Evidence map›Paper›PMID 38076217›Full record

ArticleHealth equity2023

Promoting Anti-Racism in Clinical Practice: Lessons Learned in the Process of Removing the Race Coefficient from the Estimated Glomerular Filtration Rate Algorithm.

Carla Boutin-Foster, Camille A Clare, Jameela Yusuff, Moro Salifu

Abstract read
In one paragraph

Article in Health equity, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Carla Boutin-FosterDepartment of Medicine, SUNY Downstate Health Sciences University College of Medicine, Brooklyn, New York, USA.
Camille A ClareDepartment of Obstetrics and Gynecology, SUNY Downstate Health Sciences University College of Medicine, Brooklyn, New York, USA.ORCID https://orcid.org/0000-0001-8784-5118
Jameela YusuffDepartment of Medicine, SUNY Downstate Health Sciences University College of Medicine, Brooklyn, New York, USA.
Moro SalifuDepartment of Medicine, SUNY Downstate Health Sciences University College of Medicine, Brooklyn, New York, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Promoting anti-racism in medicine entails naming racism as a contributor to health inequities and being intentional about changing race-based practices in health care. Unscientific assumptions about race have led to the proliferation of race-based coefficients in clinical algorithms. Identifying and eliminating this practice is a critical step to promoting anti-racism in health care. The New York City Department of Health and Mental Hygiene (NYC-DOHMH) formed the Coalition to End Racism in Clinical Algorithms (CERCA), a health system consortium charged with eliminating clinical practices and policies that perpetuate racism. Objective: This article describes the process by which an academic medical center guided by the NYC-DOHMH tackled race-based clinical algorithms. Methods: Multiple key interested parties representing department chairs, hospital leaders, researchers, legal experts, and clinical pathologists were convened. A series of steps ensued, including selecting a specific clinical algorithm to address, conducting key informant interviews, reviewing relevant literature, reviewing clinical data, and identifying alternative and valid algorithms. Key Outcomes: Given the disproportionately higher rates of chronic kidney disease risk factors, estimated glomerular filtration rate (eGFR) was prioritized for change. Key informant interviews revealed concerns about the clinical impact that removing race from the equation would have on patients, potential legal implications, challenges of integrating revised algorithms in practice, and aligning this change in clinical practice with medical education. This collaborative process enabled us to tackle these concerns and successfully eliminate race as a coefficient in the eGFR algorithm. Conclusions: CERCA serves as a model for developing academic and public health department partnerships that advance health equity and promote anti-racism in practice. Lessons learned can be adapted to identify, review, and remove the use of race as a coefficient from other clinical guidelines.

Indexed as

anti-racismclinical practicemedicinerace based clinical algorithms

Identifiers

PMID38076217
PMCPMC10698764

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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.