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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
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
1462-0324
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
fc7a1efc-fede-4432-87a2-4b488f61bd89
Dunn, Kate
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Judge, Andrew
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Protheroe, Joanne
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Wilkie, Ross
d5eac53d-8aff-447b-be07-31baa14ffc46
Yu, Dahai
727121be-98f3-4a88-ab96-94dcbf17a2ad
Peat, George
e7056312-3bd0-430e-8ee7-f81f4c064c85
Jordan, Kelvin P
f528b5c7-959d-4a46-b20a-9a765586a787
Bailey, James
b8bfa91b-d43a-49f1-914d-245de202429d
Prieto-Alhambra, Daniel
e596722a-2f01-4201-bd9d-be3e180e76a9
Strauss, Victoria Y.
7f805b15-394f-4a91-8dde-eb3cbae3036a
Walker-Bone, Karen
ad7d1336-ed2c-4f39-ade5-da84eb412109
Silman, Alan J.
1ab1fc13-51f5-44c8-92f1-0bb32a5c5754
Mamas, Mamas
41515b72-75ff-4922-bb9f-8f9c63f9f5af
Blackburn, Steven
9368c6a5-0303-4251-91d6-3dacb8289e50
Dent, Stephen
fc7a1efc-fede-4432-87a2-4b488f61bd89
Dunn, Kate
1cd91223-522f-4c50-97c4-d13b1b93de7f
Judge, Andrew
b853f89f-dc44-428e-9fe2-35e925544abe
Protheroe, Joanne
ef666365-4f77-4c8c-9471-967b084dff81
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), 4832-4843. (doi:10.1093/rheumatology/keab109).

Record type: Article

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.

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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
ORCID for Karen Walker-Bone: ORCID iD orcid.org/0000-0002-5992-1459

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Date deposited: 04 Mar 2021 17:32
Last modified: 17 Mar 2024 06:21

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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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