Objectives: This study aimed to assess whether recently proposed alternatives to the quality-adjusted life-year (QALY), intended to address concerns about discrimination, are suitable for informing resource allocation decisions.
Methods: We consider 2 alternatives to the QALY: the health years in total (HYT), recently proposed by Basu et al, and the equal value of life-years gained (evLYG), currently used by the Institute for Clinical and Economic Review. For completeness we also consider unweighted life-years (LYs). Using a hypothetical example comparing 3 mutually exclusive treatment options, we consider how calculations are performed under each approach and whether the resulting rankings are logically consistent. We also explore some further challenges that arise from the unique properties of the HYT approach.
Results: The HYT and evLYG approaches can result in logical inconsistencies that do not arise under the QALY or LY approaches. HYT can violate the independence of irrelevant alternatives axiom, whereas the evLYG can produce an unstable ranking of treatment options. HYT have additional issues, including an implausible assumption that the utilities associated with health-related quality of life and LYs are "separable," and a consideration of "counterfactual" health-related quality of life for patients who are dead.
Conclusions: The HYT and evLYG approaches can result in logically inconsistent decisions. We recommend that decision makers avoid these approaches and that the logical consistency of any approaches proposed in future be thoroughly explored before considering their use in practice.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.jval.2023.11.009 | DOI Listing |
Sensors (Basel)
December 2024
Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy.
Microbiol Spectr
December 2024
Department of Clinical Laboratory and Biomedical Sciences, Laboratory of Medical Microbiology and Microbiome, Division of Health Sciences, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
Prog Biomed Eng (Bristol)
September 2024
University of Coimbra, CISUC, Department of Informatics Engineering, Coimbra 3030-290, Portugal.
Mammography imaging remains the gold standard for breast cancer detection and diagnosis, but challenges in image quality can lead to misdiagnosis, increased radiation exposure, and higher healthcare costs. This comprehensive review evaluates traditional and machine learning-based techniques for improving mammography image quality, aiming to benefit clinicians and enhance diagnostic accuracy. Our literature search, spanning 2015 - 2024, identified 115 articles focusing on contrast enhancement and noise reduction methods, including histogram equalization, filtering, unsharp masking, fuzzy logic, transform-based techniques, and advanced machine learning approaches.
View Article and Find Full Text PDFPeerJ Comput Sci
December 2024
Department of Computer Science and Engineering, Konkuk University, Seoul, Republic of South Korea.
Stance detection is a critical task in natural language processing that determines an author's viewpoint toward a specific target, playing a pivotal role in social science research and various applications. Traditional approaches incorporating Wikipedia-sourced data into small language models (SLMs) to compensate for limited target knowledge often suffer from inconsistencies in article quality and length due to the diverse pool of Wikipedia contributors. To address these limitations, we utilize large language models (LLMs) pretrained on expansive datasets to generate accurate and contextually relevant target knowledge.
View Article and Find Full Text PDFIn recent years, medical sociology has produced a significant amount of publications about the effects of the COVID-19 pandemic on medical care provision and healthcare professionalism around the globe. This study builds on this line of research by looking at a rarely discussed case of pandemic management-the case of Russia's centralized and state-dominated medical sector. In our analysis, we focus on the organizational level and the institutional work of front-line health professionals.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!