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: Collaborative engagement with individuals invested in or affected by health research, beyond researchers themselves, is advantageous and encouraged by major funding bodies. However, the degree of collaborative engagement in health state valuation is unclear. A scoping review was conducted to (i) identify recommendations on best practice in collaborative engagement in health economics and related literature; (ii) identify examples of collaborative engagement in valuation studies; and (iii) map (ii) onto (i) to identify current practice and future recommendations.

Methods: Eight databases were searched in March-May 2024, with grey literature searches in August-September 2024. For objective (i), reports or manuscripts in health economics or patient-reported outcome measure development/evaluation of any date providing recommendations for collaborative engagement were included. For objective (ii), articles published since 2019 featuring health state valuation and collaborative engagement were included. Best practice recommendations were extracted and thematically synthesised. Examples of collaborative engagement were extracted and mapped against recommendations.

Results: Twenty-two records featuring recommendations and 15 valuation studies were included. A 15-item framework of emerging best practice recommendations for collaborative engagement was synthesised. Most examples of collaborative engagement involved patients and/or experts helping inform health states for valuation. There was no evidence for 9 out of 15 synthesised recommendations having been applied in any of the valuation studies and only minimal evidence was extracted for the remaining six.

Conclusions: Collaborative engagement in health state valuation is underdeveloped and unaligned with literature recommendations. A 15-point framework has been developed as a strategic starting point for developing guidance to improve practice in the field.

Background and objective: In cost-effectiveness analysis, treatment decisions are analysed at the population level. Combinations of treatment strategies that account for the heterogeneity of costs and effects across patients can be more cost-effective than a "one size fits all" approach. Individualized treatment rules (ITRs) assign a specific treatment to every patient based on their relevant characteristics, such that overall cost-effectiveness is optimized, but do not include feasibility or ethical considerations. We propose an approach for the design of ITRs based on simulated patient data from microsimulation models using statistical learning techniques.

Methods: We mathematically define the optimal ITR and how to measure the value of an ITR in a cost-effectiveness context. We explore least absolute shrinkage and selection operator (LASSO) regression, classification trees, and policy trees to illustrate how standard statistical learning techniques can be used to derive ITRs. We compare the strengths and limitations of these three approaches in terms of three criteria: the incremental value of the ITRs compared to optimal treatment assignment in terms of net monetary benefit (NMB), computational speed, and the interpretability of the ITRs. We propose methods to describe the impact of parameter uncertainty on the ITRs. We also explore how stochastic uncertainty can impact the ITR incremental value. We illustrate the methods by applying them to a microsimulation model for haemophilia B comparing four treatment strategies as a case study. The relevant patient characteristics in this model are the annualized bleeding rate, age, and sex.

Results: In our case study, a simple two-layer-deep classification tree is best suited based on the three criteria. This classification tree allocates treatments depending on whether the annualized bleeding rate of a patient is above or below 30 and whether their age is above or below 51. The optimal threshold values are uncertain based on the 95% credible ranges from the probabilistic analysis: 21-46 for annualized bleeding rate and 42-56 for age. Scenarios show that stochastic uncertainty has an impact on the incremental value of the ITR.

Discussion: Based on methodological considerations and the empirical findings in our case study, we expect the superiority of classification trees for the derivation of ITRs to be generalizable to other microsimulation models. This finding needs to be confirmed in future applications. Stochastic uncertainty has significant impacts on the ITRs, such that accurate representations of individual patient pathways are particularly crucial when designing ITRs. Future research could explore further empirical models and analytical approaches for ITRs or consider the translation of ITRs into the real-world decision-making context.

Background and objective: The reliability of a decision model to guide decision making depends on its ability to accurately predict patient outcomes. We present results of an external validation of the MicroSimulation Core Obesity Model (MS-COM) that was developed to compare the cost effectiveness of obesity management interventions in adults.

Methods: We updated a 2018 systematic literature review of economic models in overweight and obesity and conducted additional targeted searches to identify suitable sources and outcomes to validate against MS-COM in people with overweight or obesity with or without type 2 diabetes. We extracted baseline characteristics and cardiovascular and mortality outcomes, where these were closely matched with MS-COM, and incidence of type 2 diabetes. We performed external-dependent (sources used in MS-COM) and external-independent (sources not used in MS-COM) validation. The extent of concordance between predicted and observed outcomes was assessed using the coefficient of determination (R²), ordinary least-squares linear regression line (OLS LRL), mean absolute percentage error, root mean square percentage error and mean squared log of accuracy ratio.

Results: Ninety-nine potential independent validation sources were identified from 6381 screened records, of which nine studies reported cardiovascular and mortality outcomes that were closely matched with MS-COM, along with two studies that reported type 2 diabetes incidence (number of endpoints = 106). The dependent validation of cardiovascular and mortality outcomes (N = 18), based on the QRisk3 risk equation (normoglycaemia/prediabetes population) and UKPDS 82 (type 2 diabetes population), showed a good linear correlation with observed outcomes (R² = 0.99 and 0.98, respectively). There was some slight overprediction of QRisk3 (OLS LRL slope = 1.11) and underprediction of UKPDS 82 (OLS LRL slope = 0.97). The independent validation of cardiovascular and mortality outcomes also showed a good linear correlation with observed outcomes, particularly in adults with normoglycaemia/prediabetes (R² = 0.90; OLS LRL slope = 0.86); however, an independent validation of type 2 diabetes incidence showed a poorer fit with some degree of underprediction (R² = 0.74; OLS LRL slope = 0.66). Mean error estimates were lower in the dependent validation, showing good concordance between predicted and observed values.

Conclusions: External validation of MS-COM showed good concordance with dependent and independent sources, suggesting the model accurately predicts obesity-related complications in an overweight/obese population with normoglycaemia/prediabetes and type 2 diabetes.

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: An economic evaluation is widely used to facilitate decision making regarding drug reimbursement in many healthcare systems. However, the absence of preference-based measurement in clinical trials has hindered the health economic evaluation of drugs for rare diseases.

Objective: This study aims to develop mapping algorithms that convert disease-specific scales-Spinal Muscular Atrophy Independence Scale (SMAIS) for spinal muscular atrophy and Functional Assessment of Cancer Therapy-Anemia (FACT-An) for paroxysmal nocturnal hemoglobinuria-into five-level EQ-5D (EQ-5D-5L) and SF-6D version 2 (SF-6Dv2) utility values, thereby enabling the economic evaluation of related drugs.

Methods: Data were collected from two online surveys conducted in China. Both direct and indirect mapping methods were explored, including ordinary least squares regression, Tobit regression model, censored least absolute deviation, generalized linear model, beta mixture regression, adjusted limited dependent variable mixture model, ordinal logistic regression (OLOGIT), and multinomial logistic regression (MLOGIT). Model performance was assessed by mean absolute error (MAE), root mean squared error (RMSE), and adjusted R-square (adjusted R²). The optimal model was selected based on the lowest average ranking value, derived from the MAE and RMSE through five-fold cross-validation.

Results: A total of 192 patients with spinal muscular atrophy and 306 patients with paroxysmal nocturnal hemoglobinuria were included in the analysis. For spinal muscular atrophy, the MLOGIT, which included SMAIS total score and sex as predictors, demonstrated the best performance, with the lowest MAE and RMSE (EQ-5D-5L: MAE: 0.1471; RMSE: 0.1839; adjusted R²: 0.5932; SF-6Dv2: MAE: 0.1208; RMSE: 0.1563; adjusted R²: 0.4323) after five-fold cross-validation. For paroxysmal nocturnal hemoglobinuria, the OLOGIT model using the FACT-An dimension score performed best (EQ-5D-5L: MAE: 0.1068; RMSE: 0.1431; adjusted R²: 0.5394; SF-6Dv2: MAE: 0.0877; RMSE: 0.1162; adjusted R²: 0.6754).

Conclusions: These newly developed mapping algorithms enable the estimation of EQ-5D-5L and SF-6Dv2 utilities in the absence of a preference-based measurement, thus supporting health economic evaluations of therapies for spinal muscular atrophy and paroxysmal nocturnal hemoglobinuria.

National Institute for Health and Care Excellence (NICE) technology appraisal processes assume that the standard of care (SoC) is itself cost effective. However, many treatments in use in the UK National Health Service (NHS), particularly in rare diseases, were historically commissioned without formal value assessment and are priced without reference to cost-effectiveness thresholds. Cost-ineffective comparators distort how value is ascribed to new technologies, undermining the coherence of NICE's decision-making framework, and imposing substantial opportunity costs on the NHS. Using late-onset Pompe disease (LOPD) as an exemplar, we demonstrate the implications of a cost-ineffective comparator in assessments of innovative therapies. A clinically superior enzyme replacement therapy (ERT) may command a lower value-based price than current ERTs, whilst a hypothetical curative gene therapy is valued at over £4 million against current ERT, but just £629,392 when re-anchored against best supportive care. Here, value is driven by displacement of costs rather than health gain, raising affordability concerns that may limit access to genuine innovation. The 2025 NHS 10-Year Plan grants new NICE statutory powers to withdraw access to cost-ineffective therapies, presenting an opportunity to reform technology appraisal. We propose several policy responses, including comprehensive reassessment of active guidance with decisions made with respect to a standard cost-effectiveness frontier, reviews triggered by new comparators, and use of flexible decision rules within existing frameworks. These changes could allow the evolving value of medicines to be reflected in NHS practice, redefining NICE as a body that takes a dynamic, whole-lifecycle view of value. Deliberative public and stakeholder engagement is essential for success, given the potential consequences for manufacturers and patients.

Cardiovascular disease (CVD) is a major contributor to the health and economic burden of disease globally. In this paper we discuss the literature on the health economics of the prevention and early intervention in CVD. We reveal the large economic impact of CVD and provide the economic argument supporting the calls for early detection and diagnosis of CVD outlined in the Global Heart Hub's patient-led Manifesto for Change. Many challenges in conducting cost-effectiveness analyses of interventions for CVD prevention are identified, as well as the emerging statistical and economic methods to help overcome these issues. Lastly, we acknowledge the profound disparities in cardiovascular health faced by minority or underserved populations, and the important role that prevention and early intervention can play in improving health equity.

Objective: We aimed to compare EQ-5D-Y-5L health state preferences among children, adolescents, and adults in Canada using a discrete choice experiment (DCE), and to explore the feasibility of a rescaling latent DCE using anchoring tasks collected from adolescents.

Methods: An online survey was conducted to elicit preferences for EQ-5D-Y-5L health states from children (aged 12-15 years), adolescents (aged 16-17 years), and adults (aged ≥ 18 years). All respondents completed 12 latent DCE tasks. Adults and adolescents were randomly assigned to three additional anchoring tasks using a DCE with duration or with dead. The tasks were framed from the perspective of a 10-year-old child for adults and their own perspective for children and adolescents. Respondents provided feedback on the difficulty of latent DCE tasks. Mixed logit models were used to analyze latent DCE data. Anchored DCE models using duration/dead tasks were estimated and compared between adults and adolescents.

Results: Overall, 546 children, 508 adolescents, and 908 adults were included in the analyses. A higher proportion of children indicated it easy to complete DCE tasks compared with adolescents and adults. Monotonicity of coefficients were observed in latent DCE models among adults but not among children and adolescents. Anchored DCE modeling performed better in adults than in adolescents regarding monotonicity and statistical significance of coefficients, and the DCE with duration performed slightly better than the DCE with dead.

Conclusions: There were differences in health state preferences elicited using DCEs between children/adolescents and adults. Anchoring tasks appeared feasible for adolescents, with a DCE with duration performing slightly better than a DCE with dead.

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.