Biological age and predicting future health care utilisation

We explore the role of epigenetic biological age in predicting subsequent health care utilisation. We use longitudinal data from the UK Understanding Society panel, capitalising 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 is selected as a predictor of future GP consultations and IP care, while chronological rather than biological age is selected 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.

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Publication Details

Journal
Journal of Health Economics
Published
2024-12-06
DOI
https://doi.org/10.1016/j.jhealeco.2024.102956
Citations
2
Primary Topic
Global Health Care Issues
Type
article
Field-Weighted Citation Impact
1.70
Controls
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article

Biological age and predicting future health care utilisation

Apostolos Davillas, Andrew M. Jones
2 citations
Journal of Health Economics
Global Health Care Issues
1.70
article

Biological age and predicting future health care utilisation

Apostolos Davillas, Andrew M. Jones
article en
2 citations

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, capitalising 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 is selected as a predictor of future GP consultations and IP care, while chronological rather than biological age is selected 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.

Journal of Health EconomicsVol. 99
IZA - Institute of Labor Economics (DE), University of York (GB)
Zero hunger
Openalex Percentile: Top 13%
Global Health Care Issues
1.70
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