A TALE OF TWO DRUG PRICES Rena M. Conti Assistant Professor of Health Policy and Economics The University of Chicago Ernst R. Berndt Alfred P. Sloan School of Management Massachusetts Institute of Technology and National Bureau of Economic Research

Acknowledgements and Disclaimer Conti: Support from National Cancer Institute K07 CA grant to the University of Chicago. Both authors: Data and data support obtained by National Bureau of Economic Research under license from IMS Health National Sales Perspective. Both authors: Usual disclaimer – findings and opinions expressed here are those of the authors, and not necessarily those of the research sponsor, IMS Health, its affiliates or subsidiaries, or the institutions with whom the authors are affiliated.

Agenda The Institutional Setting Theoretical Considerations Data Methods Preliminary Results Next Steps

The Institutional Setting & Theoretical Considerations

IMS Institute for Healthcare Informatics Medicine Use and Shifting Costs of Healthcare. Page 41. Specialty spending is highly concentrated in treatments for inflammatory conditions, MS, cancer and HIV.

Pricing in the market for anticancer drugs 2004: bevacizumab (Avastin), colorectal cancer, $50,000, 5 months 2009: sipuleucel-T (Provenge), prostate cancer, $93,000, 3 months 2011: ipilimumab (Yervoy), skin cancer, $120,000, 3 months

Agency issues likely drive spending Patients have first dollar coverage, are often facing imminent death. Physicians profit off specialty drugs due to “buy and bill” reimbursement.

A Note on “Prices” vs. “Reimbursement” Supply side price, acquisition price: – MD office practices and hospitals acquire 5i drugs directly from manufacturer or through group purchasing organizations (GPOs) from specialty wholesalers or pharmacies. – Office practices, hospitals or through their GPOs search for manufacturers’ lowest prices a la Bertrand competition, with generic manufacturer competing on price to be awarded GPO contract and be preferred vendor.

GPOs, Medicaid and 340B Provide Discounted Acquisition Costs 10

Demand Side Reimbursement If patient on Medicare, reimbursed by Medicare Part B program. – Before 2004, provider reimbursed at 95% of Average Wholesale Price which was >> average acquisition cost implying a “spread” for office practice. – In 2006, Part B changed to reimburse at Average Sales Price lagged two quarters + 6% (ASP + 6%). – ASP calculated as manufacturer’s sales to all purchasers in US divided by number of units sold. – But ASP excludes 340B discounts, Medicaid rebates. In private sector, reimbursement tends to be based on percent off AWP, or ASP + x%, where x>6 11

Among recently shorted drugs, 17% are branded or branded-generics, 83% are generics 14 Source: IMS National Sales Perspectives, Sep 2006 – Aug %

Proximal cause - significant lapses in maintenance of facilities that produce the fill and finished dosage of the drug among many manufacturers starting around Accumulating evidence suggests FDA actions have not demonstratively alleviated the persistence of ongoing shortages. *Emanuel 2011; Gatesman and Smith, 2011; Link et al, 2012; Yurukogli, 2012.

Previous experiences with shortages in electricity in the U.S. and food droughts in international markets suggest lagged administrative pricing systems can substantially obfuscate the role of the price signal to increase production when supply of these products declines for whatever reason. Because of the two quarter lagged ASP-based Medicare Part B reimbursement policy, Medicare pays less (and providers are reimbursed less) than market prices as prices increase, but when prices are falling, Medicare pays more (and providers are reimbursed more) than actual supplier transaction prices. The exclusion of selected discounts/rebates may distort the signaling role of reimbursement could play if they are significant or grow in significance over time. The extent of co-movements between insurer reimbursements for and acquisition prices of generic injectable drugs is an open empirical question.

Study Objectives Test two hypotheses:  “Prices” have differential rather than reinforcing movements preceding shortage reports.  Differential price movements are larger for shorted than non-shorted drugs during the period preceding shortage reports.

Data

All drugs covered by Part B National spending, use, other attributes: FDA approval date, generic entry date IMS Health NSP Medicare reimbursement = amount paid per molecule unit stated in 2014 USD Price = NSP sales/extended units stated in 2014 USD Inclusive of prompt pay, GPO discounts. Excludes 340B/Medicaid rebates.

Outcome variables of interest Quarterly reimbursement trends over time within molecule. Quarterly first differenced reimbursement, prices within molecule. Quarterly first differences between reimbursement & price within molecule.

Data on shortages Matched all molecules-forms-strengths listed in the NSP with the University of Utah Drug Information Service to determine dates of shortages if present.

Preliminary data is restricted L01-L03 designation based on the World Health Organization’s Anatomic Therapeutic Classification system (n=50), experiencing generic entry prior to January Never shorted drugs include dimenhydrinate (J1240), hyoscymine (J1940), lidocaine (J2001), cladribine (J9065), cyclophosamide (J9070), dactinomycin (J9120), epirubicin (J9120), floxuridine (J9200), goserelin (J9202), mechlorethamine (J9230), melphalan (J9245), pegaspargase (J9266), pentostatin (J9268), mitoxantrone (J9293), teniposide (Q2017), vinblastine (J9360), vinorelbine (J9390). Shorted drugs include atropine (J0460/J0461), bleomycin sulfate (J9040), busulfan (JJ8510), carboplatin (J9045), carmustine (J9050), cisplatin (J9060), cytarabine (J9100), dacarbazine (J9130), daunorubicin (J9150), doxorubicin (J9000), etoposide (J9181), fludaribine (J9185), fluorouracil (J9190), mesna (J9209), leuprolide (J9218), methotrexate (J9250), mitomycin (J9280), paclitaxel (J9265), steptozocin (J9350), thiotepa (J9340), vincristine (J9370). 2006Q1 thru 2011Q3 (23 time periods).

Methods

Summary Descriptive statistics of price changes post 2005 by quarter between molecules. Using vector autoregression methods we undertook several statistical tests to assess whether any discrepancy between them is significant and stationary or non-stationary preceding shortage reports. – We examine whether first differenced ln Medicare reimbursement levels and changes received by physicians using these drugs to treat patients differ substantially from the first differenced ln average transaction prices per standard unit received by these drug suppliers for their sale to practices. – We report the results of linear regression models examining the molecule and market level factors contributing to hypothesized discrepant price movements.

Stigler-Sherwin examined whether two products were in the same geographic-based market by quantifying the correlation in their percentage change in price [∆ln (P it ) and ∆ln (P jt ), i≠j] over a given time interval. Here, we examine the extent to which P 1t -- ASP-based reimbursements faced by physicians, move in tandem with P 2t -- unit value prices faced by suppliers, and whether the nature of these price co-movements differs between never shorted and shorted oncologics, assuming the market is a national one, by quantifying the correlation in their percentage changes.

We pooled quarterly P 1t and P 2t observations and estimated the following first differenced equation by maximum likelihood, specifying random effects: ln P 1it – ln P 1i,t-1 = ln (P 1it / P 1i,t-1 ) = a + b*ln (P 2it / P 2i,t-1 ) + time + error term. (Eqn. 1)

The R 2 from this equation is informative The R 2 in a bivariate OLS regression equation is the square of the sample correlation between the dependent and explanatory variables. If prices moved in perfect tandem, a = 0 and b = 1, and the R 2 should be very high. If reimbursement and manufacturer average unit price changes generate disparate price signals, b ≠ 1 and R 2 will be modest. Stigler-Sherwin suggest different markets were likely present if correlation coefficients were below 0.4 and likely not present if correlation coefficients were 0.8 or above.

We tested whether the correlation between unit transaction prices and ASP follows a different trajectory among ultimately shorted compared to never shorted oncologics. Let S i be a dummy variable equal to one if the oncologic was eventually shorted, and zero otherwise (never shorted). Adding the S i dummy variable to Eqn. 1, and adding an interaction term S i *ln (P 2it /P 2i,t-1 ), we estimate the following equation by maximum liklihood: ln(P 1it /P 1i,t-1 ) = a + a’S i + b*ln(P 2it /P 2i,t-1 ) + b’S i *ln(P 2it /P 2i,t-1 ) + time + error term. (Eqn. 2)

Since the presence of autocorrelation may affect standard error estimates in these models – We run Arellano-Bond-Blundell regressions of ln ASP reimbursement on lagged ln average unit prices assuming a one period lag structure of the dependent variable and AR(1) and AR(2) error term processes, and assess the robustness of the a, a’, b and b’ parameter estimates and hypothesis test results.

To assess sources contributing to these discrepant price movements, we estimate a difference in differences regression equation by OLS: ln(P 1it /P 1i,t-1 ) - ln(P 2it /P 2i,t-1 ) = α + ∑ k β k X kt + time + error term. (Eqn. 3) where the regressors X kt include overall market variables (e.g., ln Medicaid beneficiaries, ln Medicare beneficiaries, ln 340B entities and share of oncologics administered in hospitals and paid for by Medicare fee for service) and molecule-specific variables (e.g., ln number approved indications) but exclude the S i shortage report dummy variable.

Estimation of parameters in Eqns. 1, 2 and 3 by OLS are inconsistent if the data generating process is a random walk (has a unit root). We test whether levels and/or first differences of ln ASP reimbursement, ln average unit prices and the difference between ln ASP reimbursement and ln average unit prices exhibited stationary trends throughout the study period. We implement the Harris Tzavalis unit root test over all oncologics and among only oncologics in short supply, with critical values adjusted for unit roots, since the null hypothesis of this test is that these prices exhibit non- stationary shifts in their trend (a unit root). We also test for unit roots using an alternative asymptotic test procedure by Levin, Lin and Chu.

Additional sensitivity analyses Redefined “shorted” by including several other shortage measures including whether the shortage was ultimately reported to be resolved as of May 2013 and whether non- shorted oncologics in our sample were reported in short supply after December For the regression in Eqn 3, we only have annual data for all of these covariates, yet pricing data is available quarterly. We implemented Stata’s imputation process to generate quarterly estimates for each variable and re-estimated the model. STATA version 11.2 was used for all estimations; estimated coefficients were considered statistically significant at standard values (p-values < 0.05).

Preliminary results

Figure 3. Mean Medicare ASP reimbursement for always generic injectable oncologics covered under medical benefit ($2012),

Figure 4. Annual trends in mean unit value transaction prices among always generic injectable oncologics, stratified by shortage report. For a list of included oncologics, see notes in Figure 3. Source: IMS National Sales Perspectives®, January October 2011, IMS Health Incorporated. All Rights Reserved Mean unit value transactions prices for shorted oncologics declined 37% cumulatively, from $145 in 2006 to $92 in We also observe price declines among non-shorted oncologics, but they are not as steep as those observed among shorted oncologics; for non-shorted oncologics, the cumulative decline is 23%, from approximately $200 to $150.

Table 6. Regression results testing the equality of ASP reimbursement and transaction unit prices overall sample drugs, between shorted and non-shorted drugs. For a list of included oncologics see Caption under Figure 3. Source: Centers for Medicare and Medicaid, ASP drug pricing files and Source: IMS National Sales Perspectives®, January 2006, September 2011, IMS Health Incorporated. All Rights Reserved.

Table 7. Regression results examining correlates of the difference between ASP reimbursement and transaction prices overall drugs. For a list of included oncologics see Caption under Figure 3. Source: Centers for Medicare and Medicaid, ASP drug pricing files and Source: IMS National Sales Perspectives®, January 2006, September 2011, IMS Health Incorporated. All Rights Reserved.

Table 8. Unit root tests for price variables over the study period. For a list of included oncologics see Caption under Figure 3. Source: Centers for Medicare and Medicaid, ASP drug pricing files and IMS National Sales Perspectives®, January 2006, September 2011, IMS Health Incorporated. All Rights Reserved.

Preliminary results summary Medicare reimbursement and average unit prices do not move in tandem for many generic physician-administered drugs used to treat cancer. This suggests that “underwater” reimbursement is largely confined to drugs considered to be part of backbone chemotherapy. Intriguingly, for oncologics that are reported in short supply during our study period, these price co-movements are almost completely independent. Thus, the price signals generated by these two price measures of the same product are not only inconsistent and ambiguous, we find their movements to be almost completely independent preceding domestic shortage reports. Our results imply lagged ASP reimbursement policy may obfuscate the role of the price signal to increase production among existing manufacturers when supply of these products declines or is interrupted among current domestic drug shortages.

Next steps Add all Part B covered drugs (not vaccines). Classify by patent protection, therapeutic class, number of FDA approved indication. Describe which drugs are “underwater” for at least two quarters during study time period. Extend study period.

"We stand in the midst of medicines too costly to be sustainable members of our therapeutic armamentarium and others too cheap to ensure high-quality manufacturing and availability...All of us -- those who discover, develop, manufacture, regulate, market, pay for, prescribe and take medicines -- ’own’ the responsibility to ensure access to the very best medicine and science have to offer.” - Stephen Spielberg. Editor-in- Chief’s Commentary: Integrating Economics Into Innovation. Therapeutic Innovation & Regulatory Science July :