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Using biomarkers to predict healthcare costs: Evidence from a UK household panel

Apostolos Davillas () and Stephen Pudney

Journal of Health Economics, 2020, vol. 73, issue C

Abstract: We investigate the extent to which healthcare service utilisation and costs can be predicted from biomarkers, using the UK Understanding Society panel. We use a sample of 2314 adults who reported no history of diagnosed long-lasting health conditions at baseline (2010/11), when biomarkers were collected. Five years later, their GP, outpatient (OP) and inpatient (IP) utilisation was observed. We develop an econometric technique for count data observed within ranges and a method of combining administrative reference cost data with the survey data without exact individual-level matching. Our composite biomarker index (allostatic load) is a powerful predictor of costs: for those with a baseline allostatic load of at least one standard deviation (1-s.d.) above mean, a 1-s.d. reduction reduces GP, OP and IP costs by around 18%.

Keywords: Healthcare costs; Socioeconomic gradient; Biomarkers; Allostatic load; Understanding society (search for similar items in EconPapers)
JEL-codes: C3 C8 I10 I18 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1016/j.jhealeco.2020.102356

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Journal of Health Economics is currently edited by J. P. Newhouse, A. J. Culyer, R. Frank, K. Claxton and T. McGuire

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