The limitations of large-scale volunteer databases to address inequalities and global challenges in health and aging
Open accessAbstract
Large-scale volunteer databanks (LSVD) have emerged from the recognized value of cohorts, attracting substantial funding and promising great scientific value. A major focus is their size, with the implicit and sometimes explicit assumption that large size (thus power) creates generalizability. We contend that this is open to challenge. In the context of aging and age-related disease research, LSVD typically have limitations such as healthy volunteer, white ethnicity and high-education biases, and they omit early and late life stages critical for understanding aging. Their outputs are heavily focused on biomedical pathways of single chronic diseases. LSVD outputs increasingly dominate the funding and the publication landscapes. This Perspective discusses LSVD limitations and calls for more transparent reporting in LSVD research, as well as a greater reflection on the value of LSVD in relation to resources consumed. We invite funders and researchers to examine whether LSVD do actually contribute knowledge needed for our acute global health challenges including inequalities. Carol Brayne and Terrie Moffit discuss the limitations of large-scale volunteer databanks (LSVD) for understanding aging and disease, call for further evaluation of their value and offer their thoughts on how to make the reporting of LSVD studies more transparent.
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.
Integrity signals
Registry
Cites
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.
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?
Many 'big data' sources from volunteers by their nature exclude those most at risk.
“Many 'big data' sources from volunteers by their nature exclude those most at risk.(32)”
From Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission (2024) · cites this paper as [32]
We used worldwide figures of prevalence where possible, which include disproportionate numbers from HICs.
“We used worldwide figures of prevalence where possible, which include disproportionate numbers from HICs although we have included evidence that risk factors prevalence varies between countries.(32) (33)”
From Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission (2024) · cites this paper as [32]
The section ‘Estimation of disease occurrence and risk’ cautions that volunteer databases are unsuitable for estimating population prevalence, but it does not describe the country composition of worldwide prevalence figures. Searches for high-income-country overrepresentation in global prevalence data found no passage addressing that point. (LLM, conf=0.72)
Experimental — accuracy not yet measured
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
1 retracted
Davis, K. A. S., Coleman, J. R. I., Adams, M. J., Allen, N. E., Breen, G. D., Cullen, B., Dickens, C. M., Fox, E., Graham, N., Holliday, J., et al. (2018) · BJPsych Open · doi:10.1192/bjo.2018.12 · Cited by 236 · see citing works
RetractedSudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., et al. (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001779 · Cited by 12,728 · see citing works
Fry, A., Littlejohns, T. J., Sudlow, C., Doherty, N., Adamska, L., Sprosen, T., Collins, R., & Allen, N. E. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx246 · Cited by 3,979 · see citing works
The changing landscape of atherosclerosis
Libby, P. (2021) · Nature · doi:10.1038/s41586-021-03392-8 · Cited by 2,873 · see citing works
The “All of Us” Research Program
The All of Us Research Program Investigators (2019) · New England Journal of Medicine · doi:10.1056/nejmsr1809937 · Cited by 2,440 · see citing works
Mixed brain pathologies account for most dementia cases in community-dwelling older persons
Schneider, J. A., Arvanitakis, Z., Bang, W., & Bennett, D. A. (2007) · Neurology · doi:10.1212/01.wnl.0000271090.28148.24 · Cited by 1,992 · see citing works
Health equity in England: the Marmot review 10 years on
Marmot, M. (2020) · BMJ · doi:10.1136/bmj.m693 · Cited by 1,834 · see citing works
Matthews, F. E., Arthur, A., Barnes, L. E., Bond, J., Jagger, C., Robinson, L., & Brayne, C. (2013) · The Lancet · doi:10.1016/s0140-6736(13)61570-6 · Cited by 1,155 · see citing works
Self-citationWhat makes UK Biobank special?
Collins, R. (2012) · The Lancet · doi:10.1016/s0140-6736(12)60404-8 · Cited by 1,128 · see citing works
Baldwin, J. R., Reuben, A., Newbury, J. B., & Danese, A. (2019) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.0097 · Cited by 1,094 · see citing works
Million Veteran Program: A mega-biobank to study genetic influences on health and disease
Gaziano, J. M., Concato, J., Brophy, M., Fiore, L., Pyarajan, S., Breeling, J., Whitbourne, S., Deen, J., Shannon, C., Humphries, D., et al. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2015.09.016 · Cited by 1,055 · see citing works
Chen, Z., Chen, J., Collins, R., Guo, Y., Peto, R., Wu, F., Li, L., & on behalf of the China Kadoorie Biobank (CKB) collaborative group (2011) · International Journal of Epidemiology · doi:10.1093/ije/dyr120 · Cited by 1,034 · see citing works
How sample size influences research outcomes
Faber, J., & Fonseca, L. M. (2014) · Dental Press Journal of Orthodontics · doi:10.1590/2176-9451.19.4.027-029.ebo · Cited by 957 · see citing works
Why Representativeness Should Be Avoided
Rothman, K. J., Gallacher, J. E., & Hatch, E. E. (2013) · International Journal of Epidemiology · doi:10.1093/ije/dys223 · Cited by 861 · see citing works
The “All of Us” Research Program
Sullivan, F., McKinstry, B., & Vasishta, S. (2019) · New England Journal of Medicine · doi:10.1056/nejmc1912496 · Cited by 855 · see citing works
Collider bias undermines our understanding of COVID-19 disease risk and severity
Griffith, G. J., Morris, T. T., Tudball, M. J., Herbert, A., Mancano, G., Pike, L., Sharp, G. C., Sterne, J., Palmer, T. M., Davey Smith, G., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19478-2 · Cited by 826 · see citing works
Batty, G. D., Gale, C. R., Kivimäki, M., Deary, I. J., & Bell, S. (2020) · BMJ · doi:10.1136/bmj.m131 · Cited by 635 · see citing works
UK Biobank: Current status and what it means for epidemiology
Allen, N., Sudlow, C., Downey, P., Peakman, T., Danesh, J., Elliott, P., Gallacher, J., Green, J., Matthews, P., Pell, J., et al. (2012) · Health Policy and Technology · doi:10.1016/j.hlpt.2012.07.003 · Cited by 521 · see citing works
A two decade dementia incidence comparison from the Cognitive Function and Ageing Studies I and II
Matthews, F. E., Stephan, B. C. M., Robinson, L., Jagger, C., Barnes, L. E., Arthur, A., Brayne, C., Cognitive Function and Ageing Studies (CFAS) Collaboration, Comas-Herrera, A., Wittenberg, R., et al. (2016) · Nature Communications · doi:10.1038/ncomms11398 · Cited by 426 · see citing works
Self-citationBig Data and Large Sample Size: A Cautionary Note on the Potential for Bias
Kaplan, R. M., Chambers, D., & Glasgow, R. E. (2014) · Clinical and Translational Science · doi:10.1111/cts.12178 · Cited by 416 · see citing works
Poulton, R., Moffitt, T. E., & Silva, P. A. (2015) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s00127-015-1048-8 · Cited by 394 · see citing works
Self-citationDavis, K. A. S., Coleman, J. R. I., Adams, M. J., Allen, N. E., Breen, G. D., Cullen, B., Dickens, C. M., Fox, E., Graham, N., Holliday, J., et al. (2020) · BJPsych Open · doi:10.1192/bjo.2019.100 · Cited by 360 · see citing works
Cohort Profile: The Irish Longitudinal Study on Ageing
Kearney, P. M., Cronin, H., O’Regan, C., Kamiya, Y., Savva, G. M., Whelan, B., & Kenny, R. A. (2011) · International Journal of Epidemiology · doi:10.1093/ije/dyr116 · Cited by 351 · see citing works
Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis
Haworth, S., Mitchell, R., Corbin, L., Wade, K. H., Dudding, T., Budu-Aggrey, A., Carslake, D., Hemani, G., Paternoster, L., Smith, G. D., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-08219-1 · Cited by 314 · see citing works
Population-based Cohort Studies
Szklo, M. (1998) · Epidemiologic Reviews · doi:10.1093/oxfordjournals.epirev.a017974 · Cited by 299 · see citing works
Matthews, F. E., Brayne, C., Lowe, J., McKeith, I., Wharton, S. B., & Ince, P. (2009) · PLoS Medicine · doi:10.1371/journal.pmed.1000180 · Cited by 279 · see citing works
Self-citationGenetic analyses identify widespread sex-differential participation bias
Pirastu, N., Cordioli, M., Nandakumar, P., Mignogna, G., Abdellaoui, A., Hollis, B., Kanai, M., Rajagopal, V. M., Parolo, P. D. B., Baya, N., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00846-7 · Cited by 251 · see citing works
Holmberg, M. J., & Andersen, L. W. (2022) · JAMA · doi:10.1001/jama.2022.1820 · Cited by 229 · see citing works
Commentary: Representativeness is usually not necessary and often should be avoided
Richiardi, L., Pizzi, C., & Pearce, N. (2013) · International Journal of Epidemiology · doi:10.1093/ije/dyt103 · Cited by 214 · see citing works
Taylor, A. E., Jones, H. J., Sallis, H., Euesden, J., Stergiakouli, E., Davies, N. M., Zammit, S., Lawlor, D. A., Munafò, M. R., Davey Smith, G., et al. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyy060 · Cited by 192 · see citing works
UK Biobank, big data, and the consequences of non-representativeness
Keyes, K. M., & Westreich, D. (2019) · The Lancet · doi:10.1016/s0140-6736(18)33067-8 · Cited by 187 · see citing works
Genetic predictors of participation in optional components of UK Biobank
Tyrrell, J., Zheng, J., Beaumont, R., Hinton, K., Richardson, T. G., Wood, A. R., Davey Smith, G., Frayling, T. M., & Tilling, K. (2021) · Nature Communications · doi:10.1038/s41467-021-21073-y · Cited by 162 · see citing works
The Longitudinal Study of Aging in Human Young Adults: Knowledge Gaps and Research Agenda
Moffitt, T. E., Belsky, D. W., Danese, A., Poulton, R., & Caspi, A. (2016) · The Journals of Gerontology Series A · doi:10.1093/gerona/glw191 · Cited by 159 · see citing works
Self-citationDementia before Death in Ageing Societies— The Promise of Prevention and the Reality
Brayne, C., Gao, L., Dewey, M. E., & Matthews, F. E. (2006) · PLoS Medicine · doi:10.1371/journal.pmed.0030397 · Cited by 158 · see citing works
Self-citationThe UK Biobank and selection bias
Swanson, J. M. (2012) · The Lancet · doi:10.1016/s0140-6736(12)61179-9 · Cited by 130 · see citing works
“Chronic obstructive pulmonary disease and phenotypes: a state-of-the-art.”
Corlăteanu, A., Mendez, Y. H., Wang, Y., Garnica, R. D. J. A., Botnaru, V., & Siafakas, N. M. (2019) · Pulmonology · doi:10.1016/j.pulmoe.2019.10.006 · Cited by 123 · see citing works
Chalmers, D., Nicol, D., Kaye, J. S., Bell, J., Campbell, A. V., Ho, C. W.-L., Kato, K., Minari, J., Ho, C., Mitchell, C., et al. (2016) · BMC Medical Ethics · doi:10.1186/s12910-016-0124-2 · Cited by 122 · see citing works
Sample selection and validity of exposure–disease association estimates in cohort studies
Pizzi, C., Stavola, B. L. D., Merletti, F., Bellocco, R., dos‐Santos‐Silva, I., Pearce, N. E., & Richiardi, L. (2010) · Journal of Epidemiology & Community Health · doi:10.1136/jech.2009.107185 · Cited by 111 · see citing works
Identification and estimation of survivor average causal effects
Tchetgen, E. J. T. (2014) · Statistics in Medicine · doi:10.1002/sim.6181 · Cited by 107 · see citing works
Barr, P. B., Bigdeli, T. B., & Meyers, J. L. (2022) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2022.0685 · Cited by 96 · see citing works
Metten, M., Costet, N., Multigner, L., Viel, J., & Chauvet, G. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01533-9 · Cited by 85 · see citing works
Ethnic Differences in Dementia Risk: A Systematic Review and Meta-Analysis
Shiekh, S. I., Cadogan, S. L., Lin, L.-Y., Mathur, R., Smeeth, L. L., & Warren‐Gash, C. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-201209 · Cited by 82 · see citing works
Adams, M. J., Hill, W. D., Howard, D. M., Dashti, H. S., Davis, K. A. S., Campbell, A., Clarke, T., Deary, I. J., Hayward, C., Porteous, D. J., et al. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyz134 · Cited by 78 · see citing works
Taiwan Biobank: making cross-database convergence possible in the Big Data era
Lin, J.-C., Fan, C.-T., Liao, C.-C., & Chen, Y. (2017) · GigaScience · doi:10.1093/gigascience/gix110 · Cited by 73 · see citing works
Cornish, R., Macleod, J., Boyd, A., & Tilling, K. M. (2020) · International Journal of Epidemiology · doi:10.1093/ije/dyaa192 · Cited by 73 · see citing works
Progress With the All of Us Research Program
Ramirez, A. H., Gebo, K. A., & Harris, P. A. (2021) · JAMA · doi:10.1001/jama.2021.7702 · Cited by 65 · see citing works
Simmons, C., Conley, M. I., Gee, D. G., Baskin-sommers, A. R., Barch, D. M., Hoffman, E. A., Huber, R. S., Iacono, W. G., Nagel, B. J., Palmer, C. E., et al. (2021) · Psychological Science · doi:10.1177/09567976211003564 · Cited by 55 · see citing works
Undiagnosed dementia in primary care: a record linkage study
Aldus, C. F., Arthur, A. J., Dennington-Price, A., Millac, P. A. H., Richmond, P., Dening, T., Fox, C., Matthews, F. E., Robinson, L. A., Stephan, B. C. M., et al. (2020) · Health Services and Delivery Research · doi:10.3310/hsdr08200 · Cited by 46 · see citing works
Self-citationBennett, D., Brayne, C., Feigin, V. L., Barker-Collo, S. L., Brainin, M., Davis, D. H. J., Gallo, V., Jetté, N., Karch, A., Kurtzke, J. F., et al. (2015) · European Journal of Epidemiology · doi:10.1007/s10654-015-0034-5 · Cited by 45 · see citing works
Self-citationCan Survival Bias Explain the Age Attenuation of Racial Inequalities in Stroke Incidence?
Mayeda, E. R., Banack, H. R., Bibbins-Domingo, K. B., Hazzouri, A. Z. A., Marden, J. R., Whitmer, R. A., & Glymour, M. M. (2018) · Epidemiology · doi:10.1097/ede.0000000000000834 · Cited by 31 · see citing works
Cognitive profiles in older males and females
Jockwitz, C., Wiersch, L., Stumme, J., & Caspers, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-84134-8 · Cited by 25 · see citing works
Kuß, O., Becher, H., Wienke, A., Ittermann, T., Ostrzinski, S., Schipf, S., Schmidt, C. O., Leitzmann, M. F., Pischon, T., Krist, L., et al. (2022) · European Journal of Epidemiology · doi:10.1007/s10654-022-00880-7 · Cited by 25 · see citing works
Fisch, L., Ernsting, J., Winter, N. R., Holstein, V., Leenings, R., Beisemann, M., Sarink, K., Emden, D., Opel, N., Redlich, R., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.11695 · Cited by 9 · see citing works
Norberg, S. J., Toohey, A. M., Jones, S., et al. (2021) · Health Promotion and Chronic Disease Prevention in Canada · doi:10.24095/hpcdp.41.2.03 · Cited by 7 · see citing works
Strategies to Enhance Retention in a Cohort Study Among Adults of Turkish Descent Living in Berlin
Dornquast, C., Reinhold, T., Solak, S., Durak, M., Becher, H., Riens, B., Icke, K., Danquah, I., Willich, S. N., Keil, T. O. P., et al. (2021) · Journal of Immigrant and Minority Health · doi:10.1007/s10903-021-01309-1 · Cited by 7 · see citing works
Biobank Governance: The Cautionary Tale of Taiwan Biobank
Yen, S.-Y., & Harmon, S. (2018) · SCRIPTed A Journal of Law Technology & Society · doi:10.2966/scrip.150118.103 · Cited by 5 · see citing works
Eisner, M., & Fearon, P. R. M. (2021) · Psychological Science · doi:10.1177/0956797621989967 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [61]Oxford English Dictionary (Oxford University Press, 2022).
Cited by
Sorted by citations
Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission
Livingston, G., Huntley, J., Liu, K. Y., Costafreda, S. G., Selbæk, G., Alladi, S., Ames, D., Banerjee, S., Burns, A., Brayne, C., et al. (2024) · The Lancet · doi:10.1016/s0140-6736(24)01296-0 · Cited by 3,860 · see citing works
Self-citationSocial participation and risk of developing dementia
Sommerlad, A., Kivimäki, M., Larson, E. B., Röhr, S., Shirai, K., Singh-Manoux, A., & Livingston, G. (2023) · Nature Aging · doi:10.1038/s43587-023-00387-0 · Cited by 207 · see citing works
Survey on Machine Learning Biases and Mitigation Techniques
Siddique, S., Haque, M. A., George, R., Gupta, K. D., Gupta, D., & Faruk, M. J. H. (2023) · Digital · doi:10.3390/digital4010001 · Cited by 78 · see citing works
Whitman, E. T., Elliott, M. L., Knodt, A. R., Abraham, W. C., Anderson, T. J., Cutfield, N. J., Hogan, S., Ireland, D., Melzer, T. R., Ramrakha, S., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00897-z · Cited by 37 · see citing works
Self-citationWalsh, S., Wallace, L., Mukadam, N., Mytton, O., Lafortune, L., Wills, W., & Brayne, C. (2023) · Public Health · doi:10.1016/j.puhe.2023.09.019 · Cited by 17 · see citing works
Self-citation
- Authors
- Brayne, C., & Moffitt, T. E.
- Affiliations
- University of Cambridge (GB)
- Duke University (US)
- Corresponding author
- Brayne, Carol
- Venue
- Nature Aging
- Published
- 2022-09-13
- Volume
- 2
- Issue
- 9
- Pages
- 775-783
- ISSN
- 2662-8465 (ISSN-L)
- PMC
- PMC10154032
- OpenAlex
- W4295419447
- Cited by
- 89
- References
- 57
- Citations by year
- 2026: 14 · 2025: 28 · 2024: 33 · 2023: 13 · 2022: 1
- FWCI
- 3.72
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Aging (publishedVersion, other-oa)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, other-oa)
- Research Portal (King's College London) (submittedVersion, other-oa)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- Funders
- Alzheimer's Society
- National Institute for Health and Care Research
- Medical Research Council (MR/P005918, P005918, MR/P005918/1, AG032282, P005918/1)
- Economic and Social Research Council
- National Institute on Aging (R01AG069939, R01AG032282, R01AG073207)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (P2CHD065563)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics in Clinical Research
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- aging research, age-related diseases, health disparities, generalizability, life course, research funding, research transparency, reporting quality
- MeSH terms
- Aging, Humans, Volunteers
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
