Evidence map›Paper›PMID 40898164›Full record

ReviewInternational journal for equity in health2025

Writing about health inequality: recommendations for accurate and impactful presentation of evidence.

Nicole Bergen, Katherine Kirkby, Aluisio J D Barros, Paula Braveman, Peter Goldblatt, Theadora Swift Koller, Oscar J Mujica, Devaki Nambiar, Owen O'Donnell, Anne Schlotheuber and 2 more

Abstract readReview
In one paragraph

Review in International journal for equity in health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. 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

12 authors.

Nicole BergenDepartment of Data, Digital Health, Analytics and AI, World Health Organization, 20 Avenue Appia, Geneva 27, CH-1211, Switzerland.
Katherine KirkbyDepartment of Data, Digital Health, Analytics and AI, World Health Organization, 20 Avenue Appia, Geneva 27, CH-1211, Switzerland.
Aluisio J D BarrosInternational Center for Equity in Health, Universidade Federal de Pelotas, Pelotas, Brazil.
Paula BravemanCenter for Health Equity, School of Medicine, University of California, San Francisco, United States of America.
Peter GoldblattInstitute of Health Equity, University College London, London, UK.
Theadora Swift KollerDepartment of Gender, Rights, Equity & Sexual Misconduct Prevention, World Health Organization, 20 Avenue Appia, Geneva 27, CH-1211, Switzerland.
Oscar J MujicaSocial Epidemiology, Department of Evidence and Intelligence for Action in Health, Pan American Health Organization, Washington, DC, United States of America.
Devaki NambiarDepartment of Data, Digital Health, Analytics and AI, World Health Organization, 20 Avenue Appia, Geneva 27, CH-1211, Switzerland.
Owen O'DonnellErasmus School of Economics, Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, the Netherlands.
Anne SchlotheuberDepartment of Data, Digital Health, Analytics and AI, World Health Organization, 20 Avenue Appia, Geneva 27, CH-1211, Switzerland.
Vivian WelchBruyère Health Research Institute and School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada.
Ahmad Reza HosseinpoorDepartment of Data, Digital Health, Analytics and AI, World Health Organization, 20 Avenue Appia, Geneva 27, CH-1211, Switzerland. hosseinpoora@who.int.

Funding

World Health Organization 001
6 · The paper itself

Abstract

Health and development agendas and programmes often prioritize the reduction of unfair and remediable health inequalities. There is a growing amount of data pertaining to health inequalities. Written outputs, including academic research papers, are key tools for describing health inequalities. Epidemiologists, data analysts, policy advisors and health equity scholars can have greater impact through accurate, concise and compelling presentation of this evidence and so assist those advocating for action to close health gaps. We make recommendations to improve the accuracy and impact of written evidence on health inequality. Focusing on the micro, macro and meta aspects of developing written reports, we drew from our varied experiences promoting health inequality monitoring to identify key strategies specific to this field, which were further expanded and explored through literature searches and consultation with experts. We recommend four general strategies: (i) using terminology deliberately and consistently; (ii) presenting statistical content accurately and with sufficient detail; (iii) adhering to guidelines and best practices for reporting; and (iv) respecting and upholding the interests of affected communities. Specifically, we address the use of terminology related to health inequality and health inequity, dimensions of inequality and determinants of health, economic inequality and economic-related inequality, sex and gender, and race and ethnicity. We present common pitfalls related to reporting statistical content, underscoring the importance of clarity when reporting association and causation. We advocate for engaged and inclusive writing processes that use affirming language and adopt strength-based messaging. This guidance is intended to increase the impact of written evidence on efforts to tackle avoidable health inequalities.

Indexed as

Health InequitiesHealth Status DisparitiesWritingHealth EquityHumansSocioeconomic FactorsAffirming languageAnalysisBest practicesHealth equityHealth inequalityLanguage useReportingStatisticsTerminologyWriting

Identifiers

PMID40898164
PMCPMC12406599

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.