Evidence map›Paper›PMID 33272926›Full record

ArticleCancer research2021

Racial/Ethnic Disparities in All-Cause Mortality among Patients Diagnosed with Triple-Negative Breast Cancer.

Fei Wang, Wei Zheng, Christina E Bailey, Ingrid A Mayer, Jennifer A Pietenpol, Xiao-Ou Shu

Open access · bronzeAbstract read
In one paragraph

Article in Cancer research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 28% 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

24 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.

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  16. Inherent Bias in Electronic Health Records: A Scoping Review of Sources of Bias.medRxiv : the preprint server for health sciences · 2024
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  18. Performance of Statistical and Machine Learning Risk Prediction Models for Surveillance Benefits and Failures in Breast Cancer Survivors.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2023
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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

6 authors at 2 institutions in 2 countries.

Fei WangDivision of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee.
Wei ZhengDivision of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee.
Christina E BaileyDivision of Surgical Oncology and Endocrine Surgery, Department of Surgery, Vanderbilt University Medical Center, Nashville, Tennessee.
Ingrid A MayerDivision of Hematology/Oncology, Breast Cancer Program, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee.
Jennifer A PietenpolVanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee.
Xiao-Ou ShuDivision of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee. xiao-ou.shu@vanderbilt.edu.
Vanderbilt University Medical Center · USSecond Hospital of Shandong University · CN

Funding

TISSUE CoreP50CA098131 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI PARK, BEN HO, PIETENPOL, JENNIFER A · 2003 to 2024
$48.7M
NCI NIH HHS P50 CA098131
6 · The paper itself

Abstract

It is unclear whether racial/ethnic disparities in triple-negative breast cancer (TNBC) mortality remain after accounting for clinical characteristics, treatment, and access-to-care-related factors. In this study, women with a primary diagnosis of TNBC during 2010-2014 were identified from the National Cancer Database. Hazard ratios (HR) and 95% confidence intervals (CI) for 3- and 5-year all-cause mortality associated with race/ethnicity were estimated using Cox proportional hazards models with stepwise adjustments for age, clinical characteristics, treatment, and access-to-care-related factors. Of 78,708 patients, non-Hispanic (NH) black women had the lowest 3-year overall survival rates (79.4%), followed by NH-whites (83.1%), Hispanics (86.0%), and Asians (87.1%). After adjustment for clinical characteristics, NH-blacks had a 12% higher risk of dying 3 years post-diagnosis (HR, 1.12; 95% CI, 1.07-1.17), whereas Hispanics and Asians had a 24% (HR, 0.76; 95% CI, 0.70-0.83) and 17% (HR, 0.83; 95% CI, 0.73-0.94) lower risk than their NH-white counterparts. The black-white disparity became non-significant after combined adjustment for treatment and access-to-care-related factors (HR, 1.04; 95% CI, 0.99-1.09), whereas the white-Hispanic and white-Asian differences remained. Stratified analyses revealed that among women aged less than or equal to 50 with stage III cancer, the elevated risk among NH-blacks persisted (HR, 1.20; 95% CI, 1.04-1.39) after full adjustments. Similar results were seen for 5-year mortality. Overall, clinical characteristics, treatment, and access-to-care-related factors accounted for most of the white-black differences in all-cause mortality of TNBC but explained little about Hispanic- and Asian-white differences. SIGNIFICANCE: These findings highlight the need for equal healthcare to mitigate the black-white disparity and for investigations of contributors beyond healthcare for lower mortality among Asians and Hispanics.

Indexed as

Health Status DisparitiesAdolescentAdultAgedAged, 80 and overCause of DeathDisease-Free SurvivalEthnicityFemaleHealthcare DisparitiesHumansIncidenceMiddle AgedRegistriesSEER ProgramSocioeconomic Factors

Identifiers

PMID33272926
PMCPMC10571320
OpenAlexW3109589872

What OpenQuestion holds

Textmetadata
LicenceTDM
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.