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Record W2099036286 · doi:10.1093/jpids/piu025

The Medicaid Cost of Palivizumab

2014· letter· en· W2099036286 on OpenAlexaboutno aff
Christopher S. Ambrose, Kimmie McLaurin

Bibliographic record

VenueJournal of the Pediatric Infectious Diseases Society · 2014
Typeletter
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsnot available
FundersAstraZeneca
KeywordsMedicaidPalivizumabReimbursementPaymentMedicineActuarial scienceFood and drug administrationPediatricsBusinessEconomicsFinanceMedical emergencyHealth careRespiratory system

Abstract

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To the editor—Borse et al. [1] describe an economic analysis of respiratory syncytial virus (RSV) prophylaxis for infants in Alaska's Yukon-Kuskokwim Delta. The authors should be congratulated for evaluating this important question with a robust analysis. As these infants are almost entirely insured by Medicaid, we wish to clarify the net Medicaid cost of palivizumab. Based on the 2010 average wholesale price (AWP), Alaska Medicaid's published reimbursement rate, and the minimum Medicaid rebate, the authors estimated a cost of $1055 per 50 mg [1]. However, using Alaska Medicaid's reported pre-rebate expenditures for palivizumab and incorporating all components of the Medicaid rebate yields a more accurate estimate, which is substantially lower at $588 per 50 mg [2, 3]. AWP is determined by drug reference companies and does not reflect a price at which manufacturers sell products to wholesalers. The US Office of the Inspector General has described AWP-based reimbursement as “fundamentally flawed” and has recommended payment based on a single national pricing benchmark based on average drug acquisition costs [4]. Given this, a better source is wholesale acquisition cost (WAC), or the list price for a drug sold by a manufacturer to wholesalers. The 2010 WAC for 50 mg of palivizumab was $1074. For Alaska Medicaid specifically, the reported pre-rebate palivizumab expenditure for 2010–2011 was $1082 per 50 mg [2]. Precise estimates of Medicaid rebates for drugs are difficult to calculate because rebate payments are not publicly available. However, they can be estimated based on the two components of the Medicaid rebate [5]. The authors note that the first component is 17.1% of average manufacturer price. However, there is a second consumer price index (CPI)–based component, which effectively precludes drug cost increases in excess of changes in CPI [5]. Based on data from 10 large states, these two rebate components were estimated to result in a 41% reduction in the 2010 palivizumab cost for Medicaid [6]. Other drugs have rebates of similar magnitude; Medicaid rebates recouped 45% of expenditures on 100 brand-name drugs in 2009 [4]. For Alaska Medicaid specifically, the estimated net 2010–2011 cost of palivizumab after rebates is $588 per 50 mg [2, 3]. This cost is considerably lower than the Borse et al. estimate and approaches one of the modeled cost-neutral thresholds of $486 [1]. Lastly, the authors incorrectly state that palivizumab AWP increased by 184% from 2001–2010 (20% annually), referencing Red Book data [7, 8]. Red Book [7, 8] reports a palivizumab AWP increase of 78% from 2001 to 2010, a 7% annual increase. After Medicaid rebates, the net US cost increase was approximately 60% from 2001 to 2010, or 5.5% annually. This increase is lower than the 83% inflation rate for US inpatient hospital services from 2001 to 2010 [9]. Similarly, the cost of an RSV hospitalization among US infants increased by 89% from 2000 to 2009 [10]. In closing, we congratulate the authors on a robust analysis and hope that this additional information regarding the net Medicaid costs of palivizumab will help inform policy decisions and future research regarding RSV prophylaxis in the United States.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0070.011
Insufficient payload (model declined to judge)0.0150.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.314
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2014
Admission routes1
Has abstractyes

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