Davillas, A. and Pudney, S. orcid.org/0000-0002-5697-0976 (2020) Using biomarkers to predict healthcare costs: Evidence from a UK household panel. Journal of Health Economics, 73. 102356. ISSN 0167-6296
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 2,314 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%.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Healthcare costs; Socioeconomic gradient; Biomarkers; Allostatic load; Understanding Society |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield) > ScHARR - Sheffield Centre for Health and Related Research |
Funding Information: | Funder Grant number ECONOMIC & SOCIAL RESEARCH COUNCIL ES/M008592/1 ECONOMIC & SOCIAL RESEARCH COUNCIL ES/K005146/1 ECONOMIC & SOCIAL RESEARCH COUNCIL ES/L009153/1 ECONOMIC & SOCIAL RESEARCH COUNCIL ES/M008592/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 06 Jul 2020 11:26 |
Last Modified: | 29 Oct 2021 14:51 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.jhealeco.2020.102356 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:162718 |