Determinants of the Financial Impact of Orphan Drugs in Italy: Differences Between Expected and Observed Pharmaceutical Expenditure.

IF 1.9 Q3 PHARMACOLOGY & PHARMACY Drugs - Real World Outcomes Pub Date : 2025-03-01 Epub Date: 2024-12-07 DOI:10.1007/s40801-024-00463-w
Francesco Saverio Mennini, Americo Cicchetti, Paolo Sciattella, Filippo Rumi, Matteo Zanuzzi, Angelica Carletto, Annalisa Sammarco, Federica Romano, Pierluigi Russo
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Abstract

Background: The increasing demand for orphan drugs and the financial challenges associated with their reimbursement highlights the need to understand the dynamics between expected and actual pharmaceutical expenditures, particularly in the context of pricing and reimbursement negotiations.

Objective: The study aims to identify the potential determinants of the difference in expected pharmaceutical spending after negotiation and the actual expenditure incurred (ΔS), as well as the difference in expected pharmaceutical spending before and after the price and reimbursement negotiation process (ΔSn) for rare disease drugs in Italy.

Methods: The analysis focused on orphan drugs authorised by European Medicines Agency, with reimbursement applications to the Italian Medicines Agency from January 2013 to January 2019. Expected post-negotiation spending was estimated by applying negotiated discounts and financial-based market entry agreements to the expected pharmaceutical expenditure. The actual expenditure was approximated through company turnovers. Beta regression models were applied to identify potential determinants of ΔS and ΔSn.

Results: The study analysed 52 reimbursed orphan drugs, with 41 (78.8%) with a single indication, 25 (48.1%) antineoplastics and immunomodulators and 18 (34.6%) conditionally/fully innovative. The median expenditure in the first 3 years post-commercialisation was 7.6% lower than expected post-negotiation. The reduction was less pronounced for innovative drugs (p = 0.011), drugs with a prices and reimbursement procedure in the subsequent 3 years (p = 0.007) and those with multiple indications (p = 0.021). Payment-by-result was the only significant variable associated with the expected spending ratio before and after negotiation (p = 0.002).

Conclusion: The actual expenditure for orphan drugs aligns with the expected post-negotiation. Yet, innovative orphan drugs exhibit a higher actual expenditure than estimated, suggesting the market values their added therapeutic value or the actual therapeutic need they meet, and configuring innovativeness status as the main determinant of the orphan drugs financial impact in the multiple regression analysis.

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意大利孤儿药财务影响的决定因素:预期和观察到的药品支出之间的差异。
背景:对孤儿药的需求不断增加,以及与这些药物报销相关的财务挑战,突出表明需要了解预期和实际药物支出之间的动态关系,特别是在定价和报销谈判的背景下。目的:本研究旨在确定意大利罕见病药物谈判后预期药品支出与实际发生的药品支出差异(ΔS)的潜在决定因素,以及价格和报销谈判过程前后的预期药品支出差异(ΔSn)。方法:分析重点是2013年1月至2019年1月向意大利药品管理局申请报销的欧洲药品管理局批准的孤儿药。预期的谈判后支出是通过将谈判折扣和基于财务的市场进入协议应用于预期的药品支出来估计的。实际开支是通过公司的营业额估算出来的。Beta回归模型用于确定ΔS和ΔSn的潜在决定因素。结果:本研究分析了52种报销孤儿药,其中单一适应症41种(78.8%),抗肿瘤和免疫调节剂25种(48.1%),有条件/完全创新18种(34.6%)。商业化后前3年的中位数支出比谈判后的预期低7.6%。创新药(p = 0.011)、有后续3年价格和报销程序的药物(p = 0.007)和有多种适应症的药物(p = 0.021)的降幅较小。结果支付是唯一与谈判前后预期支出比率相关的显著变量(p = 0.002)。结论:孤儿药的实际支出与谈判后的预期一致。然而,创新孤儿药的实际支出高于预期,这表明其增加的治疗价值或满足的实际治疗需求的市场价值,并在多元回归分析中配置创新状态作为孤儿药财务影响的主要决定因素。
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来源期刊
Drugs - Real World Outcomes
Drugs - Real World Outcomes PHARMACOLOGY & PHARMACY-
CiteScore
3.60
自引率
5.00%
发文量
49
审稿时长
8 weeks
期刊介绍: Drugs - Real World Outcomes targets original research and definitive reviews regarding the use of real-world data to evaluate health outcomes and inform healthcare decision-making on drugs, devices and other interventions in clinical practice. The journal includes, but is not limited to, the following research areas: Using registries/databases/health records and other non-selected observational datasets to investigate: drug use and treatment outcomes prescription patterns drug safety signals adherence to treatment guidelines benefit : risk profiles comparative effectiveness economic analyses including cost-of-illness Data-driven research methodologies, including the capture, curation, search, sharing, analysis and interpretation of ‘big data’ Techniques and approaches to optimise real-world modelling.
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