PharmacoEconomics最新文献

Background: Evaluating healthcare interventions for their impacts beyond health outcomes may result in recognition of changes in human capital, income level, tax revenue, and government spending, which could affect economic growth and population health. In this paper, we document instances where current health technology assessment (HTA) practices fail to account for the impacts of healthcare interventions on broader society beyond the healthcare sector.

Methods: We propose a novel conceptual framework, highlighting its three components (distributional cost-effectiveness analysis [DCEA], input-output model, and voting scheme) and their contributions to capturing the economic and societal ripple effects of healthcare interventions. This manuscript also outlines a case study in which the framework is applied to the reassessment of a previously evaluated digital health therapeutic for the treatment of opioid use disorder (OUD) compared with standard of care, demonstrating its practical application.

Results: The DCEA health value metric indicates that digital therapeutic is more equitable, favoring socioeconomically disadvantaged groups, while standard of care exacerbates health inequality by benefiting the already advantaged. Additionally, digital therapeutic shows potential for boosting productivity, raising income, and creating jobs, supporting its consideration by employer-sponsored health plans to optimize resource allocation for treating OUD.

Conclusion: The conceptual framework provides insights for enhancing HTAs to incorporate the broader economic and societal impacts of healthcare interventions. By integrating DCEA, extended HTA analysis with input-output modeling, and a voting scheme, decision makers can make informed choices aligned with societal priorities, although further research and validation are necessary for practical implementation across diverse healthcare contexts.

Background: Skin cancer's rising incidence demands understanding of its economic impact. The current understanding is fragmented because of the various methodological approaches applied in skin cancer cost-of-illness studies.

Objective: This study systematically reviews melanoma and keratinocyte carcinoma cost-of-illness studies to provide an overview of the applied methodological approaches and to identify the main cost drivers.

Methods: This systematic review was conducted adhering to the 2020 PRISMA guidelines. PubMed, Embase, and Web of Science were searched from December 2022 until December 2023 using a search strategy with entry terms related to the concepts of skin cancer and cost of illness. The records were screened on the basis of the title and abstract and subsequently on full text against predetermined eligibility criteria. Articles published before 2012 were excluded. A nine-item checklist adapted for cost-of-illness studies was used to assess the methodological quality of the articles.

Results: This review included a total of 45 studies, together evaluating more than half a million patients. The majority of the studies (n = 36) focused on melanoma skin cancer, a few (n = 3) focused on keratinocyte carcinomas, and 6 studies examined both. Direct costs were estimated in all studies, while indirect costs were only estimated in nine studies. Considerable heterogeneity was observed across studies, mainly owing to disparities in study population, methodological approaches, included cost categories, and differences in healthcare systems. In melanoma skin cancer, both direct and indirect costs increased with progressing tumor stage. In advanced stage melanoma, systemic therapy emerged as the main cost driver. In contrast, for keratinocyte carcinoma no obvious cost drivers were identified.

Conclusions: A homogeneous skin cancer cost-of-illness study design would be beneficial to enhance between-studies comparability, identification of cost drivers, and support evidence-based decision-making for skin cancer.

Researchers incorporate health state utility values as inputs to inform economic models. However, for a particular health state or condition, multiple utility values derived from different studies typically exist and a single study is often insufficient to represent the best available source of utility needed to inform policy decisions. The purpose of this paper is to provide an introductory guidance for conducting Bayesian meta-analysis of health state utility values to generate a single parameter input for economic evaluation, using R. The tutorial is illustrated using data from a systematic review of health state utilities of patients with heart failure, with 21 studies that reported utilities measured using the EuroQol-5D (EQ-5D). Explanations, key considerations and suggested readings are provided for each step of the tutorial, adhering to a clear workflow for conducting Bayesian meta-analysis: (1) setting-up the data; (2) employing methods to impute missing standard deviations; (3) defining the priors; (4) fitting the model; (5) diagnosing model convergence; (6) interpreting the results; and (7) performing sensitivity analyses. The posterior distributions for the pooled effect size (i.e. mean health state utility) and between-study heterogeneity are discussed and interpreted in light of the data, priors and models used. We hope that this tutorial will foster interest in Bayesian methods and their applications in the meta-analysis of utilities.

Background and objectives: Fremanezumab is an effective treatment for episodic (EM) and chronic migraine (CM) patients in Japan, but its cost effectiveness remains unknown. The objective of this study was to determine the cost effectiveness of fremanezumab compared with standard of care (SOC) in previously treated EM and CM patients from a Japanese healthcare perspective.

Methods: Estimated regression models were implemented in a probabilistic Markov model to inform effectiveness and health-related quality-of-life data for fremanezumab and SOC. The model was further populated with data from the literature. The adjusted Japanese healthcare perspective included productivity losses. The main model outcomes were quality-adjusted life-years (QALYs), costs (2022 Japanese Yen [¥]), and incremental outcomes including the incremental cost-effectiveness ratio (ICER). Analyses were performed separately for the EM and CM patients and combined. Costs and effects were discounted at an annual rate of 2.0%.

Results: The mean QALYs over a 25-year time horizon for the EM and CM populations combined were 13.03 for SOC and 13.15 for fremanezumab. The associated costs were ¥27,550,292 for SOC and ¥28,371,048 for fremanezumab. QALYs were higher and costs lower for EM patients compared with CM patients for both fremanezumab and SOC. The deterministic ICERs of fremanezumab versus SOC were ¥6,334,861 for EM, ¥7,393,824 for CM, and ¥6,530,398 for EM and CM combined. Indirect costs and choice of mean migraine days model distribution had a substantial impact on the ICER.

Conclusion: Using fremanezumab in a heterogeneous mixture of Japanese EM and CM patients resulted in a reduction of monthly migraine days and thus more QALYs compared with SOC. The cost effectiveness of fremanezumab versus SOC in EM and CM patients resulted in an ICER of ¥6,530,398, from an adjusted Japanese public healthcare perspective.

Expanding flexible vaccine manufacturing capacity (FVMC) for routine vaccines could facilitate more timely access to novel vaccines during future pandemics. Vaccine manufacturing capacity is 'flexible' if it is built on a technology platform that allows rapid adaption to new infectious agents. The added value of routine vaccines produced using a flexible platform for pandemic preparedness is not currently recognised in conventional health technology assessment (HTA) methods. We start by examining the current state of play of incentives for FVMC and exploring the relation between flexible and spare capacity. We then establish the key factors for estimating FVMC and draw from established frameworks to identify relevant value drivers. The role of FVMC as a countermeasure against pandemic risks is deemed an additional value attribute that should be recognised. Next, we address the gap in the vaccine-valuation literature between the conceptual understanding of the value of additional FVMC and the availability of accurate and reliable tools for its estimation to facilitate integration into HTA. Three practical approaches for estimating the value of additional FVMC are discussed: stated and revealed preference studies, macroeconomic modelling, and benefit-cost analysis. Lastly, we review how value recognition of additional FVMC can be realised within the HTA process for routine vaccines manufactured on flexible platforms. We argue that, while the value of additional FVMC is uncertain and further research is needed to help to better estimate it, the value of increased pandemic preparedness is likely to be too large to be ignored.

Introduction: The rate of development and complexity of digital health interventions (DHIs) in recent years has to some extent outpaced the methodological development in economic evaluation and costing. Particularly, the choice of cost components included in intervention or program costs of DHIs have received scant attention. The aim of this study was to build a literature-informed checklist of program cost components of DHIs. The checklist was next tested by applying it to an empirical case, Mamma Mia, a DHI developed to prevent perinatal depression.

Method: A scoping review with a structured literature search identified peer-reviewed literature from 2010 to 2022 that offers guidance on program costs of DHIs. Relevant guidance was summarized and extracted elements were organized into categories of main cost components and their associated activities following the standard three-step approach, that is, activities, resource use and unit costs.

Results: Of the 3448 records reviewed, 12 studies met the criteria for data extraction. The main cost categories identified were development, research, maintenance, implementation and health personnel involvement (HPI). Costs are largely considered to be context-specific, may decrease as the DHI matures and vary with number of users. The five categories and their associated activities constitute the checklist. This was applied to estimate program costs per user for Mamma Mia Self-Guided and Blended, the latter including additional guidance from public health nurses during standard maternal check-ups. Excluding research, the program cost per mother was more than double for Blended compared with Self-Guided (€140.5 versus €56.6, 2022 Euros) due to increased implementation and HPI costs. Including research increased the program costs to €190.8 and €106.9, respectively. One-way sensitivity analyses showed sensitivity to changes in number of users, lifespan of the app, salaries and license fee.

Conclusion: The checklist can help increase transparency of cost calculation and improve future comparison across studies.

Introduction: Children may find self-reporting health-related quality of life (HRQoL) using patient-reported outcome measures (PROMs) presented in text-based formats difficult, particularly younger children and children with developmental delays or chronic illness. In such cases, pictorial PROMs (where pictorial representations are used alongside or to replace text) may offer a valid alternative.

Aim: This systematic literature review focused on identifying and describing paediatric PROMs that incorporate pictorial approaches, providing children with more effective means to express their HRQoL.

Methods: Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. Seven electronic databases were searched from inception to 1 March 2022. There were no country restrictions applied to the search; all English-language studies were considered for inclusion in the review. Characteristics and development methods of the identified pictorial PROMs were evaluated against context-specific good practice guidelines published by The Professional Society for Health Economics and Outcomes Research (ISPOR).

Results: A total of 22 paediatric pictorial PROMs, comprising 28 unique versions, were identified. These PROMs were predominantly developed in the USA and the UK, targeting children aged 3-18 years. Likert scales with pictorial anchors, particularly happy-sad faces, were commonly used for response options, appearing in 15 (54%) of the PROMs. Various graphic methods, such as happy-sad faces, cartoons, and thermometers, were adapted to specific content domains. These PROMs covered a wide range of domains, including physical and emotional health and social functioning. Emphasis was placed on content validity, including active child participation in developing pictorial elements. Notably, children's participation was sought during the development of the pictorial elements for 13 (46%) of the PROMs. Various development methods were employed, with 43% of paediatric PROMs using literature reviews, 43% using focus groups, and 32% involving expert consultation. Interviews emerged as the primary method, being employed in 61% of the studies. Additionally, three measures specifically addressed cross-cultural considerations.

Conclusion: Paediatric pictorial PROMs offer child-friendly tools for assessing HRQoL for application with children who find reading and understanding text-based PROMs challenging. There is some evidence that pictorial PROMs facilitate self-report in this population and improve measurement properties compared to text-only PROMs. Further research is needed to develop, validate, and test paediatric pictorial PROMs, with an emphasis on including children from the inception in the co-design process.