Using biomarkers to predict healthcare costs: Evidence from a UK household panel
Apostolos Davillas () and
Journal of Health Economics, 2020, vol. 73, issue C
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)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:jhecon:v:73:y:2020:i:c:s0167629619308495
Access Statistics for this article
Journal of Health Economics is currently edited by J. P. Newhouse, A. J. Culyer, R. Frank, K. Claxton and T. McGuire
More articles in Journal of Health Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().