PharmacoEconomics最新文献

Objectives: Obstructive hypertrophic cardiomyopathy (oHCM) is a myocardial disease, characterised by left ventricular hypertrophy, hampering the ventricular blood outflow. Standard of care (SoC) includes medications such as beta-blockers (BB) and calcium channel blockers (CCB) and septal reduction therapies. Recently, mavacamten, a first-in-class myosin inhibitor, became available to oHCM patients. The objective was to develop a decision analytic model to evaluate the cost effectiveness of mavacamten compared with SoC in oHCM patients from a Dutch societal perspective.

Methods: A Markov model was developed in R based on the Decision Analysis in R for Technologies in Health framework with data from the EXPLORER-HCM trial. This trial compared mavacamten in combination with background therapy (BB and CCB) versus placebo, including oHCM patients (n = 251; mean age 59 years) in New York Heart Association (NYHA) functional classes II (72.9%) and III (27.1%). For the model, four health states were defined based on the NYHA classes, including NYHA I-NYHA III/IV and death. The model evaluated mavacamten with SoC versus SoC alone over a lifetime horizon with a cycle length of 4 weeks, following the most recent Dutch guidelines. Health state utilities and societal costs were derived from the AFFECT-HCM study, with utilities measured using the EQ-5D-5L. Outcomes included (incremental) societal costs, life years (LYs), quality-adjusted life years (QALYs) and the incremental cost-effectiveness ratio (ICER). The Dutch willingness-to-pay thresholds of €50,000 and €80,000 per QALY were applied. Uncertainty of parameters was assessed in deterministic and probabilistic sensitivity and scenario analyses.

Results: Results indicate mavacamten being more effective (Δ4.75 LYs; Δ3.36 QALYs) and more costly (Δ€235,951) compared with SoC with an ICER of €70,223 per QALY gained. Varying parameters by 20% showed that the utility value of patients in NYHA class I (ICER: €57,199; €111,506 per QALY) and drug costs (ICER: €53,985; €86,555 per QALY) were most sensitive. Mavacamten accumulated most LYs, QALYs and costs by patients improving to NYHA class I, compared with SoC, and patients remained longer in that state throughout the model. For men, incremental QALYs (Δ 3.36) and costs (Δ €239,743) were slightly higher compared with women. The probability of the intervention being cost effective at the willingness-to-pay thresholds €50,000 and €80,000 per QALY was 1.3% and 87.4%, respectively. Conclusion The results show that mavacamten increased LYs and QALYs compared with SoC, however, at substantial additional costs. The probability of mavacamten being cost effective depends on the selected willingness-to-pay threshold.

Patient and public involvement (PPI) in health economics modelling is increasingly recommended, yet formal guidance for how to structure or evaluate it remains limited. The Values in Modelling (VIM) framework was developed to address this gap by helping teams identify and deliberate on value-laden decisions in modelling. Drawing on philosophical theory, the framework defines five steps to guide collaboration between modellers and transdisciplinary participators and to document their influence on decision making: (1) identify ethical issues and perspectives; (2) characterize modelling decisions; (3) select decision-making strategies; (4) deliberate 'open' decisions; and (5) report and evaluate. We applied the VIM framework in the Lifetime Exposures and Asthma Outcomes Projection (LEAP) model project, which models the cost effectiveness of high-efficiency particulate air (HEPA) filters for asthma prevention and management. In this application, the framework helped prioritize modelling decisions for PPI, supported transparent deliberation about uncertainty, and led to concrete methodological changes-including new sensitivity analyses and revised outcome measures. These results demonstrate how a theory-informed process can enhance PPI in modelling, improving transparency, justification, and adequacy-for-purpose in health economics research.

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.

Background: In China, human papillomavirus (HPV) vaccination for girls has been included in the National Immunization Program (NIP), providing free 2-valent HPV vaccine (2vHPV) for 13-year-old girls. This study assessed the public health impact and cost-effectiveness of routine 2vHPV/9vHPV for girls aged 13 years, with/without a catch-up strategy for females aged ≤ 26 years, in six cities with free 2vHPV programs but differing socioeconomic development across China (Wuxi, Suzhou, Guangzhou, Yuxi, Chengdu, and Shijiazhuang).

Methods: A discrete-time Markov model was developed from the healthcare system perspective over a 100-year horizon. Routine (aged 13 years) and catch-up (aged ≤ 26 years) vaccination coverages were assumed at 90% and 70%, respectively. Model outputs included costs (2023 USD), quality-adjusted life years (QALYs), incremental cost-effectiveness ratios (ICERs), and cases of HPV-related diseases averted. The willingness-to-pay (WTP) thresholds were set corresponding to 1 times the per capita GDP of each city. Scenario and sensitivity analyses were conducted to test robustness.

Results: Routine 9vHPV strategy prevented an additional 1921-9630 cases of cervical cancer, compared with the routine 2vHPV strategy. Furthermore, incorporating a catch-up strategy yielded a marginal additional reduction (29-176 cases by 9vHPV and 18-108 cases by 2vHPV) in cervical cancer. Under ideal pricing assumptions, the routine 9vHPV strategy was cost-saving and cost-effective across all six cities, compared with routine/routine plus catch-up 2vHPV strategies, with a cost saving of USD 1730-2792/1769-2817 per person, and a gain of 0.195-0.315/0.193-0.311 QALYs per person. Routine plus catch-up 2vHPV and 9vHPV strategies remained cost-effective in five and four cities, respectively. Notably, scenario analysis indicated at current domestic 9vHPV market price, the routine 9vHPV strategy remained cost-effective in six cities, while the catch-up strategy became cost-ineffective across all cities.

Conclusions: The routine 9vHPV strategy may be cost-effective compared with the 2vHPV strategy in China. However, a 9vHPV catch-up strategy varies in cost-effectiveness by regional socioeconomic development.

Simulation models inform health policy decisions by integrating data from multiple sources and forecasting outcomes when there is a lack of comprehensive evidence from empirical studies. Such models have long supported health policy for cancer, the first or second leading cause of death in over 100 countries. Discrete-event simulation (DES) and Bayesian calibration have gained traction in the field of decision science because they enable flexible modeling of complex health conditions and produce estimates of model parameters that reflect real-world disease epidemiology and data uncertainty given model constraints. This uncertainty is then propagated to model-generated outputs, enabling decision-makers to assess confidence in recommendations and estimate the value of collecting additional information. However, there is limited end-to-end guidance on structuring a DES model for cancer progression, estimating its parameters using Bayesian calibration, and applying the calibration outputs to policy evaluation. To fill this gap, we introduce the DES Modeling Framework for Cancer Interventions and Population Health in R (DESCIPHR), an open-source codebase integrating a flexible DES model for the natural history of cancer, Bayesian calibration for parameter estimation, and an example application of screening strategy evaluation. To illustrate the framework, we apply DESCIPHR to calibrate bladder and colorectal cancer models to real-world cancer registry targets. We also introduce an automated method for generating data-informed parameter prior distributions and increase the functionality of a neural network emulator-based Bayesian calibration algorithm. We anticipate that the adaptable DESCIPHR modeling template will facilitate the construction of future decision models evaluating the risks and benefits of health interventions.

Background: Overweight and obesity is a prevalent and growing global health concern associated with a significant healthcare burden. With recent advancements in weight management interventions demonstrating cardioprotective benefits, there is a need for risk equations that can accurately predict cardiovascular event risk in health economic models to support healthcare decision making and resource allocation.

Objective: We aimed to derive risk equations using SELECT trial data that estimate the risk of acute coronary syndrome and stroke in people with established cardiovascular disease and overweight or obesity but without diabetes mellitus for use in health economic modelling.

Methods: Risk equations estimating first in-trial observed acute coronary syndrome and stroke were derived from patient-level data from the SELECT trial, a double-blind randomised placebo-controlled trial comparing semaglutide 2.4 mg with placebo. Risk factors were identified from the literature and by clinical experts. Risk equations were developed using cause-specific Cox proportional hazard models with all-cause mortality as a competing risk. Least absolute shrinkage and selection operator (LASSO) penalisation was applied 100 times in bootstrap samples to refine the risk equations. Final risk equations were validated with clinical experts and using SELECT trial data.

Results: Sex, coronary heart disease, a history of acute coronary syndrome and use of angina medications had the strongest associations with acute coronary syndrome (hazard ratio 1.67-1.82). For stroke, a history of atrial fibrillation, cerebrovascular disorder, stroke and transient ischaemic attack had the strongest associations (hazard ratio 1.40-1.70). In terms of discrimination, the risk equations had an area under the receiver operating characteristic curve (AUC) of 0.66-0.68 for acute coronary syndrome and 0.68-0.71 for stroke, and C-indices of 0.67-0.69 for acute coronary syndrome and 0.66-0.69 for stroke. The risk equations showed good cohort-level predictive capabilities over a 4-year time horizon.

Conclusions: These novel, treatment-specific, trial-derived risk equations are the first to predict the risk of secondary cardiovascular events in people with overweight or obesity and established cardiovascular disease but without diabetes. Incorporating these risk equations into health economic models may improve the accuracy of economic evaluations in this population.

Objective: We aimed to facilitate the comparison and communication of magnitudes of health inequality impact across interventions for different diseases, and to indicate the potential range of such impacts.

Methods: We propose rescaling the slope index of inequality to measure the health inequality impact as the change in the gap in total predicted quality-adjusted life-years between the least and most socially disadvantaged groups, with linear regression predictions used to account for effects on intermediate groups. We suggest reporting the inequality impact relative to the total health opportunity cost to facilitate comparison across interventions varying in scale and unit costs. We illustrated the approach with aggregate distributional cost-effectiveness analyses of hypothetical treatments for 1336 diseases in England. We approximated benefit shares for neighbourhood deprivation quintile groups using disease-specific hospital admissions. We tested between-group equality using generalised linear regression and constructed uncertainty intervals using Monte Carlo simulation. We assumed an equal total health opportunity cost and benefit-cost ratio of one, with alternative scenarios in a sensitivity analysis.

Results: Health inequality impacts of hypothetical treatments ranged from - 33.1% of the total health opportunity cost (inequality increasing) to + 45.3% (inequality decreasing), and were ≤ - 5% for 1.6% of diseases, ≥ + 5% for 41.8% and ≥ + 20% for 1.6%. The impact was positively associated with the benefit-cost ratio and decreased when more deprived groups were assumed to incur proportionately more total health opportunity costs.

Conclusions: Health inequality impacts can be compared using the change in the total predicted quality-adjusted life-year gap between the least and most socially disadvantaged groups as a proportion of the total health opportunity cost.