Implications of Risk Factors for Alzheimer’s Disease in Canada’s Indigenous Population
Bibliographic record
Abstract
BACKGROUND: Indigenous peoples in Canada have higher prevalence of modifiable risk factors for Alzheimer's disease (AD). The relative importance of these risk factors on AD risk management is poorly understood. METHODS: Relative risks from literature and prevalence of risk factors from Statistics Canada or the First Nations Regional Health Survey were used to determine projected population attributable risk (PAR) associated with modifiable risk factors for AD (low education and vascular risk factors) among on- and off-reserve Indigenous and non-Indigenous people in Canada using the Levin formula. RESULTS: Physical inactivity had the highest PAR for AD among Indigenous and non-Indigenous peoples in Canada (32.5% [10.1%-51.1%] and 30.5% [9.2%-48.8%] respectively). The PAR for most modifiable risk factors was higher among Indigenous peoples in Canada, particularly among on-reserve groups. The greatest differences in PAR were for low educational attainment and smoking, which were approximately 10% higher among Indigenous peoples in Canada. The combined PAR for AD for all six modifiable risk factors was 79.6% among on-reserve Indigenous, 74.9% among off-reserve Indigenous, and 67.1% among non-Indigenous peoples in Canada. (All differences significant to p < .001.). CONCLUSIONS: Modifiable risk factors are responsible for the most AD cases among Indigenous peoples in Canada. Further research is necessary to determine the prevalence of AD and the impact of risk factor modification among Indigenous peoples in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".