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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 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.

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  • Davillas, Apostolos & Pudney, Stephen, 2020. "Biomarkers, disability and health care demand," GLO Discussion Paper Series 517, Global Labor Organization (GLO).
  • Handle: RePEc:zbw:glodps:517
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    2. Davillas, Apostolos & de Oliveira, Victor Hugo & Jones, Andrew M., 2022. "Model of Errors in BMI Based on Self‐reported and Measured Anthropometrics with Evidence from Brazilian Data," CINCH Working Paper Series (since 2020) 76143, Duisburg-Essen University Library, DuEPublico.

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    More about this item

    Keywords

    Health Services; Healthcare 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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