Trends in Socioeconomic Inequalities in Breast Cancer Incidence Among Women in Canada
Bibliographic record
Abstract
INTRODUCTION: Breast cancer is the most common cancer among females in Canada. This study examines trends in socioeconomic inequalities in the incidence of breast cancer in Canada over time from 1992 to 2010. METHODS: A census division level dataset was constructed using the Canadian Cancer Registry, Canadian Census of the Population and National Household Survey. A summary measure of the Concentration index (C), which captures inequality across socioeconomic groups, was used to measure income and education inequalities in breast cancer incidence over the 19-year period. RESULTS: The crude breast cancer incidence increased in Canada between 1992 and 2010. Age-standardized C values indicated no income or education inequalities in breast cancer incidence in the years from 1992 to 2004. However, the incidence was significantly concentrated among females in high income and highly educated neighbourhoods almost half the time in the 6 most recent years (2005-2010). The trend analysis indicated an increase in breast cancer incidence among females living in high income and highly educated neighbourhoods. CONCLUSION: Breast cancer incidence in Canada was associated with increased socioeconomic status in some more recent years. Our study findings provide previously unavailable empirical evidence to inform discussions on socioeconomic inequalities in breast incidence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".