Cited by 305Chronic Disease Management StrategiesMedical Coding and Health InformationGlobal Health and Epidemiology

A chronological map of 308 physical and mental health conditions from 4 million individuals in the English National Health Service

Open access
Kuan, Valerie, Denaxas, Spiros, Gonzalez-Izquierdo, Arturo, Direk, Kenan, Bhatti, Osman, Husain, Shanaz, Sutaria, Shailen, Hingorani, Melanie +9 more·2019·The Lancet Digital Health

Abstract

Background: To effectively prevent, detect, and treat health conditions that affect people during their lifecourse, health-care professionals and researchers need to know which sections of the population are susceptible to which health conditions and at which ages. Hence, we aimed to map the course of human health by identifying the 50 most common health conditions in each decade of life and estimating the median age at first diagnosis. Methods: We developed phenotyping algorithms and codelists for physical and mental health conditions that involve intensive use of health-care resources. Individuals older than 1 year were included in the study if their primary-care and hospital-admission records met research standards set by the Clinical Practice Research Datalink and they had been registered in a general practice in England contributing up-to-standard data for at least 1 year during the study period. We used linked records of individuals from the CALIBER platform to calculate the sex-standardised cumulative incidence for these conditions by 10-year age groups between April 1, 2010, and March 31, 2015. We also derived the median age at diagnosis and prevalence estimates stratified by age, sex, and ethnicity (black, white, south Asian) over the study period from the primary-care and secondary-care records of patients. Findings: We developed case definitions for 308 disease phenotypes. We used records of 2 784 138 patients for the calculation of cumulative incidence and of 3 872 451 patients for the calculation of period prevalence and median age at diagnosis of these conditions. Conditions that first gained prominence at key stages of life were: atopic conditions and infections that led to hospital admission in children (<10 years); acne and menstrual disorders in the teenage years (10-19 years); mental health conditions, obesity, and migraine in individuals aged 20-29 years; soft-tissue disorders and gastro-oesophageal reflux disease in individuals aged 30-39 years; dyslipidaemia, hypertension, and erectile dysfunction in individuals aged 40-59 years; cancer, osteoarthritis, benign prostatic hyperplasia, cataract, diverticular disease, type 2 diabetes, and deafness in individuals aged 60-79 years; and atrial fibrillation, dementia, acute and chronic kidney disease, heart failure, ischaemic heart disease, anaemia, and osteoporosis in individuals aged 80 years or older. Black or south-Asian individuals were diagnosed earlier than white individuals for 258 (84%) of the 308 conditions. Bone fractures and atopic conditions were recorded earlier in male individuals, whereas female individuals were diagnosed at younger ages with nutritional anaemias, tubulointerstitial nephritis, and urinary disorders. Interpretation: We have produced the first chronological map of human health with cumulative-incidence and period-prevalence estimates for multiple morbidities in parallel from birth to advanced age. This can guide clinicians, policy makers, and researchers on how to formulate differential diagnoses, allocate resources, and target research priorities on the basis of the knowledge of who gets which diseases when. We have published our phenotyping algorithms on the CALIBER open-access Portal which will facilitate future research by providing a curated list of reusable case definitions. Funding: Wellcome Trust, National Institute for Health Research, Medical Research Council, Arthritis Research UK, British Heart Foundation, Cancer Research UK, Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Department of Health and Social Care (England), Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), Economic and Social Research Council, Engineering and Physical Sciences Research Council, National Institute for Social Care and Health Research, and The Alan Turing Institute.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/s2589-7500(19)30012-3

Cites

24 of 24 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Kuan, V., Denaxas, S., Gonzalez-Izquierdo, A., Direk, K., Bhatti, O., Husain, S., Sutaria, S., Hingorani, M., Nitsch, D., Parisinos, C. A., Lumbers, R. T., Mathur, R., Sofat, R., Casas, J. P., Wong, I. C. K., Hemingway, H., & Hingorani, A. D.
Affiliations
  • Health Data Research UK (GB)
  • The Alan Turing Institute (GB)
  • London Borough of Tower Hamlets (GB)
  • Queen Mary University of London (GB)
  • Moorfields Eye Hospital (GB)
  • London School of Hygiene & Tropical Medicine (GB)
  • University College London (GB)
  • St Bartholomew's Hospital (GB)
  • VA Boston Healthcare System (US)
  • UCL School of Pharmacy (GB)
  • National Institute for Health and Care Research (GB)
Corresponding author
Kuan, Valerie
Venue
The Lancet Digital Health
Publisher
Elsevier BV
Published
2019-06
Volume
1
Issue
2
Pages
e63-e77
ISSN
2589-7500 (ISSN-L)
OpenAlex
W2944890781
Cited by
305
References
24
Citations by year
2026: 24 · 2025: 55 · 2024: 51 · 2023: 58 · 2022: 42 · 2021: 46 · 2020: 25 · 2019: 4
FWCI
22.27
Type
journal article
Language
en
Open access
Locations
  • The Lancet Digital Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • UCL Discovery (University College London) (submittedVersion)
Funders
  • British Heart Foundation
  • National Institute for Health Research
  • National Institute for Social Care and Health Research (MR/K006584/1)
  • Arthritis Research UK
  • Engineering and Physical Sciences Research Council
  • Alan Turing Institute
  • Chief Scientist Office, Scottish Government Health and Social Care Directorate
  • Cancer Research UK
  • Wellcome Trust (WT 110284/Z/15/Z, WT 201375/Z/16/Z)
  • Public Health Agency
  • Manchester Biomedical Research Centre
  • Medical Research Council
  • UCL Institute for Global Health, University College London (UCL)
  • University Research Council, DePaul University
Topics
  • Health Sciences › Medicine › Epidemiology › Chronic Disease Management Strategies
  • Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
  • Health Sciences › Health Professions › Health Information Management › Global Health and Epidemiology
Keywords
age at diagnosis, cumulative incidence, period prevalence, disease phenotypes, mental illness, physical health conditions, sex differences in diagnosis, calibers, Clinical Practice Research Datalink, case definition, code lists, hospitalization, dementia, atrial fibrillation
MeSH terms
Adolescent, Adult, Aged, Aged, 80 and over, Algorithms, Mental Disorders, Child, England, Female, Forecasting, Health Status, Humans, Male, Middle Aged, Population Surveillance, State Medicine, Databases, Factual, Age of Onset, Young Adult, Electronic Health Records

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review