Estimating disease burden using national linked electronic health records: a study using an English population-based cohort.
Year: 2023
Type: article
Study design: Observational Study
Abstract: 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.
Source: Wellcome Open Research
Authors: Robert W. Aldridge, Hannah E. R. Evans, Alexei Yavlinsky, Alireza Moayyeri, Krishnan Bhaskaran, Rohini Mathur, Kelvin P. Jordan, Peter Croft, Spiros Denaxas, Anoop D. Shah, Ruth M. Blackburn, Henrik Moller, Edmond S. W. Ng, Andrew Hughes, Sebastian Fox, Julian Flowers, Jurgen Schmidt, Andrew Hayward, Ruth Gilbert, Liam Smeeth, Harry Hemingway
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,128
Funders: Wellcome Trust, Scottish Government, European Federation of Pharmaceutical Industries and Associations, UK Research and Innovation, National Institute for Health and Care Research, British Heart Foundation, Department of Health and Social Care, Academy of Medical Sciences, University College London, European Commission, Public Health Agency, Public Health England, Chief Scientist Office, Scottish Government Health and Social Care Directorate, Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Health and Social Care Research and Development Division, THIS Institute, University of Cambridge, Health Data Research UK
Awards: Long-term vascular health effects of cancer and its treatment, Infrastructure and Services - Useable Data, G2259971274, Big Data 4 Better Hearts - Sofia ref.: 116074, G4034630279, G434337789, Public health data science to investigate and improve migrant health., G6238742689, G6298484593, G6582518128, G6755285657, G7631291304, Long-term vascular health effects of cancer and its treatment, Social contagion? : using data science to characterise the distribution and dispersion of health behaviours in adolescence
OpenAlex ID: W4381660955