Cited by 152Glioma Diagnosis and TreatmentHealth disparities and outcomesGlobal Cancer Incidence and Screening

Racial, ethnic and socioeconomic disparities in the treatment of brain tumors

Curry, William T., Barker, Fred G.·2009·Journal of Neuro-Oncology

Abstract

Disparities in American health care based on socially-defined patient characteristics such as race, ethnicity, and socioeconomic position are well-documented. We review differences and disparities in incidence, pathobiology, processes and outcomes of care, and survival based on social factors for brain tumors of all histologies. In the US, black patients have lower incidences of most brain tumor types and lower-income patients have lower incidences of low grade glioma, meningioma and acoustic neuroma; ascertainment bias may contribute to these findings. Pathogenetic differences between malignant gliomas in patients of different races have been demonstrated, but their clinical significance is unclear. Patients in disadvantaged groups are less often treated by high-volume providers. Mortality and morbidity of initial treatment are higher for brain tumor patients in disadvantaged groups, and they present with markers of more severe disease. Long term survival differences between malignant glioma patients of different races have not yet been shown. Clinical trial enrollment appears to be lower among brain tumor patients from disadvantaged groups. We propose future research both to better define disparities and to alleviate them.

Integrity signals

Registry

Crossref · 10.1007/s11060-009-9840-5

Cites

126 of 126 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Increasing the data size for model training without considerations relating to data quality, reference labels, and potential site bias might not always improve results.

    “However, increasing the data size for model training without considerations relating to data quality, reference labels, and potential site bias (e.g., scanner acquisition protocols, demographics, or sociocultural considerations, such as more advanced presentation of disease at diagnosis in low-income regions45) might not always improve results.”

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Curry, W. T., & Barker, F. G.
Affiliations
Harvard University (US)
Corresponding author
Curry, William T.
Venue
Journal of Neuro-Oncology
Published
2009-05
Volume
93
Issue
1
Pages
25-39
ISSN
  • 0167-594X (ISSN-L)
  • 1573-7373
OpenAlex
W2005131672
Cited by
152
References
126
Citations by year
2026: 1 · 2025: 15 · 2024: 16 · 2023: 15 · 2022: 12 · 2021: 10 · 2020: 11 · 2019: 12 · 2018: 15 · 2017: 10 · 2016: 4 · 2015: 5 · 2014: 8 · 2013: 6 · 2012: 6
FWCI
1.88
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of Neuro-Oncology (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Genetics › Glioma Diagnosis and Treatment
  • Social Sciences › Health › Health disparities and outcomes
  • Health Sciences › Medicine › Oncology › Global Cancer Incidence and Screening
Keywords
racial disparities, ethnic disparities, socioeconomic disparities, brain tumors, high-grade glioma, low-grade glioma, meningioma, vestibular schwannoma, cancer incidence, treatment outcomes, survival, ascertainment bias, clinical trial recruitment, health care access
MeSH terms
Brain Neoplasms, Humans, Quality of Health Care, Socioeconomic Factors, Racial Groups, Healthcare Disparities

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review