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Educational inequalities in Global Activity Limitation Indicator disability in 28 European Countries: Does the choice of survey matter?

Author

Listed:
  • Jose R. Rubio-Valverde

    (Erasmus Medical Center)

  • Wilma J. Nusselder

    (Erasmus Medical Center)

  • Johan P. Mackenbach

    (Erasmus Medical Center)

Abstract

Objectives To assess the sensitivity of prevalence and inequality estimates of Global Activity Limitation Indicator (GALI) to the choice of survey in European countries. Methods We use logistic regression to estimate adjusted risk ratios, quantifying differences in prevalence and educational inequalities, the impact of survey characteristics and Kendall’s tau to assess similarity in country rankings between surveys. We include the European Health Interview Survey (EHIS), European Social Survey (ESS) and European Union Statistics on Income and Living Conditions (EU-SILC). Results EHIS estimates higher prevalence than EU-SILC 17% (men) and 23% (women), and ESS 24% (men) and 29% (women). Prevalence does not differ significantly between EU-SILC and ESS. EU-SILC estimates 52.5% (men) and 28.1% (women) higher inequalities than EHIS and 63.2% (men) and 32.7% (women) higher inequalities than ESS. Survey characteristics do not account for differences in prevalence or inequalities. Country rankings do not agree for prevalence or inequalities. Conclusions Survey choice strongly impacts estimates of GALI prevalence and educational inequalities. Further study is necessary to understand these discrepancies. Caution is required when using these surveys for cross-country comparisons of (educational inequalities in) GALI disability.

Suggested Citation

  • Jose R. Rubio-Valverde & Wilma J. Nusselder & Johan P. Mackenbach, 2019. "Educational inequalities in Global Activity Limitation Indicator disability in 28 European Countries: Does the choice of survey matter?," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 64(3), pages 461-474, April.
    • Handle: RePEc:spr:ijphth:v:64:y:2019:i:3:d:10.1007_s00038-018-1174-7
    DOI: 10.1007/s00038-018-1174-7
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    References listed on IDEAS

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    1. Christophe Croux & Catherine Dehon, 2010. "Influence functions of the Spearman and Kendall correlation measures," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(4), pages 497-515, November.
    2. Margarete Kulik & Terje Eikemo & Enrique Regidor & Gwenn Menvielle & Johan Mackenbach, 2014. "Does the pattern of educational inequalities in smoking in Western Europe depend on the choice of survey?," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 59(4), pages 587-597, August.
    3. Edward C. Norton & Morgen M. Miller & Lawrence C. Kleinman, 2013. "Computing adjusted risk ratios and risk differences in Stata," Stata Journal, StataCorp LP, vol. 13(3), pages 492-509, September.
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    Cited by:

    1. Sonja Spitzer, 2020. "Biases in health expectancies due to educational differences in survey participation of older Europeans: It’s worth weighting for," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(4), pages 573-605, June.
    2. María-José Foncubierta-Rodríguez & Rafael Ravina-Ripoll & José Antonio López-Sánchez, 2021. "Generational Portrait of Spanish Society in the Face of Climate Change. A Question to Consider for the Green Economy under the Well-Being Approach," Energies, MDPI, vol. 14(4), pages 1-14, February.
    3. Amilon, Anna & Hansen, Kasper M. & Kjær, Agnete Aslaug & Steffensen, Tinne, 2021. "Estimating disability prevalence and disability-related inequalities: Does the choice of measure matter?," Social Science & Medicine, Elsevier, vol. 272(C).
    4. Alaimo, Leonardo Salvatore & Levantesi, Susanna & Nigri, Andrea, 2024. "Fuzzy clustering of the healthy life expectancy decomposition: A multi-population analysis," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).

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