PharmacoEconomics最新文献

Objective: To construct a framework and calculation tool to compare the consequences of implementing different payment models for high-cost, one-off potentially curative therapies and enable decision making to ultimately enhance timely patient access to innovative health interventions.

Methods: A framework outlining steps to determine potentially suitable payment models was developed. Based on the framework, a supporting calculation tool operationalised as an Excel-based model was constructed to quantify the associated costs for an average patient during the timeframe of the intended payment agreement, the total budget impact and associated benefits expressed in quality-adjusted life-years for the total expected lifetime of the patient population. To demonstrate the potential of the framework, three case studies were used: onasemnogene abeparvovec (Zolgensma^®), brexucabtagene autoleucel (Tecartus^®) and etranacogene dezaparvovec (Hemgenix^®). A hypothetical case study was used to illustrate the output of the calculation tool.

Results: Part 1 of the framework presents steps for matching a suitable reimbursement and payment model with the disease and treatment characteristics. The reimbursement and payment models are further specified in Part 2. Part 3 guides end users through the setup of a calculation tool with which the financial impact can be calculated of two payment models: a price discount model and an outcome-based spread payment model with a discount. Part 4 concerns the output of the calculation tool, showing how different payment models lead to different financial consequences under three assumptions of longer term effectiveness.

Conclusions: The presented framework provides decision makers with insight into the financial consequences of their chosen payment model under different assumptions. This can aid reimbursement negotiations by clarifying the optimal choice given a therapy's characteristics.

Background: Detailed information on the efficiency of health services targeting opioid use disorder (OUD) and treatment with opioid agonist treatment (OAT) is sparse. Many countries, including Norway, are still falling short of universal health coverage (UHC) of OAT. This study aims to evaluate the incremental lifetime costs and effects of treating OUD with OAT as compared to no OAT in Norway and scaling up the treatment to a universal coverage level using equity-adjusted health economic evaluations.

Methods: We conducted cost-utility and budget impact analyses and constructed a two-state Markov model to compare the lifetime costs and outcomes among patients with OUD with and without OAT. Model inputs were derived from routine health information systems and the literature, with costs reported in 2023 Norwegian Kroner (NOK). The analyses were conducted from a Norwegian extended health-service and societal perspectives, with a lifetime time horizon. Quality-adjusted life years (QALYs) was the metric of health benefits. Outcomes were reported as incremental cost-effectiveness ratios (ICERs). The willingness-to-pay (WTP) threshold was equity-adjusted according to the future prognostic healthy life year loss method in Norway (severity of disease criterion), which is sensitive to the size of future undiscounted healthy life year loss due to the affected conditions. The WTP threshold is NOK 825,000 per QALY gained in Norwegian policy for conditions with undiscounted future QALY loss > 20. Uncertainty in the parameters and robustness of the results were assessed with one-way and probabilistic sensitivity analyses and scenario analyses.

Findings: The mean results from probabilistic sensitivity analysis estimated that OAT was associated with 3.03 additional discounted QALYs gain and incremental lifetime discounted cost of NOK 1.45 million, leading to an ICER of NOK 479,099 per QALY gained when compared with not providing OAT, with the extended health-service perspective. From a societal perspective, OAT was cost-saving, i.e. OAT produced greater health benefits while resulting in lower overall societal costs compared to no OAT. The mean undiscounted future health loss was estimated to be 21.34 QALYs for the Norwegian patient group with OUD. A total 5-year budget increase of NOK 1.208 billion was estimated if OAT was going to be scaled up from the current coverage level of 70% to UHC. Compared with the current coverage, 100% coverage of OAT was associated with an additional lifetime cost of NOK 4.332 billion but also an additional 6760 QALYs gained.

Conclusion: Our analysis suggests that OAT is cost-effective in Norway and has the potential to be cost-saving from a societal perspective. Therefore, Norwegian policy should consider scaling up treatment to extend the coverage of OAT.

Background and objective: In a context of growing clinical and financial uncertainty, pricing and payment schemes can act as possible solutions to the problems of affordability and access to health technologies. However, a comprehensive categorization of the available schemes to help decision makers tackle these challenges is lacking. This work aims at mapping existing types of pricing and payment schemes, and proposes a new approach for their classification, in order to help decision makers and other stakeholders select the best type of scheme to meet their needs.

Methods: A Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR)-compliant scoping literature review was performed between 2010 and 2023 in three databases (PubMed, Web of Science, Scopus). The search strategy was developed around two groups of keywords, "pricing/payment schemes" and "scheme innovativeness". Eligible studies were those illustrating the unique design and features of each scheme type, which were extracted by two independent reviewers, and synthesized using a narrative format, including a detailed tabular description of each type of scheme.

Results: A total of 70 unique types of pricing and payment schemes were identified. Around one third (33%) was only specified in principle, while two thirds (67%) had been implemented in practice. About half of the scheme types were proposed for drugs (34/70, 49%), and the vast majority were not designed for a specific therapeutic area (55/70, 79%). Each scheme type was categorized based on distinctive characteristics: the objectives, the outcome component, the timing/modalities of payments, and the evidence collection requirements.

Conclusions: Instead of trying to fit the retrieved schemes into a rigid taxonomy, we propose a new approach that suggests a flexible need-driven use of the available scheme types, driven primarily by the specific objective that one might have, and allows leveraging of the other key characteristics of each type of scheme.

Background: Pulmonary arterial hypertension (PAH) is a progressive disease characterized by elevated pressure in the pulmonary arteries, commonly resulting in right heart failure. PAH is associated with a high economic burden throughout the duration of the disease.

Methods: This retrospective cohort study of the Milliman Contributor Health Source Data, the Medicare 100% Research Identifiable Files, and the Merative Marketscan^® Commercial dataset between 2018 and 2020 identified adult patients with prevalent PAH based on the earliest qualifying diagnosis date or medication date ('index date') between January 1, 2019 and November 30, 2020. Outcomes were assessed using patient data from index date through the earliest of end of enrollment, end of data, or death (Medicare fee-for-service [FFS] only). All-cause and PAH-related medical and pharmacy costs per-patient per-month (PPPM) and healthcare resource utilization per 1000 patients were summarized.

Results: The study included 11,670 Medicare FFS, 1021 Medicare Advantage, 274 Medicaid, and 1174 commercially insured patients in the US. The annual national burden to payers was estimated to be US$3.1 billion. The PPPM payer costs ranged from US$6500 to US$14,742; out-of-pocket (OOP) costs ranged from US$341 to US$907 PPPM. Inpatient utilization rate ranged from 435 to 770 per 1000 patients for all-cause admissions and from 15 to 58 per 1000 patients for PAH-related admissions.

Conclusions: This study demonstrates that PAH continues to be associated with a high economic burden and healthcare resource utilization across all payer types within the US healthcare system.

Background: Cost-utility analyses (CUAs) increasingly use models to predict long-term outcomes and translate trial data to real-world settings. Model structure uncertainty affects these predictions. This study compares pharmacometric against traditional pharmacoeconomic model evaluations for CUAs of sunitinib in gastrointestinal stromal tumors (GIST).

Methods: A two-arm trial comparing sunitinib 37.5 mg daily with no treatment was simulated using a pharmacometric-based pharmacoeconomic model framework. Overall, four existing models [time-to-event (TTE) and Markov models] were re-estimated to the survival data and linked to logistic regression models describing the toxicity data [neutropenia, thrombocytopenia, hypertension, fatigue, and hand-foot syndrome (HFS)] to create traditional pharmacoeconomic model frameworks. All five frameworks were used to simulate clinical outcomes and sunitinib treatment costs, including a therapeutic drug monitoring (TDM) scenario.

Results: The pharmacometric model framework predicted that sunitinib treatment costs an additional 142,756 euros per quality adjusted life year (QALY) compared with no treatment, with deviations - 21.2% (discrete Markov), - 15.1% (continuous Markov), + 7.2% (TTE Weibull), and + 39.6% (TTE exponential) from the traditional model frameworks. The pharmacometric framework captured the change in toxicity over treatment cycles (e.g., increased HFS incidence until cycle 4 with a decrease thereafter), a pattern not observed in the pharmacoeconomic frameworks (e.g., stable HFS incidence over all treatment cycles). Furthermore, the pharmacoeconomic frameworks excessively forecasted the percentage of patients encountering subtherapeutic concentrations of sunitinib over the course of time (pharmacoeconomic: 24.6% at cycle 2 to 98.7% at cycle 16, versus pharmacometric: 13.7% at cycle 2 to 34.1% at cycle 16).

Conclusions: Model structure significantly influences CUA predictions. The pharmacometric-based model framework more closely represented real-world toxicity trends and drug exposure changes. The relevance of these findings depends on the specific question a CUA seeks to address.

Objectives: This study was designed to test hypotheses regarding the path dependence of health-outcome values in the form of linear additivity of health-state utilities and diminishing marginal utility of health outcomes.

Methods: We employed a discrete-choice experiment to quantify patient treatment preferences for major depressive disorder. In a series of choice questions, participants evaluated seven symptom-improvement sequences and out-of-pocket costs over 6-week durations. Money-equivalent values were derived from a deductive latent-class mixed-logit analysis.

Results: The discrete-choice experiment was completed by 751 respondents with self-reported major depressive disorder recruited from an online commercial panel. The class-membership probability was 0.83 for latent-class preferences consistent with supporting relative importance weights for all symptom-improvement sequences in the study design. First, we found strong support for diminishing marginal utility in symptom-improvement sequences. The money-equivalent value of an initial week of normal mood was $147 (95% confidence interval: $128, $166) and a second week of normal mood was $70 ($49, $91). Furthermore, for short treatment durations where conventional discounting was not a factor, equivalent changes in health status were valued more highly for an earlier onset of effect: holding subsequent symptom patterns constant, $338 (211, 454) versus $70 (49, 91) for improvements starting in week 2 versus week 3 and $147 ($128, $166) versus $29 (-$4, $64) for improvements starting in week 3 versus week 4.

Conclusions: Our findings imply that conventional quality-adjusted life-year calculations in which health values are assumed to be path independent can understate the value of health improvements that appear earlier in a sequence.

Objectives: We conducted a distributional cost-effectiveness analysis to evaluate how coverage of tocilizumab for inpatients with COVID-19 from 2021 to present impacted health equity in the USA.

Methods: A published, payer-perspective, distributional cost-effectiveness analysis for inpatient COVID-19 treatments was adapted to include information on baseline health disparities across 25 equity-relevant subgroups based on race and ethnicity (5 census-based groups), and county-level social vulnerability (5 geographic quintiles). The underlying cost-effectiveness analysis was updated to reflect patient characteristics at admission, standard of care outcomes, tocilizumab efficacy, and contemporary unit costs. The distributional cost-effectiveness analysis inputs for COVID-19 hospitalization and subgroup risk adjustments based on social vulnerability were derived from published estimates. Opportunity costs were estimated by converting total tocilizumab spend into quality-adjusted life-years (QALYs), distributed equally across subgroups.

Results: Tocilizumab treatment was cost effective across all subgroups. Treatment resulted in larger relative QALY gains in more socially vulnerable subgroups than less socially vulnerable subgroups, given higher hospitalization rates and inpatient mortality. Using an opportunity cost threshold of US$150,000/QALY and an Atkinson index of 11, tocilizumab was estimated to have improved social welfare by increasing population health (53,252 QALYs gained) and reducing existing overall US health inequalities by 0.003% since 2021.

Conclusions: Use of tocilizumab for COVID-19 since 2021 increased population health while improving health equity, as more patients with lower baseline health were eligible for treatment and received larger relative health gains. Future equitable access to tocilizumab for inpatients with COVID-19 is expected to lead to continued increases in population health and reductions in disparities.

Current approaches to the pricing and funding of new pharmaceuticals often focus on a one-time decision about a product for each clinical indication. This can result in multiple options being available to health systems without a clear signal about how to prioritise between them. This runs the risk that, as available treatments, evidence, and drug prices evolve, clinical and patient choices may not be aligned with the objective of allocating resources to promote population health. We propose a framework for using cost-effectiveness analysis to support pricing and funding policies for new pharmaceuticals in multi-comparator indications, some of the key aspects of which evolve over time. The framework comprises three core considerations: (1) designing proportionate processes, (2) assessing the costs and benefits of recommending multiple treatment options, and (3) appropriate application of cost-effectiveness analysis 'decision rules' to support recommendations and price negotiations. We highlight that proportionate processes require prioritisation of topics for reassessment to be aligned with clear objectives, the need for full flexibility of decision making at the point of reassessment, and that in some contexts contractual re-specification rather than typical deliberative health technology assessment processes may be more appropriate. We discuss reasons why the recommendation of multiple treatment options rather than a single cost-effective treatment may be appropriate and urge health technology assessment bodies to explicitly address the trade-offs that may be associated with recommending multiple treatments. Finally, we discuss how value-based pricing could be achieved when multiple competitor manufacturers offer confidential discounts.

Objectives: For US Medicare and Medicaid, single drug prices do not reflect the value of supplemental indications. Value-based indication-specific and weighted-average pricing has been suggested for drugs with multiple indications. Under indication-specific pricing, a distinct price is assigned to the differential value a drug offers in each indication. Under weighted-average pricing, a single drug price is calculated that reflects the value and/or volume of each indication. This study estimates price reductions and cost savings for cancer drugs under value-based indication-specific pricing and weighted-average pricing.

Methods: We collected data on US Food and Drug Administration (FDA)-approved cancer drugs and indications (2003-2020) from FDA labels, the Global Burden of Disease study, clinicaltrials.gov, and Medicare and Medicaid. A multivariable regression analysis, informed by characteristics of original indications, was used to predict value-based indication-specific prices for supplemental indications. These indication-specific prices were combined with each indication's prevalence data to estimate value-based weighted-average prices and potential cost savings under each policy.

Results: We identified 123 cancer drugs with 308 indications. Medicare and Medicaid spent a total of $28.2 billion on these drugs in 2020. Adopting value-based indication-specific pricing would increase drug prices by an average of 3.9%, with cost savings of $3.0 billion (10.6%). However, 43.7% higher prices for ultra-rare diseases would increase spending by 16.8% ($44 million). Adopting value-based weighted-average pricing would reduce prices by an average of 4.6% and spending by $3.0 billion (10.6%). Under weighted-average pricing, prices for and spending on ultra-rare diseases would be reduced by 22.6% and $55 million, respectively.

Conclusions: Value-based indication-specific and weighted-average pricing could help to align the value and price of new indications, thereby reducing expenditure on drugs with multiple indications.