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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, 2020. "Biomarkers as precursors of disability," Economics & Human Biology, Elsevier, vol. 36(C).
  • Handle: RePEc:eee:ehbiol:v:36:y:2020:i:c:s1570677x18300959
    DOI: 10.1016/j.ehb.2019.100814
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    Cited by:

    1. Davillas, Apostolos & Pudney, Stephen, 2020. "Using biomarkers to predict healthcare costs: Evidence from a UK household panel," Journal of Health Economics, Elsevier, vol. 73(C).
    2. Apostolos Davillas & Victor Hugo Oliveira & Andrew M. Jones, 2024. "A model of errors in BMI based on self-reported and measured anthropometrics with evidence from Brazilian data," Empirical Economics, Springer, vol. 67(5), pages 2371-2410, November.
    3. Davillas, Apostolos & Pudney, Stephen, 2020. "Biomarkers, disability and health care demand," Economics & Human Biology, Elsevier, vol. 39(C).
    4. Davillas, Apostolos & Pudney, Stephen, 2019. "Baseline health and public healthcare costs five years on: a predictive analysis using biomarker data in a prospective household panel," ISER Working Paper Series 2019-01, Institute for Social and Economic Research.
    5. Burlinson, Andrew & Davillas, Apostolos & Giulietti, Monica & Price, Catherine Waddams, 2024. "Household energy price resilience in the face of gas and electricity market crises," Energy Economics, Elsevier, vol. 132(C).
    6. Atkins, Rose & Turner, Alex James & Chandola, Tarani & Sutton, Matt, 2020. "Going beyond the mean in examining relationships of adolescent non-cognitive skills with health-related quality of life and biomarkers in later-life," Economics & Human Biology, Elsevier, vol. 39(C).
    7. Davillas, Apostolos & de Oliveira, Victor Hugo & Jones, Andrew M., 2023. "Is inconsistent reporting of self-assessed health persistent and systematic? Evidence from the UKHLS," Economics & Human Biology, Elsevier, vol. 49(C).
    8. Barry, L.E. & O'Neill, S. & Heaney, L.G. & O'Neill, C., 2021. "Stress-related health depreciation: Using allostatic load to predict self-rated health," Social Science & Medicine, Elsevier, vol. 283(C).

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    More about this item

    Keywords

    Biomarkers; Disability; Prediction; Self-assessed health; Understanding Society;
    All these keywords.

    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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