Evidence map›Paper›PMID 39092423›Full record

ArticleWellcome open research2023

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

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 and 11 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Wellcome open research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Robert W Aldridge *Institute of Health Informatics, University College London, London, England, NW1 2DA, UK.ORCID https://orcid.org/0000-0003-0542-0816
Hannah E R Evans *Institute of Health Informatics, University College London, London, England, NW1 2DA, UK.
Alexei YavlinskyInstitute of Health Informatics, University College London, London, England, NW1 2DA, UK.ORCID https://orcid.org/0000-0002-4100-4257
Alireza MoayyeriInstitute of Health Informatics, University College London, London, England, NW1 2DA, UK.
Krishnan BhaskaranFaculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.ORCID https://orcid.org/0000-0001-5364-8757
Rohini MathurWolfson Institute of Population Health, Queen Mary University of London, London, England, EC1M 6BQ, UK.ORCID https://orcid.org/0000-0002-3817-8790
Kelvin P JordanSchool of Medicine, Keele University, Staffordshire, England, ST5 5BG, UK.
Peter CroftSchool of Medicine, Keele University, Staffordshire, England, ST5 5BG, UK.
Spiros DenaxasInstitute of Health Informatics, University College London, London, England, NW1 2DA, UK.ORCID https://orcid.org/0000-0001-9612-7791
Anoop D ShahInstitute of Health Informatics, University College London, London, England, NW1 2DA, UK.
Ruth M BlackburnInstitute of Health Informatics, University College London, London, England, NW1 2DA, UK.
Henrik MollerCancer Epidemiology & Population Health, King's College London, London, England, WC2R 2LS, UK.
Edmond S W NgFaculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.ORCID https://orcid.org/0000-0002-3923-0802
Andrew HughesDepartment of Health and Social Care, Office for Health Improvement and Disparities, London, SW1H 0EU, UK.
Sebastian FoxDepartment of Health and Social Care, Office for Health Improvement and Disparities, London, SW1H 0EU, UK.
Julian FlowersInstitute of Health Informatics, University College London, London, England, NW1 2DA, UK.
Jurgen SchmidtDepartment of Health and Social Care, Office for Health Improvement and Disparities, London, SW1H 0EU, UK.
Andrew HaywardInstitute of Epidemiology and Health Care, University College London, London, England, WC1E 6BT, UK.
Ruth GilbertInstitute of Health Informatics, University College London, London, England, NW1 2DA, UK.ORCID https://orcid.org/0000-0001-9347-2709
Liam SmeethFaculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.ORCID https://orcid.org/0000-0002-9168-6022
Harry HemingwayInstitute of Health Informatics, University College London, London, England, NW1 2DA, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

burden of diseaseelectronic health records

Identifiers

PMID39092423
PMCPMC11292189

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.