Quality of Life Research

, Volume 19, Issue 6, pp 853–864 | Cite as

Discriminative capacity of the EQ-5D, SF-6D, and SF-12 as measures of health status in population health survey

  • Oriol Cunillera
  • Ricard Tresserras
  • Luis Rajmil
  • Gemma Vilagut
  • Pilar Brugulat
  • Mike Herdman
  • Anna Mompart
  • Antonia Medina
  • Yolanda Pardo
  • Jordi Alonso
  • John Brazier
  • Montse Ferrer
Article

Abstract

Purpose

To compare the EQ-5D, SF-6D, and SF-12 in terms of their capacity to discriminate between groups defined by relevant socio-demographic and health characteristics in a general population survey.

Methods

Data were obtained from the 2006 Catalan Health Interview Survey, a representative sample (n = 4,319) of the general population of Catalonia (Spain). Effect sizes (ES) and Receiver Operating Characteristic (ROC) curves were calculated to evaluate the instruments’ capacity to distinguish between groups based on socio-demographic variables, recent health problems, perceived health, psychological distress, and selected chronic conditions.

Results

All instruments showed a similar discriminative capacity between groups based on socio-demographic variables, recent medical visit (ES = 0.47–0.55), activity limitations (ES = 0.92–0.98), perceived health (ES = 0.97–1.33), and psychological well-being (ES = 1.17–1.57). Effect sizes between respondents with and without any of fourteen selected chronic conditions were large (0.76–1.04) for 4, moderate (0.55–0.74) for 8, and small (0.17–0.39) for two on the EQ-5D index. A similar pattern was observed for the SF-12 but ES were predominantly moderate (7 conditions) or small (6 conditions) on the SF-6D.

Conclusions

The EQ-5D and SF-12 were largely comparable in estimating the health burden of chronic conditions, recent health problems, and social inequalities. The SF-6D was less sensitive than the EQ-5D index and SF-12, particularly for physical chronic conditions.

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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Oriol Cunillera
    • 1
    • 2
  • Ricard Tresserras
    • 3
  • Luis Rajmil
    • 1
    • 2
    • 4
  • Gemma Vilagut
    • 1
    • 2
  • Pilar Brugulat
    • 3
  • Mike Herdman
    • 1
    • 2
  • Anna Mompart
    • 3
  • Antonia Medina
    • 3
  • Yolanda Pardo
    • 2
  • Jordi Alonso
    • 1
    • 2
    • 5
  • John Brazier
    • 6
  • Montse Ferrer
    • 7
    • 8
    • 9
  1. 1.CIBER en Epidemiología y Salud Pública (CIBERESP)BarcelonaSpain
  2. 2.Institut Municipal d’Investigació Mèdica (IMIM-Hospital del Mar), Health Services Research UnitBarcelonaSpain
  3. 3.Direcció General de Planificació i Avaluació del Departament de Salut de la Generalitat de CatalunyaBarcelonaSpain
  4. 4.Catalan Agency for Health Technology Assessment and ResearchBarcelonaSpain
  5. 5.Universitat Pompeu FabraBarcelonaSpain
  6. 6.Sheffield Health Economics GroupUniversity of SheffieldSheffieldUK
  7. 7.Institut Municipal d’Investigació Mèdica (IMIM-Hospital del Mar), Research Unit (IMIM-Hospital del Mar)BarcelonaSpain
  8. 8.CIBER en Epidemiología y Salud Pública (CIBERESP), Health ServicesBarcelonaSpain
  9. 9.Universitat Autònoma de BarcelonaBarcelonaSpain

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