Applied Health Economics and Health Policy最新文献

Background: Sequential next-generation sequencing (NGS) testing has demonstrated cost-effectiveness in guiding targeted therapy with tyrosine kinase inhibitors (TKIs) for advanced non-small cell lung cancer (aNSCLC) in developed countries. However, its cost-effectiveness in developing countries remains uncertain.

Objective: The aim was to conduct a cost-utility analysis comparing sequential NGS testing with the current approach of single epidermal growth factor receptor (EGFR) testing combined with first-line targeted therapy, as implemented under Thailand's Universal Health Coverage scheme for aNSCLC.

Method: Hybrid decision tree and Markov models were developed to estimate the lifetime costs and quality-adjusted life years (QALYs) associated with each strategy. The models simulate cohorts of aNSCLC patients who receive platinum-based chemotherapy or TKIs based on identified gene alterations. Patients enter the model at 60 years of age. The incremental cost-effectiveness ratio (ICER) was computed from a societal perspective. The analysis employed a lifetime horizon and discounted costs and outcomes at a rate of 3%. Furthermore, uncertainty and scenario analyses were conducted.

Findings: A sequential NGS testing strategy could identify an additional 19% of patients with biomarker-positive findings who subsequently received biomarker-driven targeted therapy compared to a single EGFR testing strategy. The number needed to screen to identify a single gene mutation and administer first-line TKI was six for the sequential NGS testing strategy. Compared to the single EGFR testing, the ICER of the sequential NGS testing strategy was 1,851,150 THB/QALY (US$51,335). At a willingness-to-pay threshold of 160,000 THB/QALY (US$4437), the single EGFR testing strategy demonstrated 100% cost-effectiveness. In contrast, the sequential NGS testing was not deemed cost-effective. The sensitivity of the ICER was influenced by the overall survival rates associated with anaplastic lymphoma kinase (ALK) inhibitors and platinum-based chemotherapy.

Interpretation: Sequential NGS testing identified a greater number of patients with aNSCLC eligible for targeted therapies, resulting in improved survival rates and enhanced QALYs compared to single EGFR testing. However, in the context of Thailand, sequential NGS testing was not cost-effective. The single EGFR testing strategy emerged as the most cost-effective option for guiding first-line targeted therapy.

Background: The quality-adjusted life year (QALY) is widely used to measure health outcome that combines the length of life and health-related quality of life (HRQoL). To be a reliable QALY measure, HRQoL measurements with a preference-based scoring algorithm need to be converted into health utilities on a scale from zero (dead) to one (perfect health). However, preference-based health utility data are often not available. We address this gap by developing a predictive model for health utilities.

Objectives: To develop a predictive model for health utilities using available demographic and morbidity variables in a health administrative dataset for non-institutionalised populations in Ontario, Canada.

Methods: The data were obtained from the 2009 to 2010 Canadian Community Health Survey containing Health Utilities Index Mark3 (HUI3), a generic multi-attribute preference-based health utility instrument linked with Ontario health administrative (OHA) data that were collected for administrative or billing purposes for patient encounters with the health care system. We employed four regression models (linear, Tobit, single-part beta mixture, and two-part beta mixture) and a calibration technique to identify the best-fit regression model.

Results: Our findings indicate that the two-part beta mixture model is the best-fit for predicting health utilities in the OHA data. The proposed predictive model reflects the original distribution of HUI3 in the population.

Conclusion: Our proposed predictive model generates reasonably accurate health utility predictions from OHA data. Our model-based prediction approach is a useful strategy for real-world applications, particularly when preference-based utility data are unavailable.

Introduction: Genomic medicine has features that make it preference sensitive and amenable to model-based health economic evaluation. Preferences of patients, caregivers, and clinicians related to the uptake and delivery of genomic medicine technologies and services that are not captured in health state utility weights can affect the intervention's cost-effectiveness and budget impact. However, there is currently no established or agreed-on approach for integrating preference information into economic evaluations. The objective of this study was to explore approaches for incorporating preferences into model-based economic evaluations of genomic medicine and to develop a conceptual framework to consider preferences in health economic models.

Methods: We conducted a critical interpretive synthesis of published literature guided by the following question: how have preferences been incorporated into model-based economic evaluations of genomic medicine interventions? We integrated findings from the literature and expert opinion to develop a conceptual framework of ways in which preferences influence economic value in the context of genomic medicine.

Results: Our synthesis included 14 articles. Revealed and stated preference data were used to estimate choice probabilities and to value outcomes. Our conceptual framework situates preference data in the context of health system, patient, clinician, and family characteristics. Preference data were sourced from clinicians, patients and families impacted by a condition or intervention, and the general public. Evaluations employed various types of models, including discrete event simulation, microsimulation, Markov, and decision tree models.

Conclusion: When evaluating the broad benefits and costs of implementing new interventions, sufficiently accounting for preferences in the form of model inputs and valuation of outcomes in economic evaluations is important to avoid biased implementation decisions. Incorporation of preference data may improve alignment between predicted and real-world uptake and more accurately estimate welfare impacts, and this study provides critical insights to support researchers who seek to incorporate preference information into model-based health economic evaluations.

Background: Non-invasive prenatal testing has the potential to be a useful genetic screening tool in Australia. However, concerns have been raised about its cost, commercial provision, the psychological impacts of the screening process, and disparities in access experienced by rural and regional communities.

Aims: The aims of this study are (1) to estimate Australian preferences for features of prenatal screening; (2) to explore potential variations in preferences between metropolitan and rural/regional communities; (3) to estimate the extent to which respondents are willing to trade-off between attributes, using willingness to pay (WTP) and willingness to wait estimates.

Methods: A discrete choice experiment (DCE) was conducted with 12 choice tasks. The DCE recruited participants from metropolitan (n = 160) and rural/regional (n = 168) locations across Australia. Mixed logit and latent class analyses were conducted and WTP and willingness to wait were calculated.

Results: Both metropolitan and rural/regional preferences were significantly impacted by the false-positive rate, false-negative rate, and cost. In addition, rural preferences were significantly impacted by the scope of the conditions covered, the inconclusive rate, and wait times. The number of screening tests and revealing the sex of the foetus were not significant within either group. Willingness to pay estimates ranged from AU$13 to avoid a test with a 1% increase in the false-positive rate to AU$323 to screen for a wide range of conditions.

Conclusions: This study highlights the importance of considering differing preferences between rural and metropolitan populations when delivering prenatal screening. Further, this study provides Australian-specific WTP estimates to be incorporated into economic evaluations.

Introduction: Healthcare payers in the USA increasingly cover genetic testing, including exome sequencing (ES), for pediatric indications. Analysis of claims data enables understanding of utilization and costs in real-world settings. The objective of this study was to describe genetic test utilization, diagnostic outcomes, and costs for children who received ES as well as for those who received less comprehensive forms of genetic testing, along with their families.

Patients and methods: We analyzed linked family claims data for commercially insured members of a large regional health plan. The sample included children younger than 18 years of age who had at least 1 year of continuous plan enrollment and at least one claim for genetic testing from 2016 to 2022, as well as their family members. We compared outcomes for children who ever had a claim for ES (ES cohort) with those for children who had claims for only less comprehensive genetic testing (other genetic testing (OGT) cohort). We evaluated the frequency of ICD-10 codes indicating genetic diagnoses, health care utilization, and out-of-pocket costs in relation to the timing of the index genetic test using t-tests and inverse-probability-of-treatment weighted regression models to control for observable clinical and demographic characteristics associated with type of testing received.

Results: Our sample included 182 children (mean comorbidity index 4.78) in the ES cohort and 1789 children in the OGT cohort (3.63; p < 0.001). ES led to an average of 1.44 (95% confidence interval [CI] 0.67-2.20) more new genetic diagnostic codes after testing than OGT. A larger proportion of the proband's family members had subsequent genetic testing in the ES cohort (mean 33.3%) than in the OGT cohort (0.5%; p < 0.001), but no differences in the number of new genetic diagnoses in family members were observed. Out-of-pocket costs for genetic testing did not differ between the two cohorts stratified by clinical severity.

Conclusions: In our sample of commercially insured pediatric patients, claims for ES were less frequent and occurred among children with more clinical complexity than those for less comprehensive genetic testing. Children in the ES cohort had a higher number of new genetic diagnoses post-testing than those in the OGT cohort with no significant differences in out-of-pocket cost of testing to families. Our findings suggest that ES is being reimbursed for children who may be difficult to diagnose.