Biological age and predicting future health care utilisation - Apostolos Davillas; Andrew M. Jones (University of Essex, Institute for Social and Economic Research (ISER), 2024)
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.
Keywords: C5; C81; I10; I18; 330; epigenetics; biological age; health care utilisation; red herring hypothesis; LASSO; supervised machine learning