Yanyu Wu, PhD

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.

 However, such methods ignore aspects of therapies that matter to patients, like the value of hope, insurance value to healthy individuals, and the increased value people place on life near its end.

Such components of value can be difficult to quantify in HTA because they are subjective valuations based on unobserved patient preferences. However, 1 relevant component of value is rooted in real-world survival data, the “option value” of therapy. Option value is the benefit a therapy provides patients by enabling them to survive to the next innovation. For example, in cancer care, any survival gains today hold additional value because they increase the likelihood that a patient may live to access even more effective treatment in the future. This concept is particularly important in disease areas with traditionally poor outcomes and rapid innovation. 

Although metastatic non—small cell lung cancer (NSCLC) and metastatic renal cell carcinoma (RCC) have had low survival rates,

 innovation to treat both cancers is currently rapid. A number of breakthrough targeted therapies and immuno-oncology (IO) therapies, which harness the immune system to fight cancer, are available, such as nivolumab, pembrolizumab, osimertinib, and ceritinib for NSCLC and nivolumab and lenvatinib for RCC.

 These therapies are improving 1 or more clinical endpoints, such as survival rates for cancers with traditionally poor survival odds.

 Furthermore, the pipelines for both tumors are active.

Given the proliferation of treatment options and the prospect of future innovation, payers, policymakers, clinicians, and patients require multiple criteria to make care-related decisions.

 A therapy’s option value accounts for the future benefits of a life-extending therapy in addition to its immediate survival effects. With that in mind, we looked at the option value of nivolumab, the first IO therapy given breakthrough status and approved by the FDA for NSCLC and RCC.

 NSCLC is histologically divided into squamous and nonsquamous (adenocarcinoma, large cell) tumor types,

 and individuals face different prognosis and treatment options depending on the histology. Squamous NSCLC (about 25%-30% of all lung cancers) is typically smoking-related, whereas nonsquamous NSCLC is the most common type of lung cancer in nonsmokers (although smokers are still at greater risk than nonsmokers).

 We examined squamous and nonsquamous NSCLC separately.

We adapted a framework for estimating a therapy’s option value

 to the contexts of metastatic NSCLC and metastatic RCC. In particular, we examined the option value of nivolumab, the first programmed cell death protein-1 (PD-1) inhibitor in both disease states. In order to estimate nivolumab’s option value, a survival curve for patients taking the pre-nivolumab standard of care was needed first. The pre-nivolumab survival curves were modified using mortality hazard ratios (HRs) from clinical trial publications to obtain a nivolumab survival curve in each setting. Forecasting methods were used to project likely future survival improvements patients with either NSCLC or RCC may live to see. The conventional survival gain from nivolumab was obtained by comparing life expectancy with nivolumab, assuming no future innovation, against life expectancy with the pre-nivolumab standard of care. The option value of nivolumab was obtained by comparing life expectancy with nivolumab under 2 scenarios: allowing for likely future survival gains versus assuming no future innovation. Finally, the option value estimates were converted to economic terms. Additional methodological detail is available in the 

Survival trends in metastatic NSCLC and metastatic RCC were estimated using the Surveillance, Epidemiology and End Results (SEER) cancer registry.

 SEER, which tracks cancer incidence and mortality using data reported by registries across the United States, currently covers about 30% of the US population.

Long-term all-cause mortality rates were taken from the Human Mortality Database (HMD).

 The HMD contains detailed population and mortality data for 37 countries, and currently provides US life tables for 1933 through 2013.

Survival curves from SEER were modified to reflect nivolumab patients’ survival using mortality HRs from clinical trial publications.

 Specifically, in both squamous and nonsquamous NSCLC, nivolumab was compared with docetaxel, and in RCC, nivolumab was compared with everolimus. These comparisons reflect those in the trials noted on the nivolumab FDA label.

 The comparator drugs were assumed to represent the pre-nivolumab standard of care. 

We identified 3 study populations: metastatic squamous NSCLC, metastatic nonsquamous NSCLC, and metastatic RCC. First, we identified patients with NSCLC in SEER by requiring a primary cancer site of “lung or bronchus” or “trachea.” We distinguished squamous from nonsquamous histology types using 

International Classification of Diseases for Oncology, Third Edition

) histology codes. For both histology types, we defined metastatic NSCLC as American Joint Committee on Cancer (AJCC) stage IV, IIIb, or III with no surgery. The latter category addressed the fact that not all patients have a substage listed (eg, IIIa vs IIIb), and a lack of surgery suggested the case was inoperable.

To identify the RCC population, we required a primary site of “kidney,” “renal pelvis,” or “ureter,” and identified the RCC histology using 

 histology codes. To limit to patients with RCC with metastatic disease, we selected the “distant” stage using “SEER historic stage A.” Although AJCC staging is commonly used in clinical practice for treatment decisions and in clinical trials for eligibility,

 it was not well documented for RCC in SEER before 2004. Because metastatic RCC is a smaller sample than NSCLC, preserving sample size was important. Moreover, in order to track longer-term trends, it was necessary to be able to identify patients before 2004. In general, SEER distant stage approximates AJCC stage IV. Therefore, we identified metastatic RCC using the SEER distant stage. 

We estimated the option value of nivolumab in the 3 populations by taking these 5 steps.

Step 1: estimate pre-nivolumab survival curves. 

To establish a pre-nivolumab baseline, we estimated survival curves using the SEER data. We identified metastatic squamous and nonsquamous NSCLC and metastatic RCC patients diagnosed between 2001 and 2010. Survival curves for each population were estimated parametrically using a log-normal distribution. Covariates included age at diagnosis, age squared, gender, race, ethnicity, marital status, tumor grade, and a quadratic time trend.

Our survival estimation was limited by the patients’ duration of follow-up in SEER. Although we preferred using patients with a more recent diagnosis due to the pace of innovation in cancer care, this limited the number of years of follow-up available. Rather than project survival for many years beyond the duration of follow-up in the SEER data, we assumed that the minority of patients surviving more than 20 years past diagnosis had the same mortality rates as the general population, using US life tables from the HMD.

Specifically, we calculated mortality rates in the first 20 years after diagnosis using the estimated survival curves from SEER and then appended the HMD mortality rates. This lifetime mortality table was used as an input in the Lee-Carter model described in Step 2. 

The SEER survival curves estimated in Step 1 showed a clear trend of improving survival over time in all 3 populations. Based on these trends, we used the Lee-Carter method

 to forecast survival improvements for each population. The Lee-Carter method involves modeling age-specific mortality rates as a function of a long-term trend in overall mortality improvements and an age-specific response to the overall trend. This method is widely used as a benchmark for long-term mortality forecasts, including by the US Census Bureau and the Social Security Administration.

Step 3: estimate survival with nivolumab.

 To obtain survival curves for patients taking nivolumab in each population, we began with the pre-nivolumab survival curves estimated in Step 1. Based on the FDA label, which calls for second- or later-line use in both the NSCLC and RCC indications,

 we assumed that nivolumab would be given as a second-line therapy starting 1 year after diagnosis. Following the clinical trial evidence,

 we assumed that nivolumab patients would experience diminished mortality risk compared with patients on the prior standard of care. Specifically, we applied the mortality HRs from nivolumab trial publications to the pre-nivolumab survival curves estimated in Step 1. We conservatively assumed that any mortality benefits of nivolumab would disappear 4 years after beginning nivolumab therapy.

Step 4: calculate option value in terms of life expectancy gains. 

Using the survival curves from Steps 1 and 3 and the survival forecasts from Step 2, we calculated the undiscounted life expectancies of patients in each population for several scenarios. First, we calculated life expectancy under the pre-nivolumab standard of care, assuming no future innovation; call this 

. Second, we calculated life expectancy with nivolumab, assuming no future innovation; call this 

. Finally, we calculated life expectancy with nivolumab allowing for future innovation based on the forecasts from Step 2; call this 

We calculated the conventional survival gain with nivolumab by comparing life expectancy with nivolumab to that with the pre-nivolumab standard of care. Specifically:

Conventional survival gain with nivolumab = 

To calculate the option value of nivolumab, we compared life expectancy with nivolumab assuming no further innovation to expected lifetime with nivolumab allowing for future innovation. Specifically:

Step 5: calculate option value in economic terms. 

Finally, we expressed the conventional and option values in economic terms by applying an economic value of a life year to the additional life years gained. We selected a mid-range value from the literature of $150,000 per life year

 and applied a discount rate of 3% to obtain present discounted values. The per-patient option value was multiplied by the estimated size of each patient population to obtain the total economic values in each population. Population sizes were estimated using several sources.

To test the robustness of the study results to the underlying assumptions, several sensitivity analyses were performed. First, we estimated an alternative parametric survival model (log logistic). This tested the sensitivity of the results to our choice of parametric model. Second, we estimated survival exclusively using the parametric model rather than taking HMD mortality rates after 20 years. This tested the effect of our use of the HMD data. Third, we assumed nivolumab would be taken in the first year after diagnosis, rather than the second. This reflected the fact that metastatic cancer can progress rapidly and, therefore, some patients could potentially reach second-line therapy in the first rather than second year from diagnosis. Fourth, we varied the duration post diagnosis that nivolumab would be effective, from 2 to 10 years. This addressed the limited duration of clinical trial data and the accompanying uncertainty about the duration of nivolumab’s effects. Fifth, we estimated nivolumab’s mortality benefits using the confidence intervals of the published mortality HRs in order to address uncertainty about the exact magnitude of nivolumab’s effects of survival. Sixth, we varied the discount rate from 0% to 6%. Seventh, we varied the value of a life year from $50,000 to $250,000.

 Last, we varied the stage definition used for the selection of the SEER RCC data, using AJCC stage IV instead of the historic “distant” stage. (We used the historic stage definition in our base case analysis because it was better populated and we wanted to test the sensitivity of our results to this choice.)

 presents descriptive statistics on the 3 study populations in SEER. The nonsquamous NSCLC population was the largest, at 210,419 individuals, followed by squamous NSCLC (49,194) and RCC (12,868). The RCC population was younger than the squamous and nonsquamous NSCLC populations, with average ages of 64.5, 69.0, and 68.0 years, respectively. A higher share of patients with nonsquamous NSCLC (44%) was female compared with squamous NSCLC (35%) and RCC (33%). Patients with RCC were more likely to be married (60%) compared with patients with squamous (52%) or nonsquamous (53%) NSCLC. Racial composition was similar, with whites composing over 80% of all 3 groups. Median survival was 8 months in RCC and 7 months in both squamous and nonsquamous NSCLC.

All 3 populations showed an increasing 1-year survival rate in the 2001 to 2010 SEER data (

). Projected survival in 2011 to 2060 according to the Lee-Carter method is shown via the dashed lines. Survival improved the most quickly in RCC (0.44% per year), followed by nonsquamous NSCLC (0.39%) and squamous NSCLC (0.27%).

Life Expectancy Estimates and Option Value

Life expectancy for patients taking the pre-nivolumab standard of care was estimated to be 18.2 months in RCC, 11.7 months in nonsquamous NSCLC, and 11.6 months in squamous NSCLC. Ignoring future innovations, patients taking nivolumab were expected to survive 24.5 months with RCC, 16.2 months with nonsquamous NSCLC, and 19.1 months with squamous NSCLC, implying gains of 6.3 months, 4.5 months, and 7.5 months, respectively. These gains are shown in blue in 

 and represent the conventional survival gains from nivolumab.

However, during the time that nivolumab patients’ survival was extended, we allowed for additional innovations to come to market. Incorporating expected innovation, nivolumab extends survival by an additional 1.2 months in RCC, 0.5 months in nonsquamous NSCLC, and 0.4 months in squamous NSCLC. These additional gains due to option value represent 18% of nivolumab’s conventionally estimated survival gain in RCC, 10% in nonsquamous NSCLC, and 5% in squamous NSCLC.

Valuing the per-patient survival gains over the full incident metastatic population with each tumor, we found a conventional economic value from nivolumab of $775 million in RCC and an option value of $105 million (

). Due to its higher incidence, economic values were larger in NSCLC. The conventional value of nivolumab was $2.6 billion in nonsquamous NSCLC and $2.0 billion in squamous NSCLC, while the option values were $203 million and $73 million, respectively. Although these valuations are based on a mid-range value of a quality-adjusted life year, it should be noted that debate exists about the best way to provide economic valuations of incremental changes in survival, such as those in this study.

Articles

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

The Option Value of Innovative Treatments for Non–Small Cell Lung Cancer and Renal Cell Carcinoma

 To determine the lifetime social value of using the guideline-recommended vaccines for children born in the United States in 2009. 

This study utilized an economic model with parameter values sourced from clinical and observational data, as well as the literature. 

The model quantified the health effects of routine vaccination for 14 diseases in terms of quality-adjusted life-years (QALYs) saved. The health effects were then valued by applying an economic value of a QALY. Producers’ profits were estimated using data on vaccine prices, profit margins, and the number of vaccines administrated in the 2009 US birth cohort. The costs of producing the vaccines were subtracted from the value of the health effects to yield the total social value of vaccination. The producers’ and consumers’ shares of this social value were calculated. Sensitivity analyses were conducted to determine how results depend on underlying parameter assumptions. 

Estimates indicated that vaccination of this cohort will save 1.2 million QALYs, relative to no vaccination. Of those health gains, 88% stemmed from reduced mortality and 12% from reduced morbidity. We estimated a social value of $184.1 billion from these gains, of which $3.4 billion accrues to manufacturers as profits, while $180.7 billion accrues to the rest of society. In sensitivity analysis, the total social value ranged from $40 billion to $675 billion, and the manufacturers’ share ranged from 0.3% to 11.5%. 

 Policy makers should account for this social value when considering policies affecting incentives to vaccinate and develop new vaccines.

		By preventing illness and premature deaths, vaccination of children born in the United States in 2009 will generate $184 billion in lifetime social value above the costs of the vaccines. 

		Because saving a child’s life yields many healthy life-years, the large majority (88%) of the health benefits of vaccines is due to avoided premature deaths rather than reduced morbidity (12%). 

		The high social value of vaccines has improved population health and provided economic benefit to multiple stakeholders, including patients, health plans, and vaccine manufacturers, whose profits in this cohort amount to approximately 2% ($3.2 billion) of the total social value.

The innovation of childhood vaccines has resulted in a decline in infectious disease, as well as gains in length and quality of life. Smallpox has been eradicated, poliomyelitis is nearly eliminated, and many other vaccine-preventable diseases have seen declines in incidence.

 Although adverse events (AEs) can occur with vaccines,

 and recent research has focused on their rising costs,

 the postvaccine era has seen life expectancy increase 15 to 25 years compared with the pre-vaccine era, and further gains are expected.

 Evidence suggests a large share of these survival gains is due to the control of infectious disease through vaccination.

When encouraged by public health policies, vaccination also provides a benefit to government and private payers by reducing overall costs and increasing population health. The CDC has cited evidence that common childhood vaccinations save over $5 in direct medical costs and effects on productivity for every $1 spent.

 Maciosek and colleagues found that preventative childhood immunization produced annual net medical savings of $267 per person.

 Vaccination also generates community (herd) immunity by reducing disease incidence and transmission, thus resulting in a healthier population.

Because vaccines have been successful at preventing disease, the public is no longer regularly confronted with many vaccine-preventable diseases, and the health and economic benefits of vaccination may be underappreciated.

 As childhood vaccines have reduced disease prevalence, real and perceived AEs of vaccination have become more salient to parents than the vaccine-targeted diseases.

 Consequently, vaccination rates in many US states have declined in recent years.

 As vaccination rates slip, the risk of new outbreaks increases.

Moreover, although consumers are insulated from the cost of many vaccines, vaccine cost is an important consideration for payers and providers and has been criticized.

 This focus on AEs and costs has obscured vaccines’ overall value to individuals and society. Previous research has yet to show how the total social value of vaccines is divided between innovators who develop these technologies and patients and the broader society who benefit from them. Therefore, in this study, we sought to measure the social value of childhood vaccines in the United States and the distribution of that value to manufacturers versus the rest of society.

The concept of social value of therapies and its distribution between manufacturers and patients has been described in other disease areas. For instance, Grabrowski et al found that statin usage resulted in a social value of $1.25 trillion, of which patients received 76%.

 Yin et al performed a similar analysis on tyrosine kinase inhibitors for the treatment of chronic myeloid leukemia and found a social value of $143 billion—90% of which was retained by patients.

 Recent gains in cancer survival have provided $1.9 trillion of additional social value, with 81% to 95% of that being retained by patients.

 Lastly, HIV/AIDS therapies have generated $1.38 trillion in social value, with 95% accruing to patients.

 Such analyses are net monetary benefit analyses, which is a common economic way of thinking about value which is distinct from cost-effectiveness analysis. The aim is to measure the total value a given health intervention generates for society, and how that value is distributed across patients and manufacturers.

This study applied similar methods to determine the social value of childhood vaccines for a birth cohort in the United States. Consistent with previous research, social value was defined from an economic perspective as the quantity of resources, in monetary terms, that society would be willing to give up in order to retain the health gains attributable to vaccines. Put another way, the overall social value of vaccines equals the aggregate value retained by consumers (above the actual payments for vaccines) plus the value retained by manufacturers (in the form of vaccine profits). We decomposed the social value into the shares accruing to manufacturers versus the rest of society. For infectious diseases, the social value includes not only those vaccinated, but also those not vaccinated who benefit from the reduction in disease incidence.

The study entailed constructing an economic model based on observational and clinical data. The model calculates the social value of the routine pediatric vaccination schedule used in the United States in 2009. We do so by quantifying the health effects of routine vaccination of children born in the United States in 2009. In particular, vaccines to prevent the following 14 diseases were considered: congenital rubella syndrome, diphtheria, haemophilus influenzae type b (Hib), hepatitis A, hepatitis B, measles, mumps, pertussis, pneumococcus-related diseases (including pneumococcal disease, otitis media, pneumonia, and meningitis), polio, rotavirus, rubella, tetanus, and varicella. The influenza vaccine was not included because its changing seasonal nature would have required different methods.

The social value was estimated by applying an economic value to the health effects of vaccines, measured in terms of quality-adjusted life-years (QALYs) saved through vaccination. QALYs take into account both duration and quality of life. A year in perfect health would be measured as 1 QALY, whereas death counts as 0. From the value of the QALYs gained, the costs to produce vaccines were subtracted. This yielded the social value—or in economic terms, the total surplus&mdash;of vaccines, and represents the economic value of the health gains from vaccines minus the resources society spent to produce them. The shares of the total surplus accruing to manufacturers (producer surplus) versus the rest of society (consumer surplus) were also calculated.

It should be noted that vaccine-preventable illnesses impose additional costs on society beyond the utility loss infected individuals experience, including caregiver utility loss and the use of special services for persistent disability. Therefore our estimate of the health value of vaccination should be considered a lower bound.

In addition, one should exercise caution in interpreting the results of this framework for rotavirus, since in industrialized countries like the United States, the costs of rotavirus are mainly hospitalization and caregiver utility loss, as rotavirus mortality and morbidity are lower in the industrialized setting. In contrast, many of the other studied vaccines target illnesses that imposed a high mortality and morbidity burden in the pre-vaccine era.

According to the CDC, 4,130,665 children were born in the United States in 2009.

 The health effects of vaccination in this cohort were estimated by combining data from the literature with life tables from the Human Mortality Database.

 From the literature, we obtained for each disease data on cases of illness prevented, premature deaths avoided, average age of onset, average age at death from the disease, average duration of disease, and utility loss. Specific parameter values and sources are available in the eAppendix (

). The survival benefits of vaccination were net of adverse reactions to vaccination. To obtain the economic value of a QALY, we considered values generated by revealed and stated preference studies.

 A mid-range value of $150,000 was used and varied in sensitivity analysis.

Estimating manufacturers’ profits required 3 types of data: 1) data on vaccine prices, 2) data on manufacturers’ profit margins, and 3) data on the number of vaccines administered in the 2009 US birth cohort. We obtained data on prices (available in the eAppendix) from the CDC Vaccines for Children Program website, which contains archived data on public and private vaccine prices from 2008 to 2015.

We obtained data on vaccine manufacturers’ profit margins from annual reports and financial statements. When measuring manufacturers’ profits, we used the gross profit margin, which represents the sales volume minus production costs. Obtaining a companywide average across the top 5 vaccine manufacturers produced an average gross profit of 75%.

 This is a conservative approach, as gross profits do not subtract out manufacturer research and development (R&D) and marketing expenses. By using the gross profit margin, we can view vaccines’ social value as society’s benefit from vaccination, and society’s investment in R&D as the cost of inventing and developing the vaccines. (Subtracting R&D from profits would negate this framing.) This framing is useful because investments should be undertaken when the benefits (ie, the return on investment) exceeds the cost; social value is an important part of this equation. Moreover, R&D costs include the costs of many failures that the innovator encountered on the way to the given successful product; there is not an established method for measuring R&D costs for vaccines.

To estimate the number of vaccines administered, we required data on vaccine coverage rates, dosage schedules, wastage, and the cohort size. Following previous work, we assumed that 53% of vaccine doses were publicly (vs privately) administered and the wastage rate—the rate at which additional vaccines must be purchased beyond those needed for each vaccinated child because some vaccines will be unused&mdash;was 5%.

 from the CDC. Doses administered between ages 0 and 18 were included, but the costs of any adult booster doses were excluded. Given that any adult booster doses occur many years into the child’s life, whereas the lives saved and illnesses avoided from vaccination are realized mainly in early childhood, the effect of the focus on childhood doses should be minimal.

The 3 analytic steps are described broadly below. Additional detail is provided in the eAppendix. Throughout the analysis, monetary values were inflation-adjusted to 2014 US dollars using the Consumer Price Index,

 and an annual discount rate of 3% was applied.

Step 1: Value Health Effects of Vaccination

The health effects of vaccines were calculated by summing the changes in morbidity and mortality among the 2009 US birth cohort due to vaccination. The mortality effects were calculated as the number of deaths averted from vaccination multiplied by the QALYs the typical child would lose from dying of the given disease (calculated as average life expectancy minus average age at death from the given disease times aged-adjusted utility). The morbidity effects were calculated as the number of cases of illness prevented through vaccination times the typical duration of illness times the disutility from the given illness. The health effects of vaccines in QALYs were then converted to economic terms by valuing each QALY at $150,000.

 This yielded the economic value of the health effects of vaccination.

Step 2: Estimate Manufacturers’ Profits

Vaccine manufacturers’ profits from a given vaccine were estimated by multiplying the vaccine’s price by the number of vaccines sold by the profit margin. Although vaccines typically consist of multiple doses, and vaccination rates vary by dose, in the profit calculations we assumed that all children who received the first dose of a vaccine would also receive all subsequent doses. This assumption overestimates profits because some children will not receive all doses, and manufacturers’ profits will be lower than they would be had these children received all of their doses.

Using the results of the previous 2 steps, we calculated consumer, producer, and total surplus (ie, social value). Consumer surplus was calculated as the value of the health effects of vaccines (from Step 1) minus the cost of the vaccines (from Step 2). Producer surplus equaled the manufacturer profits (from Step 2). Consumer and producer surplus together yielded the total surplus.

We performed analyses to test the sensitivity of the model to the parameters. Specifically, we varied all parameters by 

10%, except for the disease-specific vaccination rates, which were varied by 

 5% to avoid specifying rates over 100%. In addition, we also varied the value of a QALY from $50,000 through $250,000 to reflect the wide range of values in the literature, and varied the discount rate from 0% to 6%. Lastly, we conducted an analysis, which included the parents’ time cost to take the children to receive the vaccines, taking into account that multiple vaccines may be given in the same visit.

The health effects of vaccination are reported in 

. Compared with no vaccination, an estimated 1.2 million QALYs will be saved due to vaccination among children born in the United States in 2009, for a value of $185.2 billion. Because vaccines typically prevent deaths that would have occurred in childhood, these avoided deaths save a large number of QALYs, whereas QALY gains from avoided illness are more modest. Consequently, 88% of the health value of vaccines is due to avoided death compared with 12% due to avoided illness. Among vaccines, diphtheria, tetanus, and pertussis (DTaP/Tdap) will have the largest health value by an order of magnitude, at nearly 800,000 QALYs saved for a value of $119 billion. Other vaccines with large health values are pneumococcus-related diseases (154,000 QALYs, $23 billion); measles, mumps, and rubella (MMR) (135,000 QALYs, $19 billion); and hepatitis B (79,000 QALYs, $12 billion). The smallest health value will be for rotavirus, at 5264 QALYs and $790 million.

 reports estimates of manufacturers’ revenues, costs, gross and net profits by vaccines given to the 2009 US birth cohort. Estimated profits are the lowest for the hepatitis A vaccine ($109 million gross, $36 million net), and the highest for pneumococcus-related diseases ($1.0 billion gross, $350 million net). Across all 14 diseases, vaccines generate an estimated $4.5 billion in revenues, $1.1 billion in costs, $3.4 billion in gross profits, and $1.1 billion in net profits.

The total social value by vaccine and its distribution across manufacturers and consumers are reported in 

. Consumers’ share of value, or surplus, ranges from $10 million for rotavirus to $119 billion for DTaP/Tdap. Vaccines producing the greatest consumer surplus do not necessarily provide the largest profit, or producer surplus, to manufacturers. Total social value ranges from $595 million from rotavirus to $119 billion from DTaP/Tdap, with the full vaccination schedule generating $184 billion in social value (

), or $45,000 per child. Of that total social value, 1.8% accrues to manufacturers, whereas 98.2% accrues to the rest of society. The manufacturers’ share ranges from 0.3% for DTaP/Tdap to 98.3% for rotavirus, and is less than 15% for 7 out of the 9 vaccines.

In sensitivity analyses, we found that the model is most sensitive to the economic value of a QALY, the premature deaths prevented from vaccination, and the discount rate, which together contributed 98.1% of the variance in results (

). In simulations, the total social value ranged from $153 billion to $227 billion, and the manufacturers’ share ranged from 1.3% to 2.6%. In other words, varying parameters by 

10% maintained a social value of at least $37,172 per child, with at least 97% of the value retained as consumer surplus. In addition, varying the value of a QALY from $50,000 to $250,000 led to social value ranging from $61 billion to $308 billion, and varying the discount rate from 0% to 6% led to social value ranging from $447 billion to $107 billion. Subtracting parents’ time cost led to a social value of $182 billion and manufacture share of 1.8%.

Our estimates suggest that routine childhood vaccination produces a large social value. In particular, we found that vaccination will save 1.2 million QALYs among children born in the United States in 2009, relative to a counterfactual of no vaccination. Vaccines treating common diseases, such as DTaP/Tdap, pneumococcus, and MMR, generate a particularly large share of the health benefits.

Childhood illness is often transitory, but many childhood diseases have nontrivial fatality rates, and a death in childhood costs many years of healthy life. Therefore, it is not surprising that the bulk of these health gains (88%) stems from reduced mortality, with the remainder (12%) from reduced morbidity. Valuing each life-year at $150,000 and subtracting the production costs of vaccines, we found that vaccination of the 2009 birth cohort generated $184 billion in social value, or $45,000 per child. Of this, $3.4 billion (2%) accrues to manufacturers in the form of profits, while $180.7 billion (98%) is retained by the rest of society. Sensitivity analysis showed that the large social value and large share retained by society are robust to a wide range of plausible parameters.

The 2% manufacturers’ share of social value we found is smaller than that found in similar analyses of other health interventions. The manufacturers’ share of social value was between 5% to 24% in HIV/AIDS, chronic myeloid leukemia, and heart disease.

 The distribution of gains between manufacturers and the rest of society is determined by the cost of the intervention and the health gains it produces. Health gains will generally be larger in the context of diseases that are prevalent or take life early. Therefore, it is not surprising that childhood vaccines come out favorably in these comparisons, as they are among the most cost-effective health interventions

 and they preserve the health of children, who, in the absence of a devastating childhood illness, typically go on to lead a long, healthy life. To put the health results in context, in other industries, the producers’ share of total social value has ranged from 4% for minivans

The fact that 98% of the social value from existing childhood vaccines goes to children and their families is good news for access to current vaccines; however, it has longer-term implications for innovation. A 2% share of value suggests relatively weak incentives for the development of new and improved vaccines, relative to the incentives to develop cancer drugs, cholesterol-lowering drugs, or broadband Internet. However, there is debate in the literature about the exact manufacturer share of surplus required to optimally incentivize innovation, and therefore, although this study informs the debate, it does not determine optimal vaccine pricing.

Because the model relies on inputs from the literature, it is limited by their availability and quality. When exact parameters were not available, conservative assumptions were made. For example, costs and profits were overestimated by assuming children who receive 1 dose of a vaccine receive all subsequent doses, thus underestimating social value and overestimating manufacturers’ share. In cases in which illness is usually short and mild but can be severe and long-lasting (eg, polio), we assumed a short and mild case in our utility calculations. In calculating profit margins, we assumed the companywide profit margin applied to a given vaccine, although vaccines may comprise a less profitable division of pharmaceutical firms.

 We also used gross margin, which does not take R&D and marketing costs into account, rather than net margin, so that we could consider the social value as the return on society’s investment in developing the vaccine.

Additionally, while the 2009 birth cohort was selected for this analysis due to the availability of data on the health effects of vaccination,

 it has the drawback of including in the analysis a year of shortage of the Hib vaccine.

 However, given the scale of the findings of this study, this consideration likely had minimal impact.

Vaccination of children born in the United States in 2009 will save 1.2 million quality-adjusted life-years, generating $184 billion in social value net of vaccination costs.

The Social Value of Childhood Vaccination in the United States

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.

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.

Yanyu Wu, PhD

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