Evidence map›Paper›PMID 37520147›Full record

ArticleThe Lancet regional health. Europe2023

Trajectories in chronic disease accrual and mortality across the lifespan in Wales, UK (2005-2019), by area deprivation profile: linked electronic health records cohort study on 965,905 individuals.

Jane Lyons, Ashley Akbari, Keith R Abrams, Amaya Azcoaga Lorenzo, Thamer Ba Dhafari, James Chess, Spiros Denaxas, Richard Fry, Chris P Gale, John Gallacher and 12 more

Abstract read
In one paragraph

Article in The Lancet regional health. Europe, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
–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

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  7. Exploring the views of key stakeholders on dementia risk prediction in primary care in areas of socioeconomic deprivation: a qualitative study.The British journal of general practice : the journal of the Royal College of General Practitioners · 2026
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  17. Tackling socioeconomic disparities in multimorbidity.The Lancet regional health. Europe · 2023
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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

22 authors.

Jane LyonsPopulation Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Swansea, Wales, UK.
Ashley AkbariPopulation Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Swansea, Wales, UK.
Keith R AbramsDepartment of Statistics, University of Warwick, Coventry, UK.
Amaya Azcoaga LorenzoInstituto Investigación Sanitaria Fundación Jimenez Diaz, Madrid, Spain.
Thamer Ba DhafariDivision of Informatics, Imaging and Data Science, School of Health Sciences, University of Manchester, Manchester, UK.
James ChessSwansea Bay Health Board, Morriston Hospital, Swansea, Wales, UK.
Spiros DenaxasInstitute of Health Informatics, University College London, London, UK.
Richard FryPopulation Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Swansea, Wales, UK.
Chris P GaleSchool of Medicine, University of Leeds, Leeds, UK.
John GallacherDementias Platform UK, Department of Psychiatry, University of Oxford, Oxford, UK.
Lucy J GriffithsPopulation Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Swansea, Wales, UK.
Bruce GuthrieAdvanced Care Research Centre, Usher Institute, University of Edinburgh, Edinburgh, UK.
Marlous HallLeeds Institute of Cardiovascular and Metabolic Medicine and Leeds Institute for Data Analytics, University of Leeds, Leeds, UK.
Farideh Jalali-NajafabadiCentre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK.
Ann JohnPopulation Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Swansea, Wales, UK.
Clare MacRaeAdvanced Care Research Centre, Usher Institute, University of Edinburgh, Edinburgh, UK.
Colin McCowanSchool of Medicine, University of St Andrews, St Andrews, UK.
Niels PeekDivision of Informatics, Imaging and Data Science, School of Health Sciences, University of Manchester, Manchester, UK.
Dermot O'ReillySchool of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK.
James RaffertySwansea Trials Unit, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Swansea, Wales, UK.
Ronan A LyonsPopulation Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Swansea, Wales, UK.
Rhiannon K OwenPopulation Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Swansea, Wales, UK.

Funding

Medical Research Council MC_PC_20030Medical Research Council MC_PC_20051Medical Research Council MC_PC_20059Medical Research Council MR/S027750/1Medical Research Council MR/T033371/1Medical Research Council MR/V028367/1Wellcome Trust
6 · The paper itself

Abstract

Background: Understanding and quantifying the differences in disease development in different socioeconomic groups of people across the lifespan is important for planning healthcare and preventive services. The study aimed to measure chronic disease accrual, and examine the differences in time to individual morbidities, multimorbidity, and mortality between socioeconomic groups in Wales, UK. Methods: Population-wide electronic linked cohort study, following Welsh residents for up to 20 years (2000-2019). Chronic disease diagnoses were obtained from general practice and hospitalisation records using the CALIBER disease phenotype register. Multi-state models were used to examine trajectories of accrual of 132 diseases and mortality, adjusted for sex, age and area-level deprivation. Restricted mean survival time was calculated to measure time spent free of chronic disease(s) or mortality between socioeconomic groups. Findings: In total, 965,905 individuals aged 5-104 were included, from a possible 2.9 m individuals following a 5-year clearance period, with an average follow-up of 13.2 years (12.7 million person-years). Some 673,189 (69.7%) individuals developed at least one chronic disease or died within the study period. From ages 10 years upwards, the individuals living in the most deprived areas consistently experienced reduced time between health states, demonstrating accelerated transitions to first and subsequent morbidities and death compared to their demographic equivalent living in the least deprived areas. The largest difference were observed in 10 and 20 year old males developing multimorbidity (-0.45 years (99% CI: -0.45, -0.44)) and in 70 year old males dying after developing multimorbidity (-1.98 years (99% CI: -2.01, -1.95)). Interpretation: This study adds to the existing literature on health inequalities by demonstrating that individuals living in more deprived areas consistently experience accelerated time to diagnosis of chronic disease and death across all ages, accounting for competing risks. Funding: UK Medical Research Council, Health Data Research UK, and Administrative Data Research Wales.

Indexed as

Chronic diseaseDisease trajectoriesHealth equityMortalityPopulation-wide

Identifiers

PMID37520147
PMCPMC10372901

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.