PharmacoEconomics最新文献

Importance: The exceedingly high US spending per capita on prescription medications is mediated, at least in part, by the inefficiencies of existing generic pharmaceutical distribution and reimbursement systems; yet, the extent of potential savings and areas for targeted interventions for generic drug prescribers remains underexplored.

Objective: We aimed to analyze 2021 Medicare Part D spending on generic drugs in comparison with pricing of a low-cost generic drug program, the Mark Cuban Cost Plus Drug Company (MCCPDC), to gauge the extent of achievable potential savings.

Design, setting, and participants: In this retrospective, observational study, we performed a systematic analysis of potential Medicare Part D savings when using MCCPDC generic pricing. The 2023 MCCPDC data, as of August 2023, were obtained from the provider's publicly available database. The 2021 Medicare Part D data and prescriber datasets were obtained from the US Centers for Medicare and Medicaid Services.

Main outcomes and measures: Outcomes included total prescription volume, proportion of drugs with savings, total US dollar Medicare savings, and average weighted price reduction per unit drug. Results were stratified by medical and surgical subspecialties to identify areas for targeted interventions. Subspecialty-wise contribution to total savings versus contribution to total prescription volume was characterized.

Results: Total estimated Medicare Part D savings were $8.6 billion using 90-day MCCPDC pricing, with surgical drugs accounting for over $900 million. Nearly 80% of the examined drugs were more price effective through MCCPDC using 90-day supply. Commonly prescribed drugs in cardiology, psychiatry, neurology, transplant surgery, and urology demonstrated the highest estimated absolute savings. The most disproportionate savings relative to prescription volume were observed for drugs in oncology, gynecology, infectious disease, transplant surgery, and colorectal surgery.

Conclusions and relevance: This study underscores the significant potential for Medicare Part D savings through strategies that address the systemic overpayment for generic medications. We identified key areas for reform as well as specific medical and surgical subspecialties where targeted interventions could yield substantial savings.

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: 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.

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.

Background: Reporting standards of discrete choice experiments (DCEs) in health have not kept pace with the growth of this method, with multiple reviews calling for better reporting to improve transparency, assessment of validity and translation. A key missing piece has been the absence of a reporting checklist that details minimum standards of what should be reported, as exists for many other methods used in health economics.

Methods: This paper reports the development of a reporting checklist for DCEs in health, which involved a scoping review to identify potential items and a Delphi consensus study among 45 DCE experts internationally to select items and guide the wording and structure of the checklist. The Delphi study included a best-worst scaling study for prioritisation.

Conclusions: The final checklist is presented along with guidance on how to apply it. This checklist can be used by authors to ensure that sufficient detail of a DCE's methods are reported, providing reviewers and readers with the information they need to assess the quality of the study for themselves. Embedding this reporting checklist into standard practice for health DCEs offers an opportunity to improve consistency of reporting standards, thereby enabling transparency of review and facilitating comparison of studies and their translation into policy and practice.

There is increasing interest in the use of cure modelling to inform health technology assessment (HTA) due to the development of new treatments that appear to offer the potential for cure in some patients. However, cure models are often not included in evidence dossiers submitted to HTA agencies, and they are relatively rarely relied upon to inform decision-making. This is likely due to a lack of understanding of how cure models work, what they assume, and how reliable they are. In this tutorial we explain why and when cure models may be useful for HTA, describe the key characteristics of mixture and non-mixture cure models, and demonstrate their use in a range of scenarios, providing Stata code. We highlight key issues that must be taken into account by analysts when fitting these models and by reviewers and decision-makers when interpreting their predictions. In particular, we note that flexible parametric non-mixture cure models have not been used in HTA, but they offer advantages that make them well suited to an HTA context when a cure assumption is valid but follow-up is limited.

Background: Health systems are moving towards value-based care, implementing new care models that allegedly aim beyond patient outcomes. Therefore, a policy and academic debate is underway regarding the definition of value in healthcare, the inclusion of costs in value metrics, and the importance of each value element. This study aimed to define healthcare value elements and assess their relative importance (RI) to the public in England.

Method: Using data from 26 semi-structured interviews and a literature review, and applying decision-theory axioms, we selected a comprehensive and applicable set of value-based elements. Their RI was determined using two discrete choice experiments (DCEs) based on Bayesian D-efficient DCE designs, with one DCE incorporating healthcare costs expressed as income tax rise. Respondent preferences were analysed using mixed logit models.

Results: Six value elements were identified: additional life-years, health-related quality of life, patient experience, target population size, equity, and cost. The DCE surveys were completed by 402 participants. All utility coefficients had the expected signs and were statistically significant (p < 0.05). Additional life-years (25.3%; 95% confidence interval [CI] 22.5-28.6%) and patient experience (25.2%; 95% CI 21.6-28.9%) received the highest RI, followed by target population size (22.4%; 95% CI 19.1-25.6%) and quality of life (17.6%; 95% CI 15.0-20.3%). Equity had the lowest RI (9.6%; 95% CI 6.4-12.1%), decreasing by 8.8 percentage points with cost inclusion. A similar reduction was observed in the RI of quality of life when cost was included.

Conclusion: The public prioritizes value elements not captured by conventional metrics, such as quality-adjusted life-years. Although cost inclusion did not alter the preference ranking, its inclusion in the value metric warrants careful consideration.