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Placement of Selected New FDA-Approved Drugs in Medicare Part D Formularies, 2009-2013
Bruce C. Stuart, PhD; Sarah E. Tom, PhD; Michelle Choi, PharmD; Abree Johnson, MS; Kai Sun, MS; Danya Qato, PhD; Engels N. Obi, PhD; Christopher Zacker, PhD; Yujin Park, PharmD; and Steve Arcona, PhD
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Cost Sharing for Antiepileptic Drugs: Medication Utilization and Health Plan Costs
Nina R. Joyce, PhD; Jesse Fishman, PharmD; Sarah Green, BA; David M. Labiner, MD; Imane Wild, PhD, MBA; and David C. Grabowski, PhD

Placement of Selected New FDA-Approved Drugs in Medicare Part D Formularies, 2009-2013

Bruce C. Stuart, PhD; Sarah E. Tom, PhD; Michelle Choi, PharmD; Abree Johnson, MS; Kai Sun, MS; Danya Qato, PhD; Engels N. Obi, PhD; Christopher Zacker, PhD; Yujin Park, PharmD; and Steve Arcona, PhD
There is significant heterogeneity in formulary placement and restrictions on new drug approvals in the Part D marketplace.

Our analysis of 33 new FDA drug approvals in 8 therapeutic classes between 2009 and 2013 identified 3 sources of delay in the diffusion of new products within the Medicare marketplace. First were potential delays in assigning NDC codes to each new product. Second was the delay before the first Part D formulary places the drug. Third was the time it takes for formulary placement to diffuse within the Part D marketplace following first placement.

Normally, delays caused by failure to promptly assign NDCs are minimal, ranging from a few days to a month. Under certain circumstances, however, it can be months before NDCs are assigned. Across all of the drugs in our study sample, the mean delay between FDA approval date and NDC assignment date was 35.6 days, but this was heavily weighted by long delays for the 3 antiepileptic drugs, due in part to contention about if and where these medications were to be scheduled as controlled substances. Perampanel (Fycompa) was eventually placed into Schedule III and both exogabine (Potiga) and lacosamide (Vimpat) into Schedule V.

Delays between NDC assignment date and first Part D plan formulary placement averaged 4.6 months across the entire sample, with wide variation by therapeutic class: 2 to 4 months for antiepileptics and antipsychotics and up to 9 months for other products. The early placement of antiepileptics and antipsychotics was likely due to their protected class status. Variation in timing of first placement for drugs in nonprotected classes is more difficult to explain. One might surmise that products considered either highly efficacious or having some unique clinical advantage would be quickly adopted by plan formularies, but if that were the case, one would also expect to see a strong correlation between first placement and subsequent rapid diffusion. There was no such correlation in our data. Overall, 56.7% of all drugs had been placed on plan formularies within 6 months after first placement and 64.1% within 12 months following first placement. These adoption rates were heavily influenced by drugs in protected classes. Removing these from the averages dropped the mean adoption rate to 47.5% at 6 months and 57.2% at 12 months. As expected, the rate of adoption was fastest for LEs and CPs, as plans typically place these products on formulary without formal pharmacy and therapeutic (P&T) committee review or a waiting period.

The role that Part D plan characteristics play in formulary adoption decisions largely met our expectations. Nonbenchmark plans and those offering enhanced benefits had higher uptake rates for new drugs, as did plans with higher star ratings. After controlling for differences in plan characteristics, we found similar formulary adoption rates and time to placement among PDPs and MAPDs. However, the fact that MAPDs were predominantly enhanced benefit plans with high star ratings—both significant predictors of higher formulary placement rates—meant that MAPDs placed more new drugs on their formularies than did PDPs: 58.0% versus 52.3% at 6 months and 65.5% versus 60.0% at 12 months. Higher adoption rates for MAPDs are consistent with observations made in prior literature.2-5 However, these earlier studies did not correlate placement rates with application of utilization management tools. The fact that MAPDs were more likely than PDPs to impose ST and PA restrictions means that patient access to some newer medications could be more limited in MAPDs.


These results should be interpreted in light of several caveats. Most important is the fact that we evaluated a relatively small nonrandom sample of all FDA drug approvals between 2009 and 2013. Although our sample included a few LEs and CPs, it was weighted toward NCEs. Samples with different proportions of NCEs, LEs, and CPs would produce different estimates of delays in formulary placements by Part D plans.

Second, we restricted the analysis to all new approvals in just 8 therapeutic classes, albeit representative of a diverse set of classes used in treating common chronic conditions. Nonetheless, we make no claim that our results apply to therapeutic classes we did not investigate. Further research is necessary to determine whether the patterns observed in our analysis apply to other drug classes.
Third, we lacked data on final manufacturer prices (transaction prices minus rebates) faced by plans making formulary decisions for new drug products. Health plans have relatively little bargaining power when considering agents that are first in class. Subsequent approvals increase competition and generally lower acquisition prices.

Fourth, we restricted the study sample of Part D plans to those with continuous contracts from 2008 through 2013. This restriction was necessary in order to compute delays in plans’ formulary adoption behavior, but it also meant that our results are not universally generalizable to all Part D plans over the study period.

Fifth, limitations in the CMS formulary files precluded any formal evaluation of generosity of coverage of newly approved drugs. The files contained tier assignment numbers. However, the interpretability of tier numbers was hampered by the fact that cost-sharing tier levels varied both across plans and within plans over time (ie, a tier number of 3 could represent either a nonpreferred brand in a 4-tier plan with a single generic tier or a preferred brand in a 5-tier plan with 2 generic tiers).

Finally, we did not consider the clinical effectiveness of new drugs for Medicare beneficiaries in plan decision making regarding formulary placements. CMS rules and conventional practice by P&T committees place clinical effectiveness at the forefront of formulary considerations,8-10 but even when following standardized protocols for new drug evaluations, individual P&T committees may come to very different conclusions. Moreover, under CMS regulations, P&T committee decisions are recommendations that may be overruled by plan sponsors. The result is a very heterogeneous pattern of formulary adoptions of newly approved drug products across the Part D marketplace.


We found significant heterogeneity in formulary placement and restrictions for 33 newly FDA-approved drugs in the Part D marketplace between 2009 and 2013. Further research is necessary to determine whether this pattern applies to other drug classes.

Author Affiliations: University of Maryland Baltimore (BCS, SET, MC, AJ, KS, DQ), Baltimore, MD; Novartis Pharmaceuticals Corporation (ENO, CZ, YP, SA), East Hanover, NJ.

Source of Funding: Novartis Pharmaceuticals Corporation.

Author Disclosures: Dr Stuart and Dr Tom report receiving grant support from Novartis Pharmaceuticals Corporation. Dr Obi, Dr Zacker, Dr Park, and Dr Arcona report being employed by Novartis. Dr Obi, Dr Zacker, and Dr Arcona report stock ownership in Novartis. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (BCS, SET, ENO, CZ, YP, SA); acquisition of data (BCS, AJ, KS); analysis and interpretation of data (BCS, SET, MC, AJ, KS, DQ, ENO, CZ, YP); drafting of the manuscript (BCS, AJ, DQ, ENO, CZ, YP); critical revision of the manuscript for important intellectual content (BCS, SET, MC, AJ, DQ, ENO, CZ, YP, SA); statistical analysis (BCS, KS); obtaining funding (BCS, CZ, YP, SA); administrative, technical, or logistic support (MC, AJ, ENO, YP, SA); and supervision (BCS, CZ, YP).

Address Correspondence to: Bruce C. Stuart, PhD, Department of Pharmaceutical Health Services Research, University of Maryland Baltimore, 220 Arch St, 12th Fl, Baltimore, MD 21201. Email:

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10. Schiff GD, Galanter WL, Duhig J, et al. A prescription for improving drug formulary decision making. PloS Med. 2012;9(5):1-7. doi: 10.1371/journal.pmed.1001220.
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