Applied Health Economics and Health Policy最新文献

Background and objective: Rising healthcare costs challenge the financial sustainability of healthcare systems. Interventional pharmacoeconomics has emerged as a vital discipline to improve the cost-effective and sustainable use of drugs in clinical practice. However, current efforts are often fragmented, highlighting the need for an integrated hospital-wide approach. This study aimed to develop a scalable framework to systematically identify and implement cost-effective and sustainable drug use practices in hospitals.

Methods: This study was conducted at the Erasmus University Medical Centre in Rotterdam between December 2022 and July 2023. A novel '8-Step Efficiency Model' was designed to systematically identify and evaluate strategies for cost-effective and sustainable drug use. The process involved identifying high-expenditure drugs, systematically assessing these drugs using the Efficiency Model, and conducting a multi-disciplinary evaluation of the proposed cost-effectiveness strategies.

Results: The study assessed 39 high-cost drugs, representing 57% of the Dutch national expensive drug expenditure in 2021. Initiatives for enhancing cost-effectiveness and sustainability were identified or developed for 27 out of the 39 assessed drugs (51% of the national drug expenditure in 2021). Case examples of infliximab (e.g., wastage prevention) and intravenous immunoglobulins (e.g., lean body weight dosing) illustrate practical applications of the framework, resulting in substantial cost savings and improved sustainability.

Conclusions: This study presents a systematic scalable model for enhancing the cost-effectiveness of high-expenditure drugs in hospital settings. This approach not only addresses financial sustainability but also promotes the quality of patient care and sustainable drug use. This model could serve as a generic blueprint for other institutions to identify and implement cost-effective and sustainable drug use strategies.

Objectives: Quality-adjusted life expectancy (QALE) norms reflect the normative profiles or reference data of QALE of the general population and provide a meaningful anchor for comparison to inform healthcare decision-making. This study aimed to develop the QALE norms for the Chinese population by using a representative dataset of health utility values collected using the EQ-5D-5L and short-form 6-dimension version 2 (SF-6Dv2) instruments.

Methods: Age-specific population norms of health utility values calculated using the EQ-5D-5L and SF-6Dv2 were used. Both utility norms were combined with the latest version of the National Life Tables of China published in 2021 to calculate QALE estimates on the basis of age, sex, and urban/rural residence area. QALE estimates were further discounted using 1.5%, 3.5%, 5.0%, and 8.0% discount rates.

Results: When using the health utility values evaluated by the SF-6Dv2, the QALE at age 0 years was 66.34 years at the discount rate of 0% and 16.65 years at the discount rate of 5%. For the EQ-5D-5L, the QALE at age 0 years was 76.50 years at the discount rate of 0% and 19.45 years at the discount rate of 5%. At birth, females exhibited a higher QALE, while the difference between females and males initially increased before subsequently declining overtime, ultimately resulting in females having a lower QALE. Rural population had a monotonically lower QALE than urban population.

Conclusion: This study constructed age-stratified QALE norms for the Chinese population categorized by sex and residence area using mortality data alongside corresponding health utility values derived from the EQ-5D-5L and SF-6Dv2.

Economic evaluations play a crucial role in health resource allocation by assessing the costs and effects of various interventions. However, existing methodologies often overlook significant differences related to sex and gender, leading to a 'blind spot' in understanding patient heterogeneity. This paper highlights how biological and social factors influence costs and health outcomes differently for women, emphasising the need for a more explicit consideration of these differences in economic evaluations to ensure efficient and equitable resource allocation. The paper is structured to first outline how sex and gender factors impact costs and outcomes. It then identifies biases in current economic evaluation methods and practices, using real-world examples to illustrate the implications of these biases on policymaking and health equity. Notably, we argue that neglecting gender considerations can lead to inefficiencies and inequities in healthcare resource distribution. Key areas of gender bias include the estimation of productivity losses, quality of life variations and the secondary household effects of interventions. The analysis reveals that women often face higher healthcare costs and experience different health outcomes due to systemic biases in treatment and care. The paper concludes with practical recommendations for analysts, decision makers and research funders, advocating for the integration of sex and gender-responsive methodologies in health economic evaluations. Ultimately, this work calls for a paradigm shift in health economics to better reflect the complexities of sex and gender and improve health outcomes for all.

Background: The dead state can affect the value sets derived from discrete choice experiments (DCEs). Our aim was to empirically assess the direct impact of the immediate death state on health utilities using discrete choice experiment with time (DCE_TTO).

Methods: A sample of the general population in Quebec, Canada, completed two approaches: DCE_TTO followed by a best-worst scaling with time (BWS_TTO) (hereafter referred to as DCE_BWS), versus DCE_TTO followed by the dominated option and the immediate death state (hereafter referred to as DCE_DOD), both designed with the SF-6Dv2. In DCE_BWS, all participants first completed 10 DCE_TTO choices (i.e., option A vs B), followed by 3 BWS_TTO. In DCE_DOD, the same participants first completed the same 10 DCE_TTO choices, followed by a repeated choice between the dominated option (i.e., A or B) and the immediate death state. A conditional logit model was used to estimate value sets. The performance of models was assessed using goodness of fit using Bayesian information criterion, parameters' logical consistency, and levels' significance. The direct impact of the death state on DCE latent utilities was evaluated by examining the magnitude of coefficients, assessing the agreement among the value sets estimated by DCE_TTO with DCE_BWS and with DCE_DOD using Bland-Altman plots, the proportion of worst-than-dead (WTD) health states, and analyzing the range of estimated values.

Results: From 398 participants, a total of 348 participants were included for final analysis. The number of parameters with illogical consistency and non-significant coefficients was lower in DCE_BWS. The observed consistency in the relative importance of dimensions across all approaches suggests a stable and reliable ranking. The utility range for DCE_DOD (- 0.921 to 1) was narrower than for DCE_TTO (- 1.578 to 1) and DCE_BWS (- 1.150 to 1). The DCE_DOD estimated a lower percentage of WTD health states (20.01 %) compared to DCE_TTO (47.19 %) and DCE_BWS (33.73 %). The agreement between DCE_TTO and DCE_BWS was slightly stronger than between DCE_TTO and DCE_DOD, and the mean utility values were higher in DCE_DOD than in DCE_BWS.

Conclusions: The inclusion of the immediate death state directly within DCE increased utility values. This increase was higher when the immediate death was included in a sequence within a DCE_TTO (i.e., DCE_DOD) than when it was included in a continuum of DCE_TTO (i.e., DCE_BWS). The use of DCE_DOD was potentially better suited to incorporate the dead state into a DCE.

Background: Hemophilia B, a severe genetic disorder, involves substantial treatment costs and frequent interventions. Etranacogene dezaparvovec (EDZ) is a recently approved gene therapy for hemophilia B.

Objective: This study evaluates the cost-effectiveness of EDZ compared with conventional factor IX (FIX) prophylaxis.

Methods: A semi-Markov model simulated a cohort of adult males with severe hemophilia B to assess the economic impact of EDZ versus FIX prophylaxis over a lifetime horizon from a health system perspective in the USA. Inputs derived from clinical trials included therapy durability and transition probabilities based on Pettersson Scores. Scenario analyses incorporated frameworks suggested by the Institute for Clinical and Economic Review for single or short-term transformative therapies.

Results: Base-case analysis showed that at a cost of US$3.5 million, EDZ led to lifetime cost savings of US$11 million and an additional 0.64 quality-adjusted life years (QALYs) compared with FIX. However, FIX has extremely high annual costs. When annual cost offsets attributed to EDZ were capped at US$150,000, EDZ was found to have a threshold price of US$3.1 million at a willingness-to-pay of US$150,000 per QALY.

Conclusion: EDZ proved to be a dominant strategy over FIX prophylaxis in the base-case scenario, providing large cost savings and slightly better outcomes. The substantial costs associated with FIX are a primary driver behind these results. The introduction of cost-offset caps significantly affects the value-based price of EDZ. Using caps on cost offsets in considering price can help to balance affordability and value in the health system.

Objectives: The UK has recently established subscription-payment agreements for two antimicrobials: cefiderocol and ceftazidime-avibactam. This article summarises the novel value assessments that informed this process and lessons learned for future pricing and funding decisions.

Methods: The evaluations used decision modelling to predict population incremental net health effects (INHEs), informed by systematic reviews, evidence syntheses, national surveillance data and structured expert elicitation.

Results: Significant challenges faced during the development of the evaluations led to profound uncertainty in the estimates of INHEs. The value assessment required definition of the population expected to receive the new antimicrobials; estimating value within this heterogenous population; assessing comparative efficacy using antimicrobial susceptibility data due to the absence of relevant clinical data; and predicting population-level benefits despite poor data on current numbers of drug-resistant infections and uncertainties around emerging resistance. Though both antimicrobials offer the potential to treat multi-drug resistant infections, the benefits estimated were modest due to the rarity of true pan-resistance, low life expectancy of the patient population and difficulty of identifying and quantifying additional sources of value.

Conclusions: Assessing the population INHEs of new antimicrobials was complex and resource intensive. Future evaluations should continue to assemble evidence relating to areas of expected usage, patient numbers over time and comparative effectiveness and safety. Projections of patient numbers could be greatly enhanced by the development of national level linked clinical, prescribing and laboratory data. A practical approach to synthesising these data would be to combine expert assessments of key parameters with a simple generic decision model.

Background: The majority of cost-effectiveness analyses of pharmaceuticals do not incorporate future price changes that are expected once generic competition enters the market. A common rationale for not doing so is the uncertainty around what postloss of exclusivity price to model. The objective of this study is to assess if a drug's price postloss of exclusivity can be estimated on the basis of its cost of goods sold (COGS).

Methods: First, stakeholders were engaged to understand pricing practices for generic drugs. Then peer-reviewed literature, gray literature, and manufacturer financial statements were reviewed to estimate the typical manufacturer profit margin over COGS. Using pricing data from the Mark Cuban CostPlus Drug Company (MCCPDC), estimates of COGS were calculated by drug form. Finally, a COGS-based approach to estimating a drug's price postloss of exclusivity was tested.

Results: COGS were estimated for 2168 unique National Drug Codes (NDCs) reported in the MCCPDC price list by removing the typical manufacturer profit margin (50%) from the manufacturer prices (net of any markup or fees from the MCCPDC). A tablet/capsule, the most common form in the price list, had a median COGS of $0.10 per tablet/capsule. Estimating a drug's price postloss of exclusivity as the median COGS for that drug form times two (to reflect a 50% profit margin), produces estimates of postloss of exclusivity prices that account for drug-specific attributes.

Conclusions: This study provides an evidence-based approach to estimate a drug's price postloss of exclusivity as a function of its COGS for incorporation into cost-effectiveness analyses.

Objective: Genomic risk stratification methods for myeloid malignancies have moved beyond conventional karyotyping and single gene approaches to better define disease behaviour. Next-generation sequencing has been established as the new standard-of-care tool to accurately define prognosis at diagnosis and guide therapy decisions. We aimed to determine the economic value of a 37-gene panel test for informing subsequent care for patients with intermediate-risk myeloid malignancies.

Method: We performed an exploratory cost-utility analysis of a 37-gene panel test to inform stem cell transplantation therapy in patients with myeloid malignancies in Queensland, Australia. Clinician surveys provided data on management choice with and without genomics information while both published and individual-level data were used for healthcare costs, quality of life, relapse rates and survival data. We used a decision-analytic cohort model with Markov chains and 5000 simulations to derive the incremental cost per quality-adjusted life year (QALY) gained. Scenario, one-way and probabilistic sensitivity analyses were undertaken to test input variation on the stability of the main findings.

Results: Over 10 years, the model predicted mean costs of AU$125,561 for the panel testing strategy and AU$117,045 for usual care, indicating an incremental cost of AU$8516 for panel testing. The corresponding mean QALYs were 4.52 for panel testing and 4.46 for usual care, producing a cost of AU$153,854 per QALY gained. In the Australian system, the likelihood that panel testing would be cost effective was <1 % and would have a more favourable cost-effective profile at a willingness-to-pay of AU$140,000 per QALY gained.

Conclusions: Driven by small gains in survival and relapse rates following therapies, genomic panel sequencing for myeloid malignancies in people with intermediate-risk disease is unlikely to be cost effective in Australia.

Background and objective: Mutations in KRAS and NRAS are predictive of poor response to cetuximab and panitumumab, two anti-epidermal growth factor receptor (EGFR) monoclonal antibodies used in metastatic colorectal cancer (mCRC). Our objective was to explore the value of using KRAS and NRAS mutation status to inform third-line anti-EGFR therapy for mCRC using the value of heterogeneity (VOH) framework.

Methods: We used administrative data to identify mCRC patients who were potentially eligible for third-line therapy in 2006-2019 in British Columbia (BC), Canada. We compared three alternative stratification policies in place during the study period: the unstratified policy where anti-EGFR therapy was not offered (2006-2009), stratification by KRAS mutation (2009-2016), and stratification by KRAS+NRAS mutation (2016-2019). We used inverse-probability-of-treatment weighting to balance covariates across the three groups. Cost and survival time were calculated using a 3-year time horizon and adjusted for censoring, with bootstrapping to characterize uncertainty. Mean net monetary benefit (NMB) was calculated at a range of threshold values. The VOH of using KRAS and NRAS mutation status to inform treatment selection was calculated as the change in NMB with increasing stratification, under current (static VOH) or perfect (dynamic VOH) information.

Results: We included 2664 patients in the analysis. At a willingness-to-pay of CA$100,000/ life-year gained (LYG), stratification on KRAS mutation status provided a static VOH of CA$1565 per patient; further stratification on KRAS+NRAS provided additional static VOH of CA$594. The static VOH exceeded the marginal cost of genomic testing under both policies.

Conclusions: Stratification of anti-EGFR therapy by KRAS and NRAS mutation status can provide additional value at a threshold of CA$100,000/LYG. There is diminishing marginal value and increasing marginal costs as the policy becomes more stratified. The VOH framework can illustrate the value of subgroup-specific decisions in a comprehensive way, to better inform targeted treatment policies.