Evidence map›Paper›PMID 35007354›Full record

SynthesisRisk analysis : an official publication of the Society for Risk Analysis2022

Taxonomies for synthesizing the evidence on communicating numbers in health: Goals, format, and structure.

Jessica S Ancker, Natalie C Benda, Mohit M Sharma, Stephen B Johnson, Stephanie Weiner, Brian J Zikmund-Fisher

Abstract readSystematic Review
In one paragraph

Synthesis in Risk analysis : an official publication of the Society for Risk Analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 2 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

22 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Using Separate Single-Outcome Risk Presentations Instead of Integrated Multioutcome Formats Improves Comprehension in Discrete Choice Experiments.Medical decision making : an international journal of the Society for Medical Decision Making · 2024
    Trial
  4. Article
  5. How to Report Research on the Communication of Health-Related Numbers: The Research on Communicating Numbers (ReCoN) Guidelines.Medical decision making : an international journal of the Society for Medical Decision Making · 2025
    Article
  6. Article
  7. Article
  8. Article
  9. Visualizing machine learning-based predictions of postpartum depression risk for lay audiences.Journal of the American Medical Informatics Association : JAMIA · 2024
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Review
  17. Article
  18. Review
  19. Review
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jessica S AnckerVanderbilt University Medical Center, Department of Biomedical Informatics, Nashville, Tennessee, USA.ORCID 0000-0002-3859-9130
Natalie C BendaWeill Cornell Medicine, Department of Population Health Sciences, New York, New York, USA.ORCID 0000-0002-3256-0243
Mohit M SharmaWeill Cornell Medicine, Department of Population Health Sciences, New York, New York, USA.ORCID 0000-0003-0091-5510
Stephen B JohnsonNew York University Langone Health, Department of Population Health, New York, New York, USA.ORCID 0000-0002-7663-4355
Stephanie WeinerWeill Cornell Medicine, Department of Population Health Sciences, New York, New York, USA.ORCID 0000-0001-8506-8140
Brian J Zikmund-FisherUniversity of Michigan School of Public Health, Department of Health Behavior and Health Education, Ann Arbor, Michigan, USA.ORCID 0000-0002-1637-4176

Funding

Making Numbers Meaningful: Promoting Evidence-based Communication of Numbers in HealthR01LM012964 · NLM · WEILL MEDICAL COLL OF CORNELL UNIV · PI ANCKER, JESSICA S. · 2018 to 2021
$1.4M
NLM NIH HHS R01 LM012964
6 · The paper itself

Abstract

Many people, especially those with low numeracy, are known to have difficulty interpreting and applying quantitative information to health decisions. These difficulties have resulted in a rich body of research about better ways to communicate numbers. Synthesizing this body of research into evidence-based guidance, however, is complicated by inconsistencies in research terminology and researcher goals. In this article, we introduce three taxonomies intended to systematize terminology in the literature, derived from an ongoing systematic literature review. The first taxonomy provides a systematic nomenclature for the outcome measures assessed in the studies, including perceptions, decisions, and actions. The second taxonomy is a nomenclature for the data formats assessed, including numbers (and different formats for numbers) and graphics. The third taxonomy describes the quantitative concepts being conveyed, from the simplest (a single value at a single point in time) to more complex ones (including a risk-benefit trade-off and a trend over time). Finally, we demonstrate how these three taxonomies can be used to resolve ambiguities and apparent contradictions in the literature.

Indexed as

CommunicationGoalsHumansRisk Assessmentdata graphicshealth numeracynumeracyrisk communicationtaxonomy

Identifiers

PMID35007354
PMCPMC10241486

What OpenQuestion holds

Textmetadata
LicenceTDM
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.