Estimating the population health burden of musculoskeletal conditions using primary care electronic health records
Estimating the population health burden of musculoskeletal conditions using primary care electronic health records
Objectives: Better indicators from affordable, sustainable data sources are needed to monitor population burden of musculoskeletal conditions. We propose five indicators of musculoskeletal health and assessed if routinely available primary care electronic health records (EHR) can estimate population levels in musculoskeletal consulters. Methods: We collected validated patient-reported measures of pain experience, function and health status through a local survey of adults (≥35 years) presenting to English general practices over 12 months for low back pain, shoulder pain, osteoarthritis and other regional musculoskeletal disorders. Using EHR data we derived and validated models for estimating population levels of five self-reported indicators: prevalence of high impact chronic pain, overall musculoskeletal health (based on Musculoskeletal Health Questionnaire), quality of life (based on EuroQoL health utility measure), and prevalence of moderate-to-severe low back pain and moderate-to-severe shoulder pain. We applied models to a national EHR database (Clinical Practice Research Datalink) to obtain national estimates of each indicator for three successive years. Results: The optimal models included recorded demographics, deprivation, consultation frequency, analgesic and antidepressant prescriptions, and multimorbidity. Applying models to national EHR, we estimated that 31.9% of adults (≥35 years) presenting with non-inflammatory musculoskeletal disorders in England in 2016/17 experienced high impact chronic pain. Estimated population health levels were worse in women, older aged and those in the most deprived neighbourhoods, and changed little over 3 years. Conclusion: National and subnational estimates for a range of subjective indicators of non-inflammatory musculoskeletal health conditions can be obtained using information from routine electronic health records.
Electronic health records, Health services research, Musculoskeletal, Primary care, back pain, pain, quality of life, shoulder pain, surveillance
4832-4843
Yu, Dahai
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Peat, George
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Jordan, Kelvin P
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Bailey, James
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Prieto-Alhambra, Daniel
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Strauss, Victoria Y.
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Walker-Bone, Karen
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Silman, Alan J.
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Mamas, Mamas
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Blackburn, Steven
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Dent, Stephen
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Dunn, Kate
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Judge, Andrew
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Protheroe, Joanne
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Wilkie, Ross
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1 October 2021
Yu, Dahai
727121be-98f3-4a88-ab96-94dcbf17a2ad
Peat, George
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Jordan, Kelvin P
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Bailey, James
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Prieto-Alhambra, Daniel
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Strauss, Victoria Y.
7f805b15-394f-4a91-8dde-eb3cbae3036a
Walker-Bone, Karen
ad7d1336-ed2c-4f39-ade5-da84eb412109
Silman, Alan J.
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Mamas, Mamas
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Blackburn, Steven
9368c6a5-0303-4251-91d6-3dacb8289e50
Dent, Stephen
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Dunn, Kate
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Judge, Andrew
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Protheroe, Joanne
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Wilkie, Ross
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Yu, Dahai, Peat, George, Jordan, Kelvin P, Bailey, James, Prieto-Alhambra, Daniel, Strauss, Victoria Y., Walker-Bone, Karen, Silman, Alan J., Mamas, Mamas, Blackburn, Steven, Dent, Stephen, Dunn, Kate, Judge, Andrew, Protheroe, Joanne and Wilkie, Ross
(2021)
Estimating the population health burden of musculoskeletal conditions using primary care electronic health records.
Rheumatology, 60 (10), .
(doi:10.1093/rheumatology/keab109).
Abstract
Objectives: Better indicators from affordable, sustainable data sources are needed to monitor population burden of musculoskeletal conditions. We propose five indicators of musculoskeletal health and assessed if routinely available primary care electronic health records (EHR) can estimate population levels in musculoskeletal consulters. Methods: We collected validated patient-reported measures of pain experience, function and health status through a local survey of adults (≥35 years) presenting to English general practices over 12 months for low back pain, shoulder pain, osteoarthritis and other regional musculoskeletal disorders. Using EHR data we derived and validated models for estimating population levels of five self-reported indicators: prevalence of high impact chronic pain, overall musculoskeletal health (based on Musculoskeletal Health Questionnaire), quality of life (based on EuroQoL health utility measure), and prevalence of moderate-to-severe low back pain and moderate-to-severe shoulder pain. We applied models to a national EHR database (Clinical Practice Research Datalink) to obtain national estimates of each indicator for three successive years. Results: The optimal models included recorded demographics, deprivation, consultation frequency, analgesic and antidepressant prescriptions, and multimorbidity. Applying models to national EHR, we estimated that 31.9% of adults (≥35 years) presenting with non-inflammatory musculoskeletal disorders in England in 2016/17 experienced high impact chronic pain. Estimated population health levels were worse in women, older aged and those in the most deprived neighbourhoods, and changed little over 3 years. Conclusion: National and subnational estimates for a range of subjective indicators of non-inflammatory musculoskeletal health conditions can be obtained using information from routine electronic health records.
Text
rheumatology FINAL
- Accepted Manuscript
More information
Accepted/In Press date: 18 January 2021
e-pub ahead of print date: 9 February 2021
Published date: 1 October 2021
Additional Information:
Funding Information:
PRELIM was funded by Versus Arthritis (21403). G.P., K.J., R.W. and D.Y. hold Honorary Academic Consultant Contracts from Public Health England. K.J. is supported by matched funding awarded to the NIHR Applied Research Collaboration (West Midlands). This research was supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). D.P.A. is funded through a NIHR Senior Research Fellowship (Grant Number SRF- 2018-11-ST2-004). A.J. was supported by the NIHR Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol.
Publisher Copyright:
© 2021 The Author(s) 2021.
Keywords:
Electronic health records, Health services research, Musculoskeletal, Primary care, back pain, pain, quality of life, shoulder pain, surveillance
Identifiers
Local EPrints ID: 447142
URI: http://eprints.soton.ac.uk/id/eprint/447142
ISSN: 1462-0324
PURE UUID: 51b22519-7e6e-4f00-a177-a57442a78335
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Date deposited: 04 Mar 2021 17:32
Last modified: 03 Mar 2023 05:01
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Contributors
Author:
Dahai Yu
Author:
George Peat
Author:
Kelvin P Jordan
Author:
James Bailey
Author:
Daniel Prieto-Alhambra
Author:
Victoria Y. Strauss
Author:
Alan J. Silman
Author:
Mamas Mamas
Author:
Steven Blackburn
Author:
Stephen Dent
Author:
Kate Dunn
Author:
Andrew Judge
Author:
Joanne Protheroe
Author:
Ross Wilkie
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