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Biological age and predicting future health care utilisation
Apostolos Davillas1, Andrew M Jones2
1Department of Economics, University of Macedonia, Bonn, IZA, Greece; IZA Bonn, Germany.
Insights
Epigenetic biological age predicts future healthcare use, particularly hospitalizations. While chronological age is better for predicting outpatient visits, biological age offers deeper insights into inpatient care needs.
Area of Science:
- Biomedical Gerontology
- Epigenetics
- Health Services Research
Background:
- Epigenetic clocks measure biological age, reflecting cumulative molecular damage.
- Healthcare utilization patterns are influenced by health status and aging.
- Predicting future healthcare needs is crucial for resource allocation.
Purpose of the Study:
- To investigate the predictive power of epigenetic biological age for future healthcare utilization.
- To compare the predictive performance of biological age versus chronological age for different healthcare services.
Main Methods:
- Utilized longitudinal data from the UK Understanding Society panel.
- Employed LASSO regression to identify predictors of healthcare utilization.
- Analyzed general practitioner (GP) consultations, outpatient (OP) visits, and hospital inpatient (IP) care.
- Accounted for pre-existing conditions, baseline health, and socio-economic factors.
Main Results:
- Epigenetic biological age predicted future GP consultations and IP care.
- Chronological age, not biological age, predicted future OP visits.
- Biological aging showed a stronger role in predicting future IP care compared to GP consultations.
Conclusions:
- Epigenetic biological age is a significant predictor of future healthcare utilization, especially for inpatient services.
- The predictive utility of biological age varies across different healthcare service types.
- Understanding biological aging can enhance predictions of future healthcare demands.
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.
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