학술논문

Socio-demographic indicators of self-reported health based on EQ-5D-3L: A cross-country analysis of population surveys from 18 countries
Document Type
article
Source
Frontiers in Public Health, Vol 10 (2023)
Subject
EQ-5D-3L
EuroQol
health-related quality of life
self-reported health
population health
health inequalities
Public aspects of medicine
RA1-1270
Language
English
ISSN
2296-2565
Abstract
BackgroundGeneric health-related quality of life instruments, such as the EQ-5D, are increasingly used by countries to monitor population health via general population health surveys. Our aim was to demonstrate analytic options to measure socio-demographic differences in self-reported health using the EuroQol Group's archive of EQ-5D-3L population surveys that accumulated over the past two decades.MethodsAnalyses captured self-reported EQ-5D-3L data on over 100,000 individuals from 18 countries with nationally representative population surveys. Socio-demographic indicators employed were age, sex, educational level and income. Logistic regression odds ratios and the health concentration index methodology were used in the socio-demographic analysis of EQ-5D-3L data.ResultsStatistically significant socio-demographic differences existed in all countries (p < 0.01) with the EQ VAS based health concentration index varying from 0.090 to 0.157 across countries. Age had generally the largest contributing share, while educational level also had a consistent role in explaining lower levels of self-reported health. Further analysis in a subset of 7 countries with income data showed that, beyond educational level, income itself had an additional significant impact on self-reported health. Among the 5 dimensions of the EQ-5D-3L descriptive system, problems with usual activities and pain/discomfort had the largest contribution to the concentration of overall self-assessed health measured on the EQ VAS in most countries.ConclusionThe EQ-5D-3L was shown to be a powerful multi-dimensional instrument in the analyses of socio-demographic differences in self-reported health using various analytic methods. It offered a unique insight of inequalities by health dimensions.