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Biomarkers as precursors of disability

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  • Davillas, Apostolos
  • Pudney, Stephen

Abstract

Some social surveys now collect physical measurements and markers derived from biological samples, in addition to self-reported health assessments. This information is expensive to collect; its value in medical epidemiology has been clearly established, but its potential contribution to social science research is less certain. We focused on disability, which results from biological processes but is defined in terms of its implications for social functioning and wellbeing. Using data from waves 2 and 3 of the UK Understanding Society panel survey as our baseline, we estimated predictive models for disability 2-4 years ahead, using a wide range of biomarkers in addition to self-assessed health (SAH) and other socio-economic covariates. We found a quantitatively and statistically significant predictive role for a large set of nurse-collected and blood-based biomarkers, over and above the strong predictive power of self-assessed health. We also applied a latent variable model accounting for the longitudinal nature of observed disability outcomes and measurement error in in SAH and biomarkers. Although SAH performed well as a summary measure, it has shortcomings as a leading indicator of disability, since we found it to be biased in the sense of over- or under-sensitivity to certain biological pathways.

Suggested Citation

  • Davillas, Apostolos & Pudney, Stephen, 2018. "Biomarkers as precursors of disability," ISER Working Paper Series 2018-11, Institute for Social and Economic Research.
  • Handle: RePEc:ese:iserwp:2018-11
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    Cited by:

    1. Davillas, Apostolos & Pudney, Stephen, 2020. "Biomarkers, disability and health care demand," ISER Working Paper Series 2020-04, Institute for Social and Economic Research.

    More about this item

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • I10 - Health, Education, and Welfare - - Health - - - General

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