Predictors of health state utility values using SF-6D for Chinese adult patients with β-thalassemia major
Bibliographic Data
| ID | 22080909 |
|---|---|
| Authors | Runqi Zhang (0000-0003-1588-6345, Shandong University), Shuo Zhang (0000-0002-7812-4260, Shandong University), Jing Ming (0000-0001-5527-3768, Shandong University), Jing Xie (0000-0001-8156-0322, Shandong University), Baoguo Liu (0000-0002-5534-9598, Shandong University), Cuiqian Chen, Xiaojie Sun (0000-0002-3693-3203, Shandong University, corresponding author), Xuemei Zhen (0000-0002-1071-7326, Shandong University, corresponding author) |
| Year | 2023 |
| Volume | 10 |
| Pages | 1072866-1072866 |
| Publication date | 2023-01-19 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2022.1072866 |
| PMID | 36743186 |
| OpenAlex | W4317399515 |
| Language | EN |
| References cited | 55 |
Background Patients with β-thalassemia major (β-TM), predominantly adult patients, are associated with physical, mental, and social problems, that result in decreased quality of life (QoL). However, there is a paucity of data on QoL and health status utility (HSU) among adult patients with β-TM in mainland China. Our study aimed to evaluate the QoL by short form 36 questionnaire (SF-36) of adult patients with β-TM in mainland China and to estimate their HSU by SF-6D. In addition, we aimed to identify predictors of HSU. Methods In this cross-sectional descriptive study, a total of 75 adult patients with β-TM were included by the snowball sampling method that applied involving seven provinces with a relatively high prevalence of thalassemia across mainland China between September 1, 2021 and January 31, 2022. The collected information included social-demographic characteristics, health conditions, treatment, social support (social support rating scale), caregiver burden (Zarit burden interview), and QoL (SF-36). HSU scores were calculated for each adult patient from their SF-36 responses using the SF-6D algorithm with Hong Kong's tariff. The frequency of participants' responses to the SF-6D for each item of the options was described. Mean HSU scores between different subgroups were calculated. Ordinary least squares (OLS) regression modeling was performed to identify factors associated with HSU. Results A total of 75 adult patients with β-TM were included in this study. The mean SF-36 score was 50.2 ± 10.70, of which physical and mental scores were 47.57 ± 11.28 and 52.85 ± 14.21, respectively. In addition, the mean SF-6D utility score was estimated to be 0.598 ± 0.112, ranging from 0.391 to 0.962. Univariate analyses showed that interruption of iron chelation treatment significantly affected HSU values ( P = 0.038); diagnosis with comorbidity very slightly affected HSU values ( P = 0.0996). In the multivariate analysis, diagnosis with comorbidity ( P = 0.042) was significantly negatively associated with HSU values; the minimum pre-transfusion hemoglobin concentration ( P = 0.047) and social support ( P = 0.068) were positively associated with HSU values. Conclusion This study presents poor QoL and HSU outcomes in Chinese adult patients with β-TM. The study also highlights the importance of social support and treatment compliance, which can increase hemoglobin content and reduce comorbidities, further to ensure the QoL of patients. These findings can be used for future clinical and economic studies
Beta thalassemia · Thalassemia · Childhood Cancer Survivors' Quality of Life · Chronic Disease Management Strategies · Hemoglobinopathies and Related Disorders · Medicine · Internal Medicine · Pediatrics
Deriving a Preference-Based Single Index from the UK SF-36 Health Survey
A comparison of the EQ‐5D and SF‐6D across seven patient groups
The estimation of a preference-based measure of health from the SF-36
Relatives of the Impaired Elderly
Quality of life in Sardinian patients with transfusion-dependent Thalassemia
Modeling and variable selection in epidemiologic analysis
Social Support
| Highly cited | No |
|---|