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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.
Lyons, Jane; Akbari, Ashley; Abrams, Keith R; Azcoaga Lorenzo, Amaya; Ba Dhafari, Thamer; Chess, James; Denaxas, Spiros; Fry, Richard; Gale, Chris P; Gallacher, John; Griffiths, Lucy J; Guthrie, Bruce; Hall, Marlous; Jalali-Najafabadi, Farideh; John, Ann; MacRae, Clare; McCowan, Colin; Peek, Niels; O'Reilly, Dermot; Rafferty, James; Lyons, Ronan A; Owen, Rhiannon K.
Afiliação
  • Lyons J; Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Swansea, Wales, UK.
  • Akbari A; Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Swansea, Wales, UK.
  • Abrams KR; Department of Statistics, University of Warwick, Coventry, UK.
  • Azcoaga Lorenzo A; Centre for Health Economics, University of York, York, UK.
  • Ba Dhafari T; Instituto Investigación Sanitaria Fundación Jimenez Diaz, Madrid, Spain.
  • Chess J; School of Medicine, University of St Andrews, St Andrews, UK.
  • Denaxas S; Division of Informatics, Imaging and Data Science, School of Health Sciences, University of Manchester, Manchester, UK.
  • Fry R; Swansea Bay Health Board, Morriston Hospital, Swansea, Wales, UK.
  • Gale CP; Institute of Health Informatics, University College London, London, UK.
  • Gallacher J; Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Swansea, Wales, UK.
  • Griffiths LJ; School of Medicine, University of Leeds, Leeds, UK.
  • Guthrie B; Dementias Platform UK, Department of Psychiatry, University of Oxford, Oxford, UK.
  • Hall M; Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Swansea, Wales, UK.
  • Jalali-Najafabadi F; Advanced Care Research Centre, Usher Institute, University of Edinburgh, Edinburgh, UK.
  • John A; Leeds Institute of Cardiovascular and Metabolic Medicine and Leeds Institute for Data Analytics, University of Leeds, Leeds, UK.
  • MacRae C; Centre 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.
  • McCowan C; Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Swansea, Wales, UK.
  • Peek N; Advanced Care Research Centre, Usher Institute, University of Edinburgh, Edinburgh, UK.
  • O'Reilly D; School of Medicine, University of St Andrews, St Andrews, UK.
  • Rafferty J; Division of Informatics, Imaging and Data Science, School of Health Sciences, University of Manchester, Manchester, UK.
  • Lyons RA; School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK.
  • Owen RK; Swansea Trials Unit, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Swansea, Wales, UK.
Lancet Reg Health Eur ; 32: 100687, 2023 Sep.
Article em En | MEDLINE | ID: mdl-37520147
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.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article