Evidence map›Paper›PMID 32075640›Full record

ArticleBMC medical ethics2020

Structural racism in precision medicine: leaving no one behind.

Lester Darryl Geneviève, Andrea Martani, David Shaw, Bernice Simone Elger, Tenzin Wangmo

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in BMC medical ethics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05697198 (PRospective REgistry of Advanced Stage CancER), which is not on this map. Cited by 59 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
59citing papers in PubMed, 7 pooled it
13.7field-weighted citation impact, top 1% of its field
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.

NCT05697198 completednot on this mapstarted 2021, after this paper: background citation

PRospective REgistry of Advanced Stage CancER (PREFER) Patients to Assess Prevalence of Actionable Biomarkers and Driver Mutations Using the OmniSeq Test and Creation of a Biobank from Community Cancer Clinics in the United States to Address Disparities in Precision Medicine

Typeobservational_patient_registrySponsorLabcorp Corporation of America Holdings, IncRan2021 to 2024Enrolled1,429ConditionsLung Cancer, Ovarian Cancer, Uterine Cancer, Colorectal CancerArmsOmniSeq Test
3 · Its place in the literature

Who cites it

59 citing papers in PubMed, 7 syntheses or guidelines pooled it, 121 citations in OpenAlex.

  1. Pooled it
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  5. Big Data and Digitalization in Dentistry: A Systematic Review of the Ethical Issues.International journal of environmental research and public health · 2020
    Pooled it
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  8. Trial
  9. The Continuity Trap in Data Science Health Research.Journal of medical Internet research · 2026
    Article
  10. Review
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  17. Health Equity and Artificial Intelligence in Nephrology.Clinical journal of the American Society of Nephrology : CJASN · 2025
    Article
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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 at 2 institutions in 2 countries.

Lester Darryl GenevièveInstitute for Biomedical Ethics, University of Basel, Basel, Switzerland. lester.genevieve@unibas.ch.ORCID 0000-0001-5834-2220
Andrea MartaniInstitute for Biomedical Ethics, University of Basel, Basel, Switzerland.
David ShawInstitute for Biomedical Ethics, University of Basel, Basel, Switzerland.
Bernice Simone ElgerInstitute for Biomedical Ethics, University of Basel, Basel, Switzerland.
Tenzin WangmoInstitute for Biomedical Ethics, University of Basel, Basel, Switzerland.
University of Basel · CHUniversity of Geneva · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrecision medicine (PM) is an emerging approach to individualized care. It aims to help physicians better comprehend and predict the needs of their patients while effectively adopting in a timely manner the most suitable treatment by promoting the sharing of health data and the implementation of learning healthcare systems. Alongside its promises, PM also entails the risk of exacerbating healthcare inequalities, in particular between ethnoracial groups. One often-neglected underlying reason why this might happen is the impact of structural racism on PM initiatives. Raising awareness as to how structural racism can influence PM initiatives is paramount to avoid that PM ends up reproducing the pre-existing health inequalities between different ethnoracial groups and contributing to the loss of trust in healthcare by minority groups. MAIN BODY: We analyse three nodes of a process flow where structural racism can affect PM's implementation. These are: (i) the collection of biased health data during the initial encounter of minority groups with the healthcare system and researchers, (ii) the integration of biased health data for minority groups in PM initiatives and (iii) the influence of structural racism on the deliverables of PM initiatives for minority groups. We underscore that underappreciation of structural racism by stakeholders involved in the PM ecosystem can be at odds with the ambition of ensuring social and racial justice. Potential specific actions related to the analysed nodes are then formulated to help ensure that PM truly adheres to the goal of leaving no one behind, as endorsed by member states of the United Nations for the 2030 Agenda for Sustainable Development.

conclusionStructural racism has been entrenched in our societies for centuries and it would be naïve to believe that its impacts will not spill over in the era of PM. PM initiatives need to pay special attention to the discriminatory and harmful impacts that structural racism could have on minority groups involved in their respective projects. It is only by acknowledging and discussing the existence of implicit racial biases and trust issues in healthcare and research domains that proper interventions to remedy them can be implemented.

Indexed as

Healthcare DisparitiesPrecision MedicineRacismHumansEthics, researchHealthcare inequalitiesPrecision MedicineRacial biasRacial discriminationSocial justice

Identifiers

PMID32075640
PMCPMC7031946
OpenAlexW3008680281

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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.