Cancer incidence and mortality among the Métis population of Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Cancer has been identified as a major cause of morbidity and mortality in Canada over the last decade. However, there is a paucity of information about cancer patterns in Aboriginal people, particularly for Métis. This study aims to explore cancer incidence and mortality burden among Métis and to compare disease estimates with non-Métis population. METHODS: This population-based descriptive epidemiological study used cancer incidence and mortality data from 2007 to 2012 obtained from Alberta Health Care Insurance Plan (AHCIP) - Central Stakeholder Registry - and Alberta Cancer Registry (ACR). To identify cancer cases in Métis, the ACR was linked with the Métis Nation of Alberta (MNA) Identification Registry. In Métis and non-Métis people, age-standardized cancer incidence and mortality rates were estimated and subsequently compared between both groups. RESULTS: A higher incidence of bronchus/lung cancer was found among Métis men compared with their non-Métis counterparts (RR=1.69, CI 1.28-2.09; p=0.01). No other statistically significant differences in cancer incidence or mortality were found between Métis and non-Métis people living in Alberta over the course of the 6 years studied. CONCLUSIONS: Overall incidence and mortality associated with cancer were not higher among Métis people compared with non-Métis people. However, special efforts should be considered to decrease the higher incidence of bronchus/lung cancer in Métis men. Further development and maintenance of new and existing institutional collaborations are necessary to continue cancer research and health status surveillance in Métis population.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".