Trends in Area-Socioeconomic and Race-Ethnic Disparities in Breast Cancer Incidence, Stage at Diagnosis, Screening, Mortality, and Survival among Women Ages 50 Years and Over (1987-2005)
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Breast cancer is the most commonly diagnosed cancer and the second leading cause of cancer death among women in the United States and varies systematically by race-ethnicity and socioeconomic status. Previous research has often focused on disparities between particular groups, but few studies have summarized disparities across multiple subgroups defined by race-ethnic and socioeconomic position. METHODS: Data on breast cancer incidence, stage, mortality, and 5-year cause-specific probability of death (100 - survival) were obtained from the Surveillance, Epidemiology, and End Results program and data on mammography screening from the National Health Interview Survey from 1987 to 2005. We used four area-socioeconomic groups based on the percentage of poverty in the county of residence (<10, 10-15, 15-20, +20%) and five race-ethnic groups (White, Black, Asian, American Indian, and Hispanic). We used summary measures of disparity based on both rate differences and rate ratios. RESULTS: From 1987 to 2004, area-socioeconomic disparities declined by 20% to 30% for incidence, stage at diagnosis, and 5-year cause-specific probability of death, and by roughly 100% for mortality, whether measured on the absolute or relative scale. In contrast, relative area-socioeconomic disparities in mammography use increased by 161%. Absolute race-ethnic disparities declined across all outcomes, with the largest reduction for mammography (56% decline). Relative race-ethnic disparities for mortality and 5-year cause-specific probability of death increased by 24% and 17%, respectively. CONCLUSIONS: Our analysis suggests progress towards race-ethnic and area-socioeconomic disparity goals for breast cancer, especially when measured on the absolute scale. However, greater progress is needed to address increasing relative socioeconomic disparities in mammography and race-ethnic disparities in mortality and 5-year cause-specific probability of death.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it