Value in Health最新文献

Objectives: Patients' time spent receiving care incurs an opportunity cost, which ought to be considered when conducting an economic evaluation from a societal perspective. Instruments for capturing time-related costs are presently lacking, especially for children and young people (CYP). To address this gap, we developed and pretested the Children and Young People's Time-Use Questionnaire for use in Economic Evaluation (CYP-TUQEE), producing versions for direct completion by CYP aged 11 to 17 years, and proxy completion by parents/carers of CYP aged up to 10 years.

Methods: The CYP-TUQEE was developed using an iterative process involving scoping reviews, consultation with a Working Group of experts, and pretesting through think-aloud interviews with 20 CYP and 9 parents/carers. This process aimed to produce a comprehensive, adaptable questionnaire that is not onerous to complete by CYP or parents/carers within the target age ranges.

Results: Participants engaged well with the think-aloud process and provided feedback to inform the development of a novel, standardized instrument to facilitate the collection and inclusion of resource- and time-use data for pediatric economic evaluations. Feedback indicates that the CYP-TUQEE is easy to complete, clear, and ready for additional validation.

Conclusions: The CYP-TUQEE addresses a prominent gap by providing an accessible tool for resource-use and time-use data collection, tailored to CYP. Inclusion of patient time costs can assist in decision making and ensure prioritization of interventions respectful of patients' time. Future research will involve additional testing of the CYP-TUQEE in a real-world setting for further validation and refinement, and elicitation of a value ('unit cost') for CYP's time.

Objectives: The study objectives were (1) to demonstrate the feasibility of constructing a stated-preference evidence base and its use to quantify patients' consensus risk tolerance for treatment efficacy and (2) to use the evidence base to inform a new, parsimonious choice experiment to test a hypothesis for which there is no evidence-base information.

Methods: Nine original data sets from 5 discrete-choice-experiment studies that included inflammatory bowel disease symptom-remission and serious-infection risk attributes were obtained, totaling 2247 respondents and 25 017 choice questions. All 9 data sets were pooled and fused in a single scale-adjusted, random-parameters logit, latent-class model describing risk-tolerant and risk-averse class preferences plus a statistically uninformative class. We used a 7-data set fusion model to predict maximum acceptable risk for 2 holdout data sets.

Results: Class-membership probabilities for the risk-tolerant, risk-averse, and statistically uninformative classes were 0.53, 0.35, and 0.12, respectively. Consensus maximum acceptable 1-year risks of serious infection for 1 month of symptom remission were 9.5% (8.5, 10.6) and 5.8% (4.5, 7.1) for the risk-tolerant and risk-averse preference classes, respectively. The 7-data set fusion model performed well for combined inflammatory bowel disease out-of-sample predictions but predicted disease-specific values less accurately.

Conclusions: Maturation of the stated-preference literature offers opportunities to treat multiple quantitative preference studies similar to how multiple clinical studies are evaluated to estimate consensus effect sizes. There is significant value in developing and utilizing stated-preference evidence bases to provide benefit-transfer values, as well as to identify information gaps and inform efficient de novo study designs to close those gaps.

Objectives: Health economic models for type 1 diabetes (T1D) typically require utilities or disutilities associated with diabetes-related complications. We conducted a systematic review of studies reporting utilities and disutilities associated with T1D-related complications and assessed their methodological quality to identify a set of preferred disutilities.

Methods: We searched 6 databases from inception to 30 April 2024. Data were extracted on study design, participant characteristics, complications, utility measurement methods, and reported values. Study quality was assessed based on sample size, population representativeness, appropriateness of the value sets, and statistical methods. Preferred disutilities for economic evaluations were selected from higher-quality studies.

Results: From the 14 122 records identified, 25 were included for data extraction. Most studies identified complications via self-reporting (n = 12) or clinical assessment (n = 9). Of 22 studies analyzing health utilities derived from multiattribute utility instruments, only 8 used value sets from the same countries as the study cohorts, and 14 did not report the value sets used. We derived disutilities for 66 complications/conditions. Fifteen studies used statistical models to estimate disutilities for 44 complications. Disutilities for several complications varied widely, eg, stroke (-0.470 to -0.015), end-stage renal disease (-0.340 to -0.021), and diabetic neuropathy (-0.358 to -0.045). Quality assessment yielded preferred disutilities for 26 complications.

Conclusions: This review provides a comprehensive database of utilities and disutilities for T1D complications and a recommended set of disutilities for economic evaluations. Because of methodological and patient heterogeneity, these values should be used cautiously, with careful alignment between modeled health states and source study characteristics.

Objectives: Heterogeneous treatment effects (HTEs) refer to differences in how individual patients or subgroups respond to the same treatment. Estimating HTEs helps target care to those most likely to benefit, improving outcomes and avoiding unnecessary interventions. Machine learning (ML) enables the use of real-world data (RWD) to estimate HTEs when randomized controlled trials are not feasible. However, practical guidance for applying these methods in health economics is lacking. To support method selection, we identified and categorized ML approaches to estimating HTEs in RWD and assessed the methodological quality of studies applying them.

Methods: We conducted a scoping review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. PubMed, Scopus, Web of Science, EBSCO, and MEDLINE were searched for studies published between 2014 and 2025 that applied ML to estimate HTEs from RWD. Methodological quality was assessed using a standardized checklist.

Results: Of 1743 records screened, 74 met the inclusion criteria. We grouped the included studies into 3 categories: those using prediction-only approaches unsuited to HTE estimation (n = 8), those applying outcome modeling (n = 9), and those using customized conditional average treatment effect estimation (n = 58). Most innovations originated in the ML and statistics communities, with minimal uptake in health economics. Methodological quality was inconsistent and requires improvement.

Conclusions: ML methods for HTE estimation are increasingly applied to RWD. Tree-based models are most common, and interest in customized conditional average treatment effect approaches is growing. Better evaluation standards and more transparent reporting are needed for these methods to become reliable tools for health economics research.

Objective: Introduce the rescaled value set regression for estimating EQ-5D-5L health state values as an alternative way to report the nonparametric crosswalk.

Method: The rescaled value set regression and the nonparametric crosswalk methods were applied to estimate EQ-5D-5L state values from EQ-5D-3L value sets for three example countries (United Kingdom, the Netherlands, Spain). The rescaled value set regression converted the original three-level value set regression parameters comprising dichotomous independent variables into regression parameters for the five-level version. The health state values for twenty-eight common EQ-5D-5L response profiles were then estimated by the rescaled value set regression and nonparametric crosswalk to assess whether they produced the same results using value sets from the three different countries.

Results: When applied to EQ-5D-3L value sets, the rescaled value set regression demonstrated that a level-two response and level-three response using the EQ-5D-3L, respectively, corresponded with a level-three response and level-five response using the EQ-5D-5L. The analysis of twenty-eight common EQ-5D-5L response profiles produced identical health state values for the United Kingdom, the Netherlands, and Spain's value sets under both the rescaled value set regression and nonparametric crosswalk.

Conclusion: The rescaled value set regression provides improved transparency than the nonparametric crosswalk when estimating EQ-5D-5L health state values anchored to EQ-5D-3L value sets. Both methods may be used in combination for jurisdictions where new EQ-5D-5L valuation studies are not planned but a relevant EQ-5D-3L value set is available.

Objectives: Respiratory syncytial virus is known to cause severe bronchiolitis and pneumonia among infants. There are 2 main ways to protect infants from respiratory syncytial virus (RSV)-related diseases, namely, vaccination of pregnant women with recombinant subunit RSV pre-fusion F3 (RSVpreF) and prophylaxis of neonates/infants with nirsevimab. In 2024, both products were approved in Japan. We evaluated the cost-effectiveness of multiple immunization strategies for the protection of Japanese infants from RSV diseases by using nirsevimab and/or RSVpreF.

Methods: A decision tree with Markov model was adopted. Incremental cost-effectiveness ratio (ICER) from payers' perspective was calculated. Variables used in the model were either calculated or extracted from literature. Costs per RSVpreF and nirsevimab vaccination was assumed to be at JPY23 948/US$160 and JPY45 000/US$300, respectively.

Results: Vaccination of pregnant women with RSVpreF strategies (seasonally or year-round), prophylaxis of infants with the nirsevimab strategy, and a combination of seasonally RSVpreF and nirsevimab strategies, all reduced disease treatment costs; however, the reduction could not offset the vaccination/prophylaxis cost. RSVpreF_year-round strategy and nirsevimab strategy were either extended or absolutely dominated by the other 2 strategies and were excluded from being considered as an option. The ICER of seasonal RSVpreF strategy was JPY3 227 850/US$21 519/quality-adjusted life-years (QALY), whereas the Combination strategy's ICER was JPY23 236 084/US$154 907/QALY per QALY gained. One-way sensitivity analyses revealed that the probability of hospitalization, vaccination costs, and effectiveness of both products influence the ICER the most. The cost-effectiveness acceptance curve revealed that the curve reached 98.7% at a willingness to pay of JPY5 000 000/US$33 333 per QALY.

Conclusions: Only seasonal RSVpreF strategy is cost-effective under the JPY5 000 000/US$33 333 per QALY willingness-to-pay threshold.

The argument-based approach to validation, adopted in the US Food and Drug Administration's most recent Patient-Focused Drug Development draft guidance on clinical outcome assessments (COAs), emphasizes the importance of constructing explicit rationales to support proposed interpretations of COA scores. To assist researchers and sponsors in building such rationales, we describe 2 complementary strategies: (1) reviewing steps in the assessment process to identify essential assumptions and (2) evaluating potential threats to validity from construct underrepresentation and construct irrelevance. Using these strategies, we offer initial generic rationales tailored to 4 types of COAs: patient-reported outcomes (PROs), observer-reported outcomes (ObsROs), clinician-reported outcomes (ClinROs), and performance outcome (PerfO) measures. The generic rationales serve as starting points, with the expectation that they will be adapted to specific contexts of use. Greater discussion within the field is needed to advance consensus on the construction and evaluation of evidence-based rationales, with attention to the pragmatic and iterative nature of validation work.

Objectives: Children of parents with a mental illness (COPMI) face a higher risk of developing mental disorders, leading to significant long-term societal and health-related costs. Although preventive interventions exist, few studies assess their cost-effectiveness, and, to our knowledge, none model long-term outcomes. This study aims to develop a Markov model to evaluate the cost-effectiveness of preventive interventions for COPMI in The Netherlands.

Methods: A decision-analytic model was constructed using data from the Avon Longitudinal Study of Parents and Children. The model simulates COPMI progression over time, with health states including healthy, depression/anxiety, comorbidity, remission, and death. The time horizon spans 28 years, from ages 7 to 35, and outcomes are evaluated from both healthcare and societal perspectives. Results are expressed as total costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios. A group-based cognitive behavioral therapy (GB-CBT) intervention was evaluated against a reference scenario.

Results: The preventive CBT intervention yielded an additional 0.02 QALYs at an additional cost of €188 per patient, resulting in an incremental cost-effectiveness ratio of €9495 per QALY. The intervention had a 74% probability of being cost-effective at a willingness-to-pay threshold of €20 000 per QALY.

Conclusions: The Markov model provides a flexible tool for evaluating the cost-utility of user-defined COPMI interventions to support informed decision making in mental health care. It is freely available for academic purposes from the authors upon request. Results suggest GB-CBT may be a cost-effective strategy for preventing mental disorders in COPMI.

Objectives: We build on research of Cutler et al on the value of healthcare spending using a period life-expectancy framework. We use the framework to track health-adjusted life expectancy (HALE) and lifetime spending for all ages, show the value of improvements in healthcare, and demonstrate the contribution of expanding research to the full age range.

Methods: We used population-level results on mortality and years lived with disability from the 2019 Global Burden of Disease, Injuries, and Risk Factor Study and spending from the 2016 Disease Expenditure study. We used cause replacement methods to simulate effects of changes in healthcare. For 132 causes, we replaced cause-specific outcomes (or spending) per case from 1996 with those measures for 2016; effect is the difference between base year and simulated calculations. Spending is reported in 2016 US dollars ($).

Results: For all-cause aggregate calculated at birth, lifetime spending effect was $234 111 (95% uncertainty interval (UI): 221 395, 242 456) and HALE effect was 1.285 (95% UI: 1.161, 1.422) years per person. Value of improvements is the ratio of these 2 effects, $182 201 (95% UI: 181 494, 182 912) per HALE gained. Seventy-nine (60%) causes had an increase in mean HALE and lifetime spending. Value was $9315 (95% UI: 9204, 9427) for HIV/AIDS and $63 184 (95% UI: 62 352, 64 030) for ischemic heart disease. For drug use disorders, HALE effect was -0.331 (95%UI: -0.370, -0.296), which offset other gains. Increases in HALE often occur at older ages than lifetime spending.

Conclusions: Comprehensive measures for all ages show value of healthcare by cause.