Socio-demographic characteristics associated with SF-6D v2 utility scores in patients undergoing dialysis in China: contributions of the quantile regression.

Publisher: BioMed Central Country of Publication: England NLM ID: 101153626 Publication Model: Electronic Cited Medium: Internet ISSN: 1477-7525 (Electronic) Linking ISSN: 14777525 NLM ISO Abbreviation: Health Qual Life Outcomes Subsets: MEDLINE

Original Publication: [London] : BioMed Central, c2003-

Renal Dialysis*/psychology ; Quality of Life*/psychology; Humans ; Male ; Female ; China ; Middle Aged ; Adult ; Aged ; Surveys and Questionnaires ; Socioeconomic Factors ; Kidney Failure, Chronic/therapy ; Regression Analysis

Competing Interests: Declarations. Ethics approval and consent to participate: This study was approved by the Ethics Committee for Research at Renmin University of China. Informed consent was obtained from all participants prior to their inclusion in the study. Participants were provided with full information about the study’s purpose, procedures, and their rights, ensuring voluntary participation. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.
Background: Generic preference-based instruments, such as the Short Form 6-Dimensions (SF-6D) and EuroQol 5-Dimensions (EQ-5D), can generate utility scores that facilitate the estimation of health-related quality of life (HRQoL) which is commonly used in cost-utility analysis. This study investigated the associations between utility scores and potential socio-demographic factors in Chinese patients with dialysis using quantile regression.
Methods: Patients were recruited in a multicenter survey conducted between November 2023 and January 2024 for dialysis patients in China. Patient responses to the SF-6D version 2 (SF-6Dv2) instruments were used to calculate utility scores. The relationships between utility scores and potential socio-demographic factors were examined using both ordinary least squares (OLS) and quantile regression models. The Wald test was employed to test the differences in coefficients across quantiles in quantile regression. Model performance was assessed using 5-fold cross-validation.
Results: A total of 378 patients were included. Age, education level, having a loan due to illness, currently working, monthly income > 8000 RMB and number of comorbidities were associated with utility scores. The quantile regression coefficients and Wald test suggested that the size of the associations between the utility scores and factors varied along with the utility score distribution. Quantile regression yielded more accurate fitted and predicted values compared to OLS regression.
Conclusion: Quantile regression offers a valuable complement in analyzing factors associated with utility scores among Chinese dialysis patients. For policymakers, differentiated nonclinical strategies may be needed to improve HRQoL across varying health states within this population.
(© 2025. The Author(s).)

Pharmacoeconomics. 2021 May;39(5):521-535. (PMID: 33598860)
JAMA Intern Med. 2023 Apr 1;183(4):298-310. (PMID: 36804760)
Ann Med. 2001 Jul;33(5):375-84. (PMID: 11491197)
Healthcare (Basel). 2022 Oct 16;10(10):. (PMID: 36292490)
Health Qual Life Outcomes. 2020 Aug 3;18(1):266. (PMID: 32746855)
Qual Life Res. 2017 Nov;26(11):3035-3048. (PMID: 28638967)
Int Urol Nephrol. 2022 Jan;54(1):165-172. (PMID: 33772421)
Qual Life Res. 2020 May;29(5):1385-1391. (PMID: 31950328)
Front Public Health. 2021 Jun 29;9:675523. (PMID: 34268287)
Eur J Health Econ. 2015 Dec;16(9):1019-26. (PMID: 25519850)
Eur J Health Econ. 2010 Apr;11(2):215-25. (PMID: 19585162)
Rev Lat Am Enfermagem. 2012 Sep-Oct;20(5):838-46. (PMID: 23174827)
Health Qual Life Outcomes. 2023 Apr 17;21(1):36. (PMID: 37069562)
BMC Nephrol. 2021 Aug 16;22(1):280. (PMID: 34399714)
Indian J Palliat Care. 2020 Jan-Mar;26(1):47-53. (PMID: 32132784)
Curr Opin Nephrol Hypertens. 2010 Mar;19(2):153-9. (PMID: 20051850)
BMJ Open. 2020 Aug 6;10(8):e040286. (PMID: 32764092)
Am J Epidemiol. 2013 Dec 15;178(12):1681-6. (PMID: 24077092)
Am J Kidney Dis. 1997 Apr;29(4):584-92. (PMID: 9100049)
Health Qual Life Outcomes. 2020 Apr 23;18(1):106. (PMID: 32326945)
Surgery. 2017 Dec;162(6):1278-1285. (PMID: 28923697)
Clin J Am Soc Nephrol. 2020 Feb 1;15(2):162-164. (PMID: 37095653)
BMC Psychol. 2025 May 12;13(1):498. (PMID: 40355945)
Clin Psychol Psychother. 2024 Nov-Dec;31(6):e70022. (PMID: 39581845)
BMC Nephrol. 2023 Oct 26;24(1):316. (PMID: 37884858)
Am J Epidemiol. 2014 Aug 1;180(3):330-1. (PMID: 24989240)
Health Econ. 2010 Jun;19(6):683-96. (PMID: 19504545)
Eur J Health Econ. 2019 Nov;20(8):1195-1206. (PMID: 31338698)
Pharmacoeconomics. 2013 Feb;31(2):163-71. (PMID: 23329427)
Am J Kidney Dis. 2021 Jun;77(6):889-897.e1. (PMID: 33421457)
J Clin Nurs. 2021 Jun;30(11-12):1787-1796. (PMID: 33656217)

Keywords: Dialysis; Health-related quality of life; Quantile regression; SF-6Dv2; Utility scores

Date Created: 20250729 Date Completed: 20250730 Latest Revision: 20250731

20250731

PMC12305961

10.1186/s12955-025-02401-y

40722091