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Biomarkers, disability and health care demand

Author

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  • Apostolos Davillas
  • Stephen Pudney

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 health affects future health service utilisation very strongly, via functional disability as a mediating outcome. Our model reveals that observed income inequality in the access to health care, is driven by the fact that higher-income people tend to make greater use of healthcare treatment, for any given health and disability status. This leads to a slight rise in utilisation with income, despite the lower average need for treatment shown by the negative income gradients for both baseline health and disability outcomes. 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.

Suggested Citation

  • Apostolos Davillas & Stephen Pudney, 2020. "Biomarkers, disability and health care demand," Health, Econometrics and Data Group (HEDG) Working Papers 20/06, HEDG, c/o Department of Economics, University of York.
  • Handle: RePEc:yor:hectdg:20/06
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    Cited by:

    1. Davillas, Apostolos & Jones, Andrew M., 2025. "Biological age and predicting future health care utilisation," Journal of Health Economics, Elsevier, vol. 99(C).
    2. 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.
    3. 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.
    4. 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).
    5. 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).
    6. Aizawa, Toshiaki & Okudaira, Hiroko & Kitagawa, Ritsu & Kuroda, Sachiko & Owan, Hideo, 2024. "Employee well-being in the digital age: Assessing the impacts of a smartphone application in the workplace," Economics & Human Biology, Elsevier, vol. 55(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.
    8. Davillas, Apostolos & Pudney, Stephen, 2020. "Biomarkers as precursors of disability," Economics & Human Biology, Elsevier, vol. 36(C).

    More about this item

    Keywords

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    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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