Evidence map›Paper›PMID 34880504›Full record

ArticleNature2021

Unrepresentative big surveys significantly overestimated US vaccine uptake.

Valerie C Bradley, Shiro Kuriwaki, Michael Isakov, Dino Sejdinovic, Xiao-Li Meng, Seth Flaxman

Abstract read
In one paragraph

Article in Nature, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers.

0numbers the graph read from it
0cells of the map it votes in
93citing papers in PubMed
–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

93 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Treating gaps and biases in biodiversity data as a missing data problem.Biological reviews of the Cambridge Philosophical Society · 2025
    Review
  13. Comparing patient-reported outcomes measurement information systemQuality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025
    Article
  14. Observational
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. A framework for understanding selection bias in real-world healthcare data.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2024
    Article

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

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Valerie C Bradley *Department of Statistics, University of Oxford, Oxford, UK.
Shiro Kuriwaki *Department of Political Science, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-5687-2647
Michael IsakovHarvard College, Harvard University, Cambridge, MA, USA.ORCID http://orcid.org/0000-0001-6468-5908
Dino SejdinovicDepartment of Statistics, University of Oxford, Oxford, UK.
Xiao-Li MengDepartment of Statistics, Harvard University, Cambridge, MA, USA.
Seth FlaxmanDepartment of Computer Science, University of Oxford, Oxford, UK. seth.flaxman@cs.ox.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Surveys are a crucial tool for understanding public opinion and behaviour, and their accuracy depends on maintaining statistical representativeness of their target populations by minimizing biases from all sources. Increasing data size shrinks confidence intervals but magnifies the effect of survey bias: an instance of the Big Data Paradox

Indexed as

Health Care SurveysBenchmarkingBiasBig DataCenters for Disease Control and Prevention, U.S.COVID-19COVID-19 VaccinesDatasets as TopicFemaleHumansMaleResearch DesignSample SizeSocial MediaUnited StatesVaccinationCOVID-19 Vaccines

Identifiers

PMID34880504
PMCPMC8653636

What OpenQuestion holds

Textmetadata
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.