Estimating disease burden using national linked electronic health records: a study using an English population-based cohort. [version 2; peer review: 2 approved]

Hannah E. R. Evans; Robert W. Aldridge; Rohini Mathur; Liam Smeeth; Alireza Moayyeri; Alexei Yavlinsky; Krishnan Bhaskaran; Peter Croft; Kelvin P. Jordan; Ruth M. Blackburn; Anoop D. Shah; Edmond S. W. Ng; Henrik Moller; Sebastian Fox; Andrew Hughes; Jurgen Schmidt; Julian Flowers; Andrew Hayward; Spiros Denaxas; Ruth Gilbert; Harry Hemingway

Wellcome Open Research (Jun 2024)

Estimating disease burden using national linked electronic health records: a study using an English population-based cohort. [version 2; peer review: 2 approved]

Hannah E. R. Evans,
Robert W. Aldridge,
Rohini Mathur,
Liam Smeeth,
Alireza Moayyeri,
Alexei Yavlinsky,
Krishnan Bhaskaran,
Peter Croft,
Kelvin P. Jordan,
Ruth M. Blackburn,
Anoop D. Shah,
Edmond S. W. Ng,
Henrik Moller,
Sebastian Fox,
Andrew Hughes,
Jurgen Schmidt,
Julian Flowers,
Andrew Hayward,
Spiros Denaxas,
Ruth Gilbert,
Harry Hemingway

Affiliations

Hannah E. R. Evans: Institute of Health Informatics, University College London, London, England, NW1 2DA, UK
Robert W. Aldridge: ORCiD; Institute of Health Informatics, University College London, London, England, NW1 2DA, UK
Rohini Mathur: ORCiD; Wolfson Institute of Population Health, Queen Mary University of London, London, England, EC1M 6BQ, UK
Liam Smeeth: ORCiD; Faculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
Alireza Moayyeri: Institute of Health Informatics, University College London, London, England, NW1 2DA, UK
Alexei Yavlinsky: ORCiD; Institute of Health Informatics, University College London, London, England, NW1 2DA, UK
Krishnan Bhaskaran: ORCiD; Faculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
Peter Croft: School of Medicine, Keele University, Staffordshire, England, ST5 5BG, UK
Kelvin P. Jordan: School of Medicine, Keele University, Staffordshire, England, ST5 5BG, UK
Ruth M. Blackburn: Institute of Health Informatics, University College London, London, England, NW1 2DA, UK
Anoop D. Shah: Institute of Health Informatics, University College London, London, England, NW1 2DA, UK
Edmond S. W. Ng: ORCiD; Faculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
Henrik Moller: Cancer Epidemiology & Population Health, King's College London, London, England, WC2R 2LS, UK
Sebastian Fox: Department of Health and Social Care, Office for Health Improvement and Disparities, London, SW1H 0EU, UK
Andrew Hughes: Department of Health and Social Care, Office for Health Improvement and Disparities, London, SW1H 0EU, UK
Jurgen Schmidt: Department of Health and Social Care, Office for Health Improvement and Disparities, London, SW1H 0EU, UK
Julian Flowers: Institute of Health Informatics, University College London, London, England, NW1 2DA, UK
Andrew Hayward: Institute of Epidemiology and Health Care, University College London, London, England, WC1E 6BT, UK
Spiros Denaxas: ORCiD; Institute of Health Informatics, University College London, London, England, NW1 2DA, UK
Ruth Gilbert: ORCiD; Institute of Health Informatics, University College London, London, England, NW1 2DA, UK
Harry Hemingway: Institute of Health Informatics, University College London, London, England, NW1 2DA, UK

Journal volume & issue: Vol. 8

Abstract

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Background Electronic health records (EHRs) have the potential to be used to produce detailed disease burden estimates. In this study we created disease estimates using national EHR for three high burden conditions, compared estimates between linked and unlinked datasets and produced stratified estimates by age, sex, ethnicity, socio-economic deprivation and geographical region. Methods EHRs containing primary care (Clinical Practice Research Datalink), secondary care (Hospital Episode Statistics) and mortality records (Office for National Statistics) were used. We used existing disease phenotyping algorithms to identify cases of cancer (breast, lung, colorectal and prostate), type 1 and 2 diabetes, and lower back pain. We calculated age-standardised incidence of first cancer, point prevalence for diabetes, and primary care consultation prevalence for low back pain. Results 7.2 million people contributing 45.3 million person-years of active follow-up between 2000–2014 were included. CPRD-HES combined and CPRD-HES-ONS combined lung and bowel cancer incidence estimates by sex were similar to cancer registry estimates. Linked CPRD-HES estimates for combined Type 1 and Type 2 diabetes were consistently higher than those of CPRD alone, with the difference steadily increasing over time from 0.26% (2.99% for CPRD-HES vs. 2.73 for CPRD) in 2002 to 0.58% (6.17% vs. 5.59) in 2013. Low back pain prevalence was highest in the most deprived quintile and when compared to the least deprived quintile the difference in prevalence increased over time between 2000 and 2013, with the largest difference of 27% (558.70 per 10,000 people vs 438.20) in 2013. Conclusions We use national EHRs to produce estimates of burden of disease to produce detailed estimates by deprivation, ethnicity and geographical region. National EHRs have the potential to improve disease burden estimates at a local and global level and may serve as more automated, timely and precise inputs for policy making and global burden of disease estimation.

Published in Wellcome Open Research

ISSN: 2398-502X (Online)
Publisher: Wellcome
Country of publisher: United Kingdom
LCC subjects: Medicine; Science
Website: https://wellcomeopenresearch.org/

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