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. 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. 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. 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.

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. 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. 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. 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.

This study evaluated, in a Swedish setting, the cost effectiveness of fenfluramine (FFA) as an add-on to standard of care (SoC) for reducing seizure frequency in Dravet syndrome, a severe developmental epileptic encephalopathy. Cost effectiveness of FFA+SoC compared with SoC only was evaluated using a patient-level simulation model with a lifetime horizon. Patient characteristics and treatment effects, including convulsive seizures, seizure-free days and mortality, were derived from FFA clinical trials. Resource use and costs included cost of drug acquisition, routine care and monitoring, as well as ongoing and emergency resources. Quality of life (QoL) estimates for patients and their caregivers were derived from clinical trial data. Robustness was evaluated by one-way sensitivity analysis, probabilistic sensitivity analysis and scenario analyses. Lifetime cost of FFA+SoC was ~3 million SEK per patient compared with ~1.5 million SEK for SoC only. FFA+SoC generated 15% more QALYs than SoC only (21.2 vs 18.5 over a lifetime), resulting in an incremental cost-effectiveness ratio (ICER) of ~540,000SEK. Moreover, FFA+SoC had a higher probability of being cost effective than SoC only from a willingness-to-pay threshold of 710,000SEK. Results remained generally consistent across scenario analyses, with only few exceptions (exclusions of carer utility or FFA effect on sudden unexpected death in epilepsy). Due to better seizure control, FFA is a clinically meaningful add-on therapy and was estimated to be a cost-effective addition to current SoC for patients with this rare disease in Sweden at a willingness-to-pay threshold of 1,000,000 SEK.

To develop preliminary good practice recommendations for synthesising and linking evidence of treatment effectiveness when modelling the cost-effectiveness of diagnostic tests. We conducted a targeted review of guidance from key Health Technology Assessment (HTA) bodies to summarise current recommendations on synthesis and linkage of treatment effectiveness evidence within economic evaluations of diagnostic tests. We then focused on a specific case study, the cost-effectiveness of troponin for the diagnosis of myocardial infarction, and reviewed the approach taken to synthesise and link treatment effectiveness evidence in different modelling studies. The Australian and UK HTA bodies provided advice for synthesising and linking treatment effectiveness in diagnostic models, acknowledging that linking test results to treatment options and their outcomes is common. Across all reviewed models for the case study, uniform test-directed treatment decision making was assumed, i.e., all those who tested positive were treated. Treatment outcome data from a variety of sources, including expert opinion, were utilised for linked clinical outcomes. Preliminary good practice recommendations for data identification, integration and description are proposed. Modelling the cost-effectiveness of diagnostic tests poses unique challenges in linking evidence on test accuracy to treatment effectiveness data to understand how a test impacts patient outcomes and costs. Upfront consideration of how a test and its results will likely be incorporated into patient diagnostic pathways is key to exploring the optimal design of such models. We propose some preliminary good practice recommendations to improve the quality of cost-effectiveness evaluations of diagnostics tests going forward.

About one-fifth of US adolescents experienced major depressive symptoms, but few studies have examined longitudinal trends of adolescents developing depression or recovering by demographic factors. We estimated new transition probability inputs, and then used them in a simulation model to project the epidemiologic burden and trajectory of depression of diverse adolescents by sex and race or ethnicity combinations. Transition probabilities were first derived using parametric survival analysis of data from the National Longitudinal Study of Adolescent to Adult Health and then calibrated to cross-sectional data from the National Survey on Drug Use and Health. We developed a cohort state-transition model to simulate age-specific depression outcomes of US adolescents. A hypothetical adolescent cohort was modeled from 12-22years with annual transitions. Model outcomes included proportions of youth experiencing depression, recovery, or depression-free casesand were reported for a US adolescent population by sex, race or ethnicity, and sex and race or ethnicity combinations. At 22 years of age, approximately 16% of adolescents had depression, 12% were in recovery, and 72% had never developed depression. Depression prevalence peaked around 16-17years-old. Adolescents of multiracial or other race or ethnicity, White, American Indian or Alaska Native, and Hispanic, Latino, or Spanish descent were more likely to experience depression than other racial or ethnic groups. Depression trajectories generated by the model matched well with historical observational studies by sex and race or ethnicity, except for individuals from American Indian or Alaska Native and multiracial or other race or ethnicity backgrounds. This study validated new transition probabilities for future use in decision models evaluating adolescentdepression policies or interventions. Different sets of transition parameters by demographic factors (sex and race or ethnicity combinations) were generated to support future health equity research, including distributional cost-effectiveness analysis. Further data disaggregated with respect to race, ethnicity, religion, income, geography, gender identity, sexual orientation, and disability would be helpful to project accurate estimates for historicallyminoritized communities.

Valuation surveys of preference-based measures are typically conducted face-to-face or on web panels. In this survey, we considered whether face-to-face and online surveys were reliable using three tasks: composite time trade-off (cTTO), discrete choice experiment (DCE), and DCE with duration. Respondents (aged 20-69 years) for both face-to-face (N = 1000, target sample size) and web surveys were selected through quota sampling by sex and age from each panel of the general population in Japan. They were then allocated to one of the three tasks and divided into six groups (two survey modes × three tasks, N = 334 per group). For the cTTO, respondents were asked to rate ten health states described by the EQ-5D-5L. For the DCE and DCE with duration surveys, respondents were asked about 15 health-state pairs. For all participants, as in the second-stage survey, a similar process was repeated two weeks after the first survey. Reliability was evaluated by calculating the percentage of agreement and intraclass correlation coefficients. The cTTO scores of the face-to-face and web surveys were systematically different. Between the face-to-face and web surveys, the agreement of the TTO survey was not good. The intraclass correlation coefficient (ICC) was 0.37 for the face-to-face test-test and 0.59 for the web test-retest. Discrete choice experiment (DCE) and DCE with duration had similarly good agreement (more than 70%), regardless of face-to-face or web surveys. However, between the first and second surveys (test-retest) of DCE and DCE with duration, the agreement depends on whether the positions of the two cards (health states) are identical. If the face-to-face cTTO score is the gold standard, a web-based survey of cTTO is not recommended regardless of the ICC. If a DCE survey is performed, positioning effects should be considered.

Participatory value evaluation (PVE) has recently been introduced in the field of health as a new method to elicit stated preferences for public policies. PVE is a method in which respondents in a choice experiment are presented with various policy options and their attributes, and are asked to compose their portfolio of preference given a public-resource constraint. This paper aims to illustrate PVE's potential for informing healthcare decision making and to position it relative to established preference-elicitation methods. We first describe PVE and its theoretical background. Next, by means of a narrative review of the eight existing PVE applications within and outside the health domain, we illustrate the different implementations of the main features of the method. We then compare PVE to several established preference-elicitation methods in terms of the structure and nature of the choice tasks presented to respondents. The portfolio-based choice task in a PVE requires respondents to consider a set of policy alternatives in relation to each other and to make trade-offs subject to one or more constraints, which more closely resembles decision making by policymakers. When using a flexible budget constraint, respondents can trade-off their private income with public expenditures. Relative to other methods, a PVE may be cognitively more demanding and is less efficient; however, it seems a promising complementary method for the preference-based assessment of health policies. Further research into the feasibility and validity of the method is required before researchers and policymakers can fully appreciate the advantages and disadvantages of the PVE as a preference-elicitation method.

Adding gene expression profiles (GEPs) to the current diagnostic work-up of aggressive large B-cell lymphomas may lead to the reclassification of patients, treatment changes and improved outcomes. A GEP test is in development using TempO-Seq® technology to distinguish Burkitt lymphoma (BL) and primary mediastinal large B-cell lymphoma (PMBCL) from diffuse large B-cell lymphoma (DLBCL), and to classify patients with DLBLC and to predict the benefit of (e.g.) adding bortezomib to R-CHOP therapy (RB-CHOP). This study aims to estimate the potential impact of a GEP test on costs and health outcomes to inform pricing and evidence generation strategies. Three decision models were developed comparing diagnostic strategies with and without GEP signatures over a lifetime horizon using a UK health and social care perspective. Inputs were taken from a recent clinical trial, literature and expert opinion. We estimated the maximum price of the test using a threshold of Great Britain Pound (GBP) 30,000 per quality-adjusted life-year (QALY). Sensitivity analyses were conducted. The estimated maximum threshold price for a combined test to be cost effective is GBP 15,352. At base-case values, the BL signature delivers QALY gains of 0.054 at an additional cost of GBP 275. This results in a net monetary benefit at a threshold of GBP 30,000 per QALY of GBP 1345. For PMBCL, the QALY gain was 0.0011 at a cost saving of GBP 406 and the net monetary benefit was GBP 437. The hazard ratio for the impact of treating BL less intensively must be at least 1.2 for a positive net monetary benefit. For identifying patients with the DLBCL subtype responsive to bortezomib, QALY gain was 0.2465 at a cost saving of GBP 6175, resulting in a net monetary benefit of GBP 13,570. In a probabilistic sensitivity analysis using 1000 simulations, a testing strategy was superior to a treat all with R-CHOP strategy in 81% of the simulations and with a cost saving in 92% assuming a cost price of zero. Our estimates show that the combined test has a high probability of being cost effective. There is good quality evidence for the benefit of subtyping DLBCL but the evidence on the number of patients reclassified to or from BL and PMBCL and the impact of a more precise diagnosis and the cost of treatment is weak. The developers can use the price estimate to inform a return on investment calculations. Evidence will be required of how well the TempO-Seq® technology performs compared to the testing GEP technology used for subtyping in the recent clinical trial. For BL and PMBCL elements of the test, evidence would be required of the number of patients reclassified and improved costing information would be useful. The diagnostic and therapeutic environment in haematological malignancies is fast moving, which increases the risk for developers of diagnostic tests.