Racial, ethnic and socioeconomic disparities in the treatment of brain tumors
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.
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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.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [45]
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Federated learning enables big data for rare cancer boundary detection
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- Authors
- Curry, W. T., & Barker, F. G.
- Affiliations
- Harvard University (US)
- Corresponding author
- Curry, William T.
- ORCID
- 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
- Licence
- www.springer.com/tdm (tdm)
- 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
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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.
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