Quantifying the Effect of Financial Burden on Health-Related Quality of Life among Patients with Non-Hodgkin's Lymphomas
Cancers(2020)SCI 2区SCI 3区
Chinese Univ Hong Kong
Abstract
Objective: This study aimed to assess the association of health-related quality of life (HRQoL) with financial burden among patients with non-Hodgkin’s lymphoma (NHL) in China. Methods: The data used for the analyses came from a nationwide survey to investigate the health status of patients with lymphomas in China. The EQ-5D and EORTC QLQ-C30 were used to assess the patients’ HRQoL. The financial burden was calculated using both subjective and objective methods. The chi-squared test, Kruskal–Wallis one-way analysis of variance, ordinal least squared model, and Tobit regression model were used to estimate the relationship between financial burden and HRQoL. Results: Data from 1549 patients who reported living with 11 subtypes of NHL were elicited for our analysis. Approximately 60% of respondents reported suffering moderate to high financial burdens. A significant relationship between increased financial burden and reduced HRQoL scores, including the EQ-Index, physical, emotional, and social functioning, was identified. Compared with using an objective method to measure financial burden, patients with NHL indicated a poorer HRQoL when using a subjective method to measure financial burden. Conclusion: Medical professionals should select highly cost-effective treatments and ensure that patients understand the potential financial consequences of those treatments.
MoreTranslated text
Key words
non-Hodgkin’s lymphomas,health-related quality of life,financial burden,cancer
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
BMJ open 2023
被引用133
Supportive Care in Cancer 2022
被引用16
Cancers 2022
被引用2
A systematic review of financial toxicity among cancer patients in China
Asia-Pacific Journal of Oncology Nursing 2022
被引用16
Living with or Beyond Lymphoma: A Rapid Review of the Unmet Needs of Lymphoma Survivors
Psycho-oncology 2022
被引用8
Leukemia & lymphoma/Leukemia and lymphoma 2022
被引用5
EQ-5D and SF-6D Health Utility Scores in Patients with Spinal and Bulbar Muscular Atrophy
EUROPEAN JOURNAL OF HEALTH ECONOMICS 2023
被引用3
SUPPORTIVE CARE IN CANCER 2023
被引用5
Journal of Affective Disorders 2023
被引用1
Health and Quality of Life Outcomes 2023
被引用0
Psychometric Performance of EQ-5D-5L and SF-6Dv2 in Patients with Lymphoma in China.
EUROPEAN JOURNAL OF HEALTH ECONOMICS 2024
被引用2
Supportive Care in Cancer 2024
被引用0
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话