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Identifying and visualising multimorbidity and comorbidity patterns in patients in the English National Health Service: a population-based study

  • Valerie Kuan
  • , Spiros Denaxas
  • , Praveetha Patalay
  • , Dorothea Nitsch
  • , Rohini Mathur
  • , Arturo Gonzalez-Izquierdo
  • , Reecha Sofat
  • , Linda Partridge
  • , Amanda Roberts
  • , Ian C K Wong
  • , Melanie Hingorani
  • , Nishi Chaturvedi
  • , Harry Hemingway
  • , Aroon D Hingorani
  • , Multimorbidity Mechanism and Therapeutic Research Collaborative (MMTRC)
  • University College London
  • London School Of Hygiene and Tropical Medicine
  • Queen Mary University of London
  • University of Liverpool
  • Nottingham Support Group for Carers of Children with Eczema
  • Moorfields Eye Hospital

Research output: Contribution to journalArticlepeer-review

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Abstract

BACKGROUND: Globally, there is a paucity of multimorbidity and comorbidity data, especially for minority ethnic groups and younger people. We estimated the frequency of common disease combinations and identified non-random disease associations for all ages in a multiethnic population.

METHODS: In this population-based study, we examined multimorbidity and comorbidity patterns stratified by ethnicity or race, sex, and age for 308 health conditions using electronic health records from individuals included on the Clinical Practice Research Datalink linked with the Hospital Episode Statistics admitted patient care dataset in England. We included individuals who were older than 1 year and who had been registered for at least 1 year in a participating general practice during the study period (between April 1, 2010, and March 31, 2015). We identified the most common combinations of conditions and comorbidities for index conditions. We defined comorbidity as the accumulation of additional conditions to an index condition over an individual's lifetime. We used network analysis to identify conditions that co-occurred more often than expected by chance. We developed online interactive tools to explore multimorbidity and comorbidity patterns overall and by subgroup based on ethnicity, sex, and age.

FINDINGS: We collected data for 3 872 451 eligible patients, of whom 1 955 700 (50·5%) were women and girls, 1 916 751 (49·5%) were men and boys, 2 666 234 (68·9%) were White, 155 435 (4·0%) were south Asian, and 98 815 (2·6%) were Black. We found that a higher proportion of boys aged 1-9 years (132 506 [47·8%] of 277 158) had two or more diagnosed conditions than did girls in the same age group (106 982 [40·3%] of 265 179), but more women and girls were diagnosed with multimorbidity than were boys aged 10 years and older and men (1 361 232 [80·5%] of 1 690 521 vs 1 161 308 [70·8%] of 1 639 593). White individuals (2 097 536 [78·7%] of 2 666 234) were more likely to be diagnosed with two or more conditions than were Black (59 339 [60·1%] of 98 815) or south Asian individuals (93 617 [60·2%] of 155 435). Depression commonly co-occurred with anxiety, migraine, obesity, atopic conditions, deafness, soft-tissue disorders, and gastrointestinal disorders across all subgroups. Heart failure often co-occurred with hypertension, atrial fibrillation, osteoarthritis, stable angina, myocardial infarction, chronic kidney disease, type 2 diabetes, and chronic obstructive pulmonary disease. Spinal fractures were most strongly non-randomly associated with malignancy in Black individuals, but with osteoporosis in White individuals. Hypertension was most strongly associated with kidney disorders in those aged 20-29 years, but with dyslipidaemia, obesity, and type 2 diabetes in individuals aged 40 years and older. Breast cancer was associated with different comorbidities in individuals from different ethnic groups. Asthma was associated with different comorbidities between males and females. Bipolar disorder was associated with different comorbidities in younger age groups compared with older age groups.

INTERPRETATION: Our findings and interactive online tools are a resource for: patients and their clinicians, to prevent and detect comorbid conditions; research funders and policy makers, to redesign service provision, training priorities, and guideline development; and biomedical researchers and manufacturers of medicines, to provide leads for research into common or sequential pathways of disease and inform the design of clinical trials.

Original languageEnglish
Pages (from-to)e16-e27
JournalThe Lancet. Digital health
Volume5
Issue number1
Early online date29 Nov 2022
DOIs
Publication statusPublished - Jan 2023

Bibliographical note

Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.

Data Access Statement

Summary-level data are provided in the appendix (pp 14–93) and on our shiny apps interactive tools. Partial pairwise correlations for each stratified subgroup are available on the CALIBER platform. Code for the shiny apps tools is available on GitHub.

Funding

The study, VK, and AR are supported by the UK Research and Innovation (UKRI) Strategic Priority Fund Tackling multimorbidity at scale programme (grant number R/V033867/1) delivered by the Medical Research Council and the National Institute for Health and Care Research (NIHR) in partnership with the Economic and Social Research Council and in collaboration with the Engineering and Physical Sciences Research Council. Additional support was provided by the Rosetrees Trust. ADH and HH are NIHR Senior Investigators. SD is supported by the British Heart Foundation (BHF) Data Science Centre (grant number SP/19/3/34678), the NIHR-UKRI CONVALESCENCE study, and the Longitudinal Health and Wellbeing COVID-19 National Core Study, which was established by the UK Chief Scientific Officer in October, 2020, and funded by UKRI (grant references MC_PC_20030 and MC_PC_20059). SD is supported by the BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking (under grant agreement number 116074). SD, HH, and ADH are funded by the NIHR UCL Hospitals Biomedical Research Centre and supported by the UCL BHF Research Accelerator (grant number AA/18/6/34223). Work at Health Data Research UK (award reference LOND1) is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and Wellcome Trust. RM is supported by a Sir Henry Wellcome Postdoctoral Fellowship from the Wellcome Trust (WT 201375/Z/16/Z).

Keywords

  • Male
  • Humans
  • Female
  • Adult
  • Middle Aged
  • Aged
  • Multimorbidity
  • State Medicine
  • Diabetes Mellitus, Type 2/epidemiology
  • Comorbidity
  • Hypertension/epidemiology
  • Obesity/epidemiology

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