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. 2024 Jun 19:8:262.
doi: 10.12688/wellcomeopenres.19470.2. eCollection 2023.

Estimating disease burden using national linked electronic health records: a study using an English population-based cohort

Robert W Aldridge #  1 , Hannah E R Evans #  1 , Alexei Yavlinsky  1 , Alireza Moayyeri  1 , Krishnan Bhaskaran  2 , Rohini Mathur  3 , Kelvin P Jordan  4 , Peter Croft  4 , Spiros Denaxas  1 , Anoop D Shah  1 , Ruth M Blackburn  1 , Henrik Moller  5 , Edmond S W Ng  2 , Andrew Hughes  6 , Sebastian Fox  6 , Julian Flowers  1 , Jurgen Schmidt  6 , Andrew Hayward  7 , Ruth Gilbert  1 , Liam Smeeth  2 , Harry Hemingway  1
Affiliations

Estimating disease burden using national linked electronic health records: a study using an English population-based cohort

Robert W Aldridge et al. Wellcome Open Res. .

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.

Keywords: burden of disease; electronic health records.

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Conflict of interest statement

Competing interests: JS, AH and SF work at Department of Health and Social Care (England) and ACH at the UK Health Security Agency.

Figures

Figure 1.
Figure 1.. Directly standardized by age estimates of cancer for male and females (2000–2013) comparing estimates created using the following: 1) primary care records; 2) linked primary care and hospitalisation records; 3) linked primary care, hospitalisations and death records; and 4) National bespoke cancer registry records.
Figure 2.
Figure 2.. Incidence of cancer for male and females by age category comparing national bespoke cancer registry estimates (2013) to linked primary care, hospitalisations and death records.
Error bars represent 95% confidence intervals.
Figure 3.
Figure 3.
Annual point-prevalence for diabetes comparing national primary care quality outcome framework data to linked CPRD-HES for ( a) Type 1 and Type 2 combined ( b) Type 1 and Type 2 combined and separate; ( c) Type 2 by gender, ( d) Type 2 by age group, ( e) Type 2 by ethnicity (Error bars represent 95% confidence intervals) and ( f) Type 2 by region comparing unlinked CPRD and linked CPRD-HES (Error bars represent 95% confidence intervals).
Figure 4.
Figure 4.
Prevalence estimates (per 10 000 person) for Low Back Pain (LBP) by ( a) source, ( b) gender, ( c) ethnicity, ( d) age group, ( e) region and ( f) Index of Multiple Deprivation (IMD). Primary care records used for all figures except ( a) and ( e) where unlinked primary care and linked primary care and hospitalisation records were used (Error bars represent 95% confidence intervals.).
Figure 5.
Figure 5.. 1-year consultation prevalence for low back pain nationally, the West Midlands and Consultations in Primary Care Archive for North Staffordshire (CiPCA; includes regional data from North Staffordshire general practices) in 2010 using primary care records with secondary care data in CIPCA recorded by primary care from hospital correspondance.
Error bars represent 95% confidence intervals. Note: CiPCA Estimates are from Jordan et al. 2014 .
See this image and copyright information in PMC

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