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

Rheumatology ◽  
2021 ◽  
Author(s):  
Dahai Yu ◽  
George Peat ◽  
Kelvin P Jordan ◽  
James Bailey ◽  
Daniel Prieto-Alhambra ◽  
...  
Keyword(s):  
Primary Care ◽  
Chronic Pain ◽  
Low Back Pain ◽  
Back Pain ◽  
Electronic Health Records ◽  
High Impact ◽  
Low Back ◽  
Musculoskeletal Health ◽  
Health Records ◽  
Electronic Health

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.

scholarly journals Use of Electronic Health Records for Early Detection of High-Cost, Low Back Pain Patients

2015 ◽  
Vol 20 (5) ◽  
pp. 234-240 ◽  
Author(s):  
Daniel D Maeng ◽  
Walter F Stewart ◽  
Xiaowei Yan ◽  
Joseph A Boscarino ◽  
Jack Mardekian ◽  
...  
Keyword(s):  
Primary Care ◽  
Logistic Regression ◽  
Low Back Pain ◽  
Back Pain ◽  
Care Provider ◽  
Regression Models ◽  
Low Back ◽  
Health Records ◽  
Logistic Regression Models ◽  
Electronic Health

BACKGROUND: Low back pain (LBP) is a debilitating condition that is complex to manage. One reason is that clinicians lack means to identify early on patients who are likely to become high care utilizers.OBJECTIVE: To explore the feasibility of developing a ‘dynamic’ predictive model using electronic health record data to identify costly LBP patients within the first year after their initial LBP encounter with a primary care provider. Dynamic, in this context, indicates a process in which the decision on how to manage patients is dependent on whether they are at their first, second or third LBP visit with the provider.METHODS: A series of logistic regression models was developed to predict who will be a high-cost patient (defined as top 30% of the cost distribution) at each of the first three LBP visits.RESULTS: The c-statistics of the three logistic regression models corresponding to each of the first three visits were 0.683, 0.795 and 0.741, respectively. The overall sensitivity of the model was 42%, the specificity was 86% and the positive predictive value was 48%. Men were more likely to become expensive than women, while patients who had workers’ compensation as their primary payer type had higher use of prescription opioid drugs or were smokers before the first LBP visit were also more likely to become expensive.CONCLUSION: The results suggest that it is feasible to develop a dynamic, primary care provider visit-based predictive model for LBP care based on longitudinal data obtained via electronic health records.

A prognostic approach to defining chronic pain: Replication in a UK primary care low back pain population

Pain ◽  
2008 ◽  
Vol 135 (1) ◽  
pp. 48-54 ◽  
Author(s):  
Kate M. Dunn ◽  
Peter R. Croft ◽  
Chris J. Main ◽  
Michael Von Korff
Keyword(s):  
Primary Care ◽  
Chronic Pain ◽  
Low Back Pain ◽  
Back Pain ◽  
Low Back

scholarly journals Exploring the prevalence and construct validity of high-impact chronic pain across chronic low-back pain study samples

2019 ◽  
Vol 19 (8) ◽  
pp. 1369-1377 ◽  
Author(s):  
Patricia M. Herman ◽  
Nicholas Broten ◽  
Tara A. Lavelle ◽  
Melony E. Sorbero ◽  
Ian D. Coulter
Keyword(s):  
Chronic Pain ◽  
Low Back Pain ◽  
Back Pain ◽  
Construct Validity ◽  
Chronic Low Back Pain ◽  
High Impact ◽  
Low Back

scholarly journals 338Modelling the population distribution of patient-reported outcomes using electronic health records: a UK study

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Dahai Yu ◽  
Kelvin Jordan ◽  
James Bailey ◽  
George Peat ◽  
Ross Wilkie
Keyword(s):  
Primary Care ◽  
Health Status ◽  
Electronic Health Records ◽  
Prediction Models ◽  
Population Level ◽  
High Impact ◽  
Health Records ◽  
Final Model ◽  
Patient Reported ◽  
Electronic Health

Abstract Background Patient Reported Outcome Measures (PROM) collected in surveys allow understanding of the health at the population-level. We aimed to test the feasibility of estimating health status as measured by PROMs through ecological prediction models which use local linked survey and primary care electronic health records (EHR) and applying the models to national EHR. Methods 3,710 musculoskeletal consulters registered in 11 general practiced in North Staffordshire, UK and consented the linkage of their survey PROM (high impact pain, (HICP)) with EHR were included in this study. HICP was modelled by EHR predictors covering demographic, lifestyle risk factors, musculoskeletal diagnostic/problems, analgesic prescriptions, comorbidities, and deprivation. Final set of predictors were selected through backward elimination (p > 0.1). Individual-level prediction models (binary logistic regression) were fitted and evaluated in terms of model fit statistics (AIC, BIC, R-square), discrimination (C-statistics) and calibration-slope with internal validation. The final model was cross-mapped to a national UK primary EHR database (Clinical Practice Research Database) to obtain national population estimates of high impact pain. Results The C-statistics and calibration-slope of the final model was 0.77 (95% confidence interval: 0.70-0.79) and 1.00 (0.92-1.08), respectively. The estimated HICP was 51.2% overall among 49,788 UK musculoskeletal consulters and matched hypothesised variation by gender, age, deprivation and geographical regions. Conclusions Estimation of population-level health status as measured by PROM using EHR appears feasible and has potential application in assessing health inequalities. Further independent external validation studies are warranted. Key messages Primary care EHR data could be modelled to predict population-level PROMs.

Preliminary results from the COPE study using primary-care electronic health records and environmental modelling to examine COPD exacerbations

2018 ◽  
Vol 68 (suppl 1) ◽  
pp. bjgp18X696749 ◽  
Author(s):  
Maimoona Hashmi ◽  
Mark Wright ◽  
Kirin Sultana ◽  
Benjamin Barratt ◽  
Lia Chatzidiakou ◽  
...  
Keyword(s):  
Primary Care ◽  
Electronic Health Records ◽  
Health Management ◽  
Practice Research ◽  
Chronic Obstructive ◽  
Clinical Practice Research Datalink ◽  
Health Records ◽  
Preliminary Results ◽  
Copd Exacerbations ◽  
Electronic Health

BackgroundChronic Obstructive Airway Disease (COPD) is marked by often severely debilitating exacerbations. Efficient patient-centric research approaches are needed to better inform health management primary-care.AimThe ‘COPE study’ aims to develop a method of predicting COPD exacerbations utilising personal air quality sensors, environmental exposure modelling and electronic health records through the recruitment of patients from consenting GPs contributing to the Clinical Practice Research Datalink (CPRD).MethodThe study made use of Electronic Healthcare Records (EHR) from CPRD, an anonymised GP records database to screen and locate patients within GP practices in Central London. Personal air monitors were used to capture data on individual activities and environmental exposures. Output from the monitors were then linked with the EHR data to obtain information on COPD management, severity, comorbidities and exacerbations. Symptom changes not equating to full exacerbations were captured on diary cards. Linear regression was used to investigate the relationship between subject peak flow, symptoms, exacerbation events and exposure data.ResultsPreliminary results on the first 80 patients who have completed the study indicate variable susceptibility to environmental stressors in COPD patients. Some individuals appear highly susceptible to environmental stress and others appear to have unrelated triggers.ConclusionRecruiting patients through EHR for a study is feasible and allows easy collection of data for long term follow up. Portable environmental sensors could now be used to develop personalised models to predict risk of COPD exacerbations in susceptible individuals. Identification of direct links between participant health and activities would allow improved health management thus cost savings.

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