[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp17159.html
   My bibliography  Save this paper

Biological Age and Predicting Future Health Care Utilisation

Author

Listed:
  • Davillas, Apostolos

    (University of Macedonia)

  • Jones, Andrew M.

    (University of York)

Abstract
We explore the role of epigenetic biological age in predicting subsequent health care utilisation. We use longitudinal data from the UK Understanding Society panel, capitalizing on the availability of baseline epigenetic biological age measures along with data on general practitioner (GP) consultations, outpatient (OP) visits, and hospital inpatient (IP) care collected 5-12 years from baseline. Using least absolute shrinkage and selection operator (LASSO) regression analyses and accounting for participants' pre-existing health conditions, baseline biological underlying health, and socio-economic predictors we find that biological age predicts future GP consultations and IP care, while chronological rather than biological age matters for future OP visits. Post-selection prediction analysis and Shapley-Shorrocks decompositions, comparing our preferred prediction models to models that replace biological age with chronological age, suggest that biological ageing has a stronger role in the models predicting future IP care as opposed to "gatekeeping" GP consultations.

Suggested Citation

  • Davillas, Apostolos & Jones, Andrew M., 2024. "Biological Age and Predicting Future Health Care Utilisation," IZA Discussion Papers 17159, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp17159
    as

    Download full text from publisher

    File URL: https://docs.iza.org/dp17159.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Carl-Johan Dalgaard & Holger Strulik, 2014. "Optimal Aging And Death: Understanding The Preston Curve," Journal of the European Economic Association, European Economic Association, vol. 12(3), pages 672-701, June.
    3. Joan Costa‐Font & Cristina Vilaplana‐Prieto, 2020. "‘More than one red herring'? Heterogeneous effects of ageing on health care utilisation," Health Economics, John Wiley & Sons, Ltd., vol. 29(S1), pages 8-29, October.
    4. Michaela Benzeval & Edith Aguirre & Meena Kumari, 2023. "Understanding Society: health, biomarker and genetic data," Fiscal Studies, John Wiley & Sons, vol. 44(4), pages 399-415, December.
    5. Davillas, Apostolos & Pudney, Stephen, 2020. "Biomarkers, disability and health care demand," Economics & Human Biology, Elsevier, vol. 39(C).
    6. de Meijer, Claudine & Koopmanschap, Marc & d' Uva, Teresa Bago & van Doorslaer, Eddy, 2011. "Determinants of long-term care spending: Age, time to death or disability?," Journal of Health Economics, Elsevier, vol. 30(2), pages 425-438, March.
    7. Brilleman, Samuel L. & Gravelle, Hugh & Hollinghurst, Sandra & Purdy, Sarah & Salisbury, Chris & Windmeijer, Frank, 2014. "Keep it simple? Predicting primary health care costs with clinical morbidity measures," Journal of Health Economics, Elsevier, vol. 35(C), pages 109-122.
    8. Marc Carreras & Pere Ibern & José María Inoriza, 2018. "Ageing and healthcare expenditures: Exploring the role of individual health status," Health Economics, John Wiley & Sons, Ltd., vol. 27(5), pages 865-876, May.
    9. Eddy van Doorslaer & Xander Koolman & Andrew M. Jones, 2004. "Explaining income‐related inequalities in doctor utilisation in Europe," Health Economics, John Wiley & Sons, Ltd., vol. 13(7), pages 629-647, July.
    10. Anthony Shorrocks, 2013. "Decomposition procedures for distributional analysis: a unified framework based on the Shapley value," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 11(1), pages 99-126, March.
    11. Peter Zweifel & Stefan Felder & Markus Meiers, 1999. "Ageing of population and health care expenditure: a red herring?," Health Economics, John Wiley & Sons, Ltd., vol. 8(6), pages 485-496, September.
    12. Howdon, Daniel & Rice, Nigel, 2018. "Health care expenditures, age, proximity to death and morbidity: Implications for an ageing population," Journal of Health Economics, Elsevier, vol. 57(C), pages 60-74.
    13. Michael Grossman, 1972. "The Demand for Health: A Theoretical and Empirical Investigation," NBER Books, National Bureau of Economic Research, Inc, number gros72-1.
    14. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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).
    3. Davillas, Apostolos & Pudney, Stephen, 2020. "Biomarkers, disability and health care demand," Economics & Human Biology, Elsevier, vol. 39(C).
    4. Maynou, Laia & Street, Andrew & García−Altés, Anna, 2023. "Living longer in declining health: Factors driving healthcare costs among older people," Social Science & Medicine, Elsevier, vol. 327(C).
    5. Torrini, Irene & Grassetti, Luca & Rizzi, Laura, 2023. "Under-spending, over-spending or substitution among services? Spatial patterns of unexplained shares of health care expenditures," Health Policy, Elsevier, vol. 137(C).
    6. Viktor von Wyl, 2019. "Proximity to death and health care expenditure increase revisited: A 15-year panel analysis of elderly persons," Health Economics Review, Springer, vol. 9(1), pages 1-16, December.
    7. Joan Costa‐Font & Cristina Vilaplana‐Prieto, 2020. "‘More than one red herring'? Heterogeneous effects of ageing on health care utilisation," Health Economics, John Wiley & Sons, Ltd., vol. 29(S1), pages 8-29, October.
    8. Calciolari, Stefano & Luini, Cecilia, 2023. "Effects of the bio-psycho-social frailty dimensions on healthcare utilisation among elderly in Europe: A cross-country longitudinal analysis," Social Science & Medicine, Elsevier, vol. 339(C).
    9. Anne Mason & Idaira Rodriguez Santana & María José Aragón & Nigel Rice & Martin Chalkley & Raphael Wittenberg & Jose-Luis Fernandez, 2019. "Drivers of health care expenditure: Final report," Working Papers 169cherp, Centre for Health Economics, University of York.
    10. Howdon, Daniel & Rice, Nigel, 2018. "Health care expenditures, age, proximity to death and morbidity: Implications for an ageing population," Journal of Health Economics, Elsevier, vol. 57(C), pages 60-74.
    11. Owen O'Donnell & Eddy Van Doorslaer & Tom Van Ourti, 2013. "Health and Inequality," Tinbergen Institute Discussion Papers 13-170/V, Tinbergen Institute.
    12. Mauro Laudicella & Paolo Li Donni & Kim Rose Olsen & Dorte Gyrd‐Hansen, 2022. "Age, morbidity, or something else? A residual approach using microdata to measure the impact of technological progress on health care expenditure," Health Economics, John Wiley & Sons, Ltd., vol. 31(6), pages 1184-1201, June.
    13. Keegan, Conor & Brick, Aoife & Bergin, Adele & Wren, Maev-Ann & Whyte, Richard & Henry, Edward, 2020. "Projections of expenditure for public hospitals in Ireland, 2018–2035, based on the Hippocrates Model," Research Series, Economic and Social Research Institute (ESRI), number RS117.
    14. Friedrich Breyer & Normann Lorenz, 2021. "The “red herring” after 20 years: ageing and health care expenditures," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(5), pages 661-667, July.
    15. Eric French & John Bailey Jones & Elaine Kelly & Jeremy McCauley, 2018. "End-of-Life Medical Expenses," Working Paper 18-18, Federal Reserve Bank of Richmond.
    16. Moore, Patrick V. & Bennett, Kathleen & Normand, Charles, 2017. "Counting the time lived, the time left or illness? Age, proximity to death, morbidity and prescribing expenditures," Social Science & Medicine, Elsevier, vol. 184(C), pages 1-14.
    17. Anna Kollerup & Jakob Kjellberg & Rikke Ibsen, 2022. "Ageing and health care expenditures: the importance of age per se, steepening of the individual-level expenditure curve, and the role of morbidity," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(7), pages 1121-1149, September.
    18. Kasteridis, Panagiotis & Rice, Nigel & Santos, Rita, 2022. "Heterogeneity in end of life health care expenditure trajectory profiles," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 221-251.
    19. Jonas Krämer & Jonas Schreyögg, 2019. "Demand-side determinants of rising hospital admissions in Germany: the role of ageing," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(5), pages 715-728, July.
    20. Carl-Johan Dalgaard & Casper Worm Hansen & Holger Strulik, 2017. "Accounting for Fetal Origins: Health Capital vs. Health Deficits," Discussion Papers 17-11, University of Copenhagen. Department of Economics.

    More about this item

    Keywords

    red herring hypothesis; health care utilisation; biological age; epigenetics; LASSO; supervised machine learning;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • I10 - Health, Education, and Welfare - - Health - - - General
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:iza:izadps:dp17159. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Holger Hinte (email available below). General contact details of provider: https://edirc.repec.org/data/izaaade.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.