Evidence map›Paper›PMID 42811325›Full record

ArticleInternational journal for equity in health2026

Moving beyond the average: a method to measure health related-inequalities within randomised trials.

Zachary D V Abel, Sophie Cole, Guido Erreygers, Laurence S J Roope, Philip M Clarke

Abstract read
In one paragraph

Article in International journal for equity in health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Zachary D V AbelHealth Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK. zdvabel@gmail.com.ORCID http://orcid.org/0009-0004-2010-6950
Sophie ColeHealth Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK.ORCID http://orcid.org/0000-0002-5853-2427
Guido ErreygersFaculty of Business and Economics, University of Antwerp, Antwerp, 2000, Belgium.ORCID http://orcid.org/0000-0002-4512-9567
Laurence S J Roope *Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK.ORCID http://orcid.org/0000-0001-9098-9331
Philip M Clarke *Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK.ORCID http://orcid.org/0000-0002-7555-5348

Funding

NIHR Oxford Biomedical Research Centre NIHR-BRC-1215-20008
6 · The paper itself

Abstract

backgroundRandomised trials seldom investigate the impacts interventions have on socioeconomic inequality in health, focusing instead on their average treatment effects.

methodsWe illustrate how standard measures of inequality such as the corrected concentration index can be reported in randomised experiments to quantify the interventions' socio-economic distribution effect alongside more traditional average treatment effects. We illustrate this with a proof-of-concept example using a study on the impact of information and financial incentives on verified COVID-19 vaccination uptake in rural Ghana (n = 2,271). We estimate the distributional treatment effects of informational public health messaging, low- and high-cash financial incentives relative to a placebo group and present this information on an achievement plane.

resultsHealth messaging had a pro-rich socioeconomic inequality impact of -0.132 [95% CI: -0.28-0.02] relative to the placebo group, while the low and high cash incentives had impacts of -0.108 [95% CI: -0.29-0.07] and - 0.081 [95% CI: -0.26-0.11] respectively, providing no significant evidence for changes in inequality relative to the placebo group. A low-cash incentive was the only intervention that was shown to increase vaccination uptake.

conclusionsA socioeconomic inequality of health distributional indicator could routinely be calculated in experimental results and presented alongside average treatment effects. This would enable the identification of whether there are equality-efficiency trade-offs. The methods presented in this paper can be used to support policy decision making with representative datasets provided there is appropriate statistical power.

trial registrationThe RCT investigated in this paper was registered with the American Economic Association on January 10, 2022, RCT ID: AEARCTR-0008775.

Indexed as

COVID-19Randomized Controlled Trials as TopicCOVID-19 VaccinesGhanaHumansMotivationSARS-CoV-2Socioeconomic Disparities in HealthSocioeconomic FactorsCOVID-19 VaccinesDistributional effectsHealth inequalityHealth policyInequality in RCTs

Identifiers

PMID42811325
PMCPMC13621616

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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.