Evidence map›Paper›PMID 30333222›Full record

ArticleCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology2019

Breast Cancer Mortality in Older and Younger Patients in California.

Li Tao, Richard B Schwab, Yazmin San Miguel, Scarlett Lin Gomez, Alison J Canchola, Manuela Gago-Dominguez, Ian K Komenaka, James D Murphy, Alfredo A Molinolo, Maria Elena Martinez

Open access · greenAbstract read
In one paragraph

Article in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
0.9field-weighted citation impact, top 31% 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

14 citing papers in PubMed, 24 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

10 authors at 3 institutions in 2 countries.

Li TaoGreater Bay Area Cancer Registry, Cancer Prevention Institute of California, Fremont, California.
Richard B SchwabMoores Cancer Center, University of California, San Diego, La Jolla, California.
Yazmin San MiguelMoores Cancer Center, University of California, San Diego, La Jolla, California.ORCID 0000-0002-3682-228X
Scarlett Lin GomezGreater Bay Area Cancer Registry, Cancer Prevention Institute of California, Fremont, California.
Alison J CancholaGreater Bay Area Cancer Registry, Cancer Prevention Institute of California, Fremont, California.
Manuela Gago-DominguezMoores Cancer Center, University of California, San Diego, La Jolla, California.
Ian K KomenakaMaricopa Medical Center, Phoenix, Arizona.
James D MurphyMoores Cancer Center, University of California, San Diego, La Jolla, California.
Alfredo A MolinoloMoores Cancer Center, University of California, San Diego, La Jolla, California.ORCID 0000-0001-8412-2889
Maria Elena MartinezMoores Cancer Center, University of California, San Diego, La Jolla, California. e8martinez@ucsd.edu.
University of California San Diego · USCancer Prevention Institute of California · USMaricopa Medical Center · US

Funding

VIRAL MALIGNANCYP30CA023100 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI DIANE M SIMEONE · 1985 to 2026
$124.9M
Research Education CoreU54CA132384 · NCI · SAN DIEGO STATE UNIVERSITY · PI CRIPPS, RICHARD MATTHEW, CROWE, SHEILA E. · 2008 to 2022
$20.7M
Research Education CoreU54CA132379 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ARREDONDO, ELVA M, CROWE, SHEILA E. · 2008 to 2021
$16.1M
NCCDPHP CDC HHS NU58DP006344NCI NIH HHS HHSN261201800009CNCI NIH HHS HHSN261201800009INCI NIH HHS HHSN261201800015CNCI NIH HHS HHSN261201800015INCI NIH HHS HHSN261201800032CNCI NIH HHS HHSN261201800032INCI NIH HHS P30 CA023100NCI NIH HHS U54 CA132379NCI NIH HHS U54 CA132384
6 · The paper itself

Abstract

backgroundBreast cancer in younger patients is reported to be more aggressive and associated with lower survival; however, factors associated with age-specific mortality differences have not been adequately assessed.

methodsWe used data from the population-based California Cancer Registry for 38,509 younger (18-49 years) and 121,573 older (50 years and older) women diagnosed with stage I to III breast cancer, 2005-2014. Multivariable Cox regression models were used to estimate breast cancer-specific mortality rate ratios (MRR) and 95% confidence intervals (CI), stratified by tumor subtype, guideline treatment, and care at an NCI-designated cancer center (NCICC).

resultsOlder breast cancer patients at diagnosis experienced 17% higher disease-specific mortality than younger patients, after multivariable adjustment (MRR = 1.17; 95% CI, 1.11-1.23). Higher MRRs (95% CI) were observed for older versus younger patients with hormone receptor (HR)

conclusionsAlthough younger women tend to be diagnosed with more aggressive breast cancers, adjusting for these aggressive features results in older patients having higher mortality than younger patients, with variations by age, tumor subtype, receipt of guideline treatment, and being cared for at an NCICC. IMPACT: Higher breast cancer mortality in older compared with younger women could partly be addressed by ensuring optimal treatment and comprehensive patient-centered care.

Indexed as

AdultAge FactorsBiomarkers, TumorBreast NeoplasmsCaliforniaErb-b2 Receptor Tyrosine KinasesFemaleHumansMiddle AgedProportional Hazards ModelsReceptors, EstrogenReceptors, ProgesteroneYoung AdultBiomarkers, TumorErb-b2 Receptor Tyrosine KinasesReceptors, EstrogenReceptors, Progesterone

Identifiers

PMID30333222
PMCPMC6363871
OpenAlexW2911470796

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.