG 1), LDL-C levels >190 mg/dL (SBG 2), and diabetes (SBG 3).

 Previous research estimated that up to 75% of high-risk patients treated with statins fail to achieve LDL-C of ≤70 mg/dL.

 High CVD event rates in this population, coupled with a substantial proportion of patients not at conventional LDL-C goals, suggest potentially large economic value from reducing the burden of hyperlipidemia. Previously, eliminating deaths resulting from CVD has been estimated to be worth nearly $50 trillion in economic value.

 For those initiating statin therapy between 1997 and 2008 alone, the value of averted deaths and CVD events may exceed $1 trillion.

Members of a novel class of therapies called PCSK9 inhibitors have recently been approved for treatment of hyperlipidemia in patients in SBGs 1 and 2 who do not achieve LDL-C of ≤70 mg/dL despite receiving maximally tolerated LLT. In phase 2 and 3 trials, PCSK9 inhibitors have been shown to reduce LDL-C by between 50% and 77%, on average.

 Preliminary clinical outcomes from these studies suggest that PCSK9 inhibitors may reduce rates of major adverse cardiac events (MACEs) by up to 50%.

 Based on these findings, the FDA approved alirocumab and evolocumab for use in adults with heterozygous familial hypercholesterolemia or ASCVD, who require additional LDL-C reduction.

 Amidst these developments, payers and policy makers have expressed concern that PCSK9 inhibitors’ costs—announced at $14,100 and $14,600 annually for evolocumab and alirocumab, respectively&mdash;will dramatically increase healthcare spending.

In this study, we estimated the economic value to the United States over the next 20 years (2015-2035) of reducing hyperlipidemia burden among those at high risk for MACEs (SBGs 1 and 2) who have not achieved conventional LDL-C goals despite use of standard LLT therapy. As a case study, we estimated the value to the United States of using PCSK9 inhibitors in these patients who are currently on LLT but have not achieved an LDL-C of ≤70 mg/dL.

Our study had 2 objectives: first, we sought to quantify the clinical and economic values of reducing the burden of hyperlipidemia among patients in the United States at high risk for MACEs or CVD mortality who have been treated with standard LLT but have not achieved conventional LDL-C goals. Second, we sought to project the clinical and economic value of PCSK9 inhibitors in patients at particularly high risk of MACEs or CVD mortality who may be eligible for treatment with these agents—those in SBGs 1 and 2&mdash;who have been treated with standard LLT but have not achieved conventional LDL-C goals. We defined conventional LDL-C goals to be ≤70 mg/dL and analyzed the sensitivity to a higher threshold of 

We used the 2011 to 2012 National Health and Nutrition Examination Study (NHANES)

 to first estimate the proportion of the US adult population (nonpregnant adults 18 years or older) in SBGs 1 and 2, as outlined by ACC and AHA guidelines

 We then divided each of these groups into 3 risk subgroups based on their use of, and LDL-C response to, LLT: subgroup A—patients on LLT who have not successfully reduced LDL-C to ≤70 mg/dL (or ≤100 mg/dL, in sensitivity analyses); subgroup B&mdash;patients at goal LDL-C on LLT; and subgroup C&mdash;patients not on LLT.

 We used US Census projections to estimate the size of each these populations from 2015 to 2035.

 We used the Truven Marketscan insurance claims database to estimate the rates of nonfatal CVD events (ie, unstable angina, myocardial infarction, coronary arterial revascularization, and ischemic stroke) and a combination of the NHANES mortality files and National Vital Statistics Mortality Report (2012) to estimate mortality rates for each of these groups (see eAppendix Tables 3-5).

With projected prevalence of patients not at goal LDL-C despite being on LLT (or who are intolerant to statins), we estimated the number of CVD-related deaths and MACEs averted for subgroup A in the time period 2015 to 2035 if LDL-C levels were hypothetically reduced by 50%, as suggested by the ACC and AHA.

 To estimate the effects of LDL-C reductions, we used the quantitative relationship between LDL-C and relative risk of CVD events and mortality from the Cholesterol Treatment Trialists’ (CTT) Collaboration meta-analysis.

 We then quantified the economic value to the United States of averted CVD-related deaths and MACEs using standard health economic valuation approaches.

We simulated the effects of PCSK9 inhibitor use for SBGs 1 and 2 in subgroup A patients. We projected the number of CVD-related deaths and MACEs averted in the time period 2015 to 2035, and the associated economic value if these patients were to receive PCSK9 inhibitors for further LDL-C reduction. We estimated the effects of PCSK9 inhibitors in 2 ways. First, we combined the LDL-C—lowering effects from PCSK9 inhibitor trials with the LDL-C relationship from the CTT Collaboration meta-analysis.

 Second, we used data on the effects of PCSK9 inhibitors on CVD-related death and MACEs from a meta-analysis of recent randomized controlled trials of these drugs.

 For a more detailed description of our methods for population and prevalence projections and event and mortality rate estimates, please see the eAppendix.

Across all PCSK9 inhibitor trials, the mean difference in LDL-C between those receiving LLT plus PCKS9 inhibitors compared with LLT alone was 59% (95% CI, 57%-61%). We used data from the CTT Collaboration meta-analysis (described in the eAppendix) to estimate the effect on clinical outcomes.

 Using this method, PCSK9 inhibitor use in SBG 1 was associated with a reduction in MACE risk ranging from 43% to 50%, and a 32% reduction in CVD mortality risk, assuming the same baseline LDL-C level as indicated above. In SBG 2, the mortality reduction was 45%. The estimated impact of PCSK9 inhibitors on clinical outcomes in this scenario was termed the “conservative efficacy” treatment scenario. Next, we modeled the impact of PCSK9 inhibitors on MACEs and CVD deaths by using direct outcomes data from a meta-analysis of PCSK9 trials.

 In the meta-analysis, PCSK9 inhibitor use was associated with a 50% relative risk reduction in rates of MACEs and CVD mortality. We termed this scenario the “high-efficacy” treatment scenario.

It should be noted that the studies in this meta-analysis were not weighted to detect differences in MACE outcomes, but for changes in LDL-C levels. Therefore, these results should be interpreted with caution.

We then modeled 3 uptake scenarios for PCSK9 inhibitors. In each scenario, PCSK9 inhibitor uptake began at 3% of the eligible population in 2015 (the eligible population included patients in SBGs 1 and 2 treated with standard LLT, but with LDL-C >70 mg/dL). Starting in 2016, uptake was assumed to increase linearly by 1%, 2%, or 3% for scenarios 1, 2, and 3, respectively, until 2023, when treatment uptake reached the maximum of 10% (scenario 1), 20% (scenario 2), or 30% (scenario 3) of the eligible population, and then remained constant through 2035.

Translating Clinical Outcomes Into Estimates of Economic Value

Reducing lipid levels in high-risk patients can significantly reduce disease burden and, depending on final negotiated prices, PCSK9 inhibitors can make an economic contribution to this goal.

Dana P. Goldman, PhD

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Value of Improved Lipid Control in Patients at High Risk for Adverse Cardiac Events

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