CANCER INCIDENCE IN FIRST NATIONS AND MéTIS ADULTS IN CANADA: FOLLOW-UP OF THE 1991 CENSUS MORTALITY COHORT
Bibliographic record
Abstract
Introduction Cancer incidence amongst First Nations has historically been lower than that in the general population, but preliminary province-level evidence suggests it is rising to meet or exceed that in the general population. Objectives We aim to estimate incidence rates of lung, prostate, breast and colorectal cancers in First Nations and Métis (FN&M) and compare these to the rates in non-Aboriginal Canadians nationwide. Methods A cohort of 1991 Long Form Census respondents was probabilistically linked to the Canadian Cancer Registry (1992 to 2003) and the Canadian Mortality Database (1992 to 2006). Age-standardized, site-specific incidence rates and time trends will be generated over the next 4 months and results will be available for presentation. This will be the first national study of cancer incidence in FN&M. Further, this linked cohort provides a rare opportunity to include groups typically under-represented in health research (e.g. Métis and non-Status Indians) since FN&M will be identified based on self-report of Aboriginal ancestry, Registration and band membership in the Census. Results The cohort includes 2.7 million adults 25 and older on Census day in 1991, equivalent to a 15% sample of the population of that age at the time. Of these, approximately 62,000 reported either First Nations ancestry or are registered under the Indian Act of Canada. Preliminary analyses show that crudely, cancer incidence from 1992 to 2003 was higher in non-Aboriginals than in FN&M nationwide. FN&M have similar incidence of breast and colorectal cancers, both lower than the general population. The Métis have significantly lower incidence of prostate cancer than the general population, but significantly higher rates than First Nations. Incidence rates of lung cancer in Métis are comparable to those in the general population, while First Nations have lower lung cancer incidence. Conclusions Using an innovative approach, this study will provide much needed data to inform effective, culturally-appropriate cancer prevention and control activities for FN&M in Canada. New cancer incidence data will help justify and re-prioritize FN&M cancer programs by identifying issues that require the most attention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.002 | 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".