PharmacoEconomics最新文献

Background and objective: Aztreonam-avibactam (ATM-AVI) is a novel combination antibiotic approved in Europe for the treatment of complicated intra-abdominal infection, hospital-acquired pneumonia, including ventilator-associated pneumonia; complicated urinary tract infection, including pyelonephritis and for infections due to aerobic Gram-negative organisms with limited treatment options. This analysis assessed the cost effectiveness of ATM-AVI ± metronidazole versus colistin + meropenem (COL + MER) for the treatment of patients with complicated intra-abdominal infection and hospital-acquired pneumonia/ventilator-associated pneumonia, including infections with suspected metallo-β-lactamase-producing Enterobacterales from the public payer perspective in Italy using phase III trial data.

Methods: The cost-effectiveness analysis adopted a decision tree model to simulate the clinical pathway of complicated intra-abdominal infection and hospital-acquired pneumonia/ventilator-associated pneumonia, followed by a Markov model to capture lifetime health outcomes on cured patients, with costs valued in 2024 Euros and discounted at 3%. The model captures the impact of resistant pathogens and side effects (i.e. nephrotoxicity). Model uncertainty was assessed using a probabilistic and deterministic sensitivity analysis.

Results: The ATM-AVI treatment sequence (ATM-AVI ± metronidazole followed by cefiderocol after treatment failure) had improved clinical outcomes and higher cure rates, shorter hospital stays and higher quality-adjusted life-year gains compared with the COL + MER sequence (COL + MER followed by cefiderocol after treatment failure). The incremental cost-effectiveness ratio in the ATM-AVI sequence was dominant for complicated intra-abdominal infection and was €1552 per quality-adjusted life-year for hospital-acquired pneumonia/ventilator-associated pneumonia, well below the willingness-to-pay threshold of €30,000 in Italy.

Conclusions: Our analysis suggests that ATM-AVI is expected to be a cost-effective use of Italian healthcare resources for treating suspected metallo-β-lactamase-producing Enterobacterales, including complicated intra-abdominal infection and hospital-acquired pneumonia/ventilator-associated pneumonia.

Background: Immune checkpoint inhibitors (ICIs) are clinically beneficial but associated with high costs that represent a growing challenge for healthcare budgets and may affect affordability, especially in resource-limited settings. Moreover, the healthcare sector is a significant source of greenhouse gas emissions, and medication-related waste-such as that from vial-based therapies-has been identified as a contributing factor. Alternative dosing strategies could reduce the environmental and financial impact of ICI therapy while maintaining clinical safety and efficacy.

Methods: Population pharmacokinetic simulations were performed using virtual cohorts representative of the original cancer populations treated with ICIs. The analysis was conducted from a Western European hospital perspective, using Dutch public data to estimate costs (based on volume-dependent pricing) and carbon emissions from drug production, travel, and medical waste.

Results: Under the US Food and Drug Administration exposure-matching criteria, optimized dosing regimens reduced drug costs by up to €23,311 (- 28%) and carbon emissions by up to 255 kgCO₂e (- 30%) per patient, depending on the drug and dosing strategy. Using a broader therapeutic window approach, cost savings reached up to €40,135 (- 69%) and carbon reductions up to 501 kgCO₂e (- 63%) per patient. Incorporating vial sharing further increased potential cost savings to €5,721 per patient (- 31%). All estimates reflect European pricing and emissions factors, modeled over an 8-month treatment period.

Conclusions: These findings suggest that optimizing dosing strategies can yield meaningful economic and environmental benefits in ICI therapy while maintaining drug exposure within levels defined by US Food and Drug Administration criteria or broader therapeutic windows. A user-friendly application developed in this study allows users to generate virtual populations and evaluate tailored dosing strategies, facilitating practical implementation in diverse healthcare settings.

Objectives: The EQ-5D-5L is a multi-attribute utility instrument recommended by many health technology assessment agencies. This study aimed to develop an EQ-5D-5L value set for Singapore.

Methods: A 'lite' version of the EuroQol Research Foundation's EQ-5D-5L valuation protocol, which was designed to value a total of 91 health states using a composite time trade-off (cTTO) method, was followed. Five hundred members of the general public in Singapore were quota-sampled and invited to a personal interview face-to-face or via Zoom. All participants completed 20 cTTO tasks administered using the EuroQol Valuation Technology (EQ-VT) program. Cross-validation analysis was performed to identify the best-performing model for estimating the values of all the 3,125 EQ-5D-5L health states.

Results: A 20-parameter main-effects model with two two-way interaction terms outperformed other models in the cross-validation analysis. The value set estimated using this model ranges from - 0.851 (for state 55555) to 1.000 (for state 11111), with pain/discomfort and anxiety/depression dimensions associated with the greatest disutility.

Conclusions: We developed an EQ-5D-5L value set based on the health preferences of Singaporeans. We recommend EQ-5D-5L users in Singapore to use this value set and encourage a more systematic and dedicated methodological effort to understand interaction effects and potential non-linearities in the valuation of multi-attribute health descriptive systems.

Objectives: The clinical pathway for patients with moderate-to-severe Crohn's disease (CD) typically includes sequential pharmacologic treatment as well as surgery, but positioning of different therapies within these sequences remains challenging. Cost-utility analysis rarely captures these sequences and does not incorporate registry data on long-term effectiveness. In this study, we aim to overcome these limitations.

Methods: We developed an individual state transition model with four health states (active disease, remission, and remission due to surgery and death), five sequential treatment lines, and surgery. Efficacy data from network meta-analyses (NMA) for biologic naive and biologic exposed patients were combined with Dutch registry data to forecast long-term benefit, calculate costs, and estimate utilities. Analyses had a Dutch societal perspective with a lifetime time horizon. Costs were reported in 2023 euros and discounted with 3%. Effects were reported in quality-adjusted life years (QALYs) and discounted with 1.5%. The cost-per-QALY threshold was €20,000. Deterministic analyses for the base case, three scenarios (including recently published trials or price declines for ustekinumab), and one-way sensitivity analysis were run with 30,000 patients. The probabilistic sensitivity analysis was conducted by sampling 1000 patients in 1000 model runs.

Results: When opting for step-up sequences, the most cost-effective sequence (out of 156 sequences) starts with either azathiopurine/6-mp or methotrexate and is followed by combination therapy (infliximab + azathioprine) when patients discontinue their first line owing to disease activity or discontinuation. The most cost-effective top-down sequence (out of 72) starts with combination therapy (infliximab + azathioprine). After two lines of treatment, differences in cost-effectiveness between biologics become smaller. To be equally cost-effective as anti-tumor necrosis factor (TNF) combination therapy, a price decline for ustekinumab (biosimilars) of 81% is required or 50% to become the preferred option after combination therapy. Validation against external data suggested good predictive capabilities of the model.

Conclusions: Integrating NMA and registry data improves the quality of cost-effectiveness models for treatment sequences in CD. This open-source model can be easily updated for future therapies and holds the potential to become a standard model for use in clinical guideline development and the economic evaluation of new drugs.

Objective: To understand the application of productivity-adjusted life years (PALYs) as an outcome measure across various disease contexts.

Methods: We conducted a scoping review of studies published between 2018 and April 2025 that utilised PALYs to illustrate their potential applications and identify methodological approaches that have been applied. Using a citation-based search, we selected studies that applied PALYs to quantify societal health burdens in specific diseases or contexts. Extracted data included health conditions, country, timeframe, model type, outcomes, productivity index components, gross domestic product and sensitivity analysis. Findings were summarised through narrative synthesis.

Results: A total of 41 studies conducted between 2018 and 2025 were reviewed, including chronic diseases such as diabetes and cardiovascular diseases, as well as environmental factors. Conditions such as breast cancer, leukaemia, kidney disease, mental health, knee osteoarthritis, epilepsy and sleep apnoea had the lowest productivity indices. Most of these studies originated from high-income countries (n = 27), followed by upper-middle-income (n = 10), and lower-middle-income (n = 4) settings. Life table models were the most common methodological approach adopted (n = 26), followed by dynamic models (n = 10). Studies focused on disease prevention (n = 21) outnumbered those addressing disease management (n = 18). Most studies accounted for both absenteeism and presenteeism (n = 30). Estimates of productivity loss per person using gross domestic product ranged from US$1137 to AU$217,983 annually.

Conclusions: PALYs have been utilised in diverse diseases and contexts, highlighting their utility in measuring societal health impacts. However, adding unpaid and informal work makes burden estimates more accurate. The increasing emphasis on prevention indicates a strategic change in health policy and economic assessment.

Background: Advances in the availability and regimen optimization of highly effective disease-modifying treatments (DMTs) for relapsing-remitting multiple sclerosis (RRMS) have led to questions about their comparative worth.

Objectives: This study evaluates the costs and effects of natalizumab versus other highly effective DMTs and the impact, in terms of times and costs, of the new subcutaneous natalizumab formulation versus the intravenous formulation in patients with RRMS in Italy.

Methods: This is a cost-consequence analysis from the Italian national health service and societal perspectives. A Markov model was developed to assess clinical and cost outcomes related to disease and DMTs. The model simulated two scenarios: one comparing natalizumab extended-dose regimen and ofatumumab and ocrelizumab, focusing on efficacy outcomes and costs, and one comparing intravenous and subcutaneous natalizumab with a focus on administration resource consumption, times, and costs. Model input data came from the literature.

Results: DMTs had similar clinical and social outcomes: natalizumab slightly reduced disease progression, increased quality-adjusted life-years, and reduced the impact on days of productivity loss and informal care. Natalizumab also resulted in statistically significant 5-year cost reductions compared with ocrelizumab and ofatumumab. Subcutaneous natalizumab improved resource consumption compared with intravenous natalizumab, saving the time of healthcare professionals, patients, and caregivers and reducing administration costs. The subcutaneous formulation was associated with statistically significant total direct and indirect cost reductions at 5 years.

Conclusion: 6-week dosing regimen of natalizumab showed a slight improvement of clinical and social outcomes and a statistically significant cost reduction compared with ocrelizumab and ofatumumab over a 5-year simulation. Moreover, subcutaneous administration reduced administration times and costs.

Background and objective: Inflammatory arthritis is a common condition treated in rheumatology clinics, contributing significantly to healthcare costs and societal burden. Understanding the economic impact of inflammatory arthritis requires a comprehensive analysis through cost-of-illness studies. This systematic review aims to gather up-to-date cost-of-illness data on inflammatory arthritis from various countries, identify the primary cost drivers, describe shifts in cost components and appraise the quality of cost-of-illness study reporting in this field.

Methods: An electronic search was performed across four databases, including MEDLINE, Embase, the Cochrane Database of Systematic Reviews and the Health Management Information Consortium, to identify cost-of-illness studies on inflammatory arthritis published over the past two decades. The primary outcome was the annual cost per patient with inflammatory arthritis, categorised by cost components. All costs were standardised to 2024 US dollar values. The quality of the included studies was evaluated using the Larg and Moss checklist and the modified Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist.

Results: From an initial 12,264 publications, 82 studies were included in this review, covering axial spondyloarthritis (n = 49), psoriatic arthritis (n = 30), reactive arthritis (n = 2), rheumatoid arthritis (n = 13; 2019 onwards) and seronegative/seropositive rheumatoid arthritis (n = 8). Annual total societal costs varied considerably across inflammatory arthritis subtypes and countries. Medication expenditures consistently emerged as the primary direct healthcare cost driver, while productivity losses due to morbidity constituted the major component of indirect costs. Carer productivity loss represented a substantial proportion of indirect costs (up to 60.9%), yet was infrequently reported. Over time, we observed an increasing proportion of medication-related costs and a decreasing proportion of productivity losses for axial spondyloarthritis, alongside a reduction in inpatient care costs for psoriatic arthritis. These evolving cost distributions mirror patterns previously reported in rheumatoid arthritis. Methodological gaps were evident, with most studies lacking sensitivity analyses and comprehensive cost perspectives.

Conclusions: A substantial economic impact of inflammatory arthritis across different regions and subtypes was identified. This review emphasises the importance of including comprehensive cost components to fully assess the economic burden of inflammatory arthritis and provides methodological recommendations for future studies.

Objectives: Most medication errors occur in primary and long-term care, and a wide range of medication safety interventions have been implemented, but these are often expensive, with little evidence around cost-effectiveness. We report a systematic review of economic evaluations of these interventions within primary and long-term healthcare settings.

Methods: A comprehensive search was conducted in databases (Medline, Embase, Econlit and PsycINFO) for full economic evaluations of primary care interventions targeting all errors in the medication use process (January 2004 to September 2025). Methodological and reporting qualities were assessed using standard tools.

Results: From 8523 records, 44 studies evaluating interventions in general/family practice (22), community pharmacy (11) and nursing/care/residential homes (11) met the inclusion criteria, 24 of which were either pharmacy led (19) or multidisciplinary medication reviews (5). All but one study looked at prescribing or monitoring interventions only. A total of 12 studies included all patients, with 24 focusing on older adults (> 65 years) and 3 focusing on condition-specific groups. Most studies only included costs from a healthcare perspective (39). Outcomes ranged from prescribing errors (9), hospital utilisation (13) and health-related quality of life (15) to falls (6) and adverse drug events (6). In total, 21 studies carried out an incremental cost-effectiveness analysis (16 including the incremental cost per quality-adjusted life year gained), and 14 reported the intervention cost-effectiveness. Remaining studies were cost-consequence (18) and cost-benefit analyses (5). Study reporting quality varied considerably, with lack of transparency in the design of the decision-analytic model, varied reporting of costs, little consideration of indirect costs or the impact of loss of trust on future use of healthcare, limitations in handling of uncertainty or discounting and very little patient involvement around targeting patients or designing interventions. Of the ten studies using decision models, all scored poorly for model validation. The quality of studies has not improved over time.

Conclusions: While some interventions demonstrated cost-effectiveness, study quality was variable, with generally poorly validated models. Study heterogeneity precluded meaningful direct comparison between studies. Significant research gaps remain as studies focused mainly on prescribing and monitoring errors, there was little or no investigation of technology-based interventions and there was inadequate targeting of patients most vulnerable to harm.

This illustration uses the Scottish Cardiovascular Disease (CVD) Policy Model as a case study to provide a comprehensive, step-by-step guide to building a discrete event simulation (DES) model in R. It is specifically designed for practitioners who are familiar with constructing Markov models in R and wish to transition their theoretical knowledge of DES into practical implementation. The Scottish CVD Policy Model was originally developed as an Excel-based Markov model with a sophisticated structure: a primary Markov model for first events and nested sub-Markov models for subsequent events. Later replicated in R by Xin, Yiqiao et al., the model's source code was made publicly available on GitHub, underscoring its potential as a teaching tool. The intricate structure of this model presents several challenges in health economic modeling, making it an ideal candidate for demonstrating how DES techniques can address such complexities effectively. In this illustration, we deliberately avoid using R packages developed specifically for DES to enhance transparency. Instead, we rely on base R functions, and the tidyverse package for tidy data wrangling. This approach ensures that every step of the DES implementation is clear and reproducible. In addition to covering fundamental topics such as how to simulate a time to event according to an assumed distribution, and continuous discounting, the illustration also provides solutions to more advanced modeling challenges, such as handling piecewise-modeled cost and utility. By discussing both general principles and complex scenarios, this paper equips readers with the practical tools needed to transition from Markov to DES frameworks, enhancing the accuracy and flexibility of health economic evaluations.