Estimating the population health burden of musculoskeletal conditions using primary care electronic health records

Dahai Yu; George Peat; Kelvin P Jordan; James Bailey; Daniel Prieto-Alhambra; Danielle E Robinson; Victoria Y Strauss; Karen Walker-Bone; Alan Silman; Mamas Mamas; Steven Blackburn; Stephen Dent; Kate Dunn; Andrew Judge; Joanne Protheroe; Ross Wilkie

doi:10.1093/rheumatology/keab109

Estimating the population health burden of musculoskeletal conditions using primary care electronic health records

Dahai Yu^*, George Peat, Kelvin P Jordan, James Bailey, Daniel Prieto-Alhambra, Danielle E Robinson, Victoria Y Strauss, Karen Walker-Bone, Alan Silman, Mamas Mamas, Steven Blackburn, Stephen Dent, Kate Dunn, Andrew Judge, Joanne Protheroe, Ross Wilkie

^*Corresponding author for this work

Bristol Medical School (THS)

Research output: Contribution to journal › Article (Academic Journal) › peer-review

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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, health status through a local survey of adults (≧34 years) presenting to English general practices over 12-months for low back pain (LBP), 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 LBP 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 (≧34 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 three 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.

Original language	English
Article number	keab109
Pages (from-to)	4832-4843
Number of pages	12
Journal	Rheumatology
Volume	60
Issue number	10
Early online date	9 Feb 2021
DOIs	https://doi.org/10.1093/rheumatology/keab109
Publication status	Published - 1 Oct 2021

Bibliographical note

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
primary care
musculoskeletal
health services research
surveillance
pain
quality of life
back pain
shoulder pain

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10.1093/rheumatology/keab109

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Yu, D., Peat, G., Jordan, K. P., Bailey, J., Prieto-Alhambra, D., Robinson, D. E., Strauss, V. Y., Walker-Bone, K., Silman, A., Mamas, M., Blackburn, S., Dent, S., Dunn, K., Judge, A., Protheroe, J., & Wilkie, R. (2021). Estimating the population health burden of musculoskeletal conditions using primary care electronic health records. Rheumatology, 60(10), 4832-4843. [keab109]. https://doi.org/10.1093/rheumatology/keab109

Yu, Dahai ; Peat, George ; Jordan, Kelvin P ; Bailey, James ; Prieto-Alhambra, Daniel ; Robinson, Danielle E ; Strauss, Victoria Y ; Walker-Bone, Karen ; Silman, Alan ; Mamas, Mamas ; Blackburn, Steven ; Dent, Stephen ; Dunn, Kate ; Judge, Andrew ; Protheroe, Joanne ; Wilkie, Ross. / Estimating the population health burden of musculoskeletal conditions using primary care electronic health records. In: Rheumatology. 2021 ; Vol. 60, No. 10. pp. 4832-4843.

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title = "Estimating the population health burden of musculoskeletal conditions using primary care electronic health records",

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, health status through a local survey of adults (≧34 years) presenting to English general practices over 12-months for low back pain (LBP), 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 LBP 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 (≧34 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 three 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.",

keywords = "electronic health records, primary care, musculoskeletal, health services research, surveillance, pain, quality of life, back pain, shoulder pain",

author = "Dahai Yu and George Peat and Jordan, {Kelvin P} and James Bailey and Daniel Prieto-Alhambra and Robinson, {Danielle E} and Strauss, {Victoria Y} and Karen Walker-Bone and Alan Silman and Mamas Mamas and Steven Blackburn and Stephen Dent and Kate Dunn and Andrew Judge and Joanne Protheroe and Ross Wilkie",

note = "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: {\textcopyright} 2021 The Author(s) 2021.",

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Yu, D, Peat, G, Jordan, KP, Bailey, J, Prieto-Alhambra, D, Robinson, DE, Strauss, VY, Walker-Bone, K, Silman, A, Mamas, M, Blackburn, S, Dent, S, Dunn, K, Judge, A, Protheroe, J & Wilkie, R 2021, 'Estimating the population health burden of musculoskeletal conditions using primary care electronic health records', Rheumatology, vol. 60, no. 10, keab109, pp. 4832-4843. https://doi.org/10.1093/rheumatology/keab109

Estimating the population health burden of musculoskeletal conditions using primary care electronic health records. / Yu, Dahai; Peat, George; Jordan, Kelvin P; Bailey, James; Prieto-Alhambra, Daniel; Robinson, Danielle E; Strauss, Victoria Y; Walker-Bone, Karen; Silman, Alan; Mamas, Mamas; Blackburn, Steven; Dent, Stephen; Dunn, Kate; Judge, Andrew; Protheroe, Joanne; Wilkie, Ross.

In: Rheumatology, Vol. 60, No. 10, keab109, 01.10.2021, p. 4832-4843.

Research output: Contribution to journal › Article (Academic Journal) › peer-review

TY - JOUR

T1 - Estimating the population health burden of musculoskeletal conditions using primary care electronic health records

AU - Yu, Dahai

AU - Peat, George

AU - Jordan, Kelvin P

AU - Bailey, James

AU - Prieto-Alhambra, Daniel

AU - Robinson, Danielle E

AU - Strauss, Victoria Y

AU - Walker-Bone, Karen

AU - Silman, Alan

AU - Mamas, Mamas

AU - Blackburn, Steven

AU - Dent, Stephen

AU - Dunn, Kate

AU - Judge, Andrew

AU - Protheroe, Joanne

AU - Wilkie, Ross

N1 - 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.

PY - 2021/10/1

Y1 - 2021/10/1

N2 - 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, health status through a local survey of adults (≧34 years) presenting to English general practices over 12-months for low back pain (LBP), 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 LBP 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 (≧34 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 three 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.

AB - 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, health status through a local survey of adults (≧34 years) presenting to English general practices over 12-months for low back pain (LBP), 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 LBP 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 (≧34 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 three 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.

KW - electronic health records

KW - primary care

KW - musculoskeletal

KW - health services research

KW - surveillance

KW - pain

KW - quality of life

KW - back pain

KW - shoulder pain

U2 - 10.1093/rheumatology/keab109

DO - 10.1093/rheumatology/keab109

M3 - Article (Academic Journal)

C2 - 33560340

VL - 60

SP - 4832

EP - 4843

JO - Rheumatology

JF - Rheumatology

SN - 1462-0324

IS - 10

M1 - keab109

ER -

Estimating the population health burden of musculoskeletal conditions using primary care electronic health records

Abstract

Bibliographical note

Keywords

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