Differences in site-specific cancer incidence by individual- and area-level income in Canada from 2006-2015
Bibliographic record
Abstract
ABSTRACT Introduction Income, a component of socioeconomic status, influences cancer risk as a social determinant of health. We evaluated the independent associations between individual- and area-level income, and site-specific cancer incidence in Canada. Methods We used data from the 2006 and 2011 Canadian Census Health and Environment Cohorts, which are probabilistically linked datasets constituted by 5.9 million and 6.5 million respondents of the 2006 Canadian long-form census and 2011 National Household Survey, respectively. Individuals were linked to the Canadian Cancer Registry through 2015. Individual-level income was derived using after-tax household income adjusted for household size. Annual tax return postal codes were used to assign area-level household income quintiles to individuals for each year of follow-up. We calculated age-standardized incidence rates (ASIR) and rate ratios for cancers overall and by site. We conducted multivariable negative binomial regression to adjust these rates for other demographic and socioeconomic variables. Results Individuals of lower individual- and area-level income had higher ASIRs compared to those in the wealthiest income quintile for head and neck, oropharyngeal, esophageal, stomach, colorectal, anal, liver, pancreas, lung, cervical, and kidney and renal pelvis cancers. Conversely, individuals of wealthier individual- and area-level income had higher ASIRs for melanoma, leukemia, Hodgkin’s lymphoma, and non-Hodgkin’s lymphoma, breast, uterine, prostate, and testicular cancers. Most differences in site-specific incidence by income quintile remained after adjustment. Conclusions Although Canada’s publicly funded healthcare system provides universal coverage, inequalities in cancer incidence persist across individual- and area-level income gradients. Our estimates suggest that individual- and area-level income affect cancer incidence through independent mechanisms.
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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 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".