Income and Education Inequalities in Brain and Central Nervous System Cancer Incidence in Canada: Trends over Two Decades
Bibliographic record
Abstract
The socioeconomic gradient of brain and central nervous system (CNS) cancer incidence in Canada is poorly understood. This study aimed to measure socioeconomic inequalities in brain and CNS cancer incidence in Canada from 1992 to 2010. Using a unique census division level dataset (n = 280) pooled from the Canadian Cancer Registry (CCR), the Canadian Census of Population and the National Household Survey, we measured brain and CNS cancer incidence in Canada. The age-adjusted concentration index (C) was used to measure income- and education-related inequalities in brain and CNS cancers in Canada, and for men and women, separately. Time trend analyses were conducted to examine the changes in socioeconomic inequalities in brain and CNS cancers in Canada over time. The results indicated that the crude brain and CNS cancer incidence increased from 7.29 to 8.17 per 100,000 (annual percentage change: 0.70) over the study period. The age-adjusted C results suggested that the brain and CNS cancer incidence was not generally significantly different for census division of different income and educational levels. There was insufficient evidence to support changes in income and education-related inequalities over time. Since the incidence of brain and CNS cancers in Canada showed no significant association with socioeconomic status, future cancer control programs should focus on other risk factors for this cancer subset.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".