Evidence map›Paper›PMID 34240065›Full record

ReviewThe lancet. Healthy longevity2021

A call for standardised age-disaggregated health data.

Theresa Diaz, Kathleen L Strong, Bochen Cao, Regina Guthold, Allisyn C Moran, Ann-Beth Moller, Jennifer Requejo, Ritu Sadana, Jotheeswaran Amuthavalli Thiyagarajan, Emmanuel Adebayo and 35 more

Erratum issuedAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in The lancet. Healthy longevity, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 70 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
70citing papers in PubMed, 7 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

70 citing papers in PubMed, 7 syntheses or guidelines pooled it.

  1. Pooled it
  2. Guideline
  3. Pooled it
  4. Pooled it
  5. NMR in biomedicine · 2025
    Pooled it
  6. Shifts in food consumption patterns in the Levant: a systematic review of the last six decades.The international journal of behavioral nutrition and physical activity · 2025
    Pooled it
  7. Pooled it
  8. Explainable Plasma Proteomics-Based Machine Learning for Osteoporosis Diagnosis, Prognosis, and Protein Biomarker Discovery in the UK Biobank.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026
    Article
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  14. Observational
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  16. Review
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  18. Observational
  19. Review
  20. Article

10 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

45 authors.

Theresa DiazMaternal, Newborn, Child and Adolescent Health, and Ageing Department, WHO, Geneva, Switzerland.
Kathleen L StrongMaternal, Newborn, Child and Adolescent Health, and Ageing Department, WHO, Geneva, Switzerland.
Bochen CaoData and Analytics Department, WHO, Geneva, Switzerland.
Regina GutholdMaternal, Newborn, Child and Adolescent Health, and Ageing Department, WHO, Geneva, Switzerland.
Allisyn C MoranMaternal, Newborn, Child and Adolescent Health, and Ageing Department, WHO, Geneva, Switzerland.
Ann-Beth MollerUNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction, Department of Sexual and Reproductive Health and Research, WHO, Geneva, Switzerland.
Jennifer RequejoDivision of Data, Analysis, Planning and Monitoring, UNICEF, New York, NY, USA.
Ritu SadanaMaternal, Newborn, Child and Adolescent Health, and Ageing Department, WHO, Geneva, Switzerland.
Jotheeswaran Amuthavalli ThiyagarajanMaternal, Newborn, Child and Adolescent Health, and Ageing Department, WHO, Geneva, Switzerland.
Emmanuel AdebayoAdolescent Health Unit, Institute of Child Health, College of Medicine, University of Ibadan, Ibadan, Nigeria.
Elsie AkwaraUNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction, Department of Sexual and Reproductive Health and Research, WHO, Geneva, Switzerland.
Agbessi AmouzouJohns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
John J Aponte VaronGlobal Malaria Programme, WHO, Geneva, Switzerland.
Peter S AzzopardiGlobal Adolescent Health Group, Maternal Child and Adolescent Health Program, Burnet Institute, Melbourne, VIC, Australia.
Cynthia Boschi-PintoDepartment of Epidemiology and Biostatistics, Fluminense Federal University, Rio de Janeiro, Brazil.
Liliana CarvajalDivision of Data, Analysis, Planning and Monitoring, UNICEF, New York, NY, USA.
Venkatraman Chandra-MouliUNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction, Department of Sexual and Reproductive Health and Research, WHO, Geneva, Switzerland.
Sarah CroftsOffice for National Statistics, London, UK.
Saeed DastgiriTabriz Health Services Management Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
Jeremiah S DeryGhana Statistical Service, Cape Coast, Ghana.
Shatha ElnakibUNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction, Department of Sexual and Reproductive Health and Research, WHO, Geneva, Switzerland.
Laura FaganUN Major Group for Children and Youth, New York, NY, USA.
B Jane FergusonTannay, Switzerland.
Julia FitznerGlobal Infectious Hazard Preparedness Department, WHO, Geneva, Switzerland.
Howard S FriedmanUN Population Fund, New York, NY, USA.
Ann HagellAssociation for Young People's Health, London, UK.
Eduard JongstraUN Population Fund, New York, NY, USA.
Laura KannPrinceton, IL, USA.
Somnath ChatterjiData and Analytics Department, WHO, Geneva, Switzerland.
Mike EnglishKEMRI-Wellcome Trust, Nairobi, Kenya.
Philippe GlaziouDepartment of Global Tuberculosis, WHO, Geneva, Switzerland.
Claudia HansonDepartment of Global Public Health, Karolinska Institute, Stockholm, Sweden.
Ahmad R HosseinpoorData and Analytics Department, WHO, Geneva, Switzerland.
Andrew MarshMaternal, Newborn, Child and Adolescent Health, and Ageing Department, WHO, Geneva, Switzerland.
Alison P MorganNossal Institute for Global Health, Melbourne School of Population and Global Health, VIC, Australia.
Melinda K MunosJohns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
Abdisalan NoorGlobal Malaria Programme, WHO, Geneva, Switzerland.
Boris I PavlinHealth Emergency Information and Risk Assessment Department, WHO, Geneva, Switzerland.
Rich PereiraOffice for National Statistics, London, UK.
Tyler A PorthDivision of Data, Analysis, Planning and Monitoring, UNICEF, New York, NY, USA.
Joanna SchellenbergLondon School of Hygiene & Tropical Medicine, London, UK.
Rizwana SiddiquePakistan Bureau of Statistics, Islamabad, Pakistan.
Danzhen YouDivision of Data, Analysis, Planning and Monitoring, UNICEF, New York, NY, USA.
Lara M E VazPopulation Reference Bureau, Washington, DC, USA.
Anshu BanerjeeMaternal, Newborn, Child and Adolescent Health, and Ageing Department, WHO, Geneva, Switzerland.

Funding

World Health Organization 001
6 · The paper itself

Abstract

The 2030 Sustainable Development Goals agenda calls for health data to be disaggregated by age. However, age groupings used to record and report health data vary greatly, hindering the harmonisation, comparability, and usefulness of these data, within and across countries. This variability has become especially evident during the COVID-19 pandemic, when there was an urgent need for rapid cross-country analyses of epidemiological patterns by age to direct public health action, but such analyses were limited by the lack of standard age categories. In this Personal View, we propose a recommended set of age groupings to address this issue. These groupings are informed by age-specific patterns of morbidity, mortality, and health risks, and by opportunities for prevention and disease intervention. We recommend age groupings of 5 years for all health data, except for those younger than 5 years, during which time there are rapid biological and physiological changes that justify a finer disaggregation. Although the focus of this Personal View is on the standardisation of the analysis and display of age groups, we also outline the challenges faced in collecting data on exact age, especially for health facilities and surveillance data. The proposed age disaggregation should facilitate targeted, age-specific policies and actions for health care and disease management.

Indexed as

COVID-19PandemicsChild, PreschoolHumansMorbiditySustainable Development

Identifiers

PMID34240065
PMCPMC8245325

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.