PharmacoEconomics最新文献

Background and objectives: Metastatic castration-sensitive prostate cancer (mCSPC) imposes a significant economic burden and necessitates more cost-effective treatment strategies. The variability among the components of published economic evaluation models leads to methodological inconsistencies, underscoring the need for an optimal framework to minimise unwarranted structural variation. This paper reviews existing economic evaluations, establishes a comprehensive framework and aims to support future economic evaluations and decision-making in mCSPC.

Methods: A systematic literature review (SLR) was conducted to identify relevant economic evaluations in mCSPC. Health technology assessments (HTAs) by the National Institute for Health and Care Excellence, and Canada's Drug Agency were reviewed to gather insights on critiques and limitations. On the basis of these findings, a comprehensive cost-effectiveness modelling framework was established. Furthermore, two additional SLRs were conducted to identify cost and resource utilisation inputs, as well as health state utility scores derived from published studies and HTA assessments.

Results: Markov models and partitioned survival models (PSMs) were commonly reported in literature and published HTA evaluations. Despite the strong precedence of PSMs, we propose an optimal framework for mCSPC utilising a semi-Markov structure. This approach offers increased flexibility, allowing transition rates from progressed states to depend on time since progression occurred. We also present key sources of cost and utility data identified in the SLR.

Discussion: This work aligns with methodologies recommended by the Innovative Medicine Initiative (IMI) PIONEER external group and published studies. The optimal framework, including healthcare resource utilisation and utility data, consolidates existing modelling precedents in mCSPC and will assist the cost-effectiveness assessment of treatments for this condition.

Background and objective: Heart failure (HF) is a complex clinical syndrome associated with high mortality and extensive healthcare use. Using longitudinal data, we aimed to estimate the lifetime healthcare and long-term care (LTC) use and costs of Dutch patients with HF.

Methods: We used linked administrative data on mortality, LTC, and healthcare use covering the entire Dutch population for the period 2013-2024. Newly diagnosed patients with HF were defined as patients who were not hospitalized for HF 2 years before their index hospitalization for HF in 2015. Using regression modeling, we estimated hospitalized patients' life expectancy and lifetime healthcare costs as a function of age, sex, comorbidity, and income (all costs were adjusted to 2021 values).

Results: We identified 21,011 unique hospitalized patients with HF (mean age 80 years), of whom 86% died during follow-up. Estimated lifetime total healthcare and LTC costs varied between €35,000 and €170,000, depending on patient characteristics. Lifetime LTC costs varied between €9000 and €50,000. While comorbidity affects life expectancy substantially, it did not have a strong impact on lifetime costs. Income level affects costs more than comorbidities, and lower-income groups incur higher lifetime LTC costs.

Conclusions: Despite people with lower incomes having shorter lifespans than those with higher incomes, their lifetime LTC costs are higher. However, people with higher incomes have higher hospital costs, partly owing to their longer life expectancy.

Manufacturers of orphan drugs face several obstacles in meeting health technology assessment requirements because of poor availability of natural history data, small sample sizes, single-arm trials, and a paucity of established disease-specific endpoints. There is a need for specific considerations and modified approaches in health technology assessments that would account for the challenges in orphan drug development. Multistakeholder collaborations can benefit patients, their families, and the broader society and reduce the inequity faced by patients with rare diseases.

Cohort-level models in Microsoft Excel^® remain the standard for cost-effectiveness modeling to inform health technology assessment (HTA), despite calls and rationale for more flexible approaches. Their limited ability to capture patient-level characteristics can, in the presence of patient heterogeneity or the need to track patient characteristics to accurately capture a technology's implications, introduce bias. Their continued prevalence is explained by key stakeholders' familiarity with spreadsheet software, and the lower computational burden of cohort-level versus patient-level models. However, contemporary Excel functions have opened up possibilities for calculations within native Excel that enable more flexible, patient-level approaches to be implemented in familiar spreadsheet-based software, without use of any Visual Basic for Applications (VBA) code. Therefore, this tutorial aims to provide step-by-step guidance on how to implement a previously published and freely available individual-level discrete event simulation (DES) in Excel, using contemporary Excel functions and without any VBA code.

The cost effectiveness of pharmacotherapies for obesity (such as semaglutide, tirzepatide, liraglutide, and newer agents) is increasingly being appraised by health technology assessment (HTA) bodies. Modelling is required to extrapolate weight change observed over relatively short clinical trial durations to long-term weight loss and associated cardio-metabolic outcomes and costs. Extrapolation is a common issue in HTA, but there is a unique challenge for anti-obesity drugs because of the number of interacting uncertainties. This is a particular concern given the substantial eligible population sizes and associated high financial decision risk of providing lifetime treatment. We describe four key challenges in modelling pharmacotherapies for obesity: (1) modelling long-term body mass index (BMI) trajectories with and without obesity pharmacotherapy, (2) modelling time on treatment, (3) using risk equations to link changes in BMI to clinical outcomes, and (4) modelling clinical outcomes not (solely) related to BMI changes. We discuss each of these challenges and the impact they have had in global HTA appraisals for pharmacotherapies. We speculate how these challenges relating to short-term clinical trials could be overcome to more robustly predict long-term outcomes and the role that observational data may play. As clinical trial and real-world evidence for technologies for obesity evolves, analysts and decision-makers need to determine which evidence sources are most appropriate and how they should be combined.

Background: Randomized controlled trials are the standard for health technology assessment, but when they are infeasible or unethical, single-arm trials (SATs) are submitted.

Objectives: This study examined when SATs were accepted for added benefit by the Institute for Quality and Efficiency in Health Care (IQWiG) and/or the Federal Joint Committee (G-BA) in Germany.

Methods: We identified health technology assessments via the AMNOG-Monitor database through December 2024, with additional details from G-BA documents. We compared the SATs and other evidence for added benefit decisions (granted/not granted), stratified by orphan drug status, special marketing authorization, approved indication (chronic hepatitis C/others), and population (adults/children). Added benefit claims by manufacturers, IQWiG recommendations, and G-BA appraisals were compared.

Results: Among 1738 G-BA decisions, 85.8% (1491/1738) of the subpopulations were fully assessed by IQWiG, with 13.5% (202/1491) based on SATs. Among the 247 orphan drugs assessed by the G-BA, 37.7% (93/247) were SAT-based. Overall, SAT-based assessments demonstrated an added benefit in 12.2% (36/295) of cases. This included 13.4% (27/202) of full assessments and 9.7% (9/93) of orphan drug assessments. IQWiG accepted only 18.5% (5/27) of the SATs endorsed by the G-BA. Statistical tests revealed significant differences between manufacturers' claims, IQWiG recommendations, and G-BA appraisals. SATs were most frequently accepted for chronic hepatitis C treatments (mostly with non-standard marketing authorization) and paediatric indications. The G-BA cited reasons such as dramatic effects, rare diseases, a lack of alternatives, or fewer side effects, although justifications were often unclear.

Conclusion: Acceptance rates for SATs remain low, and criteria for added benefit are not always explicitly defined. To enable benefit assessments when randomised controlled trials are infeasible or unethical, clear and binding criteria developed in collaboration with the G-BA are essential.

Objective: To what extent a care pathway, due to its associated pollution, may be more detrimental to future health than beneficial to contemporary patients is still an open question. We present a methodological framework to integrate pollutant-induced future health damages in health technology assessment (HTA) metrics like quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios (ICERs) for a better evaluation of the cost effectiveness of care pathways.

Methods: We used the ReCiPe model to estimate the future detrimental health impact (in disability-adjusted life years [DALY]) of pollutants from the US healthcare system, showing the major impact of GHG emissions compared with other pollutants. An adapted version of the ReCiPe model was used to convert GHG emissions from care pathways into future DALY_GHG, QALY_GHG, and life years (LY_GHG), as well as the associated confidence intervals. For a given care pathway, future health damages were compared with patient benefits (e.g., QALY_GHG/QALY_patient). Damages may also be integrated in the ICER_GHG by subtracting future health losses from patient health benefits. Case applications are provided.

Results: Future damages to health emerging from pollutants emitted by the US healthcare system were estimated at 7,363,000 DALYs per year. Focusing on GHG emissions to estimate pollutant impact is reasonable, as they represent >90% of future damages. We provide estimates to convert GHG emissions into future health damages in DALY, QALY, or LY (and associated uncertainty), taking into account future impacts over different time horizons (20, 100, or 500-1000 years) and using different discount rates for future health impact (0 or 3%). We recommend estimating future damages using an egalitarian perspective (with a 0% discount rate) to maintain intergenerational equity. The QALY_GHG/QALY_patient ratio allows weighting future detrimental effects of care pathways against their benefits. For health economic evaluations, we recommend integrating GHG emissions into the ICER, preferably in its denominator (QALY, DALY, LY). When focusing on specific care pathways, health gains may be substantially limited by future GHG-related detrimental impacts, especially for chronic treatments in low-risk populations. Some care pathways, like influenza vaccination, improve patient health while mitigating GHG. Accounting for GHG emissions may substantially favor or penalize one strategy over another in terms of ICER. Confidence intervals of the results were wide due to large uncertainties regarding long-term predictions.

Conclusion: HTA should consider care pathways' impact on future health to better assess the impact and cost effectiveness of health technologies. Under the hypothesis of intergenerational equity, GHG accounting has a substantial

Background and objective: Informal caregivers play a critical role in supporting individuals with musculoskeletal conditions. This systematic review aimed to evaluate the psychological and economic burdens associated with caregiving for musculoskeletal conditions.

Methods: We conducted a systematic review in accordance with PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines, searching MEDLINE, Embase, CINAHL, EconLIT, and APA PsycINFO for studies published between 2000 and April 2025. Studies were eligible if they examined the psychological and economic burden of informal caregiving for adults with musculoskeletal conditions. Screening and data extraction were conducted using EndNote 21 and Covidence. Risk of bias was assessed using the CASP checklist for psychological burden studies and the EVERS criteria for economic burden studies. Data were synthesized narratively. An exploratory meta-analysis of informal care hours was conducted using a subset of studies with sufficient statistical data.

Results: A total of 41 studies were included, with 24 reporting psychological burdens, 16 economic burdens, and one for both. Caregiving burden included emotional, social, financial, and time-related impacts, impacting the caregivers' quality of life. Higher anxiety and depression were correlated with a greater caregiver burden. Informal care costs varied by musculoskeletal condition type, location, severity, intensity, and valuation method. Reported informal care hours showed substantial variation across studies. The overall risk of bias across included studies was low.

Conclusions: This systematic review highlights the considerable psychological, economic, and time-related burdens faced by informal caregivers of individuals with musculoskeletal conditions. Caregivers face high stress, physical strain, and opportunity costs. The lack of standardized assessments hinders accurate burden quantification, economic evaluation, and policy responses. Future efforts should focus on adopting consistent measurement instruments and valuation methods, alongside implementing structured policies, financial support, and psychological interventions to better support the caregivers.

Background: Developing health economic decision-analytic models requires making modelling choices to simplify reality while addressing the decision context. Finding the right balance between a decision-analytic model's simplicity and its adequacy is important but can be challenging.

Objective: We aimed to develop a tool that supports the systematic reporting and justification of modelling choices in a decision-analytic model, ensuring it is adequate and only as complex as necessary for addressing the decision context.

Methods: We identified decision-analytic model features from the key literature and our expertise. For each feature, we defined both simple and complex modelling choices that could be selected, and the consequences of simplifying a feature contrary to requirements of the decision context. Next, we designed the tool and assessed its clarity and completeness through interviews and expert workshops. To ensure consistency of use, we developed a glossary sheet and applied the tool in an illustrative case: a decision-analytic model on a repurposed drug for treatment-resistant hypertension.

Results: We conducted five interviews and two workshops with 18 decision-analytic model experts. The developed SMART (Systematic Model adequacy Assessment and Reporting Tool) consists of a framework of 28 model features, allowing users to select modelling choices per feature, then assessing the consequences of their choices for validity and transparency. SMART also includes a glossary sheet. The treatment resistant hypertension case example is provided separately.

Conclusions: SMART supports decision-analytic model development and assessment, by promoting clear reporting and justification of modelling choices, and highlighting their consequences for model validity and transparency. Thoughtful and well-justified modelling choices can help optimise the use of resources and time for model development, while ensuring the model is adequate to support decision making.