PharmacoEconomics最新文献

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.

Introduction: Immunocompromised host pneumonia (ICHP) is an important cause of morbidity and mortality, yet usual care (UC) diagnostic tests often fail to identify an infectious etiology. A US-based, multicenter study (PICKUP) among ICHP patients with hematological malignancies, including hematological cell transplant recipients, showed that plasma microbial cell-free DNA (mcfDNA) sequencing provided significant additive diagnostic value.

Aim: The objective of this study was to perform a cost-effectiveness analysis (CEA) of adding mcfDNA sequencing to UC diagnostic testing for hospitalized ICHP patients.

Methods: A semi-Markov model was utilized from the US third-party payer's perspective such that only direct costs were included, using a lifetime time horizon with discount rates of 3% for costs and benefits. Three comparators were considered: (1) All UC, which included non-invasive (NI) and invasive testing and early bronchoscopy; (2) All UC & mcfDNA; and (3) NI UC & mcfDNA & conditional UC Bronch (later bronchoscopy if the initial tests are negative). The model considered whether a probable causative infectious etiology was identified and if the patient received appropriate antimicrobial treatment through expert adjudication, and if the patient died in-hospital. The primary endpoints were total costs, life-years (LYs), equal value life-years (evLYs), quality-adjusted life-years (QALYs), and the incremental cost-effectiveness ratio per QALY. Extensive scenario and probabilistic sensitivity analyses (PSA) were conducted.

Results: At a price of $2000 (2023 USD) for the plasma mcfDNA, All UC & mcfDNA was more costly ($165,247 vs $153,642) but more effective (13.39 vs 12.47 LYs gained; 10.20 vs 9.42 evLYs gained; 10.11 vs 9.42 QALYs gained) compared to All UC alone, giving a cost/QALY of $16,761. NI UC & mcfDNA & conditional UC Bronch was also more costly ($162,655 vs $153,642) and more effective (13.19 vs 12.47 LYs gained; 9.96 vs 9.42 evLYs gained; 9.96 vs 9.42 QALYs gained) compared to All UC alone, with a cost/QALY of $16,729. The PSA showed that above a willingness-to-pay threshold of $50,000/QALY, All UC & mcfDNA was the preferred scenario on cost-effectiveness grounds (as it provides the most QALYs gained). Further scenario analyses found that All UC & mcfDNA always improved patient outcomes but was not cost saving, even when the price of mcfDNA was set to $0.

Conclusions: Based on the evidence available at the time of this analysis, this CEA suggests that mcfDNA may be cost-effective when added to All UC, as well as in a scenario using conditional bronchoscopy when NI testing fails to identify a probable infectious etiology for ICHP. Adding mcfDNA testing to UC diagnostic testing should allow more patients to receive appropriate therapy earlier and improve patient outcomes.

We propose a short-cut heuristic approach to rapidly estimate value of information (VOI) using information commonly reported in a research funding application to make a case for the need for further evaluative research. We develop a "Rapid VOI" approach, which focuses on uncertainty in the primary outcome of clinical effectiveness and uses this to explore the health consequences of decision uncertainty. We develop a freely accessible online tool, Rapid Assessment of the Need for Evidence (RANE), to allow for the efficient computation of the value of research. As a case study, the method was applied to a proposal for research on shoulder pain rehabilitation. The analysis was included as part of a successful application for research funding to the UK National Institute for Health and Care Research. Our approach enables research funders and applicants to rapidly estimate the value of proposed research. Rapid VOI relies on information that is readily available and reported in research funding applications. Rapid VOI supports research prioritisation and commissioning decisions where there is insufficient time and resources available to develop and validate complex decision-analytic models. The method provides a practical means for implementing VOI in practice, thus providing a starting point for deliberation and contributing to the transparency and accountability of research prioritisation decisions.