Associations between community income and cancer incidence in Canada and the United States
Bibliographic record
Abstract
BACKGROUND: Associations between socioeconomic status (SES) and the incidence of cancer have been reported previously in the U.S. Canada has more comprehensive health care and social programs than the U.S. The purpose of this study was to compare the strength of associations between SES and cancer incidence in Canada and the U.S. METHODS: The regions studied were the Canadian province of Ontario and the areas of the U.S. covered by the Surveillance, Epidemiology, and End Results (SEER) program. The populations at risk were defined using the 1991 Canadian Census and the 1990 U.S. Census. The populations of Ontario and of the SEER areas of the U.S. were each divided into deciles on the basis of median household income. Population-based cancer registries were used to identify incident cases. Age-standardized incidence rates for all major groups of malignant diseases were calculated for each SES decile in Ontario and in the U.S. Income-associated incidence gradients observed in Ontario and the U.S. were compared. RESULTS: The incidence of most types of cancer was similar in Ontario and the U.S. In both countries, there were moderately strong, inverse associations between income level and the incidence of carcinomas of the cervix, the head and neck region, the lung, and the gastrointestinal tract. In both Ontario and the U.S., several of these diseases were twice as common in the bottom income decile than they were in the top decile. In contrast, carcinoma of the female breast and carcinoma of the prostate were more common among higher income communities in both countries, but the observed associations were weaker in Ontario. CONCLUSIONS: Despite Canada's universal health insurance and more comprehensive social security system, the association between lower socioeconomic status and the incidence of many common cancers is just as strong in Ontario as it is in the U.S. The mechanisms responsible for these associations require further investigation.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".