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Biomarkers, disability and health care demand

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  • Davillas, Apostolos
  • Pudney, Stephen

Abstract

Using longitudinal data from a representative UK panel, we focus on a group of apparently healthy individuals with no history of disability or major chronic health condition at baseline. A latent variable structural equation model is used to analyse the predictive role of latent baseline biological health, indicated by a rich set of biomarkers, and other personal characteristics, in determining the individual’s disability state and health service utilisation five years later. We find that baseline biological health affects future health service utilisation very strongly, via progression to functional disability channel. We also find systematic income gradients in future disability risks, with those of higher income experiencing a lower progress to disability. Our model reveals that observed pro-rich inequity in health care utilisation, is driven by the fact that higher-income people tend to make greater use of health care treatment, for any given biological health and disability status; this is despite the lower average need for treatment shown by the negative association of income with both baseline ill biological health and disability progression risk. Factor loadings for latent baseline health show that a broader set of blood-based biomarkers, rather than the current focus mainly on blood pressure, cholesterol and adiposity, may need to be considered for public health screening programs.

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  • Davillas, Apostolos & Pudney, Stephen, 2020. "Biomarkers, disability and health care demand," Economics & Human Biology, Elsevier, vol. 39(C).
  • Handle: RePEc:eee:ehbiol:v:39:y:2020:i:c:s1570677x20301994
    DOI: 10.1016/j.ehb.2020.100929
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    Cited by:

    1. Davillas, Apostolos & Pudney, Stephen, 2020. "Biomarkers as precursors of disability," Economics & Human Biology, Elsevier, vol. 36(C).
    2. Davillas, Apostolos & Jones, Andrew M., 2025. "Biological age and predicting future health care utilisation," Journal of Health Economics, Elsevier, vol. 99(C).
    3. 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).
    4. Davillas, Apostolos & M. Jones, Andrew, 2024. "Biological age and predicting future health care utilisation," ISER Working Paper Series 2024-03, Institute for Social and Economic Research.
    5. 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.
    6. 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).
    7. Davillas, Apostolos & Pudney, Stephen, 2017. "Concordance of health states in couples: Analysis of self-reported, nurse administered and blood-based biomarker data in the UK Understanding Society panel," Journal of Health Economics, Elsevier, vol. 56(C), pages 87-102.

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    More about this item

    Keywords

    Health services; Health care demand; Biomarkers; Disability;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • I10 - Health, Education, and Welfare - - Health - - - General
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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