Evidence map›Paper›PMID 37840113›Full record

ArticleAnnals of surgical oncology2024

Addressing Data Aggregation and Data Inequity in Race and Ethnicity Reporting and the Impact on Breast Cancer Disparities.

Brianna Wilson, Jane Mendez, Lisa Newman, Sharon Lum, Kathie-Ann Joseph

Abstract read
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In one paragraph

Article in Annals of surgical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.4field-weighted citation impact, top 16% 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.

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

5 citing papers in PubMed, 6 citations in OpenAlex.

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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 5 institutions in 1 country.

Brianna WilsonUT Southwestern Medical School, Dallas, TX, USA.
Jane MendezMiami Cancer Institute, Miami, FL, USA.
Lisa NewmanDepartment of Surgery, Weill Cornell Medicine, New York, NY, USA.
Sharon LumDepartment of Surgery, Loma Linda University School of Medicine, Loma Linda, CA, USA.
Kathie-Ann JosephDepartments of Surgery and Population Health, New York University Grossman School of Medicine, New York, NY, USA. Kathie-Ann.Joseph@nyulangone.org.
Cornell University · USLoma Linda University · USMiami Heart Research Institute · USNYU Langone Health · USSouthwestern Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Collecting and reporting data on race and ethnicity is vital to understanding and addressing health disparities in the United States. These health disparities can include increased prevalence and severity of disease, poorer health outcomes, decreased access to healthcare, etc., in disadvantaged populations compared with advantaged groups. Without these data, researchers, administrators, public health practitioners, and policymakers are unable to identify the need for targeted interventions and assistance. When researching or reporting on race and ethnicity, typically broad racial categories are used. These include White or Caucasian, Black or African American, Asian American, Native Hawaiian or Other Pacific Islander, or American Indian and Alaska Native, as well as categories for ethnicity such as Latino or Hispanic or not Latino or Hispanic. These categories, defined by the Office of Management and Budget, are the minimum standards for collecting and reporting race and ethnicity data across federal agencies. Of note, these categories have not been updated since 1997. The lack of accurate and comprehensive data on marginalized racial and ethnic groups limits our understanding of and ability to address health disparities. This has implications for breast cancer outcomes in various populations in this country. In this paper, we examine the impact data inequity and the lack of data equity centered processes have in providing appropriate prevention and intervention efforts and resource allocations.

Indexed as

Breast NeoplasmsEthnicityHealthcare DisparitiesHealth Status DisparitiesRacial GroupsData AggregationFemaleHumansUnited States

Identifiers

PMID37840113
OpenAlexW4387653120

What OpenQuestion holds

Textmetadata
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.