PharmacoEconomics最新文献

Background: Children with multidrug-resistant (MDR)/rifampicin-resistant (RR) tuberculosis (TB) are an important but neglected group in cost-effectiveness research. Digital health information systems enable new approaches to health-service cost analysis. The Provincial Health Data Centre (PHDC) in the Western Cape, South Africa, collates disparate health system data including hospital inpatient and outpatient data, medications, laboratory tests, and primary health care utilisation.

Methods: A health-service cost analysis used anonymised, integrated PHDC data for children treated for MDR/RR-TB between 2018 and 2021. Health-service utilisation was costed using local unit prices, and total per-patient costs were summarised by key patient and disease characteristics (age, sex, resistance profile, site of disease, and HIV status) and reported in 2021 USD. A log-linear regression model identified cost drivers, and alternative parametric distributions were fitted to total costs to assess distributional fit.

Results: There was significant total cost variation across the 271 children in the data sample (median US$7576; interquartile range 2725-22,986). Regression analysis indicates younger age, extrapulmonary disease site, living with HIV, and treatment duration had significant impact on costs; impact of resistance profile was significant but subject to modelling assumptions. The distribution of total per-patient costs fitted a gamma distribution (α = 0.93, β = 14,496).

Conclusion: Treatment for MDR/RR-TB in children remains costly for health systems. Utilising routinely collected, real-world data from an established health information system enables accurate and representative insights to overall costs and major cost drivers. Costs were highly skewed, with a small proportion of patients incurring very high costs. This cost analysis can assist in decision making and programme development at local and international levels and as an input to secondary analysis.

Background and objective: Efanesoctocog alfa is a first-in-class high-sustained factor VIII therapy approved for prophylaxis, on-demand treatment, and peri-operative management of bleeding in hemophilia A. This analysis aimed to compare the cost effectiveness of efanesoctocog alfa prophylaxis with factor VIII extended half-life prophylaxis.

Methods: A lifetime Markov model was developed from a US payer perspective, using clinical data from an indirect treatment comparison of phase III studies and inputs derived from the literature. A cohort of patients aged ≥ 12 years with severe hemophilia A without inhibitors, who received prophylaxis using any regimen or on-demand treatment, entered the model. Outcomes included joint and non-joint bleeds, quality-adjusted life-years, total direct costs, and the incremental cost-effectiveness ratio. Costs were expressed in US dollars and inflated to January 2023 prices. Discount rates of 3% were used. One-way probabilistic and scenario analyses were conducted. The willingness-to-pay threshold was assumed at $150,000 per quality-adjusted life-year.

Results: Efanesoctocog alfa was more effective and less costly (also referred to as 'dominant') versus factor VIII extended half-life therapies, with a lower lifetime number of joint (undiscounted 34.00 vs 140.65) and non-joint (undiscounted 13.33 vs 55.99) bleeds, higher quality-adjusted life-years (24.00 vs 22.92), and lower total costs ($30,716,640 vs $32,953,485). A broad range of scenario analyses and probabilistic sensitivity analyses resulted in 100% of simulations being cost effective. Dosing level and drug costs had the largest impact on results in the deterministic sensitivity analyses.

Conclusions: Our analysis suggests that efanesoctocog alfa was dominant versus prophylaxis with standard and elevated factor VIII extended half-life dosing regimens. Efanesoctocog alfa was associated with better joint health and, hence, contributed to fewer bleeds, lower costs, and higher quality-adjusted life-years.

Combination vaccines combine several components in a single dose administration. They offer programmatic and public health advantages, particularly as vaccine schedules become increasingly crowded. They are often more expensive to develop and produce, which discourages manufacturer investment without clear market signals. Hence their benefits need to be captured with existing health economic evaluation reference cases used by decision-makers to guide vaccine investments. We propose that the value of combination vaccines can be captured through at least four domains: (1) reductions in tangible and intangible costs to caregivers, (2) operational efficiencies to the health system, (3) opportunity costs of vaccine schedule slots, and (4) more streamlined vaccine schedules. We demonstrate the practicality of our framework by comparing the value of introducing a hypothetical vaccine to a crowded schedule as a standalone formulation, a replacement for a vaccine already in the schedule, or a combination product. The framework could also be applied to estimate the value of reducing the number of separate administrations needed for a standalone vaccine. Applying it in real-world situations could be facilitated by further data collection, particularly on collating results on the value of existing vaccines in the schedule and estimating willingness-to-pay for fewer vaccine administrations.

Background: Cure models are becoming more popular for modelling survival data where long-term survival, or 'cure', is considered plausible. One criterion for considering fitting cure models is evidence for a plateau in the Kaplan-Meier survival curve. However, what constitutes a mathematical definition of a plateau in survival probability is unclear, and visual inspections of survival curves are subjective.

Objective: We investigate these issues and clarify what is meant by a plateau in this context.

Methods: We begin by describing an activity where five experts were presented with 10 survival curves from oncology trials. They were asked to rank these curves in order of their potential suitability for mixture cure modelling. We explore mathematically what features of data are required to produce a positive estimated cure fraction under an exponential mixture cure model. We show how these results can be generalised to a Weibull mixture cure model. A case study was performed using one of the survival curves.

Results: We found weak correlations between the experts' rankings. Mathematical investigations revealed the features of data required for mixture cure models to be potentially useful, such as a decreasing event rate, but this is highly model dependent. The case study illustrated similar statistical issues.

Conclusions: We conclude that a precise definition of the extent to which a Kaplan-Meier survival curve demonstrates a plateau is likely to prove elusive. External evidence or subject matter expert knowledge about the plausibility of cure must therefore play a key role.

Current methods for modelling spillover effects on carers in economic evaluation include four main methods: (absolute) utilities, disutilities, increments and multipliers. Each of these approaches assumes that the spillover effect is one-dimensional. We aimed to develop a new approach that better reflects the complexity of caring and the nuances of how a new treatment may impact the caregiver. We propose a new method based on the established concepts of the 'family effect' (or caring about someone) and the 'caregiving effect' (providing care for someone). These effects can be disentangled through analysis of carer-patient dyads or using patient and carer (dis)utilities and estimates from the literature. We consider case studies in Duchenne Muscular Dystrophy, Spinal Muscular Atrophy and Alzheimer's Disease. Our approach models a small carer health-related quality of life (HRQoL) gain for each intervention, whereas the utility approach consistently models a substantial carer HRQoL gain, and the disutility approach models a carer HRQoL loss in two case studies. Our method allows explicit consideration of the benefits to carers of extending patient survival or improving patient health, with the negative HRQoL impact of increased caregiving burden. We propose that our method can be used with published data at present, and further research should analyse the family and caregiving effects in different conditions.

Background and objective: Health technology assessments traditionally rely on clinical trial data, leaving uncertainties about real-world cost effectiveness. This post-market economic evaluation used registry data to estimate the real-world cost effectiveness of bortezomib, lenalidomide and dexamethasone (VRd) versus standard of care as it existed prior to VRd funding for newly diagnosed, transplant eligible and ineligible multiple myeloma, as subsidised by the Australian government in 2019.

Methods: We conducted the economic evaluation from the perspective of the Australian healthcare system using the EpiMAP Myeloma model, a discrete event simulation model powered by risk equations based on data from the Australia & New Zealand Myeloma and Related Diseases Registry. This approach captured individual patient heterogeneity and treatment pathways through up to nine lines of therapy. We assessed differences in quality-adjusted life-years and costs over a lifetime horizon, discounting both at the standard Australian rate of 5% per annum. Costs were valued in 2025 Australian dollars and non-parametric bootstrapping was used to quantify parameter uncertainty.

Results: Brtezomib, lenalidomide and dexamethasone was associated with 0.16 incremental quality-adjusted life-years (95% confidence interval [CI] 0.10, 0.21) and A$16K incremental costs (95% CI A$12K, A$120K). Improved response to therapy with VRd was predicted to marginally increase receipt of autologous stem cell transplantation by 1.1% (95% CI 0.6, 1.7), significantly increase receipt of maintenance therapy by 13.8% (95% CI 10.4, 17.3) and marginally decrease the proportion of patients progressing to subsequent lines. None of the bootstrap iterations fell below the traditional A$50K/quality-adjusted life-year threshold.

Conclusions: The 2019 decision to universally fund VRd for newly diagnosed multiple myeloma did not result in a cost-effective allocation of healthcare resources when judged against the traditional A$50K/quality-adjusted life-year threshold. Our findings provide nuanced insights into the real-world cost effectiveness of VRd, highlighting how post-market evaluations can inform refinement of funding decisions for complex therapeutic interventions.