Background: Changes over time in health state values from a societal perspective may be an important reason to consider updating societal value sets for preference-based measures of health.
Objective: The aim was to examine whether stated health preferences are different between 2002 and 2017, controlling for demographic changes in the United States.
Methods: Data from 2002 and 2017 US EQ-5D-3L valuation studies were combined. The primary analysis compared valuations of better-than-dead (BTD) states only, as both studies used the same time trade-off (TTO) method for these states. For worse-than-dead (WTD) states, the 2017 study used the lead-time TTO and the 2002 study used the conventional TTO, which necessitated transformation. Regression models were fitted to BTD values to estimate time-specific differences, adjusting for respondent characteristics. Secondary analyses examined models that fitted WTD values (using linear and nonlinear transformations of the 2002 data) and all values.
Results: The adjusted BTD-only model showed mean values were higher for 2017 compared with 2002 (βY2017=0.05, P<0.001). WTD-only models showed negative changes over time but that were dependent on the transformation method (linear βY2017=-0.72; nonlinear βY2017=-0.35; both P<0.001). Using all values, 2017 mean valuations were lower using a linear transformation (βY2017=-0.11; P<0.001) but did not differ with the nonlinear transformation.
Conclusions: Individuals in 2017 are generally less willing to trade quantity for quality of life compared with 2002. This study provides evidence of time-specific differences in a society's preferences, suggesting that the era in which values were elicited may be an important reason to consider updating societal value sets.
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http://dx.doi.org/10.1097/MLR.0000000000001714 | DOI Listing |
ArXiv
December 2024
Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75235, USA.
Purpose: A reliable and comprehensive cancer prognosis model for clear cell renal cell carcinoma (ccRCC) could better assist in personalizing treatment. In this work, we developed a multi-modal ensemble model (MMEM) which integrates pretreatment clinical information, multi-omics data, and histopathology whole slide image (WSI) data to learn complementary information to predict overall survival (OS) and disease-free survival (DFS) for patients with ccRCC.
Methods And Materials: We collected 226 patients from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma dataset (TCGA-KIRC).
Front Immunol
December 2024
Microbiome-X, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
Arch Med Res
October 2024
Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia "Renzo Canestrari", Campus di Cesena, Alma Mater Studiorum Università di Bologna, Cesena, Italy; Centro de Investigación en Neuropsicología y Neurociencias Cognitivas, Universidad Católica Del Maule, Talca, Chile. Electronic address:
Background: Aging can lead to a decline in motor control. While age-related motor impairments have been documented, the underlying changes in cortico-cortical interactions remain poorly understood.
Methods: We took advantage of the high temporal resolution of dual-site transcranial magnetic stimulation (dsTMS) to investigate how communication between higher-order rostral premotor regions and the primary motor cortex (M1) influences motor control in young and elderly adults.
J Dairy Sci
November 2024
Ruminant Nutrition and Emissions, Agroscope, Posieux, Switzerland. Electronic address:
To investigate dietary influences on the volatilome, the volatile subcategory of the metabolome, we performed a comparative untargeted volatilome analysis of exhaled breath, ruminal fluid, serum, urine, and milk from lactating Holstein cows fed different diets. Thirty-two cows (79.4 ± 31.
View Article and Find Full Text PDFSci Rep
October 2024
Department of MPH, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
Identifying COVID-19 patients' clinical phenotypes based on characteristics and comorbidities, as well as their differences, helps in terms of clinical care and potential crises. Our goal is to use Latent Class Analysis (LCA) to identify COVID-19 patient profiles based on demographics, symptoms, and comorbidities, and evaluate their correlation with ICU admission, hospitalization duration, and mortality in a cross-sectional study. We included hospitalized patients with positive SARS-CoV-2 tests in two referral hospitals in the south of Iran between January 2020 and July 2021.
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