PharmacoEconomics最新文献

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.

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.

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.

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.

Background: The majority of recent estimates on the direct medical cost attributable to hospital-onset infections (HOIs) has focused on device- or procedure-associated HOIs. The attributable costs of HOIs that are not associated with device use or procedures have not been extensively studied.

Objective: We developed simulation models of attributable cost for 16 HOIs and estimated the total direct medical cost, including nondevice-related HOIs in the USA for 2011 and 2015.

Data and methods: We used total discharge costs associated with HOI-related hospitalization from the National Inpatient Sample and applied an analogy costing methodology to develop simulation models of the costs attributable to HOIs. The mean attributable cost estimate from the simulation analysis was then multiplied by previously published estimates of the number of HOIs for 2011 and 2015 to generate national estimates of direct medical costs.

Results: After adjusting all estimates to 2017 US dollars, attributable cost estimates for select nondevice-related infections attributable cost estimates ranged from $7661 for ear, eye, nose, throat, and mouth (EENTM) infections to $27,709 for cardiovascular system infections in 2011; and from $8394 for EENTM to $26,445 for central nervous system infections in 2016 (based on 2015 incidence data). The national direct medical costs for all HOIs were $14.6 billion in 2011 and $12.1 billion in 2016. Nondevice- and nonprocedure-associated HOIs comprise approximately 26-28% of total HOI costs.

Conclusion: Results suggest that nondevice- and nonprocedure-related HOIs result in considerable costs to the healthcare system.

Care as usual has failed to stem the tide of mental health challenges in children and young people. Transformed models of care and prevention are required, including targeting the social determinants of mental health. Robust economic evidence is crucial to guide investment towards prioritised interventions that are effective and cost-effective to optimise health outcomes and ensure value for money. Mental healthcare and prevention exhibit the characteristics of complex dynamic systems, yet dynamic simulation modelling has to date only rarely been used to conduct economic evaluation in this area. This article proposes an integrated decision-making and planning framework for mental health that includes system dynamics modelling, cost-effectiveness analysis, and participatory model-building methods, in a circular process that is constantly reviewed and updated in a 'living model' ecosystem. We describe a case study of this approach for mental health system policy and planning that synergises the unique attributes of a system dynamics approach within the context of economic evaluation. This kind of approach can help decision makers make the most of precious, limited resources in healthcare. The application of modelling to organise and enable better responses to the youth mental health crisis offers positive benefits for individuals and their families, as well as for taxpayers.

Background: Pandemic influenza poses a recurring threat to public health. Antiviral drugs are vital in combating influenza pandemics. Baloxavir marboxil (BXM) is a novel agent that provides clinical and public health benefits in influenza treatment.

Methods: We constructed a linked dynamic transmission-economic evaluation model combining a modified susceptible-exposed-infected-recovered (SEIR) model and a decision tree model to evaluate the cost-effectiveness of adding BXM to oseltamivir in China's influenza pandemic scenario. The cost-effectiveness was evaluated for the general population from the Chinese healthcare system perspective, although the users of BXM and oseltamivir were influenza-infected persons. The SEIR model simulated the transmission dynamics, dividing the population into four compartments: susceptible, exposed, infected, and recovered, while the decision tree model assessed disease severity and costs. We utilized data from clinical trials and observational studies in the literature to parameterize the models. Costs were based on 2021 CN¥ and not discounted due to a short time-frame of one year in the model. One-way, two-way, and probabilistic sensitivity analyses were also conducted.

Results: The integrated model demonstrated that adding BXM to treatment choices reduced the cumulative incidence of infection from 49.49% to 43.26% and increased quality-adjusted life years (QALYs) by 0.00021 per person compared with oseltamivir alone in the base-case scenario. The intervention also amounted to a positive net monetary benefit of CN¥77.85 per person at the willingness to pay of CN¥80,976 per QALY. Sensitivity analysis confirmed the robustness of these findings, with consistent results across varied key parameters and assumptions.

Conclusions: Adding BXM to treatment choices instead of only treating with oseltamivir for influenza pandemic control in China appears to be cost-effective compared with oseltamivir alone. The dual-agent strategy not only enhances population health outcomes and conserves resources, but also mitigates influenza transmission and alleviates healthcare burden.

To assign values to the health states of children, some health economists have suggested relying on the 'proxy' preferences among the health states of children expressed by a random sample of the adult population. These preferences have been elicited in several ways, with respondents sometimes asked to express their (adult) preferences among the health states of children, and sometimes asked to imagine themselves as children and to express what they think their preferences would be. This essay discusses three grounds for eliciting the preferences of a random sample of adults that have been suggested as ways to assign values to the health states in the EQ-5D-Y, and criticizes the first two: (1) the evidential ground: the preferences of the population sample are good evidence of how good or bad the health states of children are; (2) the 'taxpayer' ground: the adult population has the authority to assign values to health states, therefore their preferences are determinative; and (3) the pragmatic grounds: surveying is straightforward and shifts the responsibility from health economists to the population. I argue that instead of surveying a random sample of the population, health economists should rely on deliberative groups that include older children, experts on children's health and development, as well as members of the population at large. These groups should engage with the reasons that lie behind preferences among health states.