Wes Yin, PhD

This study examined the relationship between medication adherence, cost sharing measured as out-of-pocket spending, and total annual spending in Medicare beneficiaries with type 2 diabetes (T2D) to evaluate whether pharmacy cost-sharing programs have the potential to decrease adherence. These programs may unintentionally increase the risk of medical complications and may result in higher spending overall.

This retrospective study used 2006 to 2009 Medicare claims data. The sample included patients 65 years or older with T2D (at least 1 claim with International Classification of Diseases, 9th Revision, Clinical Modification codes 250.x0 and 250.x2 and at least 1 antidiabetes drug claim). 

Medication adherence was measured as proportion of days covered over the first 12 months of observation. Spending and adherence outcomes were defined in deciles. 

The sample included 12,305 patient-year observations. Pharmacy spending for patients in the most adherent (10th) decile was 59% higher than that for patients in the least adherent (1st) decile ($4839 vs $3046). Yet, patients in the 10th decile had 49% lower total ($12,531 vs $24,468) and 64% lower medical spending ($7692 vs $21,421) than patients in the 1st decile. Greater out-of-pocket spending was correlated with lower adherence and higher total and medical spending. 

 This study describes a widespread variation in medication adherence, pharmacy cost sharing, and medical spending in a sample of Medicare beneficiaries with T2D. We found that lower adherence was correlated with higher cost sharing in the Medicare population, perhaps because of unobserved confounding factors. However, the existing literature on patients with employer-sponsored insurance suggests some of this correlation may be indicative of causal relationships.

		Increased cost sharing could decrease the likelihood that patients with type 2 diabetes will adhere to their medication and increase diabetes complications and total diabetes spending. 

		Using Medicare claims data (2006-2009), we examined the relationships between medication adherence, cost sharing measured as out-of-pocket spending, and total annual spending to evaluate whether pharmacy cost-sharing programs have the potential to decrease adherence. 

		We found a widespread variation in medication adherence, pharmacy cost sharing, and medical spending, and that lower adherence is correlated with higher cost sharing in the sample of Medicare beneficiaries with type 2 diabetes.

In 2014, nearly 30 million Americans (9.3%) were living with diabetes, including an estimated 11.2 million who were 65 years or older.

 In 2012, the total national spending on diabetes was $245 billion, including $176 billion in direct medical spending. Patients 65 years or older use more healthcare services than younger patients and incur the majority of costs associated with diabetes. Ultimately, the Medicare program—in which 28% of beneficiaries have diabetes&mdash;bears most of this burden.

 Interventions targeted at individuals using the most resources and who have the greatest expenditures have the potential for a disproportionate impact on spending.

Several studies evaluating the economic burden of diabetes have estimated that antidiabetes agents and supplies account for 12% of total healthcare spending.

 Payers have implemented various cost-sharing schemes to reduce pharmacy spending, including incentive-based formularies, coinsurance or co-payments, and benefit caps.

 Research consistently shows that higher co-payments or coinsurance reduce pharmaceutical utilization and adherence among patients with chronic diseases like type 2 diabetes (T2D), heart failure, and schizophrenia,

 and that reducing co-payments in employer-sponsored insurance plans can increase adherence.

Although cost sharing may reduce overall pharmacy spending, it could also have consequences for patient health and result in unintended increases in nonpharmacy healthcare spending, thereby offsetting the savings on pharmaceuticals. The limited existing research shows that cost sharing increases emergency department visits and hospitalizations, especially among chronically ill patients.

 The impact of cost sharing on therapeutic adherence is particularly relevant for patients with diabetes, given that improved adherence is associated with a greater likelihood of reaching glycated hemoglobin (A1C) targets—the primary measure of effective disease management

—and cost sharing has increased since the introduction of Medicare Part D.

 By some estimates, a 1% increase in antidiabetes prescription utilization would reduce medical spending for Medicare beneficiaries with T2D by 0.83%.

Despite the large overarching financial burden of T2D, few studies have examined the extent to which pharmacy and medical spending contribute to total diabetes-related spending.

 This retrospective claims study of a Medicare population examined the relationship between adherence, cost sharing as measured by out-of-pocket (OOP) spending, and total annual spending.

The study used claims data for a 20% random sample of age-eligible Medicare fee-for-service beneficiaries for the years 2006 to 2009. Data were acquired from the CMS Medicare Research Identifiable Files, which provide medical and pharmacy claims, including diagnoses, procedure codes, and measures of charges and spending. Overall spending in any category (total healthcare, medical, or pharmacy) equaled the sum of the patient OOP component and the payer-covered component.

A sample was created using the following criteria: 1) Medicare beneficiaries 65 years or older; and 2) with at least 1 claim with an 

International Classification of Diseases, 9th Revision, Clinical Modification

codes 250.x0 and 250.x2) and at least 1 claim for an antidiabetes medication in the period following the first observed claim with diabetes diagnosis. The index date for patients in the sample was the date of the first observed claim with a T2D diagnosis. Only patients 65 years or older at the time of the first observed claim with a diabetes diagnosis were included. The sample included patients with a T2D diagnosis or antidiabetes medication use in the year prior to the first observed claim in the database if the patient’s index date was less than 1 year after the date of Medicare enrollment. Patients with a follow-up period of less than 1 year after the index date or those who were pregnant or less than 6 months post partum were excluded.

Adherence was measured as the proportion of days covered (PDC), a well-established method that has been used for several decades to evaluate adherence in diabetes and other chronic conditions (eg, cardiovascular disease, dyslipidemia, hypertension).

 PDC was calculated as the total number days when a patient had medication on hand during the observation window (ie, date of first claim to exhaustion of last prescription, segmented into calendar years).

This retrospective study used descriptive analysis to evaluate the extent to which patients’ health status differed across deciles of spending. In particular, we calculated the prevalence of diabetic comorbidities, complications, and drug therapy type by decile of total annual spending. To investigate the relationship between medication adherence and spending, average pharmacy, medical, and total spending were calculated within each adherence decile. To examine the relationship between cost sharing—as measured by OOP spending (total, medical, and pharmacy)&mdash;and medication adherence, average OOP pharmacy, medical, and total spending were calculated within each decile of adherence.

Dividing the sample into deciles created enough bins to characterize the spending distribution. Using deciles to map the distribution of total, pharmaceutical, and medical spending among Medicare beneficiaries with T2D also enabled the identification and description of the degree of skew in annual spending distribution.

Based on the inclusion and exclusion criteria, the sample included 12,305 patients with a mean follow-up period of 3.2 years and a mean age of 74.4 years. The sample was 64% female and 36% male.

Distribution of Total, Medical, and Pharmacy Spending

Considerably higher average total and medical spending were observed for patients in the upper deciles of total spending compared with patients in the lower deciles, but the between-decile difference in pharmacy spending for patients was not as substantial (Figure 1). The average annual total spend for patients in the lowest spending (1st) decile was $794; this increased by nearly 100-fold, to $78,641, for patients in the highest spending (10th) decile. The average annual medical spend for patients in the 1st decile was $417 and increased more than 170-fold, to $71,203, for patients in the 10th decile. However, the average annual pharmacy spend for patients in the 1st decile was $377 compared with $7439 for patients in the 10th decile, a less than 20-fold increase.

Patient Descriptive Statistics and Health Status by Spending

Patient age and sex were similar across all total annual deciles of spending; however, patients in the upper deciles were substantially more likely to have comorbid conditions, including congestive heart failure (74% in the 10th decile vs 17% in the 1st decile), cardiac arrhythmias (68% vs 24%), complicated hypertension (57% vs 17%), or moderate/severe renal impairment (18% vs 1%), among others. The use of certain antihyperglycemic drug therapies was also more prevalent in the upper deciles of total spending. Compared with patients in the 1st annual spending decile, patients with T2D in the 10th decile had a nearly 5-fold higher probability of using long- or rapid-acting insulin to treat their diabetes (31%-34% vs 3%-7%, respectively). Dipeptidyl peptidase-4 inhibitors, meglitinide, and thiazolidinedione use was also higher for patients in the upper deciles of total annual spending, whereas metformin (a biguanide) use was less common in the 10th versus 1st decile (31% vs 53%). Use of sulfonylureas and glucagon-like peptide-1 receptor agonists did not vary significantly across the deciles of total annual spending.

Mean PDC was divided into deciles and ranged from 0.20 for patients in the 1st decile to 1.00 for patients in the 10th decile. As shown in Figure 2, patients with greater medication adherence, as measured by PDC, had lower average annual medical and total spending and only moderately increased pharmacy spending. For patients in the 1st decile of PDC (lowest adherence) to the 10th decile, average annual total spending decreased 49% (from $24,468 to $12,531), and average annual medical spending fell 64% (from $21,421 to $7692). By comparison, pharmacy spending between the 1st decile of PDC and the 10th decile increased 59% (from $3046 to $4839).

An examination of the relationship between total OOP spending and adherence found analogous results. Average annual pharmacy OOP spending for patients in the 1st to 10th PDC decile increased 67% (from $441 to $737), but average annual total OOP spending decreased 36% (from $2116 to $1344), and average annual medical OOP spending decreased 64% (from $1675 to $607).

Based on the present study’s findings, Medicare patients with T2D have highly skewed total annual spending; patients in the upper deciles of total spending account for a disproportionate share of aggregate total spending. As previous studies had similar findings,

 this analysis confirms that medical spending accounts for the vast majority of the skew in total spending on T2D treatment, and that pharmacy spending is a relatively small proportion of total spending.

An analysis of comorbidities and medication use according to total spending showed that patients in the highest deciles were more likely to have comorbid conditions and to use certain antidiabetes drugs (in particular, long-acting, short-acting, and premix insulins). It is likely that the differences by decile in the prevalence of diabetic comorbidities, complications, and therapy utilization in patients play a large role in driving healthcare spending variation. This finding is supported by existing studies.

This study also examined the distribution of total, medical, and pharmacy spending in Medicare patients with T2D based on their level of medication adherence. More adherent patients had substantially lower total spending and medical spending, but moderately increased pharmacy spending, suggesting that, for Medicare patients with T2D, lower adherence is related to increased spending in general. Increased spending may be the result of poorer outcomes, increased comorbidities, and a need for additional treatment, as medication adherence has been significantly associated with improved health status, fewer complications, and less medical resource utilization in patients with T2D.

This study corroborates previous research and suggests that increasing adherence among Medicare beneficiaries with T2D could generate significant cost savings.

 The use of pharmacy cost sharing in Medicare has gone up since the introduction of Medicare Part D, which extended pharmacy benefit coverage and increased pharmaceutical utilization,

 but the availability of generic drugs has prevented significant increases in OOP spending. If this cost-sharing trend continues, adherence may fall and any savings on pharmacy spending could be offset by increased medical spending.

 Recent proposals to limit annual OOP spending for Medicare beneficiaries would reduce the financial burden on high-spend Medicare beneficiaries with T2D, potentially improving medication adherence and health outcomes.

This study has certain limitations. The analysis was descriptive, with data randomly drawn from an administrative claims database representing the years 2006 to 2009. Although the levels of pharmacy and medical spending may have changed since 2009, our qualitative results and the correlations between cost sharing, adherence, and medical spending should not be impaired by the age of the data, as many similar studies have found the same relationships in other patient samples and time periods.

 Additionally, because the study drew from the Medicare database, and because only patients 65 years or older at the time of the first observed T2D diagnosis claim were included, the mean age of the sample (74 years) was higher than the average age of T2D diagnosis (54 years) in the United States.

 Last, only limited medical history is available for patients in claims data research, thereby increasing the potential for confounding, and the date of patients’ initial T2D diagnosis could not be determined with certainty.

This study has several implications. First, the study results demonstrates the existence of a skew in spending on T2D treatment. Medicare patients in the highest deciles of total spending had dramatically higher total and medical spending, clearly indicating that most spending on diabetes-related treatment is from medical—and not pharmacy&mdash;spending among a subset of high-cost patients. In addition, the study expands on available studies of patients with employer-sponsored insurance to demonstrate that improved adherence is related to reduced resource use and spending in Medicare beneficiaries with T2D and suggest that interventions aimed at improving adherence among high-cost patients have the greatest potential to reduce health expenditures. Most importantly, the study results confirm the negative connection established in the existing literature between cost sharing and medication adherence, which varied widely among Medicare beneficiaries with T2D.

 If pharmacy cost-sharing programs decrease medication adherence, initiatives that promote pharmacy cost sharing may unintentionally increase the risk of medical complications offsetting some (or in targeting cases, all) of the savings associated with lower pharmaceutical spending.&ensp;

 Precision Health Economics (JPM, JV-O, JS, DP), Los Angeles, CA; AstraZeneca (JJS, IK), Fort Washington, PA; University of California, Los Angeles (WY), Los Angeles, CA; University of Southern California (ALP), Los Angeles, CA.

 This study was supported by AstraZeneca.

 Dr MacEwan, Mr Sullivan, and Ms Peneva are employees of Precision Health Economics (PHE), which is a consultant for biotech/pharma companies and was compensated by AstraZeneca to perform the study. Dr Vanderpuye-Orgle was an employee of PHE during the time of the study and is currently employed by Amgen. Dr Yin is a consultant for PHE. Dr Sheehan has been employed by AstraZeneca, and Dr Kalsekar has been employed by and holds stock in AstraZeneca and Johnson & Johnson, which manufacture drugs for the treatment of diabetes. Dr Peters has been a consultant and grant recipient from numerous pharmaceutical companies including AstraZeneca, and receives editorial fees from Medscape.

 Concept and design (JPM, JJS, WY, JV-O, JS, IK, ALP); acquisition of data (JS, IK); analysis and interpretation of data (JPM, JJS, WY, JV-O, JS, DP, IK); drafting of the manuscript (JPM, WY, DP); critical revision of the manuscript for important intellectual content (JPM, JJS, WY, JS, ALP); statistical analysis (WY, JS); provision of patients or study materials (ALP); obtaining funding (JJS, IK); administrative, technical, or logistic support (DP, IK); and supervision (JJS, WY, JV-O, IK).

 Joanna P. MacEwan, PhD, Precision Health Economics, 11100 Santa Monica Blvd, Suite 500, Los Angeles, CA 90025. E-mail: joanna.macewan@pheconomics.com. 

1. National diabetes statistics report. CDC website. https://www.cdc.gov/diabetes/pubs/statsreport14/national-diabetes-report-web.pdf. Published October 24, 2014. Accessed January 21, 2015.

2. American Diabetes Association. Economic costs of diabetes in the US in 2012. 

. 2013;36(4):1033-1046. doi: 10.2337/dc12-2625.

3. American Diabetes Association. Economic costs of diabetes in the US in 2007. 

. 2008;31(3):596-615. doi: 10.2337/dc08-9017.

4. Goldman DP, Joyce GF, Zheng Y. Prescription drug cost sharing: associations with medication and medical utilization and spending and health. 

5. Goldman DP, Joyce GF, Escarce JJ, et al. Pharmacy benefits and the use of drugs by the chronically ill. 

6. Sokol MC, McGuigan KA, Verbrugge RR, Epstein RS. Impact of medication adherence on hospitalization risk and healthcare cost. 

Articles

This study describes a widespread variation in medication adherence, pharmacy cost sharing, and medical spending. Increased cost sharing may decrease adherence and increase total diabetes spending.

The Relationship Between Adherence and Total Spending Among Medicare Beneficiaries With Type 2 Diabetes

 To measure the value of survival gains attributable to the introduction of 3 novel therapies for myelodysplastic syndromes (MDS).

 Retrospective study of patients diagnosed with MDS in the Surveillance, Epidemiology and End Results Program (SEER) registry, clinical trial evidence for MDS therapies, and claims data.

 We used multivariate Cox proportional hazards models to estimate the increase in survival associated with the introduction of the 3 new therapies for patients diagnosed with MDS from 2001 to 2011 in the SEER cancer registry. Increases in survival associated with the 3 novel therapies were estimated using retrospective survival analyses and published clinical trial evidence. MDS treatment costs were estimated using Ingenix claims data and used to calculate the share of the value of survival gains retained by patients.

 We estimated that the introduction of these 3 therapies is associated with a hazard ratio of 0.901 (

<.10), and a 73% increase in median survival from 33 to 57 months. We estimated that for current and future MDS patients, these 3 therapies will generate over $193 billion in cumulative value through extensions in patient survival.

 This study demonstrates that the value of recently approved innovative therapies in MDS is large and that the value of survival gains in MDS far outweighs their costs.

		Innovative treatments for myelodysplastic syndromes (MDS) have generated significant benefits for patients by markedly extending survival. 

		The value of survival gains far outweighs their cost, suggesting that improving patient access to these MDS therapies is warranted. 

		The value of survival gains represents a conservative estimate of the total value of innovative treatments for MDS to society, which also includes the value of medical cost offsets, productivity gains, and other sources of value, such as reduced caregiver burden.

Over the past several decades, innovation in both the detection and treatment of cancer has resulted in improved survival rates.

—have experienced dramatic gains in survival, due in part to the introduction of targeted therapies, such as tyrosine kinase inhibitors (TKIs), vascular endothelial growth factor (VEGF) targeted drugs, immunomodulators (IMiDs), and proteasome inhibitors, among others.

 In contrast to conventional chemotherapies that kill rapidly dividing cells—whether cancerous or not&mdash;targeted therapies inhibit cancer cell growth and improve survival by interfering with specific molecular targets.

Several clinical trials investigating novel targeted therapies for myelodysplastic syndromes (MDS) have demonstrated improvement in overall survival (OS) and progression-free survival (PFS).

 For example, azacitidine has been shown to successfully improve OS and quality of life,

 increasing median OS by nearly 10 months, while reducing the risk of fatal complications.

 Similarly, patients with MDS being treated with decitabine exhibited better overall response and PFS, and patients responsive to lenalidomide had a significantly lower risk of death or progression to acute myeloid leukemia (AML) after 6 months, compared with patients with MDS being treated with a placebo.

The existing literature demonstrates that the return on investment in advances in treatments for cancer and acute myocardial infarction, among others, has been large, but may not be constant over time.

 This study contributes to the larger debate surrounding the value of medical innovation and the need for studying value in specific disease, time, and geographic contexts, given ongoing movement toward linking reimbursement to value. The introduction of novel targeted therapies, in MDS and elsewhere, coincides with increased scrutiny toward the relative value and cost of cancer therapies more generally.

 This attention motivates rigorous evaluation of the economic value of new cancer therapies, where value captures improved survival, improved quality of life, reduced caregiver burden, as well as other dimensions of value.

 In one recent analysis of CML, Yin et al (2012) measured the value generated by new novel targeted therapies along 1 dimension of value: survival gains. They estimated that the survival gains attributed to the introduction of TKIs generated significant social value.

 Similarly, studies on the value of improvements in cancer survival and mortality find that these gains have significant societal value.

For MDS, to our knowledge, there are currently no analyses that measure the value of survival gains attributable to innovative therapies relative to their cost.

 Moreover, no study has measured MDS survival gains in the real world, where benefits may differ from those observed in clinical trials. This study addresses this gap in the literature. We estimated the value of survival gains in MDS from the introduction of 3 targeted therapies: azacitidine, decitabine, and lenalidomide. Following the analytical framework applied in Yin et al (2012),

 this study used data from the Surveillance, Epidemiology, and End Results Program (SEER) cancer registry database for 2001 to 2011, to estimate survival gains in the real world as a result of the introduction of these 3 therapies. We then calibrated an existing economic model to value the estimated increases in survival.

 Finally, we used the Ingenix Touchstone commercial claims database (Ingenix) to estimate treatment costs, which we compared with the total value of survival gains.

We conducted 3 distinct subanalyses. In the first, we used SEER registry data from 2001 to 2011 for patients with MDS, and Cox proportional hazards models to estimate the increase in survival in the real world due to the introductions of key new therapies for MDS between 2001 and 2011. The primary independent variables in the survival models were the binary variables that indicated when new therapies were introduced (

 classified MDS as a blood disorder rather than a neoplasm, making the identification of MDS (diagnosed prior to 2001 in SEER) problematic. Therefore, we restricted our analysis to patients diagnosed in 2001 or later.

We included a binary independent variable—

—that takes a value of 1 in (diagnosis) years 2004 and 2005, and another binary independent variable&mdash;

—that takes a value of 1 in years 2006 to 2011. The regression coefficient on 

captures the survival effect of the introduction of azacitidine (introduced in 2004); and the coefficient on 

captures the effect of the introductions of lenalidomide (approved in late 2005) and decitabine (introduced in 2006), as well the availability of azacitidine. Because all 3 therapies have been available from 2006 on, we were only able to estimate the combined effect of the availability of all 3 therapies for MDS from 2006 to 2011. The hazard ratio (HR) associated with the 

variable is the primary estimate of the combined effect of new therapies on MDS survival, and is the key input into our economic model of the value of survival gains.

Importantly, in order to interpret the HR associated with the 

as the combined effect of the 3 therapies

, we included a set of flexible time variables to capture the secular trends in survival that are unrelated to the introduction of azacitidine, lenalidomide, and decitabine. In this way, we would not confound the estimated impact of new therapies on survival with these unrelated changes in MDS survival. In our final specification, we included a cubic annual time trend. We also controlled for a rich set of patient demographic characteristics, including age at diagnosis, gender, tumor sequence, and race. This ensured that changes in survival due to changes in case mix over time would not be incorrectly attributed to the impact of new therapies for MDS.

Because SEER lacks information on which specific therapies patients utilize, the hazard model allowed us to estimate the average impact of new therapy introduction on the entire MDS population, including patients undergoing treatment with therapies other than azacitidine, lenalidomide, and decitabine. Hence, our regression estimates captured the “intended to treat” (ITT) effect of the 3 therapies on MDS survival. To estimate the survival impact on those treated by the new therapies (ie, the “treatment on the treated” (TOT) effect), we used the Ingenix claims data to calculate the average share of patients with MDS being treated by the new therapies between 2006 and 2011. We then scaled the ITT effect by the share of patients treated with the novel therapies to calculate the TOT effect.

To estimate the value of survival gains in MDS, we applied methods developed by Becker, Philipson and Soares (2005),

 which allowed us to estimate the value patients place on increased longevity. This method involved calibrating an economic model to calculate the utility of patients with cancer when facing the baseline survival curve, as well as the utility curve when faced with an improved survival curve attributable to new treatments. This model also allowed us to estimate the dollar amount that would make baseline patients with cancer as well off as patients with the improved survival, holding all else equal. Following Becker, Philipson, and Soares (2005), we interpreted this dollar amount as the value of the survival gain.

Because utility in this framework depends on the value of consumption while alive, this dollar amount depends on patient income levels, which, at a very general level, dictates patients’ ability to consume goods, including healthcare. The model was therefore calibrated to the income level of patients with cancer, in addition to discount rates, marginal utility of consumption, and other standard economic parameters, as summarized in Becker, Philipson, and Soares (2005) (see 

The economic model takes as inputs the estimated survival gains, as well as patient “full” income levels, as the total value of time available for work and leisure.

 We estimated patient income using the Medical Expenditure Panel Survey (MEPS) data from 2010 and 2011.

 However, MEPS did not have a sufficient number of patients with MDS (who fall into the “other” cancer category) to allow for the estimation of tumor-specific estimates of income; therefore, to produce reliable estimates of annual income, we estimated the expected annual earnings and full income of all patients with cancer, 18 years or older, assuming earned income reflects 8 hours of work per day and, therefore, full income equals double earned income.

We estimated the annual treatment costs of these 3 therapies using claims data from the Ingenix database for years 2006 to 2012, averaging across years and inflating to 2013 dollars using the Bureau of Labor Statistics Consumer Price Index.

 The Ingenix database is a private sector health insurance claims—based database that combines information from a wide array of different insurance providers. The database captures all healthcare claims, including prescription drugs, and inpatient, emergency, and ambulatory services for elderly and nonelderly individuals with employer-provided insurance from more than 50 large Fortune 500 firms. We calculated the present discounted value (PDV) of the increase in survival for a single patient over his or her lifetime, where value and costs in later years are weighted by each year’s estimated survival probability. Finally, we calculated the PDV of the lifetime value and costs for all future patient cohorts.

The total PDV of the lifetime survival gains and costs considers future MDS incident cohorts together with the current cohort, assuming a 3% discount rate, and accounts for patent expiry: generic versions of azacitidine and decitabine became available in 2013, and the lenalidomide patent will expire in 2027. How much prices decline following patent expiry depends on several factors, including the number of generic manufacturers that enter the market, the complexity of producing the molecule, outpatient rather than pharmacy benefit coverage, branded-drug market share, the number of competing therapies, and the length of time since patent expiry.

 For these reasons, the effect of patent expiry on the price of specialty drugs like oncologics may be different than the effect of expiry on nonspecialty drugs. To accommodate for this uncertainty in post patent expiry drug prices, we used a conservative estimate of the effect of patent expiration on drug prices. A recent study estimates that intravenously administered specialty drugs decline by 34% after patent expiry, and that oral specialty drugs decline by 21% after patent expiry.

 We used these price declines when forecasting future costs of generic azacitidine, lenalidomide, and decitabine treatment in MDS.

The sample of patients diagnosed with MDS between 2001 and 2011 from the SEER cancer registry database contained 38,025 individuals. Descriptive statistics for the study sample are displayed in 

. Sixty percent of these patients were diagnosed in 2006 or later; patients diagnosed in 2006 or later were slightly older at diagnosis and less likely to be white, but overall, the 2 populations had very similar characteristics.

Change in MDS Survival Curves Attributable to New Therapies

The coefficient on the post 2006 indicator reflected the decline in the hazard rate associated with the introduction of the 3 therapies relative to the hazard rate in years prior to their introduction (2001-2003). In our preferred model, which included more flexible controls for secular trends in survival, we estimated that the introduction of these 3 therapies is associated with an HR of 0.901 (

). That is, we estimated that patients with MDS diagnosed in 2006 or later had an approximately 10% lower chance of dying within the year (conditional on surviving to that year) than patients diagnosed in 2006, but under the counterfactual that the 3 new therapies were not introduced (Table 3). Note that the counterfactual survival rate in 2006 is the estimated survival rate in the pre-introduction (2003) period, adjusted for the secular increase in survival between 2003 and 2006.

Approximately one-fourth of patients with MDS had claims for at least 1 of the 3 novel treatments consistently over this period, resulting in an overall average increase from 33 to 57.5 months in median survival for treated individuals (

Societal Value of Survival Gains Attributable to New Therapies

The annual value of survival gains associated with innovative treatments—roughly $208,000 per year&mdash;equals the estimated amount a patient would pay for the higher survival profile associated with the new therapies. Note that this is conservative compared with the value of a 24-month (from 33 to 57.5 months) increase in median survival, as implied by conventional health technology assessment methods, which would range from $400,000 to $600,000 depending on the assumed valued of a statistical life-year.

 These differences stem from diminishing utility in longevity, which appeared in the model in 2 places: 1) the size of the survival gain and 2) the baseline survival prior to the introduction of the new therapies. If either of these are small, the marginal utility, and hence, valuation, of an incremental survival gain is larger than for the same incremental survival when the gain is large or when the baseline survival is already large.

 compares the estimated value of survival gains to costs, where the annual treatment costs of the new therapies,

 Precision Health Economics (JPM), Los Angeles, CA; University of California, Los Angeles (WY), Los Angeles, CA; Celgene Corporation (SK, ZMK), Summit, NJ

 Dr MacEwan is an employee of Precision Health Economics, a research consulting firm owned by Precision Medicine Group, which was compensated by Celgene to perform the study. Dr Yin is a consultant to Precision Health Economics. Dr Kahn and Mr Kaura are employees and stockholders of Celgene, which manufactures a product to treat MDS.

 Concept and design (WY, JPM, SK, ZMK); acquisition of data (WY, JPM); analysis and interpretation of data (WY, JPM, SK, ZMK); drafting of the manuscript (WY, JPM, SK, ZMK); critical revision of the manuscript for important intellectual content (WY, JPM, SK, ZMK); statistical analysis (JPM); obtaining funding (SK, ZMK); administrative, technical, or logistic support (WY, JPM); and supervision (WY, ZMK).

 Joanna P. MacEwan, PhD, Precision Health Economics, 11100 Santa Monica Blvd, Los Angeles, CA 90025. E-mail: Joanna.MacEwan@pheconomics.com. 

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This study measured the value of survival gains attributable to the introduction of 3 novel therapies for myelodysplastic syndromes.

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