Disparities among Minority Women with Breast Cancer Living in Impoverished Areas of California
Bibliographic record
Abstract
BACKGROUND: Interaction effects of poverty and health care insurance coverage on overall survival rates of breast cancer among women of color and non-Hispanic white women were explored. METHODS: We analyzed California registry data for 2,024 women of color (black, Hispanic, Asian, Pacific Islander, American Indian, or other ethnicity) and 4,276 non-Hispanic white women (Anglo-European ancestries and no Hispanic-Latin ethnic backgrounds) diagnosed with breast cancer between the years 1996 and 2000 who were then followed until 2011. The 2000 US census categorized rates of neighborhood poverty. Health care insurance coverage was either private, Medicare, Medicaid, or none. Cox regression was used to model rates of survival. RESULTS: A 3-way interaction between ethnicity, health care insurance coverage, and poverty was observed. Women of color inadequately insured and living in poor or near-poor neighborhoods in California were the most disadvantaged. Women of color adequately insured and who lived in such neighborhoods in California were also disadvantaged. The incomes of such women of color were typically lower than the incomes of non-Hispanic white women. CONCLUSIONS: Women of color with or without insurance coverage are disadvantaged in poor and near-poor neighborhoods of California. Such women may be less able to bare the indirect, direct, or uncovered costs of health care for breast cancer treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".