PharmacoEconomics最新文献

Background: People's time is a finite resource and a valuable input that ought to be considered in economic evaluations taking a broad, societal perspective. Yet, evaluations of interventions focusing on children and young people (CYP) rarely account for the opportunity cost of time in this population. As a key reason for this, health economists have pointed to uncertainty around when it is appropriate to include CYP time-related costs in an economic evaluation and highlighted the lack of clear guidance on the topic.

Methods: With this in mind, we carried out a Delphi study to establish a list of relevant considerations for researchers to utilise whilst making decisions about whether and when to include CYP time in their economic evaluations. Delphi panellists were asked to propose and rate a set of possible considerations and provide additional thoughts on their ratings. Ratings were summarised using descriptive statistics, and text comments were interrogated through thematic analysis.

Findings: A total of 73 panellists across 16 countries completed both rounds of a two-round Delphi study. Panellists' ratings showed that, when thinking about whether to include displaced CYP time in an economic evaluation, it is very important to consider whether: (1) inclusion would be in line with specified perspective(s) (median score: 9), (2) CYP's time may already be accounted for in other parts of the evaluation (median score: 8), (3) the amount of forgone time is substantial, either in absolute or relative terms (median score: 7) and (4) inclusion of CYP's time costs would be of interest to decision-makers (median score: 7). Respondents thought that considerations such as (1) whether inclusion would be of interest to the research community (median score: 6), (2) whether CYP's time displaced by receiving treatment is 'school' or 'play' time (median score: 5), and (3) whether CYP's are old enough for their time to be considered valuable (median score: 5) are moderately important. A range of views was offered to support beliefs and ratings, many of which were underpinned by compelling normative questions.

Objectives: Published literature has levied criticism against the cost-minimisation analysis (CMA) approach to economic evaluation over the past two decades, with multiple papers declaring its 'death'. However, since introducing the requirements for economic evaluations as part of health technology (HTA) decision-making in 1992, the cost-minimisation analysis (CMA) approach has been widely used to inform recommendations about the public subsidy of medicines in Australia. This research aimed to highlight the breadth of use of CMA in Australia and assess the influence of preconditions for the approach on subsidy recommendations METHODS: Relevant information was extracted from Public Summary Documents of Pharmaceutical Benefits Advisory Committee (PBAC) meetings in Australia considering submissions for the subsidy of medicines that included a CMA and were assessed between July 2005 and December 2022. A generalised linear model was used to explore the relationship between whether medicines were recommended and variables that reflected the primary preconditions for using CMA set out in the published PBAC Methodology Guidelines. Other control variables were selected through the Bolasso Method. Subgroup analysis was undertaken which replicated this modelling process.

Results: While the potential for inferior safety or efficacy reduced the likelihood of recommendation (p < 0.01), the effect sizes suggest that the requirements for CMA were not requisite for recommendation.

Conclusion: The Australian practice of CMA does not strictly align with the PBAC Methodology Guidelines and the theoretically appropriate application of CMA. However, within the confines of a deliberative HTA decision-making process that balances values and judgement with available evidence, this may be considered acceptable, particularly if stakeholders consider the current approach delivers sufficient clarity of process and enables patients to access medicines at an affordable cost.

Background: The advancement of diagnostic and therapeutic interventions in early Alzheimer's disease (AD) has demanded the economic evaluation of such interventions. Resource utilization and cost estimates in early AD and, more specifically, the amyloid-positive population are still lacking. We aimed to provide cost estimates in AD in relation to disease severity and compare these with the control population. We also aimed to provide cost estimates for a subset of the AD population with both clinical diagnosis and amyloid-positive confirmation.

Materials and methods: This was a retrospective longitudinal analysis of resource utilization using data from national registries. A cohort from the national Swedish registry for cognitive/dementia disorders (SveDem) includes all clinically diagnosed AD between 2013 and 2020. The study population included 31,951 people with AD and 63,902 age- and sex-matched controls (1:2). The population was followed until death, the end of December 2020, or 2 years from the last clinic visit. Direct medical and social costs were estimated from other national registries. Direct medical costs include costs for medications and inpatient and outpatient clinical visits. Direct social costs include costs for institutionalization, home care, short-term care, support for daytime activities, and housing support. Mean annual costs and 95% confidence intervals were obtained by bootstrapping, presented in 2021 Swedish Krona (SEK) (1 SEK = 0.117 USD, 1 SEK = 0.0985 EUR in 2021), and disaggregated by AD severity, cost component, sex, age group, and care setting.

Results: Mean annual costs for individuals with clinically diagnosed AD were SEK 99,906, SEK 290,972, SEK 479,524, and SEK 795,617 in mild cognitive impairment (MCI), mild, moderate, and severe AD. The mean annual costs for the population with both clinical diagnosis and amyloid-positive AD confirmation (N = 5610) were SEK 57,625, SEK 179,153, SEK 333,095, and SEK 668,073 in MCI, mild, moderate, and severe AD, respectively. The mean annual costs were higher in institutionalized than non-institutionalized patients, females than males, and older than younger age groups. Inpatient and drug costs were similar in all AD severity stages, but outpatient costs decreased with AD severity. Costs for institutionalization, home care, support for daytime activities, and short-term care increased with AD severity, whereas the cost of housing support decreased with AD severity.

Conclusions: This is the first study estimating annual costs in people with AD from MCI to severe AD, including those for the amyloid-positive population. The study provides cost estimates by AD severity, cost components, care settings, sex, and age groups, allowing health economic modelers to apply the costs based on different model structures and populations.

Objectives: The objective of this study is to compare different information retrieval methods that can be used to identify utility inputs for health economic models.

Methods: The usual practice of using systematic review methods was compared with two alternatives (iterative searching and rapid review), using a health technology assessment (HTA) case study in ulcerative colitis (UC). We analysed whether there were differences in the utility values identified when using the alternative search methods. Success was evaluated in terms of time, burden and relevance of identified information. The identified utility values were tested in an executable health economic model developed for UC, and the model results were compared.

Results: The usual practice of using systematic review search approaches identified the most publications but was also the least precise method and took longest to complete. The inclusion of data from the different search methods in the model did not lead to different conclusions across search methods.

Conclusions: In this case study, usual practice was less efficient and resulted in the same health economic model conclusions as the alternative search methods. Further case studies are required to examine whether this conclusion might be generalisable.

Treatment effect waning (TEW) refers to the attenuation of treatment effects over time. Assumptions of a sustained immuno-oncologic treatment effect have been a source of contention in health technology assessment (HTA). We review how TEW has been addressed in HTA and in the wider scientific literature. We analysed company submissions to English language HTA agencies and summarised methods and assumptions used. We subsequently reviewed TEW-related work in the ISPOR Scientific Presentations Database and conducted a targeted literature review (TLR) for evidence of the maintenance of immuno-oncology (IO) treatment effects post-treatment discontinuation. We found no standardised approach adopted by companies in submissions to HTA agencies, with immediate TEW most used in scenario analyses. Independently fitted survival models do however suggest TEW may often be implicitly modelled. Materials in the ISPOR scientific database suggest gradual TEW is more plausible than immediate TEW. The TLR uncovered evidence of durable survival in patients treated with IOs but no evidence that directly addresses the presence or absence of TEW. Our HTA review shows the need for a consistent and appropriate implementation of TEW in oncology appraisals. However, the TLR highlights the absence of direct evidence on TEW in literature, as TEW is defined in terms of relative treatment effects-not absolute survival. We propose a sequence of steps for analysts to use when assessing whether a TEW scenario is necessary and appropriate to present in appraisals of IOs.

Background: Atrial fibrillation (AF) is associated with increased morbidity and mortality and exerts an increasingly significant burden on global healthcare resources, with its prevalence rising with an ageing population. Despite a substantial thromboembolic risk, particularly in the period immediately following diagnosis, oral anti-coagulation is frequently not initiated or is delayed. The aim of this study was to evaluate healthcare costs in people with AF, comparing those who were commenced on oral anti-coagulation in the immediate period following the index diagnosis date with those in whom initiation was late and those who never started anti-coagulation.

Methods: This retrospective cost analysis used linked Scottish health data to identify adults newly diagnosed with AF between January 1st 2012 and April 30th 2019 with a baseline CHA₂DS₂-VASc score of ≥ 2. This AF population was sub-divided according to timing of the first prescription of oral anti-coagulant (OAC) during a 2-year follow-up period: never started (OAC never initiated), immediate OAC (OAC prescribed within 60 days of incident AF diagnosis), and delayed OAC (OAC prescribed more than 60 days after incident AF diagnosis). A two-part model was developed, adjusted for key covariates, including age, sex, and frailty, to estimate costs for inpatient admissions, outpatient care, prescriptions, and care home admissions, and overall costs.

Results: Of an overall AF population of 54,385, 26,805 (49.3%) never commenced OAC, 7654 (14.1%) initiated an OAC late, and 19,926 (36.6%) were prescribed anti-coagulation immediately. The mean adjusted cost for the overall AF population was £7807 per person per year (unadjusted: £8491). Delayed OAC initiation was associated with the greatest adjusted estimated mean annual cost (unadjusted: £13,983; adjusted: £9763), compared to those that never started (unadjusted: £10,433; adjusted: £7981) and those that received an immediate OAC prescription (unadjusted: £3976; adjusted: £6621). Increasing frailty, mortality, and female sex were associated with greater healthcare costs.

Conclusion: AF is associated with significant healthcare resource utilisation and costs, particularly in the context of delayed or non-initiation of anti-coagulation. Indeed, there exists substantial opportunity to improve the utilisation and prompt initiation in people newly diagnosed with AF in Scotland. Interventions to mitigate against the growing economic burden of AF should focus on reducing admissions to hospitals and care homes, which are the principal drivers of costs; prescriptions and outpatient appointments account for a relatively small proportion of overall costs for AF.

The coronavirus disease 2019 (COVID-19) pandemic has increased public awareness of the influence of epidemiological and economic decision models on public policy decisions. Alongside this is an increased scrutiny on the development, analysis, reporting and utilisation of decision models for public policy making. Therefore, it is important that model developers can clearly explain and justify to all stakeholders what is included and excluded from a model developed to support decision-making, to both improve transparency and trust in decision-making. Our aim is to provide tools for improving communication between modellers and decision-makers, leading to improved transparency in decision-making. To do so, we extend the recently described directed acyclic graphs with omitted objects displayed (DAGWOOD) approach from Haber et al. (Ann Epidemiol 68:64-71, 2022) to decision analytic models, giving the decision analytic models with omitted objects displayed (DAMWOOD) approach. DAMWOOD is a framework for the identification of objects omitted from a decision model, as well as for consideration of the effects of omissions on model outcomes. Objects omitted from a decision model are classed as either an exclusion (known and unknown confounders), misdirection (alternative model pathways) or structure (e.g. model type, methods for estimating relationships between objects). DAMWOOD requires model developers to use explicit statements and provide illustration of included and omitted objects, supporting communication with model users and stakeholders, allowing them to provide input and feedback to modellers about which objects to include or omit in a model. In developing DAMWOOD, we considered two challenges we encountered in modelling for pandemic policy response. First, the scope of the decision problem is not always made sufficiently explicit by decision-makers, requiring modellers to intuit which policy options should be considered, and/or which outcomes should be considered in their evaluation. Second, there is rarely sufficient transparency to ensure stakeholders can see what is included in models and why. This limits stakeholders' ability to advocate to decision-makers for the prioritisation of specific outcomes and challenge the model results. To illustrate the application of DAMWOOD, we apply it to a previously published COVID-19 vaccine allocation optimisation model. The DAMWOOD diagrams illustrate the ways in which it is possible to improve the communication of model assumptions. The diagrams make explicit which outcomes are omitted and provide information on the expected impact of the omissions on model results. We discuss the usefulness of DAMWOOD for framing the decision problem, communicating the model structure and results and engaging with those making and affected by the decisions the model is developed to inform.

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.

Background: Knowing the health opportunity costs of funding decisions is crucial to assess whether the health gains associated with new interventions are larger than the health losses imposed by the displacement of resources. Empirical estimates based on the effect of health spending on health outcomes have been proposed in several countries, including Spain, as a proxy to capture these opportunity costs. However, there is a need to regularly update existing health opportunity cost estimates and to explore the role of omitted variable bias in these estimations.

Objective: The aim of this paper is to provide an updated and refined estimate of the causal impact of health spending on health in Spain that can be translated into an estimate of the incremental cost per quality-adjusted life-year produced by the Spanish national health system.

Methods: We applied fixed-effect models using data for 17 Spanish regions from 2002 until 2022 to estimate the impact of public health spending on health outcomes and explored the extent of omitted variable bias. Changes in these estimates over time were assessed and alternative specifications were tested.

Results: Based on fixed-effect models with control variables, the estimated spending elasticity was 0.061, which translated into an incremental cost per quality-adjusted life-year of approximately €34,000. The bias-corrected elasticity was 0.075, with a corresponding incremental cost per quality-adjusted life-year of €27,000. We found that the estimated impact of spending on health decreases when recent years of data are added, and that the extent of omitted variable bias appears to increase, particularly when adding the COVID-19 pandemic period.

Conclusions: This study provides an updated estimation of the incremental cost per quality-adjusted life-year produced by the Spanish national health system. The estimates provided can be easily updatable as new data become accessible, and the methods applied might be transferable to other settings with similar available data.