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Objective <p>The EQ-5D is increasingly being used in studies of health inequalities, providing further evidence of a social gradient in health; i.e. consistent positive associations between a socioeconomic indicator and health. However, the steepness in the social gradients in HRQoL differs depending on which of the two EQ-5D measures is used; whether based on respondents’ EQ-5D-5L <i>descriptions</i> or by their <i>direct valuation</i> in EQ-VAS. This study aims to provide new knowledge as to why the two HRQoL measures suggest different degrees of health inequalities.</p> Methods <p>Based on two large unique data sets (Tromsø Study Wave 7, N = 21,083; MIC study, N = 8022), cross-sectional analyses were conducted. We identified the most prevalent EQ-5D-5L profiles. Within each of ten EQ-5D-5L profile groups, we examine response heterogeneities in EQ-VAS scores using linear regressions, as explained by respondents’ level of educational attainment, controlling for age and sex.</p> Results <p>We showed significantly increasing EQ-VAS scores along with educational attainments. For instance, in the most prevalent health state (11121), a consistent education-health gradient was observed: compared to individuals with primary education, the EQ-VAS was 1.7 higher among those with secondary education; 2.6 higher among individuals with short tertiary education; and, 3.7 higher among individuals with long tertiary education.</p> Conclusions <p>This paper provides new insights into the use of EQ-5D in health inequality studies by suggesting an additional underlying education gradient in HRQoL than what is revealed through the EQ-5D-5L values. Broader psychosocial domains and aspects of adaptation should be considered when monitoring health inequalities.</p>

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Measuring inequality in quality of life: Why the EQ-5D may underestimate it

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月11日

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  • Jan Abel Olsen,
  • Gang Chen,
  • Admassu Lamu

摘要

Objective

The EQ-5D is increasingly being used in studies of health inequalities, providing further evidence of a social gradient in health; i.e. consistent positive associations between a socioeconomic indicator and health. However, the steepness in the social gradients in HRQoL differs depending on which of the two EQ-5D measures is used; whether based on respondents’ EQ-5D-5L descriptions or by their direct valuation in EQ-VAS. This study aims to provide new knowledge as to why the two HRQoL measures suggest different degrees of health inequalities.

Methods

Based on two large unique data sets (Tromsø Study Wave 7, N = 21,083; MIC study, N = 8022), cross-sectional analyses were conducted. We identified the most prevalent EQ-5D-5L profiles. Within each of ten EQ-5D-5L profile groups, we examine response heterogeneities in EQ-VAS scores using linear regressions, as explained by respondents’ level of educational attainment, controlling for age and sex.

Results

We showed significantly increasing EQ-VAS scores along with educational attainments. For instance, in the most prevalent health state (11121), a consistent education-health gradient was observed: compared to individuals with primary education, the EQ-VAS was 1.7 higher among those with secondary education; 2.6 higher among individuals with short tertiary education; and, 3.7 higher among individuals with long tertiary education.

Conclusions

This paper provides new insights into the use of EQ-5D in health inequality studies by suggesting an additional underlying education gradient in HRQoL than what is revealed through the EQ-5D-5L values. Broader psychosocial domains and aspects of adaptation should be considered when monitoring health inequalities.